Search

• A novel joint optimal allocation model of regional water and land resources (JOAMRWLS). • A three-level unit division method of "basic units-computing units-hydrological response units". • Use of a two-layer nested algorithm involving successive approximation and nonlinear programming (SA-NLP). • The model improves the regional economy and water resource utilization efficiency. Water and land resources are indispensable prerequisites for the sustenance and advancement of human civilization. In recent decades, China has faced significant challenges in managing its water and land resources due to the intensifying competition resulting from rapid urbanization, industrialization, and population growth. The joint optimal allocation of water and land resources can effectively address these issues. However, there have been limited accomplishments in investigating the integrated water and land resources allocation system's interaction with social, economic, and environmental development. This paper develops a novel joint optimal allocation model of regional water and land resources (JOAMRWLS). The model integrates water and land resources for integrated allocation, taking into account the mutual feed relationship between them, and thereby achieving comprehensive utilization and coordination of water and land resources within the region. Then a two-layer nesting algorithm based on successive approximation and nonlinear programming (SA-NLP) is proposed to solve the model, obtaining the regional optimal land use pattern and optimal water allocation scheme. Subsequently, this paper uses Luoyang City in Henan Province, China, as a case study to verify the proposed JOAMRWLS. The results indicate that the water volume and area of each land use type tend to stabilize after the fifth iteration of the calculation with a consistent water volume of 18.65 × 108 m3. Compared with the conventional optimization model of water resources (COMWR), the JOAMRWLS encompasses five cities experiencing water scarcity, whereas the latter includes twelve such cities. Furthermore, the economic benefit of the JOAMRWLS is 2.65 × 1011 CNY, surpassing that of the former at 2.56 × 1011 CNY, highlighting its superiority. [ABSTRACT FROM AUTHOR]

The paper introduces a transformer-based deep reinforcement learning (T-DRL) approach designed to address the multiobjective multihydropower reservoir operation optimization (MMROO) problem. Unlike existing literature that primarily focuses on maximizing power generation from individual reservoirs, the MMROO model in this study considers the broader context of multiple reservoirs, encompassing total power generation, ecological protection, and residential area water supply. The computational challenges posed by the numerous constraints and nonlinearities of multiple reservoirs render conventional multiobjective evolutionary algorithms both expensive and lacking in generalization capabilities for solving the MMROO problem. To overcome these challenges, the paper proposes a T-DRL approach that leverages the multihead attention mechanism within the encoder module to adeptly extract complex information from reservoirs and residential areas. The two-stage encoder effectively processes diverse information separately. The multireservoir network of the decoder then generates optimal decisions based on contextual information. The case study focusing on Lake Mead and Lake Powell in the Colorado River Basin demonstrates the efficacy of the T-DRL approach, producing operation strategies that outperform a state-of-the-art method. Specifically, the proposed approach yields a 10.11% increase in electricity generation, a 39.69% reduction in amended annual proportional flow deviation, and a 4.10% rise in water supply revenue. Overall, the T-DRL approach emerges as an effective method for the multiobjective operation of multihydropower reservoir systems. • A multiobjective multihydropower reservoir operation problem is addressed. • A T-DRL method is proposed to deal with the multiobjective optimization. • The T-DRL method outperforms current methods in the quality of the obtained PFs. • The proposed strategies obtain more electricity and water supply revenue. [ABSTRACT FROM AUTHOR]

[Display omitted] • A model for multi-objective optimization operation of multiple water sources under uncertainties was proposed. • Uncertainties in inflow and water demand forecasts can be considered simultaneously, and the correlation between uncertainties can be considered quantitatively. • The impact of inflow-water demand forecast uncertainties and their correlations on water resource operations are explored for water resources managers. Uncertainties in inflow and water demand forecasts bring risks to water resources management. Therefore, the research on multi-objective optimization operation of multiple water sources under uncertainties holds critical research significance and application value. This paper proposes a model for the multi-objective optimization operation of multiple water sources that can consider the inflow and water demand forecast dual uncertainties. In handling uncertainties, this paper adopts a scenario generation method based on joint distribution-Monte Carlo (JDMC), which allows for a quantitative consideration of the correlation between uncertainties. Moreover, the variable weight method and LINGO 20.0 are employed for solving multi-objective optimization issues to obtain stable Pareto frontiers. The proposed model is applied to the water receiving area inside Jiangsu Province of the South-to-North Water Transfer East Route Project (SNWTERP) in China. The main findings are as follows: 1) The impact of the inflow and water demand forecast uncertainties on the operation results is synergistic rather than antagonistic. Considering only a single uncertainty may result in water receiving area facing more severe water shortages or purchasing spot water at high prices. 2) There is a strong spatial correlation between uncertainties. If we ignore or only qualitatively consider the correlation between uncertainties, the impact of uncertainties on operation results will be underestimated or overestimated. 3) The Pareto frontiers are convex functions, indicating that decision-makers should choose a solution in the middle part. [ABSTRACT FROM AUTHOR]

[Display omitted] • New framework for integrated surface water quantity-quality modelling and forecasts. • Real time monitoring and short term forecasting of surface water availability. • Computationally efficient water quality classification using data-driven techniques. • Application for complex surface water system with various conflicting water users. Climate change and population increase are major challenges when guaranteeing a sustainable water supply in densely populated river basins worldwide, where different water users such as industry, drinking water production and agriculture typically come into conflict. During dry periods, real time monitoring and short term forecasting of the water availability are crucial to prepare and take appropriate action. Integrated water quantity and water quality models of complex surface water systems are indispensable to achieve this. However, existing model techniques often focus on a single aspect of the water system, are computationally too demanding and are hard to integrate with other sub models. This paper proposes a framework for developing efficient, integrated water quantity/quality models in support of real-time management and forecasting of complex surface water systems. Its three main components are: a low-flow prediction for the supplying river(s) during dry periods, a conceptual water quantity-quality model with semi-automated retrieval of necessary boundary conditions and input data, and classification models to predict the water quality state of the considered system during forecasts or scenario analyses. The novelty of the methodology lies in the use of parsimonious, computationally efficient sub-models that still allow to consider numerous operational rules, competing water users and water supply and quality constraints. The framework was tested in the context of water availability and salt intrusion management during droughts in the Campine Canals in the Scheldt-Meuse-Rhine delta, considering boundary conditions of shipping traffic, pumping at locks, hydropower stations, level regulation, and water abstractions for drinking water supply, industry, agriculture and nature reserves. Despite the complexity of the considered system, the model is able to describe the state of the water system in terms of water levels, discharges and salt intrusion impacts on water production with an acceptable accuracy. The research shows that the Campine Canal network is susceptible to water scarcity due to the increased risk of low flows in summer as a consequence of climate change. Local decision makers should adopt both effective adaptation strategies as well as real-time forecasts in acute drought situations to increase the region's defence and resiliency against water shortages. [ABSTRACT FROM AUTHOR]

• Groundwater contributes 26–78 % of streamflow during the low-flow period in a large Himalayan watershed. • The groundwater contribution varies substantially from the upstream to downstream sub-catchments. • Young water fraction ranges from 36 % in the middle catchment to 55 % in the lower catchment. • Hydraulic conductivity of the underlying bedrock is a key control on groundwater discharge to the stream. • Our study emphasizes fractured bedrock's critical role in mountain baseflow. Mountains in general are important sources of water resources and correspondingly, the Upper Indus Basin (UIB) in the Himalayas provides water for hundreds of millions of users. During low-flow periods, streamflow is fed by a combination of groundwater (baseflow), snow/glacier melt and recent precipitation. However, the proportion of stream baseflow generated by different sources and the factors that control these contributions remain poorly understood. In this paper, we focus on the Western Himalayan Liddar watershed (1243 km2) in the UIB and use a multi-method approach to characterize aquifers and baseflow generation in three nested catchments which represent varying geology and topography. Our multi-method approach includes geophysical surveys, slug tests, stable isotope compositions, young water fraction (F yw) estimation and a Bayesian end member mixing model. Specifically, we hypothesize that K sat of the subsurface plays a critical role in the contribution of groundwater to baseflow. The watershed is subdivided into three nested catchments (Limestone, hardrock and alluvium). Our results indicate that the upstream, steep, high-permeability limestone catchment has the relatively largest groundwater contribution to streamflow (63–78 %) and a low F yw (38 %) than the other two sites. The mid-elevation hard rock catchment has a lower permeability on average but is highly variable due to fracture networks and geologic contacts, allowing a substantial groundwater contribution to streamflow (38–66 %) and a similar F yw (36 %) to the limestone catchment. The downstream, low-slope catchment underlain by alluvium has a highly variable hydraulic conductivity and a substantially lower groundwater contribution to streamflow (26–45 %) and a higher F yw (55 %). We find that both hydraulic conductivity and topography exert control on the magnitude of baseflow contribution to the stream. However, K sat acts as a key control in groundwater contribution to baseflow as observed in headwaters and downstream region of the watershed. While unconsolidated deposits are often thought of as important high-porosity mountain aquifers, our results point to the importance of fractured and karstified bedrock in baseflow generation in high mountains. As climate change alters the snow- and ice-melt regimes of UIB rivers, our study underscores the critical importance of improving our understanding of baseflow sources. [ABSTRACT FROM AUTHOR]

Equitable water allocation in real-world irrigation systems is hampered by supply fluctuations, posing a significant challenge to the goal of promoting fairness among consumers. In this paper, we concern ourselves with the limits of equity achievable for any water allocation scheme across the entire spectrum of water supply conditions. In the process, we develop a typology of canonical water allocation mechanisms that categorizes mechanisms w.r.t. the distribution of fulfilled demand across the users. Adopting specific notions of supply reliability and distribution equity, we derive the theoretical performance limits for all canonical mechanisms and extend the analysis to arbitrary allocation mechanisms. We show that for any value of supply reliability, the best possible equity is realized by mechanisms that uniformly distribute water among users, whereas the worst possible equity is associated with mechanisms that prioritize the demand of some users before allocating water to others. We also show that any intermediate equity level can be realized by adjusting the initial entitlements prior to allocating water to fulfill demands, in an approach we categorize as hybrid allocation. We parameterize the performance boundaries for such allocation schemes based on the fraction of supply allocated to initial entitlements. We discuss how this parameter can serve as a policy tool to balance the goals of equitable water access with other system-level objectives. In the end, we complement the analytical results with numerical simulations of a selected agricultural district from a real-world irrigation system and speculate about the application of our study to large-scale hierarchical systems. • A categorization of water allocation mechanisms into three canonical types. • Evaluated the performance limits of allocation mechanisms for equity and reliability. • Validation of the theoretical performance limits through Monte-Carlo simulations. [ABSTRACT FROM AUTHOR]

• Integrating an agent-based model into a large-scale distributed hydrologic model. • Evaluating the co-evolution of the CNHS at multiple spatial scales. • Demonstrating the trade-off among FEW sectors under climate change. Managing water resources to meet increasing energy and food demands while maintaining environmental sustainability under climate change is a major challenge, especially when this nexus occurred in a coupled natural–human system (CNHS), where heterogeneous human activities affect the natural hydrologic cycle and vice versa. The relevant research has been limited by the lack of models that can effectively integrate human dynamics and hydrologic conditions with spatial details to examine co-evolutionary systems. To address this challenge, this paper develops a modeling framework that integrates an agent-based model (ABM; human behavior model) into a large-scale, process-based distributed hydrologic model to simulate human decisions endogenously in the hydrologic cycle. We then apply the Decision Scaling approach, an ex-post scenario analysis method, with our integrated model to study the bidirectional feedback of the CNHS under future changing climate conditions. With the Columbia River Basin (CRB) selected as the case study area, the calibration results show that the integrated model can simultaneously capture the historical irrigated water consumption and streamflow dynamics. Modeling results show that the trade-off between irrigated water consumption, hydropower generation, and streamflow will become more pronounced under hotter and wetter climate conditions at both the entire basin and regional (states and provinces) levels. Special attention should be given to "temperature thresholds" of different regions when the trade-off pattern started. The trade-off results can potentially inform the Columbia River Treaty renegotiation and provide insights for long-term water management policies. [ABSTRACT FROM AUTHOR]

[Display omitted] • Hydrological model predictions are crucial for risk-based water management decisions. • Errors in hydrological predictions are typically neglected for risk-based decision-making. • New framework shows neglecting errors over-estimates yield, under-estimates risks. • Outcomes underscore the need to account for hydrological errors in decision-making. Risk-based decision making for water resource systems often relies on streamflow predictions from hydrological models. These predictions are integral for estimating the frequency of high consequence extreme events, such as floods and droughts. However, streamflow predictions are known to have errors due to various factors such as incomplete hydrological understanding, parameter misspecification, and uncertain data. Despite these errors being well known, they are frequently neglected when undertaking risk-based decision-making. This paper demonstrates that neglecting hydrological errors can impact on drought risk estimation for high stakes decisions with potentially severe consequences for water resource system performance. A generic framework is introduced to evaluate the impact of hydrological errors for a wide range of water resource system properties. This framework is applied in two Australian case study catchments, where we use a stochastic rainfall model, the GR4J hydrological model, a residual error model, and a simplified reservoir storage model to estimate water resource performance metrics (risk and yield). The results underscore the impact of neglecting hydrological errors on decision-making. In one case study catchment, the yield was over-estimated by ∼15%–55%, resulting in the (actual) risk of running out of water being ∼2–30 times larger than reservoir design. The magnitude of these errors in water resource performance metrics is striking, especially considering that the streamflow predictions appear reasonable based on typical performance metrics (e.g., NSE of ∼0.7). The errors in performance metrics stem from the complex propagation of hydrological errors through the water resource system modelling chain. By accounting for critically important hydrological errors we can mitigate highly erroneous risk estimates and improve decision-making related to water resource management. [ABSTRACT FROM AUTHOR]

• The adaptive irrigation behaviors might worsen the water scarcity in the CRB. • Clustering identified bold, prudent, and forward-looking farmer classes. • We indicated specific parameters for uncertainty reduction in human systems. The Colorado River Basin (CRB) supports the water supply for seven states and forty million people in the Western United States (US) and has been suffering an extensive drought for more than two decades. As climate change continues to reshape water resources distribution in the CRB, its impact can differ in intensity and location, resulting in variations in human adaptation behaviors. The feedback from human systems in response to the environmental changes and the associated uncertainty is critical to water resources management, especially for water-stressed basins. This paper investigates how human adaptation affects water scarcity uncertainty in the CRB and highlights the uncertainties in human behavior modeling. Our focus is on agricultural water consumption, as approximately 80% of the water consumption in the CRB is used in agriculture. We adopted a coupled agent-based and water resources modeling approach for exploring human-water system dynamics, in which an agent is a human behavior model that simulates a farmer's water consumption decisions. We examined uncertainties at the system, agent, and parameter levels through uncertainty, clustering, and sensitivity analyses. The uncertainty analysis results suggest that the CRB water system may experience 13 to 30 years of water shortage during the 2019–2060 simulation period, depending on the paths of farmers' adaptation. The clustering analysis identified three decision-making classes: bold, prudent, and forward-looking, and quantified the probabilities of an agent belonging to each class. The sensitivity analysis results indicated agents whose decision-making models require further investigation and the parameters with the higher uncertainty reduction potentials. By conducting numerical experiments with the coupled model, this paper presents quantitative and qualitative information about farmers' adaptation, water scarcity uncertainties, and future research directions for improving human behavior modeling. [ABSTRACT FROM AUTHOR]

[Display omitted] • Quantifies water consumption of 135 sectors caused by tourism activities in China. • Illustrates direct and indirect water used driven by direct tourism sector in China. • Compared the accommodation water consumption per guest per day in different regions. • China exported a net 898 million m3 tourism water footprint to worldwide. • Assesses the global scarce water footprint transferred from Chinese tourist. Tourism depends largely on a high water consumption. Previous studies have primarily focused on virtual water embodied in goods or products through trade. Our study provides novel insights into the virtual water embodied in goods and services purchased by tourists. We identified the tourism blue water footprint (TWF) of China according to the environmentally extended input–output table of 135 sectors (11 direct + 124 indirect tourism sectors) together with tourism satellite account data. The results show that the agriculture, electricity, and petroleum sectors majorly contribute to the indirect TWF, while the accommodation and air transport sectors majorly contribute to the direct TWF and were water intensive. Using a constant ratio of 1.5 relating local water footprints to tourist water footprints, we then estimate that China's outbound tourists consume more water than inbound tourists (net TWF of 906 million m3). Nearly half of the net TWF from Chinese tourists is consumed in water deficient countries. Thus, the pressure caused by Chinese tourists on global water resources should be considered. This paper quantitatively evaluates the impact of China's tourism-induced water transfer on global water resources from the perspective of direct and indirect tourism consumption. The results of this paper enrich the research cases of the impact of final consumption on water resources utilization. [ABSTRACT FROM AUTHOR]

• Developed a Decision Support Framework to classify uncertainties and management options using participatory scoping for scenarios analysis. • DSS includes: reservoirs water balance, statistical downscaling, hydrological modelling, sediment assessment, water management and decision support for water management plans. • DSS framework was applied to Tandula reservoir Complex, India and performance was evaluated using multi-criteria decision support in the three-layer hierarchal process. • DSS framework provides a solution for strategic water resources planning and management in a basin using the participation of farmers, technocrats, and water resource managers. The climate uncertainties, data scarcity, and socio-techno challenges pose significant threats and options respectively to the future planning of complex water projects in developing countries. This paper attempts to develop a decision support system (DSS) framework to classify uncertainties and management options using participatory scoping for scenarios analysis with the aid of six interrelated modules namely (i) water balance of reservoirs for computation of runoff (ii) statistical downscaling for climate projection (iii) hydrological modelling (iv) sediment assessment (v) water management and (vi) decision support for the assessment of the performance of multiple management plans designed for three future assessment periods of 2020-35, 2046-64 and 2081-99. The proposed framework was applied to the Tandula complex system of Chhattisgarh state of India consists of three reservoirs namely Tandula, Kharkhara, and Gondli having water transfer in their commands and for industrial use. The developed DSS framework provides the opportunity to analyze the management plans with climate uncertainties for the complex water resources projects having data scarcity. Here, in the present paper, the developed DSS framework incorporates eighty-four diverse management plans devised from constant interaction with stakeholders and technocrats resulting to deal with climate uncertainties using possible improvement in efficiencies and consumptive use for three future assessment periods. Furthermore, the performance of the developed plans was evaluated using multi-criteria decision support in the three-layer hierarchal process. The decision support analysis suggested that the second assessment period (FP-2: 2046-64) will be more crucial from the management point of view where 54 out of 81 plans could not perform as par with the present performance of the system. The developed DSS framework provides a solution for strategic water resources planning and management under projected climate scenarios in a basin having problems of scarcity, surplus, and non-uniform distribution of surface water using the participation of farmers, technocrats, and water resource managers. [ABSTRACT FROM AUTHOR]

• This paper extends the residual mass concept to propose a new method to quantify water availability. • The method automates the identification of deficit periods in the residual mass curve. • The method is validated against SPI and streamflow for Australia. Water supply management for hydroclimatic extremes has commonly been analysed using the cumulative difference between inflow and demand, a concept known as the Residual Mass Curve. This paper extends the residual mass concept to develop a new method to quantify water availability, termed the Residual Mass Severity Index (RMSI). The RMSI improves on the Residual Mass Curve by allowing extreme deficit periods to be easily identified without the need for manual inspection of the Residual Mass Curve. Another advantage of the RMSI is that it does not require a temporal window to be specified to characterise extreme deficit periods, which is needed for many drought indices such as the Standardized Precipitation Index (SPI). This paper develops the method for calculating the RMSI using precipitation time series and compares it to the SPI for different drought durations across Australia. Major hydroclimatic extremes during the instrumental period are assessed using RMSI, SPI, and by comparing the RMSI estimates with streamflow data from 467 catchments with varying sizes and climate across Australia. RMSI is a useful index for water managers interested in assessing water availability with potential applicability in summarising the features of historical hydroclimatic extremes as well as future changes in water availability due to anthropogenic climate change. [ABSTRACT FROM AUTHOR]

Floods are one of the deadliest natural hazards and are exacerbated by changes in land-use and climate. Urban development decreases infiltration by reducing pervious areas and increases the accumulation of pollutants during dry weather. It also decreases infiltration by reducing pervious areas and increases the accumulation of pollutants during dry periods. During rainy events, there is an increase in pollution concentrations and runoff that may be a source of water supply during drought periods. Modeling the quantity and quality dynamics of stormwater runoff requires a coupled hydrodynamic module capable of estimating the transport and fate of pollutants. In this paper, we evaluate the applicability of a distributed hydrodynamic model coupled with a water quality model (HydroPol2D). First, the model is compared to GSSHA and WCA2D in the V-Tilted catchment, and the limitation of the critical velocity of WCA2D is investigated. We also applied the model in a laboratory wooden board catchment, focusing on the validation of the numerical approach to simulate water quality dynamics. Then, we apply HydroPol2D in the Tijuco Preto catchment, in São Carlos - Brazil, and compare the modeled results with the full momentum solver of the HEC-RAS 2D. This catchment shares similar characteristics with many poorly-gauged and human-impacted catchments worldwide. The implementation of the model, the governing equations, and the estimation of input data are discussed, indicating the challenges and opportunities to scale HydroPol2D into the reality of data scarcity of larger poorly-gauged catchments. For a 1-yr return period of rainfall and antecedent dry days, and assuming an uncertainty of 40% in the water quality parameters, the results indicate that the maximum concentration of total suspended solids (TSS), the maximum load and the mass of the pollutant washed in 30% of the volume are, 456 ± 260 mgL-1km-2, 2. 56 ± 0. 4 kgs-1km-2, and 89 % ± 10 % , respectively. [Display omitted] • A new coupled hydrodynamic and water quality 2D model is developed (HydroPol2D). • The spatial–temporal dynamics of Total Suspended Solids (TSS) is investigated. • We estimate TSS transport rates and first flush for a 1-year return period. • The maximum TSS load is 2. 56 ± 0. 4 kg s − 1 km − 2 for a 40% uncertainty in water quality parameters. • HydroPol2D can also be used for flood inundation mapping, infiltration, and evapotranspiration modeling. [ABSTRACT FROM AUTHOR]

• A Transformer-based network for large-scale lake extraction from Sentinel-2 imagery. • The proposed Transformer-based network addresses the interference of cloud shadows. • Adding cloud shadows in training data helps to eliminate cloud shadow interference. Lake extraction from remote sensing images is an important approach to monitoring water resources. It provides guidance for aiding in maintaining natural ecological balance through the artificial adjustment of lake water resources. Cloud shadows are common interference factors in lake extraction from optical images. In this paper, we proposed a deep learning network based on a novel Transformer architecture to extract lakes from Sentinel-2 imagery. To determine how training datasets affect misclassifications of cloud shadows, we produced five training datasets that contained different proportions of cloud shadows to train the network; the highest proportion of cloud shadows among them was 4%. We found that, when the training dataset contained 4% cloud shadows, the obtained model could effectively reduce the misclassification of cloud shadows and improve the accuracy of lake extraction, and with this model, the evaluation results over the validation dataset achieved an overall accuracy (OA) of 0.9954 and a Kappa of 0.9568. In addition, the Transformer-based network was applied in the endorheic basin of Tibetan Plateau to further evaluate the generalization ability of the network over a large area. The Global Surface Water (GSW) dataset from the European Commission Joint Research Centre was used as a reference to validate our results in this area. Our experiment and results demonstrated the underlying potential of the Transformer-based network for improving image classification of optical imagery by reducing cloud shadow inferences. [ABSTRACT FROM AUTHOR]

• An innovative drainage technology based on the groundwater management is proposed. • The technology provides solutions for mine production safety and ecological water security. • Mathematical concepts of the ecological water volume and level of mine drainage is presented. • Numerical simulation model of mine area with unstructured grid is established and validated. The two main factors of groundwater system disturbance in coal mines are mine water inflow caused by overburden destruction and advance drainage projects as the main means of mine water hazard control. An urgent problem to be solved in the ecologically fragile area is how to minimize the impact of advance drainage on the groundwater ecological environment while reducing mine water inflow rate to ensure safe production. This paper proposes an optimal advance drainage technology based on multi-objective groundwater management. Based on the analysis of the respective relationship between the drainage groundwater level and the capture supply and mine water inflow rate, the concept and mathematical expression of the drainage threshold of the mine and the ecological environment are put forward for the first time. Based on the numerical simulation model, this technology fully describes the dynamic changes of the groundwater system and establishes a groundwater management model with multi-objective constraints such as mine production safety, ecological water resources protection and efficient drainage. The optimal drainage scheme for the boreholes location, the drainage volume and the time distribution is obtained through objective programming. While reducing drainage volume and ensuring mine production safety, the impact on the locally important phreatic groundwater is significantly reduced, effectively resolving the contradiction between ecological water resource protection and mine safety production. In the example of a coal mine in Northwest China, by comparing the traditional equal drainage scheme, the optimized advance drainage technology fully considers the water-richness characteristics and the dynamic changes of the groundwater system during the drainage process. The number of boreholes in the optimization scheme is reduced by 50 %, and the total drainage volume is reduced by 40.60 %. More importantly, under the condition that the decrease of mine water inflow rate in the two schemes is basically the same, the average increase of Quaternary leakage in the optimization scheme decreases by 53.36 %. Through this technology, the ecological critical value in the study area is evaluated. When the regional Quaternary groundwater level decreases by more than 0.73 m, the impact range of the drawdown funnel and the drawdown rate are obviously accelerated. It provides a scientific foundation for groundwater resource management and low-carbon mining in ecologically fragile areas. [ABSTRACT FROM AUTHOR]

• We modify a method to assess yield of carryover reservoir with environmental flow. • The method can be applied at large scales, under historic and future climates. • We quantify yield declines due to climate change and environmental flow provision. Protecting freshwater systems to ensure sustainability requires diagnosing threats over a broad range of scales, from local to global. There is growing acceptance that environmental flows must be protected to sustain river channels and floodplain ecosystems downstream of reservoirs. Meeting environmental water requirements has proven difficult because they reduce water yield available for human use – a particular challenge in regions already experiencing declining water availability with climate change. A further challenge is the difficulty of assessing the impact of environmental water requirements on water availability for other purposes. This paper provides a modification of the Gould-Dincer method to assess the yield of carry-over reservoirs subject to environmental water requirements. The method characterizes reservoir inflows using readily available flow characteristics (annual mean and C v). We develop and test the method using ten examples with well-understood environmental water requirements and a range of annual flow C v s. We then demonstrate this method at continental scale, to investigate the impact on yield of providing high-reliability supply including environmental water requirements, under a changing climate. According to our results, considering climate change and environmental flow provision simultaneously, the decline in water yield is projected to be about 27% and 18% respectively for Australia and US (based on hypothetical reservoirs capacity of one times mean annual inflow). Adapting environmental flow demands to future hydrological conditions results in a 2% and 3% increase in Australia and US-wide yield for human use respectively. While further testing is needed, the method provides a simple and rapid way to estimate the impact of providing environmental flow on yield, applicable at global scale. [ABSTRACT FROM AUTHOR]

• BMA improved estimation of prediction uncertainty for daily streamflow simulations. • Applying BMA provided a better understanding of SWAT-HS reliability and precision. • BMA models had similar estimations for model reliability, but different for precision. • BMA showed that MNSE had the highest predictive ability in simulating streamflow. In this paper, multiple objective functions were used to conduct uncertainty analysis on calibrated streamflow simulations of SWAT-hillslope model (SWAT-HS), which is a semi-distributed model suited for humid, mountainous regions. SWAT-HS was set up for six watersheds of NYC water supply system and streamflow was simulated at a daily time step. Each SWAT-HS model setup was then passed through a calibration process for parameter optimization. This process involved constraining the model using several alternative objective functions which produce statistically acceptable streamflow simulations, which served as an ensemble of predictions for uncertainty analysis. We hypothesize that the uncertainty introduced by selecting a single objective function can be large with each objective function being sensitive to a specific hydrologic signature. Using multiple objective functions result in a wider range in optimum parameter values and an ensemble of simulations based on different objective functions can capture the overall prediction uncertainty through differences in parameter estimates. The Bayesian Model Averaging (BMA) method was then applied to ensemble predictions to quantify overall prediction uncertainty. The uncertainty analysis showed the similarity of uncertainty interval characteristics for all BMA based predictions across all watersheds. More than 94–97% of the observations were covered by uncertainty intervals estimated using multiple objective functions when compared to 73–79% of the observations when using a single objective function. No significant differences were observed among the six watersheds, and the results of their uncertainty analysis were similar. We propose the use of multiple objective functions as an option in ensemble modeling of streamflow and uncertainty analysis. [ABSTRACT FROM AUTHOR]

• Removal of GEDI outliers and water level evaluation. • Bias adjustment between mission ICESat-2 and GEDI. • Water level dynamics from integrating ICESat-2 and GEDI. • Water levels cross validation with DAHITI and Hydroweb datasets. • Factors that affecting the accuracy of water level monitoring. The water levels of inland lakes on the Tibetan Plateau are extremely sensitive to global climate change and can objectively reflect the temporal and spatial changes in local water resources. However, monthly and seasonal variations in lake water levels are difficult to monitor due to the lack of sufficient in situ gauges across mountainous areas. Moreover, GEDI products exhibit large uncertainties in mountainous surroundings. In this paper, taking Qinghai Lake as an example, we first refined the raw data of the GEDI and ICESat-2 missions by implementing a quality control procedure involving outlier removal tailored to the characteristics of each mission; then, we analyzed the accuracy of each mission, especially targeting factors that affect the water level retrievals in the GEDI products. Third, the bias between the two missions was adjusted by selecting the overlapping or adjacent observation dates. Finally, we constructed dense temporal water-level data by integrating the refined ICESat-2 and GEDI data. Data from water level stations and the DAHITI and Hydroweb datasets were also utilized for validation. The results show that (1) very accurate results can be obtained from the ICESat-2 ATL13 product, and the standard deviations of most observed days are under 0.05 m; (2) the GEDI products derived from algorithm 2 can offer more effective footprints than those from algorithm 1, with an improvement of approximately 9.78 %. Moreover, large differences exist among the different GEDI beams, and beams 1 and 2 are recommended for further analysis. Overall, most beams overestimated the lake levels with a bias of 0.264 ± 0.357 m; (3) the long-time-series water levels showed a mean increasing trend of 0.243 m/yr from 2018 to 2021. The relatively high-water-level periods were distributed mostly in August and September, while the low-water-level periods were distributed mostly in February and March. The combined water levels were very correlated with the DAHITI and Hydroweb datasets, with R values larger than 0.8, and highly consistent with the observations from hydrological stations (the inter-year change range spanned from 0.015 m to 0.327 m, and the intra-year difference range varied from 0.03 m to 0.16 m); and (4) Integrating the GEDI and ICESat-2 missions allowed us to capture the monthly, seasonal and annual dynamics of the lake water level, and the results indicate that the combined dataset presents a valuable resource for hydrological and climatic change studies. [ABSTRACT FROM AUTHOR]

• A preference decision method (PDM) is developed to assist in making trade-offs. • The PDM provides solid quantitative support for subjective preferences. • The PDM draws on economic theories and has physical and mathematical connotations. • The PDM is based on the Pareto set itself and can be extended into other areas. The key to formulating a multi-objective reservoir operation scheme is coordinating the high/low priorities and achieving balance among various targets accordingly in the decision-making process. However, the traditional decision-making methods are either completely subjective or neglect the decision-maker's preferences, making it essential to optimize these methods to address such unsatisfactory aspects. Therefore, through drawing on some universally acknowledged economic theories and studying the geometric relationships among the Pareto solutions to such multi-objective decision-making problems, the concepts of "replacement rate", "profit-loss sensitivity ratio", and "preference equilibrium degree" are introduced, deduced, and elaborated on in this paper. On the basis of them, the preference decision method (PDM), entrusted with strict mathematical and physical connotations, is constructed and derived in detail, especially for addressing the bi-objective and tri-objective scenarios. The PDM can quantify the complex internal feedback relationship among various targets, providing objective support for decision-makers in the subjective trade-offs of the interests of multiple subjects and achieving the unity of subjective and objective. Furthermore, the proposed method is applied to the Wujiang cascade reservoirs in China, through which the benefits of power generation, water supply and ecological protection are enabled to be considered simultaneously. The application shows that the PDM can effectively shrink the Pareto set, which greatly reduces the difficulty of decision-making, and the obtained results prove feasible and satisfactory. In addition, the PDM is based entirely on the Pareto set itself, making it simple to utilize, and can be extended into solving other multi-objective decision-making problems. [ABSTRACT FROM AUTHOR]

• A bed-steadying discharge is proposed. • A flow-sediment model and an LSTM model are integrated. • The riverbed deformation is predicted. River morphology plays an important role in water environment and resources. The dominant discharge (Q D) is a crucial indicator for understanding river morphology and bed evolution under the impact of various interacting processes. At present, the identification of Q D mainly depends on the analysis of a large number of hydrological data derived from measuring stations, leading to difficulty in obtaining detailed Q D distributions along the study reach. In this paper, Q D is approximately expressed as the bed-steadying discharge (Q S) which is based on major factors of water level and sediment-carrying capacity. Subsequently, an integrated model combining a numerical fluid-flow and sediment model with a deep-learning algorithm is applied to analyze the changing process of the Q S. The flow and sediment transport processes are simulated by the calibrated mathematical model, which are then adopted as the input sequences for the long short-term memory (LSTM) module. The verification results of the established LSTM model show robustness and accuracy in predicting the flow and sediment transport processes under multi-stage incoming flow and sediment conditions. Furthermore, the proposed integrated model is applied to identify the detailed process of Q S in the Middle Huaihe River. Results show that the changing process of Q S along the study reach is characterized by a particular trend of "increase-decrease-rapid increase" due to natural changes and human activities. Additionally, the Q S agrees well with Q D at the hydrological station, showing that Q S can be applied as an approximation for Q D along the study reach. By analyzing longitudinal and transverse profiles, the rationality of using Q S as obtained by the newly presented model is demonstrated. Its temporal variation is also consistent with the cross-sectional changes for the specified stations. [ABSTRACT FROM AUTHOR]

• A bed-steadying discharge is proposed. • A flow-sediment model and an LSTM model are integrated. • The riverbed deformation is predicted. River morphology plays an important role in water environment and resources. The dominant discharge (Q D) is a crucial indicator for understanding river morphology and bed evolution under the impact of various interacting processes. At present, the identification of Q D mainly depends on the analysis of a large number of hydrological data derived from measuring stations, leading to difficulty in obtaining detailed Q D distributions along the study reach. In this paper, Q D is approximately expressed as the bed-steadying discharge (Q S) which is based on major factors of water level and sediment-carrying capacity. Subsequently, an integrated model combining a numerical fluid-flow and sediment model with a deep-learning algorithm is applied to analyze the changing process of the Q S. The flow and sediment transport processes are simulated by the calibrated mathematical model, which are then adopted as the input sequences for the long short-term memory (LSTM) module. The verification results of the established LSTM model show robustness and accuracy in predicting the flow and sediment transport processes under multi-stage incoming flow and sediment conditions. Furthermore, the proposed integrated model is applied to identify the detailed process of Q S in the Middle Huaihe River. Results show that the changing process of Q S along the study reach is characterized by a particular trend of "increase-decrease-rapid increase" due to natural changes and human activities. Additionally, the Q S agrees well with Q D at the hydrological station, showing that Q S can be applied as an approximation for Q D along the study reach. By analyzing longitudinal and transverse profiles, the rationality of using Q S as obtained by the newly presented model is demonstrated. Its temporal variation is also consistent with the cross-sectional changes for the specified stations. [ABSTRACT FROM AUTHOR]

• A field-scale problem of pumping energy consumption minimization was solved. • Coupling of a groundwater flow model with an optimization algorithm was performed. • The optimization algorithm is based on an analytical solution. • The optimal pumping strategy under various problem setups was examined. • The importance of including friction losses in such problems was highlighted. Global population growth and rapid economic development, along with modern goals for creating sustainable communities, render it necessary to focus on fundamental, finite resources such as water and energy. The interdependencies between water and energy are well known, while recently the interest of the scientific and policy communities on their dependency relationship has grown noticeably. On the one hand, water is used throughout the energy industry, while, on the other hand, the water system needs energy for collecting, pumping, supplying and treating water. Considering the latter case, great importance should be given when specifically referring to groundwater resources for the abstraction of which a large amount of energy is generally required. Therefore, minimizing the energy consumed for pumping groundwater is extremely important, and, for this reason, a large number of studies have been conducted in order to address this certain issue. However, most of the studies dealing with pumping cost minimization problems ignore the effect of friction losses on the total energy consumed for pumping and transporting groundwater. Under this perspective, the present paper attempts to highlight the importance of including friction losses in such type of problems by combining a simulation model and an optimization algorithm. Through the formulation of different setups of the same problem, that is the energy cost minimization for pumping groundwater from a system of wells and transporting it through transmission lines, it was found that only when including friction losses in the optimization problem, all managed wells are being used for pumping water, thus resulting in more reasonable and workable solutions. [ABSTRACT FROM AUTHOR]

[Display omitted] • Wildfires are becoming more severe and frequent and their effects increasingly expose water resources to stress. • Climate, wildfire, streamflow records (1975 to 2018), topography, and landcover data from 92 catchments were combined to quantify the contribution of wildfire to streamflow variability across Australian temperate forests. • On average wildfire regimes relatively results in 8.8% increases in streamflow across Australian temperate forests. • Wildfire effects on regional streamflow varied spatially depending on hydroclimate factors, particularly precipitation. As wildfires become more frequent and severe, there are concerns regarding their impacts on water yield from forested catchments. While there are many studies in Australia about the effects of individual wildfires on streamflow at fine scales (<1 km2) in specific geographic settings, the effects of wildfire regimes on streamflow at broad spatial scales across temperate forests in Australia are not well understood. In this paper, we combined climate, wildfire, streamflow records (1975 to 2018), topographic, and landcover data from 92 catchments (74 – 4740 km2) in the Australian temperate zone to quantify the contribution of wildfire regimes over time to streamflow variability in different hydroclimatic settings (humid, dry sub-humid, and semi-arid) and geographic regions (Southeast Australia (SEA), Southwest Western Australia (SWWA), and Tasmania (TAS)). Wildfire regimes were represented by two metrics: the burnt area to drainage area (BDA) ratio for wildfire events in each year and a spatially averaged metric of Time Since Fire (TSF), which is a spatial average of time since the last wildfire in the catchment. By comparing prefire and postfire runoff ratios our study found that on average there was a short-term increase in runoff ratio (∼3% in year 1 and ∼6% in year 2 post-fire) after wildfires with BDA > 25%. No influence of fire was found in the long-term (15–20 years after the wildfire) runoff ratio. We found that wildfire regime, measured by TSF, explained ∼8.8% of the variation in annual streamflow across the Australian temperate zone, and that with decreasing TSF (i.e., increased wildfire impact), the average streamflow increased. Streamflow variation explained by wildfire regimes varied with hydroclimate. The explained variance of streamflow by wildfire regimes in semi-arid catchments (23%) and dry sub-humid catchments (13%) were higher than humid catchments (5%). Our results provide a broad-scale understanding of how wildfire regimes influence streamflow variability at broad temporal and spatial scales, and provided important context and baseline information for determining the implications of changes in climate and fire regimes for regional water availability. [ABSTRACT FROM AUTHOR]

• A hydrological modeling approach was proposed to distinguish the relative contribution of climate changes and human activities on Daihai Lake's recession. • In the absence of natural runoff and human-induced water withdrawal observations, the natural state's lake water storage changes were reconstructed. • Without considering the high water years, the anthropogenic factor took the responsibility for the lake shrinkage about 58%, while the climatic factor took roughly about 42% in period 1974–2018. • This investigation serves as a useful example of applying a distributed model to the assessment of lake hydrology, especially for the lake catchments with limited observations. In recent decades, water levels in many closed inland lakes over north-western China experienced severe declines, among which the Daihai Lake in Inner Mongolia, China was particularly significant. Remotely sensed investigations indicated that the Daihai Lake had shrunk approximately 64% in an area with the dramatic decline of water level approximately 9 m from the 1980 s to 2010 s, which has caused serious concern on the sustainable development of the surrounding region. Consequently, several previous studies have concentrated on the possible causes of lake shrinkage. However, predicting climate variability and anthropogenic activities' relative contributions to lake water decline is challenging due to the lack of direct water consumption upstream and watershed-scale natural runoff observations. In this paper, a hydrological modeling approach was presented to quantitatively evaluate and separate the relative contributions of climate variations and intensifying anthropogenic activities. The simulation accuracy was evaluated through comparisons with observed data and cross-validation with previous studies. Based on a baseline period, the natural streamflow draining into the lake was reconstructed using the model. The results revealed that the water loss of the Daihai Lake was caused predominately by human activities rather than climate variability. Specifically, for wet conditions, the climate factors posed a negative effect on the water level decline of the lake and delayed the shrinking process of the lake area. The anthropogenic factors took a dominant role (roughly 58%) of lake shrinkage at the annual average scale for normal and dry conditions, while the climate factors accounted for approximately 42%. Hence, the rational policy for water resource management, ecological restoration, and dam and reservoir constructions/operations should be the essential precautions for sustainable development of the Daihai Lake and its surroundings. Additionally, the hydrological modeling approach adopted in this investigation serves as a useful example of the quantitative assessment of climate variations and human activities, especially for the ungauged catchments. [ABSTRACT FROM AUTHOR]

• The mean groundwater table depth in China dropped by approximately 0.2 m (P < 0.01) during 1979–2018. • The Noah-MP land model forced with reanalysis climate data can reasonably capture terrestrial water and radiation processes across China. • Incorporating the groundwater lateral flow process enhances the seasonality of modeled terrestrial water and radiation processes on a regional scale. Faced with intensified climate change and ever-increasing water demand, the groundwater table depth is vital to sustaining terrestrial environments in a country such as China; however, on a regional scale, insufficient research has been conducted on its variability thus far. Using a land model forced with reanalysis climate data, this paper investigates groundwater table depth changes across China for the 1979–2018 period. The results show a significant overall reduction in the groundwater table depth in China during the past 40 years without explicit consideration of direct human influence. There is an approximately 0.2 m decrease on average, but it is approximately 0.5 m in southern China and 0.8 m to the north of 35°N despite a 1.2 m rise in part of this region due to an increasingly wet climate and a topographic water convergence. The findings provide a first-order pattern of historical groundwater table responses to climate change, highlighting inherent links among subsurface-surface-atmosphere water cycles and worsened terrestrial water availability in China. A model evaluation compared with in situ observations has demonstrated the model's ability to realistically portray terrestrial water and energy balances on regional scales, as well as the enhancement of their seasonality with incorporated lateral groundwater flow processes. This ability gives us reasonable confidence in the simulated trends and spatial variability. Nonetheless, the mean groundwater table still shows large biases relative to observations, and the problems of human influence, two-way interactions between groundwater and surface water, and local topographical complexity have yet to be properly addressed in the present study and deserve further research. [ABSTRACT FROM AUTHOR]

• A framework for country-scale water-energy-food security overall analysis is developed. • The developed approach integrated TOPSIS, Spearman and radar graph methods. • Method can promote water-energy-food security evaluation in data-limited countries. • Water-energy-food security and relationships in Central Asia is evaluated. • The factors reducing water-energy-food security in Central Asia are identified. Thoroughly understanding the security of water, energy and food (WEF) and the factors that influence them is essential for sustainable development management in any region. This study proposed a new analytical framework for WEF security evaluation in both individual sectors and the whole system, using the technique for order preference by similarity to an ideal solution (TOPSIS) and four dimensions of security indicators: availability, self-sufficiency, productivity and accessibility. The internal relationships among the three sectors and the main factors influencing WEF security were analysed by Spearman's rank correlation and radar graphs, respectively. The five countries in Central Asia (CA), which are experiencing WEF crises and facing great challenges in achieving their sustainable development goals (SDGs), were chosen as a case study in this paper. Our results showed that Kazakhstan attained the highest WEF security level, followed by Kyrgyzstan, Turkmenistan and Uzbekistan; Tajikistan exhibited the lowest security level from 2000 to 2014. Three types of internal relationships among the three sectors were identified: synergies, trade-offs and unclassified. The unclassified relationship type accounted for the largest share of 54% in CA, suggesting great potential for synergetic improvement among the three sectors. Approaches for improving the country WEF security based on our research are as follows: Kazakhstan should prioritize food allocation and supply, Kyrgyzstan and Tajikistan should increase energy and food production and raise the supply level and usage efficiency of water and food, Turkmenistan should increase the available water resources and food production and improve the supply level and usage efficiency of water and energy, and Uzbekistan should both increase the available amount and enhance the WEF management performance. [ABSTRACT FROM AUTHOR]

• Pi-VAT is a post-processing, interactive visualization, and analysis tool. • Simple implementation to facilitate 'what-if' analysis using WEPP and SWAT outputs. • Suitable for assessing areas for targeted management. • Synthesize and visualize multiple watersheds and scenarios. • An approach to address the need for integrating extensive outputs from process-based models into decision support tools. Effective watershed management and protection of water resources from non-point source pollution require identification, prioritization, and targeting of pollutant source areas. Process-based hydrology and water quality models are powerful heuristic tools for land and water resources managers. However, because of their complexity, such models are often under-utilized as management prioritization and planning tools. In this paper, we present a prioritization, interactive visualization, and analysis tool (Pi-VAT) that is programmed to synthesize multi-scenario, multi-watershed outputs from process-based geospatial models. We demonstrate the utility of Pi-VAT to examine simulated hydrologic, sediment, and water quality response at the hillslope/hydrologic response unit (HRU) scale. We apply Pi-VAT to output from multiple watersheds and for multiple management scenarios and treatments from two geospatial models for watershed management: Water Erosion Prediction Project (WEPP) and Soil & Water Assessment Tool (SWAT). Pi-VAT was developed using the Shiny web application framework for the R programming language. In a matter of minutes, Pi-VAT can synthesize overwhelming amounts of output from process-based models into information useful for land and water resources managers. We illustrate the use of Pi-VAT to interactively identify, quantify, and visualize areas that are most susceptible to disturbance under different scenarios and provide a synthesis approach based on land use, soil type, and slope steepness. This approach guides land and water resources managers in prioritizing the areas of the watershed that provide the maximum reduction in pollutant loads while treating the least amount of area. Pi-VAT provides a flexible reactive platform for the development of decision support tools based on process-based models intended for watershed management and research applications. [ABSTRACT FROM AUTHOR]

• Propose an indirect way of cooperation in transboundary river management. • Appropriate data sharing and selection in water system operation is vital. • Sharing data on available water storages foster hydropower production. Water resources management in the Lancang-Mekong River Basin (LMRB) is challenging in face of the complex trans-national geopolitical context, the acceleration of hydropower dam expansion, and the growing demands for energy, food, and riverine ecosystem preservation across the basin. Centralised management through coordinated operation of all the basin water infrastructures ("hard cooperation") across the river basin is ideal but hard to achieve given the existing geopolitical context of the region. To overcome these barriers and facilitate a more adaptive and reliable operation of the LMRB's water system, this paper focuses on the concept of "soft-cooperation", i.e. the idea of indirectly fostering cooperation by sharing data across countries to inform the non-cooperative (or independent) operation of their systems. Here, we first quantify existing riparian conflicts and synergies for the non-cooperative scenario, where upstream and downstream act independently without any form of cooperation, and for the "hard cooperation" configuration, where an ideal centralised operator jointly operates all the infrastructures. Then a "soft cooperation" scheme assuming data sharing across riparian countries is evaluated. The most effective and informative data is selected via machine learning and used to inform the design of the river reservoir system operation via evolutionary multiobjective direct policy search. Results show that the riparian conflicts around water mainly exist between the sectors, i.e. hydropower production vs ecosystem conservation, rather than among countries. For reservoirs with various capacities and locations, the value of information varies. Generally, sharing data about the water currently available in the different river storages results in a large improvement in hydropower production. This study highlights the value of appropriate data sharing and selection in water system operation as an indirect way of promoting cooperation in transboundary river management. [ABSTRACT FROM AUTHOR]

• Frequent changes in groundwater trend direction are detected according to time series length. • Groundwater levels in France show a low-frequency variability that could explain these changes. • Groundwater level trends are highly sensitive to the presence of low-frequency variations. Estimating groundwater level evolution is a major issue in the context of climate change. Groundwater is a key resource and can even account in some countries for more than half of the water supply. Groundwater trend estimates are often used for describing this evolution. However, the estimated trend obviously strongly depends on available time series length, which may be caused by the existence of long-term variability of groundwater resources. In this paper, using a groundwater level database in Metropolitan France as an example, we address this issue by exploring how much trend estimates are sensitive to low-frequency variability of groundwater levels. Database consists of relatively undisturbed groundwater level time series regarding anthropogenic influence (water abstraction by either continuous or periodic pumping). Frequent changes in trend direction and magnitude are detected according to time series length, which can eventually lead to contradictory interpretations of the groundwater resource evolution, as presented in first part of this article. To assess whether low-frequency variability – known to originate from climate variability – can induce such modifications of trends, we explored in a second step the multi-time scale variability of groundwater levels using a methodology based on discrete wavelet transform. Most of the time series displaying changing trends depending on time series length corresponded to aquifers with high-amplitude low-frequency variability of groundwater levels. Two predominant low-frequency components were detected: multi-annual (∼7 years) and decadal (∼17 years). We finally examined how much those two low-frequency components may affect trend estimates on the longer time period available. For this purpose, we individually removed each of both components from the original times series by discrete wavelet filtering and re-estimated trends in the filtered groundwater level time series. The results showed that the groundwater level trends were highly sensitive to the presence of any of these low-frequency components, which may then strongly influence the estimated trends either by exaggerating or mitigating them. These results emphasize that i) attributing the estimated trends only to climate change would be hazardous given the large influence of low-frequency variability on groundwater level trends, ii) estimation of trends in hydrological projections resulting from General Circulation Models (GCM) outputs in which low-frequency variability is not well represented would be subject to strong uncertainty, iii) a potential change in the amplitude of internal climate variability – e.g. increasing or decreasing low-frequency variability – in the next decades may lead to substantial changes in groundwater level trends. [ABSTRACT FROM AUTHOR]

• We developed a novel analytical model for regional groundwater transit time distributions s accounting for the impact of stormwater infiltration systems. • Global sensitivity analysis successfully identifies the most important parameters for urban groundwater TTDs. • The urban groundwater flow regime cannot be fully restored to the pre-urban conditions even with the aid of stormwater infiltration systems. The stormwater infiltration system (SIS) has been recognized as a feasible way to manage stormwater and restore streamflow. As a lumped representation of the flow and transport processes, the groundwater transit time distribution (TTD) is highly relevant to this restoration goal. However, the influence of the SIS on groundwater TTDs has rarely been studied. In this paper, we propose a novel analytical model for groundwater TTDs in regional urban aquifers accounting for the impact of the SIS. This analytical solution is developed for a homogeneous 2-D cross-section of an idealized unconfined aquifer following the Dupuit-Forchheimer assumption. The analytical solution is verified through the comparison with simulated TTDs using the Lagrangian particle tracking algorithm. The global sensitivity analysis reveals that the stormwater infiltration rate, the number of infiltration facilities, the size of the urban area, and the evapotranspiration are the most important factors for groundwater mean transit times (MTTs). Although groundwater discharge and MTT can be restored to the pre-urban one, groundwater TTDs will inevitably deviate from the pre-urban ones due to the alteration from diffuse recharge to infiltration-focused recharge, indicating that the urban groundwater flow regime cannot be fully restored to the pre-urban conditions. Overall, as a first-order approximation of the real-world urban groundwater system, the proposed analytical model provides decision-making support for urban water resources management and the design of the SIS. [ABSTRACT FROM AUTHOR]

• Conventional and unconventional water resources allocation is handled. • The interval multi-objective genetic algorithm is developed for model solving. • Combineing synergetic theory and information entropy theory to construct the synergetic goal. The optimal allocation of water resources is a critical means of resolving the supply–demand paradox. To optimize the allocation of regional water resources, an Interval Numeral-Hierarchical Planning Model (INHPM) was established to integrate unconventional water resources into the water resource allocation system while simultaneously considering the interests of decision makers at different levels and the uncertainty in a water resource allocation system. The INHPM directs conventional and unconventional water supply departments to achieve coordinated development through the objective function of the synergy degree. The theory of synergy and the theory of information entropy serve as the theoretical foundations for the objective function of the synergy degree. In addition, the interval number was introduced to allow the INHPM to address uncertainty in a water resource allocation system. The hierarchical planning model served as the primary framework for assisting decision makers in minimizing conflicts of interest among various stakeholders in a water allocation system. In this paper, the Interval Multi-Objective Genetic Algorithm (IMOGA) was established to solve the INHPM, as opposed to the previous method of transforming the uncertainty model into a certainty model and solving it (IMOGA-INHPM). Finally, using the optimal allocation of conventional and unconventional water resources in Ordos, Northwest China, as an example, the results and comparison analysis of a case study demonstrate the IMOGA-practicability INHPM's and efficiency. [ABSTRACT FROM AUTHOR]

• First global review of EOCs in karst aquifers. • 144 different EOCs detected, max. conc. > 100 ng/L for almost half. • EOCs are useful as tracers for karst characterisation. • EOCs may improve contaminant source identification in karst. A quarter of the world's population uses groundwater from karst aquifers. A range of emerging organic contaminants (EOCs) are considered a potential threat to water resources and dependant ecosystems, and karst aquifers are the most vulnerable groundwater systems to anthropogenic pollution. This paper provides the first global compilation (based on 50 studies) of EOCs in karst aquifers and explores EOC occurrence and the use of EOCs to understand karst systems. Of the 144 compounds detected in the reviewed studies, the vast majority in karst groundwater are pharmaceuticals and pesticides. Maximum concentrations of compounds varied over five orders of magnitude, and nearly half of the detected compounds exceed 100 ng/L. Karst groundwater is shown to have lower frequency of detection and lower concentrations compared to surface waters and local shallow intergranular aquifers, but overall higher concentrations compared to other major aquifer types. A growing number of studies have demonstrated the utility of EOCs and some legacy compounds for groundwater quality assessment and as tracers for characterising karst systems. They can improve understanding of vulnerability, storage, attenuation mechanisms, and in some cases have been used to assist with catchment delineation. This is a growing research area for karst hydrogeology, and more research is needed to understand EOC contamination of karst aquifers, and to develop EOCs as tracers within karst to improve our understanding of this critical water resource. [ABSTRACT FROM AUTHOR]

• DRASTIC-AgLU-CC is applied to examine future groundwater contamination risk. • Climate and land use factors are treated as dynamic model inputs. • The influence of land use scheme complexity on model output is assessed. • Detailed agricultural land use representation may improve model predictions. • Predicted contamination risk varies depending on land use representation. Rural regions, which are commonly reliant on groundwater for water supply, often face groundwater quality challenges related to agricultural activity. To sustainably manage and protect groundwater resources, it is necessary to have a clear understanding of inherent groundwater vulnerability and the contamination risk posed by pollutant loading. Index-based methods typically assess groundwater contamination risk in a static manner and commonly represent agricultural land as a single land use class despite the complexity of land management practices that may be present. This paper illustrates modification of the conventional DRASTIC-LU methodology, through a newly developed DRASTIC-AgLU-CC approach, to better understand how groundwater contamination risk may change by mid-century while simultaneously examining the potential influence of land use scheme complexity on model performance and prediction. Recharge, depth to water table, and land use are treated as dynamic factors through the integration of downscaled global climate model data, empirically estimated water table fluctuation and recharge, and trend-based land change modelling. The Upper Parkhill watershed in southwestern Ontario, Canada serves as a case study for method application. Study results indicate that more detailed agricultural land use representation, specifically the inclusion of crop rotation and tile drainage data, has the potential to improve model predictive ability; furthermore, predicted future changes in groundwater contamination risk may vary depending on the manner in which land use is represented. Within the Upper Parkhill watershed, mid-century land use and climate conditions may increase groundwater contamination risk, although minimal change is anticipated. The presented approach provides a valuable screening tool to better understand the potential for changing groundwater contamination risk in rural regions and can be used to guide groundwater monitoring programs and water supply development. [ABSTRACT FROM AUTHOR]

• IBIS may be more impacted by climate change in UIB than local hydrology. • Sedimentation combined with climate change exacerbates dry season water shortages. • Water availability reductions in the dry season could impact Pakistan's food security. Pakistan relies greatly on the water of the Upper Indus basin (UIB) for its vast agriculture in the Indus Basin Irrigation System (IBIS). There are already concerns about the extent of unsustainable water use in the IBIS, particularly of groundwater, and these concerns are likely to increase in the future due to population increase and associated increased demand for food. In addition, climate change, continuing reservoir sedimentation in the major dams, and other impacts on water supply and demand are likely to have an impact on the practical implementation of the landmark inter-provincial Water Apportionment Accord (the Accord), which governs surface water allocation in the IBIS. There is, therefore, a need to assess the sustainability of irrigation in light of the potential variations of water availability in the basin under a series of plausible climate change and reservoir sedimentation scenarios. In this paper, we examine the impact of continuing reservoir sedimentation and climate change by 2050 on the water balance of canal commands in the IBIS. Firstly, we used a river system model, the first to include water management rules as in the Accord, to propagate climate induced changes on flows in the UIB and their impact on IBIS flow regulation and canal deliveries (I cw), with and without reservoir sedimentation. Secondly, we used a selection of General Circulation Models (GCMs) that showed skill at simulating the local climate of the IBIS to assess the projected changes to rainfall (P n) and evapotranspiration (ET a). The analysis revealed that water balances in IBIS canal command areas are more significantly affected by changed inflows from the UIB than by localised climate induced changes (i.e. P n and ET a). Continuing reservoir sedimentation in the dams to 2050 will diminish the capacity of the system to store flows during the wet summer Kharif season for its later release in the dry winter Rabi season. This will decrease the supply of I cw in the Rabi season to the downstream IBIS areas in Sindh and Balochistan provinces, and potentially increase supplies in the Kharif season. The impact of reservoir sedimentation on the Rabi water supply (33 mm (13%) decrease in median I cw , equivalent to 1.89 km3), is greater than the impact of projected localised climate change within the IBIS (2 mm (12%) decrease in median P n , equivalent to 0.13 km3). Sedimentation combined with climate change decreases the median I cw by 51 mm (20%, equivalent to 2.91 km3) in Rabi in the low flow scenario and increases it by 48 mm (8%, equivalent to 2.72 km3) in Kharif in the high flow scenario. In the upstream Punjab province, however, reservoir sedimentation will have limited impact by 2050 due to lower ET a in Rabi and the availability of groundwater. Continued reservoir sedimentation beyond 2050 will also affect Punjab, again diminishing the supply of I cw in Rabi and increasing it in Kharif. Concurrently, declining I cw could lead to a substantial reduction in crop areas in the Rabi season. To avoid this undesirable scenario, Pakistan needs to explore both demand and supply management options to achieve food and economic security, as well as controlling sedimentation rates in reservoirs. [ABSTRACT FROM AUTHOR]

• Use of CFSR data for hydrological modelling in KRB leads to useful estimates of hydrological parameters. • Water resources in KRB can be assessed with satisfactory accuracy using CFSR data. • Statistics show model results are very good and may be used in ungauged catchments. • The spatial variability of water yield shows water stress and excess areas. The Zambezi River Basin is among many basins in Southern African region with inadequate, low resolution, poorly maintained and distributed network of hydrometeorological stations. This scenario entails scarcity of hydrological data in the basin and subsequently leads to poor assessment of water resources. This paper investigates alternative technologies in the assessment of the hydrology and water resources in the basin. The high-resolution gridded climate data together with land use and soil data were used as inputs into SWAT model to simulate hydrological processes. Calibration and validation of model were performed using SWAT-CUP. Comparison of average monthly rainfall data for gridded and observed shows 97% correlation. The statistics show NS to be 0.73 while R2 was 0.73. The uncertainty analysis was determined with a P-factor at 0.75 while the R-factor was 0.75. Therefore, use of gridded climate data for hydrological modelling in data scarce regions could be a useful tool to estimate the main hydrological parameters and water resources of an area with satisfactory accuracy. [ABSTRACT FROM AUTHOR]

• Energy consumption is introduced in the optimization model. • Eliminate the negative influence caused by weight distribution. • Optimal planting schemes were obtained. In order to multi objectives and uncertainties in the optimization allocation of irrigation water resources, this paper developed an improved interval multi-objectives programming method. The proposed model was then applied to a case study in Jinghuiqu irrigation district, Shaanxi Province, China. Maximum economic benefit and minimum energy consumption are regarded as planning objectives. A series of planting structure optimal schemes were obtained under different representative hydrological years. From the result, shortage of water resources has a negative effect on the development of Jinghuiqu irrigation district. Moreover, the remaining water resources prefer vegetable to wheat after meeting the minimum demand of the developed model, which characterized with high economic benefit and lower energy consumption. Moreover, due to the introduction of effective precipitation, a lot of irrigation water resources have been saved, such as 47.14% of total water consumption was saved when P = 25%. Furthermore, the desired optimal schemes can be obtained by the decision-makers based on the results of the developed model. [ABSTRACT FROM AUTHOR]

• Our research object focuses on large-scale consecutive missing data. • A hybrid transfer learning-based deep learning method is proposed and illustrated. • The feasibility of the proposed algorithm in actual water monitoring is explored. • Negative Transfer is controlled within limits by a novel and classical algorithm. In recent years, water quality monitoring has been crucial to improve water resource protection and management. Under the relevant laws and regulations, environmental protection department agencies monitor lakes, streams, rivers, and other types of water bodies to assess water quality conditions. The valid and high-quality data generated from these monitoring activities help water resource managers understand the existing pollution situations, energy consumption problems and pollution control needs. However, there are inevitably many problems with water quality data in the real world due to human mistakes or system failures. One of the most frequently occurring issues is missing data. Although most existing studies have explored classic statistical methods or emerging machine/deep learning methods to fill gaps in data, these methods are not suitable for large-scale consecutive missing data problems. To address this issue, this paper proposes a novel algorithm called TrAdaBoost-LSTM, which integrates state-of-the-art deep learning theory through long short-term memory (LSTM) and instance-based transfer learning through TrAdaBoost. This model inherits the full advantages of the LSTM model and transfer learning technique, namely the powerful ability to capture the long-term dependencies among time series and the flexibility of leveraging the related knowledge from complete datasets to fill in large-scale consecutive missing data. A case study involving Dissolved Oxygen concentrations obtained from water quality monitoring stations is conducted to validate the effectiveness and superiority of the proposed method. The results show that the proposed TrAdaBoost-LSTM model not only improves the imputation accuracy by 15%~25% compared with that of alternative models based on the obtained performance indicators, but also provides potential ideas for similar future research. [ABSTRACT FROM AUTHOR]