Responses of streamflow to forest expansion in a typical subhumid watershed under future climate conditions.

Water availability in the subhumid region is highly vulnerable to frequent droughts. Water scarcity in this region has become a limiting factor for ecosystem health, human livelihood, and regional economic development. A notable pattern of land cover change in the subhumid region of the United States is the increasing forest area due to afforestation/reforestation and woody plant encroachment (WPE). Given the distinct hydrological processes and runoff generation between forests and grasslands, it is important to evaluate the impacts of forest expansion on water resources, especially under future climate conditions. In this study, we focused on a typical subhumid watershed in the United States - the Little River Watershed (LRW). Utilizing SWAT + simulations, we projected streamflow dynamics at the end of the 21st century in two climate scenarios (RCP45 and RCP85) and eleven forest expansion scenarios. In comparison to the period of 2000-2019, future climate change during 2080-2099 will increase streamflow in the Little River by 5.1% in the RCP45 but reduce streamflow significantly by 30.1% in the RCP85. Additionally, our simulations revealed a linear decline in streamflow with increasing forest coverage. If all grasslands in LRW were converted into forests, it would lead to an additional 41% reduction in streamflow. Of significant concern is Lake Thunderbird, the primary reservoir supplying drinking water to the Oklahoma City metropolitan area. Our simulation showed that if all grasslands were replaced by forests, Lake Thunderbird during 2080-2099 would experience an average of 8.6 years in the RCP45 and 9.4 years in the RCP85 with water inflow amount lower than that during the extreme drought event in 2011/2012. These findings hold crucial implications for the formulation of policies related to afforestation/reforestation and WPE management in subhumid regions, which is essential to ensuring the sustainability of water resources.

Evaluation of sustainable development capacity of water sources: a case study of China.

The issue of water scarcity has drawn attention from all over the world. The coordination of the interaction between ecological and environmental development of water sources and socio-economic development is currently an essential issue that needs to be solved in order to safeguard the water resources environment for human survival. In this essay, we suggest a paradigm for assessing the sustainable exploitation of water resources. First, three ecological, economic, and social factors are investigated. Twenty essential evaluation indexes are then constructed using the Delphi approach, along with an index system for assessing the potential of water sources for sustainable development. The weights of each evaluation index were then determined using the combination assignment approach, which was then suggested. The coupled degree evaluation model of the capability for sustainable development of water sources was then developed. In order to confirm the viability and validity of the suggested model, the model was used to assess the Liwu River water source's capacity for sustainable growth in the context of the South-North Water Transfer in Shandong, China. It is believed that the aforementioned study would serve as a helpful resource when evaluating the capacity of water sources for sustainable development.

Impact of climate change on the water resources of the Atbara River using novel hydrological models.

Rivers respond directly to climate change, as well as incorporating the effects of climate-driven changes occurring within their watersheds. In this research, climate change's impact on the Atbara River, one of the main tributaries of the Nile River, was studied. Various statistical methods of analysis were applied to study the basic characteristics of the climatic parameters that affect the discharge of the Atbara River. The three hydrological gauging stations on the Atbara River, namely, the Upper Atbara and Setit reservoirs, Khashm el-Girba reservoir, and Atbara Kilo 3 station, were included in the study. The correlation between the meteorological parameters and the hydrology of the Atbara River and the prediction of the future hydrology of the Atbara River Basin was determined. Many hydrological models were developed and tested to predict the hydrology of the river. Finally, forecasting for river hydrology was built. No significant trend was found in the precipitation in the study area. The developed model simulates the observed data with a high coefficient of determination ranging from 0.7 to 0.91 for the three hydrological gauging stations studied. Results predicted a slight decrease in river discharge in future years.

A full-scale operational digital twin for a water resource recovery facility-A case study of Eindhoven Water Resource Recovery Facility.

Digital transformation for the water sector has gained momentum in recent years, and many water resource recovery facilities modelers have already started transitioning from developing traditional models to digital twin (DT) applications. DTs simulate the operation of treatment plants in near real time and provide a powerful tool to the operators and process engineers for real-time scenario analysis and calamity mitigation, online process optimization, predictive maintenance, model-based control, and so forth. So far, only a few mature examples of full-scale DT implementations can be found in the literature, which only address some of the key requirements of a DT. This paper presents the development of a full-scale operational DT for the Eindhoven water resource recovery facility in The Netherlands, which includes a fully automated data-pipeline combined with a detailed mechanistic full-plant process model and a user interface co-created with the plant's operators. The automated data preprocessing pipeline provides continuous access to validated data, an influent generator provides dynamic predictions of influent composition data and allows forecasting 48 h into the future, and an advanced compartmental model of the aeration and anoxic bioreactors ensures high predictive power. The DT runs near real-time simulations every 2 h. Visualization and interaction with the DT is facilitated by the cloud-based TwinPlant technology, which was developed in close interaction with the plant's operators. A set of predefined handles are made available, allowing users to simulate hypothetical scenarios such as process and equipment failures and changes in controller settings. The combination of the advanced data pipeline and process model development used in the Eindhoven DT and the active involvement of the operators/process engineers/managers in the development process makes the twin a valuable asset for decision making with long-term reliability. PRACTITIONER POINTS: A full-scale digital twin (DT) has been developed for the Eindhoven WRRF. The Eindhoven DT includes an automated continuous data preprocessing and reconciliation pipeline. A full-plant mechanistic compartmental process model of the plant has been developed based on hydrodynamic studies. The interactive user interface of the Eindhoven DT allows operators to perform what-if scenarios on various operational settings and process inputs. Plant operators were actively involved in the DT development process to make a reliable and relevant tool with the expected added value.

Effectiveness evaluation of China's water resource tax reform pilot and path optimization from the perspective of policy field.

The water resource tax reform played an important role in promoting sustainable development in China. Subsequent to the seven-year reform, the effectiveness evaluation of the policy in each pilot area and the exploration of the optimization path directly affected the promotion of water resource tax policy and the improvement of water use efficiency. Therefore, the theoretical framework of the water resource tax policy field was constructed to examine the mechanism of the three subsystems of policy scenario, policy orientation, and policy effect; fuzzy-set qualitative comparative analysis (fsQCA) was then used to evaluate and quantitatively compare the policy implementation effect and policy path in each pilot area, with emphasis put on three policy orientations, i.e., the decision and decomposition effect of policy goals, the selection and im plementation effect of policy tools, and the policy supervision and security effect. As shown by the research results: ① the water resource tax reform had effectively improved the efficiency of water resource utilization in the pilot areas; ② three pilot models of water resource tax policy had been extracted, namely the policy goal and tool-driven model centering on a single dimension of the policy field, the implementation-supervision dual drive model emphasizing the supervision and security effect of the policy, and the three-dimensional policy orientation linkage model that focused on the synergistic effect of the policy field; ③ strong heterogeneity existed in water resource tax policy implementation paths and effects in each pilot area. Accordingly, regional heterogeneity could be considered in the process of reform to construct institutionalized, precise, and differentiated reform implementation methods from the perspective of the policy field.

Vegetation restoration strategies based on plant water use patterns.

The study on the water source of plants in alpine mountainous is of great significance to optimize the allocation and management of water resources, and can also provide important reference for ecological restoration and protection. However, the controls of water sources for different plants in alpine mountainous region remain poorly understood. Based on the advantages of stable isotope tracer and Bayesian (MixSIAR) model, the water source of plants in Qilian Mountains was quantitatively analyzed. The results showed that the water sources of plants in Qilian Mountain mainly included two parts: direct source and indirect source. The direct source is soil water, which provides most of the water that plants need. The highest contribution of soil water to shrubs was 80 %, followed by trees (73 %) and herbs (72 %). It is worth mentioning that trees mainly use deeper soil water (below 60 cm), shrubs mainly use surface and intermediate soil water (0-60 cm), and herbs mainly use surface soil water (0-40 cm). What is more noteworthy is that indirect sources, such as precipitation, glacier and snow meltwater, and groundwater, are also water sources that cannot be ignored for plant growth in study area. Shrubs and Herbs use more soil water in the range of 40-60 cm, which leads to the possibility of water competition between these two planting types. Therefore, attention should be paid to this phenomenon in the process of vegetation restoration and water resources management. Especially when planting or restoring artificial plants, it is necessary to consider the water use strategy of the two plants to avoid unnecessary water competition and water waste. This is of great significance for ecological stability and sustainable utilization of water resources in the study region.

A new strategy for groundwater level prediction using a hybrid deep learning model under Ecological Water Replenishment.

Accurate prediction of the groundwater level (GWL) is crucial for sustainable groundwater resource management. Ecological water replenishment (EWR) involves artificially diverting water to replenish the ecological flow and water resources of both surface water and groundwater within the basin. However, fluctuations in GWLs during the EWR process exhibit high nonlinearity and complexity in their time series, making it challenging for single data-driven models to predict the trend of groundwater level changes under the backdrop of EWR. This study introduced a new GWL prediction strategy based on a hybrid deep learning model, STL-IWOA-GRU. It integrated the LOESS-based seasonal trend decomposition algorithm (STL), improved whale optimization algorithm (IWOA), and Gated recurrent unit (GRU). The aim was to accurately predict GWLs in the context of EWR. This study gathered GWL, precipitation, and surface runoff data from 21 monitoring wells in the Yongding River Basin (Beijing Section) over a period of 731 days. The research results demonstrate that the improvement strategy implemented for the IWOA enhances the convergence speed and global search capabilities of the algorithm. In the case analysis, evaluation metrics including the root mean square error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE), and Nash-Sutcliffe efficiency (NSE) were employed. STL-IWOA-GRU exhibited commendable performance, with MAE achieving the best result, averaging at 0.266. When compared to other models such as Variance Mode Decomposition-Gated Recurrent Unit (VMD-GRU), Ant Lion Optimizer-Support Vector Machine (ALO-SVM), STL-Particle Swarm Optimization-GRU (STL-PSO-GRU), and STL-Sine Cosine Algorithm-GRU (STL-SCA-GRU), MAE was reduced by 18%, 26%, 11%, and 29%, respectively. This indicates that the model proposed in this study exhibited high prediction accuracy and robust versatility, making it a potent strategic choice for forecasting GWL changes in the context of EWR.

A fuzzy interval dynamic optimization model for surface and groundwater resources allocation under water shortage conditions, the case of West Azerbaijan Province, Iran.

The allocation of water in areas which face shortage of water especially during hot dry seasons is of utmost importance. This is normally affected by various factors, the management of which takes a lot of time and energy with efforts falling infertile in many cases. In recent years, scholars have been trying to investigate the applicability of fuzzy interval optimization models in attempts to address the problem. However, a review of literature indicates that in applicating such models, the dynamic nature of the problem has mostly been overlooked. Therefore, the aim of the present study is to provide a fuzzy interval dynamic optimization model for the allocation of surface and groundwater resources under water shortage conditions in West Azerbaijan Province, Iran. In so doing, an optimization model for the allocation of water resources was designed and then was validated by removing surface and groundwater resources and analyzing its performance once these resources were removed. The model was then applied in the case study of ten regions in West Azerbaijan Province and the optimal allocation values and water supply percentages were determined for each region over 12 periods. The results showed that the increase in total demand has the greatest effect while the increase in groundwater industrial demand has the least effect on the supply reduction rate. The increase of uncertainty up to 50% in the fuzzy interval programming would lead to subsequent increases in groundwater extraction by up to 19% and decreases in water supply by up to 10%. The increase of uncertainty in the fuzzy interval dynamic model would cause an increase in groundwater extraction to slightly more than 10% and a decrease in water supply to 0.05%. Therefore, implementing the fuzzy interval dynamic programming model would result in better gains and would reduce uncertainty effects. This would imply that using a mathematical model can result in better gains and can provide better footings for more informed decisions by authorities for managing water resources.

The role of interacting social and institutional norms in stressed groundwater systems.

Groundwater resources play an important role for irrigation, particularly in arid and semi-arid regions, where groundwater depletion poses a critical threat to agricultural production and associated local livelihoods. However, the relationship between groundwater use, farming, and poverty, particularly with regards to informal mechanisms of resources management, remains poorly understood. Here, we assess this relationship by developing a behavioural model of groundwater user groups, empirically grounded in the politically fragile context of Tunisia. The model integrates biophysical aquifer dynamics, institutional governance, and farmer decision-making, all of which are co-occurring under conditions of aquifer depletion and illicit groundwater extraction. The paper examines how community-level norms drive distributional outcomes of farmer behaviours and traces pathways of local system collapse - whether hydrogeological or financial. Through this model, we explore how varying levels of trust and leadership, ecological conditions, and agricultural strategies can delay or avoid collapse of the social-ecological system. Results indicate limits to collective action under path-dependent aquifer depletion, which ultimately leads to the hydrogeological collapse of groundwater user groups independent of social and institutional norms. Despite this inevitable hydrogeological collapse of user groups, the most common cause of water user group failure is bankruptcy, which is linked to the erosion of social norms regarding fee payment. Social and institutional norms, however, can serve to delay the financial collapse of user groups. In the politically fragile system of Tunisia, low levels of trust in government result in low social penalties for illicit water withdrawals. In the absence of alternative irrigation sources, this serves as a temporary buffer against income-poverty. These results highlight the need for polycentric coordination at the aquifer-level as well as income diversification beyond agriculture to sustain local livelihoods.

A comprehensive research on open surface drinking water resources in Istanbul using remote sensing technologies.

Istanbul is a megacity with a population of 15.5 million and is one of the fastest-growing cities in Europe. Due to the rapidly increasing population and urbanization, Istanbul's daily water needs are constantly increasing. In this study, eight drinking water basins that supply water to Istanbul were comprehensively examined using remote sensing observations and techniques. Water surface area changes were determined monthly, and their relationships with meteorological parameters and climate change were investigated. Monthly water surface areas of natural lakes and dams were determined with the Normalized Difference Water Index (NDWI) applied to Sentinel-2 satellite images. Sentinel-1 Synthetic Aperture Radar (SAR) images were used in months when optical images were unavailable. The study was carried out using 3705 optical and 1167 SAR images on the Google Earth Engine (GEE) platform. Additionally, to determine which areas of water resources are shrinking, water frequency maps of the major drinking water resources were produced. Land use/land cover (LULC) changes that occurred over time were determined, and the effects of the increase in urbanization, especially on drinking water surface areas, were investigated. ESRI LULC data was used to determine LULC changes in watersheds, and the increase in urbanization areas from 2017 to 2022 ranged from 1 to 91.43%. While the basin with the least change was in Istranca, the highest increase in the artificial surface was determined to be in the Büyükçekmece basin with 1833.03 ha (2.89%). While there was a 1-12.35% decrease in the surface areas of seven water resources from 2016 to 2022, an increase of 2.65-93% was observed in three water resources (Büyükçekmece, Sazlidere, and Elmali), each in different categories depending on their size. In the overall analysis, total WSA decreased by 62.33 ha from 2016 to 2022, a percentage change of 0.70%. Besides the areal change analysis, the algae contents of the drinking water resources over the years were examined for the major water basins using the Normalized Difference Chlorophyll Index (NDCI) and revealed their relationship with meteorological factors and urbanization.

A new interpretable streamflow prediction approach based on SWAT-BiLSTM and SHAP.

Streamflow is a crucial variable for assessing the available water resources for both human and environmental use. Accurate streamflow prediction plays a significant role in water resource management and assessing the impacts of climate change. This study explores the potential of coupling conceptual hydrological models based on physical processes with machine learning algorithms to enhance the performance of streamflow simulations. Four coupled models, namely SWAT-Transformer, SWAT-LSTM, SWAT-GRU, and SWAT-BiLSTM, were constructed in this research. SWAT served as a transfer function to convert four meteorological features, including precipitation, temperature, relative humidity, and wind speed, into six hydrological features: soil water content, lateral flow, percolation, groundwater discharge, surface runoff, and evapotranspiration. Machine learning algorithms were employed to capture the underlying relationships between these ten feature variables and the target variable (streamflow) to predict daily streamflow in the Sandu-River Basin (SRB). Among the four coupled models and the calibrated SWAT model, SWAT-BiLSTM exhibited the best streamflow simulation performance. During the calibration period (training period), it achieved R2 and NSE values of 0.92 and 0.91, respectively, and maintained them at 0.90 during the validation period (testing period). Additionally, the performance of all four coupled models surpassed that of the calibrated SWAT model. Compared to the tendency of the SWAT model to underestimate streamflow, the absolute values of PBIAS for all coupled models are below 10%, which indicates that there is no significant systematic bias evident. SHapley Additive exPlanations (SHAP) were used to analyze the impact of different feature variables on streamflow prediction. The results indicated that precipitation contributed the most to streamflow prediction, with a global importance of 29.7%. Hydrological feature variable output by the SWAT model played a dominant role in the Bi-LSTM's prediction process. Coupling conceptual hydrological models with machine learning algorithms can significantly enhance the predictive performance of streamflow. The application of SHAP improves the interpretability of the coupled models and enhances researchers' confidence in the prediction results.

A system dynamics simulation-based strategic analysis of integrated water resources utilization and management in Shenzhen city.

As one of the most rapidly developing cities in China, Shenzhen grapples with an increasing challenge in managing water resources due to escalating conflicts with its soaring water demand. This study established a system dynamics (SD) model based on a causal loop diagram to explore the intricate interconnections within the urban water resources system. Through simulating water supply and demand in Shenzhen from 2021 to 2035, the model identified key sensitive factors and examined various utilization scenarios for multiple water resources. Results indicated that water scarcity posed a significant obstacle to Shenzhen's development. To tackle this challenge, several effective measures should be implemented, including enhancing water conservation capabilities, developing seawater resources, promoting water reuse, optimizing the economic structure, and managing population growth. Prioritizing water conservation efforts and maximizing the utilization of seawater resources were regarded as the most impactful strategies in alleviating the water crisis. Furthermore, the relationship between water conservation capabilities and seawater utilization scale was analyzed using the SD model, contributing to the development of a comprehensive water resources management strategy. The findings from this study would provide insights into robust methods for allocating water resources, thereby enhancing sustainable water management strategies applicable to regions facing similar challenges.

One to twelve-month-ahead forecasting of MODIS-derived Qinghai Lake area, using neuro-fuzzy system hybridized by firefly optimization.

Lakes, as the main sources of surface water, are of great environmental and ecological importance and largely affect the climatic conditions of the surrounding areas. Lake area fluctuations are very effective on plant and animal biodiversity in the areas covered. Hence, accurate and reliable forecasts of ​lake area might provide the awareness of water and climate resources and the survival of various species dependent on area fluctuations. Using machine learning methods, the current study numerically predicted area fluctuations of ​China's largest lake, Qinghai, over 1 to 12 months ahead of lead time. To this end, Moderate Resolution Imaging Spectroradiometer (MODIS) sensor images were used to monitor the monthly changes in the area of ​the lake from 2000 to 2021. Predictive inputs included the MODIS-derived lake area time latency specified by the autocorrelation function. The data was divided into two periods of the train (initial 75%) and test (final 25%), and the input combinations were arranged so that the model in the test period could be used to predict 12 scenarios, including forecast horizons for the next 1 to 12 months. The adaptive neuro-fuzzy inference system (ANFIS) was utilized as a predictive model. The firefly algorithm (FA) was also used to optimize ANFIS and improve its accuracy, as a hybrid model ANFIS-FA. Based on evaluation criteria such as root mean square error (RMSE) (477-594 km2) and R2 (88-92%), the results confirmed the acceptable accuracy of the models in all forecast horizons, even long-term horizons (10 months, 11 months, and 12 months). Based on the normalized RMSE criterion (0.095-0.125), the models' performance was reported to be appropriate. Furthermore, the firefly algorithm improved the prediction accuracy of the ANFIS model by an average of 16.9%. In the inter-month survey, the models had fewer forecast errors in the dry months (February-March) than in the wet months (October-November). Using the current method can provide remarkable information about the future state of lakes, which is very important for managers and planners of water resources, environment, and natural ecosystems. According to the results, the current approach is satisfactory in predicting MODIS-derived fluctuations of Qinghai Lake area and has research value for other lakes.

Water resource dynamics and protection strategies for inland lakes: A case study of Hongjiannao Lake.

Globally, lakes are drying up and shrinking and inland lakes, in particular, face severe water shortage problems. Thus, the degradation mechanisms and protection measures for inland lakes urgently need to be explored. Hongjiannao Lake (HL), an inland lake on the border of Shaanxi Province and Inner Mongolia Autonomous Region of China, was selected for the present case study. The evolution of HL was analyzed and the current lake water storage was measured on site. The driving factors of water resource changes in HL were discussed based on meteorological and landcover data. The results showed that (1) from 1929 to 2021, the lake area of HL experienced four stages: formation, stability, shrinkage and recovery. The smallest water area was 31.08 km2 in 2015, half the size of lake in the 1960s. (2) Spatially, the morphological changes of HL mainly occurred where the rivers entered the lake. (3) In 2021, the average depth of HL was 3.77 m, and the water storage capacity was 140.56 million m3. (4) The annual average evaporation was 3.36 times the amount of the annual average precipitation in Hongjiannao Basin (HB), but climate change was not the main driver of changes in the HL area. (5) In the past 20 years, cultivated land and artificial surface increased by 3.11% and 1.04%, respectively, whereas grassland and water body decreased by 3.51% and 0.45%, respectively. The expansion of cultivated land and artificial surface, as well as the construction of reservoirs upstream of the lake, hindered the replenishment of water resources to HL. This study recommends a range of strategies for water resource protection in inland lakes, including implementing ecological restoration projects, carrying out inter-basin water transfer measures, improving the efficiency of regional water resource use, and improving industrial structure and distribution.

Assessing ecological health in a semi-arid basin: a case study of the Wei River Basin, China.

A healthy water ecosystem within a river basin is essential for maintaining ecological security, preserving species diversity, and ensuring sustainable socio-economic development. Unfortunately, human activities have significantly threatened the health of water ecosystems in various basins. Consequently, timely restoration and targeted protection of damaged river ecosystems have become crucial objectives in watershed management. As a prerequisite and cornerstone for river protection and management, assessing river ecological health has emerged as a primary focus in current research. In this study, we selected the Wei River Basin, a representative area of the Yellow River Basin, as our research subject. We identified multiple influencing factors, including society, biology, water quality, and habitat, which collectively impact this semi-arid region. To assess the overall impact of these factors on ecological health, we developed a comprehensive River Ecological Health Assessment Index (REHAI) system. The research findings indicate that the Wei River system, as a whole, is currently in a healthy state, while the Jing and Luo River systems are classified as sub-healthy. Furthermore, we observed variations within the Wei River system itself; the upper reaches of the Wei River exhibit higher levels of health compared to the middle reaches, whereas the water environment in the lower reaches is the most compromised. This degradation can be attributed to downstream subsidence, increased pollution, and rapid urbanization. By establishing a river ecosystem health assessment methodology and conducting a comprehensive evaluation of the health status of river ecosystems, this paper puts forward management recommendations for river basins. These findings provide a scientific basis for the sustainable utilization of water resources in river basins and promote the harmonious coexistence of humanity and nature.

Dynamic changes in water resources and comprehensive assessment of water resource utilization efficiency in the Aral Sea basin, Central Asia.

The Aral Sea Basin in Central Asia faces significant challenges in improving water utilization and treatment because of frequent transboundary river water disputes and shortages of water resources. However, the traditional water resource utilization efficiency (WRUE) assessment models generally have the defect of over-validating evaluation results. To solve this problem, this study used the Coefficient of Variation method to constrain the self-contained weights in the traditional Data Envelopment Analysis (DEA) to construct an improved CV-DEA model, and assessed the WRUE of the Aral Sea Basin countries during 2000-2018 and compared the WRUE with that of the countries in the Mekong River Basin and Northeast Asia, then explored the factors influencing water utilization. The conclusions were drawn: since 1960, the runoff from the upper Amu Darya and Syr Darya rivers increased significantly, while the runoff from the lower Amu Darya River into the Aral Sea declined. Meanwhile, the water area of the Aral Sea shrank from 2.56 × 104 km2 to 0.70 × 104 km2 in 2000-2018, with the Northern Aral Sea remaining stable while the southern part shrinking sharply. The WRUE of the Aral Sea Basin (0.599, on average) was higher than that of the Mekong River Basin (0.547) and lower than that of Northeast Asia (0.885). Kazakhstan and Uzbekistan had the highest WRUE of 0.819 and 0.685 respectively, and the WRUE in both two countries improved from 2000 to 2018. Tajikistan (0.495) and Turkmenistan (0.402) experienced decreases in WRUEs. The high input redundancy of agricultural water consumption was the main driving force affecting WRUE in the basin.

Deep learning in water protection of resources, environment, and ecology: achievement and challenges.

The breathtaking economic development put a heavy toll on ecology, especially on water pollution. Efficient water resource management has a long-term influence on the sustainable development of the economy and society. Economic development and ecology preservation are tangled together, and the growth of one is not possible without the other. Deep learning (DL) is ubiquitous in autonomous driving, medical imaging, speech recognition, etc. The spectacular success of deep learning comes from its power of richer representation of data. In view of the bright prospects of DL, this review comprehensively focuses on the development of DL applications in water resources management, water environment protection, and water ecology. First, the concept and modeling steps of DL are briefly introduced, including data preparation, algorithm selection, and model evaluation. Finally, the advantages and disadvantages of commonly used algorithms are analyzed according to their structures and mechanisms, and recommendations on the selection of DL algorithms for different studies, as well as prospects for the application and development of DL in water science are proposed. This review provides references for solving a wider range of water-related problems and brings further insights into the intelligent development of water science.

Analysis of sustainable water resource management and driving mechanism in arid region: a case study of Xinjiang, China, from 2005 to 2020.

The long-term dynamic comprehensive evaluation of the water resource carrying capacity (WRCC) and the analysis of its potential driving mechanism in arid areas are contemporary research issues and technical means of mitigating and coordinating the conflict between severe resource shortages and human needs. The purpose of this study was to explore the distribution of the WRCC and the spatiotemporal heterogeneity of drivers in arid areas based on an improved two-dimensional spatiotemporal dynamic evaluation model. The results show that (1) the spatial distribution of the WRCC in Xinjiang, China, is high in the north, low in the south, high in the west, and low in the east. (2) From 2005 to 2020, the centers of gravity of the WRCC in northern and southern Xinjiang moved to the southeast and west, respectively, and the spatial distribution exhibited slight diffusion. (3) The factors influencing the WRCC exhibit more obvious spatial and temporal heterogeneity. The domestic waste disposal rate and ecological water use rate were the main factors influencing the WRCC in the early stage, while the GDP per capita gradually played a dominant role in the later stage. (4) In the next 30 years, the WRCC in Xinjiang will increase. The results provide a theoretical reference for the sustainable development of water resources in arid areas.

A bridge over sustainable water: Politicians' perceptions about the preconditions for collective action.

Sustainable water resource management is a core interest for all societies. As water systems are often common resources, the management of water systems requires coordinated action among actors along the water. For flowing water, a complication for coordination is upstream-downstream relations where what happens upstream affects downstream, but not the other way around. In this study we present results from a survey experiment with politicians in Sweden, focusing on whether and to what extent their willingness to cooperate is affected by their placement upstream or downstream along a fictive water system. Our findings indicate that politicians from upstream and downstream municipalities share the view that upstream politicians bear greater responsibility for undertaking preventive actions and are willing to assume remedial responsibility for problems caused by them. These results challenge the notion that self-interest is the primary obstacle to resolving environmental collective action problems.

Does interstate trade of agricultural products in the U.S. alleviate land and water stress?

Interregional free-trade of agricultural products is expected to transfer embodied (virtual) water from more to less water-productive regions. However, irrigation in semi-arid to arid regions may significantly push up agricultural productivity but cause local water scarcity. This may result in a puzzle: inter-regional trade may save overall water consumption but lead to more severe local water scarcity. An analogous puzzle may exist for farmland, for instance, trade may save farmland but not address farmland scarcity. To test the existence of these two important puzzles, we applied environmentally extended multi-regional input-output models to obtain the inter-regional virtual agricultural water and land transfer across 48 states of the conterminous U.S. and estimated their agricultural land and water footprints in 2017. Such a detailed analysis showed that while the land-abundant Midwestern states exported a sizable amount of virtual farmland to other densely populated areas and foreign nations, the water-stressed Western U.S. and Southwestern U.S. states, like California, Arizona, and New Mexico, exported considerable amounts of water-intensive crops such as fruits, vegetables and tree nuts to the Eastern U.S. and overseas, thus worsen the local water scarcity of those water scarce states. Our analysis highlights a critical dilemma inherent in an economic productivity-focused incentive regime: It frequently leads to increased withdrawal of scarce water. Therefore, resource scarcity rents need to be reflected in inter-regional trade with the support of local environmental policies.