Water rights trading planning and its application in water resources management: A water-ecology-food nexus perspective

Nexus approach provides an effective perspective for implementing synergetic management of water resources. In this study, an interval two-stage chance-constrained water rights trading planning model under water-ecology-food nexus perspective (ITCWR-WEF) is proposed to analyze the interaction between water trading and water-ecology-food (WEF) nexus, which fills in the water resources management gaps from a novel nexus perspective. ITCWR-WEF incorporates hydrological simulation with soil and water assessment tool (SWAT), water rights configuration with interval two-stage chance-constrained programming (ITCP), and multi-criterion analysis with Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS). The developed ITCWR-WEF is applied to a real case of Daguhe watershed, which has characteristics of water scarcity, food producing areas and fragile ecosystem. Initial water rights allocation is addressed before the trading. Mechanisms analysis is designed to reveal mutual effect of water rights trading and WEF nexus. Optimal water management scenario is identified through multi-criterion analysis. Results reveal that the mechanism of water rights trading with WEF nexus under low constraint-violation risk level of water availability and environment capacity is recommended to promote the rational water resources allocation to balance the economic goals, water environment and water supply security, as well as ecological and food water demand guarantees.

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.

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 - andPO 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.

A high-resolution dataset of water bodies distribution over the Tibetan Plateau

Water body (WB) extraction is the basic work of water resources management. Tibetan Plateau is one of the largest alpine lake systems in the world. However, research on the characteristics of water bodies (WBs) is mainly focused on large and medium WBs due to spatial resolution. This research presents a dataset containing a 2-m resolution map of WBs in 2020 based on Gaofen-1 data, and morphometric and landscape indices of WBs across the Tibetan Plateau. The Swin-UNet model is well performed with overall accuracy at 98%. The total area of WBs is 56354.6 km2 across Tibetan Plateau in 2020. The abundance compared with that from size-abundance relationship indicate WBs in the Tibetan Plateau conformed to the classic power scaling law. We evaluate the influence of spatial-resolution in WB extraction, which shows the dataset could be valuable to fill the gap of existing WBs map, especially for small waters. The dataset is valuable for revealing the spatial patterns of WBs, and understanding the impacts of climate change on water resources in Plateau.

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.

Steroid hormones in surface water resources in China: systematic review and meta-analysis and probabilistic ecological risk assessment

A Search was conducted in international databases including Scopus, PubMed, Embase, and Web of Science from 10 January 2005 to 15 January 2023. The risk quotient (RQ) of Estrone (E1), 17β-E2 (E2), and Estriol (E3) on the surface water resources of China was calculated by Monte Carlo Simulation (MCS) technique. The rank order of steroid hormones based on pooled (weighted average) concentration in surface water was E3 (2.15 ng/l) > E2 (2.01 ng/l) > E1 (1.385 ng/l). The concentration of E1 in Dianchi lake (236.50.00 ng/l), 17β-E2 in Licun river (78.50 ng/l), and E3 in Dianchi lake (103.1 ng/l) were higher than in other surface water resources in China. RQ related to E1, 17β-E2 and E3 in 68.00%, 88.89% and 3.92% of surface water resources were high ecological risk, respectively. Therefore, carrying out source control plans for steroid hormones in surface water sources should be conducted continuously.

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.

Different policies constrained agricultural non-point pollutants emission trading management for water system under interval, fuzzy, and stochastic information

Formulating suitable policies is essential for resources and environmental management. In this study, an agricultural pollutants emission trading management model driven by water resources and pollutants control is developed to search reasonable policies for agricultural water resources allocation under multiple uncertainties. Random-fuzzy and interval information in water resources system that have directly impact on the effectiveness of management schemes is reflected through interval two-stage stochastic fuzzy-probability programming. The model was root from regional agricultural water resources system in Jining City, China under considering the relationship among effective precipitation, crop water demand, and pollutants emission. Two types policies (water consumption-control and pollutants emission-control) are designed for searching the related interaction on water resources management and water quality improvement. The results indicated that water resources policies would be of water and environmental double benefits, and a large rainfall would reduce irrigation amount from water sources and lead to a larger pollutants emission trading. The results will help for defining scientific and effective water resources protection and management policies and analyzing the related interacted effects on water consumption, pollutants control and system benefit.

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.

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.

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 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.

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.

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.

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.

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, Sazlıdere, and Elmalı), 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.

HYADES - A Global Archive of Annual Maxima Daily Precipitation

Time series of annual maxima daily precipitation are crucial for understanding extreme precipitation behavior and its shifts toward nonstationarity with global warming. Extreme precipitation insight assists hydraulic infrastructure design, water resource management, natural hazard prevention, and climate change adaptation. However, not even a third of the records are of sufficient length, and the number of active stations keeps decreasing. Herein, we present HYADES: archive of yearly maxima of daily precipitation records, a global dataset derived from the Global Historical Climatology Network database of daily records (GHCN-Daily). The HYADES dataset contains records from 39 206 stations (heterogeneously distributed worldwide) with record lengths varying from 16 to 200 years between 1805 and 2023. HYADES was extracted through a methodology designed to accurately capture the true maxima even in the presence of missing values within the records. The method's thresholds were determined and evaluated through Monte Carlo simulations. Our approach demonstrates a 96.73% success rate in detecting the true maxima while preserving time series statistical properties of interest (L-moments and temporal monotonic trend).

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.

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.

Security-differentiated Water Pricing as a Mechanism for Mitigating Drought Impacts. Insights from a Case Study in the Mediterranean Basin

Water is becoming an increasingly scarce resource due to growing multi-sector demand and the effects of climate change. During droughts, the proportional rule is the most widespread water allocation method applied in irrigation systems. However, this method fails to guarantee efficient water allocation or to provide a fair method of water allocation. This paper aims to verify whether by replacing the water allocation methods based on a proportional rule with methods based on a priority rule could improve the allocation of water resources and minimize the negative economic impacts of water shortages. The ultimate objective of this research is to design a water pricing scheme capable of guaranteeing efficient water reallocation during drought conditions. Therefore, an experiment was carried out for the largest irrigated area in southern Italy, covered by the Capitanata Reclamation and Irrigation board (CBC). A positive mathematical programming model was implemented in order to simulate the effects of the proposed mechanism. The findings show that priority mechanisms have the potential to improve overall economic efficiency in the event of water shortages. However, results also point to the need for optimal design of a differentiated water pricing scheme.

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.

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.

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.

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.

Simulation-Optimization Approach for Siting Injection Wells in Urban Area with Complex Hydrogeology

Managed aquifer recharge has become a standard water resources management practice to promote the development of locally sustainable water supplies and combat water scarcity. However, installation of injection wells for replenishment purposes in urban areas with complex hydrogeology faces many challenges, such as limited land availability, potential impacts on municipal production wells and known subsurface contamination plumes, and complex spatially variable hydraulic connections between aquifer units. To assess the feasibility and cost-effectiveness of injecting advanced treated water (ATW) into a complex urban aquifer system, a Simulation-Optimization (SO) model was developed to automate a systematic search for the most cost-effective locations to install new wells for injecting various quantities of ATW, if feasible. The generalized workflow presented here uses an existing MODFLOW groundwater model-along with advanced optimization routines that are publicly available-to flexibly accommodate a multiobjective function, complex constraints, and specific project requirements. The model successfully placed wells for injection of 1 to 4 MGD of ATW in aquifers underlying the study area. The injection well placement was primarily constrained by avoiding excessive impact on environmental sites with underlying groundwater plumes. The largest costs were for well installation and piping to the wells from the existing ATW pipes. This workflow is readily adaptable to other sites with different complexities, decision variables, or constraints.

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.

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.

Sustainable management of water resources and assessment of the vulnerability of Moroccan oases to climate change

In the oases of Morocco, climate trends show an increase in average temperatures of 2.2 °C and exacerbated precipitation by + 20% between 2020 and 2050 according to climate change scenarios. The consequences of these changes have a clear decrease in water availability and an increase in water needs. Therefore, analyzing water resource capacity and searching for adequate solutions to water scarcity in oases are essential for developing drylands. In this study, we assess the possible effects of climate change on water scarcity and the oasis ecosystem and its components. The calculated water stress index (WSI) remains very low due to a decrease in the resource impacted by the combined increase in precipitation and temperature. The obtained results indicate that for scenario 1 the WSI varies from 904 to 699 m3/inhab/year in 2030 and for scenario 2 the WSI varies between 583 in 2030 and 451 m3/inhab/year in 2050. The water availability indicator takes a value in scenario 1 of 75% for Zagora and 50% for Ouarzazate at the horizons 2030 and 2050, then increase in scenario 2 to 89% for Zagora and 78% for Ouarzazate at the horizons2030 and 2050. These results were used to develop the adaptation process, which aims to identify needs, activities, and projects in the short, medium, and long term at the horizons 2030 and 2050. In addition, it could shed light on sustainable development in this region. In addition, this study could be a reference for researchers and a decision-support document for decision-makers to place economic development within an environmental management framework.

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.

Availability of the current and future water resources in Equatorial Central Africa: case of the Nyong forest catchment in Cameroon

To anticipate disasters (drought, floods, etc.) caused by environmental forcing and reduce their impacts on its fragile economy, sub-Saharan Africa needs a good knowledge of the availability of current water resources and reliable hydroclimatic forecasts. This study has an objective to quantify the availability of water resources in the Nyong basin and predict its future evolution (2024-2050). For this, the SWAT (Soil and Water Assessment Tool) model was used. The performance of this model is satisfactory in calibration (2001-2005) and validation (2006-2010), with R2, NSE, and KGE greater than 0.64. Biases of - 11.8% and - 13.9% in calibration and validation also attest to this good performance. In the investigated basin, infiltration (GW_RCH), evapotranspiration (ETP), surface runoff (SURQ), and water yield (WYLD) are greater in the East, probably due to more abundant rainfall in this part. The flows and sediment load (SED) are greater in the middle zone and in the Southwest of the basin, certainly because of the flat topography of this part, which corresponds to the valley floor. Two climate models (CCCma and REMO) predict a decline in water resources in this basin, and two others (HIRHAM5 and RCA4) are the opposite. However, based on a statistical study carried out over the historical period (2001-2005), the CCCma model seems the most reliable. It forecasts a drop in precipitation and runoff, which do not exceed - 19% and - 18%, respectively, whatever the emission scenario (RCP4.5 or RCP8.5). Climate variability (CV) is the only forcing whose impact is visible in the dynamics of current and future flows, due to the modest current (increase of + 102 km2 in builds and roads) and future (increase of + 114 km2 in builds and roads) changes observed in the evolution of land use and land cover (LULC). The results of this study could contribute to improving water resource management in the basin studied and the region.

Towards a better understanding of atmospheric water harvesting (AWH) technology

Atmospheric water harvesting (AWH) technology is an emerging sustainable development strategy to deal with global water scarcity. To better understand the current state of AWH technology development, we conducted a bibliometric analysis highlighting three water harvesting technologies (fog harvesting, condensation, and sorption). By comprehensively reviewing the research progress and performing a comparative assessment of these technologies, we summarized past achievements and critically analyzed the different technologies. Traditional fog collectors are more mature, but their efficiency still needs to be improved. External field-driven fog harvesting and active condensation need to be driven by external forces, and passive condensation has high requirements for environmental humidity. Emerging bio-inspired fog harvesting and sorption technology provide new possibilities for atmospheric water collection, but they have high requirements for materials, and their commercial application is still to be further promoted. Based on the key characteristics of each technology, we presented the development prospects for the joint use of integrated/hybrid systems. Next, the water-energy relationship is used as a link to clarify the future development strategy of AWH technology in energy driving and conversion. Finally, we outlined the core ideas of AWH for both basic research and practical applications and described its limitless possibilities for drinking water supply and agricultural irrigation. This review provides an essential reference for the development and practical application of AWH technologies, which contribute to the sustainable utilization of water resources globally.

Recombinant Organophosphorus acid anhydrolase (OPAA) enzyme-carbon quantum dot (CQDs)-immobilized thin film biosensors for the specific detection of Ethyl Paraoxon and Methyl Parathion in water resources

Organophosphates pesticide (OP) toxicity through water resources is a large concern globally among all the emerging pollutants. Detection of OPs is a challenge which needs to be addressed considering the hazardous effects on the health of human beings. In the current research thin film biosensors of recombinant, Organophosphorus acid anhydrolase (OPAA) enzyme along with carbon quantum dots (CQDs) immobilized in thin films were developed. OPAA-CQDs thin film biosensors were used for the specific detection of two OPs Ethyl Paraoxon (EP) and Methyl Parathion (MP) in river water and household water supply. Recombinant OPAA enzyme was expressed in E. Coli, purified and immobilized on the CQD containing chitosan thin films. The CQDs used for this purpose were developed by a one-pot hydrothermal method from phthalic acid and Tri ethylene diamine. The properties of CQDs, OPAA and thin films were characterized using techniques like XPS, TEM, XRD, enzyme activity and CLSM measurements. Biosensing studies of EP and MP were performed by taking fluorescence measurements using a fiber optic spectrometer. The analytical parameters of biosensing were compared against an estimation carried out using the HPLC method. The biosensing performance indicates that the OPAA-CQDs thin film-based biosensors were able to detect both EP and MP in a range of 0-100 μM having a detection limit of 0.18 ppm/0.69 ppm for EP/MP, respectively with a response time of 5 min. The accuracy of estimation of EP/MP when spiked in water resources lie in the range of ∼100-102% which clearly indicates the OPAA-CQD based thin film biosensors can function as a point-of-use method for the detection of OP pesticides in complex water resources.

Adaptable and comprehensive vulnerability assessments for water resources systems in a rapidly changing world

Comprehensive and adaptive approaches to vulnerability assessment are crucial for guiding effective adaptation in global water resources systems. A common approach to quantify vulnerability is through indicators, which capture the 'spirit of vulnerability' while retaining practical ease-of-use benefits. However, a comprehensive meta-analysis of reveals two specific limitations of global indicator-based vulnerability assessments for water resources systems: 1) vulnerability is influenced by complex interactions among multi-domain factors, for which indicator quality and data vary; and 2) vulnerability is dynamic and evolves over time, an aspect overlooked in most approaches. In response to these identified challenges, we propose a new dynamic "build-your-own" approach to vulnerability assessment. Our approach focuses on correcting for the identified gaps and biases in indicators and data to improve assessment comprehensiveness. This approach also incorporates guidance around adapting assessments over time to better reflect vulnerability under changing conditions. The open-source nature of our approach and underlying data can facilitate the development and customization of indicator-based vulnerability assessments for diverse applications, supporting practical and relevant planning for more resilient water resources systems.

Water footprints and crop water use of 175 individual crops for 1990-2019 simulated with a global crop model

The water footprint of a crop (WF) is a common metric for assessing agricultural water consumption and productivity. To provide an update and methodological enhancement of existing WF datasets, we apply a global process-based crop model to quantify consumptive WFs of 175 individual crops at a 5 arcminute resolution over the 1990-2019 period. This model simulates the daily crop growth and vertical water balance considering local environmental conditions, crop characteristics, and farm management. We partition WFs into green (water from precipitation) and blue (from irrigation or capillary rise), and differentiate between rainfed and irrigated production systems. The outputs include gridded datasets and national averages for unit water footprints (expressed in m3 t-1 yr-1), water footprints of production (m3 yr-1), and crop water use (mm yr-1). We compare our estimates to other global studies covering different historical periods and methodological approaches. Provided outputs can offer insights into spatial and temporal patterns of agricultural water consumption and serve as inputs for further virtual water trade studies, life cycle and water footprint assessments.

Developing managed aquifer recharge (MAR) to augment irrigation water resources in the sand and gravel (Crag) aquifer of coastal Suffolk, UK

Managed aquifer recharge (MAR) offers a potential innovative solution for addressing groundwater resource issues, enabling excess surface water to be stored underground for later abstraction. Given its favourable hydrogeological properties, the Pliocene sand and gravel (Crag) aquifer in Suffolk, UK, was selected for a demonstration MAR scheme, with the goal of supplying additional summer irrigation water. The recharge source was a 4.6 km drainage channel that discharges to the River Deben estuary. Trialling the scheme in June 2022, 12,262 m3 of source water were recharged to the aquifer over 12 days via a lagoon and an array of 565 m of buried slotted pipes. Groundwater levels were raised by 0.3 m at the centre of the recharge mound with an approximate radius of 250 m, with no detrimental impact on local water features observed. The source water quality remained stable during the trial with a mean chloride concentration (133 mg L-1) below the regulatory requirement (165 mg L-1). The fraction of recharge water mixing with the groundwater ranged from 69% close to the centre and 5% at the boundary of the recharge mound, leading to a reduction in nitrate-N concentration of 23.6 mg L-1 at the centre of the mound. During July-September 2022, 12,301 m3 of recharge water were abstracted from two, 18 m boreholes to supplement surface irrigation reservoirs during drought conditions. However, the hydraulic conductivity of the Crag aquifer (∼10 m day-1) restricted the yield and thereby reduced the economic viability of the scheme. Construction costs for the MAR system were comparatively low but the high costs of data collection and securing regulatory permits brought the overall capital costs to within 18% of an equivalent surface storage reservoir, demonstrating that market-based mechanisms and more streamlined regulatory processes are required to incentivise similar MAR schemes.

An innovative approach for predicting groundwater TDS using optimized ensemble machine learning algorithms at two levels of modeling strategy

Groundwater salinization in coastal aquifers is a major socioeconomic challenge in Oman and many other regions worldwide due to several anthropogenic activities and natural drivers. Therefore, assessing the salinization of groundwater resources is crucial to ensure the protection of water resources and sustainable management. The aim of this study is to apply a novel approach using predictive optimized ensemble trees-based (ETB) machine learning models, namely Catboost regression (CBR), Extra trees regression (ETR), and Bagging regression (BA), at two levels of modeling strategy for predicting groundwater TDS as an indicator for seawater intrusion in a coastal aquifer, Oman. At level 1, ETR and CBR models were used as base models or inputs for BA in level 2. The results show that the models at level 1 (i.e., ETR and CBR) yielded satisfactory results using a limited number of inputs (Cl, K, and Sr) from a few sets of 40 groundwater wells. The BA model at level 2 improved the overall performance of the modeling by extracting more information from ETR and CBR models at level 1 models. At level 2, the BA model achieved a significant improvement in accuracy (MSE = 0.0002, RSR = 0.062, R2 = 0.995 and NSE = 0.996) compared to each individual model of ETR (MSE = 0.0007, RSR = 0.245, R2 = 0.98 and NSE = 0.94), and CBR (MSE = 0.0035, RSR = 0.258, R2 = 0.933 and NSE = 0.934) at level 1 models in the testing dataset. BA model at level 2 outperformed all models regarding predictive accuracy, best generalization of new data, and matching the locations of the polluted and unpolluted wells. Our approach predicts groundwater TDS with high accuracy and thus provides early warnings of water quality deterioration along coastal aquifers which will improve water resources sustainability.

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.

Physical and virtual water transfers in China and their implication for water planetary boundary

China is an extremely water-scarce country with an uneven distribution of regional water resources. We define two absolute sustainability indicators, using the multi-regional input‒output (MRIO) model to outline the contribution of China's physical and virtual water transfers in mitigating the problem of regional water boundary-exceeding. Although the overall use of freshwater resources is within the safe operation space, 55% of province's water resource development transgresses the local water planetary boundary. Physical and virtual water transfers effectively mitigate the stress of water supply to the water planetary boundary in China's water-scarce regions. Among them, the role of virtual water transfers occupies the main part. The cost of using physical water in water-receiving regions and the situation of virtual water flowing from water-scarce regions to developed water-rich regions cannot be ignored, and a small number of provinces are responsible for most of the virtual water net imports and exports. The obtained results are helpful for the redistribution of water planetary boundary transgressing responsibilities among provinces and the formulation of absolute sustainable water resources management policies.

Coupling coordination analysis and key factors between urbanization and water resources in ecologically fragile areas: a case study of the Yellow River Basin, China

The rapid urbanization (UR) and industrialization in the Yellow River Basin (YRB) have resulted in a significant scarcity of water resources (WRs), highlighting the need to investigate the complex and dynamic relationship between UR and WR for sustainable urban development in ecologically fragile areas. This study utilizes the coupling coordination degree model (CCDM), spatial correlation analysis, and Tobit model to examine the coupling coordination relationship, spatial effects, and key factors between UR and WR in sixty prefecture-level cities within the YRB. The empirical findings reveal that the development of the WR subsystem lags behind the UR subsystem and that there is a significant spatial disequilibrium in the CCD between UR and WR. Specifically, the high-high clusters are located in the northwest and east, while the low-low clusters are spread in the southwest. Furthermore, investment in science and technology and economic development have a positive impact on the CCD, while government capacity, urban construction, and industrial structure have a negative impact. These results can provide valuable guidance for decision-making in urban planning for ecologically fragile areas facing water supply constraints.

Watershed groundwater level multistep ahead forecasts by fusing convolutional-based autoencoder and LSTM models

The development of deep learning-based groundwater level forecast models can tackle the challenge of high dimensional groundwater dynamics, predict groundwater variation trends accurately, and manage groundwater resources effectively, thereby contributing to sustainable water resources management. This study proposed a novel ConvAE-LSTM model, which fused a Convolutional-based Autoencoder model (ConvAE) and a Long Short-Term Memory Neural Network model (LSTM), to provide accurate spatiotemporal groundwater level forecasts over the next three months. The HBV-light and LSTM models are chosen as benchmarks. An ensemble of point data and the corresponding derived images concerning the past (observations) and the future (forecasts from a conceptual model) of groundwater levels at 33 groundwater wells in Jhuoshuei River basin of Taiwan between 2000 and 2019 constituted the case study. The findings showcase the effectiveness of the ConvAE-LSTM model in extracting crucial features from both point and imagery datasets. This model successfully establishes spatiotemporal dependencies between regional images and groundwater level data over diverse time frames, leading to accurate multi-step-ahead forecasts of groundwater levels. Notably, the ConvAE-LSTM model exhibits a substantial improvement, with the R-squared values showing an increase of more than 18%, 22%, and 49% for the R1, R2, and R3 regions, respectively, compared to the HBV-light model. Additionally, it outperforms the LSTM model in this regard. This study represents a noteworthy milestone in environmental modeling, offering key insights for designing sustainable groundwater management strategies to ensure the long-term availability of this vital resource.

Quantifying climate change impacts on future water resources and salinity transport in a high semi-arid watershed

High salinity mobilization and movement from salt-laden deposits in semi-arid landscapes impair soils and water resources worldwide. Semi-arid regions worldwide are expected to experience rising temperatures and lower precipitation, impacting water supply and spatio-temporal patterns of salinity loads and affecting downstream water quality. This study quantifies the impact of future climate on hydrologic fluxes and salt loads in the Gunnison River Watershed (GRW) (14,608 km2), Colorado, using the APEX-MODFLOW-Salt hydro-chemical watershed model and three different CMIP5 climate models projection downscaled by Multivariate Adaptive Constructed Analogs (MACA) for the period 2020-2099. The APEX-MODFLOW-Salt model accounts for the reactive transport of major salt ions (SO42-, Cl-, CO32-, HCO3-, Ca2+, Na+, Mg2+, and K+) to streams via surface runoff, rainfall erosional runoff, soil lateral flow, quick return flow and groundwater-stream exchange. Model results are analyzed for spatial and temporal trends in water yield and salt loading pathways. Although streamflow is primarily derived from surface runoff (65%), the predominant source of salt loads is the aquifer (73%) due to elevated concentrations of groundwater salt. Annual salt loading from the watershed is 582 Mkg, approximately 10% of the salt load in the Colorado River measured at Lee's Ferry, AZ. For future climate scenarios, annual salt loads from the watershed increased between 4.1% and 9.6% from the historical period due to increased salt loading from groundwater and quick return flow. From the results, applying the APEX-MODFLOW-Salt model with downscaled future climate forcings can be a helpful modeling framework for investigating hydrology and salt mobilization, transport, and export in historical and predictive settings for salt-affected watersheds.

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.

The impact of human activities on blue-green water resources and quantification of water resource scarcity in the Yangtze River Basin

Under the influence of climate change and human activities, water scarcity and uneven spatial distribution have become critical factors constraining societal development and threatening ecological security. Accurately assessing changes in blue and green water resources (BW and GW) caused by human activities can reveal the actual situation of water scarcity. However, previous research often overlooked the calibration of GW and human water usage, and it rarely delved into the primary human factors leading to water scarcity and potential impact mechanisms. Therefore, based on the PCR-GLOBWB model that considers human impacts, and with reasonable calibration of B/GW and human water usage, hydrological processes were simulated under both human-influenced and natural conditions. A comprehensive assessment of the impact of human activities on BW and GW was conducted. The results show that: (1) BW and GW exhibit a spatial pattern of increasing from northwest to southeast in the basin. From 1961 to 2020, the proportion of BW showed an upward trend, while GW was decreasing; (2) The impact of human activities on changes in water resources is mainly concentrated in the midstream and dowmstream of the basin. Due to human influences, the green water flow (GWF) increased by 3-24.4 mm, and the BW volume increased by 67.2-146.4 mm. However, the green water storage (GWS) decreased by 5.6-75.4 mm; (3) The impact of human activities on blue water scarcity (BWscarcity) is significantly greater than green water scarcity (GWscarcity). The worsening of GWscarcity does not exceed 0.2, while areas where BW reaches significant deterioration (BWscarcity > 1.5) account for 1.3 %, 9.8 %, and 17 % of the upstream, midstream and downstream, respectively. (4) Irrigation activities are the main factor causing water resource scarcity. In the future, it is important to reasonably develop the potential for GW utilization and optimize BW management measures to address water resource crises.

Envisioning the innovative approaches to achieve circular economy in the water and wastewater sector

Given the challenges of urbanization and rapid resource depletion, policymakers have been compelled to abandon the old sequential paradigm of "take-make-use-dispose" to a circular approach that prioritizes preservation of natural resources. The circular economy represents a sustainable management concept that focuses on reducing, recovering, reusing, and recycling waste. While significant strides have been made in implementing circular economy principles in various industries such as automotive, electronics, and construction, particular attention has been given to the water and wastewater domains due to imbalances in water resources. Here we review the global progress of circular economy adoptability in the water and wastewater domains, considering technical, environmental, economic, and social perspectives. It assesses the current state of circular economy integration in the wastewater domain worldwide and presents approaches to promote and accelerate its adoption. The study critically examines the principles of waste management, known as the 6Rs (reclaim, restore, recycle, reduce, recover, reuse), in order to formulate effective strategies for integrating circular economy practices in the water and wastewater domains. Additionally, the study provides an overview of existing research conducted on different aspects of circular economy. Finally, the study analyzes the challenges and opportunities associated with implementing circular economy principles in the water sector.

Managing open loop water resource value chain through IoT focused decision and information integration (DII) modelling using fuzzy MCDM approach

Water is a limited and invaluable resource that is essential for human survival. Negligence and unregulated water use have brought about a global water crisis. Proper management with a relevant decision and information integration approach can aid water to continue as a renewable resource. The water and wastewater industry must shift from outmoded, inefficient techniques to more sustainable, data-driven solutions to address water concerns and improve public health. The Internet of Things (IoT) has emerged as an innovative strategy for decision and information integration to drive an open-loop Water Value Chain (WVC) efficiently. The IoT-driven network allows objects to connect and communicate, gather data in real-time, analyze data and develop reasonable decision - making insights instantaneously. This study aims to find the enablers of IoT for an open-loop WVC. It examines 25 factors for IoT implementation in the open-loop WVC. The 25 factors are clustered into seven enablers using Principal Component Analysis (PCA). These principal components are analyzed by employing a Multi-Criteria Decision Making (MCDM) approach, i.e., the Fuzzy Decision-Making Trial and Evaluation Laboratory (DEMATEL), which helps to find the cause-effect relationship to prioritize the enablers. The fuzzy set theory is used to address the uncertainty and vagueness in experts' opinions and data deficiency problems. The study reveals that the Ecosystem of an IoT network, IoT network configuration and adaptation and data mobility in an IoT network are the most prominent enablers to consider for the implementation of IoT in an open loop WVC. The study may be helpful for regulatory agencies and enterprises in water distribution and processing for identifying and prioritizing the potential enablers of IoT in an open-loop WVC.

New approach into human health risk assessment associated with heavy metals in surface water and groundwater using Monte Carlo Method

This study assessed the environmental and health risks associated with heavy metals in the water resources of Egypt's northwestern desert. The current approaches included the Spearman correlation matrix, principal component analysis, and cluster analysis to identify pollution sources and quality-controlling factors. Various indices (HPI, MI, HQ, HI, and CR) were applied to evaluate environmental and human health risks. Additionally, the Monte Carlo method was employed for probabilistic carcinogenic and non-carcinogenic risk assessment via oral and dermal exposure routes in adults and children. Notably, all water resources exhibited high pollution risks with HPI and MI values exceeding permissible limits (HPI > 100 and MI > 6), respectively. Furthermore, HI oral values indicated significant non-carcinogenic risks to both adults and children, while dermal contact posed a high risk to 19.4% of samples for adults and 77.6% of samples for children (HI > 1). Most water samples exhibited CR values exceeding 1 × 10-4 for Cd, Cr, and Pb, suggesting vulnerability to carcinogenic effects in both age groups. Monte Carlo simulations reinforced these findings, indicating a significant carcinogenic impact on children and adults. Consequently, comprehensive water treatment measures are urgently needed to mitigate carcinogenic and non-carcinogenic health risks in Siwa Oasis.

Impacts of recent rainfall changes on agricultural productivity and water resources within the Southern Western Ghats of Kerala, India

Significant changes in rainfall patterns are critical to agriculture, and the dependency of cropping systems on rainfall variability would engender appropriate farming practices and agriculture policies for a climate-resilient agriculture system. This study analyses the significance of rainfall variability on agriculture productivity in the Wayanad district of Kerala (India) using time series data on rainfall (1989-2019) and crop yield (2000-2019). The spatial variability of rainfall patterns reveals a dichotomy between the rain gauge stations in the northern and southern parts of the region. Despite the absence of statistically significant trends in the monthly, seasonal and annual rainfall, based on the Mann-Kendall trend analysis, an increase in the yield of many crops (e.g., winter paddy, banana) is evident, which emphasises the critical role of irrigation in driving the crop productivity. As an adaptation strategy to changing rainfall patterns, irrigation would meet the additional crop water requirement for sustainable agricultural production under the varying rainfall distributions. However, the increase in the area under irrigation in recent years has had significant implications for both surface water and groundwater resources. The conclusive findings suggest that the region requires climate-resilient agriculture, focusing on optimising irrigation and developing sustainable agriculture and water conservation strategies.