Pollution, spatial distribution, and health risks assessment of nutrient concentration in surface water resources of Maroon-Jarahi Basin in southwestern Iran.

Nowadays, the introduction of nutrients caused by human activities is considered an environmental issue and a significant problem in river basins and coastal ecosystems. In this study, the concentration of nutrients ( NO 3 - and PO 4 3 - ) in the surface water sources of the Maroon-Jarahi watershed in the southwest of Iran was determined, and the pollution status and health risk assessment were done. The average concentration of nitrate and phosphate in Ludab, Maroon, Zard, Allah, Jarahi rivers, and Shadegan wetland were obtained at 2.25-0.59, 4.59-1.84, 4.07-2.02, 5.40-2.81, 11.51-4.67, 21.63 and 6.20 (mg/l), respectively. A comparison of the results with the World Health Organization (WHO) limit showed that nitrate was lower than in all stations, but phosphate was higher than the limit in some stations of the Maroon, Allah, Jarahi rivers, and Shadegan wetland. Calculation of linear regression analysis showed significant positive relationships between nitrate and phosphate in all surface water sources (except Ludab) and based on the N/P ratio, nitrogen was estimated as the limiting factor in phytoplankton growth (N/P < 16). The evaluation of the status of the Nutrient pollution index (NPI) was observed as: Shadegan > Jarahi > Allah > Maroon > Zard > Ludab that the Jarahi River and Shadegan wetland were in the medium pollution class (1 < NPI ≤ 3) and other waterbodies were in the non-polluted to low pollution state (NPI < 1). Calculation of the chronic daily intake (CDI) showed that water body nutrients cause more non-carcinogenic health risks through the oral route than dermal exposure, and according to HI, children's health is more at risk than adults. Findings showed that surface water resources especially downstream of the Maroon-Jarahi watershed are at eutrophication risk, and to control the nearby human activities and as a result increase the nutrients in these water resources, measures should be taken.

Human health risk assessment framework for new water resource recovery-based bio-composite materials.

A new type of bio-composite material is being produced from water-recovered resources such as cellulose fibres from wastewater, calcite from the drinking water softening process, and grass and reed from waterboard sites. These raw materials may be contaminated with pathogens and chemicals such as Escherichia coli, heavy metals, and resin compounds. A novel risk assessment framework is proposed here, addressing human health risks during the production of new bio-composite materials. The developed framework consists of a combination of existing risk assessment methods and is based on three main steps: hazard identification, qualitative risk mapping, and quantitative risk assessment. The HAZOP and Event Tree Analysis methodologies were used for hazard identification and risk mapping stages. Then, human health risks were quantitatively assessed using quantitative chemical risk assessment, evaluating cancer and non-cancer risk, and quantitative microbial risk assessment. The deterministic and the stochastic approaches were performed for this purpose. The contamination of raw materials may pose human health concerns, resulting in cancer risk above the threshold. Microbial risk is also above the safety threshold. Additional analysis would be significant as future research to better assess the microbial risk in biocomposite production. The framework has been effectively used for chemical and microbial risk assessment.

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.

Performance of water indices for large-scale water resources monitoring using Sentinel-2 data in Ethiopia.

Evaluating the performance of water indices and water-related ecosystems is crucial for Ethiopia. This is due to limited information on the availability and distribution of water resources at the country scale, despite its critical role in sustainable water management, biodiversity conservation, and ecosystem resilience. The objective of this study is to evaluate the performance of seven water indices and select the best-performing indices for detecting surface water at country scale. Sentinel-2 data from December 1, 2021, to November 30, 2022, were used for the evaluation and processed using the Google Earth Engine. The indices were evaluated using qualitative visual inspection and quantitative accuracy indicators of overall accuracy, producer's accuracy, and user's accuracy. Results showed that the water index (WI) and automatic water extraction index with shadow (AWEIsh) were the most accurate ones to extract surface water. For the latter, WI and AWEIsh obtained an overall accuracy of 96% and 95%, respectively. Both indices had approximately the same spatial coverage of surface water with 82,650 km2 (WI) and 86,530 km2 (AWEIsh) for the whole of Ethiopia. The results provide a valuable insight into the extent of surface water bodies, which is essential for water resource planners and decision-makers. Such data can also play a role in monitoring the country's reservoirs, which are important for the country's energy and economic development. These results suggest that by applying the best-performing indices, better monitoring and management of water resources would be possible to achieve the Sustainable Development Goal 6 at the regional level.

Climate change and land cover effects on water yield in a subtropical watershed spanning the yungas-chaco transition of Argentina.

The demand for mountain water resources is increasing, and their availability is threatened by climate change, emphasizing the urgency for effective protection and management. The upper Sali-Dulce watershed holds vital significance as it contributes the majority of the Sali-Dulce water resources, supporting a densely populated dry region in Northwestern Argentina, covering an area of 24,217 km2. However, the potential impact of climate change and land use/land cover change on water yield in this watershed remains uncertain. This study employs the InVEST Annual Water Yield model to analyze the average water yield in the watershed and evaluate its potential changes under future scenarios of climate and land use/land cover change. InVEST was calibrated using data from multiple river gauges located across the watershed, indicating satisfactory performance (R2 = 0.751, p-value = 0.0054). Precipitation and evapotranspiration were the most important variables explaining water yield in the area, followed by land use. Water yield showed a notable concentration in the montane area with 40% of the watershed accounting for 80% of the water yield, underscoring the importance of conserving natural land cover in this critical zone. Climate change scenarios project an increase in water yield ranging from 21 to 75%, while the effects of land cover change scenarios on water yield vary, with reforestation scenarios leading to reductions of up to 15% and expansions in non-irrigated agriculture resulting in increases of up to 40%. Additionally, water yield distribution may become more concentrated or dispersed, largely dependent on the type of land cover. The combined scenarios highlight the pivotal role of land cover in adapting to climate change. Our findings provide valuable insights for designing future studies and developing policies aimed at implementing effective adaptation strategies to climate change within the Salí-Dulce watershed.

Evaluation of water resources security in Anhui Province based on GA-BP model.

Water resources security is an important cornerstone of regional sustainable development, but the current evaluation system of water resources security is not scientific, and the measurement of safety level has not been optimized by combining algorithms. In this paper, indicators are selected according to the actual situation in Anhui Province. Firstly, correlation analysis (CA) and principal component analysis (PCA) are used to reduce the dimensionality of indicators, and then, the scientific evaluation is carried out based on genetic algorithm optimized back propagation neural network (GA-BP). This paper improves the generalization ability of the evaluation model and overcomes the shortcomings of the traditional model, which is slow in convergence and easy to fall into local optimality. The results showed that the water resources security level showed an obvious improvement trend from 2006 to 2020 and stabilized at a relatively safe level from 2014 to 2020. The subsystem of water resources environmental security is the least secure, followed by the subsystem of social and economic security, and the security of water resources regulation and response is basically stable at a relatively safe level. The conclusion of this study can provide decision-making basis for the relevant research of government, society, and scientific community.

Water scarcity in the Kingdom of Saudi Arabia.

Saudi Arabia (SA) is one of the world's arid, most water-scarce nations without permanent water resources. The purpose of this article is to provide a comprehensive overview of Saudi Arabia's water resources availability and reliability in terms of water supply, demand, and the major challenges that water faces. Saudi has an annual water supply of roughly 89.5 m3 per person as consumption is rising in parallel with the country's rapid population growth and development. SA produces the most desalinated seawater in the world, accounting for 22% of worldwide consumption. Due to changes in agricultural demand, Saudi Arabia's overall water needs in 2020 were 15.98 BCM. Apart from agricultural use, the food industry utilizes up to 80% of freshwater supplies, with only around 20% of rain recharging the aquifer, meaning that the region will still be water-stressed by 2025. In addition to wastewater reuse, water expenses should be split between private investors and the government, and water losses in cities should be collected and recycled. Water development projects must also be safeguarded and have long-term viability for the community's future and well-being. Despite previous conservation efforts (public awareness campaigns, television and other public media messages, drip irrigation, and so on), more work is required, including improving water resource infrastructure, implementing environmental use of friendly technologies, and increasing economic feasibility, social acceptability, and management in light of the Sustainable Development Goals (SDG).

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 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.

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.

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.

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.

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.

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.

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.

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.