Agricultural Water Demand Research Articles

A Copula Function–Monte Carlo Method-Based Assessment of the Risk of Agricultural Water Demand in Xinjiang, China

A Copula Function–Monte Carlo Method-Based Assessment of the Risk of Agricultural Water Demand in Xinjiang, China

Prominent Increase in Air Temperatures on Two Small Mediterranean Islands, Lastovo and Lošinj, Since 1998 and Its Effect on the Frequency of Extreme Droughts

The article analyzes the development of air temperatures and precipitation on two remote islands in the Adriatic Sea from 1961 to 2023, examining annual and monthly time scales. Lastovo Island is located in the southern Adriatic, and Lošinj Island is situated 277 km north, and both exhibit a sharp rise in air temperatures since 1998, though precipitation series show no significant trends of an increase or decrease. Using the New Drought Index (NDI) method, this study calculated drought intensities for the period 1961–2023. The analyses conducted in this study undoubtedly indicate a rising frequency and intensity of droughts, with severe droughts doubling and extreme droughts increasing fourfold in the recent period (1998–2023) compared to the previous one (1961–1997). The most pronounced increase in severe and extreme droughts occurs specifically from June to August. This trend is likely applicable to many small Mediterranean Islands, which number over 10,000 and have a permanent population of more than 1.6 million people, with numbers significantly rising during the tourist season. The increased water demand for agriculture and daily use, combined with increased drought risk, not only exacerbates the potential for forest fires but also threatens social structures and ecological conditions. This is particularly critical as the combination of drier conditions and increased fire risk poses a significant challenge, endangering natural landscapes and valuable historical sites that are integral to the islands’ identity and heritage. This study’s findings indicate a dangerous trend likely to persist and worsen with continued increases in air temperatures in the Mediterranean region.

AI-driven optimization of agricultural water management for enhanced sustainability

Optimizing agricultural water resource management is crucial for food production, as effective water management can significantly improve irrigation efficiency and crop yields. Currently, precise agricultural water demand forecasting and management have become key research focuses; however, existing methods often fail to capture complex spatial and temporal dependencies. To address this, we propose a novel deep learning framework that combines remote sensing technology with the UNet-ConvLSTM (UCL) model to effectively integrate spatial and temporal features from MODIS and GLDAS datasets. Our model leverages the high-resolution spatial data from UNet and the temporal dependencies captured by ConvLSTM to significantly improve prediction accuracy. Experimental results demonstrate that our UCL model achieves the best R2\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$R^2$$\\end{document} compared to existing methods, reaching 0.927 on the MODIS dataset and 0.935 on the GLDAS dataset. This approach highlights the potential of AI and remote sensing technologies in addressing critical challenges in agricultural water management, contributing to more sustainable and efficient food production systems.

An approach to demarcate prospective areas for groundwater recharge a case study of Upper Thurinjalar Tamil Nadu, India

India, as the world's largest consumer of groundwater, faces an alarming water crisis, particularly in southern regions like Upper Thurinjalar, Tamil Nadu. The relentless extraction of groundwater has severely depleted reserves, making it increasingly difficult to meet the water demands of households, agriculture, and industry. This unsustainable usage endangers the region's water security, emphasizing the urgent need for sustainable management practices. Immediate action is essential to safeguard these vital resources and secure a sustainable water future for the region. The present study addresses the critical need to identify optimal groundwater recharge areas to mitigate the adverse effects of groundwater depletion. Utilizing Remote Sensing (RS) and Geographic Information Systems (GIS), this research focuses on delineating suitable recharge zones in Upper Thurinjalar, Tamil Nadu, to promote sustainable water resource management. Through geospatial analysis, thematic maps were generated, incorporating variables such as geomorphology, geology, subsurface lithology, lineaments, land use, drainage density, soil types, and slope. Utilizing satellite data within the GIS framework, groundwater recharge zones were classified into three categories: highly suitable (61.06 km2), moderately suitable (214.18 km2), and least suitable (48.07 km2). Additionally, prioritized interventions for 18 rural reservoirs within the same catchment area included sediment removal, depth enhancement, and infrastructure upgrades. The application of artificial groundwater recharge in these targeted areas is anticipated to significantly alleviate irrigation water deficits, thereby advancing sustainable development and rehabilitating degraded land. The use of a weighted overlay technique within Geographic Information Systems (GIS) has proven to be a highly effective method for optimizing water resource management, facilitating the sustainable development of groundwater resources. The results emphasize the importance of promptly undertaking focused interventions, such as prioritizing tank improvements and deploying artificial recharge procedures, in order to guarantee the long-term availability of water. Implementing these proactive measures is essential for reducing water scarcity and promoting the development of unused land in the designated recharge areas.

Integrated Geospatial and Analytical Hierarchy Process Approach for Assessing Sustainable Management of Groundwater Recharge Potential in Barind Tract

Groundwater depletion in Bangladesh’s Barind tract poses significant challenges for sustainable water management. This study aims to delineate groundwater recharge potential zones in this region using an integrated geospatial and Analytical Hierarchy Process (AHP) approach. The methodology combines remote-sensing data with GIS analysis, considering seven factors influencing groundwater recharge: rainfall, soil type, geology, slope, lineament density, land use/land cover, and drainage density. The AHP method was employed to assess the variability of groundwater recharge potential within the 7586 km2 study area. Thematic maps of relevant factors were processed using ArcGIS software. Results indicate that 9.23% (700.22 km2), 47.68% (3617.13 km2), 37.12% (2816.13 km2), and 5.97% (452.70 km2) of the study area exhibit poor, moderate, good, and very good recharge potential, respectively. The annual recharge volume is estimated at 2554 × 106 m3/year, constituting 22.7% of the total precipitation volume (11,227 × 106 m3/year). Analysis of individual factors revealed that geology has the highest influence (33.57%) on recharge potential, followed by land use/land cover (17.74%), soil type (17.25%), and rainfall (12.25%). The consistency ratio of the pairwise comparison matrix was 0.0904, indicating acceptable reliability of the AHP results. The spatial distribution of recharge zones shows a concentration of poor recharge potential in areas with low rainfall (1200–1400 mm/year) and high slope (6–40%). Conversely, very good recharge potential is associated with high rainfall zones (1800–2200 mm/year) and areas with favorable geology (sedimentary deposits). This study provides a quantitative framework for assessing groundwater recharge potential in the Barind tract. The resulting maps and data offer valuable insights for policymakers and water resource managers to develop targeted groundwater management strategies. These findings have significant implications for sustainable water resource management in the region, particularly in addressing challenges related to agricultural water demand and climate change adaptation.

Assessment of the impact of greenhouse rainwater harvesting managed aquifer recharge on the groundwater system in the southern Jeju Island, South Korea: Implication from a numerical modeling approach

Managed aquifer recharge (MAR) is increasingly being adopted worldwide to mitigate groundwater depletion and ensure the sustainability of water resources. Rainwater harvesting (RWH)-MAR can augment aquifer storage and reduce flood damage in rural areas with dense greenhouse facilities. This study has assessed the feasibility of greenhouse RWH-MAR in Namwon agricultural areas in the southern part of Jeju Island, South Korea, by considering the injection rate and location of MAR using a numerical model. The model results showed that groundwater level increases were directly related to the infiltration rate, although spatial differences in head rise were observed owing to the spatial variability of hydraulic conductivity. In addition, installing the RWH-MAR in highland areas (>100 masl) enhanced the water level rise when compared to the expected values, indicating that a higher hydraulic gradient and thick unsaturated zone facilitated more effective MAR outcomes than in lowland areas. To optimize the contribution of source water to the agricultural water demand, placing the RWH-MAR near the pumping well improved the availability of injected rainwater to agricultural wells. This study highlights the importance of designing RWH-MAR schemes considering MAR objectives and the topographic and hydrogeological characteristics of the site.

Assessment on Water Resource Management for Sedawgyi Dam: A WEAP Analysis Approach

This study employs the Water Evaluation and Planning (WEAP) system to analyze water resource management for the Sedawgyi Dam in Myanmar. The research integrates hydrological data, demand projections, and infrastructure information to simulate current and future water scenarios. Key steps include model setup, calibration, scenario development, and analysis. The results indicate that Mandalay City's highest domestic water demands are in Chanmyatharzi and Aungmyaetharzan townships, with agricultural water demands peaking in Mattaya township. The reference scenario highlights a significant domestic water supply shortfall, meeting only 10% of the demand, while agricultural demands are nearly fully met. The study underscores the urgent need for improved water management strategies to address rising unmet water demands, particularly for domestic use, to ensure sustainable water resource allocation in the region.

Near future variations in temperature extremes in northeastern Iran under CMIP6 projections.

Extreme air temperatures which are of significance in plant growth are influenced by climate change. The aim of this study was to assess the climate change effects on temperature extreme indices (TEIs) in northeastern Iran based on the CMIP6 projections. For this purpose, five extreme indices including maximum of maximum temperature (TXx), minimum of minimum temperature (TNn), cold nights (TN10p), warm nights (TN90p), and summer days (SU25) which are effective on plant growth were evaluated. The projections of the three Earth system models including ACCESS-CM2, MIROC6, and MRI-ESM2-0 were assessed under the three scenarios of SSP1-2.6, SSP2-4.5, and SSP5-8.5 for the period 2026-2050 compared to the historical period (1989-2014). The models' projections were evaluated by statistical tests on the changes in the average and trend of data. Results showed that the MRI-ESM2-0 model revealed the best efficiency compared to the other models. The projections of all models and scenarios indicated a significant increase in the average of TXx and SU25 indices over the study area at the confidence level of 95% by 1.6 to 2.4°C, as well as 15 to 18days under the three scenarios. Also, the results exhibited a significant increasing trend in TN90p and a decreasing one in TN10p overall province atconfidence level of 95%. These changes will lead to an increase in evapotranspiration of the plants and an increase in agricultural water demand, and as a result, a decrease in the yield of some main products like wheat and saffron which are the main products in the study area.

A multi-layer nesting and integration approach for predicting groundwater levels in agriculturally intensive areas using data-driven models

Agricultural water demand, groundwater extraction, surface water delivery and climate exhibit complex nonlinear relationships with groundwater storage in agricultural regions. As alternatives to computationally intensive physical models, data-driven machine learning methods are frequently employed as surrogates to capture these complex relationships, owing to their high computational efficiency. Inevitably, reliance on a single machine learning model may lead to underestimation of prediction uncertainty and potentially result in reduced accuracy. This paper presents a multi-layer nesting and integration approach for predicting groundwater levels in agriculturally intensive areas using data-driven models. The key contributions of the research are threefold: 1) the development of a comprehensive input variable selection process considering the lag effects and driving mechanisms of groundwater level variations; 2) the implementation of an optimization-based hyperparameter tuning method to enhance the performance of individual machine learning models; and 3) the establishment of a multi-model integration framework based on a multi-layer nesting technique. This approach combines the outputs of multiple machine learning models to consolidate predictions and expand the hypothesis space. The effectiveness of the proposed framework is demonstrated through case studies in the Huaihe River Basin, a major agricultural region in China. The results show that the multi-layer nesting and integration approach outperforms the use of individual machine learning models, providing more accurate and reliable groundwater level predictions. This framework offers valuable insights for decision-makers and water resource managers, supporting sustainable groundwater management and addressing the challenges faced in agriculturally intensive areas.

Developing a socio-hydrological model to assess community sensitivity to lake degradation

ABSTRACT The crucial challenge in arid/developed endorheic basins is to balance agricultural water demands with ecosystem health. This study addresses this dilemma by developing a semi-distributed socio-hydrological model for the basins using monthly water balance coupled with annual community sensitivity (CS)/response models. The central research question focuses on whether the degradation of lakes in the Tashk-Bakhtegan basin, our selected case study, can shift the community’s attitude towards allocating water resources for environmental rather than agricultural purposes. Given the basin’s vastness and complex water access, individuals hold diverse environmental attitudes on lake degradation. Upon model calibration, it becomes evident that downstream areas exhibit a CS variable 2.4 times higher than upstream areas, with annual fluctuations ranging from 1.15 to 6.34. Remarkably, the findings underscore a gradual transformation in downstream attitudes, where increasing CS has shifted their perspective towards prioritizing the well-being of the lake, while upstream residents continue to prioritize agro-economic benefits.

Future Agricultural Water Availability in Mediterranean Countries under Climate Change: A Systematic Review

Warming and drying trends in the Mediterranean Basin exacerbate regional water scarcity and threaten agricultural production, putting global food security at risk. This study aimed to review the most significant research on future water availability for the Mediterranean agricultural sector under climate change (CC) scenarios published during 2009–2024. Two searches were performed in the Scopus and Web of Science databases, to which previously identified significant studies from different periods were also added. By applying a methodology duly protocoled in the PRISMA2020-based guideline, a final number of 44 particularly relevant studies was selected for review. A bibliometric analysis has shown that most of the published research was focused on Southwestern European countries (i.e., Spain, Italy, Portugal) and grapevine and olive tree crops. Overall, the reviewed studies state that future Mediterranean water reserves may not meet agricultural water demands, due to reduced reservoir inflows and higher irrigation demands under future CC and socioeconomic scenarios. Regarding adaptation measures to improve water-use management in agriculture, the majority of the reviewed studies indicate that the use of integrated modelling platforms and decision–support systems can significantly contribute to the development and implementation of improved water/land-management practices.

Will there be water? Climate change, housing needs, and future water demand in California

Climate change in California is expected to alter future water availability, impacting water supplies needed to support future housing growth and agriculture demand. In groundwater-dependent regions like California's Central Coast, new land-use related water demand and decreasing recharge is already stressing depleted groundwater basins. We developed a spatially explicit state-and-transition simulation model that integrates climate, land-use change, water demand, and groundwater gain-loss to examine the impact of future climate and land use change on groundwater balance and water demand in five counties along the Central Coast from 2010 to 2060. The model incorporated downscaled groundwater recharge projections based on a Warm/Wet and a Hot/Dry climate future from a spatially explicit hydrological process-based model. Two urbanization projections from a parcel-based, regional urban growth model representing 1) recent historical and 2) state-mandated housing growth projections were used as alternative spatial targets for future urban growth. Agricultural projections were based on recent historical trends from remote sensing data. Annual projected changes in groundwater balance were calculated as the difference between land-use related water demand, based on historical estimates, and climate-driven recharge plus agriculture return flows. Results indicate that future changes in climate-driven groundwater recharge, coupled with cumulative increases in agricultural water demand, result in overall declines in future groundwater balance, with a Hot/Dry future resulting in cumulative groundwater decline in all but Santa Cruz County. Cumulative declines by 2060 are especially prominent in San Luis Obispo (−2.9 to −5.1 Bm3) and Monterey counties (−6.5 to −8.7 Bm3), despite limited changes in agricultural water demand over the model period. These two counties show declining groundwater reserves in a Warm/Wet future as well, while San Benito and Santa Barbara County barely reach equilibrium. These results suggest future groundwater supplies may not be able to keep pace with regional demand and declining climate-driven recharge, resulting in a potential reduction in water security in the region. However, our county-scale projections showed new housing and associated water demand does not conflict with California's groundwater sustainability goals. Rather, future climate coupled with increasing agricultural groundwater demand may reduce water security in some counties, potentially limiting available groundwater supplies for new housing.

Capacity configuration of cascaded hydro-wind-photovoltaic complementary systems considering comprehensive benefits based on a synergetic-orderly framework

The rational configuration of hydro-wind-photovoltaic complementary system is fundamental to fully leveraging the regulation capacity of hydropower and the complementarity between different natural resources. However, complementary systems involving cascaded reservoirs often have comprehensive utilization requirements, which current techno-economic frameworks struggle to address. To solve the above issues, this study proposes a novel framework based on the theory of synergetics to determine the optimal capacities for wind and photovoltaic power considering the comprehensive benefits. Firstly, an inner operation model aimed at maximizing overall order degree is established to optimize the system’s operational performance. Secondly, Kolmogorov entropy is utilized in the outer layer to characterize the synergy of different wind and photovoltaic capacity schemes, thereby selecting the one with the best synergistic effect. Additionally, current techno-economic evaluation indicators are introduced to validate the framework’s effectiveness. A case study of the clean energy base on the upper Yellow River was conducted, and the results indicate that: (1) The proposed framework effectively addresses the comprehensive benefits of multiple subsystems and the optimal capacity scheme performs well in economic and technical aspects. (2) Compared to variations in wind and solar resources, inflow conditions and agricultural water demand have a greater impact on the capacity configuration and operational performance. (3) The optimal capacity ratio of hydro to renewable energy in the upper Yellow River is around 1:0.59. Therefore, this study provides important theoretical support for expanding capacity configuration methods in multi-energy complementary systems.

Impact of projected climate variability on agricultural water demand of key crops in drought-prone central India region

ABSTRACT Water scarcity predominantly affects agriculture production. It is mandatory to save future water to prevent its shortage due to variations in climate. Variation in climate is a significant factor responsible for frequent droughts over the Bundelkhand region in central India. It is necessary to obtain early warning studies in this region to manage and arrange future agricultural water. Out of best Regional Climate Models (RCMs) under the Coordinated Regional Downscaling Experiment (CORDEX) and their driving Coupled Model Intercomparison Project Phase 5 (CMIP5), General Circulation Models (GCMs) for India, the most effective model has been found based on the observed multiscale data for the region. The daily projected data (2021–2100) of best GCM, Earth System Model, ESM-2M have been used to evaluate the future water requirements for major rabi and hot weather crops grown in the region. The climate change effect is seen in the majority, especially in the upper Bundelkhand part. Most of the crops will need more water than the present condition, especially from 2071 to 2100. The findings will also help researchers and policymakers to utilize the future projected data to propose suitable water management strategies for agriculture.

Trade-Off and Synergy Mechanism of Agricultural Water Resource Spatial Allocation in Monsoon Climate Areas Based on Machine Learning: A Case Study of Reservoir Layout Optimization in Shandong Province, China

Influenced by increasing global extreme weather and the uneven spatiotemporal distribution of water resources in monsoon climate areas, the balance of agricultural water resources supply and demand currently faces significant challenges. Conducting research on the spatial allocation trade-offs and synergistic mechanisms of agricultural water resources in monsoon climate areas is extremely important. This study takes the spatial layout of reservoir site selection in water conservancy projects as an example, focusing on Shandong Province as the research area. During the site selection process, the concept of water resource demand is introduced, and the suitability of reservoir siting is integrated. It clarifies ten influencing factors for suitability degree and five influencing factors for demand. A bi-objective optimization model that includes suitability degree and demand degree is established. Utilizing machine learning methods such as the GA_BP neural network model and the GA-bi-objective optimization model to balance and coordinate the supply and demand relationship of agricultural water resources in the monsoon region. The study found that: (1) in the prediction of suitability degree, the influencing factors are most strongly correlated with the regulatory storage capacity (regulatory storage capacity > total storage capacity > regulating storage coefficient); (2) compared with single-objective optimization of suitability degree, the difference between water supply and demand can be reduced by 74.3% after bi-objective optimization; (3) according to the spatial layout optimization analysis, the utilization of water resources in the central and western parts of Shandong Province is not sufficient, and the construction of agricultural reservoirs should be carried out in a targeted manner. This study provides new ideas for promoting the efficient use of water resources in monsoon climate zones and the coordinated development of humans and nature, reflecting the importance of supply and demand balance in the spatial allocation of agricultural water resources, reducing the risk of agricultural production being affected by droughts and floods.

Utilizing Artificial Intelligence Techniques for a Long–Term Water Resource Assessment in the ShihMen Reservoir for Water Resource Allocation

Accurate long–term water resource supply simulation and demand estimation are crucial for effective water resource allocation. This study proposes advanced artificial intelligence (AI)–based models for both long–term water resource supply simulation and demand estimation, specifically focusing on the ShihMen Reservoir in Taiwan. A Long Short–Term Memory (LSTM) network model was developed to simulate daily reservoir inflow. The climate factors from the Taiwan Central Weather Bureau’s one–tiered atmosphere–ocean coupled climate forecast system (TCWB1T1) were downscaled using the K–Nearest Neighbors (KNN) method and integrated with the reservoir inflow model to forecast inflow six months ahead. Additionally, Multilayer Perceptron (MLP) and Gated Recurrent Unit (GRU) were employed to estimate agricultural and public water demand, integrating both hydrological and socio–economic factors. The models were trained and validated using historical data, with the LSTM model demonstrating a strong ability to capture seasonal variations in inflow patterns and the MLP and GRU models effectively estimating water demand. The results highlight the models’ high accuracy and robustness, offering valuable insights into regional water resource allocation. This research provides a framework for integrating AI–driven models with Decision Support Systems (DSSs) to enhance water resource management, especially in regions vulnerable to climatic variability.

Predicting the impact of climate change on crop water footprint using CMIP6 in the Shule River Basin, China

Quantitatively predicting the impacts of climate change on water demands of various crops is essential for developing measures to ensure food security, sustainable agriculture, and water resources management, especially in arid regions. This study explored the water footprints (WFs) of nine major crops in the middle and downstream areas of Shule River Basin, Northwest China, from 1989 to 2020 using the WF theory and CROPWAT model and predicted the future WFs of these crops under four emission and socio-economic pathway (SSPs-RCPs) scenarios, which provides scientific support for actively responding to the negative impacts of climate change in arid regions. Results indicated: (1) an increasing trend of the overall crop WF, with blue WF accounting for 80.31–99.33% of the total WF in the last 30 years. Owing to differences of planting structure, water-conservation technologies, and other factors, the multi-year average WF per unit area of crops was 0.75 × 104 m3 hm−2 in downstream area, which was higher than that in midstream area (0.57 × 104 m3 hm−2) in the last 30 years; therefore agricultural water use efficiency in the downstream area was lower than that in the midstream area, implying that the midstream area has more efficient agricultural water utilization. (2) an initial increase and then decrease of crop WFs in the study area under SSP1-2.6, SSP2-4.5, and SSP5-8.5 scenarios by the end of the century, reaching their peak in 2030s which was higher than that from 1989 to 2020; with the maximum growth rates in the midstream area ranging from −0.85% in SSP5-8.5 to 5.33% in SSP2-4.5 and 29.74% in SSP5-8.5 to 34.71% in SSP2-4.5 in the downstream area. The local agricultural water demand would continue to increase and water scarcity issues would be more severe in the next 10–20 years, affecting downstream areas more. Under the SSP3-7.0 scenario, crop WF values of the midstream and downstream regions will be 2.63 × 108 m3 and 4.22 × 108 m3 in 2030, respectively, which is significantly higher than those of other scenarios and show a long-term growth trend. The growth rate of the midstream and downstream regions will reach 44.71% and 81.12%, respectively, by the end of this century, so the local agricultural water use would be facing more strain if this scenario materializes in the future. Therefore, the Shule River Basin should encourage development of water-saving irrigation technologies, adjust the planting ratio of high water consuming crops, and identify other measures to improve water resource utilization efficiency to cope with future water resource pressures.

Multi-objective double layer water optimal allocation and scheduling framework combing the integrated surface water – groundwater model

Balancing the water consumption of agricultural and ecological is the key point of sustainable social and economic development in an inland river basin. The growth of desert riparian forests in inland river basins mainly depends on a certain phreatic water table depth (PWTD). The main object of this study was to allocate and schedule water resources to regulate the PWTD and satisfy agricultural water demand. Therefore, a multi-objective double layer optimal allocation and scheduling framework based on the computationally efficient integrated surface water-groundwater model (ISGWM), which can simulate the surface water processes, groundwater recharge and discharge processes, and PWTD changes, was constructed and applied to the mainstream of Tarim River Basin (TRB). The top layer model of the framework is an optimal ecological water allocation model, and its optimal allocation results are used as the initial solution of the bottom layer model. The results show that under 5 different inflow frequencies, the agricultural water shortage rate is 0, 17.38 %, 17.41 %, 14.06 %, and 19.94 %, respectively. The PWTD regulation has a great performance. After the optimal scheduling, the proportions of good growth of the control area behind the gate under different inflow frequencies were 98.18 %, 98.18 %, 98.18 %, 90.91 %, and 94.55 %. Agricultural water shortage is mainly due to the non-uniformity distribution of intra-annual inflow and the lack of controlling hydraulic engineering. The regulation of PWTD can guarantee the growth of desert riparian forests on both sides of the mainstream of TRB. Besides, we explored the feasibility of exploiting groundwater to supplement agricultural water consumption. The groundwater exploitation should be controlled within the scope of not causing excessive increase of PWTD (difference between PWTD and target depth <1 m), due to the groundwater exploitation to supplement agricultural water will lead to the increase of PWTD. Overall, this framework, which regulates the PWTD with the change of ecological water supply based on the ISGWM, provides a new idea for the allocation and scheduling of agricultural and ecological water resources in arid inland river basins. It also provides a new method for the coupled cooperative operation of surface water and groundwater.

Improve food, water, and economic benefits in China’s oases through crop switching

China’s oases are the center of human life and economic development for tens of millions of inhabitants in drylands. However, oases have experienced a significant expansion of cropland during the past three decades, which has resulted in rapidly increasing agricultural water demand and increasing water shortages in the region. Although crop switching is recognized as a promising strategy for improving the sustainability of agricultural production, little is known about how much it can improve the sustainability of oasis cropping systems and whether there are trade-offs among multiple sustainability goals. Here, we used a crop-water-use model, data on crop production and nutrient content, observational data on streamflow, and oasis land-use data to assess the impacts of different crop switching scenarios on water demand, nutrient supply, and income. The results showed that oasis agricultural water demand increased from 28.5 km3 to 41.1 km3 between 1987 and 2017, with 74.6 % of the oasis counties experiencing increasing agricultural water stress. By replacing all crops with those having the lowest water demand in each respective county, the demand for irrigation can be reduced by 23.1 %, and the demand for groundwater reduced by as much as 72.8 %. Crop switching scenarios that also consider crop species diversity, in which replacing the highest water demand crops with alternative crops (those with the lowest water demands, highest protein production, or highest calorie production) in each county can effectively reduce irrigation water demand (8.5–12.9 % for streamflow and 32.7–43.3 % for groundwater), while increasing protein (21.3–35.8 %) and calorie (27.5–42.7 %) production as well as income (1.4–2.4 %). Our research provides potential solutions to effectively alleviate the conflict between agricultural development and the environment in oasis regions.

Spatio-temporal variation of depth to groundwater level and its driving factors in arid and semi-arid regions of India

Climate change and increasing anthropogenic activities, such as over-exploitation of groundwater, are exerting unavoidable stress on groundwater resources. This study investigated the spatio-temporal variation of depth to groundwater level (DGWL) and the impacts of climatic (precipitation, maximum temperature, and minimum temperature) and anthropogenic (gross district product (GDP), population, and net irrigated area (NIA)) variables on DGWL during 1994–2020. The study considered DGWL in 113 observation wells and piezometers located in arid western plains (Barmer and Jodhpur districts) and semi-arid eastern plains (Jaipur, Ajmer, Dausa, and Tonk districts) of Rajasthan State, India. Statistical methods were employed to examine the annual and seasonal patterns of DGWL, and the generalized additive model (GAM) was used to determine the impacts of climatic and anthropogenic variables on DGWL. During 1994–2020, except for Barmer District, where the mean annual DGWL was almost constant (around 26.50 m), all other districts exhibited increase in DGWL, with Ajmer District experiencing the most increase. The results also revealed that 36 observation wells and piezometers showed a statistically significant annual increasing trend in DGWL and 34 observation wells and piezometers exhibited a statistically significant decreasing trend in DGWL. Similarly, 32 observation wells and piezometers showed an statistically significant increasing trend and 37 observation wells and piezometers showed a statistically significant decreasing trend in winter; 33 observation wells and piezometers indicated a statistically significant increasing trend and 34 had a statistically significant decreasing trend in post-monsoon; 35 observation wells and piezometers exhibited a statistically significant increasing trend and 32 observation wells and piezometers showed a statistically significant decreasing trend in pre-monsoon; and 36 observation wells and piezometers reflected a statistically significant increasing trend and 30 observation wells and piezometers reflected a statistically significant decreasing trend in monsoon. Interestingly, most of the observation wells and piezometers with increasing trends of DGWL were located in Dausa and Jaipur districts. Furthermore, the GAM analysis revealed that climatic variables, such as precipitation, significantly affected DGWL in Barmer District, and DGWL in all other districts was influenced by anthropogenic variables, including GDP, NIA, and population. As a result, stringent regulations should be implemented to curb excessive groundwater extraction, manage agricultural water demand, initiate proactive aquifer recharge programs, and strengthen sustainable management in these water-scarce regions.