ABSTRACT. Hydrologists contend with combinations of several variables that result in compound extreme events such as active and break spells in monsoonal rains, heat waves, and flash flooding from snowmelt. Many compound extremes manifest at time scales of days to weeks requiring data at daily (or finer resolution) time scales. A novel multivariate approach using a time-varying interval-censored estimation method of copula models is proposed for determining conservative design magnitudes for compound extremes under (i) temporal dependence amongst variables, and (ii) when data contain significant ties or repeated values. The efficacy of this method is demonstrated over the Godavari River Basin, India, using daily precipitation and temperature data to characterize cold-wet compound extremes at various spatial scales from a recent period (1977 to 2020) during the monsoon season. The presence of ties and temporal dependence are both shown to have a profound influence on design estimates. This framework shows promise for characterization of other compound extremes in hydrology.

ABSTRACT. The assessment of flood risk is vital for effective flood risk management, traditionally accomplished through hydrological modeling that incorporates physical and empirical relationships for flood simulation and prediction. However, recent advancements in machine learning offer a promising alternative. This abstract explores the applicability of machine learning approaches and hydrological modeling, highlighting their contributions to flood risk management. Hydrological modeling techniques, such as rainfall-runoff and hydraulic models, provide a detailed understanding of hydrological phenomena by incorporating physical processes and empirical relationships from historical data. Nevertheless, they face limitations such as reliance on accurate observational input data and uncertainties in model parameters. In contrast, machine learning approaches demonstrate significant potential in improving flood risk assessment by automatically learning complex patterns from historical data, effectively handling nonlinear relationships and uncertainties. Machine learning can integrate various data sources, including remote sensing, social media, and climate projections, enhancing flood predictions and vulnerability assessments. Machine learning models, with real-time data integration and adaptive capabilities, contribute to flood risk management by enabling timely warnings, effective evacuation plans, identifying flood-prone areas, optimizing resource allocation, and designing mitigation measures. However, challenges and potential disadvantages of machine learning approaches exist, such as the need for large amounts of high-quality training data, potential biases in predictions, and difficulty in interpreting complex systems. The black-box nature of some machine learning algorithms may hinder the understanding of underlying physical processes and limit trust in predictions. In conclusion, integrating machine learning approaches with hydrological modeling has the potential to revolutionize flood risk assessment and management, leveraging the detailed understanding of physical processes offered by hydrological models and the ability of machine learning techniques to handle nonlinear relationships and uncertainties effectively. This integration can enhance flood risk assessment accuracy, improve early warning systems, optimize resource allocation, and develop robust flood management strategies.

ABSTRACT. In Sudan limited or lack of access to safe water is the main factor hindering the socio-economic development and environmental conservation in the rural areas away from the Nile corridor. Scarce water was regarded as the root cause of conflict in Darfur when started in 2003, mainly between farmers and pastoralists. Water harvesting projects are recognized by the government as a priority to provide access to safe water as a key element for stability and socio-economic development in the rural areas, where people depend solely on erratic rains as source of water. The rainfall decreases northwards from 800 mm in the southern border to less than 20 mm in the North. Variability in rainfall may reach about 50% in the northern half of the country and 30% in the central region. The objectives of work are: (1) Development of water resources away from the Nile corridor, through collection, and storage of rainwater and recharge of groundwater. (2) Development of the rural areas, socially and economically (poverty alleviation) through improved water access. (3) Supply domestic water for humans and livestock and for irrigation. (3) Enhance animal production, agriculture production and fish farming. The water harvesting techniques in Sudan aimed at collection of rainwater or catching the runoff in the rainy season (July-September) to store it for the period of shortage (December-June). A water harvesting storage facility has to be of low cost, of simple design, easy to implement, easy to operate and easy to maintain. In order to plan for the storage facilities. The concept and techniques of water harvesting is shown in the paper for artificial reservoirs and conventional hafirs. The hafirs are usually located in places where both the soil and hydrology are favorable. There are two types of haffirs with respect to feeding. On-stream hafirs and off-stream hafirs. There are four problems that affect the efficiency of a hafir: (1) Surface water evaporation, seepage losses, silting of hafirs and misuse of water. (2) The implementation of the water harvesting projects in Sudan dated back to 1940s. (3) After independence the government embarked in association with Non-Governmental Organization (NGO) and with assistance from international donors to implement water harvesting projects. The estimated overall rainfall in Sudan is about 442 billion Cubic Meter (BCM). Until 2013 the estimated volumes of the hafirs were about 15 Million Cubic Meters (MCM) and that for small earth dams was about 30 MCM. Conclusions and Recommendations. In view of the current conflict between the three countries, Egypt, Ethiopia and Sudan about the shared resources in the Blue Nile River after the construction of the Ethiopian Renaissance Dam (GERD), Sudan should on embark heavenly rainfall harvesting projects.

ABSTRACT. The Yellow River, which yearly discharges at least one billion tons of silt into the ocean, is one of the rivers in the world with the largest sediment concentration. A portion of these sediments settle at the estuary and alter the delta's shape. The Yellow River (YR) has seen major changes in recent decades because of climate change and human activity, which have had a variety of effects on its Delta. Therefore, it is essential to research these changes and create a prediction model for potential future modifications. This study aims to develop a methodology that integrates remote sensing, hydrological, and climatic data to assess and predict changes in the delta. Furthermore, this study aims to understand patterns of delta changes by comparing Landsat series data collected every four years from 1996 to 2021, as the focus was on the period after the artificial diversion of the Yellow River in 1996. We evaluate the impacts of hydrological, climatic, precipitation, and environmental management factors on deltaic changes. Satellite remote sensing data is used to monitor geographical features, and hydrological data analysis is integrated to establish a more precise model for deltaic evolution. By utilizing Remote Sensing (RS) and Geographic Information System (GIS) technologies, we were able to characterize the changes that occurred between 1996 and 2021 in the YR delta. Previous research has identified four main phases in the evolution of the YR Delta, 1. Erosion period (1996-2000), 2. Stable growth period (2000-2005), 3. Alternation period of siltation (2005-2015) and, 4. Rapid erosion period (2015-2020). Through the relevant data from the "Yellow River Basin Hydrological Yearbook", "Yellow River Sediment Bulletin" and China Meteorological Administration meteorological data analysis. Therefore, a more precise model for deltaic evolution characterization needs to be established. Thus, the findings of the current study contribute to the understanding of the geological aspects of the dynamic changes in the YR Delta, particularly in relation to hydrological processes. This knowledge can also provide insights into similar deltaic environments worldwide and their interactions with hydrological processes.

ABSTRACT. Groundwater aquifers are critical global resources, with their mapping and monitoring presenting significant challenges, particularly for developing nations. This study introduces a novel methodology for groundwater aquifer delineation using an unsupervised machine learning approach. The technique capitalizes on time-series clustering of groundwater level (GWL) data, thus offering a cost-effective solution that bypasses the need for extensive drilling, geophysical surveys, or pumping tests, which are often resource-intensive and impractical in resource-limited settings.

Our methodology employs a modular clustering framework that utilizes hierarchical agglomerative clustering in conjunction with a custom-designed hydrology-specific dissimilarity distance function. The approach accounts for variable length time series and gaps in GWL data. It temporally aligns time series before comparison based on a global dataset index, enabling comparison of uneven subsequences. This methodology offers significant advantages over traditional time-series clustering techniques, such as dynamic time warping and Euclidean distance, particularly when dealing with real-world hydrological data.

The algorithm has been optimized using Bayesian hyperparameter optimization, with initial testing conducted on a synthetic aquifer dataset simulating Texas real aquifer systems. Preliminary findings suggest that a minimum of 400 monthly timesteps (~33 years) provides a high level of accuracy in aquifer delineation. The preliminary results exhibit promising accuracy in delineating major Texan aquifers, despite uneven data coverage.

Moreover, the aquifer boundaries are visualized geographically using a novel mapping library (GeoZ) developed for this particular purpose. The proposed unsupervised learning approach, underpinned by the analysis of widely available GWL time-series, could provide a vital tool for sustainably managing groundwater resources globally. Future work will focus on optimizing the model components and testing it on additional real-world datasets to better establish its generalizability and efficacy.

ABSTRACT. Groundwater is the largest accessible freshwater source on earth but it is threatened by overdraft and pollution. For example in the United States, the greatest threat to the sustainability of irrigated agriculture is drought due to climate change, which has resulted in groundwater overdraft as farmers turn to groundwater as an insurance against drought. Some of the most productive agricultural regions in the U.S., such as California‘s Central Valley, Central Arizona, and Lower Rio Grande are experiencing unprecedented levels of groundwater overdraft. Groundwater is also threatened by salinization and nitrate contamination associated with irrigated agriculture. Sustainable use of groundwater to meet the growing global demand for nutritious food, fiber, and biofuels while ensuring co-benefits for the environment and human health remains a grand challenge. A USDA NIFA supported multistate and transdisciplinary project called GSAS (Groundwater and Sustainable Agricultural Systems) is being implemented in the Southwestern US (Central Valley of California, the Pinal Active Management Area in Arizona, and the Lower Rio Grande watershed, in New Mexico) to find solutions to sustainable use of groundwater in agriculture under a changing climate. Examples of solutions being evaluated include agricultural-managed aquifer recharge, desalination of brackish groundwater, groundwater demand management (restrictions on pumping), regenerative agriculture practices, precision water management, nutrient and salinity management, and developing socioeconomic tools for groundwater governance at basin scale, etc. As well as education and extension initiatives around groundwater and sustainability agricultural systems. Preliminary findings indicate innovative policies, technologies, and management strategies will be needed to achieve sustainable groundwater use in irrigated agriculture.

ABSTRACT. During the last decade, the volume and types of Earth Observation (EO) and environmental data has increased enormously; in the mean time, models and algorithms based on these datasets have been developed for interpreting the physical processes underlying the crop production systems. These models transform data into information for supporting good management practices in agriculture and decision-making processes at different levels, from local farmers to rural communities and nations. The efficient management of water resources for irrigation is of vital importance in arid and semi-arid countries, but often farmers lack access to data and information for adjusting irrigation in relation to the actual crop needs and the actual availability of water resources. Mature technologies based on EO data and ICTs have already been developed by the European industry sector; thanks to the Copernicus Sentinel satellite constellation of European Space Agency, EO-services for irrigation management have evolved from prototype level to complete and qualified systems (TRL7-8), i.e. IRRISAT© (www.irrisat.com). The spectral characteristics and the radiometric quality of the imaging sensor on board the Sentinel 2A and 2B satellite have allowed the application of physically-based algorithms which are able to monitor important biophysical crop parameters (e.g. Leaf Area Index, albedo, fractional vegetation cover, canopy water content) for the evaluation of evapotranspiration under a wide range of conditions. In particular, shortwave infrared (SWIR) spectral bands from Sentinel-2 can be used to evaluate the water status of soil and canopy system, allowing the estimation of actual evapotranspiration by using alternative methods to thermal infrared observations, available only from Landsat 8, with limited spatial and temporal resolution. The IRRISAT irrigation advisory system is based on the application of the classical Penman-Monteith for ET calculation and crop water requirements but using the above mentioned biophysical parameters instead of the crop coefficient. Under optimum soil water availability, the surface resistance in P-M equation is mainly dependent on Leaf Area Index. However, when considering arid and semi-arid conditions, the surface resistance are dependent also on the soil water status. IRRISAT has been improved to include SWIR observations to increase the surface resistance when aridity conditions occurs.

ABSTRACT. Nitrate contamination of groundwater is definitely an issue of major concern. This is also the case in the Maltese islands where nitrate levels in the main bodies of groundwater exceed the quality standards established under the EU’s Groundwater Directive (GWD) causing concern on their status. Current monitoring infrastructure focuses on the measurement of nitrate content in groundwater, thereby focused on assessing the existing situation. Increased focus is required on monitoring of the unsaturated zone, hence enabling the advanced (early) identification of the development of contamination.

Nitrate source tracing using natural isotopes of Nitrogen and Oxygen undertaken in Malta identified soil nitrate being the main source of groundwater contamination. Correlation of σ15N and σ18O in nitrate sources and groundwater nitrate indicated that the main source of nitrate contamination arises due to soil N derived Nitrate and therefore the over application of fertilizers in arable agriculture.

As part of the CF.PA10.0096 Project, Malta has developed an unsaturated zone monitoring network based on Flexible Time Domain Reflectometry (FTDR) and Vadose Zone Monitoring System (VMS) developed by Dahan et al. The network is comprised of 16 monitoring stations involving slanted boreholes driven in soil and rock up to a vertical depth of 23m. VMS Sampling Ports and FTDR sensors are installed every 3m (approx.) along the monitoring sleeve. Stations enable the monitoring of the % water content in the rock and the collection of water samples. Monitoring stations reflect the diversity of agricultural activities in the islands, as well as the different hydrogeological conditions giving the network a high level of spatial representativity. The monitoring network was commissioned in June 2021.

Groundwater samples are collected from the VMS ports on a monthly basis, and samples are analyzed for a suite of physicochemical parameters including nitrogen and electrical conductivity. First analysis of monitoring data shows: (i) a sustained decrease in the Nitrate content of recharge water with depth in all reporting stations, with the most significant decrease generally occurring within the first 10m of the unsaturated zone; (ii) a potential relationship between nitrate content in the first sensors (soil depth of up to 0.5m) and agricultural land use with stations representative of traditional agricultural activities showing values around 3000mg/l, whilst stations representative of vineyards (where nitrate application is highly managed) showing values of around 300mg/l; and (iii) nitrate content in the deepest stations goes below 50mg/l (GWD Quality Standard) in only five monitoring stations. Furthermore, data from a number of stations show a correlation between Electrical Conductivity and Nitrate content, possibly indicating the relevance of physical processes such as dilution and dispersion in the reduction of nitrate concentration of percolating water with depth.

The results of the monitoring network support the evaluation of the measures under Malta’s Nitrates Action Programme, enabling also an evaluation of the impacts of climate change on the development of nitrate contamination of groundwater.

The CF.PA10.0096 project “Enhancing National Monitoring and Public Engagement Capacity for improved Water Resources Management” is co-financed under the European Structural and Investment Funds 2014-2020, Cohesion Fund.

ABSTRACT. Over-exploitation of groundwater happens in the drought-prone Barind Tract, northwest Bangladesh for its rising demand mainly in domestic and agriculture sectors. The area is facing challenges from scarcity of rainfall, topographic barrier for conservation of rain/runoff water, low groundwater recharge due to thick top clay soil layer (Barind clay) of low infiltration capacity, constrain in groundwater development etc. The drainage pattern indicates scope of loss of major amount of runoff water with less natural infiltration capacity of surface soil to recharge suggesting constrain in availability of groundwater. From hydraulic head values, it is evident that the recharged groundwater eventually discharged into surrounding rivers basins from the Tract in generally in both dry and monsoon seasons. Moreover, the unsustainable water management practice consequences the rapid declination of groundwater tables (GWT) in last few decades making the system is close to a unbalance condition. Unfortunately, no proper assessment of the sustainable groundwater resource have yet made. Here water balance study is definitely a terrific and challenging task, and the rainwater harvesting (RWH) with artificial recharge of groundwater resources using Managed Aquifer Recharge (MAR) model - a viable solution has considered to revert the ongoing depletion of resources and to restore water balance. In this context, potentiality for MAR model has studied using integrated approach of RS and GIS using MCDM tool to have time saving and cost-effective results. The Tract has MAR model application potentiality of 448 km2 (20%) of ‘high suitable’; 1456 km2 (66%) of ‘moderate suitable’; and 312 km2 (14%) under unsuitable zone. Here 35% of precipitation is lost as runoff, and that of 14% remains as soil moisture. Here, groundwater resource used for irrigation equals to 24% of the annual precipitation, but only 8.5% of the precipitation infiltrated to recharge groundwater naturally, and rest 68% of rain/runoff water has scope to infiltration/injection naturally/artificially to revert balanced condition. Through this model, the cost of recharging one cubic meter of water is about US$ 0.18. Although this is little bit expensive in comparison to injected water volume, but can be an important solution to create a sustainable freshwater source for rural and peri-urban communities especially economically marginalized and unprivileged peoples facing hardship for drinking and agricultural water. Finally, the present study provides a guideline to water managers and decision makers to ascertain availability of water resource through the Integrated Water Resource Management (IWRM) approach as lauded in the Bangladesh Water Act (BWA) (2013).

ABSTRACT. An accurate prediction of water table rise (WTR) phenomenon in urban areas is crucial for groundwater resources management and protection. This study employs shallow learning (SL) and deep learning (DL) models to predict WTR in urban areas. At Level 1, interpretability-focused SL models (Catboost, XGBoost, LGB) and time-aware DL models (1D-CNN, RLSTM, GRU, transformers) are used. At Level 2, SL and DL groups are independently ensemble stacked using weighted averages. The research aims to assess SL and DL performance and combine model groups at two levels for forecasting WTR in Muscat (Oman) for three-week steps. A 13-month WTR field-dataset (Dec 2017 to Jan 2019) is divided into 90% for training and 10% for testing. Shapley Additive exPlanations (SHAP) selects feature lag times at Level 1, while label encoding and aggregation address time-related patterns. Performance is evaluated by using root mean square error-observations standard deviation ratio and Nash-Sutcliffe efficiency coefficient. The results indicate that the SL models outperform DL ones in producing matches between measured and computed water levels due to their interpretability and linearity during the training and validation phases. Whereas DL models outperform the SL models in the prediction phase where those models predict WTR phenomenon for the 3 week-period following field observations. In level 2, the ensemble stacked model improved the performance of DL models for three-week WTR future prediction compared to ensemble stacked SL models. Time series analysis (TSA) has shown robustness and proven valuable for identifying the decomposition pattern, and seasonality of fine-scale high resolution measurements of WTR. TSA is also a powerful tool in presenting larger datasets and provides meaningful visualization. The field data shows a pattern of high-water level in the mid-nights (12:00 AM-6:00 AM) and mid-days (12:00 PM-6:00 PM) which is attributed to high pressure in public water network (PWN). However, lowered water level is reported during the mornings (6:00AM-12:00AM) and evenings (6:00PM-12:00 AM) which suggest to vary the pressure of the PWN.

ABSTRACT. Today, many agro-industries face several challenges on the treatment of their wastewater, mainly due to the seasonal nature of their work and small dimension, which means that they could not rely on expensive automated treatment systems. This fact leads to the necessity of manually monitor and adjust the treatment system, in order to adapt the system to the fluctuations in the quality and quantity of the wastewater being produced. This type of continuous system adjust, dependent of human operation, is often neglected due to the little time available for workers in small companies. Current existing automation systems are a good and efficient solution but have a high cost that cannot be supported by small companies. Access to low-cost monitoring and automation solutions could therefore mean an extremely important step in terms of efficiency and sustainability. Due to technological advances, there is currently a widespread access to low-cost sensors and controllers that present sufficient reliability and quality to allow for the continuous monitoring of the main parameters that influence the treatment, in order to assist companies in the treatment of their wastewater. The use of low-cost IoT (Internet of Things) solutions presents itself as a new solution aiming at the improvement of the treatment efficiency of existing systems with economic and environmental gains. As part of this work, a prototype of a low-cost monitoring and control system was developed, which aims to validate the usefulness and functionality of the proposed system. This system allows for the system’s monitoring and treatment intensity adjustment, in order to guarantee the quality of the treated effluent, adapting the treatment to the existing conditions, and being able to issue alerts in case of identification of a failure/anomaly in the system. The ability to adapt the treatment also ensures the quality of the treated wastewater according to its destination, whether for subsequent use, discharge into a municipal collector or discharge into a natural receiving environment. The developed system is in its validation phase, having been installed in a real scale wastewater treatment system and showing promising results regarding its reliability and the identification of improvement opportunities on the treatment system operation.

This work is an integral part of the tasks developed within the scope of the PhD scholarships granted by the University of Lisbon and Instituto Superior de Agronomia, and by scholarship 2021.06015.BD financed by the Portuguese Foundation for Science and Technology to the PhD student Artur Saraiva. The work developed also had the support of the research center LEAF-Linking Landscape, Environment, Agriculture and Food Research Unit, UIDB/04129/2020 through the TARIoT project.

ABSTRACT. One of the challenges facing stakeholders is individual wastewater containing pharmaceutical residues (antibiotics). Even in trace amounts, the antibiotics present lead to the development of anti-biotic-resistant bacterial and viral strains. Operators have a number of solutions for Waste Water Treatment Plants. Among the most effective are UV treatment, reverse osmosis and tangential filtration, which can be combined. These sophisticated treatments pose complex maintenance problems and are time-consuming, leading to significant costs. For personal wastewater (<200 eqH), in the absence of wastewater collection networks, the issue of treatment efficiency (pre-filtration, decantation and addition of coagulants) is far from resolved. The authorities and agencies responsible for assessing the quality of personal wastewater can only observe not only the presence of these substances, which have been increasing steadily since 2010, but also the absence of simple, inexpensive solutions. Professionals installing individual treatment systems currently have few, if any, solutions. The challenge poses a public health issue, and the WHO has warned of the danger posed by anti-bio-resistance for the general public and healthcare professionals. While this is a major issue for developed countries, it is also vital for developing countries. The reasons for this are, on the one hand, the low level of equipment, poor regulation and compliance with standards where they exist, the cost of installation (even simple installation) in relation to the standard of living, and the low level of maintenance. And above all, the widespread and ever-increasing use of antibiotics. When this type of wastewater is analysed at the inlet and outlet, we find the presence of numerous bacteria (E. coli, faecal coliforms, enterococcus sp., total flora, and Vibrio cholera) as well as traces of drug residues (Ciprofloxacin, Doxycycline, Enoxacin, Enrofloxacin, Norfloxacin, Ofloxacin, Amoxicillin and Ampicillin). This study focuses on the analysis of wastewater from a pilot plant designed by the Ecole Polytechnique, University Cheick Anta Diop, Dakar, Senegal. It was installed in 2018 and is being monitored from the physic-chemical, bacteriological and microbiological points of view. Comparative results of the pilot plant (with and without addition of Moringa Olifeira Seeds, in terms of bacterial removal and the effect on anti-bioresistance are presented. To achieve this study, microbiological analysis, POCIS passive organic compounds sensors (from Affinisep), genomics and proteomics have been used. We observed the presence of Ciprofloxacin and Doxycycline (in the order of tens to hundreds ng/L) in the pilot plant. We found that the untreated water contained 6.43 x 105 CFU/mL of ampicillin- resistant E. coli, 1.33 x 108 CFU/mL of ampicillin- resistant faecal and more than 300 x 106 CFU/mL of ampicillin- resistant total flora. While MOS addition induces antibiotic bacteria removal by 96% for resistant E. coli and 100% for resistant faecal coliforms. These preliminary results have to be confirmed, which is planned for 2024 campaign. We conclude, that whatever the used water composition especially in term of bacterial and viral fauna, MOS is an alternative efficient compound acting as coagulant but also as an antiseptic agent.

ABSTRACT. Produced water (PW) is the highest volume wastewater related to oil and gas production. PW is usually injected into onshore deep wells for disposal after acidification, and oil and H2S removal. Qatar National Vision 2030 mandates the increased usage of highly treated wastewaters to enhance the sustainability of water sources in the country both for agricultural and landscaping irrigation practices. Use of PW could be an appealing alternative for diversification of water resources in Qatar as well as addressing the environmental and economic constraints of traditional PW disposal practices. As the demand and production of oil and gas is continuing to increase globally, the environmental footprints associated with this production and more specifically with generation of produced water (PW) are increasing. Furthermore, as the scarcity of freshwater supply is increasing, PW can be a crucial source of water after suitable treatment. Since PW is a very challenging mix of different contaminants with varying concentrations, in this study we tested the efficiency of different water treatment processes such as wetland treatment, adsorption with AC, coagulation-flocculation, and RO/NF for PW treatment to meet irrigation and landscaping quality standards. The comprehensive physicochemical analysis of PW samples before and after treatment has been performed. It was found that CWT, adsorption, and coagulation-flocculation treatment of PW provide high removal of BTEX compounds, some heavy metals, i.e., Al, Fe, Mn, and other pollutants, however such methods are not efficient enough in reducing TDS, COD, salinity, and boron levels in treated PW. On the other hand, RO/NF treatment efficiently reduced concentrations of both organic and inorganic pollutants in treated PW including TDS, COD, salinity, and boron values and successfully meet current Qatar standards for water to be used for irrigation and landscaping purposes. In addition, a combination of CWT and NF/RO membrane methods seems to provide both high productivity of water treatment and efficiency in pollutant removal to best fit for PW treatment.

ABSTRACT. The Arabian Gulf is known as one of the most unique environments in the world, facing naturally extreme conditions such as high temperatures and hypersalinity, high evaporation rates, and limited freshwater input. Over the last few decades, these conditions have been further exacerbated by the twin forces of climate change and direct human impact on the coastal system. The increase of the eutrophication-driven hypoxia in the Gulf stands out as one of the most pressing concerns related to the growth of the human population in the Gulf area. Although hypoxia typically depends on agricultural-related eutrophication, this is not the case in the Gulf, where the arid climate prevents the development of intensive coastal agricultural. Instead, coastal hypoxia is primarly due to eutrophication resulting from wastewater discharge. Despite the regulatory measures and limited reuse of treated wastewater, the Arabian Gulf continues to experience an important nutrient load and contaminants discharging resulting in importing biological and environmental effects. These effects include the proliferation of harmful algal blooms and the subsequent occurrence of fish mortalities and, potentially, an increase in coral bleaching. This work constitutes a comprehensive examination of the freshwater consumption and wastewater production, discharge and reuse within the countries bordering the Arabian Gulf. It encompasses an in-depth analysis of legal thresholds concerning the chemical contaminants, drawing comparisons with established standards in Europe. Furthermore, the geographic distribution of treatment facilities has been documented, facilitating the identification of areas where the treated effluents may directly impact sensitive ecosystems, such as coral reefs or mangroves. Lastly, this research presents an assessment of the biological consequences stemming from nutrient load, considering both existing scientific knowledge and observed events in the Gulf waters. Further investigations are imperative to advance our comprehension of the biological repercussions associated with the allowed threshold for phosphorous, nitrogen, and other contaminants.

ABSTRACT. The scarcity of water resources is a major problem in Morocco, as is the case in many other arid parts of the world. Several factors have contributed to the amplification of this issue, including rapid population rise, increasing water demand, climate change impacts, untreated wastewater contamination of water bodies, and irrational use of irrigation water. Since then, the water demand for agriculture irrigation has seen a dramatic increase, rising from 500 Mm3 in 1961 to 13,500 Mm3 in 2014, with 70% sourced from surface water and 30% from groundwater. All of which increase the pressure on water resources and have a harmful impact on health and the ecosystem. The aim of this study is to assess the treatment performance and understand the operation of a hybrid multi-soil-layering (MSL) treatment plant located at Cadi Ayyad University in Marrakech, Morocco. The treatment system incorporates a solar panel, a septic tank (primary treatment), and a sequential arrangement of a vertical flow MSL (VF-MSL, secondary treatment) unit and a subsurface horizontal flow MSL (HF-MSL, tertiary treatment) unit. Both the VF-MSL and HF-MSL units are constructed using alternating layers of gravel and soil-based materials arranged in a brick-like pattern and operate at a hydraulic loading rate (HLR) of 250 L/m2/day. Sampling is conducted on a bimonthly basis at the inlet and outlet of each unit of the treatment system. The monitored parameters include pH, electrical conductivity, chemical oxygen demand (COD), suspended solids (SS), nitrogen compounds (NH4+, NO3-, NTK, NT), phosphorus compounds (PO43-, PT), as well as microbiological parameters such as fecal coliforms, fecal streptococci, and staphylococci. Our findings indicate substantial removal efficiency of the hybrid MSL in treating domestic wastewater, with significant reductions (p<0.05) in organic matter and phosphorus, as well as notable nitrogen removal, including TSS (97%), COD (88.57%), TP (79.93%), and TN (88.49%). In addition, the hybrid MSL system achieved remarkable log removals of fecal bacteria indicators and pathogens, reaching 4.21log for fecal coliform, 3.90 log for fecal streptococci, and 2.43 log for Staphylococcus sp. In conclusion, the treatment of domestic wastewater using multi-soil-layering eco-technology produces treated water that meets Moroccan discharge and irrigation standards, making it suitable for reuse both in landscaping and agriculture to cope with water scarcity.

ABSTRACT. Global warming, water scarcity and increasing energy demand are the major issues for all developing and developed nations. The majority of energy is wasted in centralized wastewater treatment systems and unfortunately the treated effluents do not comply with the discharged norms, therefore, discharged untreated at many places and creating the stress for clean water availability. Bio-electrochemical systems have been considered as a sustainable solution to treat wastewater. The integration of microbial fuel cell (MFC) and forward osmosis (FO) technology has been termed as osmotic microbial fuel cell (OMFC) which can generate water as well as bio-energy from wastewater. OMFC has wide applications such as water recovery/reuse, resource recovery, desalination, treatment of wastewater, environmental bioremediation and bio-energy generation. The FO membranes are expensive as well as the water flux declines with time and due to which currently the major focus is to develop cost effective high quality membranes to ensure longevity. The high cost and lesser availability of the materials required to prepare FO membrane has generated need to find alternative for the fabrication of FO substrate and there have been attempts to modify the active layer side of the FO membrane to enhance the water flux and reduce the reverse salt flux. This review paper focuses on recent updates in the synthesis of FO membranes, their challenges, and finally application in osmotic microbial fuel cell for water, fertilizer, and electricity generation along with the treatment of waste.

ABSTRACT. Household wastewater termed as “Sewage’ is a combination of black and grey water. It discharged through sewer lines and collected and treated at sewage treatment plants (STPs) by centralized systems. Unfortunately, almost 70 to 80% untreated effluents are discharged through STPs and discharged into aquatic environments and reason for creating water pollution, water scarcity, climate change, global warming, nutrients deficiency, etc. Decentralized systems seem a better solution which suggests treatment based on the chemical composition of the stream. This review presents the treatment of human waste, especially human urine, and fecal material in decentralized or onsite manner with resource recoveries for sustainable development. As such human urine consists of 1% in sewage but almost contributes 80-90 % Nitrogen (N), 50-65% Phosphorous (P), and 50-80%% Potassium (K), along with 10% COD, which creates eutrophication when untreated waste is discharged. Human urine consists of 95% water and rest urea and other substances. If urine is treated properly, sufficient nutrients like N, P, K along with water can be recovered and can fulfil the demand of fertilizers to a certain extent. Many techniques are utilized in literature. Accordingly, this review presents techniques, their limitations, potential, and future directions. Human urine’s conductivity is an important parameter which increases with time. Therefore, human urine can be an excellent electrolyte for microbial fuel cells (MFC) for electricity generation. Similarly, hydrothermal techniques like hydrothermal carbonization and liquefaction are utilized for fecal material as biomass to produce biochar, oil and process water. Produce char has an almost equal caloric value of bituminous coal and coke (28.8 MJ/kg) and process water contains nutrients. Therefore, this paper reviews the above aspects and tries to suggest a cost-effective treatment and resource recovery system.

ABSTRACT. The "Integrated Flood and Water Resources Management in the ASEAN Basins for Sustainable Development" project focused on the Cagayan River Basin, particularly the Magat Dam, aiming to comprehensively address challenges arising from climate and land-use changes. The study proposes innovative protocols for dam discharges, safety, and optimized reservoir operation during extreme climate conditions. It delves into flood dynamics, morphological changes, and sustainable agriculture development, emphasizing solutions for integrated river basin management to enhance disaster risk reduction and resilience. The project yielded significant results, including the development of cutting-edge technologies like Rainfall-Runoff-Inundation (RRI) for flood prediction, Soil and Water Assessment Tool for dam inflow projections, and Telemac for sediment transport analysis. Key advancements include calibrated and validated Hydrodynamic Telemac 2D, Hydro-morphodynamic Telemac 2D, and Hydro-morphodynamic Telemac 3D Models, enhancing water dynamics understanding. GIS-based site selection for Sabo dam locations and creation of flood hazard maps under various climate change scenarios aid comprehensive risk assessment.

A highlight is the adoption of RRI with ensemble forecasting for inflow prediction by the National Irrigation Administration-Dam and Reservoir Division, supporting operational decisions during extreme rainfall. The study results in scholarly publications and Intellectual Property Rights (IPR) related to mapping and a Training Manual. The project conducts seminars, workshops, and training courses for knowledge dissemination and capacity building, covering integrated water, flood, and sediment management, advanced technologies, and institutional options for integrated watershed management. Strategic partnerships are formed with stakeholders and institutions, fostering collaboration and knowledge exchange to address river basin management complexities. Policy impact is significant, endorsing ten policy briefs on integrated river basin and water resources management. Policy briefs cover diverse subjects like climate change impacts on drought vulnerability and modeling dam tributary flow for improved decision support. The research methodology involves investigating climate and land-use effects on water resources, dam safety, flood modeling, morphological analysis, sustainable agriculture, and integrated river basin management for disaster risk reduction and resilience.

In conclusion, the research project strives to provide sustainable solutions for the intricate challenges of integrated flood and water resources management in ASEAN basins. By enhancing water dynamics understanding, optimizing reservoir operations, and fostering collaboration, it aims to promote a resilient and sustainable future for the Cagayan River Basin and beyond. The comprehensive approach and multifaceted outcomes contribute significantly to achieving sustainability in water resources management, setting an example for regions facing similar challenges.

ABSTRACT. Harmful algal bloom (HAB) occurrences are continuing to increase globally in water bodies, with detrimental impacts on lake ecosystems and socio-economics of surrounding populations. A further cause for concern is their unpredictable production of toxins that can be harmful to animals and humans. The ability to predict HAB abundance accurately is crucial for implementing timely mitigation and intervention strategies to protect both aquatic ecosystems and public health. Extensive studies are being conducted in attempts to acquire a better understanding of HAB growth for forecasting purposes. Enhancing the abilities of predictive models for HABs can benefit water management activities through timely and targeted interventions aimed at preventing or mitigating the detrimental effects of algal blooms, establishing better early warning systems to provide advance notice to authorities and communities and enabling them to take proactive measures to protect water quality and public health. Application of data-driven models for forecasting HAB occurrences is becoming increasingly prevalent. However, data-driven models rely heavily on data availability, and building a predictive model for water bodies with limited data impairs the performance and accuracy of models. This study investigated the potential impacts of data gaps on the accuracy and reliability of HAB prediction models in fifteen inland lakes in the United States. Three types of data were used, namely (i) Water quality measurements, including nutrient levels of total nitrogen, total phosphorus, dissolved oxygen, water temperature, turbidity, and chlorophyll-a concentrations, (ii) meteorological data, including air temperature, precipitation, wind speed, and solar radiation, and (iii) satellite remote sensing data of the cyanobacteria index (CI). The data utilized in this study cover short durations ranging from three to seven years for the different lakes. Three data imputation methods were examined to reconstruct missing values in these datasets: Random Forest Regression (RFR), k-Nearest Neighbors (kNN), and Multiple Imputation by Chained Equations (MICE). A machine learning model, Random Forest (RF), was employed to forecast cyanobacteria abundance. Similarity analysis was also used with Euclidean distance and Dynamic Time Warping (DTW) to calculate similarity metrics, and agglomerative clustering was used to cluster the lakes based on their similarity. Different cases of data limitations were examined to assess the role of missing data and imputation techniques on model’s predictive abilities. Results show that similarity-based clustering of lakes can be useful to improve model performance. However, similarity-based clustering may not always accurately capture the underlying patterns and variances across all lakes.

ABSTRACT. Water resource management faces significant challenges due to hydrological droughts, emphasizing the critical need for accurate forecasting. This study meticulously analyzes hydrological droughts in the Topľa River, Slovakia, employing an innovative deep-learning model based on the Streamflow Drought Index (SDI) spanning from 1989 to 2020. The assessment of hydrological droughts involves a comprehensive data preparation phase, transforming and aligning daily discharge, water level, and temperature data with SDI requirements. This process categorizes hydrological years into dry or non-dry, laying the foundation for subsequent modeling. At the core of this research is the development of a sophisticated deep-learning model that harnesses the predictive power of SDI for precise forecasting. To overcome the challenge of limited training data, the study employs the Synthetic Minority Over-sampling Technique (SMOTE) for data augmentation, ensuring a balanced representation of both dry and normal hydrological years. The model architecture is carefully designed to predict SDI for an entire hydrological year, drawing on input features from the initial six months, which include water level, discharge, and temperature data. Implemented through TensorFlow and Keras libraries, the model incorporates strategic measures to prevent overfitting, enhancing its robustness. Through extensive training and evaluation, the model exhibits exceptional performance, achieving a remarkable 100% accuracy on both training and validation datasets. Impressively, this high level of accuracy extends to the testing dataset spanning 2010 to 2020, underscoring the model's ability to generalize effectively to unseen data. This study significantly contributes to the advancement of hydrological drought prediction through the integration of deep learning and innovative data augmentation techniques. The model's exceptional accuracy and generalizability underscores its potential as a valuable tool for drought assessment and water resource management. The research highlights the importance of an accurate data transformation process, ensuring compatibility between diverse data types and the chosen model architecture. The study's results not only validate the model's robust performance but also signal a promising avenue for further exploration in the realm of hydrological variability. The potential applications across diverse hydrological contexts and geographical regions emphasize the far-reaching implications of this research in contributing to our understanding of and ability to mitigate the impacts of hydrological extremes on society and ecosystems.

ABSTRACT. In recent years, freshwater scarcity has extended across the entire world. Groundwater resources are being heavily used as surface water supplies are insufficient to meet the needs of human activities. Therefore, for their smart development and usage, evaluation of potential groundwater resources is essential. The present research focused on the evaluation efficiency of advanced machine learning algorithms for delineation of groundwater potential zones (GWPZ) in urban cluster of northern Kerala, the southern part of the India. The groundwater potential mapping was prepared using machine learning (ML) algorithm of the eXtreme Gradient Boosting (XG boost), K Nearest Neighbour (KNN), Naïve Bayes (NB) and Random Forest (RF). Urban groundwater system is dynamic and hence performance of machine learning algorithms has been assessed in order to effectively establish the relationship between the urban groundwater system and various hydrogeological elements. Influencing factors such as topographical (slope, aspect), hydrological (drainage density, rainfall, surface water proximity), geological (lithology, lineament density) and landcover factors (landuse/landcover and soi texture) were applied for the study. In total, 80 samples were collected and acquired; about 80% of the samples were selected randomly as training data set and the remaining 20% as test data set. The models have been trained, tested and deployed based on groundwater yield of the urban phreatic aquifers. Through statistical examination and receiver operating characteristic (ROC)-area under curve (AUC) analysis, the effectiveness of each model was validated. However, compared to previous studies using AHP and hybrid Fuzzy-AHP analyses, all of the novel ML models presented in this work produced better estimations of groundwater potential. The results of XG boost, one of these techniques, have been encouraging, and they are sufficiently general and adaptable to various data-scarce urban environments.

ABSTRACT. Meteorological drought is a major concern in Morocco. The availability of accurate and reliable precipitation data is essential for monitoring and assessing the extent of drought in the country, which is challenging given that ground measurements are very limited, especially in mountainous regions. This study aims to statistically evaluate satellite precipitation products CHIRPS and PERSIANN-CDR for 27 rain gauge stations across the national territory over a 30-year period. This evaluation was carried out using various performance metrics, including the coefficient of correlation (CC), mean error (ME), root mean square error (RMSE), relative bias, and mean absolute error (MAE). These satellite-estimated data, along with observed precipitation, will be used to quantify meteorological drought in Morocco based on the Standardized Precipitation-Evapotranspiration Index (SPEI) and the Mann-Kendall trend test. The quantitative statistical evaluation also pertains to the temperatures from the ERA5 reanalysis product to incorporate its data into evapotranspiration calculations and, consequently, the SPEI. The results of the quantitative statistical evaluation show good correlations between satellite-estimated precipitation from both CHIRPS and PERSIANN-CDR and observed data. The estimation error values vary from one station to another, but generally, they are low. However, there is some dispersion between the two datasets, as indicated by the RMSE values. It should be noted that the mean error (ME) and the relative bias in percentage show underestimations for some stations and overestimations for others compared to reference values. Regarding ERA5, the results indicate excellent performance of this reanalysis product. The analysis of SPEI trends using the Mann-Kendall test generally indicates an absence of significant short-term drought trends in most stations, while long-term drought shows significant trends either decreasing or increasing, primarily represented by the CHIRPS product. However, further in-depth studies and contextual analysis are needed to obtain a comprehensive understanding of drought variability in the different regions under study.

ABSTRACT. Satellite-based precipitation products are becoming reliable sources for obtaining highly detailed rainfall estimates worldwide. However, satellite products must be properly assessed and often require bias correction due to their significant uncertainty. In this study, we evaluated the effectiveness of five different precipitation products (GPM, GPCP, CHIRPS, PERSIANN-CDR, and PERSIANN) in accurately replicating observed monthly rainfall at 28 stations, located in the western region of Saudi Arabia. The most performed products were corrected using the widely used machine learning (ML) algorithms. After conducting the analysis, it was found that the GPM product had the highest correlation coefficient of 0.56 and Kling-Gupta Efficiency (KGE) of 0.5 among other products. In terms of the best bias correction method, the random forest (RF) method performed better than the k-nearest neighborhood (KNN) and Artificial neural network (ANN). Additionally, the machine learning (ML) methods were more effective than traditional bias correction methods, including linear scaling and quantile mapping methods. Overall, the findings suggest that the GPM product is a dependable tool for accurately capturing rainfall patterns in the study area.

ABSTRACT. Groundwater and harvested rainwater represent the main conventional freshwater resources in United Arab Emirates (UAE). Artificial recharge practices have been implemented in UAE since 1982, with the construction of the first major recharge dam in Wadi Ham. Recent studies indicated a significant decline in fresh groundwater storage, except during wet years where direct recharge from rainfall and stored water in dams occurs. However, the efficiency of dam-based recharge is hindered by siltation. Only around 40% of the ponded water would contribute to the groundwater storage and the other 60% is held in the unsaturated zone or evaporated. Depending on precipitation patterns and subsequent rainfall events as well as temperature, the water in the unsaturated zone might percolate or evaporate. The present study presents extensive soil infiltration surveys conducted at various sites in Wadi Bih over several years. The effect of siltation on infiltration and recharge rates is assessed. The study aims to assess the variations in soil properties in response to changes in precipitation intensity and frequency. Groundwater storage and salinity are found to be heavily influenced by precipitation rates and patterns. The recharge resulting from two consecutive peak monthly precipitation events in December 2019 and January 2020 replenished the fresh groundwater reserves and pushed saline/brackish water marginally toward the shoreline.

ABSTRACT. The Moroccan economy is classified as a developing economy with a strong agricultural component, which consumes about 85% of its water resources. The Moroccan agriculture is required to be more efficient in terms of water use and also to continue to play its driving role in the socio-economic ambitions of the country. The study aims (1) to gain in-depth understanding of current and future hydrological cycles under various changes; (2) to establish protocols for managing water-related information effectively., (3) to improve the efficiency of the use of irrigation water from a technical and socio-economic point of view towards smart agriculture and sustainable collective water management, and (4) to kickstart a platform for two-way knowledge exchange between researchers, end-users, and decision-makers. Our investigation concerns some strategic hydrological basins in Morocco embodying a gradient of aridity increasing towards the south (e.g., Souss-Massa, Tensift and Oum-Er-Rbia Basins). Limited water resources and dependent on uncertain climatic conditions characterize them, and they are home to a continuously developing irrigated agricultural activity. The consortium leading the study project is made up of universities with a panel of socio-economic partners. In order to successfully conduct this kind of study, the consortium capitalizes on the diversity of members' experiences, the scientific achievements of previous projects on water and sustainability, the network of scientific observatories installed in the project basins, and the network of national and international partners consolidated between the members of the consortium. A synergistic combination of new technologies, tools and approaches is used. This combination includes new technologies for in situ measurement, aerospace observations, multidisciplinary modelling, and the principles of collective intelligence for the co-construction of a model for the sustainable management of water resources. "Smart" protocols for managing irrigation water at the parcel based on agrometeorology technologies is carried out and introduced to farmers. Reviewing the governance of water resources in the three regions based on technological and scientific advances is among the expected impacts of the study. Other impacts include: the contribution to the digital transformation of the water and agriculture sector, the contribution to improving the living conditions of farmers by improving the efficiency of their production methods, and the increase in attractiveness of the agricultural sector to youngsters. The training and capacity building aspect is a solid component in this sense. Our investigation shows significant results in the assessment crops water needs and valuation for sustainable water integrated management. The experimental sites are a good example in this regard.

ABSTRACT. The majority of irrigation districts and water supply organizations in the U.S. and throughout the world use canals to deliver water. Supplying the correct amount of water in this manner to the intended user is difficult and often results in the loss of water. This loss occurs because of travel times from the release of water at the source to the field delivery point. In addition, sediment accumulation and vegetation growing in the canal also interferes with timely deliveries.

Many irrigation districts have installed automation equipment consisting of sensors, gate actuators, site computer control units and data communication systems to try to deliver water in an accurate and timely manner. These components help, but a feedback control system, similar to what is used in industry, would significantly improve water deliveries. This is especially true as travel times change throughout the irrigation season. It is very difficult to determine the time delay change and subsequently make an adjustment to delivery procedures.

The objective of this project was to develop a feedback control algorithm that will operate canal water control sites and provide timely water deliveries using a ratio control technique. This was accomplished by developing an 8 reach hydraulic model that solves the St. Venant open channel equations. The dimensions for the main canal of an irrigation district near Las Cruces was used for this model. This work demonstrates the robustness of this water delivery method by showing how water can be accurately transported during an irrigation season in spite of changes that typically occur in travel times. The flexibility of this control method is further demonstrated by changing the model parameters to reflect changes in vegetation growth as well as sediment accumulation. This ratio control method is further compared to an industry standard control method called proportional integral control (PI). This work concludes that the ratio control method is effective for operation of an irrigation canal system by delivering water to the desired location at the desired time and in the correct amount.

ABSTRACT. The East Nile Delta area is a natural extension of the Nile Delta, characterized by a considerable increase in groundwater head that is linked to the growth of agriculture and urbanization. In such an agricultural area, the Quaternary aquifer is a crucial supply of water for different purposes. The management of this type of aquifer necessitates the parameterization of a realistic groundwater-flow model, as well as the characterization of the aquifer shape, spatial distribution of its features, and local interconnectivity. In order to establish a secure framework for the management of the Quaternary Aquifer in the East Nile Delta, the current study offers a calibrated 3D GIS-based groundwater flow model. A 19-layer grid was constructed based on 93 borehole data in order to simulate the characteristics and the groundwater flow in upper 100 m fluvial deposits of the Quaternary aquifer. The model calibration was performed in both steady state and transient conditions, using the trial-and-error method implemented in the finite difference algorithm MODFLOW 2000. The steady state model is calibrated based on the 1991 groundwater head of the hydrogeological map of Egypt. While that for the transient, a period from 1991 to 2005, capturing the changes in the groundwater system over this time frame is employed. The transient model is subsequently calibrated using groundwater head data collected during a two-year field study conducted from 2004 to 2005. Finally, to assess the forecast accuracy of the transient model, a comparative analysis was conducted between 119 groundwater level data sourced from published literature in the years 2013 and 2014 and the corresponding model predictions. The findings of this comparison revealed negligible disparities between the observed data and the model predictions. Based on the actual and supposed extraction rates of the Quaternary aquifer, nine stress scenarios with different management schemes were suggested. Located north Mit Ghamr, the northern confined aquifer requires a maximum permissible total groundwater abstraction between 2.5 and 5.5 million m3/yr. that maintain an average drawdown less than 0.45 m where standard point production rate may approach up to 1000 m3/d. Alternatively, the potential groundwater development within the southern confined aquifer is relatively higher with additional pumping of 450 million m3/yr. and 3000 to 4000 m3/d point discharge in standard production wells that achieve less than 1.0 m drawdown. The rising water table of unconfined aquifer, however, showed significant sensitivity towards infiltration of excess irrigation water with limited response to additional pumping. Thus, it is recommended to preserve the infiltration rate within a small range, as those of 2004, that maintain with additional pumping stress an average change of 0.25 m. The calculated water budget indicated that all pumping stress is predominantly balanced by seepage from Ismaelia canal and Damietta branch with slight contributions from irrigation canals and drains. These results are crucial in delineating efficient sustainable scenarios for the Quaternary aquifer management in the East Nile Delta region, known for dynamic land development.

ABSTRACT. Emerging pollutants (EPs) find their way into municipal wastewater streams due to a combination of household discharge and improper industrial disposal practices. Membrane technology offers numerous benefits and has been successfully used in various wastewater treatment applications. However, these systems are susceptible to fouling, especially when used to remove EPs, limiting their widespread adoption. Approximately 50% of the total energy costs are attributed to addressing fouling issues, and the use of harsh chemicals for membrane cleaning adds an additional environmental burden. Enzyme active membranes (EAMs) have recently been suggested as an alternative technology that may have the potential to overcome the challenges faced by conventional EP treatment techniques. This research aims at the development of a novel bioactive membrane, by immobilizing biocatalysts on ultrafiltration membrane to be used for the treatment of wastewater contaminated with EP. Full characterization of the developed bioactive membranes is carried out to assess the practicability. In addition, the effectiveness of ibuprofen, selected as a model EP, removal is studied. A model that considers mass transfer and enzymatic reaction kinetics is developed and used to predict the behavior and optimize the process. The use of structured nanomaterials that provide high surface area, modified pore size distribution, and adjustable pore shape, are also tested as carriers for enzyme immobilization. In such a system, the interactions between the enzyme and the substrate are considerably enhanced owing to the reduced mass transfer paths, while the contact time can be controlled by the mass transfer rate. This results in reduced losses of substrate and catalyst, higher yields, cleaner products, and inhibiting fouling that would occur if these large molecules were deposited on the membrane. Successful findings of this work could open new possibilities for using fibrous EAMs in environmental protection. This research addresses both national and global trends marked by growing water scarcity. It presents an opportunity to create sustainable water treatment methods that can enhance the country's water security in the years ahead.

ABSTRACT. Water scarcity is a challenging problem in arid and semi-arid environments and great efforts are required to achieve the sustainable development goals particularly SDG2, SDG6 and SDG11. To cater for the increasing water demand from municipal, agricultural, and industrial sectors, dependance on groundwater resources is increasing because of the non-availability of adequate freshwater from surface water resources. This increased pressure is raising concerns on the sustainability of groundwater resources particularly in urban and arid or semi-arid environments. The urban environment is characterized by the changing landscape, increased population, infrastructural growth, urban sprawl of unorganized development, voluminous wastewater generation from industrial development, and urban heat island, and all these forcings complicate the management of water resources as the groundwater resources in such setup get impacted adversely in terms of both quantity and quality. The objective of this paper is to identify the factors responsible for influencing groundwater regime adversely and to present a modelling framework combining machine learning and remote sensing that can be utilized to evolve strategies for ensuring groundwater sustainability in arid and semi-arid environments. The present study explores how the developed modelling framework can be employed in urban and rural setups of semi-arid environment for evolving strategic measures for sustainable groundwater management. The study reveals that the intensified urbanization has changed the landscape dynamics and increased the surface imperviousness leading to high runoff and low groundwater recharge. This has altered the groundwater availability and quality spatially and temporally. The mismanagement of voluminous wastewater generated from diverse groups of industries find pathways to affect the groundwater quality. Whereas the rural setups of semi-arid environment show water stress and reduced agricultural productivity for the cultivated crops due to reduced availability of irrigation water of desired quality. The study conceptualizes various scenarios of urban and rural setups of semi-arid environment based on the secondary data and several strategic measures are evolved using the developed framework to offset the hydrological damages and pave the way for water sustainability.

ABSTRACT. Despite considerable site and groundwater remediation efforts, contamination of water resources is still a significant problem in the U.S. Although many of the contamination sources tend to be from decades of improper disposal of waste, recent extreme incidents also generate extensive contamination. Technologies for remediation of contaminated materials have been under continuous development, validation and implementation for almost three decades. Due to difficulties in locating contamination in the subsurface – and challenges in addressing the complex and heterogeneous physical, chemical, biological and thermal subsurface conditions – only a small fraction of contaminated sites have been cleaned and contamination is still a critical issue. Electrochemical oxidation systems offer effective strategies for in situ remediation of sites contaminated with many groups of persistent and toxic organic pollutants. The Electro-Fenton (EF) process is an Electrochemical Advanced Oxidation treatment process for many groups of persistent and toxic organic pollutants in water. In this process, H2O2 is electrogenerated in situ via 2-electron reduction of dissolved oxygen (DO) in an acidic medium with continuous regeneration of Fe2+. H2O2 is activated by Fe2+ to form highly oxidizing, non-selective hydroxyl radicals (•OH) for pollutant transformation. The strong oxidation potential of the •OH makes the EF process a potentially powerful treatment for many groups of persistent and toxic organic pollutants in water. However, despite its ease of operation and cost effectiveness, the EF process has a number of drawbacks for use in a portable system: 1) the EF process requires injection of air or O2 to facilitate formation of H2O2; 2) formation of H2O2 requires an acidic environment and the presence of a noble metal catalyst (e.g., Pd); and 3) the process is based on the Fenton reaction which requires the Fe(II)/Fe(III) redox cycle for H2O2 activation to form •OH, causing formation of an iron sludge when the system is neutralized. Based on recent findings, we tested a novel “EF-like” EAOP process for advanced oxidation that overcomes these challenges. This work is part of the PROTECT Superfund Research Center which is interdisciplinary and multi-institutional Center that studies water pollution and cleanup as well as the transport, exposure, health impact in Puerto Rico.

ABSTRACT. With increasing surface water scarcity for irrigation, brackish water is increasingly used for irrigating a variety of crops of arid and semi-arid ecosystem. This study evaluated the effects of brackish water irrigation on gas exchange and water uptake in tomato plants and proposed a new model to relate tomato evapotranspiration and yield to irrigation water salinity. A greenhouse experiment was conducted with five salinity treatments: tap water of EC 0.6, brackish water of EC 2 and 3, and reserve osmosis (RO) concentrate of EC 4 and 6 dS m-1. Results indicated that increasing irrigation water salinity reduced plant evapotranspiration (ET) and consequently increased deep percolation (DP) and leaching fraction (LF). Tomato yield decreased with increasing salinity, but moderate salinity (2 dS m-1) stimulated leaf photosynthesis rate (Pn), transpiration rate (Tr), and stomatal conductance (Gs). The higher concentrations of Na, Mg, Ca and K in soil and irrigation water increased Na and Mg but decreased K and Ca concentrations in plant organs. The accumulations of micronutrients (Fe, Mn, Cu, B, and Al) except Zn in the whole plant decreased with increasing salinity levels. Tomato yield was significantly correlated to accumulations of Na, Ca, P and N in whole plants. The simple proposed new model provided the good estimation of both yield and ET under saline conditions. The calculated soil salinity threshold values (ECe*) from the new model were 1.73 dS/m for yield and 2.52 dS/m for ET, respectively, indicating that the reduction of tomato yield was more sensitive than the decrease of ET under salinity. To achieve an optimal comprehensive plant nutrition and growth, the study showed that tomato plants can be irrigated up to a water salinity of 2 dS m-1 without causing stress.

ABSTRACT. Water scarcity is an increasing issue in most countries. And solutions such as water desalination are expensive processes and are not afforded by most third world countries. Therefore, scientists have been inspired by natural creatures in the mechanism and ability of harvesting water from the atmosphere, in which water droplets are suspended in the form of humidity or fog. Fog harvesting is a sustainable, simple, and cost-effective technique to collect water from the air. This current study proposes the use of 3D printing as an advanced processing technique for the fabrication of polymeric membranes that will be further chemically functionalized and used to collect water from fog. Accordingly, the 3D-printed membranes will be chemically functionalized to create surfaces with different degrees of hydrophilicity and hydrophobicity, allowing for the evaluation of the impact of changing the surface chemistry of the membrane on the effectiveness of fog harvesting.

ABSTRACT. Water is a key input for agriculture production. However, the increasing issue of water scarcity, often associated with climate change, poses a significant obstacle to agricultural expansion, particularly in arid and semi-arid regions. Water crises in agriculture are enhanced by the inefficient water management and competition with other sectors contending the access to the water resources. In this context, exploiting technological advances is increasingly urgent to optimize water utilization in agriculture and ensure the sustainability of food supply for future generations. Here, an advanced irrigation scheduling method to achieve irrigation water optimization for open-field crops is evaluated against field data and, in comparison with a standard approach that only exploits crop growth models. The proposed method is based on the integration of such crop growth models with data acquired by remote sensing technology to demonstrate that a sequential assimilation of satellite crop imagery can be efficiently used to support irrigation planning at farm level as well as at irrigation district level. This study presents an implementation to crops of processing tomato in Campania Region (Southern Italy), by integrating the following components: • Canopy cover data derived from multispectral images captured by Sentinel-2 satellite constellation. • Weather, soil water content and matric potential obtained from a proximal sensors. • The agro-hydrological model AquaCrop simulating both crop growth and soil water balance. Processing tomato (Solanum lycopersicum L.) was cultivated in year 2021 (41°00′26.33″ N 14°10′13.93″ E) in parcels of about 5 ha. Soil was ploughed at 40 cm depth, and tomato seedlings were transplanted on 7th of April 2021 in continuous double rows wprecipitation were collected at a complete weather station located in the study area. Soil water content and soil matric potential were monitored to establish the optimal irrigation volumes, by keeping the soil water content above 50% of the readily available water, as estimated from soil hydraulic properties. The applied total irrigation volume was 308 mm. Field irrigation data and tomato yield were taken as reference data for evaluating AquaCrop prediction performances in the following two cases: - AquaCrop implemented with parameter settings based on the best a priori knowledge of the processing tomato cultivar; - AquaCrop sequentially corrected by forcing the simulated canopy cover with the canopy covered observed by Sentinel-2 images. The results showed that second strategy could lead to better prediction performances of both the irrigation crop water requirements and crop yield, by reducing the error by 10% and 20% respectively.

ABSTRACT. Groundwater salinization in coastal aquifers is a major socioeconomic challenge in Oman and many other regions worldwide due to several anthropogenic activities and natural drivers. Therefore, assessing the salinization of groundwater resources is crucial to ensure the protection of water resources and sustainable management. This research uses multiple ensemble machine learning models (ML) of Catboost (CB), Adaboost (AD), Extra Trees (ET), and Random Forest (RF) optimized by Grid Search (GS) and Bayesian Search (BS) for predicting total dissolved solids (TDS) as metric for groundwater salinization in a coastal aquifer in Oman. To improve the overall accuracy and predictability of ML models, the ensemble models were selected as inputs in the novel committee machine ensemble model-based bagging (CMEM-BA). All models were evaluated and validated using several statistical and extensive graphical representations. The results indicated that the performance of optimized ensemble models by BS was better than of those by the GS method based on the training and testing phases. While the CMEM-BA model outperformed all models regarding predictive accuracy and generalization of new data. Our approach will help to address water scarcity/degrading water quality, which is one of the essential targets of the sustainable development vision in coastal aquifers around the world.

ABSTRACT. This study was conducted to examine how the Fujairah Emirate, United Arab Emirates (UAE), basins behave hydrologically. The landscape of the emirate is heterogeneous and characterized by rugged mountains, coastal plains, deserts, and wadis. The land use and land cover of Fujairah, like other emirates in the country, have changed tremendously in the past few decades. The diversity of landforms and changes in land use and land cover influence the hydrological process within the emirate. The impact of land use, land cover, and soil characteristics on flooding potential was investigated through hydrologic model simulations. Land use and land cover were delineated using WorldView-2 satellite high-spatial-resolution images. Moreover, the impact of land use, land cover, and soil types on hydrologic response was assessed using the Hydrologic Modeling System (HEC-1) in three basins of the Fujairah Emirate. The analysis of peak flow characteristics revealed a wide range of hydrological responses among the three basins. The model calculated the peak discharge for different return periods. It also estimated the peak flow timing and total runoff volumes. These differences result from a combination of factors, including basin size, topography, land use, and soil types. The findings of the analysis are crucial for fine-tuning hydrological models and improving flood predictions. Furthermore, it can be used for some levels of emergency response and general planning purposes.

ABSTRACT. The availability of accurate and reliable rainfall data is crucial in Saudi Arabia, which is highly vulnerable to flash floods and climate change. This study implemented a comprehensive quality control procedure for more than 300 rain gauge stations distributed across Saudi Arabia. The rainfall data was obtained from three different sources: Ministry of Environment, Water, and Agriculture (MEWA), National Oceanic and Atmospheric Administration (NOAA), and the Presidency of Meteorology and Environment (PME). The evaluation process consists of three phases: primary quality control, absolute quality estimation, and relative quality control. The final step involves comparing the rainfall data of nearby stations, considering their proximity, altitude difference, and correlation to make a complete assessment of each station. The stations were then rated as acceptable, good, or excellent based on the mentioned quality control criteria. Although Saudi Arabia has limited rainfall stations with low station density, temporal offsets, and frequent and prolonged data gaps, a thorough quality control procedure was applied and the most reliable stations were identified. A major challenge now is to provide free access to this database for research and non-commercial use due to national data protection laws.

ABSTRACT. Groundwater is a critical natural resource for sustaining various sectors and promoting sustainable development in arid regions. In Qatar, located in the Arabian Peninsula, groundwater reserves are essential for meeting agricultural water demand. However, the depletion and degradation of these resources pose significant challenges to their sustainable management, exacerbated by the increasing emphasis on local food production goals, urbanization, and climate change impacts. This work explores the application of the GIS-integrated eco-hydrological model, Soil and Water Assessment Tool (SWAT), in Qatar to enhance groundwater resource management. We integrated measured and remote sensing spatial and temporal datasets to simulate the hydrological processes governing groundwater recharge and availability from 1987 to 2016. The study covers an area of approximately 11,600 km2 and incorporates diverse data types, including elevation, land use/land cover, soil properties, and weather records. Our findings provide valuable insights into the capabilities and limitations of SWAT as a groundwater management tool in the Qatari arid context. The model was calibrated and validated by comparing the timing and volumes of observed runoff events with simulated outputs. The results demonstrate that SWAT accurately predicted groundwater recharge patterns as compared to previous literature values. Furthermore, SWAT was used to identify areas with high recharge potential. However, the model performance was sensitive to certain input parameters, such as soil properties and weather data, which require careful parameterization. Specifically, the study highlights the need for improved data accuracy and availability, the necessity of incorporating local hydrological knowledge for model calibration, and the importance of considering different management practices to enhance groundwater sustainability in the country. In summary, this study showcases the significance of GIS-integrated hydrological modeling and the use of remote sensing data in sustainable groundwater management, particularly in data-scarce regions. The outcomes of this work enhance the knowledge about Qatar's groundwater resources, providing valuable information that can inform policymakers, water managers, and stakeholders involved in decision-making processes related to groundwater use, conservation strategies, and artificial recharge programs.

ABSTRACT. Flood hazards are a global concern impacting infrastructure, property, and life, resulting in substantial economic, social, and physical losses. This is primarily attributed to climate change, human activities, and land use/land cover changes. It is thus crucial to examine flood-related indices and search for changes that influence flood hazards and risks in river basins. A field survey was conducted and using questionnaires, locals were interviewed to ascertain information about the catchment. The study further assessed the flood risk of the Mpanga River using the Rainfall Runoff Inundation Model and the Analytical Hierarchy Process method. It involved statistical analysis of the discharge data using the Gumbel method. Further analysis of the different flood events and maps for varying return periods was performed to identify the hot spots and develop flood risk maps and propose possible mitigation measures at the Mpanga River basin scale. The Nash- Sutcliffe model efficiency (NSE) for the monthly simulation was 0.83 with a coefficient of correlation of 0.97 for the calibration period while the validation period generated a Nash-Sutcliffe model efficiency (NSE) of 0.71 and coefficient of correlation of 0.89. The catchment is susceptible to flooding with the upstream part liable to a greater depth of flood. An increase in both the channel depth, width and in the soil, depth showed a reduction in the flooding extent. There is an imperative need to mitigate flood risks, particularly by implementing engineering structures such as dams aimed at reducing the volume of water flow.

ABSTRACT. Urban environments are now-a-days experiencing multifaceted hydrological problems that include inadequate stormwater drainage, water logging, urban flooding, declining groundwater level, and water scarcity. The precise assessment of urban runoff is a prior requirement for addressing such problems. The changing land use, spatial heterogeneity, and hydrological uncertainty make the runoff assessment complex, particularly the peak runoff resulting from critical storms in semi-arid urban environments. The objective of this paper is to present an integrated framework of remote sensing, geographic information system and fuzzy logic for spatial assessment of surface runoff from urban systems and their temporal dynamics under varying storm frequency. The runoff coefficient is computed based on LULC, soil and slope. The LULC and slope are derived from multispectral satellite images, whereas the rainfall and soil data are obtained from measurements and field investigation, respectively. Given the ambiguous and uncertain nature of the geological, environmental, and meteorological variables in urban regions, the fuzzy logic technique offers a flexible and adaptable strategy for modelling urban runoff. To account the effect of uncertainty associated with the meteorological measurement and urban catchment characteristics in runoff assessment, rainfall intensity and runoff coefficients have been considered as fuzzy parameters and thereby fuzzy membership functions were generated for them. The fuzzy overlay technique was employed to estimate the peak runoff resulting in the urban catchment characterized by various LULC classes. The developed modelling framework has been applied to the urban system of Delhi, capital of India, which is characterized by a significantly high urbanization and economic expansion rate and has spatially brought rapid changes in the city’s urban structure in recent past. The study uses SRTM DEM for determining slope and Landsat 8 satellite imageries for LULC. The study reveals that there have been significant changes in the land use pattern showing increased impervious area. The study shows rapid transformation of open land to imperious areas, particularly in the North, North-West, North-East, South-West and South districts which has resulted in high runoff discharge. The generated surface runoff finds its way to join contaminated drains or some pathways leading to the Yamuna River. The increased impervious areas have limited direct infiltration of rainfall for groundwater recharge. There is a need to improve the stormwater infiltration rate and reduce the runoff rate by focusing on increasing the share of permeable surfaces in the Delhi region. The study highlights that quantifying the urban runoff from different land uses is essentially required to cope with various water challenges including ensuring sustainable water management in the region due to limited and uncertain water availability.

ABSTRACT. In regions characterized by aridity, where the scarcity of water resources stands as a critical concern, the accurate assessment of rainfall holds paramount importance. This precise evaluation not only amplifies agricultural productivity but also facilitates enhanced water resource management strategies, simultaneously mitigating the vulnerabilities to both droughts and floods. Within this context, the present study delves into the application of machine learning (ML) algorithms for the purpose of predicting monthly precipitation patterns within the geographical confines of the United Arab Emirates (UAE). Diverse ML methodologies, encompassing Random Forest, Long Short-Term Memory (LSTM), Support Vector Machine (SVM), XGBoost, Multilayer Perceptron (MLP), Elastic Net, and Gradient Boosting, were employed for the analysis of the dataset, alongside ensemble models that amalgamated two or more distinct ML techniques. While Gradient Boosting, LSTM, and XGBoost exhibited the most robust correlations with observed rainfall (0.80, 0.85, and 0.78, respectively), these models encountered challenges in accurately capturing instances of intense precipitation, consequently yielding decreased precision in predicting extreme events characteristic of arid climates. This inadequacy in prediction, particularly during periods of severe drought, was pronounced. Conversely, MLP, Elastic Net, and SVM showcased limited capability in anticipating rainfall within the study region, reflected by correlation coefficients consistently falling below 0.5. The implications derived from the study underscore that both XGBoost and LSTM hold potential in effectively forecasting rainfall in the UAE, further accentuated by the potential for refinement through ensemble models. The research underscores the intricate nature of forecasting instances of exceedingly heavy rainfall and accentuates the pressing need to construct more refined models tailored to this context. Concurrently, the study underscores the promise of leveraging ML techniques to enhance rainfall prediction accuracy within the UAE. Moreover, it emphasizes the necessity of augmenting the input dataset, including parameters of climatological significance, to bolster the predictive prowess of ML models in arid settings. This accentuates the indispensability of fusing domain expertise into ML models, particularly when addressing intricate systems such as rainfall prediction in arid regions.

ABSTRACT. Studying the characteristics of rainfall and the frequency and quantities of extreme events are key factors contributing to flood risk management. Egypt recently experienced different levels of flood situations due to heavy rainfall in several regions. Due to insufficient or inaccurate observed data in Egypt, freely available satellite data is a promising resource for conducting rainfall-related studies. In this study, hourly precipitation data from the Japan Aerospace Exploration Agency (JAXA) satellite were used to conduct the temporal and spatial analysis of event-based rainfall in Sinai, Egypt. The data used were checked and cleaned, and then the investigation was performed for the most suitable minimum inter-event time (MIET) by three methods, namely, the autocorrelation analysis (AutoA), the average annual number of events analysis (AAEA), and the coefficient of variation analysis (CVA). The results indicated that an 8-h MIET duration was appropriate for the case study. Subsequently, a dataset of storm-based characteristics (intensity, duration, and dry period) was established, and spatiotemporal analysis of different rainfall characteristics was conducted across Sinai. The results of this research provided a clear perception of the rainfall situation in Sinai and could be the starting point for utilizing hourly satellite rainfall data based on the MIET approach to flood mitigation and water resources management studies.

ABSTRACT. A flash flood represents the most prevalent natural disaster that endangers the lives of people, as well as the stability of the economy and infrastructure. To effectively address this threat, watershed planning and management become crucial tasks, especially in identifying areas prone to flash floods and reducing their impact, particularly in regions like the Fujairah Emirate. These sudden floods can result in severe consequences for both individuals and infrastructure, emphasizing the importance of proactive planning. The utilization of geographic information systems (GIS) and a weighted overlay technique serves as a robust approach to predicting flash flood-prone zones (FFVZ). This approach integrates various factors, such as rainfall, elevation, slope, land use/land cover, drainage density, geology, geomorphology, and soil characteristics, derived from satellite data. This comprehensive framework helps in comprehending the complexities of flash flood susceptibility. Additionally, a classification system categorizes vulnerability levels, offering a clear understanding of the varying degrees of risk within the studied area. This classification can be a valuable tool for prioritizing mitigation efforts and allocating resources to the most vulnerable areas. Identifying critical sites with high vulnerability ratings is essential for effective emergency planning and response strategies. Furthermore, historical data from Landsat and Sentinel 2 imagery, which track changes in vegetation and urban development over time, shed light on the evolving environment and its potential link to flash flood susceptibility. This historical perspective provides valuable insights into long-term trends and guides future urban development planning.

ABSTRACT. India is one of the most flood-prone countries in the world and one-eighth of the country's geographical area is subjected to floods. The flood extent mapping is critical to disaster management, humanitarian relief work and decision-making. Efficient monitoring and prediction of floods and risk management for a large river is quasi-impossible without the use of Earth Observation (EO) data from space. As a matter of fact, one of the most important problems associated with flood monitoring is the difficulty to determine the extent of the flood area as even a dense network of observations cannot provide such information. The flood extent information is used for damage assessment and risk management, and benefits to rescuers during flooding; it is also very important for calibration and validation of hydraulic models to reconstruct what happened during the flood and determine what caused the water to go where it did. During floods the cloud coverage is more, thus very difficult to acquire and obtain information using earth observation satellites suing optical sensor . To overcome the limitation, SAR sensors can acquires the ground information even in cloud cover. Therefore risk management can be performed as early as possible. Handling of SAR information is valuable for this situation since typical optical satellite pictures will be prevented by mists and is difficult to plan water bodies. Yet, with SAR pictures we can notice the landscape despite the fact that there are numerous obstructions like mists dust and so on as it is Radar Tech dissimilar to optical one. Curtailed type of open source PC vision library which upholds python, c++, and java interfaces. We utilized a SAR level 1 data which is available in ESA’s( European Space Agency) open source data of sentinel satellites. We downloaded sentinel 1 a data as a safe file. The preprocessing of SAR data was perfomed with python first as it is the raw image which is not clear. Then after this , the binary flood mapping which basically maps the water bodies present is carried out. And finally we filter out the known water bodies and map the extra water bodies due to floods. We have implemented Flood mapping using python. This will be helpful during flood times. As we get the output in kmz format we can even view it by overlapping it on google earth and closely examining the flooded areas. This might help the flood rescue team during critical situations. It is very much useful during flood times when the water starts to enter the streets and rescue teams have to start rescue operations. This will be helpful to give an overview where water has been reached already and make a rescue plan accordingly. A CNN machine learning program would help it further more by predicting the flood areas which can be done as extension to this project

ABSTRACT. Gabčíkovo Water Structure is a crucial water structure in terms of hydropower and navigation on the Slovak part of the Danube river. Navigation and hydropower are coupled and therefore solved together on the Gabčíkovo Water Structure and Danube river under the Expert Control System (ECS). ECS has two models: Optimization Model and Simulation Model. Optimization Model controls filling and emptying process of the navigation ship lock at the Gabčíkovo Water Structure. In order to perform this process, the software had to be based on theoretical analysis of the relationships of individual physical quantities and parameters expressed by mathematical apparatus. Simulation Model controls discharges and water level regime with respect to navigation safety requirements on the entire section of the Danube river affected by the operation of the Gabčíkovo Water Structure. The software tool for such a complex hydraulic process that would be able to evaluate the impact of the operation had to be developed. The paper describes the principles on which both software work. ECS is a part of the Gabčíkovo Water Structure upgrade and modernization project.

ABSTRACT. Arid regions such as Djibouti are faced with severe water scarcity, further complicated by the lack of data, posing a challenge to the assessment of water resources. Although precipitation events remain infrequent in those regions, climate change projections indicate an intensification, which not only heightens the risk of flash floods exposing communities but also alters groundwater recharge patterns. To address the issue of water resource assessment, this study was conducted to identify flash flood-prone zones and potential groundwater-rich areas in sedimentary and deep basalt zones using several data sources along with integrated hydrologic modeling techniques. The aim is to enhance risk mitigation and water resource utilization for both existing and new settlements. The data collection process encompassed extensive field surveys, satellite imagery, topographical information, geological insights from available maps, on-site observations, meteorological data, flood records from photographs and measurements, groundwater monitoring including information on groundwater usage. Modeling was conducted using GETFLOWS, a terrestrial fluid flow simulator integrating surface and subsurface flows for robust simulation with minimal data requirement. The simulator implements physics-based equations to seamlessly simulate infiltration, groundwater recharge, and overland flooding. The 3D model was built using gathered data, specifically tailored for the Ali Faren catchment, which forms a crucial part of the vast Ambouli watershed in Djibouti. Specific to this region, the network of wadis has been accurately deduced from high-resolution DSM (AW3D provided by RESTEC), satellite imagery, specialized knowledge, and previous studies. While the model's discretization system was tailored to this wadi network, the integrated nature of the simulator also allows surface flows to deviate from modeled paths. When necessary, the model was adjusted based on the surface flow trajectories obtained from initial simulations. Survey of micro-tremor arrays has been used to estimate shallow parts with wadi sediment depths fixed at 15 to 20 meters. Historical precipitation events covering the period 2000-2010 with daily rainfall record were used to simulate the water flow at a time step smaller than a day. The model was able to simulate transitory surface flooding and groundwater recharge, to identify wadi networks where ephemeral floods propagate and infiltrate to shallow groundwater. Simulations were conducted to show groundwater recharge after precipitation events of varying intensities. High-risk flash flood zones were delineated based on model results for extreme scenarios informed by probable maximum precipitation estimates. This study shows that the proposed model can pinpoint areas susceptible to flash floods and provides insights on the impact of extreme rainfall on water resources in semi-arid region. Advanced calibration using satellite soil moisture and additional runoff observation data could further improve the model accuracy. By linking hydrologic simulation with risks to communities and infrastructure, this research can support development of early warning systems. As a result, the techniques presented show promise for water sustainability and hazard mitigation in arid climates facing climate change.

ABSTRACT. Agricultural drought monitoring is indispensable as it alters the course of the natural agricultural system, causing severe constraints on achieving the UN SDG goal of food security. Hence, monitoring agricultural drought is vital in the present scenario. The most of agricultural drought indices are mainly or solely based on rainfall at a monthly scale. Very few studies considered multiple parameters along with rainfall at an intra-monthly scale and vegetative indices for calculating drought severity. Due to climate change and erratic rainfall patterns, conventional drought indices are not suitable for addressing intra-monthly rainfall patterns. To address this challenge, the current study developed a methodology for calculating agricultural drought using the standardized precipitation index (SPI) and NVSWI from 2000 to 2022 by also accounting rainfall at an intra-monthly scale. SPI is a multiscalar drought index that can be used to monitor drought at different durations. NVSWI is the standardization of VSWI, which is the ratio of Normalized Differential Vegetation Index (NDVI) and Land Surface Temperature (LST). It will help in identifying the dry and wet spells that occurred in a very short time. This study employed IMD gridded rainfall datasets for SPI calculation at 0.250 * 0.250 spatial resolution. NDVI and LST data are acquired from MODIS data sources. This study also incorporated the deviation coefficient to monitor the drought severity efficiently. Both the results are combined with a linear weightage function and the resulting index is called the integrated drought index. Weights are assigned from the principal component analysis. The Karnataka state of India is considered as a study area to test the model. The proposed methodology can be applied to other regions with similar climatic conditions across India, which can identify the drought-vulnerable and water-stress regions with the indication of its severity in the different climatic conditions. This Index is better for understanding the agricultural drought patterns spatially and temporally across diverse climatic conditions, even in remote and data-scarce regions, which helps the agricultural community and policymakers.

ABSTRACT. Comprehending the progression and occurrence of drought holds significance in mitigating its associated impacts especially in warming regions of the world. The main objective of this study is to monitor and assess droughts in Morocco using various widely used meteorological indices. Those indices are the Rainfall Anomaly Index (RAI), the Standardized Precipitation Index (SPI), the standardized Precipitation Evapotranspiration Index (SPEI) and the self-calibrated Palmer Drought Severity Index (scPDSI). The focus of this study is Morocco which is a mediterranean country that has varying climate regimes and is heavily affected by dry spells. Gridded reanalysis data of rainfall and temperature for the period 1983-2021 is extracted from PERSIANN-CDR and ERA5, respectively. These products provide a resolution of 0.25° over the study area, and a long record of data that is required for climatic studies. In the beginning of the analysis, the study area’s climate regimes and trends were evaluated; The assessment using the aridity index unveiled three primary climatic patterns within the study area: subhumid, semi-arid, and arid. Moreover, the analysis of rainfall and temperature trends indicates a noticeable inclination toward a warming climate in the region. The evaluation of the four indices indicates that the rainfall-based index RAI exhibits subpar performance in monitoring drought within the study area. Specifically, RAI demonstrates a tendency to report drought occurrences more frequently than the other indices, in addition to slightly underestimating the severity of significant dry periods experienced in Morocco's history. Moreover, the scPDSI is computationally demanding and an overestimation of drought duration as well as an underestimation of its intensity was noticed for various dry events. Regarding overall performance, both SPI and SPEI excel in consistently identifying and assessing drought severity, with SPEI particularly suited for arid and semi-arid regions, better accommodating Morocco's warming climate conditions. The assessment of droughts in Morocco using the SPEI between the periods 1983-2000 and 2000-2021 indicated an increase of dry events frequency by 33%, duration by 5.9% and severity by 2%. SPEI also reported the years 2017 and 2020 as being the longest dry events for the study period, lasting for 9 and 8 months respectively. While the most intense events were witnessed in the years 1990, 1992 and 2016. The conclusions drawn from this research into meteorological indices and droughts serve as a pivotal foundation for drought assessments, the establishment of early warning systems, and future forecasts of drought conditions across Morocco and its various regions.

ABSTRACT. Anthropologically, water transcends its chemical composition; it serves as a cultural lens challenging simplistic notions, urging a comprehensive understanding rooted in social, historical, and local contexts. Guided by the Nine Planetary Boundaries Framework, we navigate the Anthropocene era, emphasizing the imperative to address climate change, biodiversity loss, and freshwater challenges for global environmental sustainability. Amid escalating water challenges intensified by climate change and urbanization, marginalized communities unfairly bear the burden of water scarcity. The transboundary Krishna River underscores the necessity for collaborative water management across Maharashtra, Karnataka, Telangana, and Andhra Pradesh. The study explores various water harvesting techniques, distinguishing between autogenic (rainwater capture) and allogenic (external sources like rivers and aquifers). Investigating 15 Indian ecological regions, the research tailors paradigms for each, emphasizing hybrid approaches. Autogenic techniques like surface water harvesting and roof water harvesting are analyzed. Hybrid methods, combining surface water with groundwater infiltration, are identified by Talab/Bandhis and Johads. Hybridizations observed in runoff-induced water harvesting (Jhalaras, Ahars-pynes) and flood water harvesting (Eris) enhance water conservation efficiency. The study systematically evaluates autogenic methods, identifying groundwater systems as the most threatened due to urbanization, pollution, and fluoride contamination. The study validates the mixed research methodology, combining qualitative inputs transcribed into quantitative empirical data using Johnny Saldana's methodology for qualitative coding. It offers a comprehensive understanding of water harvesting systems' sustainability, delineating characteristics, and analyzing hybrid models for efficient water resource harnessing sculpted to diverse ecological regions in India. In Telangana, the study explores unique water conservation strategies, focusing on aquifers and tank systems. It systematically examines diverse traditional water systems, emphasizing conservation rooted in India's traditional knowledge, revealing stepwells as vital components in settlements for optimizing floodwaters during the monsoon. In conclusion, the intricate interplay between settlements, surface water, and groundwater, exemplified by stepwells, serves as a valuable blueprint for contemporary water conservation efforts. The study underscores the urgency of preserving traditional water systems in Telangana to sustain agriculture and ecological balance, emphasizing the need for sustainable development practices. This contribution broadens the discourse on water management and conservation, offering insights into traditional practices that can inform contemporary strategies. The lessons from Telangana resonate globally, promoting sustainable water practices in semi-arid regions. In essence, it illuminates the intricate water management tapestry of Telangana, urging a balance between urban development and the preservation of age-old ecological systems for a sustainable future.

ABSTRACT. Global warming is leading to an increased recurrence of extreme events, triggering severe water scarcity in arid and semi-arid regions such as Morocco's Tensift Basin. Given the limited coverage of weather stations, accurately quantifying precipitation amount and variability over time and space becomes crucial. This study aims to evaluate the performance of eight satellite rain analysis, and merged precipitation products (SRMP) – PERSIANN, PERSIANN CDR, GPM IMERG, ARC2, RFE2, CHIRPS, ERA5, and MSWEP. The products were compared to data collected from fourteen weather stations in the Tensift Basin between 2001 and 2016, considering four-time steps (daily, monthly, seasonal, and annual) using volumetric metrics and categorical scores. The assessment also includes the estimation of SRMP for extreme precipitation by comparing extreme indices with those of weather stations. Similarly, it explores the behavior of SRMP during droughts by comparing their Standardized Precipitation Index (SPI) with weather station data to identify the most accurate products in predicting the onset, duration, and magnitude of droughts. To reduce discrepancies between SRMP and weather station data, bias correction was performed using cumulative distribution function (CDF) mapping. As result, PERSIANN CDR, GPM, MSWEP, and ERA5 emerged as the most accurate, with superior performance at monthly and annual time steps compared to daily ones. Seasonally, summer exhibited the poorest performance across all products, while autumn performed the best. Notably, PERSIANN CDR proved to be the most reliable product for water managers to detect extreme events. MSWEP, ERA5, and PERSIANN CDR stood out as the best products for studying the impact of climate change on drought. The results underscore the effectiveness of CDF mapping in correcting the bias, particularly in extreme conditions. This study provides valuable insights for water managers and climate monitoring in semi-arid regions, by improving the understanding of the impact of climate change on water resources through accurate simulations using precise climate data.

ABSTRACT. The water resources and allocation patterns in the Syr Darya transboundary River are affected by pluralities factors that warrant effective streamflow forecasting tools to improve regional planning and water management efficiency. This study used advanced machine learning models – LSTM-based models - to examine the contribution of rainfall data on streamflow forecasting performance by investigating five scenarios whereby rainfall data sets from different weather stations were incorporated depending on their geographical positions. Specifically, All-RF scenario included rainfall data collected in all 11 stations; Up-RF and Down-RF included only the rainfall data measured upstream and downstream of the streamflow measuring station; P-RF only included the rainfall data exhibiting the highest level of correlation with the streamflow data, and FO scenario only had streamflow data. Amongst all the scenarios, both the LSTM and BILSTM models performed best in the scenario where the streamflow was the only input feature (FO). Hence, the multivariate scenarios containing multiple input features, as opposed to the univariate scenario containing a single data type (flow), did not improve the predictive accuracy of either model, regardless of the positions of the weather stations. The P-RF scenarios yielded better prediction accuracy than all the other scenarios, including rainfall data. The results showed that the BILSTM model performed better than the LSTM model in almost all scenarios tested, achieving up to 24% and 22% reduction of RMSE and MAE values (in All-RF scenario), respectively. The findings of this study evidence the suitability of monolayer LSTM-based networks, especially BILSTM, with only streamflow data as input features for high-performance and budget-wise river flow forecast applications while minimizing data processing time.

ABSTRACT. Spatial information on plant-water requirement is the most crucial input for designing an efficient site-specific irrigation system. In quantifying this spatial information, canopy temperature-derived crop water stress maps could provide a potential solution. With the support of modern, advanced, and cost-effective remote sensing platforms like Unmanned Aerial Vehicle (UAV), aircraft, and Satellite, remote sensing data can be systematically collected with varying degrees of efficiency for spatial water stress assessment. However, each of these platforms provides remote sensing data at varying degrees of spectral and spatial resolutions, which can impact the user’s ability to develop spatial water stress maps and implementation of precision irrigation systems. Therefore, the main goals of this study were 1) to assess the feasibility and accuracy of UAV, aircraft, and satellite-based imaging for crop water stress quantification and crop health mapping; and 2) to compare and contrast the resolution of water stress zone identification for precision irrigation technology implementation. Thermal infrared (TIR) and multispectral images were obtained over a four-acre cornfield using a quadcopter (Matrice-100), aircraft (Ceres Imaging), and Satellite (Landsat-8). Spatial maps of canopy temperature and NDVI were developed using these images and analyzed for capacity to capture water requirements and crop health accurately. UAV imagery outperformed the other two platforms in providing detailed imagery and sensing changes in crop health throughout the field. For a sample area of dimension 82 m x 44 m, the UAV imagery provided 683 different types of canopy temperature values. In contrast, aircraft imagery provided 158 different values, followed by satellite imagery which provided only 5-6 variations in canopy temperature to represent the same area. Moderate and low spatial resolution imagery from aircraft (1-1.5 m/pixel) and satellite (30 m/pixel) was limited in detecting inter-row variability and outputting the average pixels of the crop canopy and inter-row space. Whereas high-resolution UAV imagery (1.5 cm/pixel – 6 cm/pixel) precisely distinguished inter-row gap from plants and provided crop-only pixels without mixing with background soil. UAV imagery was precise and sensitive in detecting crop variability between two nozzles of an irrigation pivot, while aircraft imagery was less precise and sensitive. Satellite imagery was not able to capture the variations at this small scale. So, overall, UAV and aircraft imagery remains competitive in providing infield crop health variability for site-specific management in agriculture. Satellite imagery is limited in providing infield crop health variability to design site-specific irrigation, especially for small-scale farms.

ABSTRACT. Climate change and associated droughts are major challenges to many countries especially to arid and semi-arid environments. In 2015/2016 the Tigray region (northern Ethiopia) was hit by drought, despite implementations of various interventions for four decades which include: soil and water conservation (SWC) at scale; constructions of water harvesting (WH) structures (more than 150 dams, thousands of check-dams, ponds, spate systems, etc); and groundwater development. To assess the performances/effects of previous interventions to drought resilience, we have carried out a comprehensive study on 14 representative watersheds for four years (2015 to 2018) that involved: inventory and evaluations of the implemented SWC and WH interventions on drought resilience, monitoring 68 groundwater wells (level and yield) and 33 springs (discharge), participatory stakeholder evaluations, and assessments on emerging challenges and opportunities related to groundwater development and management. Results of our study show that groundwater development in the region evolved from provision of drinking water for small communities in the 1990’s which was challenged by low productivity and drying of wells to rapid increase in groundwater development for various purposes: domestic water supply (coverage: 57%) and groundwater-based irrigation (about 20000 ha) in 2018. The scale and productivity of groundwater development was found to correlate with the scale and approach of SWC and WH interventions implemented in the region. Results of our monitoring show that: (a) the maximum and minimum groundwater levels were found to be 15.7m (May 2016) and 0.7m (November 2017) respectively, (b) the yields of groundwater wells varied between 5.5 L min-1 (May 2016) to 236.7 L min-1 (November 2017), and (c) the discharge of springs varied between dry (May 2016) to 236 L min-1 (October 2017). Groundwater wells and springs that are recharged by SWC and WH were found to be less affected by the 2015/2016 droughts and to any rainfall variability during the study period. The groundwater recharge observed in Tigray was not based on deliberate designs/constructions of recharge systems but rather due to secondary effects from unplanned leakages/seepages from WH structures. There is therefore a need to shift towards design and constructions of WH and SWC structures for enhanced groundwater recharge that considers various factors, among others: availability of excess runoff for recharge, selection of appropriate technologies/structures for recharge, identification of appropriate storages (suitable hydrogeological conditions) and climate variability. Despite the highly variable rainfall and droughts, extensive SWC and WH interventions have enhanced groundwater recharge and helped cope with droughts in semi-arid areas of Ethiopia; a lesson for further climate change adaptation in other similar environments.

ABSTRACT. Flash floods pose a significant threat to communities worldwide, causing numerous fatalities and substantial economic losses. Early prediction and mapping of flash flood risk can greatly mitigate their impact by enabling proactive evacuation plans and effective emergency response. In this study, we propose a machine learning approach utilizing XG-Boost and Random forest (RF) algorithms for flash flood prediction and mapping (FFPM) in the town of Annaba, North-eastern Algeria. Satellite imagery, historical reports, and field data were used to determine flood-inundated areas. XGBoost and RF methods yielded relatively comparable results in terms of the variables' relevance. The most significant parameters were shown to be in the following order of importance: slope, topographic wetness index (TWI), drainage density (DD), distance to stream (DS), curvature, rainfall, land use land covert (LULC), Elevation and aspect. Hyper-parameter setting optimization was used to boost the model performance. The obtained implemented models' accuracy values were 95.1 % and 92.2 %, for XGBoost and RF respectively, implying that the former performed slightly better. It was therefore adopted for mapping flash flood forecasts, and the results provided crucial direction for decision-makers about the establishment of future research sites.

ABSTRACT. The resilience to drought and climate change -in 1251 Hydrogeological Enclosures (HES)- is analyzed in this paper, as well as the suitability that these can present in the face to a possible artificial recharge that mitigate the effect of an unfavorable situation, such as a prolonged drought or the climate change. A Hydrogeological Enclosure (HES) is the basic unit of work that has been used by the Spanish hydraulic administration to evaluate and quantify groundwater resources in the Kingdom of Spain. It has been defined as each of the different hydrogeological sectors that within the same body of groundwater (GB) drain into different bodies of surface water (SB). The methodology that has been used in this paper makes use of the Standardized Precipitation Index (SPI); since this index is a direct indicator of water scarcity due to drought, as well as of the daily climatic series of precipitation provided by the State Meteorological Agency of the Government of Spain (AEMET), and of the Post-process carried out by the same Organization of the European project ENSEMBLES. This project systematically and comprehensively integrates several climate prediction models (21) that provide more concrete and accurate climate change projections on a temporal and geographical scale. As for the calculation of the SPI index for a given period and in a particular locality, which has a rainfall record for a sufficiently long period of time, it is determined by adjusting that record to a theoretical probability distribution, which is then transformed into a normal distribution so that the average SPI is zero. For the evaluation of the suitability of proposing or carrying out artificial recharge of aquifers, the depletion coefficient, and the semi-emptying time of the Hydrogeological Enclosures (HES) have been used, since the faster the discharge of an aquifer, the worse the behavior they present against an artificial recharge operation. The depletion coefficient and the semi-emptying time have been obtained, whenever possible, from data of hydrographs, obtained by foronomic control, to which the Maillet function has been adjusted. Where it is possible to proceed in this way, Rorabaugh's expression or bibliographical data have been used. The results obtained are very encouraging, since 45% of the Hydrogeological Enclosures (HES) have a favorable aptitude a in the face to the realization or proposal of artificial recharge of aquifers. Large detrital depressions are the aquifers that offer the best prospects of the artificial recharge, while the worst are provided by small karst aquifers. The effect of climate change will increase the need to propose the realization of more artificial recharge in large areas of the Spanish territory, where currently the simulations carried out do not detect deficit. The methodology designed and used can be described as easy to apply and quick to execute and interpret.

ABSTRACT. This study used geostatistical theory and ArcGIS geostatistical module to determine and evaluate the spatial changes in the groundwater level differences for a data set of 263 groundwater wells located in the Emirate of Abu Dhabi during the period from 2002 to 2022. The geostatistical spherical ordinary kriging method with cross-validation was used to evaluate the spatial changes in the level of the groundwater. Results showed an average decline in groundwater level from 2002 to 2022 is 2.81m per year. The predicted water level differences map reveals that northeast side of the study area is the critical zone facing a clear groundwater scarcity. Hence, the study concludes that geostatistics and GIS are well established technologies for evaluating groundwater levels in order to have better future management.

ABSTRACT. In arid and semi-arid areas such as UAE, natural water is a key factor affecting the structure, function, and stability of ecosystems and a central link connecting vegetation and hydrological processes. With the ongoing climate change, further concern for the management and sustainability of water resources in arid environments is needed. Consequently, investigations of recharge mechanisms and sources of groundwater in arid regions are crucial to conducting sustainable exploitation of water resources. The differences between light and heavy stable isotopes (18O/16O and 2H/H) and their fractionation patterns in the water molecules can offer a valuable understanding of recharge sources. We here assess the viability of utilizing stable isotopic techniques to better understand the dimension and extent of groundwater recharge sources in the UAE. In this respect, water samples from active pumping wells were analyzed for stable isotopes. In addition, physio-chemical parameters like pH, EC, temperature and TDS were measured in the field. Our results show that local moisture sources for the UAE originate from the Indian Ocean, Arabian Sea, Arabian Gulf and Mediterranean Sea. Evaporation alters the isotopic composition of precipitation and relative humidity which is partly reflected in the stable isotope composition of groundwater. The isotopic fingerprints of the groundwater vary from -13 to 31 ‰ for δ18O and from -32 to 71‰ for δ2H. Total Dissolved solids (TDS) vary from 120 to 19000 mg/L indicating various recharge sources and water rock interaction could have impacted the aquifers. The isotope δ18O and δ2H compositions of groundwater are located along the water evaporation line indicating the recharge occurs under different local or regional climatic conditions or undergoes geochemical change. Moreover, the distribution of the isotopic data suggests three groups of groundwater within the study area. The first group is depleted in its isotopic signature and weakly affected by evaporation. This feature could be attributed to lithologic effect and/or local structural disturbances (e.g. faults) affecting the aquifer systems that receive precipitation along the high topographic regions. In turn, faults and dominated fractured lithologies may facilitate the infiltration of meteoric water into the aquifer. In the second group, the isotopic signature located along the Local Meteoric Water Line (LMWL) indicates modern meteoric water and the direct and rapid recharge from the precipitation or local drainage systems. In group three, the isotope compositions of the groundwater depart from the LMWL suggesting interaction with deep aquifer. The study highlights the significance of further studies to accurately quantify the recharge volumes from different sources that will help to manage the aquifers both qualitatively and quantitatively.

ABSTRACT. Understanding the dynamics of land use and land cover (LULC) is essential for sustainable land use planning for environmental and natural resources management. The present study aims to detect the change in LULC for a semi-arid region, Meerut City in Uttar Pradesh, India, and its projection for the year 2027 by integrating the artificial neural network (ANN) and remotely sensed data. The spatial variables such as slope, aspect, and hillshade data obtained from the LULC map are used as input variables. The results reveal that slope, aspect, and hillshade map have significant effects on LULC and show an overall kappa coefficient (κ) of 0.91 for the observed and projected LULC map for the year 2022. The results obtained from the projection of LULC for the year 2027 using ANN reveals that the cropland area would significantly decline by 18.66 km2 and the tree cover area would significantly increase by 14.32 km2. The findings from this study may prove beneficial for policymakers and land use planners to better understand future LULC distribution and will help them to plan for future development and sustainably manage the natural resources within the study region.

ABSTRACT. Sustainable development of groundwater resources requires a holistic portrait of the water quality covering a wide range of chemical, biochemical and biological characterizations. As part of the chemical compounds, natural and anthropogenic radioactive isotopes represent a vital quality indicator in groundwater globally. The occurrence and amount of a radiative isotope in groundwater is controlled by many external (to aquifer) and internal (within aquifers) factors. Among these factors are climate, source of external radioactivity, topography and aquifer lithology. Even though groundwater radioactivity was an essential component of quality assurance in many developed countries, the program of measuring radioactivity and radioactive isotopes in the UAE started around a decade ago. The research group at the UAEU initiated the measurements and today we have a reliable amount of data covering groundwater in most parts of the UAE. The data also cover different types of aquifer systems and mainly focus on groundwater exploitation in active farming and industrial regions. Early measurements of anthropogenic radioactive isotopes such as Cs-137 and C-14 in the UAE soil and groundwater disclose concentrations at insignificant levels from the point of water quality assurance. Therefore, the radioactivity measurement program was focused on the abundance of natural radioactive isotopes and total radioactivity in the UAE groundwater. The measurements include U-238, U-235, Th-232, Ra-226, Rn-222 and total alpha and beta radioactivity. A wide range of variation in the concentrations of the radioisotopes and radioactivity was observed and partly relates to the aquifer lithology and climatic conditions. The drinking water standards of the World Health Organization and the U.S. Environmental Protection Agency were used to classify the radioactivity hazards in the investigated groundwater. Most of the groundwater systems lie within the acceptable range of safe drinking water in terms of radioactivity and radioactive isotope content. There are, however, enhanced radioactivity and concentration of radioactive isotopes in some regions of the UAE. In addition to the water quality assurance practices, radioisotopes are used to trace the source of the groundwater and evaluate the recharge capacity of the aquifers. The results suggest rather variable recharge tendencies of the aquifers which were mostly driven by topography and paleochannels. The outcomes of the groundwater radioactivity research help in the better understanding of water quality, sources and recharge conditions that are essential in the management of groundwater resources in the UAE. Among the groundwater management spinoffs is the development of monitors such as radon to estimate changes in the groundwater quality and sources.

ABSTRACT. Water is a valuable resource for the countries of the Arabian Peninsula Region. In the UAE, groundwater is one of the major water resources; however, due to the scarcity of rainfall, the recharge is lower as compared to the water discharge. The Gravity Recovery and Climate Experiment (GRACE) satellite is used to detect water movement and storage on the Earth. For the UAE, the data indicates that the water level is declining with the passage of time, and the calculated cumulative trend from 2002 to 2022 indicates a 6.4 cm EWT decline. However, the decline trend is not linear, and one can observe different phases of the EWT fall. From 2002 to 2013, one can clearly observe a falling trend, and from 2013 to 2020, the trend aligns along a straight line, and after 2020, there is a sharp decline. To comprehend the cause of these different trends, we compare our data with different climatological parameters and observe their relationship with the groundwater level time-series trends. We will present and discuss these preliminary results at the upcoming conference.

ABSTRACT. This study addresses two concepts of recharge to aquifers in the Arab region of arid and semi-arid environments: (1) recharge from rainfall to the underlying aquifers and (2) lateral recharge/discharge whether it is transboundary (regional recharge) or submarine groundwater discharge (SGD).

The concept of recharge from rainfall to brackish aquifers in arid environments is not the same as that for freshwater aquifers in semi-arid environments. The differences are due to a number of factors: first, in arid environments, recharge from rainfall does not take place every month of a typical year because dry periods (when there is no rainfall) occur over several consecutive months. Second, when rainfall percolates to water levels of aquifers, the mechanism of estimating salt mass balance is controlled by different factors for aquifers with native freshwater than those of native brackish water. Third, the mechanism of travel of recharging water from the unsaturated zone to groundwater levels may be different between arid and semi-arid environments. This paper is an attempt to discuss in detail the previously mentioned three reasons through taking case studies from the literature to investigate how the concept of recharge was used so that more insights to the recharge concept from rainfall events will be established between arid environments compared to semi-arid/wet environments. The driving force for this research is that recharge estimation is a very important for water resources planning and management in both arid and semi-arid environments. The Arab countries compromise of both arid and semi-arid environments which are experiencing climate change in terms of more frequent flash floods and a noticeable change of annual rainfall quantities. Also, changes in rainfall intensities, duration of rainy seasons and evaporation rates (thus temperature changes) are major factors that control recharge from rainfall in both arid and semi-arid environments. Therefore, the accurate estimation of aquifer recharge allows more reasonable quantification of the sustainable yields aquifers in arid and semi-arid environments. Previous studies have concentrated on approximating or estimating annual recharge to aquifers from annual rainfall without disaggregating it to monthly basis, without taking monthly climate change over time into consideration, without accurate estimation of salt mass balance when the receiving aquifer is of native brackish water or freshwater, and finally without taking into consideration the effect of accumulating the rainfall from different consecutive months on the recharge process. Avoiding these factors is inadequate for accurate estimation of recharge to aquifers in the Arab countries.

The transboundary recharge and the submarine groundwater discharge are based on developing contour maps of groundwater levels of the stressed aquifer under investigation. This study will apply this concept on Kuwait as a case study. The results will quantify the transboundary recharge to both Kuwait Group and Dammam aquifers.

By developing accurate estimations of recharge and SGD to/from the utilized aquifers in the Arab countries then, these countries will be in a better position to hold sustainable management and development of their groundwater resources.

ABSTRACT. Climate change has played a vital role in the hydrological cycle in recent years resulting in extreme events, especially in an arid region like Kuwait, where the availability of freshwater is extremely scarce. Kuwait is highly dependent on groundwater for agricultural purposes and relies on seawater desalination to supply its growing population with fresh water. Groundwater production in Kuwait made up 22.3% of the total water consumption in 2006, while desalination accounted for 54.6%, and wastewater reuse contributed 23.0%. The daily per capita consumption in Kuwaiti residential villas varies from 180 to 2,018 liters and is considered as fourth-highest per capita consumer in the GCC region. The higher-income class represents the major consumer category due to its cheap availability. The need for freshwater, the increase in population, the increase in infrastructural development, and the lack of institutional monitoring and management are Kuwait's main challenges in managing its water resources. Thus, providing solutions and strategic plans to manage water resources is crucial and essential for Kuwait's water resources' sustainability. The United Nations has been focusing on the Sustainable Development Goal 6 for the sustainable management of water thereby ensuring the availability of safe water to all. In that regard, this article aims to provide strategic solutions for future water management strategies in Kuwait. The management can be through, 1. the conservation techniques with financial expenditures like metering, artificial recharge structures, additional smart innovative materials like IoT devices, etc., and 2. The conservation without financial expenditures like the implementation of strict water policies and tariffs. However, management strategies should involve both techniques based on the needs of specific regions and utilities (agriculture, industrial and domestic). The future management could be through a web-based online platform by integrating all associated governmental sectors in Kuwait focusing on water, to be under one cloud to develop a big data depository, knowledge platform, monitoring, and management of water resources, and development of governance policies. The proposed database is planned to be achieved by the Kuwait Institute for Scientific Research (KISR) in collaboration with the Kuwait ministries (Public Authority of Agriculture Affairs & Fish Resources, Ministry of Public Works, Ministry of Electricity & Water & Renewable Energy, and Kuwait Environment Public Authority) through installing IoT devices (Internet of Things) for real-time database development and sharing of water-related data. The water-related data will include information on, water level, well depth, the physical (temperature, odor, electrical conductivity, etc.) and chemical (pH, major ions etc.) parameters of groundwater wells, their distribution network, information on wastewater treatment plants, and desalination plants such as the quantity of treated water production and amount of reject generated, will be hosted on open-source GIS platforms. The results of this study can help the decision-makers to implement sound strategies for a clean safe sustainable integrated water management plan.

ABSTRACT. According to the latest statistics available by AADC, desalination plants—with an average daily supply of 170 MGD —are Al Ain city's only source of drinking water. This statement highlights the critical need for the implementation of sustainable water management measures in the Al Ain Region, an arid region facing rapid urbanization and shifting demographics. A thorough examination of population statistics, demographic trends, and urbanization rates provided a solid basis for future water demand forecasts. The integration of several factors provided a comprehensive understanding of the spatial distribution of water demand in the Al Ain Region. It revealed the significant impact of population changes and urban growth. The study also established a basic framework for future climate modeling using forecast climate scenarios and potential changes in water availability and groundwater recharge. The model’s findings provided significant information about water supply, demand, and balance for water resources management, particularly water scarcity in the future. These results highlighted the dynamic interplay between water availability and behavior patterns, emphasizing the need for flexible water resource management techniques. Combining climate model outputs with spatially specified data sets using GIS technology improved the model's precision and dependability. This resulted in a robust tool that can adaptively respond to changing behavioral and demographic patterns, ensuring the sustainable management and exploitation of water resources. The study's implications extend beyond Al Ain Region. The findings have significant implications for arid regions facing similar water resource management challenges worldwide. The findings emphasize the importance of data-driven methods to ensure water security. The study also offers a framework for developing data-based water management approaches in regions with limited water resources. By incorporating geospatial modeling techniques with demographic trends, decision-makers can better understand spatial and temporal water demand patterns. This integration enables to make more informed decisions regarding water resource allocation and the implementation of successful water management strategies.

ABSTRACT. There is increasing recognition that the integration of geophysical techniques into hydrogeophysical studies in desert environments can significantly improve our understanding of hydrogeological setting, particularly at intermediate scales as in small basins and watersheds. Obviously, the primary hydrogeological challenges faced in hyper-arid regions are the meager rainfall and scarcity of groundwater. Due to the restricted accessibility to bedrock for direct mapping, the geological information in this area is primarily inferred. Moreover, conducting a hydrogeological evaluation in desert environments proves to be a difficult task in areas where data is limited. Therefore, the need for innovative and new techniques such as geoinformatics can fill the gap and help better understanding the behavior of geological structures for better alluvial aquifer distribution inferences. This research aimed to develop an integrated approach of remote sensing, magnetic and geoelectrical data to effectively manage the limited water resources within structurally controlled watersheds in wadi systems. As a test site, wadi Al Baroud in the Eastern Desert of Egypt is studied using the proposed approach to assess the impact of geological and structural controls on alluvial aquifer distributions. We aim to integrate the nested and cross scale magnetic and electrical measurements to construct a three-dimensional (3D) hydrogeological model of the subsurface in watersheds. Through the analysis and modelling of satellite imagery using remote sensing (RS) and geographic information system (GIS) techniques, regional geological features, geomorphology, lineaments, and active channels related to flash flood occurrences in desert areas are identified. Consequently, hydrographs and runoff models can be generated for wadi systems. Moreover, the study involves the implementation of 2D-electrical resistivity tomography (ERT) and land magnetic surveys along the main basin of Wadi Al Baroud to examine both lateral and vertical variations in alluvium thickness and the saturated zones. Airborne and land magnetic data analysis presents new insights into the subsurface geological structures. The magnetic data reveal concealed that bound the undiscovered basins in the study site. Then, the 2D-ERT technique delineates detailed subsurface layer distributions, near-surface lateral variations, shallow subsurface structures and potential groundwater zones along the main course of the wadi. The inversion results of geoelectrical data demonstrate the feasibility of delineating thick alluvium zones that can be recharged forming groundwater-bearing zones constituting a sustainable source for freshwater. It is worth noting that the presence of groundwater is primarily confined within the structurally controlled graben. Hence, the outcomes of this case study explore how the application of the proposed approach can answer critical hydrogeological science questions and provides a better understanding of groundwater resources distributions in structurally-controlled watersheds worldwide.

ABSTRACT. Climate change is accelerating due to increased human activities and the associated increase in the greenhouse gas emissions. Climate change has impacted the parameters of the hydrologic cycle, including temperature, wind and precipitation patterns. Climate change might affect hydrological regimes and increase the likelihood of extreme events such as extended drought and flash floods. Middle East is among the most vulnerable areas to climate change. The UAE, like other countries in the Middle East, is vulnerable to extreme weather events including drought and flood. Climate change represents a main challenge for water resources management in many countries. The drought is a natural hazard that might become more frequent under changing weather conditions. Drought analysis is usually conducted using different drought indices, but the Standardized Precipitation Index (SPI) is the most used method and is recommended by the World Meteorological Organization for monitoring drought conditions in various areas across the world. This study aims to assess the long-term drought in the northern area of UAE using SPI based on recorded monthly rainfall data from land stations for a period of 40 years from 1980 to 2020. SPI-12 months were calculated using DrinC to examine meteorological drought that may affect water resources. Four stations have been selected for rainfall trend and SPI analysis. Rainfall trends analysis showed that Masafi station had the highest annual rainfall (134.7 mm) and Um Ghafa station had the lowest (43.0 mm). Rainfall trend analysis showed a significant decline at most of the stations, possibly due to climate change in the last two decades. According to SPI-12 results, moderate droughts occurred in 2001 and 2005, severe droughts occurred in 2010 and 2011, while extreme droughts occurred in 2012 and 2016. The driest years were 2005, 2010, 2012, and 2016 and the estimated average return period is 3 to 4 years. All the studied stations had a decreasing trend in rainfall, indicating that climate change would have significant impact on the UAE's water resources and more drought periods are expected. The findings of the study will allow for proper management of the UAE's water resources and adaptation to climate change.

ABSTRACT. Recent years have seen an increase in the frequency of flood occurrences and the damage they cause. Effective flood control measures are urgently needed, and mapping flood inundation and identifying flood-prone areas are two key instruments for this. In the present study, the potential flood-prone areas were identified and inundation maps were created using the HEC-RAS 2-Dimensional model in the Netravati-Gurupura river basin, South-west India. For the Netravati and Gurupura river basin's upcoming return period floods as well as earlier flood episodes, flood inundation maps have been created. For the past experienced flood episodes, flood inundation maps were also created for the Mangalore city corporation. The 30 m SRTM Digital Elevation Model is used to determine the features of the terrain. Daily discharge data were utilized to simulate the flow, and statistical measures such as the Coefficient of determination, Nash-Sutcliffe efficiency, and Index of the agreement were used to calibrate the model for the ideal value of Manning's roughness coefficient of 0.032. Potential flood-prone areas were located based on the model's simulation of depth, velocity, and water surface height. When the model's effectiveness was validated for the flooding years 2004, 2009, 2011, and 2014, it was discovered that the statistical parameter findings fell within the required range. From this study, it was found that flood events can be simulated by using HEC-RAS 2-Dimensional model and flood-prone zones can be identified which would assist the decision-makers in planning out suitable flood mitigation measures.

ABSTRACT. This abstract consolidates findings from three hydrogeophysical studies in the United Arab Emirates (UAE) focused on understanding groundwater aquifers in the arid Al-Ain region, a crucial water source. The UAE, with minimal rainfall, heavily relies on shallow aquifers for freshwater supply. The Al-Jaww Plain, characterized by extensive gravel and sand deposits from neighboring Oman Mountains, holds significant groundwater resources. The first study employed audio-magnetotelluric (AMT) method, seismic reflection profiling, and borehole data to map groundwater aquifers in the Al-Jaww Plain. The resulting 2D resistivity inversion model identified three distinct geo-electrical zones representing various aquifer layers.

In a second study, microgravity monitoring was conducted at four water wells in Al-Ain City to comprehend the hydrodynamic features of the shallow groundwater aquifer. The study integrated time-lapse microgravity measurements and water levels to estimate changes in groundwater storage. Spatiotemporal changes in microgravity and water levels provided insights into groundwater storage variations over the study period, demonstrating the effectiveness of the microgravity method for monitoring shallow groundwater aquifers.

In the third study, a synergistic approach was adopted by integrating microgravity monitoring with Interferometry Synthetic Aperture Radar (InSAR) to examine land surface deformation (LSD) in the Al-Ain region. This integration offered new perspectives on LSD studies by identifying sources of surface deformation. InSAR analysis revealed periodic land surface deformation variations corresponding to seasonal changes. Additionally, the study detected land surface subsidence and uplift in specific regions, linked to groundwater extraction activities. The integration highlighted a negative correlation between land surface subsidence/uplift and microgravity changes, shedding light on the influence of groundwater exploitation on LSD in the Al-Ain area.

Overall, these hydrogeophysical studies provide valuable insights into groundwater aquifers and the hydrodynamic characteristics of the shallow groundwater system in the UAE, particularly in the Al-Ain region. Understanding these aspects is pivotal for sustainable management and efficient utilization of groundwater resources in arid regions facing water scarcity challenges.

ABSTRACT. Flash floods have emerged as a critical concern in the twenty-first century, exacerbated by the intensifying effects of climate change, especially in urban regions. Urbanization compounds the devastating impacts of flash floods, underscoring the urgent need for mitigation strategies. However, there exists a notable research gap concerning the impacts of flash flood disasters and mitigation measures. Flash floods, characterized by their rapid development within minutes or hours due to intense rainfall, necessitate vigilant measures to prevent casualties of various kinds. Diverse types of floods possess unique features and characteristics, including river flooding, where water levels surpass riverbanks due to excessive rainfall; urban/pluvial flooding, occurring in urban areas affected by heavy rainfall exceeding drainage system capacity; and reservoir flooding, resulting from dam failures. This study focuses on assessing the utility of modular barrier systems across various flooding events with distinct characteristics and site-specific considerations under varying rainfall intensities. Mobile infrastructure systems have gained prominence in recent years for flood protection. Their effectiveness and application methods vary depending on geometric attributes, installation locations, response times, installation team expertise, flood dynamics prediction, and spatial extent. The study area, situated in the city of Serres within the Central Macedonia region of Northern Greece, served as the focal point for this research. To draw meaningful results that reflect the actual flood events, this study commenced with an assessment of hydrological and water flow characteristics within the stream and its catchment area. Subsequently, hydraulic simulations were executed, encompassing the current state of the stream and multiple flooding scenarios in urban environment, incorporating mobile flood barriers and small dams with diverse types and geometric features. The outcomes of this research have culminated in the development of a comprehensive "roadmap" that is delineating how, when, and where non-permanent protective measures can be strategically implemented within urban environments. This roadmap stands as a valuable guide for local authorities and civil protection agencies tasked with safeguarding urban areas from flash flood disasters. The main outcome of the study was the implementation of a well-structured Action Plan in the context of flood protection and action measures in order to minimize flood risks with the optimal use of mobile systems as a short-term containment or diversion flood protection measures in combination with the permanently existing protection measures and infrastructures in the study area. This may serve as a guideline for civil protection agencies and local authorities that are responsible for taking prevention and reduction measures for extreme flooding events.

ABSTRACT. Channel roughness plays a critical role in the advancement of hydraulic models used for predicting floods and flood inundation maps. This study presents findings from the application of a hydrodynamic model aimed at calibrating the Manning’s roughness coefficient (n) for the lower Tapi river in India. The releases from Ukai dam and tidal levels in the Arabian sea are taken as upstream and downstream boundary conditions respectively. The calibration of Manning’s n has been performed by taking global as well as distributed parameters, i.e., Ukai dam to Kakrapar and Kakrapar to Arabian sea, for the selected reach of river. After several trials, the Manning’s value of 0.035 from Ukai dam to Kakrapar and 0.02 from Kakrapar to Arabian sea are found better with the lowest weighted RMSE. In most of the gauging station it is found that simulated water level is higher than the observed water levels. The calibrated Manning’s n has been found to perform well in validation of independent flow event, and the same can be used for design of levees as flood protection measures for the Surat city, India.

ABSTRACT. Oasis agriculture in the Ferkla valley (southeast Morocco, rainfall < 130 mm/year) is traditionally irrigated by floods from the surrounding mountains (High Atlas and Anti-Atlas). This run-off recharges the aquifers drained by the khetteras or is spread over the cultivated plots via flood harvesting systems. The pump, introduced in the 1980s, has allowed the extension of irrigation outside traditional oases by drilling boreholes, but groundwater levels are dropping. Two innovations have been developed locally to deal with this situation: i/ the supply of water to the khettaras, old groundwater mobilisation systems, which are currently dried out using solar-powered pumping from collective wells, ii/ the recharging of the water table at farm level using floodwater harvesting ponds. These innovations were assessed between 2020 and 2023, by means of interviews with farmers and institutional stakeholders, an analysis of satellite images, monitoring of surface and groundwater levels on a farm scale, and analytical modelling of artificial recharge. The context in which collective solar pumping emerged and the ways in which ancestral khettara water management rules were adjusted were identified and analysed. Our research findings indicate that employing floodwaters for both date palm irrigation and groundwater recharge at the farm level is an effective strategy for reducing evaporation losses from the floodwater harvesting pond. Our analyses, combining field measurements and analytical modelling, reveal that the geographical scope of groundwater recharge from the pond is constrained by the region's hydrogeological conditions. Additionally, excessive irrigation could contribute to aquifer recharge, particularly if multiple floods occur closely in time. As for the association of solar-powered pumping and the downstream part of the existing khettara system, which includes transport and distribution structures and management rules, it helps to protect the collective access and management of groundwater. Thus, the social organization around groundwater resources is maintained, and the traditional groundwater capture structure could be abandoned. The analysis of these innovations shows that Ferkla valley is experiencing a generalised competition for water that could ultimately compromise all forms of agriculture and life in the downstream parts of the watershed if access to surface and groundwater is not regulated. This work contributes to the discussion about ensuring the sustainable management of water resources in this fragile oasis ecosystem.

ABSTRACT. Fertilizer use in agriculture has become a major source of surface and groundwater pollution worldwide. The nitrate concentration in ground water has increased in many arable parts of the world by more than the allowable maximum nitrate-N concentration (10 mg/L). The surface water bodies like freshwater lakes are also experiencing frequent eutrophication due to high phosphorus loads from agricultural areas. Certain soil amendments, such as biochar, super absorbent polymers, and liming could help reduce water pollution, increase water and fertilizer use efficiencies, and improve soil fertility. A three-year field study was conducted in pots by growing green peppers in a loamy sand soil. The effects of biochar, lime, natural hydrogel, and their combinations on soil nutrient loss, fertilizer and water use efficiencies, and crop yield was examined. Hardwood biochar (1%w/w), lime (1%w/w), and natural hydrogel from scrap paper (0.5%w/w) were applied to the pots, arranged in a randomized complete block design with four blocks (rows). The results showed that soil amendments significantly improved (p ≤ 0.05) soil pH, OM, N, P, K, Ca, and CEC levels. In addition, biochar, hydrogel, biochar-hydrogel, hydrogel-lime, and biochar-hydrogel-lime treatments significantly reduced (p ≤ 0.05) nitrate-N losses from crop root zone by at least 50%, compared to the non-amended soils. Although soil amendments had no profound effect on orthophosphate leaching losses from the pots in the first year of the study, lime, hydrogel, hydrogel-lime, and biochar-hydrogel-lime treatments significantly reduced (p ≤ 0.05) orthophosphate losses by at least 50% in the second and third years of the study. Furthermore, mixed soil amendments, biochar-hydrogel, hydrogel-lime, and biochar-hydrogel-lime significantly increased green pepper yield (up to 40%, p ≤ 0.05), compared to the non-amended soil and the single soil amendments, due to improved soil nutrient and water retention capacity. The study concluded that the mixed soil amendments can significantly decrease environmental pollution by keeping soil nutrients from leaching out and, at the same time, enhancing crop yield. The findings of this study will be useful for the farmers experiencing declining soil fertility, water scarcity, and water pollution issues.

ABSTRACT. Nanomaterials are promising candidates to overcome the water quality issues caused by dye consumer industries. However, their efficiency restriction in water treatment hinders their practical applications, particularly in dispersed nanomaterials in water systems. We present a novel approach to immobilize ferromagnetic nanocomposite into a polymeric membrane. We embedded 100 mg L-1 Zn0.5Mn0.5Fe2O4 nanocomposites (NCPs) in poly (methyl methacrylate) (PMMA), eliminating the need to retrieve the dispersed nanomaterials after water treatment. The developed Zn0.5Mn0.5Fe2O4/PMMA nanocomposite was characterized by Scan Electron Microscope (SEM), and Fourier Transform Infrared Spectroscopy (FTIR). The removal of two commercial dyes (Brilliant Blue and Allura Red) at concentration levels of (0, 25, 35, 50, 70, and 100 mg L-1) was examined by batch adsorption studies followed by Ultraviolet-visible (UV-Vis) spectrophotometer quantification. It is observed that the maximum percentage removal was obtained for the embedded Zn0.5Mn0.5Fe2O4 nanoparticles onto PMMA polymer using 100 mg of Zn0.5Mn0.5Fe2O4 NCPs for all the four dye levels in comparison to dispersed Zn0.5Mn0.5Fe2O4 nanocomposites. It was observed that maximum adsorption of the Brilliant Blue and Allura Red was obtained under alkaline conditions pH 7.32 and 8.37, respectively. We suggest that the Zn0.5Mn0.5Fe2O4/PMMA membrane can be applied as a sustainable material to enhance water treatment efficiency by merging the advantages as a fast preparation technique and a higher adsorption capacity to remove complex organic contaminants.

ABSTRACT. A field experiment on soil moisture status and rain water use efficiency (RWUE) as influenced by different long term nutrient management practices under various production methods (Organic, integrated and conventional) in pigeonpea based crop rotation system (greengram followed by rabi sorghum and pigeonpea) in Northeastern Dry Zone of Karnataka, India was conducted during Kharif and Rabi seasons of 2021-22 and 2022-23 at Zonal Agricultural Research Station, Kalaburagi with ten treatments and three replications laid out in randomized block design. The experiment was initiated in the year 2004 and modified in 2021-22. The treatment details are as follows, T1: FYM (50%) + Vermicompost (50%) equivalent to 100% RDN/P in 100% organics treated preceding plot, T2: FYM (50%) + Vermicompost (50%) equivalent to 75% RDN/P in 100% organics treated preceding plot + Innovative organic practices, T3: FYM (50%) + Vermicompost (50%) equivalent to 50% RDN/P in 100% organics treated preceding plot + Innovative organic practices, T4: FYM (50%) + Vermicompost (50%) equivalent to 75% RDN/P in 75% organics treated preceding plot, T5: FYM (50%) + Vermicompost (50%) equivalent to 50% RDN/P in 75% organics treated preceding plot + Innovative organic practices, T6: FYM (25%) + Vermicompost (25%) + NPK fertilizers (50%) in integrated nutrient management preceding plot, T7: FYM (37.5%) + Vermicompost (37.5%) + NPK fertilizers (25%) in integrated nutrient management preceding plot + Innovative organic practices, T8: 100% NPK fertilizers in fully inorganics treated preceding plot, T9: Recommended dose of fertilizers –RDF (Recommended NPK+ Recommended FYM) in RDF treated preceding plot and T10: 50% Recommended NPK fertilizers + Recommended FYM in RDF treated preceding plot. Where, innovative organic practices include soil application of ghanajeevamrutha @ 0.5 t ha-1 in row lines at sowing and foliar spray of cow urine (5%) at flower initiation (greengram and pigeonpea)/30 DAS (rabi sorghum) alternated with foliar spray of vermiwash (10 %) at 15 days after first spray. Organics were applied equivalent to recommended N (rabi sorghum) and P (greengram and pigeonpea). The results indicated that organic manurial treatments (T1 to T5) recorded significantly higher soil moisture percentage at 25 and 50 DAS in greengram and 45, 90 and 135 DAS in pigeonpea. Whereas treatments T1, T2 and T4 recorded significantly higher soil moisture percentage at 30, 60 and 90 DAS in rabi sorghum than INM, recommended package and inorganic treatments. However, significantly higher RWUE was recorded by treatment T6 in greengram (2.03 kg ha-mm-1), rabi sorghum (7.93 kg ha-mm-1) and pigeonpea (1.74 kg ha-mm-1) because of higher yields of crops under the same treatment. It was closely followed by T1 indicating the potential of organic manures in increasing the yields and RWUE of the crops in the system. On the other hand, significantly lower soil moisture percentage was reported under inorganic treatment (T8) in all the crops grown in the rotation system. There was a saving of 25% of N/P requirement through organics as a result of higher RWUE by following innovative organic practices in the crops in rotation.

ABSTRACT. The global demand for potable quality water has been on a continuous rise. Consequently, water reuse is currently considered one of the alternatives to significantly expand supplies of freshwater. However, the presence of emerging contaminants, such as pharmaceuticals and personal care products (PPCPs), in water matrices is a serious public health concern. Unfortunately, conventional wastewater treatment plants (WWTP) are not efficient in the removal of PPCPs, which poses serious threats to the environment, humans, and animals. This study investigated the efficacy of biofiltration with and without a pre-advanced oxidation process (AOP) for the removal of these micropollutants. Rapid small-scale stainless-steel columns employing biologically activated carbon and expanded glass were fed with pre-treated effluent water sourced from an actual WWTP in the city of Al Ain. First, the feasibility of utilizing biofiltration system without pre-treatment for the removal of PPCPs from the WWTP effluent was investigated. Second, a hybrid treatment system, in which advanced oxidation process (AOP) was provided as the pre-treatment, was introduced. Photocatalysis using zinc oxide was employed as the pre-advanced oxidation process. The treatment efficiency of the biofiltration system and the hybrid system of biofiltration and AOP were studied and examined by measuring parameters such as pH, conductivity, total organic carbon (TOC), anions, BOD and dissolved oxygen (DO) before and after treatment. The removal of PPCPs was determined by performing the solid phase extraction (SPE) and the analysis using gas chromatography-mass spectrometry (GC-MS). This research investigated a novel hybrid possibility of treating wastewater for reuse. The results and findings of this research are therefore a significant contribution to the effective reuse of treated wastewater.

ABSTRACT. Water stress for several countries including Morocco, is a major concern and considerably affects the economic and social sectors of the country. Seawater desalination processes is one of the solutions adopted to meet this challenge. Direct contact membrane distillation (DCMD) processes have been proposed to meet many challenges, including the recycling and recovery of salt water from reverse osmosis units. Recently, composite polymer membranes based on ionic liquids (ILs) have been adopted for several extraction and separation processes. In this context, an original hydrophobic composite polymer membrane was prepared from a mixture of polyvinylidene fluoride (PVDF) and polysulfone (PSU) polymer supports, based on TrioctylMethylAmmonium-Oleate ionic liquid (TOMAO) and doped with activated carbon (AC). The PVDF/PSU-IL-AC membrane has been adopted in DCMD processes for recycling of salt water by diffusion and extraction of water vapor (WV). The PVDF/PSU-IL-AC composite membrane has good hydrophobicity (120.8°) and high porosity (87%). Significant, flux of 18.29 kg.m-2.h-1 and salt rejection of 99.93% were observed for the treatment of a NaCl solution (35 g.L-1) at 60°C. This membrane has good resistance and stability to highly saline feed solutions and good reproducibility of flux and salt rejection parameters (9 weeks). Several parameters were quantified, notably the activation parameters Ea, ∆H# and ∆S#. The diffusion mechanism of water molecules through the membrane has been identified and the roles of IL and AC agents have been elucidated. All results indicate that the developed membrane is an effective tool for producing pure water that meets drinking standards from highly saline waters (BRINE). Finally, seawater desalination tests were carried out under optimal conditions. The results are conclusive and confirm the performance of the developed membrane.

ABSTRACT. While the presence of microplastics and nanoplastics in the various natural compartments of planet Earth is well established, recent studies show that groundwater is also affected. This proves that these man-made materials pass through geological environments, eventually concentrating in hydrogeological formations. Our aim is to understand how this type of perennial molecule (some of which are estimated to last more than 400 years) is transported from the surface to groundwater. Our approach is based on reference plastics of the low-density polyethylene (LDPE) type injected into model columns (sand type with controlled granulometry) and to study the modes of transport, aggregation and disaggregation. The numerical models used are currently of the one-dimensional advection-diffusion type to solve the equations from a numerical point of view. The model enables the average linear water flow velocity and the longitudinal dispersion coefficient to be determined numerically. This approach provides information on transport, but the constants or rates of attachment and detachment for the medium under consideration still need to be calculated. For this we used also a first order equation accounting for nonequilibrium reversible attachment onto a solid matrix. Convergence of the results presupposes a full characterization of the parameters and materials used. For example, sand, microplastics and nanoplastics were previously analyzed in the laboratory. Each parameter was carefully checked by repeating each injection experiment with a specific quantity of plastic. Without these precautions, major discrepancies can be observed, making it impossible to draw any scientific conclusions about the behavior of these objects in a model column. The results show that these objects can remain trapped in the sand column, but also that a large proportion pass through the sandy medium with varying degrees of ease. Progress about these experiments will be fixed by online control of the MPls and NPls transport inside the column using innovative approach. International collaboration is needed to extend our simulation toward different geological formation in a first step. Then in a second step we aim to fully characterize the porosity of different type of rocks to estimate the quantity which can reach underground waters in different type of watersheds. One risk in arid region is the vulnerability of the groundwaters to the contamination by persistent plastics compounds. Actually, most of the middle east region face industrial development with many projects based on petrochemistry which require high volume of water. It could be a challenge conducting to environmental degradation of the country with huge impact onto the population. This preliminary work is part of a wider project to determine the ecotoxicological impact of PMls and NPls in groundwater intended for human use, such as drinking water!

ABSTRACT. The scarcity of water resources is one of the main barriers to further food production. On the other hand, the historical evaluation of food production systems in terms of crop water productivity is a key factor in understanding the country's food security. Thus, this paper describes the historical development of water productivity components, along with water stress and imported food, in the Gulf Cooperation Council countries (GCC) of Bahrain, Kuwait, Oman, Qatar, Saudi Arabia, and UAE from 2000 to 2020. This is to highlight the vulnerability of GCC countries’ food supply to overseas water use in imported food. Water productivity is calculated based on Gross Domestic Product (GDP) per cubic meter of total freshwater withdrawal. The annual freshwater withdrawal refers to the withdrawal from storage basins, desalination plants, and non-renewable aquifers, and also includes the withdrawal for agriculture and livestock. The review analysis is based on the Food and Agriculture Organization (FAO), AQUASTAT data, World Bank, Organization for Economic Co-operation and Development (OECD) for GDP estimates, and World Research Institute-AQUEDUCT. The results revealed that between 2000 and 2020, the annual freshwater withdrawals for livestock and crops dropped by 20.8%. The cereal production harvested for dry grain also dropped by about 36% between 2000 and 2020. The decrease in water withdrawals and cereal production has not affected water productivity, where it has increased, growing at an average rate of 121.8% from 92.8 in 2000 to 205.9 in 2020. The increase in livestock production index (meat, milk, cheese, eggs, honey, raw silk, wool, hides, and skins) by about 189% between 2000 and 2020 could be the main reason for improving water productivity. Therefore, livestock production is the most crucial component of agriculture in GCC countries, contributing the biggest percentage of water productivity. As a result, the food imports were 11.3% as a share percent of total merchandise imports. This percentage is equivalent to 90% of the food demand in the GCC countries, meaning an increase in water imported in agricultural products. This research highlighted that despite the improvements in water productivity, the GCC countries are not self-sufficient in food, as about 90% of GCC countries’ food demand is imported, and consequently, the impact on water use and productivity is overseas. Hence, it is important to understand, and account for, the impact of imported food on water productivity overseas. In the future, funders are recommended to make greater engagements with international organizations concerned with agriculture and water use to promote sustainable production chains between both GCC countries and the countries to which food is imported; water security perspectives.

ABSTRACT. With extremely high water stress conditions, water conservation is an uphill task for the municipalities in the Kingdom of Saudi Arabia (KSA). Most of these municipalities are relying on periodic active leakage control programs for their water distribution systems (WDS) to meet the ministerial target of allowable water losses of up to 10%. This target does not appear to be ambitious for the countries in arid regions where the impacts of droughts are limiting the water resources and population growth and urbanization are overexploiting them on the other. In addition to the overuse of water, consumers are least bothered about the possible leakage on their side due to low water rates. For intermittent water supplies in arid regions, a framework based on the environmental valuation of water loss was proposed to establish the economic level of leakage. The process initiates with an estimation of real losses and their comparison with the unavoidable real losses. The framework propagates the idea of reducing the real losses up to the unavoidable real losses with an infrastructure leakage index of ‘1’. Implanting the framework on a WDS in the Qassim region reveals that conventional strategies, including active and passive leakage control, pressure management, and management of consumer-side issues, can reduce the ILI from 20 to 5.26. Recognizing and implementing the environmental cost of water loss in arid environments using an environmental valuation technique (i.e., based on evoking consumer preferences about the expected degradation of existing water resources) can further reduce the ILI with a nominal increase in water prices.

ABSTRACT. Agricultural activities are frequently the main component of water use in arid or semi-arid environments with up to 90% of total water use devoted to agriculture. Fresh vegetable production in arid or semi-arid regions usually takes place in greenhouses equipped with evaporative cooling systems (pad and fan). The production of 1 kg of fresh vegetables under these conditions often requires more than 100 L of water with the majority of the water consumed by the cooling system. The purpose of the current study is to indicate how proper design of the greenhouse and the cooling system as well as proper maintenance and operation of the cooling system can result in tremendous water savings and therefore higher water use efficiency by reducing the cooling water use. The presented results are obtained by a combination of modelling computations and data obtained from research trials that took place at the greenhouse facilities of The National Research Center for Sustainable Agriculture (ESTIDAMAH) in Riyadh. Two greenhouse design elements, namely the position of the exhaust fans and the cover to ground ratio of the greenhouse are shown to affect the cooling water use with potential water savings reaching up to 50% and 30% respectively. Furthermore the cooling efficiency of the pad wall that is mainly depending on the proper wetting of its surface can also result in up to 50% cooling water saving. Finally, the outcome of the current research provide clear guidelines for the construction and operation of more water efficient evaporatively cooled greenhouses.

ABSTRACT. This article discusses the problems of online monitoring of water metering in irrigation systems in Central Asia, in particular the Republic of Uzbekistan. The development of an intelligent device and the implementation of the monitoring system is aimed at solving these problems. In Uzbekistan, 2.3 million hectares of irrigated land (53% of the total) are supplied with water by 1,693 pumping stations, annually pumping more than 50 mlr.m3 of water. The annual electricity consumption of these pumping stations is 8-8.2 billion kWh, which is about 11% of the annual electricity generation of the entire energy sector of Uzbekistan. At operated irrigation pumping stations, energy consumption is taken into account manually, most of them do not have accurate water metering devices that could take into account the amount of sediment coming with water. As a result, there was no possibility of monitoring the energy efficiency of the pumping station in real time. In addition, irrigation pumping stations consume 1-3% of electricity from the total annual consumption for their own needs. All this leads to an increase in annual electricity consumption by pumping stations. The purpose of this study is the scientific substantiation, development and implementation of the system for monitoring the energy efficiency of operated irrigation pumping stations, providing information on the consumption and supply of electricity in real time, as well as an integrated photovoltaic system for the energy supply of pumping stations' personal needs. 29 large pumping stations in seven regions of the Republic were selected as research objects: Bukhara, Navoi, Surkhandarya, Jizzakh, Andijan, Namangan, and Ferghana regions, which have great economic and strategic importance for their regions. The smart device was developed to determine the flow rate and volume of water pumped by pumping stations, its laboratory and field tests were carried out, and the certificate of quality compliance was obtained. The devices were implemented at all selected objects. The implementation of the developed monitoring system of pumping stations made it possible to quickly and efficiently manage the entire system of irrigation pumping stations, reduced annual energy consumption on 2,6% by monitoring unauthorized connection of units, registration and theft of electricity, providing electricity for personal needs of pumping stations with the help of developed and implemented mobile photovoltaic systems, as well as the introduction of effective operating modes in each pumping station. It also made it possible to accurately determine and regulate the volume of pumped water, taking into account the flow turbidity, reduce the labor costs of maintenance personnel by 14% to remove the meter readings, register in magazines, transfer them to the Pumping Stations Department, etc., which ultimately reduced operating costs at operated irrigation pumping stations in Uzbekistan by 9.8%, and also increased reliability of providing the necessary amount of water to irrigated lands.

ABSTRACT. Jordan is one of the most water-deficient countries in the world and is about to face severe water shortages in the future. Groundwater resources have been used extensively in the past and therefore groundwater levels have been declining in many areas, but their exploitation is still considered as an important contributor to meet the growing water demand. A numerical groundwater flow model was created with the finite-difference code MODFLOW to simulate the effects of a planned wellfield development in Southern Jordan, north of the city Ma'an. The exploited A7/B2 limestone aquifer is from the late Cretaceous period. The planned wellfield area has a confined aquifer (B4/B5) overlain by an aquitard (B3). The modelling results provide an estimation of possible abstraction rates and the drawdown for different wellfield designs until the year 2030. As natural groundwater recharge is low, the water resources in this area are not renewable and groundwater levels are not expected to recover again. The location is well-chosen concerning road accessibility and due to the overlying units, there is high protection and low vulnerability of the groundwater. As spatial arrangement, a linear array with 1 km distance between the wells parallel to the Desert Highway is suggested. The vicinity to the highway simplifies transport of construction material and ensures an easy accessibility for future maintenance. During several model runs an annual abstraction of 4.73 MCM of groundwater turned out as feasible with an acceptable drawdown. This amount could be used to contribute to the public water supply. This measure to pump groundwater from the proposed wellfield is planned to fill the temporary gap in the water supply until a long-term solution is framed. Challenges during modelling occurred due to the scarcity of data and therefore modelling results need to be evaluated critically. The topic of this work has been worked out within the project ‘Water D2D – Water Security: From Data to Decision’ that is aiming towards a water-secure Jordan.

ABSTRACT. Satellite derived Evapotranspiration (ETa) products are used world-wide for different studies like drought monitoring and food Security assessment. In the present research work, usability of satellite derived evapotranspiration (ETa) from two different sources has been found out. The main aim of this research work is to study a relationship between crop yield (rice, maize, barely, soybean) and ETa. Crop yield being main parameter that affects economy and food security of a county, satellite derived monthly, and yearly ETa was evaluated and usability of this for decision makers to monitor vegetation condition in drought prone and food insecure areas was found out. Zonal statistics operation was performed in QGIS, and time series graphs were plotted for ETa vs crop yield and ET anomaly vs crop yield. Conversion of NRSC (National Remote Sensing Centre) daily data to monthly and extracting single pixel ET data was done using R Studio. The results of this study indicated that out of the two ETa sources (NRSC and USGSFEWS), USGSFEWS (United States Geological Famine Early Warning System Network) souse provides more usable data due to its accuracy and availability of ETa even in monsoon seasons whereas lot of missing data were observed in the months of June, July and August in NRSC data. However, the correlation between crop yield and ETa for chosen districts of Cauvery River basin came out to be in between 12 to 35% and correlation between crop yield and ET anomaly came out to be in between 35 to 55%. Improvement in satellite-based ETa data and crop yield data is collection needed for these particular identified areas for making crucial decisions regarding crop yield. Decision makers can moderately depend upon ETa for making crucial decisions related to crop yield in the study area. Developed methodology in this study will be very much useful in basin scale water resources management.

ABSTRACT. Satellite-derived sea surface chlorophyll-a concentrations (Chl-a) and sea surface height anomalies (SSHA) were computationally analyzed to examine the variability and correlation of both parameters. The ocean waters of the western Arabian Sea and the Arabian Gulf (15N -30N, 50E -66E), around the Arid land countries, were examined using monthly 16 km data sets of Chl-a and SSHa from January 2018 to December 2022. MODIS-Aqua monthly product of Chl-a was found with 22% missing data points and then was reconstructed using the Data Interpolating Empirical Orthogonal Functions (DINEOF) computational technique. Gulf waters being an enclosed water body are expected to behave differently from the adjacent open sea waters of the western Arabian Sea. Therefore, 10 stations were selected to cover the whole study area in order to examine the variability in different regions. Pixel-based stations were computed for 4027 sea points in the study area, showing 3461 points with negative and 566 points with positive relationships for Chl-a and SSHa. During the study time period, the trend analysis of Chl-a data categorizes the whole study area into four parts: 1) the western Arabian Sea region at lower latitudes, with increasing trends 2) the Arabian Sea region at higher latitudes, with decreasing trends 3) gulf waters under direct intake from open sea, with decreasing trend 4) gulf waters far from open sea, with increasing trends. Results for correlative relationship analysis of Chl-a and SSHA showed that 86% of the area showed inverse relation whereas 14% of the area showed direct relation. The application of DINEOF and satellite-derived data sets has played an instrumental role in yielding results that are not only way more authentic than the traditional methods but also hold the potential to assist more in-depth scientific inquiry into the marine potential for sustainable economic growth. The study is beneficial for the enhancement of understanding of the seawater quality in terms of biological features, consequently it is helpful to enhance the usage of the ocean-related economy of the arid land countries in the region.

ABSTRACT. Karstic aquifers play a vital role in supplying drinking water and supporting irrigation in Morocco. However, a more comprehensive understanding is essential to enhance their sustainable management in the face of global changes. This study, which involves the chemical and isotopic analysis of 67 groundwater samples from the Rif Mountains' karst aquifer, offers crucial insights into the key factors and processes influencing groundwater recharge and residence time. Isotopic values of δ18O and δ2H suggest that recharge sources vary across the region. For instance, in Lakraa Mountain, North of Lao River, and Haouz and Dersa Mountain aquifers, recharge is predominantly originated from meteoric water at high, intermediate, and low elevations, respectively. Notably, the isotopic signature of the Atlantic Ocean influences all samples except for those from the Lakraa Mountain aquifer, which exhibits Mediterranean Sea influence. Radiocarbon dating, using the IAEA model, reveals groundwater ages spanning from modern to 1460 years. The presence of detectable tritium values (>2.7 TU) in groundwater aligns with the tritium levels observed in precipitation at the nearest GNIP stations of Gibraltar and Fez-Saiss, located approximately 100 km north and 250 km south of the study area, respectively. This evidence underscores the contemporary nature of groundwater in the Rif Mountains, with recharge occurring within the past 60 years. This finding highlights the aquifer's renewability and its vulnerability to climate variabilities and human activities. Furthermore, these results underscore the effectiveness of isotopic tracing in mountainous springs. They offer valuable insights for decision-makers tasked with managing water resources in this karstic region.

ABSTRACT. Hydrological processes creating storage of groundwater beneath the earth surface is complex and functionally nonlinear. Hence more often required to be assessed using advanced numerical solution techniques. Accuracy in physics is attributed to the assumptions considered in co-relating the point logs, whereas, mathematical precision depends upon, how close the discretized function approaches linearity. One of the major uncertainties in numerical modelling of the multi-aquifer domain, is the appreciation of the leakage that occurs in the interfaces between two layers. In the present paper, to address the problem, we take the opportunity of cluster wise lithological correlations and fitting of the exact Theim, Thies and Hantush solutions of pump test, to assess the vertical flows across the interfaces. We suggest that this technology to be practiced for each layer of a multi-aquifer domain explicitly and estimated vertical flows to be used as an input to model application, in order to gain more certainty in the model results.

ABSTRACT. Aquaculture is the production of aquatic organisms under controlled conditions to satisfy the global food demand, generating 82.1 million tons of live fish weight. Aquaculture has become the top food-producing industry in the world, surpassing fisheries production by over 16% more metric tonnes in 2017 and a steady upward trend in yield from 10% to 50% in the last 30 years. One of the most important organisms used in this activity is shrimp. Around 55% of the global shrimp market grows in farms, generating more than 10.6 billion. Moreover, their production is increasing by 10% yearly. However, the main cost related to shrimp aquaculture is because of the high cost of animal feed. The commercial feed contains fish meal (FM) and fish oils; these compounds raise the total feed cost and, consequently, a high production cost for the farmer's shrimp. Moreover, the production of FM leads to high water consumption, negatively impacting the environment. This project aimed to produce a novel food for the shrimp aquaculture industry to reduce FM and water costs. The objective was to replace FM with plant meal, reducing farmers' costs and positively impacting the shrimp growth. In this study, glandless cottonseed meal (GCSM) was used at four different diet concentrations (1= 44% GCSM/0% FM, 2= 33% GCSM/11% FM, 3= 22% GCSM/22% FM, and 4= 11% GCSM/33% FM) using commercial feed as the control diet. All diets were adjusted to 14% moisture content and processed through an Intelli-Torque Plasti-Corded Brabender single screw extruder. The extrusion process followed the next characteristics: screw compression force 1:1, screw diameter: 19mm, exit die: 3mm, screw rotation speed: 180 RPM. The extruder hot zones were set to 90°C, 110°C, and 130°C, respectively. The extrudates were provided for 30 days to different tanks containing Penaeus vannamei shrimp with a 1.2-2.0 g of starting weight. The results showed that the ash content, expansion ratio, water absorption, and water activity showed no statistical difference (P<0.05) among treatments and control. The tested physicochemical characteristics that showed statistical differences (P<0.05) compared to the control diet were crude fat, crude protein, digestible energy, metabolizable energy, and hardness; all but crude fat were higher in diets containing GCSM. The highest weight was obtained on shrimps fed with the control diet (2.97±0.006g), while diet 1 made the shrimp reach 2.92±0.011g. Even though there was a significant difference (P<0.05), it is minimal, at 0.05g average difference. Tissue analysis of the amino acid and fatty acids profiles proved that diet 1 had more amino acids and fatty acids necessary for shrimp growth across all the diets. Glandless cottonseed meal has exhibited physicochemical characteristics, in vitro and in vivo results that show superior qualitative data compared to the commercial diet at a fraction of the production price. Glandless cottonseed meal has the potential not only to feed shrimp but also other aquaculture species. However, further research must be done to expand its uses as a feed protein source.

ABSTRACT. Soil and water salinity in the arid regions are continuously increasing in globe. To tackle this situation, irrigation through drip is placed most important. A Field experiment was conducted at Agricultural Research station, Gangavathi, Karnataka, India from 2018 and 2019 with an objective to study the effect of three (Furrow-M0, Surface drip-M1 and Subsurface drip-M2) irrigation techniques and five saline water levels on soil water. Different water salinity with 0.65 dS m-1 (normal water-S0), 2 dS m-1(S1), 3 dS m-1 (S2), 4 dS m-1(S3) and 5 dS m-1(S4) were used to study the effect on tomato yield. The results revealed that maximum (27.3 t ha-1) yield was found in M2 followed by M1 (26.67 t ha-1) and lowest in M0 (20.38 t ha-1). Under sub treatments, highest yield was obtained under S0, followed by S1, which was on par with the S0 treatment. Therefore, the water salinity with 2 dS m-1 could be used for tomato cultivation without any yield loss. The two year pooled data on water productivity showed higher (9.86 kg m-3) water productivity under M2 followed by M1 (8.42 kg m-3) and least in case of M0 (3.76 kg m-3). Decreased water productivity with increased in salinity levels of irrigation was observed. In surface drip irrigation the soil moisture decreased vertically as depth increases and decreased horizontally at 20 cm distances away from the dripper. In subsurface drip irrigation, due to frequent application of water at 20 cm depth through buried drip lateral (M2), more moisture was found at root zone and at deeper depths. The quality parameters pH, TSS, acidity, ascorbic acid and lycopene content were found increased with increase in salinity levels of irrigation water and had no effect of different irrigation techniques. The sampling and analysis of soil water content is very tedious process. Hence, the HYDRUS-1D model, a computer software based model was used for simulating one dimensional soil water content under different irrigation techniques and different salinity of irrigation water for next event. Calibration and validation results of HYDRUS-1D for observed and simulated showed that, better R2 and RMSE values. Therefore, HYDRUS-1D model could be used to simulate the soil water content in different irrigation methods. The highest benefit cost ratio of 1.84 was obtained under M2S0 followed by M1S0 (1.8) and M2S1 (1.78). The minimum (0.524 year) payback period was obtained under M1S0 followed by M2S0 (0.544 year), M1S1 (0.548 year) and M2S1 (0.567 year). When there is not enough fresh or normal water available for irrigation, saline water with salinity 2 dS m-1 can be applied as an alternative water source to irrigate the field with surface drip and subsurface drip irrigation techniques without any harmful effect on the soil and yield loss in tomato.

ABSTRACT. Water shortage is becoming an alarming concern for humanity. It is expected to suffer limited access to drinkable water affecting more than half the population by 2050. Therefore, securing a sustainable source with high accessibility and less energy consumption has been the focus of scientists of different disciplines. Accordingly, water harvesting based technologies became a well-known promising solution due to the high feasibility with variable materials available across the world. Our modern module herewith empowers Al-mesh collection ability. Inspired by corresponding nature, adopts cactus flowerlike sempervivum plant and optimize a designed surface with hydrophilic-hydrophobic properties similar to the Stenocara Gracilipes desert’s beetle. The tuned architecture demonstrates a morphological flowerlike manganese oxide nanostructure with adjustable tunability and a polymeric robust linker; Polyacrylonitrile. Collectively, the fabricated composition influences the wettability of the surface and droplets growth and release. The composed NF-MnO2 / PAN nanoarchitecture is estimated to collect up to 45 L/m2 per Hour.

ABSTRACT. Kuwait is located in an arid region with limited freshwater resources and arable land. Therefore, it is heavily dependent on imports of food commodities to maintain food security. It is estimated that Kuwait imports around 90% of its food. The country, however, undergoes frequent disruptions in its food supply as a result of global food production volatility, unstable socio-political conditions, and climate change. It is necessary for Kuwait to substitute a portion of importation of the critical food items with local production in order to ensure an adequate food supply at all times and to maintain high food security levels. A large Governmental Initiative (GI) research project has recently been initiated at Kuwait Research Institute for Scientific Research (KISR) in order to develop innovative technologies and practices that will increase local food production. In particular, Kuwait needs to optimize the processes related to the production of crops, poultry, livestock, and shrimps. This GI project is aimed at identifying suitable packages for small, medium, and large farms with the most suitable activities to contribute to Kuwait's food security requirements. It is anticipated that the GI will achieve its goals through the following research packages: plant production, animal production, post-production, energy requirements, water resources, policy and reforms, and economic feasibility. This paper describes the research progress of the water resources research package. The objective of this package is to develop water management schemes that will result in enhanced agricultural production at integrated farms in Kuwait. The paper presents and discusses how this research package will achieve its main objective. Furthermore, it discusses the results obtained to date regarding the characteristics of the various types of water available at KISR's experimental farm.

ABSTRACT. Produced water (PW) is a major waste stream of oil and gas production and presents a potential environmental pollutant. Current strategies look at the re-injection of PW into the reservoir or into a disposal well. In both cases, some purification of PW is needed before injection. On the other hand, upon adequate purification, PW could become an alternate water resource in arid regions. This paper focuses on PW from an oil field in Southern Kuwait. The varying properties of PW over time are noted. The corrosive damage of PW to the subsurface production infrastructure is highlighted. Oil - water separation in PW through membrane filtration and adsorption processes is discussed. Subsequent methods for the desalination of the de-oiled PW are deliberated.