The significant impact of climate change, reduced precipitation, and drought in recent decades has led to a marked decline in both surface and groundwater levels. This crisis has particularly affected regions with arid and semi-arid climates, resulting in excessive abstraction from various aquifers. The Kerman Plain aquifer, which is located in an arid to semi-arid region, has also experienced a severe drop in groundwater levels over these years. Therefore, quantitative assessment of groundwater levels in this plain is of great importance for improving management and decision-making during serious water crises. In the present study, a 22-year simulation of groundwater levels in the plain was conducted using the GMS software and the MODFLOW numerical model. Future aquifer conditions up to the point of reaching the bedrock were predicted based on groundwater levels in the Bodaghabad well, located in the northern part of the aquifer within the Kerman urban area. According to the modeling results for the period 2002–2024, the greatest annual decline was observed in the western and eastern regions, averaging 1.23 meters, while the minimum decline 0.27 meters per year occurred in the central and southern parts of the aquifer. Furthermore, the findings showed that the diversion of Kerman’s urban wastewater for use by adjacent industries, and the consequent lack of aquifer recharge from the city’s natural wastewater drainage, have increased the groundwater level decline rate by 15.85% and shortened the time to reach bedrock in the Kerman urban area by 14 years.
In groundwater flow modeling, as in any modeling problem, a certain amount of error is inevitable. Recharge or discharge wells, acting as point sources or sinks, play a key role in modeling accuracy, and the way they are treated can either reduce or increase errors. In this study, two approaches were investigated: first, transferring the well to the nearest node in its neighborhood, and second, distributing the pumping rate of the well among the closest nodes. A hypothetical aquifer was examined under two conditions-unconfined and confined-and using both triangular and square meshes. The results indicated that simplifying the model by moving the pumping well to the nearest node is justified only for unconfined aquifers with triangular meshes. For other cases-including unconfined aquifers with square meshes and confined aquifers with either mesh-the second approach is recommended, as it significantly reduces errors in groundwater flow modeling. These findings can also be generalized to real aquifer studies. Quantitative results show that Approach 2 consistently reduces modeling errors: for unconfined aquifers, MAE values are below 0.03 for both mesh types, whereas confined aquifers exhibit larger reductions, particularly with triangular meshes, where MAE reaches 0.38 and maximum errors up to 1.17. These results highlight the robustness of Approach 2 across different mesh configurations and aquifer conditions, providing an effective and reliable numerical tool for groundwater modeling.
Accurately determining the relationship between river flow discharge and suspended sediment load in watersheds is challenging due to the influence of natural and human-induced variables on river estimations. Therefore, it is essential to employ modern methods to improve existing models. In this context, multivariate methods and copula-based modeling and simulation, given their ability to analyze data distributions, can be suitable options. In this study, joint frequency analysis of river flow discharge and suspended sediment load was conducted at the Abajalo and Tapik stations in the Nazlochai sub-basin, Lake Urmia, Iran using copula functions and marginal distributions. First, the correlation between variables was examined using Kendall’s tau coefficient, indicating a strong positive relationship between river flow discharge and suspended sediment load. For modeling marginal distributions, the Log-Normal and GEV distributions with NSE=0.99 at the Abajalo station were selected as the best distributions for flow discharge and suspended sediment load, respectively, while the GEV and Generalized Pareto distributions with NSE=0.99 at the Tapik station were chosen for river flow discharge and suspended sediment load, respectively. In the joint analysis, the Galambos and Gumbel-Hougaard copula functions demonstrated the best performance based on evaluation criteria. Bivariate analysis revealed that at the Abajalo station, with a 90% probability and river flow discharge exceeding 40 m³/s, the suspended sediment load reaches over 3,000 tons/day, while at the Tapik station, the same probability with a river flow discharge of 25 m³/s indicates a suspended sediment load exceeding 500 tons/day. Finally, based on the conditional density of copula functions and considering various probabilities, equations were proposed for simulating suspended sediment load conditioned on river flow discharge at both stations. The proposed equations were suggested for probability levels of 80–90%, 90–95%, and 95–99% and evaluated using various statistical metrics. The proposed equations for conditional estimation of suspended sediment load demonstrated high performance at the Abajalo station (NSE>0.95 and 386.20.84 and 26.8
This study offers the first comprehensive comparison among four hybrid deep learning architectures—LSTM-GRU, CNN-LSTM, Attention-LSTM, and Transformer—for multipurpose dam inflow forecasting under severe hydrological variability. The study employed a 14-year dataset (168 observations, 2010-2023) obtained from Jiroft Dam in Iran and framed with hydrological and operational parameters including precipitation, reservoir capacity, agricultural discharge, and turbine functions. The LSTM-GRU architecture yielded the best performance by attaining 0.873 R² and 29.73 m³/s root mean square error (RMSE) during the validation procedure and demonstrating the best balance among accuracy and generalizability. The model robustness was confirmed by advanced validation methods including Taylor diagrams, violin diagrams, and statistical testing (Kolmogorov-Smirnov, Ljung-Box, and Breusch-Pagan tests). Seasonal analysis revealed a seven times change in flow rates ranging across winter maxima of 391.5 m³/s and autumn minima of 56.2 m³/s. The models showed a widespread tendency to predict lower peak flows (percentage bias, PBIAS: -14.34% to -20.86%), suggesting the presence of operational safety buffers. Precipitation–agricultural interactions were identified as the key forecasting variable (importance = 0.999). The model provides real-time support for decision-making on reservoir management, flood protection, and potable water supply under changing environmental circumstances and provides a validated model for AI-accelerated water resource management.
The Lokichar Basin in Turkana, northern Kenya, is a semi-arid environment marked by pronounced rainfall variability and intense competition for water resources among domestic, livestock, and oil-driven industrial demands. This study assesses long-term rainfall trends (1981–2024), aquifer characteristics, groundwater levels, and demand dynamics to evaluate resource sustainability under oilfield development. Rainfall analyses using the Rainfall Anomaly Index, Standardized Precipitation Index, and Mann-Kendall tests show marked interannual variability, with significant increases in annual and October–December rainfall and evidence of non-stationarity in seasonal precipitation. Pumping tests from 23 boreholes reveal heterogeneous transmissivity (4.17 × 10⁻⁶–6.00 × 10⁻³ m²/s) and specific yield (0.036–0.500), with high-yield zones concentrated in Nakukulas. Continuous monitoring indicates daily groundwater fluctuations linked to domestic abstraction, while simulations project extreme midday drawdowns exceeding 60 m under full-scale oil production. Estimated natural recharge (21.8–43.6 Mm³ yr⁻¹) falls short of 2022 industrial demand (~53.0 Mm³ yr⁻¹), exposing a recharge–demand deficit. These findings highlight the vulnerability of Lokichar aquifer to over-abstraction and emphasize the need for integrated management, regulated abstraction, enhanced monitoring, and climate-responsive planning to secure water resources and support sustainable petroleum development in this fragile semi-arid basin.
The study evaluated household water supply, access, and harnessing methods in Abetifi, Kwahu East, Ghana, within a quantitative research framework, involving 400 respondents selected through stratified random sampling. It discussed seasonal changes in water supply, the primary factors affecting household access, and the efficiency of adaptation measures. During the dry season, 85 per cent of households lacked water, 70 per cent had no piped water, and people walking 3.5 km daily spent 4.8 hours a day collecting water. Contrastingly, the wet season had a minimum of 25 per cent scarcity, a 1.2 km travel distance, and an average collection time of 2.5 hours. More than 55 per cent of households spent more than 5 Ghana cedis per day on water, and 60 per cent of households experienced frequent breakdowns of the borehole or standpipe. During the wet season (70%), water was collected from rainwater; during the dry season (70%), came from boreholes, with the help of household storage systems (60%). The results of remote-sensing analysis indicated that the mean value of the NDWI (-0.582) during the wet season was larger than the mean value during the dry season (-0.461), and the results of the LULC analysis (2000-2025) demonstrated that vegetation cover and growth reduced by half and built-up areas expanded, which identified the fact of increased anthropogenic pressure on water resources. The study's originality lies in combining household survey information, NDWI, and LULC analysis within the Sustainable Livelihoods Framework to assess the interaction among seasonality in climatic conditions, human adaptation, and plateau hydrology. The results provide data-driven, practical recommendations for developing climate-resilient community-based water systems in Ghana's highlands.
This study aims to (1) evaluate the Crop Water Productivity (CWP) and gray Water Footprint (WFGray) for key agricultural systems in Lorestan province, Iran, to identify hotspots of inefficiency and pollution, and (2) develop and compare Machine Learning (ML) models for predicting these metrics to aid in management and forecasting. We calculated CWP and WFGray for major crops (including forage corn, wheat, beans, potatoes and vegetables) across multiple meteorological stations in Lorestan province. Furthermore, we employed two ML algorithms including Random Forest (RF) and Support Vector Machine (SVM) to model and predict these indices. Model performance was evaluated using the Mean Absolute Error (MAE). The assessment revealed significant regional and crop-specific disparities. Forage corn was the most efficient and sustainable system (CWP: 2.173 kg/m³, WFGray: 0.05 m³/kg), whereas bean production was the least efficient (CWP: 0.064 kg/m³). Spatially, stations like Azna (potato) demonstrated best practices, while Kuhdasht was identified as a critical area of concern due to low efficiency and high fertilizer pollution. In modeling, the optimal algorithm was target-dependent: RF was superior for predicting CWP (MAE: 0.236), while SVM performed relatively better for the more complex WFGray. This study concludes that addressing water security and agricultural pollution in the region requires tailored, crop-specific interventions and improved farm management practices. Furthermore, while ML model (particularly RF) proves to be a powerful tool for forecasting water productivity, accurately modeling the environmental impact (WFGray) remains a challenge, highlighting the need for more robust data and further research in this domain.
Improper maintenance, inadequate drainage system design, and increasing non-porous surfaces due to urbanization result in waterlogging in urban areas. Shahjalal Upashahar is a prominent urban area in Sylhet city which faces waterlogging conditions due to sediment deposition, leading to the loss of original drainage channel capacity. This study focused on reducing the sediment accumulation in the drainage channel by implementing a sand trap and bar screen mechanism. To design the sand trap for a 100-year return period, the study area was divided into five sub-watersheds using the ArcGIS tool. Rainfall data (2000-2023) were collected from the nearby meteorological station. Based on the determined slope and length of all sub-catchments, the concentration times range from 10.995 to 13.8707 minutes. Using the concentration time, the rainfall intensity was calculated from the Intensity Duration Frequency curve (IDF). The highest runoff was calculated for all sub-watersheds using the rational equation. The peak discharge for catchments 1 through 5 were 1.263, 1.784, 0.254, 1.183 and 1.326 m3/sec, respectively. The required cross-section of the rectangular sand trap was determined using the equation of continuity Q = AV. The bar screen was designed based on the size of solid waste and the prevailing velocity of flow. In this study, the designed cross-sectional areas of sand traps ranging from 0.38 to 2.26 m2 for five sub-watersheds were expected to reduce sediment accumulation by maintaining full drainage channel capacity.
Groundwater quality is influenced by the region’s natural climatic–geological setting and anthropogenic practices such as agriculture, industry, and mining. Ongoing evaluation of groundwater quality is therefore vital for secure drinking supplies, agricultural production, industrial operations, public-health protection, and efficient treatment processes. This study aims to evaluate the quality of groundwater in Sarbisheh Plain, South Khorasan, Iran. Water-quality data for 2020 and 2021 were examined and analyzed for the 18 wells supplying Sarbisheh’s water demand. The status and concentrations of 12 physico-chemical parameters during the mentioned years were evaluated and statistically analyzed using SPSS software. The overall quality of the studied water resources was also evaluated using groundwater quality index. The results showed that the average EC in the water-supply wells of Sarbisheh is approximately 4513.5 μS/cm, which exceeds the standard limit. The TDS values also ranged from 596 to 8511 mg/l, with the mean for most wells falling outside the acceptable standard range. Among the studied ions, sodium and chloride exhibited the highest concentrations at 682.1 mg/l and 677.3 mg/l, respectively, while potassium and fluoride showed the lowest levels at 28 mg/l and 0.3 mg/l. Calculations of the water quality index for the 18 wells showed that 33.33% of the wells fell into the good category, while the remaining wells ranged from poor to very poor. The results demonstrated that assessment and monitoring of groundwater quality in study area are very important; moreover, for drinking purposes, treatment is required to improve water quality and meet the necessary standards.
The aim of this research is to simulate and predict the groundwater level in the Siminehrood River Basin, which is situated south of Lake Urmia, Iran. This simulation was conducted using copula functions while accounting for changes in river discharge influenced by climate change. A total of 26 large-scale CMIP6 models were utilized in this study. Precipitation data were downscaled and simulated using the LARS WG 7.0 model. Subsequently, precipitation data for both the baseline period (1988-2018) and the future period (2031-2050) were predicted for three scenarios: SSP1-2.6, SSP2-4.5, and SSP5-8.5 through a weighted average method. Following the simulation and prediction of precipitation in the Siminehrood River Basin, copula functions were employed to simulate and predict both river discharge and groundwater levels. Prior to fitting the copula function, correlations between pair of parameters precipitation-river discharge and river discharge-groundwater level were examined using Kendall's tau coefficient; correlation values obtained were 0.43 for precipitation-river discharge and 0.44 for river discharge-groundwater level. After selecting marginal distributions and examining these correlations, ten different copula functions were fitted to each pair of parameters in order to identify the most suitable model among them. The results from predicting precipitation related to climate change indicated that annual precipitation under all three scenarios would decrease compared to the measured precipitation. Annual precipitation reductions were projected to be 5.1 mm, 31.5 mm, and 34.8 mm under the scenarios SSP1-2.6, SSP2-4.5, and SSP5-8.5, respectively. Analysis through copula functions revealed that the Clayton copula provided optimal performance when creating a joint distribution for these pair of parameters during simulation phases concerning river discharge as well as groundwater levels and its accuracy was validated based on evaluation criteria including NSE (Nash-Sutcliffe Efficiency), RMSE (root mean square error), and R² (coefficient of determination). Furthermore, it was concluded that reductions in annual precipitation would lead to decreases in annual river discharge ranging from 2.9 m³/s to 6.6 m³/s alongside an annual drop in groundwater levels estimated between 0.3 m and 1.5 m.
Dissolved oxygen (DO) fluctuations directly affect biological processes and water quality in a reservoir. It can occur gradually or rapidly as a result of a large input load of pollution. This paper proposes the Reservoir Health Indicator (RHI) as a weighted combination of reliability, resiliency, and vulnerability indices. The one-dimensional First-Order Reliability Method (FORM) and the empirical framework are applied to estimate these indices. The analysis uses 50 years of daily DO simulation results, acquired from molding a Minab dam divided into five non-overlapping 10-year periods. An indicator value greater than 0.5 reveals that the dam is healthy and sufficiently reliable in meeting the DO standard. Three weighting scenarios are applied to explore the RHI sensitivity. Results showed that in the first scenario, the approximate range of RHI variation is between 0.6 and 0.2. This indicates that after 20 years, the dam has lost its ability to improve its condition. In the second scenario, the variation is between 0.63 and 0.4, and the dam almost loses its health at 25 years. The third scenario indicates successful performance of the dam such that system has almost ability to recover itself by the end of its life. Therefore, developing such an indicator can effectively help understand the variation of a reservoir water quality by integrating three vital aspects of reliability, resiliency, and vulnerability.
Freshwater scarcity has become a pressing global challenge, driving the need for innovative and sustainable water production technologies. Atmospheric water harvesting offers a promising solution by exploiting the vast reservoir of water vapor in the air, particularly for arid and remote regions. In this study, we developed a novel two-stage moisture absorption–desorption system using a highly hygroscopic hydrogel–CaCl₂ composite (7.4% hydrogel, 92.6% CaCl₂). The device comprises an absorption compartment equipped with 10 trays (0.675 kg of the composite per tray) for capturing atmospheric moisture and a condensation–recovery compartment integrated with a refrigeration system for efficient desorbed vapor condensation. The system operates in cyclic absorption and thermally driven desorption phases, with each phase offering fully programmable and controllable duration. The desorbed vapor is subsequently directed into a condensation chamber, where it is recovered through an integrated refrigeration unit, and discharged from the system. Experimental results demonstrated a freshwater production capacity of approximately 1 L per day, under air relative humidity of approximately 33%. This integrated approach highlights the potential of hydrogel–salt composites coupled with active condensation for atmospheric water harvesting applications.
In this research, a method of drawing groundwater levels maps is developed considering the active Qanats in the Gonabad Plain, Iran. The common approach for drawing the groundwater levels map, is interpolating the groundwater level elevation data obtained from observation wells. Qanat has been ignored for drawing the groundwater levels map in the most of time. However, it is one of the main structures that should be considered in this analysis. In the introduced approach, the groundwater surface levels map of the Gonabad Plain was drawn using the groundwater level elevation data obtained from observation wells and mother well of Qanats located in the aquifer and also considering that water bearing zone of Qanats drain groundwater, therefore groundwater levels is parallel and groundwater flow line is perpendicular to it. Results lead to preparation of an accurate groundwater levels map that is important for the maintenance of groundwater resources and also helping to reveal the secret of the method of drilling deep Qanats in the plain in several thousand years ago. By using this map, the aquifer transmissivity in water bearing zone of each Qanat and the capture zone of each Qanat were estimated. The results indicated that the aquifer transmissivity in water bearing zone of Qanats varies from 23 to 77 square meters per day.
Water, this vital element, plays an irreplaceable role in our lives. From drinking water supply to energy production, agriculture, industry, and numerous other sectors depend on it. However, human activities have profoundly impacted water resource availability. Under these circumstances, a deeper understanding of the hydrological cycle and river behavior becomes more crucial than ever. One key tool for hydrological cycle simulation is the SWAT (Soil and Water Assessment Tool) model. This study investigated the effects of using regional versus global soil and land use data on SWAT model performance in the Chel-Chai watershed. Monthly river discharge data from Lazoureh and Jangaldeh stations (2006-2020 for calibration; 1997-2005 for validation) were utilized. The results showed that at Lazoureh station, during the calibration phase, regional and global data showed similar performance; the NS and R indices for both data types were 0.60 and 0.77, respectively, and the MAE and RMSE errors were both 0.78 and 1.11. Consequently, no difference was observed between regional and global data in this phase. During the validation phase at Lazoureh station, regional data performed better than global data, reducing MAE and RMSE by 2.56% and 1.92%, respectively. At Jangaldeh station, during the calibration phase, regional data also outperformed global data. The NS and R indices for regional data were 0.73 and 0.90, respectively, while for global data they were 0.58 and 0.87. Regional data also showed better performance during the validation phase. The results demonstrate that regional data can provide more accurate river discharge estimates, particularly during validation phases. This study highlights the importance of spatial data resolution in hydrological modeling accuracy.
Nowadays, heavy metal contamination in water sources is a critical environmental concern directly related to human health, necessitating the development of sustainable and cost-effective remediation techniques. Traditional adsorbents are often expensive and less environmentally friendly, highlighting the need for alternative materials with high adsorption efficiency. This study proposes using pistachio green hull, an agricultural byproduct, in both untreated and chemically treated forms as an adsorbent for removing cadmium (Cd2+) from aqueous solutions. Batch adsorption experiments were conducted using untreated, HNO3-treated, NH3/H2O2-treated, and acetone/H2O2-treated pistachio green hulls at an initial Cd2+ ions concentration of 20 mg/L. The adsorption kinetics, equilibrium isotherms, and pH dependence were analyzed to determine the adsorption efficiency and mechanism. The removal efficiencies for untreated, HNO3-treated, NH3/H2O2-treated, and acetone/H2O2-treated samples in laboratory scale were 68%, 55%, 88%, and 95%, respectively. Maximum adsorption occurred within a broad pH range of 4-9. The kinetic analysis revealed that Cd2+ adsorption follows a pseudo-second-order model, with rapid adsorption (<4 min). The adsorption isotherm followed the Langmuir model, suggesting monolayer adsorption, and electrostatic interactions were identified as a key mechanism. The findings demonstrate that pistachio green hulls, particularly after chemical modifications, serve as an effective and eco-friendly adsorbent for Cd2+ removal. This study contributes to advancing sustainable and low-cost solutions for heavy metal remediation.
This study presents a comprehensive analysis aimed at predicting the discharge of the Barandozchay River using machine learning algorithms and meteorological data from both satellite and ground sources over the period from 2002 to 2022. The research highlights the significance of incorporating snow cover data in enhancing predictive accuracy, particularly during the spring and summer seasons. Utilizing Artificial Neural Networks (ANN), Support Vector Machines (SVM), and Random Forest (RF), the study evaluates various parameters affecting river discharge, including temperature, precipitation, and solar radiation. The results indicate that the Random Forest model outperforms the others in accuracy and generalization, while SVM demonstrates improved predictive capabilities with the inclusion of snow cover data. Specifically, the integration of snow cover data significantly enhanced the simulation accuracy of river discharge. The SVM model showed notable improvements in evaluation metrics, with R2 increasing from 0.64 to 0.72, MAE decreasing from 0.4 to 0.61, and RMSE reducing from 0.81 to 0.29 in the test data. Conversely, the RF model experienced an increase in error for the test data, but the correlation coefficient R2 improved from 0.85 to 0.88. The findings underscore the necessity of employing advanced machine learning techniques for water resource management, especially in regions facing water crises due to climate change.
Identifying and prioritizing barriers to people's participation (PPBs) in is a prerequisite for implementing participatory soil and water conservation projects (SWCPs). Comparison evaluation of the local community and experts perspectives on the PPBs has rarely been investigated. Therefore, in the current study the level of agreement on the PPBs importance from the perspectives two groups were examined. For this purpose, Dastgerd, Asadli and Emarat watersheds, eastern Iran, with different socio-economic conditions were selected. In the current study the 13 important PPBs in implementation of SWCPs were identifying, which can be used as a model in future studies of other watersheds. Then the indicators were prioritized using Friedman Test. Finally, the two-sample Kolmogorov–Smirnov Test was also used to examine the agreement of the two views on the importance of the items. The results of PPBs prioritization based on 215 local people and 51expert’s viewpoints showed that lake of participatory guidelines, expert oriented decision-making process and lack of incentives economic in implementation of SWCPs are the most important PPBs. The results of two-samples Kolmogorov-Smirnov test show that the opinions of people and experts regarding the importance and role of 65% PPBs have a significant difference. The disagreement between the opinions of the two groups is a barrier to achieving the goals of participatory SWCPs. Also, removing barriers related to economic-executive factors has a high effect on increasing the level of participation and encouraging voluntary participate in SWCPs.
The sediment transport and the relation to water quality parameters and hydrological characteristics in the Sufi Chay River in Iran were investigated in this study using long-term monitoring data. Traditional statistical methods, dimensionless parameter analysis, and advanced soft computing techniques are combined within the scope of the presented comprehensive analysis. Total sediment load is the dependent variable, while the independent variables include flow rate, total dissolved solids (TDS), electrical conductivity (EC), pH, total anions, total cations, anion hardness, and cation hardness. Strong correlations were observed between total sediment load and flow rate (r = 0.82), total dissolved solids (r = 0.68), and electrical conductivity (r = 0.65). The dimensionless equation developed related sediment concentration to Reynolds number, Froude number, and normalized water quality parameters. The performance was quite good as revealed by the R2 value of 0.82. Comparison of performances using three soft computing methods, namely Artificial Neural Networks, Adaptive Neuro-Fuzzy Inference System, and Support Vector Regression, are performed. The highest R2 value of 0.91 and RMSE of 53.2 tons/ day were obtained with ANFIS model. Sensitivity analyses indicated that flow rate and TDS were the most sensitive parameters to predict total sediment load. Generally, a seasonal variability in sediment transport, showing that the maximum discharges happened in the spring season with the mean of 187.3 tons/day, while the minimum discharges happened in the summer season with the mean of 42.8 tons/day. Besides, a nonlinear relationship between flow rate and both sediment concentration and discharge in this catchment reflects a complex erosion and transport process. The investigation also resulted in some important ion-parameter relationships, which are indicative of the geochemical factors operating on the water quality and sediment activity.
The advancement of industries and the impacts of climate change are among the primary drivers of the critical challenges the world faces today. This paper reflects a profound connection to the lessons of ancient civilizations, emphasizing the importance of sustainable practices. It addresses both the necessity and the barriers to integrating digital technologies into industrial activities. While the task was highly systematic, it was carried out using reputable databases, and this article highlights some of those efforts. Using VOS Viewer software, a total of 700 articles were analyzed, with a specific focus on titles and studies published between 2023 and 2025. Nowadays, the use of Artificial Intelligence (AI) and the Internet of Things (IoT) adds significant value to optimizing water resource management. However, even the most advanced technologies and systems—designed to identify users’ water consumption patterns and provide detailed analysis and data—come with their own set of challenges. Key issues such as inadequate technological infrastructure, resistance to change, and financial constraints are critical areas that need to be addressed to improve water management practices. Without a clear strategy and proper preparation—including a thorough understanding of contemporary needs and advanced conditions—efforts to design and outline effective solutions will fall short. Where there is sufficient internal capacity and a robust organizational structure, there is no reason why innovative, non-bureaucratic approaches, such as targeted recommendations, should not be considered to address water-related challenges and engineer sustainable solutions.
Kenya’s arid lands, including Ngilai and Kalepo conservancies, face multiple challenges such as poverty, poor infrastructure, weak governance, and climate change—leading to prolonged droughts, flash floods, and declining water sources. This study investigates water accessibility, depth, and quality of 125 water sources using focus group discussions (FGDs) and water quality experiments. Rainfall analysis showed 1997 as the wettest year (RAI +4.6, El Niño) and 2017 as the driest (RAI –4.2). A Mann-Kendall trend test revealed a non-significant positive trend (p = 0.368). Most water pans deplete within two months of dry season onset, and face issues like siltation, poor infrastructure, and contamination. Capacities ranged from 150–12,000 m³ with depths of 0.5–2 m. Boreholes are the main water source, while Kalepo also features springs and seasonal rivers due to its undulating terrain. The geology comprises metamorphic and sedimentary rocks. In wet seasons, seasonal springs within 3 km buffers offer accessible domestic water. During dry seasons, water conflicts occur within 10 km zones due to human-wildlife competition. Livestock migrates to the Mathews Ranges in search of vegetation, the perennial Ewaso Ngiro River, and permanent boreholes. Groundwater recharge occurs in sandy seasonal laggas, with yields of 5 m³/hr and borehole depths of 103–122 m, aided by fault lines. Water pH ranged from 6.5- 8.64, indicating acidic to slightly alkaline conditions, while EC values ranged from 250 to 4000 μS/cm. Findings highlight the need to improve water storage and manage siltation to build climate-resilient communities.
