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• 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]

[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]

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]

• 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]

• Numerous science and policy debates occur over efficient aquifer protection measures. • This paper formulates a basin scale hydroeconomic optimization framework. • Its application is to North America's Middle Rio Grande Basin. • It discovers least cost aquifer protection measures for handling climate water stress. • Results are presented showing the marginal and total cost of aquifer protection. Where surface and groundwater are managed conjunctively, the stress on water supplies from climate change can significantly influence water use patterns as well as the economic value and sustainability of those uses. However, aquifer protection can be an expensive proposition because water uses that currently rely on aquifer pumping may produce considerable economic value that would be lost if protection measures are carried out. Evidence from climate-stressed regions has attracted research addressing the costs and benefits of aquifer protection plans. Despite these efforts, few peer-reviewed papers have examined water use patterns that minimize the economic costs of aquifer protection. This work presents an original approach to address that gap by developing and applying a basin scale hydroeconomic optimization model of North America's Middle Rio Grande Basin to explore impacts of new policies not yet implemented supporting aquifer protection. It also gives model access to readers or stakeholders to experiment with their own scenarios to assess impacts of alternative aquifer protection plans. The model accounts for surface and groundwater storage, irrigation, urban, environmental, and recreational demands, surface water inflows under various climate scenarios, groundwater pumping and recharge, substitute water prices, crop water use, evaporation, as well as institutional constraints governing water use. The objective is implemented by finding the optimized discounted net present value of economic benefits summed over uses, sectors, and regions from use of surface water and connected aquifers. Results are shown for each of six water supply scenarios, two substitute water prices, and two system operation rules. To address impacts of aquifer protection targets, groundwater sustainability targets are specified and enforced as constraints for each of the region's two major aquifers. We assess total and marginal cost of achieving two targeted aquifer protection levels by identifying optimized surface use and groundwater pumping for each of 24 scenarios. Results show that climate change, in the form of reduced and highly variable inflows, considerably drives up the cost of protecting aquifer sustainability, amplified by the conjunctive nature of the system. Future work points to a need to assess economic performance of various water conservation measures as well as reducing costs of substitute water through measures such as technical advance in desalination, recycling and reuse, substitution of other resources for water, better characterization of existing aquifers, and development of new groundwater supplies. [ABSTRACT FROM AUTHOR]

• A bootstrap-based algorithm is proposed for disclosing uncertainty of Budyko model. • Propagation of the parameter uncertainty to water availability assessment is derived. • There exists remarkable uncertainty in parameter w of Budyko model. • Impact of w uncertainty on Budyko output varies nonlinearly with climate. Parametric Budyko method has been widely used to assess water availability under changing environment. Its single parameter (w) controlling the shape of the Budyko curve, is of critical importance to water availability assessment (WAA). Here, the water availability is defined as the ratio of evapotranspiration to precipitation in the context of Budyko hypothesis. As an unobserved variable, w is generally estimated through incorporating the long-term water-balance model and Budyko hypothesis, i.e. estimating w as a function of precipitation, runoff, and potential evapotranspiration over a long-term time series of these variables. Inevitably, the estimated value of w is subject to uncertainty resulting from hydro-meteorological records with limited size. Further, this uncertainty affects WAA in context of Budyko hypothesis. The uncertainty and its propagation issue is often overlooked in past research. In this paper, we develop a bootstrap-based algorithm to reveal aforementioned uncertainty. Moreover, a partial derivative-based sensitivity analysis is performed for understanding the propagation of w uncertainty on WAA. Three catchments located in the Yellow River Basin, China, are used as case study sites. Results indicate that there exists remarkable uncertainty in parameter w , which varies with the data length. The impact of such uncertainty on WAA increases quickly under the humid condition, thereafter reaches a peak under the mild condition, and decreases slowly under the arid condition. The reason behind this phenomenon is the largest sensitivity of WAA to parameter w under the mild climate condition. Moreover, a general analytical expression for determining the critical value of climate condition which maximizes the impact of parameter w uncertainty on WAA is derived in this paper. Results of this study can enhance the reliability and robustness of Budyko-based models. [ABSTRACT FROM AUTHOR]

• Evaluate the comprehensive carrying capacity of water resources of 31 provinces in China. • Divides the comprehensive carrying capacity of water resources into three aspects. • Combine the classification method of system theory with pressure-state-response model. • Discuss the direct and indirect effects of different variables on the carrying capacity of water resources. There has been a series of water resource problems, such as imbalances of the water supply and demand and serious water pollution, in China. It is important for the Chinese government to formulate a sustainable development strategy for water resources and to effectively guarantee the national water resource security. This paper divides the comprehensive carrying capacity of water resources into the following three aspects: the water resource balance capacity, the water resource pressure and driving force, and the water resource development and utilization capacity. Taking 31 provinces, municipalities and autonomous regions of China as research objects, the catastrophe progression method is adopted to evaluate the comprehensive carrying capacity of interprovincial water resources in China from 2010 to 2016. The direct effects and spatial spillover effects of different factors on water resources are discussed by constructing a spatial Durbin model (SDM). The results show that there are obvious spatial differences in the water resource balance ability, pressure and driving force, and development and utilization ability. The spatial distribution follows a decreasing trend from east to west and from south to north. The water supply, water demand, investment in environmental management, economic development and balance of the ecosystem have obvious direct effects on the carrying capacity of water resources. The spatial spillover effect of the water supply and demand, energy consumption, the proportion of protected areas and the newly increased areas of soil erosion control are significant and will have a strong impact on the water resource carrying capacity of surrounding areas. The conclusion of this paper can provide a reference for the government in making water resource management policies. [ABSTRACT FROM AUTHOR]

• Stackelberg-Nash and Cournot-Nash games are applied to gain optimum strategies. • Uneven precipitation and stream inflow are characterized as fuzzy random variables. • An interactive-dynamic-programming-based genetic algorithm (IDP-GA) is designed. • An emergency response mechanism based on different climatic scenarios is developed. Increasing water demand aggravates water scarcity in water-scarce regions (e.g., India, Pakistan, and China), where uneven precipitation increases the risk of maintaining sustainable eco-economic development. Ignoring precipitation's effects leads to misguided policy recommendations that either deplete or underuse basin water resources. Therefore, this paper highlights the concept of uneven precipitation and uncertain stream inflow based on scenario analysis. Within a river basin, the regional authority and subarea managers both have water allocation and reallocation privileges. Motivated by sustainable development of water resource management systems, we argue that regional authorities should focus on alleviating the contradiction between supply and demand, and then seeking an optimum allocation strategy to ensure societal and economic benefits for its subareas, from which the subareas can provide reasonable reaction strategies. Authorities dominate water transactions between subareas involved in water rights distribution. This paper proposes a multi-objective multi-stage Stackelberg-Nash-Cournot (m2SNC) game model to cope with conflicts between regional authorities and subarea managers concerning multiple activities. An interactive-dynamic-programming-based genetic algorithm (IDP-GA) is designed to simulate the policies needed for the optimum allocation of water resources under various climate scenarios. The significant stress caused by unsustainable water resource allocation has been a perplexing problem for Sancha Lake, an important area in the southeast basin of the Dujiangyan Irrigation System in China. To identify the tradeoffs among stakeholders, we apply the proposed model to this basin. To gain further insight into climate change and water usage sustainability, we simulate the varying spatial and seasonal distribution of stream inflow based on historical climate data and propose different water management strategies for normal, slight drought, moderate drought, and serious drought conditions. This creates an emergency response cooperative mechanism based on different climate scenarios for developing countries that need to cope with water deficiency and uncertain situations. [ABSTRACT FROM AUTHOR]

Graphical abstract Highlights • The paper presents a comprehensive review and application of ELM model. • The review comprises all the implementations of the hydrological processes. • The reviewed studies evaluated and assessed with wide-ranging of aspects. • The enhanced ELM model performed remarkably on the river flow forecasting. • Several milestone propositions are anticipated for future research directions. Abstract Despite the massive diversity in the modeling requirements for practical hydrological applications, there remains a need to develop more reliable and intelligent expert systems used for real-time prediction purposes. The challenge in meeting the standards of an expert system is primarily due to the influence and behavior of hydrological processes that is driven by natural fluctuations over the physical scale, and the resulting variance in the underlying model input datasets. River flow forecasting is an imperative task for water resources operation and management, water demand assessments, irrigation and agriculture, early flood warning and hydropower generations. This paper aims to investigate the viability of the enhanced version of extreme learning machine (EELM) model in river flow forecasting applied in a tropical environment. Herein, we apply the complete orthogonal decomposition (COD) learning tool to tune the output-hidden layer of the ELM model's internal neuronal system, instead of the conventional multi-resolution tool (e.g., singular value decomposition). To demonstrate the application of EELM model, the Kelantan River, located in the Malaysian peninsular, selected as a case study. For a comparison of the EELM model, and further model evaluation, two distinct data-intelligent models are developed (i.e., the classical ELM and the support vector regression, SVR model). An exhaustive list of diagnostic indicators are used to evaluate the EELM model in respect to the benchmark algorithms, namely, SVR and ELM. The model performance indicators exhibit superior results for the EELM model relative to ELM and SVR models. In addition, the EELM model is presented as a more accurate, alternative predictive tool for modelling the tropical river flow patterns and its underlying characteristic perturbations in the physical space. Several statistical metrics defined as the coefficient of determination (r), Nash-Sutcliffe efficiency (E ns), Willmott's Index (WI), root-mean-square error (RMSE) and mean absolute error (MAE) are computed to assess the model's effectiveness. In quantitative terms, superiority of EELM over ELM and SVR models was exhibited by E ns = 0.7995, 0.7434 and 0.665, r = 0.894, 0.869 and 0.818 and WI = 0.9380, 0.9180 and 0.8921, respectively. Whereas, EELM model attained lower (RMSE and MAE) values by approximately (11.61–22.53%) and (8.26–8.72%) relative to ELM and SVR models, respectively. The obtained results reveal that the EELM model is a robust expert model and can be embraced practically in real-life water resources management and river sustainability decisions. As a complementary component of this paper, we also review state-of-art research works where scholars have embraced extensive implementation of the ELM model in water resource engineering problems. A comprehensive evaluation is carried out to recognize the current limitations, and also to propose potential opportunities of applying improved variants of the ELM model presented as a future research direction. [ABSTRACT FROM AUTHOR]

Highlights • More than 150 papers dealing with applications of GP in water resource engineering were reviewed. • Fundamentals of GP variants were provided and compared with each other. • Selection ways of GP parameters were discussed. • Future scope of studies were demonstrated. Abstract The state-of-the-art genetic programming (GP) method is an evolutionary algorithm for automatic generation of computer programs. In recent decades, GP has been frequently applied on various kind of engineering problems and undergone speedy advancements. A number of studies have demonstrated the advantage of GP to solve many practical problems associated with water resources engineering (WRE). GP has a unique feature of introducing explicit models for nonlinear processes in the WRE, which can provide new insight into the understanding of the process. Considering continuous growth of GP and its importance to both water industry and academia, this paper presents a comprehensive review on the recent progress and applications of GP in the WRE fields. Our review commences with brief explanations on the fundamentals of classic GP and its advanced variants (including multigene GP, linear GP, gene expression programming, and grammar-based GP), which have been proven to be useful and frequently used in the WRE. The representative papers having wide range of applications are clustered in three domains of hydrological, hydraulic, and hydroclimatological studies, and outlined or discussed at each domain. Finally, this paper was concluded with discussions of the optimum selection of GP parameters and likely future research directions in the WRE are suggested. [ABSTRACT FROM AUTHOR]

Research on water cooperation in the Eastern Nile Basin has focused on expanding policy and diplomacy tools for a better allocation of transboundary water resources confined to the river. Regional cooperation on water and related sectors such as energy and land expands the bargaining and areas for mutual gain, and thus enhances cooperation perspectives. This paper looks at the contribution and the potential benefits of a regional cooperation approach to addressing the underlying challenges of water diplomacy, such as complexity and distrust. It also promotes the understanding of river basins as a “resource basin” of integrated and linked resource-use issues, not always related to the river flow. The paper provides an analysis of priority issues for water–energy–food nexus in regional cooperation in the Eastern Nile Basin. This basin represents an illustrative case for regional cooperation and increased integration due to multiple comparative advantages inherent in the uneven endowments of water, energy and arable land resources, and to varying levels of economic and technological advancement among the three riparian countries: Egypt, Sudan and Ethiopia. The paper also analyzes institutional arrangements on a regional scale, and elaborates on the inherent trade-offs associated with them. [ABSTRACT FROM AUTHOR]

Water resources in Mediterranean regions are under increasing pressure due to climate change, economic development, and population growth. Many Mediterranean rivers have their headwaters in mountainous regions where hydrological processes are driven by snowpack dynamics and the specific variability of the Mediterranean climate. A good knowledge of the snow processes in the Mediterranean mountains is therefore a key element of water management strategies in such regions. The objective of this paper is to review the literature on snow hydrology in Mediterranean mountains to identify the existing knowledge, key research questions, and promising technologies. We collected 620 peer-reviewed papers, published between 1913 and 2016, that deal with the Mediterranean-like mountain regions in the western United States, the central Chilean Andes, and the Mediterranean basin. A large amount of studies in the western United States form a strong scientific basis for other Mediterranean mountain regions. We found that: (1) the persistence of snow cover is highly variable in space and time but mainly controlled by elevation and precipitation; (2) the snowmelt is driven by radiative fluxes, but the contribution of heat fluxes is stronger at the end of the snow season and during heat waves and rain-on-snow events; (3) the snow densification rates are higher in these regions when compared to other climate regions; and (4) the snow sublimation is an important component of snow ablation, especially in high-elevation regions. Among the pressing issues is the lack of continuous ground observation in high-elevation regions. However, a few years of snow depth (HS) and snow water equivalent (SWE) data can provide realistic information on snowpack variability. A better spatial characterization of snow cover can be achieved by combining ground observations with remotely sensed snow data. SWE reconstruction using satellite snow cover area and a melt model provides reasonable information that is suitable for hydrological applications. Further advances in our understanding of the snow processes in Mediterranean snow-dominated basins will be achieved by finer and more accurate representation of the climate forcing. While the theory on the snowpack energy and mass balance is now well established, the connections between the snow cover and the water resources involve complex interactions with the sub-surface processes, which demand future investigation. [ABSTRACT FROM AUTHOR]

The study of reservoir deterministic optimal operation can improve the utilization rate of water resource and help the hydropower stations develop more reasonable power generation schedules. However, imprecise forecasting inflow may lead to output error and hinder implementation of power generation schedules. In this paper, output error generated by the uncertainty of the forecasting inflow was regarded as a variable to develop a short-term reservoir optimal operation model for reducing operation risk. To accomplish this, the concept of Value at Risk (VaR) was first applied to present the maximum possible loss of power generation schedules, and then an extreme value theory-genetic algorithm (EVT-GA) was proposed to solve the model. The cascade reservoirs of Yalong River Basin in China were selected as a case study to verify the model, according to the results, different assurance rates of schedules can be derived by the model which can present more flexible options for decision makers, and the highest assurance rate can reach 99%, which is much higher than that without considering output error, 48%. In addition, the model can greatly improve the power generation compared with the original reservoir operation scheme under the same confidence level and risk attitude. Therefore, the model proposed in this paper can significantly improve the effectiveness of power generation schedules and provide a more scientific reference for decision makers. [ABSTRACT FROM AUTHOR]

Flood frequency analysis (FFA) is a necessary tool for water resources management and water infrastructure design. Owing to the existence of variability in sample representation, distribution selection, and distribution parameter estimation, flood quantile estimation is subjected to various levels of uncertainty, which is not negligible and avoidable. Hence, alternative methods to the conventional approach of FFA are desired for quantifying the uncertainty such as in the form of prediction interval. The primary focus of the paper was to develop a novel approach to quantify and optimize the prediction interval resulted from the non-stationarity of data set, which is reflected in the distribution parameters estimated, in FFA. This paper proposed the combination of the multi-objective optimization approach and the ensemble simulation technique to determine the optimal perturbations of distribution parameters for constructing the prediction interval of flood quantiles in FFA. To demonstrate the proposed approach, annual maximum daily flow data collected from two gauge stations on the Bow River, Alberta, Canada, were used. The results suggest that the proposed method can successfully capture the uncertainty in quantile estimates qualitatively using the prediction interval, as the number of observations falling within the constructed prediction interval is approximately maximized while the prediction interval is minimized. [ABSTRACT FROM AUTHOR]

• Proposes an MCDM framework for robust water resources management under uncertainty. • Develops an aggregation method for conflict resolution and uncertainty estimation. • Proposes a novel SMAA-GRA model to consider uncertainties in MCDM problems. • Conducts significance analysis to test effects of uncertainty on decision-making. • Enables a risk-informed water resource decision to be made with higher reliability. Water planner and managers always work in a changing and uncertain environment, since the majority of water resource-related data is imprecise or uncertain to some extent. MCDM (multi-criteria decision making) is a well-suited decision support tool for water resources management, however multiple uncertainties existed in criteria weights (CWs) and performance values (PVs) hinder the application of MCDM methods in real-world problems. This paper proposes an application-oriented stochastic MCDM framework for robust water resources management under uncertainty. A weight aggregation approach based on the game theory coupled with feasible weight space (FWS) is developed for conflict resolution and uncertainty estimation. We propose a novel SMAA-GRA model by combining the stochastic multi-criteria acceptability analysis (SMAA) theory and grey relational analysis (GRA). The risk of errors in decision making and rank uncertainty degree are utilized to assess decision-making uncertainty. We perform significance analysis to examine the impacts of input parameter uncertainties on the final assessment of alternatives. The proposed framework is demonstrated via two water resources management case studies. Numerical experiments are conducted to investigate the robustness and computational efficiency of the framework as well as the effects of uncertainty level and probability distribution type on decision-making results. The results indicate that the framework enables water managers to examine the robustness of final decisions and allows a risk-informed water resource decision to be made with higher reliabilities. [ABSTRACT FROM AUTHOR]

• Groundwater hydrochemistry evolution interpretation from the viewpoint of water flow system. • Anthropogenic activities and natural processes are distinguished in different zones. • Practical implications for regional water resources exploitation strategy. Groundwater resources are increasingly exploited for industrial and agricultural purposes in many arid/semi-arid regions globally. In order to gain a harmonious development among groundwater resources and economic benefits, urgent investigation of the impacts of anthropogenic activities on the groundwater chemistry is required. Understanding hydrochemical mechanism of groundwater in Yinchuan Plain, arid and semi-arid area in Northwestern China, plays a significant role in water resources planning in this ecologically sensitive area. In this paper, a total of 460 phreatic water samples were collected, including 58 water samples containing isotopic data (D and 2H). Results demonstrate that water chemistry type and isotopic data of phreatic water presents a zonation characteristic from west to east and from south to north. In the eastern part of the Helan Mountain (A 1 zone), the type of hydrochemistry is mainly HCO 3 ·SO 4 -Ca·Mg, SO 4 ·HCO 3 -Ca·Mg, which is mainly recharged by the bedrock fissure water of Helan Mountain. However, in the southern Qingtongxia (A 2 zone) are HCO 3 -Na HCO 3 -Ca·Mg, which receives irrigation and spring water recharge. Phreatic aquifer in Multi-layered structure zone (A 3 zone) reflect multiple recharge sources to the aquifer, the water chemistry demonstrates diverse variations. The anion was dominated by the HCO 3 , while the cation is dominated by sodium. The predominant controlling factors of phreatic water hydrochemistry are divided into anthropogenic activities and natural conditions. Natural conditions mainly include the existence form and lithology of the aquifer, sources of groundwater recharge, soil salinity, and evaporation, mineral dissolution, ion exchange and adsorption and mixing effect. Meanwhile, among natural factors, minerals dissolution in groundwater is the main process dominating the hydrochemical evolution of the groundwater. The other is anthropogenic activities which can be classified as groundwater pollution, excessive exploitation or excretion of groundwater, and excessive groundwater recharge. [ABSTRACT FROM AUTHOR]

• Instead of holistic trend model partial trend models are developed. • Practically applicable polygon trend analysis statistical values are derived. • The application of the new methodology is for three regions from World. • Transitional trend components are identified. Trend analysis is continuously in the research and application agenda due to climate change effect searches on various engineering, social, economic, agriculture, environmental and water resources design, management, operation and management studies. Classical trend analysis is useable for holistic trend identification and then statistical quantification as for its intercept and slope. The main drawbacks in these classical approaches are the set of fundamental assumptions such as the serial independence of the given time series, pre-whitening, normality of the data and non-existence of serial comparison among different sections of the same record. This paper explains a non-parametric approach to avoid almost all these difficulties by simple methodology, which is referred as the Innovative Polygonal Trend Analysis (IPTA). Such an approach helps not only to identify the trend in a given series, but also trend transitions between successive sections of the two equal segments from the original hydro-meteorological time series leading to trend polygon, which provides a productive basis for finer interpretation with linguistic and numerical interpretations and inferences from a given time series. The application of the IPTA is presented for rainfall records from New Jersey, USA, Danube River and Göksu River discharge records from Romania and Turkey. [ABSTRACT FROM AUTHOR]

• Reservoir operations are adapted to the nexus across water supply, power generation, and environment systems. • Dynamic weights for multiple objectives are considered for reservoir operations within the framework of the WPE nexus. • Tradeoffs across WPE systems and reservoir operational decisions shift from stage to stage. • The control parameter that dominates the shift in the stable levels of a given variable can be identified. Reservoir operation plays an important role in the coevolution of the nexus across water supply, power generation, and environment (WPE) systems. This paper aims to contribute to the state-of-the-art in how multi-objective reservoir operation can be modeled within the framework of the WPE nexus by incorporating the interactions among reservoirs operation, stakeholders' decision preferences, and WPE systems. Specifically, five ordinary differential equations are used to describe the dynamics of WPE states. These dynamics are driven by a multi-objective optimization model that optimizes reservoir operation decisions. Two novel response linkages that describe how the reservoir operational decisions influence socio-economic variables and how the environmental awareness influences stakeholders' preference for reservoir operation are proposed in this study. The Middle Route of China's South-to-North Water Transfer Project is selected as the case study. The results show that: (1) response linkages are effective in describing the interactions between reservoir operational decisions and WPE systems, (2) tradeoffs across WPE systems and reservoir operational decisions shift from stage to stage because of the changing socio-economic conditions, and (3) the control parameter that dominates the shift in stable levels of a given variable can be identified. These results highlight the importance of re-evaluating involved tradeoff effects and updating of operational policies periodically, and how our modeling approach can be used to detect and guide such adaptions. [ABSTRACT FROM AUTHOR]

• Literature-based and alternative equations for quick-flow runoff are tested. • A new empirical equation was found to outperform previously used equations. • Monthly maps of quick-flow runoff are produced and evaluated for the CONUS. • Patterns of increased quick-flow runoff in winter-spring months are observed. The quantitative estimation of the quick-flow runoff component of streamflow is required for many hydrologic applications. Estimation at the monthly timescale and national spatial scale would be particularly useful for national water availability modeling. This paper reviews a sample of commonly used equations for quick-flow runoff, including several currently in use in continental-scale models. The review shows the wide range of equation forms or heuristics currently in use to predict quick-flow runoff, the limited spatial scale over which these equations are often developed or calibrated, and the scarcity of well-tested equations available for quick-flow runoff at the monthly timescale. Data were gathered from a set of 1301 gaged watersheds across the United States to test a range of equations from the literature, along with several alternative equations, to assess and compare their performance in predicting quick-flow runoff at the monthly timescale. The highest-performing equation was selected for application to monthly maps of explanatory variables to produce monthly quick-flow runoff water budget contribution maps. This equation is a regression against precipitation, soil saturated hydraulic conductivity, surficial geology type, and slope data. Its application indicates that average quick-flow runoff across the conterminous United States in the winter exceeds that in the summer by up to a factor of three. The monthly maps were explored and evaluated for the timespan of 2000–2015. The comparison of equation forms and produced monthly maps will be useful for a variety of hydrologic modeling and monitoring applications. [ABSTRACT FROM AUTHOR]

• We used 1 × 1 km spatial and 1-hour temporal resolution hydro-meteorological data. • We used a parsimonious distance distribution dynamics (DDD) hydrological model. • We considered three different methods of regionalizations of the model parameters. • The three methods of regionalization perform satisfactorily. • This work contributes to water resource and flood management in small ungauged catchments. Streamflow data is important for studies of water resources and flood management, but an inherent problem is that many catchments of interest are ungauged. The lack of data is particularly the case for small catchments, where flow data with high temporal resolution is needed. This paper presents an analysis of regionalizing parameters of the Distance Distribution Dynamics (DDD) rainfall-runoff model for predicting hourly flows at small-ungauged rural catchments. The performance of the model with hourly time resolution has been evaluated (calibrated and validated) for 41 small gauged catchments in Norway (areas from 1 km2–50 km2). The model parameters needing regionalization have been regionalized using three different methods: multiple regression, physical similarity (single-donor and pooling-group based methods), and a combination of the two methods. Seven independent catchments, which are not used in the evaluation, are used for validation of the regionalization methods. All the three methods (the multiple regression, pooling-group, and combined methods) perform satisfactorily (0.5 ≤ KGE < 0.75). The combined method (which combines multiple regression and pooling-group) performed slightly better than the other methods. Some model parameters, namely those describing recession characteristics, estimated by the regionalization methods, appear to be a better choice than those estimated locally from short period of hydro-meteorological data for some test catchments. The single-donor method did not perform satisfactorily. The satisfactory performance of the combined method shows that regionalization of DDD model parameters is possible by combining multiple regression and physical similarity methods. [ABSTRACT FROM AUTHOR]

Highlights • Discharge measurements are exposed to different levels of uncertainties. • Impacts of land use change could be masked by errors in discharge measurements. • 50−75% of errors in discharge measurement can mask impact of land use change. • Data quality controls will enhance precision of land use change impact. Abstract Analysis of hydrological impacts of land use change raises questions about whether, and how much, such impacts are misrepresented because of errors in river flow observations. In this paper, land use change impacts (represented by changes in watershed storage) and different ranges of discharge measurement error are compared to assess how errors in discharge measurement can potentially mask a land use change impact. Using a watershed from the Ethiopian highlands to exemplify this, we simulated five different levels of land use change impacts with five levels of watershed storage reductions (from 10% to 50% change) and the associated time series of runoff. Different levels of observation error were then introduced into these artificial time series. Comparison was made between every pair, i.e. a time series derived from a certain level of land use change (storage reduction) versus a time series corresponding to a given level of observation error, using a step-change t -test. Significant step-changes between pairs define the detectability of land use change impact. The analysis was made for the entire 30-year time series as well as for the most extreme annual weather conditions. The results showed that for the average year and wettest year, 75% or more error in observed discharge masks the maximum simulated land use change impact on hydrology. In dry years, a 50% error in discharge is enough to mask the same impact. Knowing (and improving) the level of data quality contributes to a better understanding of hydrological uncertainties and improves the precision in assessing land use change impacts. Both of these are essential elements in water resources development planning. [ABSTRACT FROM AUTHOR]

Highlights • A differential game is proposed to model water resources with several consumers. • The general strategy sets are discretized to solve the water allocation problem. • Non-cooperative (Nash) equilibrium is given and its Pareto optimality is studied. • Pareto optimal solution is also determined which can be suggested for cooperation. • Illustrative realistic examples show the practical applicability of the results. Abstract It is indispensable for the sustainable water supply of the human society to distribute the available water resources with maximal efficiency among the different consumers while assuring the needed time for recharge. The theoretically established, effective tool for the solution of this allocation problem is mathematical modelling with special regard to the methods of game theory. In the present paper, a differential game is proposed to describe the consumption and the (natural) recharge of a limited water resource. Then the general course of the solution of the allocation problem is given aiming at maximizing the players' yields through the discretization of the strategy sets. In case of two consumers, the solution is given in more details along with several practical examples. The Pareto optimality of the non-cooperative solution (Nash equilibrium) is checked and the Pareto optimal strategy pair, which provides maximal sum payoff for the players, is suggested for cooperation (after a possible re-distribution of the payoffs). This cooperative solution generally provides higher payoff for each player than if they consumed according to conflicting non-cooperative behaviour. In addition, examples with three consumers are provided similarly. [ABSTRACT FROM AUTHOR]

Highlights • A hybrid optimization method is developed for solving two level optimization problem. • Spatio-temporal crop pattern and virtual water content is taken into consideration. • Share of green water in total crop water consumption is maximized. • A dynamical water pricing function is developed for different sectors. • Higher water allocations don't guarantee more profit for the leader. Abstract In this paper, a mathematical model for conflict resolution among a diverse set of agricultural water users in Golestan province, Iran, is developed. Given the bi-level nature of the distribution of power in the current problem, a combination of Leader–Follower game and Nash–Harsanyi bargaining solution method is employed to find optimal water and crop area allocations. The Golestan Regional Water Authority is the leader in this setting, controlling the total water allocations; and the agricultural sectors are the followers, competing over the allocated water. Two objectives for the leader are (i) maximizing profits, and (ii) maximizing share of green water in total agricultural production through selecting more efficient crop patterns. The followers' objective is merely maximizing obtained benefits for the selected crop patterns. Virtual water concept is also factored into the related objective functions, and the water allocation problem is solved considering spatio-temporal crop pattern along with a dynamic water pricing system. This involves using a hybrid optimization structure as a new approach to solving two level optimization problems. The results show that the leader's income is independent of total water allocation and is only affected by crop pattern and crop area, two factors which drive water price too. The followers' benefit also depends on crop pattern and crop area, as they influence the crop yield, cost and water price. Finally, green water plays a key role in selecting the optimal crop pattern and crop area. [ABSTRACT FROM AUTHOR]

Highlights • Artificial Neural Network approaches were used to predict water trading prices (WTP). • Prices forecast showed greater agreement with actual water prices. • Water allocations, cereal and meat prices were significant determinants of WTP. Abstract This paper proposes an integrated (hybrid) Artificial Neural Network-Bayesian (ANN-B) modelling approach to improve the accuracy of predicting seasonal water allocation prices in Australia's Murry Irrigation Area, which is part of one of the world's largest interconnected water markets. Three models (basic, intermediate and full), accommodating different levels of data availability, were considered. Data were analyzed using both ANN and hybrid ANN-B approaches. Using the ANN-B modelling approach, which can simulate complex and non-linear processes, water allocation prices were predicted with a high degree of accuracy (R BASIC = 0.93, R INTER. = 0.96 and R FULL = 0.99); this was a higher level of accuracy than realized using ANN. This approach can potentially be integrated with online data systems to predict water allocation prices, enable better water allocation trade decisions, and improve the productivity and profitability of irrigated agriculture. [ABSTRACT FROM AUTHOR]

Highlights • Water storage trends and extreme conditions was analyzed using satellite data. • Significant connection between water storage trends and climate influence. • The climatic variability and extreme condition linked to regional climate pattern due to oceanic-atmospheric interactions. • Implication of the result in water resources planning and management is clear. Abstract Climate variability along with increase in the demand for water resources highlights the need for better understanding of the link between regional climate and terrestrial water storage. This paper examined the variation of terrestrial water storage in relation to climatic influences over the Upper Blue Nile (UBN) River Basin from GRACE Terrestrial Water Storage Anomaly (TWSA), satellite altimetry, rainfall, and Multivariate El Niño-Southern Oscillation Index (MEI) anomaly data. Although there is no statistically significant (α = 0.05) long-term trend in terrestrial water variation and rainfall in the basin (lake storage, TWSA, and rainfall, p-value = 0.45, 0.48, and 0.55, respectively), in the last decade, two visible droughts occurred between 2002 and 2004, and 2009 and 2010, which resulted in water deficit in the basin, where below average rainfall, TWSA, and lake height (storage) were observed during these periods. Extreme rainfall analysis from Standardized Precipitation Index (SPI) and strong connection between wet season rainfall, lake height, and TWSA, respectively, indicate interannual terrestrial water storage dynamics in the UBN Basin is strongly influenced by climate. Further, the El Niño-Southern Oscillation (ENSO) influences rainfall in the UBN Basin, specifically the peak rainfall season (June-September), which shows negative correlation (r = −0.62) with MEI anomaly values, which indicates that the El Niño case (positive MEI values) is linked to the dry conditions in the basin. The findings of this study, combined with studies such as socioeconomic impact of drought, will facilitate better planning and management of water resources in water stressed regions. Furthermore, this study demonstrates the application of a combination of satellite and other hydrologic data in understanding the hydro-climatic condition of a remote basin. [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]

• 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]

Summary Australia is the driest continent and there is increasing competition for scarce fresh water resources between agriculture and the environment. In the Murray–Darling Basin (MDB) that conflict has largely been resolved by reallocating water from agriculture to the environment. As part of the water reform process both governments and industry are focussed on improving on-property water use efficiency (WUE), particularly of irrigated agriculture. This paper examines the potential for WUE to enable MDB irrigators to adapt to cuts in their irrigation entitlements. The paper draws on data from a case study in the Namoi Valley of New South Wales. The distinctive contribution of this paper is that we draw on survey data of the existing and intended adoption of a limited suite of currently available WUE practices. That is, we have not simply assumed that all irrigators, or a specific proportion of irrigators, will adopt each WUE option. Given survey respondents’ intended level of adoption, we calculated the potential water savings for each property and then the catchment, without extrapolating beyond the survey respondents. Those calculations suggest that water savings of up to 100.9 GL could be achieved across the Namoi catchment if those interested in doing so were to convert to existing improved WUE practices. Those savings represented 82% of the reduction in irrigator entitlements under the draft MDB Plan, and exceed the 10 GL/yr reductions required under the revised MDB Plan. These results suggest that those adopting existing WUE practices will have additional water for irrigation. To the extent that this is the case, there seems to be less justification for government support for irrigators during the adjustment process. [ABSTRACT FROM AUTHOR]

Summary Water represents an essential element for the life of all who inhabit our planet. But the random nature of this resource, which is manifested by the alternation of wet periods and dry periods, makes it even more precious. Whatever the approach (water planning, water management, drought, economy), in order to maximise the profit produced by the allocation of water it is necessary an understanding of the relationships between physical variables as precipitation, temperatures, streamflows, reservoir volumes, piezometric levels, water demands and infrastructures management. This paper attends to provide a review of fundamental water scarcity and drought indexes that enables to assess the status of a water exploitation system. With the aim of a better water management and governance under water scarcity conditions., this paper also presents a classification of indexes to help decision makers and stakeholders to select the most appropriate indexes, taking as the starting point the objectives of the analysis and the river basin features. [ABSTRACT FROM AUTHOR]

Summary Unraveling the interaction between water users in a river basin is essential for sound water resources management, particularly in a context of increasing water scarcity and the need to save water. While most attention from managers and decision makers goes to allocation and withdrawals of surface water resources, reuse of non-consumed water gets only marginal attention despite the potentially significant volumes. As a consequence, claims of water saving are often grossly exaggerated. It is the purpose of this paper to explore the processes associated with water reuse in a river basin among users of varying nature and review existing methods for directly or indirectly describing non-consumed water, recoverable flow and/or water reuse. First a conceptual representation of processes surrounding water withdrawals and associated definitions is discussed, followed by a section on connectivity between individual withdrawals and the complex dynamics arising from dependencies and tradeoffs within a river basin. The current state-of-the-art in categorizing basin hydrological flows is summarized and its applicability to a water system where reuse occurs is explored. The core of the paper focuses on a selection and demonstration of existing indicators developed for assessing water reuse and its impacts. It is concluded that although several methods for analyses of water reuse and recoverable flows have been developed, a number of essential aspects of water reuse are left out of existing indicators. Moreover, a proven methodology for obtaining crucial quantitative information on recoverable flows is currently lacking. Future studies should aim at spatiotemporal tracking of the recoverable portion of water withdrawals and showing the dependency of multiple water users on such flows to water policy makers. [ABSTRACT FROM AUTHOR]

Summary The effects of climate change on water resources have been studied extensively throughout the world through the use of hydrologic models coupled with General Circulation Model (GCM) output or climate sensitivity scenarios. This paper examines the effects of hydrologic model parameterization uncertainty or equifinality, where multiple unique hydrologic model parameter sets can result in adequate calibration metrics, on hydrologic projections from downscaled GCMs for three snowmelt-dependent watersheds (upper reaches of the Clearwater, Gunnison, and Sacramento River watersheds) in the western United States. The hydrologic model used in this study is the Soil and Water Assessment Tool (SWAT) and is calibrated for discharge at the watershed outlet in each watershed. Despite achieving similar calibration metrics, a majority of hydrologic projections of average annual streamflow during the 2080s were statistically different, with differences in magnitude and direction (increase or decrease) compared to historical annual streamflows. At the average monthly time-scale, a majority of the hydrologic projections varied in peak streamflow timing, peak streamflow magnitude, summer streamflows, as well as overall increases or decreases compared to the historical monthly streamflows. Snowmelt projections from the SWAT model also widely varied, both in depth and snowmelt peak timing, for all watersheds. Since a large portion of the runoff-producing regions in the western United States is snowmelt-dependent, this has large implications for the prediction of the amount and timing of streamflow in the coming century. This paper shows that hydrologic model parameterizations that give similar adequate calibration metrics can lead to statistically significant differences in hydrologic projections under climate change. Therefore, researchers and water resource managers should account for this uncertainty by assembling ensemble projections from both multiple parameter sets and GCMs. [ABSTRACT FROM AUTHOR]

Summary Large scale urbanization has resulted in greater withdrawals of shared waters and this withdrawal has been largely dependent on the hegemony of the riparian’s. The last few decades has seen the upward surge of many countries in terms of development as well as hegemony. Existing structures of established water sharing framework typically evaluate only parameters related to historic water use such as historic water demand and supply, contribution to flow, and hydrology. Water conflicts and cooperation is affected by various issues related with development and hegemony. Characterization and quantification of development and hegemony parameters is a very complex process. This paper establishes a novel approach to predict river basins at risk; the approach addresses the issue of water conflict and cooperation within a methodologically more rigorous predictive framework. Fuzzy synthetic evaluation technique is used in this paper to undertake the risk assessment of international transboundary rivers. In this paper the fuzzy domain of risk consists of two fuzzy sets – hegemony and development, indices of which are developed with the help of fuzzy synthetic evaluation techniques. Then the compositional rule-base is framed to ascertain the fuzzy risk. This fuzzy risk can be further used to prioritize all the international river basins which can help in the identification of potentially high risk basins. Risk identification of international river basins is not only scientifically valuable, but also practically highly useful. Identifying those basins that are likely to be particularly prone to conflict or cooperation is of high interest to policy makers. [ABSTRACT FROM AUTHOR]

Highlights • We proposed a novel panoramic fuzzy risk analysis model based credibility theory. • Different near-boundary operation risks are analyzed for the hydropower station. • High-risk areas of Jinxi hydropower station in short-term operation are identified. • A strong technical support for the decision-making of dispatchers is provided. • Water abandoning or output shortage of hydropower station can be effectively avoided. Abstract Short-term power generation operation is an important content in the hydropower station management. Because of the limitation of forecast accuracy, there is error between the forecasted runoff process and actual runoff process. When the hydropower station operation is near the water level boundaries, such as normal water level or dead water level, this error is likely to bring the risk of water abandoning or output shortage. In order to precisely evaluate the near-boundary operation risk of hydropower station caused by runoff forecasting error, a novel panoramic fuzzy risk analysis model was proposed in this paper by taking the forecasting error as a fuzzy variable. The coupling of hydrological forecasting error and the short-term operation risk of hydropower station based on credibility theory was realized, and the solving process of the model based on fuzzy simulation technology was provided in detail. Different near-boundary operation risks of hydropower station can be analyzed and evaluated by the simulation with different water level combinations in this model. The high-risk area and suggested operation area of Jinxi hydropower station in different near-boundary operation situations were clearly identified in the case study. The intuitive results of this model can provide a strong technical support for the decision-making of the dispatchers when making the power generation plans in the actual production, so that to avoid the occurrence of water abandoning or output shortage of hydropower station, and effectively improve the efficiency of water resources utilization. [ABSTRACT FROM AUTHOR]

Highlights • The paper describes annual aquifer storage change in the Rincon Valley aquifer. • Aquifer ΔS is more correlated with surface water use than with groundwater use. • Aquifer storage recovery occurred from 2014–2016 after critically short supply years. • The results lay a foundation for management to maintain the aquifer resiliency. Abstract The Rincon Valley in arid, south-central New Mexico, is especially impacted by reduced surface water supply because the contribution of groundwater is limited by aquifer constraints. Consecutive surface water allotment shortages in the Elephant Butte Irrigation District (EBID) have reduced recharge. The effects are compounded by farmers continuing to extract groundwater to meet crop requirements. Conjunctive use assumes aquifer resilience (i.e., ability to absorb pumping stress), but not necessarily in drought. This study further develops the water table fluctuation method by analyzing data from the EBID's groundwater monitoring program to reveal conjunctive use controls over the spatial and interannual variability of net storage changes from 2009 to 2016 in the Valley and introduces the term groundwater-surface water ratio of application (GSRA), that has potential for characterizing system resilience in conjunctive use settings. Regression modeling shows that variation in the annual EBID surface water allotment correlates strongly with year-end water table elevations, even more strongly than total annual groundwater extractions for irrigation, suggesting that variable surface water allotments are a primary driver of this system. Dewatering of the aquifer as of 2011 significantly altered the system hydrology such that from 2011 to 2016, net change in storage correlates strongly with the annual surface water allotment, corresponding to large river losses for the same period, but resulting in net gains in storage from 2014 to 2016. Rapid storage loss and rebound in this constrained aquifer system allowed quantification of aquifer resilience, enabling the development of a GSRA as a potential planning metric. [ABSTRACT FROM AUTHOR]

Highlights • The predictive drought capacity of a seasonal forecast system was assessed using contingency tables. • An aggregate index for assessing predictive drought capacity was proposed. • The proposed methodology is useful for assessing the skill of climate models to predict droughts. • The dynamical System4 coupled model can be used for forecasting drought events. Abstract Droughts cause significant socio-economic and environmental impacts, so it has become an extremely important element in decision-making within water resource systems. For this reason, the research in this field has increased considerably over the last few decades. In order to be capable of making early decisions and reducing drought impacts, it is necessary to predict the occurrence of such events months or even years in advance. In this sense, various methods have been used to predict the occurrence of droughts. At present, seasonal forecast data can be used to forecast meteorological, hydrological, agricultural and operational droughts. However, the seasonal forecast data of these dynamical ocean-atmosphere coupled models must be analyzed in an exhaustive way, since it is known that these models may not adequately represent the climatic variability at river basin scale. Hence, this paper presents a new methodology for assessing the skill of a climate forecasting system in order to predict the occurrence of droughts by using contingency tables. The indices obtained from the contingency tables are necessary to perform the analysis of the predictive ability of the model in a semi-distributed way. All this taking into account the intensity of droughts using different scenarios based on the threshold below which it is considered to be in drought. Finally, a single value is obtained to determine the predictive ability of the forecasting model for the entire basin. The proposed methodology is applied to the Júcar river basin in Spain. It has been found that the analyzed forecast model shows better results than those obtained using an autoregressive model. Further work is needed to enhance climate forecasting from the perspective of water resources management, however, it should be mentioned that this type of data could be used for drought forecasting, allowing possible mitigation measures. [ABSTRACT FROM AUTHOR]

There is increasing recognition of the need to incorporate the complex and dynamic interactions between society and water in studies of water resource systems. The study of human-water dynamics requires the involvement of researchers from different disciplines, including hydrology and social science. This paper tracks recent trends in socio-hydrological research using quantitative and qualitative methods. Bibliographic metrics and network analysis are used to identify general trends and illustrate the internal connections in this small but growing interdisciplinary field. Results show that hydrologists dominate research in socio-hydrology as presently defined, with far less participation from social scientists. Research questions for socio-hydrology tend to focus on system dynamics and are often not framed in a way that engages the interests and expertise of social scientists. A qualitative analysis addresses key barriers to the interdisciplinary development of socio-hydrology and identifies research needs and directions for better integration of the human dimensions of water science. Attracting social scientists to this field requires a broader conceptualization of socio-hydrology to focus on sustainable development, risk assessment, hazard management, resilience, adaptation, and knowledge mobilization in addition to hydrology’s traditional focus on systems modeling and decision support. We propose three themes for thematic focus: systemic risk and natural hazards; sustainability science; and adaptive governance. This broader framing of socio-hydrology is inherently more interdisciplinary, brings new methods and viewpoints to the field, and ensures that place-based local values are included in studies of water system dynamics. [ABSTRACT FROM AUTHOR]

Rainwater harvesting (RWH) systems are an effective low impact development practice that provides both water supply and runoff reduction benefits. A stochastic modelling approach is proposed in this paper to quantify the water supply reliability and stormwater capture efficiency of RWH systems. The input rainfall series is represented as a marked Poisson process and two typical water use patterns are analytically described. The stochastic mass balance equation is solved analytically, and based on this, explicit expressions relating system performance to system characteristics are derived. The performances of a wide variety of RWH systems located in five representative climatic regions of the United States are examined using the newly derived analytical equations. Close agreements between analytical and continuous simulation results are shown for all the compared cases. In addition, an analytical equation is obtained expressing the required storage size as a function of the desired water supply reliability, average water use rate, as well as rainfall and catchment characteristics. The equations developed herein constitute a convenient and effective tool for sizing RWH systems and evaluating their performances. [ABSTRACT FROM AUTHOR]

Climate change, population growth, and weakly developed water management institutions in many of the world’s dry communities have raised the importance of designing innovative water allocation methods that adapt to water supply fluctuations while respecting cultural sensitivities. For example, Afghanistan faces an ancient history of water supply fluctuations that have contributed to periodic food shortage and famine. Poorly designed and weakly enforced water allocation methods continue to result in agriculture sector underperformance and periodic food shortages when water shortfalls occur. To date, little research has examined alternative water sharing rules on a multi-basin scale to protect food security for a subsistence irrigation society when the community faces water shortage. This paper’s contribution examines the economic performance of three water-sharing mechanisms for three basins in Afghanistan with the goal of protecting food security for crop irrigation under ongoing threats of drought, while meeting growing demands for food in the face of anticipated population growth. We achieved this by formulating an integrated empirical optimization model to identify water-sharing measures that minimize economic losses while protecting food security when water shortages occur. Findings show that implementation of either a water trading policy or a proportional shortage policy that respects cultural sensitivities has the potential to raise economic welfare in each basin. Such a policy can reduce food insecurity risks for all trading provinces within each basin, thus being a productive institution for adapting to water shortage when it occurs. Total economic welfare gains are highest when drought is the most severe for which suffering would otherwise be greatest. Gains would be considerably higher if water storage reservoirs were built to store wet year flows for use in dry years. Our results light a path for policy makers, donors, water administrators, and farm managers, who shoulder the burden of protecting food security and rural livelihoods in the world’s poor dry communities. [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]

Southwest China represents one of the largest continuous karst regions in the world. It is estimated that around 1.7 million people are heavily dependent on water derived from karst springs in southwest China. However, there is a limited amount of water supply in this region. Moreover, there is not enough information on temporal patterns of spring discharge in the area. In this context, it is essential to accurately predict spring discharge, as well as understand karst hydrological processes in a thorough manner, so that water shortages in this area could be predicted and managed efficiently. The objectives of this study were to determine the primary factors that govern spring discharge patterns and to develop a state-space model to predict spring discharge. Spring discharge, precipitation (PT), relative humidity (RD), water temperature (WD), and electrical conductivity (EC) were the variables analyzed in the present work, and they were monitored at two different locations (referred to as karst springs A and B, respectively, in this paper) in a karst catchment area in southwest China from May to November 2015. Results showed that a state-space model using any combinations of variables outperformed a classical linear regression, a back-propagation artificial neural network model, and a least square support vector machine in modeling spring discharge time series for karst spring A. The best state-space model was obtained by using PT and RD, which accounted for 99.9% of the total variation in spring discharge. This model was then applied to an independent data set obtained from karst spring B, and it provided accurate spring discharge estimates. Therefore, state-space modeling was a useful tool for predicting spring discharge in karst regions in southwest China, and this modeling procedure may help researchers to obtain accurate results in other karst regions. [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]

Nexus approaches address the complex interconnections across coupled systems, providing a new and effective way to identify the dynamics of coevolution process. The connections across water supply, power generation and environment (WPE) systems are increasingly tight and thus can be profiled as a WPE nexus. This paper aims to model the WPE nexus in the China’s Hehuang Region by using the system dynamics approach. Six nonlinear ordinary differential equations of storage, water supply, power generation, population, biomass, and environmental awareness are formulated to dominate the coevolution process. Their constitutive relations are then obtained from observed data to complete the WPE nexus model. The results show that the system dynamics approach is competent in modeling the WPE nexus of the Hehuang Region. The coevolution process is divided into four cyclic stages (i.e., exploitation, deterioration, depression and recovery stages) and terminates with a stationary cycling after thousands of years. The dynamics of state variables are explicitly interpreted to show how different driving variables affect the variations of system states. The observations obtained by modeling the nexus are insightful, improving the understanding of interactions across coupled systems. [ABSTRACT FROM AUTHOR]

Stochastic rainfall generation is important for a range of hydrologic and water resources applications. Stochastic rainfall can be generated using a number of models; however, preserving relevant attributes of the observed rainfall—including rainfall occurrence, variability and the magnitude of extremes—continues to be difficult. This paper develops an approach to constrain stochastically generated rainfall with an aim of preserving the intensity-durationfrequency (IFD) relationships of the observed data. Two main steps are involved. First, the generated annual maximum rainfall is corrected recursively by matching the generated intensity-frequency relationships to the target (observed) relationships. Second, the remaining (non-annual maximum) rainfall is rescaled such that the mass balance of the generated rain before and after scaling is maintained. The recursive correction is performed at selected storm durations to minimise the dependence between annual maximum values of higher and lower durations for the same year. This ensures that the resulting sequences remain true to the observed rainfall as well as represent the design extremes that may have been developed separately and are needed for compliance reasons. The method is tested on simulated 6 min rainfall series across five Australian stations with different climatic characteristics. The results suggest that the annual maximum and the IFD relationships are well reproduced after constraining the simulated rainfall. While our presentation focusses on the representation of design rainfall attributes (IFDs), the proposed approach can also be easily extended to constrain other attributes of the generated rainfall, providing an effective platform for post-processing of stochastic rainfall generators. [ABSTRACT FROM AUTHOR]