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Transformation plays a significant role in solving hydrological problems. In response to the comment on Singh and Das (2015) by Deng et al. (2015) regarding the transformation, boundary conditions and subsequent analytical solution of advection dispersion equation in semi-infinite heterogeneous porous media, it is argued that the transformation used by Singh and Das (2015) for finding the analytical solution is correct and mathematically valid. Also, the different expression of f (mt) used to obtain the solution are explained with their physical relevance in the field. [ABSTRACT FROM AUTHOR]

• A hydrodynamic-hydrological coupled model suitable for simulating flood processes in narrow-valley cities was developed. • The flood disaster characteristics in narrow-valley cities differ temporally and spatially from those in plain areas. • Provides reference for modeling and flood control system construction in narrow valley cities. The flood processes and control methods in narrow-valley cities exhibit significant differences compared to other regions. Therefore, precise modeling and simulation of their flood processes is crucial. This study focuses on a typical narrow-valley city located in the Loess Plateau of northern China. A hydrodynamic-hydrological coupling model was constructed to simulate the floods, analyzing flood disaster characteristics as well as the effects and differences of various flood control measures in the narrow-valley city. The results indicate: (1) The hydrodynamic-hydrological coupling model proposed in this paper is highly appropriate for accurately simulating floods in narrow-valley cities. A hydrological model was used to generalize the hydrological process in hilly mountainous areas. While a hydrodynamic model was employed to conduct a detailed simulation of the river terrace, where the main urban area is located. After validation through two actual flood events, the model demonstrated satisfactory simulation performance. (2) The flood characteristics of narrow-valley cities differ from those of plain regions mainly because the terrain restricts the spread of floods once they surpass the embankments. This limitation leads to potentially greater inundation risks in such topographical features. Additionally, the elongated distribution of cities in these areas creates spatial disparities in flooding between upstream and downstream areas. (3) There are significant variations in the effectiveness of different flood control measures. Various flood control approaches were modeled and simulated in this paper. Their flood control capabilities were ranked in descending order as: reservoirs, flood diversion channels, and river channel dredging. (4) The combined flood control effect of multiple measures is notably superior to that of individual measures. Among them, the combination of reservoirs and flood diversion channels is optimal, as it regulates the inflow from a large upstream area. The conclusions drawn in this paper can provide a certain degree of reference and basis for the construction of flood models and flood control systems in narrow-valley cities. [ABSTRACT FROM AUTHOR]

Determining the respective attribution proportions of climate change and land use change to streamflow variations in river systems is of increasing interest to researchers and practitioners tasked with managing river basins. This paper proposes an extension to established techniques of attributing the relative proportions of climate change (CC) and land use change (LUC) drivers to streamflow change by instead considering these proportions as distributed through a probability density function (pdf), rather than as a point value. The novel method is demonstrated for the River Tweed in the UK. Results are determined by the flow, temperature and precipitation data, and upon the algorithms used to identify change points in these vectors. The ratio of the LUC and CC attribution proportions (Land Use and Climate Change Attribution Proportions, LCAP) is more appropriately expressed as a vector of values, each associated with its own probability value within a probability density function. The paper demonstrates that the LCAP ratio pdf can vary considerably over time and that it is possible to track physical changes in the catchment in the evolution of the probability density function. Results show that the land-use change/climate change attribution proportion (LCAP) ratio varies over time and can be expressed as probabilistic estimate. It can be concluded, with a high degree of confidence, that for the Tweed catchment the LUC is a significant driver of streamflow change. Hence this finding may have implications for future catchment flood management utilising nature based solutions (NbS) to reverse landscape degradation and mitigate effects of climate change, provided that the economic and social costs are outweighed by the benefits. [ABSTRACT FROM AUTHOR]

• 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]

• A novel approach for modelling subsidence induced by fluid extraction from coal seams. • Transient groundwater and subsidence models are linked. • Coal shrinkage induced by gas desorption can significantly impact subsidence. Several coal seams are contained within the formations of Great Artesian Basin (GAB), the largest natural underground water reservoir in Australia and the world. The extraction of coal seam gas (CSG) and its associated water from thousands of wells within the coal measures of the GAB has led to tens of millimetres of subsidence in CSG development areas. Since highly developed farming systems located in these areas rely on very low slope landforms, even this scale of subsidence has caused significant community concern about the potential for CSG extraction to impair farming operations and productivity through changes in land slope and drainage. Coal seam compaction associated with dewatering and gas extraction has two key components: poromechanical compaction and desorption-induced bulk shrinkage. The former results from pore pressure depletion (due to dewatering and gas extraction) and an increase of vertical effective stress, while the latter is induced by gas desorption from the coal matrix, leading to further deformation. While poromechanical compaction of fluid-bearing formations has been extensively addressed in the literature, relatively little research has been conducted on the role of coal shrinkage in CSG-induced subsidence. This paper introduces an innovative practical modelling approach for assessing CSG-induced subsidence at a subregional/regional scale. The approach utilises an analytical model for CSG-induced subsidence derived from constitutive stress–strain relations for poroelastic-sorptive media. This is a novel approach in the context of CSG-induced subsidence which considers both poromechanical compaction and desorption-induced shrinkage. A further distinction to previous work is the integration of the geomechanical-sorptive subsidence model with a numerical groundwater model. Based on this approach, this paper examines the effect of coal shrinkage on subsidence, and its proportions with respect to total compaction for one of the major coal measures in the Surat Basin. Input data are derived from three-dimensional geological, geomechanical, and groundwater models, as well as methane adsorption and desorption reports. Results show that (a) the impact of coal shrinkage on CSG-induced subsidence is likely to be significant in the study area and (b) the contribution of coal shrinkage to CSG-induced subsidence depends on gas content, Langmuir isotherm, shrinkage strain parameters, and the saturation state of coal. This study provides important insights into CSG-induced subsidence and lays the foundation for the development of robust, efficient, and localised predictive models to support environmental impact assessment and management. [ABSTRACT FROM AUTHOR]

[Display omitted] • Complex network reveals the spatiotemporal propagations of extreme precipitation events (EPEs) in China. • EPEs over 218 river basins are classified into six communities with different seasonality from May to October. • There exist propagations of EPEs along the Yellow, Haihe, Liaohe and Songhua River Basins within one to eight days. Extreme precipitation events (EPEs) cause catastrophic losses to human lives and social economy. Focusing on the wet season from May to October, this paper formulates the complex network to characterize the spatiotemporal propagations of EPEs over 218 river basins in China. Specifically, based on the daily Multi-Source Weighted-Ensemble Precipitation version 2, the event synchronization along with significance test facilitates the complex network of EPEs from 1979 to 2020. By characterizing the topology of EPEs, the results show that synchronous EPEs generally occur over the Hetao Area of Yellow River Basin, Haihe River Basin and Northeast China. The complex network of EPEs classifies the 218 river basins into six communities. In May and June, more than 50 % of EPEs generally occur in South China. From July to September, over 60 % of EPEs tend to be observed in the Huang-Huai-Hai Plain, upper-middle reaches of Yellow River Basin, Northeast China and Southwest China. In October, about 65 % of EPEs happen in South and Southwest China. In the meantime, less than 10 % of EPEs occur in Northwest China. Among the synchronous EPEs starting in late June or early July and peaking in late July to early August, there exist propagations along the Yellow River, Haihe, Liaohe and Songhua River Basin within one to eight days. Eastward moisture transport along this pathway within four to six days drives the propagation of synchronous EPEs. The complex network analysis of this paper provides useful information for early warning and forecasting of EPEs and flood hazards. [ABSTRACT FROM AUTHOR]

[Display omitted] • Geogenic processes combined with biotic activity generate diversified mineral waters. • CO 2 , N 2 and CH 4 are most widely represented dissolved gases in mineral waters. • Excess air can be used as tracer of biotic processes. • Complex regional mineral water flows are identified. • An improved regional hydrogeological conceptual model is proposed. The varied gaseous composition of thermo-mineral waters emerging in a non-active zone reflects the diversity and complexity of groundwater pathways and provides important insights into their hydrogeological behaviours. The investigated geochemical content of complex thermo-mineral springs revealed the need to use dissolved gas contents as part of a multi-tracer approach to discriminate processes, geogenic (water–gas-rock interactions), abiotic (geological confinement, flow paths) and biotic activity influencing geochemical of groundwater along regional pathways. Irrespective of the dissolved element content or the water type, examining the overall concentration of dissolved gases enables an effective delineation of regional groundwater flow paths. Using dissolved gas content further contributed to the circumvention of some analytical challenges associated with conventional isotopic or geochemical techniques, often linked to the high concentration of elements such as iron, sulfate, sulfide or other naturally occurring elements content. The primary objectives are to analyse the gas composition of individual springs, to identify the origin of these gases in the groundwater, and to use this gas composition to improve the understanding of the flow patterns contributing to the geochemical diversity observed at the surface. From field investigations in a geologically and structurally complex area of Eastern Corsica (France), three types of gas contents are identified: (type 1) CH 4 & H 2 S-rich, (type 2) N 2 -rich and (type 3) CO 2 -rich. The study of these dissolved gases highlights that the wide geochemical diversity of thermo-mineral waters observed here is not only related to the mineralogical composition of the local aquifer but also involves strong and cumulative interactions along deep regional circulation pathways. This approach also reveals a common deep crustal gaseous influence characterised by N 2 production, which interacts during up flow with groundwater and then with the local metamorphic or sedimentary rock matrix. The groundwater's isotopic and geochemical contents are then altered by local lithologies encountered through both abiotic and biotic interactions. Finally, at shallow depths, phreatic groundwater can add its geochemical and isotopic footprint and dilute this complex mixture before groundwater emerges as mineral spring. This paper answers the primary objectives yet further demonstrates that using dissolved gas as a tracer of groundwater flow paths allows a deeper interpretation of surface geochemical and isotopic observations, distinguishes local from regional flow paths, and provides information about processes at the origin of groundwater diversity. The combination of tools presented in this paper (i.e., geochemical, dissolved gas, and isotopic tools) allows the establishment of a reliable regional groundwater flow scheme for thermo-mineral waters in a non-active zone. This scheme is essential to improve thermo-mineral water management, and protection to ensure their sustainable quality in front of increasing anthropogenic and climatic pressures. [ABSTRACT FROM AUTHOR]

• A review has been conducted on the application of ML and RS in hydrology domain. • Analyses were conducted on surface hydrology, hydro-climatic extremes and GWM & WQ. • GIS and ML algorithms prove valuable in the realm of hydrological investigations. • State-of-the-art approach is attributed to hydrology and water resources. Water, one of the most valuable resources on Earth, is the subject of the study of hydrology, which is of utmost importance. Satellite remote sensing (RS) has emerged as a critical tool for comprehending Earth and atmospheric dynamics, including hydrology. With the assistance of satellite RS, the scientific community has achieved significant progress in recent years. Since machine learning (ML) and RS techniques were initially applied to the study of hydrology, there has been a tremendous increase in interest in studying potential areas for future advancements in hydrology. The growth can see in the publications of related papers. Considering these initiatives, the current review paper attempts to give a thorough analysis of the function of ML and RS techniques in four fields of hydrology. This review study considers hydrological topics of streamflow, rainfall-runoff, groundwater modelling and water quality, and hydroclimatic extremes. The use of learning strategies in the hydrological sciences is examined in all reviews and research papers. Several databases were utilised for this purpose, including Scopus-index, science direct, Web of Science, and Google Scholar. The overall results of this study show that employing RS techniques, ML and ensemble approaches is incomparably superior to using traditional methods in hydrological studies. [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]

• Hydrological digital twins (HDT) are essential tools to understand the hydrological cycle especially in a changing climate. • We present, test, and validate an open-source framework to implement HDT in the large anthropized alpine Adige catchment. • The HDT is calibrated/validated on a variety of hydrological processes and data sources providing satisfactory performances. Understanding and simulating the hydrological cycle, especially in a context of climate change, is crucial for quantitative water risk assessment and basin management. The hydrological cycle is complex as it is a combination of non-linear natural processes and anthropogenic influences that alter landforms and water flows. Human-induced changes of relevance, including changes in land uses, construction of dams and artificial reservoirs, and diversion of the river course, lead to changes in water flows throughout the basin. These should be explicitly accounted for a realistic representation of the anthropogenically altered hydrological cycle. Such a realistic representation of the hydrological cycle is a necessary input for the water risk assessment in a particular region. In this paper, we present a hydrological digital twin (HDT) model of a large anthropized alpine basin: the Adige basin located in the northeast of Italy. Most catchments model often overlook land-uses changes over time and forget to model reservoir operation and their influence over time on water flow. Yet, for example, the Adige basin has>30 reservoirs affecting the water flow. We therefore use the GEOframe modeling framework to demonstrate the ability to create a hydrological twin model accounting for these anthropogenic changes. Specifically, we model each component of the water cycle over 39 years (1980–2018) at daily timescale through calibration of the Adige HDT with a multi-site approach using discharge data of 33 stations, based on a high-resolution (1 km) temperature and precipitation dataset and a calculated crop potential evapotranspiration (PETc) dataset, which accounts for human-induced change of the land cover over time. The modeling system also includes the simulation of artificial reservoirs and dams by the dynamically zoned target release (DZTR) reservoir model. The Adige HDT is assessed/validated/compared through a variety of hydrological processes (i.e., river and reservoir discharges, PETc and actual evapotranspiration, snow, and soil moisture) and data sources (i.e., observations and remote sensing data). Overall, the HDT reproduces well the measured discharge in space and time with a Kling Gupta Efficiency (KGE) above 0.7 (0.8) for 30 (23) of the 33 gauge-stations. For 7 artificial reservoirs with available data, the reservoir turbinated discharges are successfully reproduced with an average KGE of 0.92. A comparison between modeled and MODIS remote sensing snow data showed an average error of < 10% across the entire basin; the model also presented a good spatio-temporal agreement both with GLEAMS potential (and actual evapotranspiration) with an average KGE of 0.63 (0.60) and a high-level of correlation (0.5 on average) with the ASCAT satellite retrieved soil moisture. The findings of this paper demonstrate the potential of the open-source, component-based, GEOframe system to build a HDT, to provide a reliable and long term (39 years) estimation of all the water cycle components in a complex anthropized river basin at high spatial resolution. Spatially detailed HDT models results of this type can be used to inform basin-wise adaptation policy decisions and better water management practices in a time of changing climate. [ABSTRACT FROM AUTHOR]

• The SWCC of Ili loess with high level of soluble salts are compared by two methods. • The compression of centrifuge method causes the increase of matric suction. • Solution concentration causes the effect of salt on total and osmotic suction. • A new SWCC fitting model considering soluble salt content is proposed. Soil water characteristic curve (SWCC) is a critical parameter for unsaturated soil. However, few study can be found in literature for SWCC of loess with high soluble salt content, in particular SWCC fitting model. This paper targets on Ili loess in Xinjiang in China, which has a high level of soluble salts and is a typical loess in central Asia. Centrifuge method and filter paper method are used to conduct SWCC tests of Ili loess under different salt conditions. The total and matric suction of Ili loess under different soluble salt contents are obtained. The results of two methods are compared. The corresponding influence of soluble salt on SWCC is studied. At the same time, based on the Gardner model, a new SWCC fitting model of total and matric suction for Ili loess is proposed, which is able to consider the influences of soluble salts. The test results show that the compression during centrifuge method contributes certain increase of matric suction, especially at high volumetric water content conditions. Soluble salts have large effect on total and osmotic suction of Ili loess, however limited effect on matric suction. The alteration of total and osmotic suction is caused by the change of solution concentration in the sample. Total suction is mainly affected by osmotic suction. The alteration of matric suction is caused by the precipitation of the salts. It is proved that the proposed SWCC model is capable of characterizing the total and matric suction by simultaneously considering volumetric water content and soluble salt content. [ABSTRACT FROM AUTHOR]

• We discuss the discomforts of hydrologists in their dialogue with society. • We propose 4 ways to better align scientific stance, research practices & discourse. • This helps to refocus research practices and engage in and with society. • Socio-hydrology allows a collective, interdisciplinary reflexive process. • This is potentially a way to integrate socio-hydrology in society. Although there is a deep historical relationship between hydrology and society, the relationship has considerably evolved in the last three decades. Hydrologists, in particular those involved in designing of decision-support tools, are experiencing a widening gap between an academic discipline which has progressively moved away from field-based applied natural science to computational hydrology, and the multiplication of stakeholders involved in the water-related issues addressed by research. The challenge for hydrology is now to negotiate this shift and to rethink its engagement in society. This paper provides a description of a planned process designed to improve hydrology-society interactions and to foster reflexivity in socio-hydrology. Based on an interdisciplinary reflexive process undertaken in Tunisia from 2016 to 2020, we identified three types of discomforts in the dialogue with society, inviting scientists to lucidly engage with these discomforts. We formulated four key reflexive propositions to achieve a better alignment of scientific stance, research practices and discourse. The first proposition concerns the need to explain more clearly the value systems scientists engage in and with society. The second concerns the need to position hydrology in society and not outside it, by reconsidering the functions that research fulfils in society. The third is an invitation to redefine the perimeter of the research interlocutors and the way to reach them. The fourth is to revisit scientific practices to build on the strengths of the dialogue between field-based natural science and computational hydrology. The paper concludes that adopting a reflexive posture towards these four dimensions of the dialogue between hydrology and society is an effective way to overcome discomforts and to refocus research stance, practices and discourse. It is a way to renew hydrology's place in society and to contribute to the current thinking in socio-hydrology initiated by hydrologists. [ABSTRACT FROM AUTHOR]

• Different forms of knowledge may help develop a original development model. • Sociohydrology approach fostered exchange between different knowledge. • This enhanced the research protocol based on isotope tracers. • It contributed to the active knowledge base of the oasis community. • Weaving different forms of knowledge helps developing a virtuous and durable oasis. This paper demonstrates the reciprocal benefits of a socio-hydrology approach that fosters fruitful exchanges between different 'knowers' of complex water dynamics in weaving different types of knowledge. For centuries, the managed aquifer recharge and use system in the Beni Isguen oasis in Algeria was diligently monitored by communal water stewards and the data meticulously recorded and stored. Throughout our research project on the origin of groundwater and its replenishment, intense dialogue between scientists and communal water stewards greatly enhanced the research protocol based on isotope tracers, while simultaneously contributing to the active knowledge base of the community. The dialogue was based on mutual respect, trust and a reciprocal desire to share knowledge. Our findings revealed increased mineralization of shallow groundwater during drought periods, which was attributed to geological processes, and emphasized the crucial role of floods. Also, contrary to the initial assumption that deep Continental Intercalaire groundwater was only used for drinking, it was identified as a vital external resource, responsible for substantial recharge of the phreatic aquifer of the oasis. This source of water explains the continued use of the phreatic aquifer for irrigation more than 10 years after the last major flood. This collaborative socio-hydrology approach between differently situated 'knowers' contributes to the grounding of socio-hydrology. While respecting the strong water conservation culture, weaving different forms of knowledge may help develop a virtuous and original development model in the Algerian Sahara and beyond. [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]

This paper introduces novel semi-analytical models tailored for estimating land subsidence resulting from groundwater extraction in confined aquifers. These models offer high scalability, allowing them to be applied to various well configurations and pumping schedules. Their development involves the numerical integration of two key analytical solutions: the "nucleus of strain" (NoS) (Mindlin and Chen, 1950), which represents a localised zone within the aquifer where a unit change in pore pressure leads to deformation and subsequent surface displacement, and the classic Theis equation (Theis, 1935) for the pore pressure changes induced by a constant-rate well pumping from a laterally unbounded aquifer. These integrations yield surface displacement components, both horizontal and vertical, expressed as functions of two dimensionless spatial–temporal variables, which encompass aquifer depth, thickness, well placement, pumping schedules, and critical hydro-geomechanical parameters like hydraulic conductivity, porosity, vertical compressibility, and water compressibility. Proposed are two distinct modelling approaches: one employing a lookup table (LT) derived from numerical integration results, and the other providing direct closed-form surface displacement solutions by fitting LT data with "hinge models", which use piecewise-linear functions linked by sigmoidal curves for computational efficiency. In both cases, surface displacement components are estimated by plugging in the dimensionless variables. Conditions of variable pumping from multiple wells can be addressed by applying superposition of solutions. In essence, these semi-analytical models offer swift computational capabilities for understanding and forecasting land subsidence dynamics. Their scalability makes them adaptable to a wide array of well configurations and scheduling scenarios, rendering them valuable for numerous applications. They are particularly significant for providing preliminary estimates of the impacts of groundwater development, conducting "what-if" tests, and performing sensitivity analyses to identify key factors affecting land subsidence risk. This underscores the importance of these models in sustainable groundwater resource management and in mitigating land subsidence and its associated consequences. • Innovative frameworks to assess land subsidence from groundwater extraction. • Versatile models adaptable to various pumping well configurations and schedules. • NoS solution integrated into Theis equation for surface displacement calculation. • lookup table (LT) with 2D interpolation and direct solution via hinge model fitting. • Accurate subsidence predictions with errors below 5%, enabling efficient simulations. [ABSTRACT FROM AUTHOR]

[Display omitted] • Event-based estimation of lag time provided reliable results. • DMCA method provided the best results for milder and bigger catchments. • Physically-based formulas perform better, especially for steeper and smaller catchments. • Robust predictions and consistency with velocity observations. • Simplification of physically-based formulas based on Hack's law for practical purposes. Several studies have demonstrated that response times in natural catchments decrease with increasing rainfall intensity. Consequently, event-based estimations of catchment response times are of paramount importance in applied hydrology. Specifically, they have the potential to address a major inconsistency in the use of empirical formulas. These formulas often assume response times as constant parameters, regardless of whether extreme or frequent flood events are considered, thus neglecting the role of flow velocities. In this paper, built upon previous approaches developed and/or analyzed by the authors, two different recent methods for event-based estimations of catchment response times are critically reviewed, and their predictive performances are compared. First, four "physically-based" formulas, calibrated using synthetic rainfalls in three small Italian watersheds to reproduce the results of a two-dimensional hydrodynamic-based rainfall/runoff model and, consequently, the simulated wave celerities, are considered. Then, the detrending moving-average cross-correlation analysis (DMCA) has been applied to assess the average time elapsed between the centroids of precipitation and discharge time series. The soundness of these two approaches is initially assessed based on their ability to reproduce estimated lag times from observations. Their robustness is further evaluated by analyzing the magnitude and basin scale dependence of the inferred velocities compared to observed values, following a recent approach proposed in the literature. These issues are discussed with reference to 60 rainfall-runoff events occurring across 27 watersheds in Hungary and Italy, which possess substantially different geomorphic and climatic features, highlighting both the potential and the need for further improvements. Both approaches give error rates of around 37% for the proposed dataset. [ABSTRACT FROM AUTHOR]

• Proposed a load distribution model for cascade serial diversion-type hydro station; • Unit group strategy is adopted to realize the flow matching of serial hydro plants; • The model can reduce water consumption while considering complex constraints. With the development of complex cascade serial diversion-type hydropower plants (CSDHP) in Southwest China, it has become imperative to study short-term scheduling challenges that are distinctive to this type of hydropower facility. Unlike conventional cascade hydropower stations, which have large reservoirs to regulate the outflow between stations, the CSDHP system consists of several diversion-type hydropower plants connected in series, requiring strict matching of turbine discharge between upstream and downstream facilities. In addition, these plants impose complex operation constraints, such as multiple units sharing one tunnel, which greatly increases the difficulty of short-term scheduling. This paper proposes a short-term optimal scheduling model for CSDHP systems aimed at achieving efficient load distribution among units while minimizing water consumption. The model takes into account the strict matching of turbine discharge and the complex constraints of multiple units sharing one tunnel. Subsequently, the model is transformed into a mixed integer linear programming problem using specified linearization techniques, with complexity further addressed via an iterative resolution method. A case study of a cascade illustrates the model's efficacy in facilitating effective unit load distribution under complex hydraulic and electrical constraints, which substantially reduces the system's water consumption by up to 6.1% during the dry season and 1.8% during the flood season, and reducing the number of crossing forbidden zones by 50.0% and 52.0%, respectively. The result shows the models' potential to enhance day-ahead scheduling for the CSDHP system. [ABSTRACT FROM AUTHOR]

• Determining the barometric response at early lag times (sub minute timescale) • Extending regression deconvolution methods to handle large data sets. • Demonstrating the deficiency of the static barometric efficiency. • Tools for signal decomposition and site characterization using water levels. • Side-by-side comparison of time and frequency domain response functions. A regression deconvolution method using distributed lags for the efficient calculation of barometric/loading response functions in the presence of Earth tides and background trends was applied to water pressure measurements collected every second. This method was able to simultaneously deconvolve multiple stresses that can be used for both signal decomposition (removal of noise and isolation of signals) and the determination of impulse response functions that can be interpreted for hydraulic properties and subsurface characterization. We also demonstrate how the static barometric efficiency is a deficient parameter when applied to wells having delayed responses and needs to be applied with care. The distributed lag method combined with high frequency monitoring has implications for improved site characterization, determining the continuity of subsurface features, parameter estimation, understanding the degree of confinement, and signal decomposition. The methods for this paper can be found in the R hydrorecipes package. [ABSTRACT FROM AUTHOR]

• Teleconnection cycles are skilful predictors of drought at up to four-year lead times. • NAO cycles as predictors of drought can outperform existing methods. • Skill was greatest for longer droughts or in catchments with longer response times. With global temperatures, populations and ecological stressors expected to rise, hydrological droughts are projected to have progressively severe economic and environmental impacts. As a result, hydrological drought forecasting systems have become increasingly important water resource management tools for mitigating these impacts. However, high frequency behaviours in meteorological or atmospheric conditions often limit the lead times of hydrological drought forecasts to seasonal timescales, either through poorer performance of multi-year meteorological forecasts or the lack of multi-year lags in atmosphere-hydrology systems. By contrast, low frequency behaviours in regionally important teleconnection systems (such as the North Atlantic Oscillation, NAO) offer a novel way to forecast hydrological drought at longer lead times. This paper shows that, by using a data-driven modelling approach, long-term behaviours within the NAO can be skilful predictors of hydrological drought conditions at a four-year forecasting horizon. Multi-year semi-periodic patterns in the NAO were used to forecast regional groundwater drought coverage in the UK (proportion of groundwater boreholes in drought), with the greatest forecast performance achieved for longer duration droughts, and for hydrogeological regions with longer response times. Model errors vary from 14 % (proportion of boreholes, (MAE)) in flashy hydrological regions or short droughts (<3 months), to 2 % for longer duration droughts (>8 months). Model fits of r2 up to 0.8 were produced between simulated and recorded regional drought coverage. As such our results show that teleconnection indices can be a skilful predictor of hydrological drought dynamics at multi-year timescales, opening new opportunities for long-lead groundwater drought forecasts to be integrated within existing drought management strategies in Europe and beyond. [ABSTRACT FROM AUTHOR]

• The VMD decomposition processing module can improve prediction accuracy. • VMD-CBiLSTM can improve the accuracy of drought prediction in arid and semi-arid regions. • The VMD-CBiLSTM prediction model has good stability. Drought can cause great harm and loss, so accurate and efficient drought prediction has certain research significance. In previous studies, a single machine learning model is often used to predict the factors related to drought, such as precipitation and temperature, to indirectly explain the influence degree of drought, but the overall prediction accuracy is low, which can not fully and effectively predict the nonlinear and non-stationary drought characteristic information. In this paper, the variational modal decomposition model (VMD) is used to decompose the meteorological drought time series, and the convolution neural network (CNN) and the improved bidirectional long short-term memory neural network (BiLSTM) are combined to construct the meteorological drought hybrid prediction model (VMD-CBiLSTM). The research results show that using VMD-CBiLSTM model to forecast the monthly daily evapotranspiration deficit index (DEDI) of three weather stations, compared with the results predicted by VMD-LSTM, VMD-BiLSTM and VMD-ARIMA models, the average prediction accuracy R2 is increased by 0.076, 0.034 and 0.328 respectively, and the average RMSE is decreased by 0.178, 0.094 and 1.373 respectively. Compared with single model, VMD-CBiLSTM can not only reduce the uncertainty of meteorological drought prediction caused by climate model heterogeneity, but also improve the accuracy of drought prediction in arid and semi-arid regions, which can provide reference for coping with drought occurrence and drought early warning in advance. [ABSTRACT FROM AUTHOR]

• A real-time scheduling method of cascade pumping station based on multi-scale closed-loop. • Both single point and continuous water distribution disturbance can be effectively dealt with. • Compared with a single scale closed loop has the advantage of macro guidance. • Multi-scale rolling optimization can improve water supply and economic benefits. • It can be extended to solving uncertain scheduling problems with longer time dimensions. Closed-loop control is an effective control method to deal with uncertain disturbance in water supply canal system (WSCS). However, previous researches mainly focus on closed-loop control with a single time scale, and it is difficult to solve the uncertainty problem with a long time dimension. In this paper, a multi-scale closed-loop coupled real-time scheduling framework is established and solved by considering both large and small scale closed-loop scheduling models, so as to improve the performance of real-time scheduling in a longer time dimension under uncertain perturbations. The framework is applied to the virtual simulation test of the Xuhong River WSCS of the east route of the South-to-North Water Diversion project under different water distribution disturbances. The results show that the closed-loop control method can update the system status regularly through rolling optimization, and effectively reduce the prediction error of water level and energy consumption. In the two water distribution disturbance scenarios, the proposed method reduces the average relative deviation of water supply plan execution by 3.4 % and 5.9 %, and the unit energy consumption of cascade pumping station operation by 1.7 kWh/104m3 and 1.5 kWh/104m3, respectively, compared with the single-scale closed-loop scheduling method. In addition, the proposed method can effectively deal with the uncertainty of water distribution disturbance in limited computing time, and can meet the requirements of real-time scheduling in a long time dimension under uncertain disturbance. [ABSTRACT FROM AUTHOR]

• A broad database of throughfall/stemflow chemistry was compiled for China. • Meta-analysis and boosted regression trees (BRT) were applied to uncover patterns. • A pronounced acidification effect was identified in stemflow but not throughfall. • Chemical enrichment was prevalent in both throughfall and stemflow. • BRT revealed non-linear partial effects of influential predictors. In vegetated ecosystems, the chemical composition of rainwater is strongly altered after gross rainfall (GR) partitioning into throughfall (TF) and stemflow (SF) before entering the under-canopy soil. However, quantitative syntheses of the chemical alteration are lacking, rendering an insufficient comprehension of its overarching patterns and magnitudes. Here, we furnish a comprehensive dataset related to the chemistry of rainfall partitioning by vegetation in China compiled from 131 peer-reviewed papers published between 1988 and 2023. A meta-analysis was conducted to assess the chemical alterations as GR partitions into TF and SF. We discerned a marked acidification effect in SF but not in TF, with the pH being significantly lower in SF than in GR. Additionally, we observed a persistent concentration-based chemical enrichment of total nitrogen (TN) and total phosphorous (TP) as well as 12 commonly reported nutrient-ions (except for Cu2+) in both TF and SF, with SF generally exhibiting a greater degree of chemical enrichment than TF. Using a machine learning method (boosted regression trees), we identified the key drivers from twelve biotic and abiotic predictor variables on the effect sizes of pH, TN, TP, and each nutrient-ion, and further elucidated the prevalent non-linear partial effects of these key drivers on the magnitude of chemical alterations. For instance, three predictor variables were identified as key drivers for the effect sizes of TN for SF, with a descending relative influence of diameter at breast height (56.9 %; positive effect), Human Footprint (16.5 %; positive), and bark texture (14.0 %; stands with mixed bark textures exerting a greater impact). Our study has significant implications for an accurate estimation of nutrient budgets in vegetated ecosystems throughout China, contributes to a deeper understanding of the intricate ways in which biotic and abiotic predictors influence chemical alterations in rainfall partitioning, and aids in evaluating forest health and developing sensible forest management strategies. [ABSTRACT FROM AUTHOR]

• SAC-SMA model was evaluated using machine learning-based limits-of-acceptability over streamflow. • Streamflow-based signatures were also used to identify physically unrealistic simulations. • All the 1-million parameter sets tested in this study were rejected as unfit-for-purpose. • Low flows were critical in identifying behavioral and non-behavioral models. Hydrological models are evaluated by comparisons with observed hydrological quantities such as streamflow. A model evaluation procedure should account for dominantly epistemic errors in hydrological data such as model input precipitation and streamflow and avoid type-2 errors (rejecting a good model). This study uses quantile random forest (QRF) to develop limits-of-acceptability (LoA) over streamflows that account for uncertainties in precipitation and streamflow values. A significant advantage of this method is that it can be used to evaluate models even at ungauged basins. This method was used to evaluate a hydrological model –Sacramento Soil Moisture Accounting (SAC-SMA) – over the St. Joseph River Watershed (SJRW) for both gauged and hypothetical ungauged scenarios. QRF defined wide LoAs that yielded a large number of models as behavioral, suggesting the need for additional measures to develop a more discriminating inference procedure. The paper discusses why the LoAs defined by QRF were wide, along with some ways to define more discriminating LoAs. To further constrain the model, five streamflow-based signatures (i.e., autocorrelation function, Hurst exponent, baseflow index, flow duration curve, and long-term runoff coefficient) were used. The combination of LoAs over streamflow and streamflow-based signatures helped constrain the set of behavioral models in both the gauged and the ungauged scenarios. Among the signatures used in this study, the Hurst exponent and baseflow index were the most useful ones. All the 1-million models evaluated in this study were eventually rejected as unfit-for-purpose. [ABSTRACT FROM AUTHOR]

• A novel large-scale hydrological and hydrodynamic coupled model for flow routing. • A new implicit fully coupling algorithm in coupled hydrological-hydrodynamic model. • Complex interactions among river–lake systems can be simulated accurately. • Quantifying the impacts of topographic changes on hydrological regime using limited data. In lowland alluvial river systems consisting of mainstream, distributaries, and floodplain lakes, the flow dynamics are characterized by changing backwater effects and seasonal water storage and release, and the bed morphology is unstable due to frequent sediment deposition and erosion. Hence, models that can accurately and efficiently simulate hydraulic interactions with low topographic data demands are required for such water systems. In this study, we present a coupled flow routing model for the Yangtze River-Dongting Lake system by coupling a 1D hydrodynamic model for the mainstream with hydrological models for distributaries and the lake. A new approach is proposed to generate a set of rating curves to represent the combined impacts of the inflow and outlet stage on the lake storage volume, thereby an innovative hydrological routing method fully considering backwater effects is established. Additionally, an implicit fully coupled algorithm is presented to integrate the hydrodynamic and hydrological models, which demonstrates ideal numerical stability and high computational efficiency. The coupled model is applied to flow routing in the Yangtze-Dongting system, with the Nash-Sutcliffe efficiency coefficients above 0.960, and most of the mean absolute error below 0.4 m (1000 m3/s) for stage (discharge). The model is also used to evaluate the impact of topographic changes during 1998–2016 on the flow process. The results indicate that the mainstream stage has decreased notably, while the reduction of the storage capacity of the lake is not yet remarkable. For the model proposed in this paper, only hydrological data and mainstream topographic data are necessary during model development, calibration, and application. In the context of continuous channel/lake bed adjustment in the middle reach of the Yangtze River Basin, the coupled model provides an effective tool for quantitatively evaluating the hydrological regime and flood process of a river system at a full scale. [ABSTRACT FROM AUTHOR]

• Automated framework for realizing the whole set of drainage design process. • Deeplabv3 + -based segmentation of land use combined with prior knowledge. • Sophisticated delineation of subcatchment and hydrological characterization. • Parameter optimization of automatic network topology generation algorithms. • Automatic hydraulic calculation of pipeline capacity. This paper proposed a comprehensive and automated framework that integrates the entire urban drainage design process, from refined land use segmentation, to catchment subdivision and characterization, to network topology generation, and finally to hydraulic estimation of pipeline capacity, based on multiple open-source datasets. The method incorporated deep learning-based semantic segmentation, GIS-based hydrological characterization, topology generation and hydraulic calculation algorithms. The performance and applicability of the method were validated through case studies of two cities. The results showed that the deep learning model with prior knowledge could accurately segment urban land use to obtain the detailed shape and boundary description, and achieved higher detection accuracy for water bodies, buildings, and roads. The stormwater drainage topology and conveyance capacity were automatically generated based on subcatchment division and hydrological characterization under a set of topological parameter conditions. In the optimal Critical Success Index (CSI) scenario, only 8% and 21% of the generated pipelines were inconsistent with the expert-designed networks. Compared to the traditional manual design process, the automated approach can save significant time and resources and reduce the reliance on field surveys and empirical/expert work. At the same time, the tool can be employed for system layout and capacity optimization, providing important support for the automation, design, and rehabilitation of stormwater drainage systems. [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]

• An improved PT model based on shortwave infrared soil moisture index. • The PT-SIMI model rectifies ET underestimation in alpine grassland of the QTP. • The average ET over the QTP from 2011 to 2020 remains relatively stable at 478 mm/year. • Air temperature primarily drives ET over water bodies, grasslands and forests. • Soil moisture exerts a pronounced influence on ET in bareland and grassland. Evapotranspiration (ET) is a crucial process in the land water cycle, energy balance, and carbon cycle, making it essential for understanding ecosystem health and climate change. The Qinghai-Tibet Plateau (QTP) is a vital contributor to the global water cycle and ecosystem, and its water resources and ecological environment are of great significance to the development and survival of Asia. To accurately estimate ET on the QTP, this paper proposes an improved Priestley-Taylor (PT) model based on shortwave infrared soil moisture index (SIMI). The model is applied to estimate ET on the QTP from 2011 to 2020, allowing for a comprehensive spatiotemporal analysis. The findings indicate that, (1) The PT-SIMI model has higher estimation accuracy than both the Modified Satellite-Based Priestley-Taylor (MS-PT) model and the Priestley-Taylor Jet Propulsion Laboratory (PT-JPL) model at most sites, particularly in alpine grasslands sites, and demonstrates a greater sensitivity in capturing changes in soil moisture; (2) the spatial distribution of ET on the QTP demonstrates a declining pattern from southeast to northwest; (3) the average ET from 2011 to 2020 remains relatively stable at 478 mm/year; (4) notable spatial variations in ET are observed across the plateau, with noticeable upward trends observed in the central and western regions, while the south central region experiencing a decreasing trend. This study provides valuable insights into the water cycle and ecological environment of the QTP and its findings can support sustainable development in the region. [ABSTRACT FROM AUTHOR]

This paper introduces and applies a novel methodology that integrates watershed modeling with the traditional regional flood frequency analysis. The methodology introduces a novel site-similarity measure that relies on hydrologic simulations and accounts for the effect of land depressions on streamflow generation. The new measure is tested along with other traditional measures for regional flood frequency analysis in the Canadian prairies. The case study is chosen carefully to critically test the new methodology. An application of 30 combinations of the new and traditional site similarity measures is assessed for pooling 109 sites. The homogeneity of the clustered groups is evaluated, and different probability distributions are applied to describe at-site and regional annual maximum flows. The results present enhanced groups' homogeneity when the new measure is employed due to a better representation of the hydrologic similarity between the pooled sites. Furthermore, the regionally estimated quantiles are found susceptible to the chosen site similarity measures in the pooling process, which highlights the importance of considering the proposed measure that describes a key hydrologic aspect when land depressions exist. [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]

[Display omitted] • Confined Quaternary groundwater reverse recharge contaminates buried karst water. • Upper coal mining activities pose a threat to the buried karst aquifers. • The vertical seepage of contaminated river water worsens karst water quality. • Hydrologic connectivity and source availability determine the contamination process. Karst groundwater contamination has emerged as a worldwide environmental and health hazard. Karst aquifers, even for the buried karst systems, have strong contamination sensitivity and great pollution risk due to the good pipe fracture connectivity. The large burial depth and invisible hydrologic connectivity pose a challenge for the diagnosis of the contamination sources and identification of the contamination pathways of buried karst aquifers. To address it, a comprehensive application of hydrogeological and hydrogeochemical investigation combined with multiple isotopes (δ2H, δ18O, δ34S, δ13C and 87Sr/86Sr) were performed to elucidate the multiple contamination sources and migration pathways of contaminants in Jinci Spring, a complex buried karst water system. The results obtained highlight that the hydrological connectivity and source availability determine the specific contamination process in buried karst aquifer. Karst aquifer under the river channel is observed to be influenced by the vertical infiltration of polluted river water as indicated by elevated chloride levels (mean 79.4 mg/L) and higher δ2H (mean −8.8 ‰) and δ18O (mean −64.5 ‰) values. Evidenced by the excess of sulfate (mean: 1454 mg/L) and depleted δ34S (mean: −1.6 ‰), the lower karst aquifer may receive the leaking recharge of the mining waste water in the overlying coal seams. The occurrence of nitrate in karst water may be induced by reverse recharge from neighboring confined quaternary aquifers, indicated by the elevated nitrate concentration (15.0–50.8 mg/L) with high 87Sr/86Sr ratios (mean 0.7124). Our findings suggest that over-pumping of deep karst waters may produce additional pollution sources by altering the natural recharge relationship between montanic karst waters and the neighboring pore waters within the basin. Research in this paper improves our understanding of groundwater pollution and hydrological connectivity in buried carbonate aquifers and the protection of karst water resources. [ABSTRACT FROM AUTHOR]

• A framework for evaluating the cascade effect of drought and flood events in different sub-regions and multiple levels is proposed. • The correlation degree of meteorological-hydrological drought and flood in the Yangtze River Basin is higher than that in the Yellow River Basin. • The flood-flood and drought-drought scenarios aggravated the disaster, but the probability of the same drought and flood scenarios in the two basins was higher. The flood-drought scenario is conducive to mitigating drought. Under the background of global change, the multivariate attributes, multi-temporal and multi-watershed regional characteristics of extreme drought and flood disasters are significant. It is very important to identify the drought and wetness encounter law and spatial cascade effect in the source region-upper-middle-lower reaches of the basin. In this paper, the Yangtze River and Yellow River basins are taken as the research objects. Based on the standardized precipitation index (SPI) characterizing meteorological drought and wetness and the standardized runoff index (SRI) characterizing hydrological drought and wetness, the evolution law of extreme drought and flood events in the source region-upper-middle-lower reaches of the two basins is analyzed. On this basis, the correlation analysis and transfer function analysis are used to explore the response relationship and lag effect between the two in different partitions. A two-dimensional joint distribution model is constructed by Copula function to quantitatively analyze the joint probability of drought and wetness encounters of 9 combined events, and to deeply analyze the impact of different drought and wetness encounter scenarios. The results show that: from 1957 to 2020, the two basins showed a trend from drought to wet. The SPI-12 in the Yellow River Basin ranged from-1.57 to 1.32, and that in the Yangtze River Basin ranged from-1.24 to 1.11. The severity of drought and wetness in the Yellow River Basin was higher than that in the Yangtze River Basin; The correlation coefficients between SPI and SRI in the source-upper-middle-lower reaches of the Yellow River (Yangtze River) were 0.78 (0.76), 0.23 (0.73), 0.44 (0.55) and 0.23 (0.63), respectively. The correlation degree of meteorological-hydrological drought and wetness in the Yangtze River Basin was significantly higher than that in the Yellow River Basin. The average probability of synchronous combination events of drought and wetness in the two basins is greater than 51.43 %. The probability of drought in the Yellow River Basin is greater than that of flood, while the Yangtze River Basin is the opposite. In the same drought and wetness scenario, an average of 54.1% of the event pairs were aggravated by disasters, and more than 52 % of the events in the wetness-drought scenario had obvious drought mitigation effects. The research results can provide important support for improving the early warning and risk management of river basin disasters. [ABSTRACT FROM AUTHOR]

• Developed a method to assess synergy and competition in water-energy-food systems. • Energy and food industries face intense competition for water in the Yellow River Basin. • Considering water quality exacerbates competition for water between food and energy and diminishes synergy. • Resource endowment and technological level have significant impact on the degree of competition. Water, as an indispensable component of the water-energy-food (WEF) nexus, plays a pivotal role in shaping its stability and safety. The competition for water between the food and energy systems is accentuated in the concept of the nexus. However, existing quantitative frameworks and assessment mechanisms have limitations as they often neglect to consider water quality in water competition within the WEF nexus. Therefore, a WEF nexus synergy and competition assessment method was proposed by coupling water footprint theory and the Lotka-Volterra model in this paper. This method establishes two scenarios based on the water footprint perspective, namely, water quantity and water quality-quantity. It effectively addresses the deficiencies in quantitative analysis of WEF system trade-offs and synergies. To illustrate the application of the method, this study conducted a case study in the Yellow River Basin (YRB) of China, evaluating the complex competitive and synergistic mechanisms of water use in the energy and food industries. Compared to the water quantity scenario, the results reveals that the inclusion of gray water footprint intensifies the competition for water in the food and energy industries in the YRB from 2000 to 2020, particularly in resource-based and economically developed cities. The spatial distribution characteristics of water resource competition and synergy in the YRB's WEF system align with WEF endowment and economic technology levels. To address water conflicts in the WEF nexus and enhance nexus security, this study proposes a regulatory pathway for WEF system synergistic security based on two aspects (water competition and synergy characteristics and resource endowment). It contributes to a more comprehensive understanding of water competition and synergy in the WEF nexus and provides valuable insights for resource management. [ABSTRACT FROM AUTHOR]

• A deep learning algorithm can provide more skilful nowcasting for medium and strong rainfall cases than for a weak one. • A deep learning algorithm trained by the dataset for one region can be skilfully used to nowcast rainfall for another region with the similar weather system. • For a deep learning algorithm, there is an optimal number of iterations for training the algorithm. It is a critical need to provide timely and valuable alerts of rainstorms and floods to the public. However, it still remains a world-class challenge to achieve serviceable nowcasting rainstorms with even a short lead time of one hour. Different deep learning algorithms have been adopted to improve nowcasting accuracy. Unfortunately, it is still a question which algorithm is more suitable and how to interpret the rainstorm nowcasting results from deep learning. To this end, this paper focuses on modelling the evolution of rainstorm clouds using deep learning algorithms that can be applied to nowcast rainstorms for the next few hours. Adopting three deep learning algorithms, the study provides a detailed analysis of the nowcasting results of three typical cases of different rainfall intensities from a radar echo mosaic image dataset. The dataset was collected in Guangdong, China, and the analysis interprets the performance differences. The analysis further discloses that an AI-based method can provide more skilful nowcasting for medium and strong rainfall cases than for weak ones. Moreover, a deep learning algorithm trained by the dataset for one region can be skilfully used to nowcast rainfall for another region with a similar weather system. This explains the nowcasting capability of deep learning algorithms as well as their robustness. Besides, experiments on the number of iterations reveal that more iterations do not achieve higher nowcasting accuracy. With improved interpretability of deep learning from the perspective of real-world application in the study, it is expected that the algorithms producing higher accuracy and longer lead time nowcasts will be made possible. [ABSTRACT FROM AUTHOR]

• Fitness landscape metrics are used to characterise key properties of the error surfaces of conceptual rainfall runoff models (CRRM) • Enabled large-scale analysis of error surfaces from 420 CRRM combinations with different structures/catchments/error metrics & data length. • Increases in the number of model parameters increases calibration difficulty, decreases calibration efficiency and reduces parameter identifiability. • Increased catchment wetness increases relative roughness of error surfaces and relative optima dispersion. • Opens door to improved CRRM design and selection of better calibration approaches through large-scale analysis of error surfaces. The ease and efficiency with which conceptual rainfall runoff (CRR) models can be calibrated, as well as issues related to the uniqueness of their parameters, has received significant attention in literature. While several studies have tried to gain a better understanding of the underlying factors affecting these issues by examining the features of model error surfaces, this has generally been done in an ad-hoc fashion using lower-dimensional representations of higher-dimensional surfaces. In this paper, it is suggested that exploratory landscape analysis (ELA) metrics can be used to quantify key features of the error surfaces of CRR models, including their roughness and flatness, as well as their degree of optima dispersion throughout the surface. This enables key error surface features of CRR models to be compared in a consistent, efficient and easily communicable fashion for models with different combinations of attributes (e.g. model structure, catchment climate conditions, error metrics, and calibration data set lengths). Results from the application of ELA metrics to the error surfaces of 420 CRR models with different combinations of the above attributes indicate that increasing model complexity results in an increase in relative error surface roughness and relative optima dispersion and that, while increasing catchment wetness increases the relative roughness of error surfaces, it also decreases optima dispersion. This suggests that for the models considered in this study, optimisation efficiency is likely to decrease with increasing model complexity and catchment wetness, while optimisation difficulty is likely to increase and parameter uniqueness likely to decrease with model complexity and catchment dryness. While implications for choice of model complexity will need further work, this study highlights the potential value of the proposed approach to understanding the calibration efficiency, difficulty and parameter uniqueness of conceptual rainfall runoff models. [ABSTRACT FROM AUTHOR]

• Model complexity increased the predictive capacity of regression models. • Model complexity also increased the multicollinearity in the models. • The coarser data granularity increased the correlation between model variables. • Higher model complexity and coarser data granularity led to instable parameters. Water temperature is a key factor influencing biota of stream ecosystems. Hence, it is important to comprehend the environmental drivers of stream temperature for robust prediction of conditions and effective management of stream communities. Linear regression models are commonly used for predictive purposes, but their predictive capacity and interpretability can be significantly affected by their complexity and the structure of input data. In some cases, researchers may be obligated to favor prediction power or interpretability while compromising the other. Therefore, insight into relationships between model fit, correlation among predictor variables (i.e., multicollinearity), and level of temporal aggregation of data (i.e., data granularity) may be helpful to reduce such trade-offs. In this paper, we investigated these relationships within a hierarchical set of multiple linear regression (MLR) models examining environmental factors influencing stream temperature dynamics. Our findings showed that as the number of predictor variables (i.e., model complexity) increased, the magnitude of multicollinearity in MLR models increased, but model fit also increased. The results also revealed that using data averaged over longer time frames (i.e., coarser data granularity) yielded high multicollinearity, as indexed by variance inflation factor values (VIF) for all model predictors. This led to higher variance in parameter estimates (i.e., parameter instability) and potential challenges in model interpretation as the sign of parameter estimates changed in many streams examined. Multicollinearity was not the only reason for these changes in the sign of parameter estimates as they were also observed in simple linear regression models across varying levels of data granularity. Based on our findings, we conclude that the selection of data granularity is an important consideration in multiple regression modeling, with profound implications for model interpretability. [ABSTRACT FROM AUTHOR]

• A flexible approach to a coupled 1D-2D hydrodynamic model is provided. • The coupling model presented can reasonably simulate significant floods. • Flood intensification weakens the backwater effect and increases flood risk. • The evacuation generation time significantly influences the extent of the D-zone. Large-scale earthquakes and huge landslide surges may cause the failure of cascade reservoirs, resulting in massive downstream flooding. The flood risk zoning of cascade reservoir dam breaks is crucial for local governments to formulate emergency plans and for individuals to garner their awareness of the flood risks. This paper presents a flood risk zoning approach based on a one-dimensional and two-dimensional coupled hydrodynamic model. The flooding area simulated by the coupled hydrodynamic model was divided into a Deadly Zone (D-zone) and an Escape Zone based on whether there is sufficient time for the public to evacuate the danger area under a specific evacuation generation time (EGT). A case study was conducted on the Jinsha-Yalong River (JYR), a confluence river located in Panzhihua City, to map the risk zones. This study considered a dam-break flood in Yalong River encountering two river flood scenarios in Jinsha River (scenario A: the annual average flow; scenario B: a 1000-year return period flood). Two evacuation scenarios (EGT of 30 and 60 min) were also considered; thereafter, four combined evacuation scenarios (A-EGT30, A-EGT60, B-EGT30, and B-EGT60) were simulated. The coupled hydrodynamic model is demonstrated to be effective through the analysis of water balance and hydrographs of special section discharge. The results show that: (1) in comparison to scenario A, scenario B reached its maximum flood extent approximately 5 min earlier, signifying that flood spread speed increases with the increase in flood magnitude; (2) the D-zone area was 127.7 % larger in scenario A-EGT60 compared to A-EGT30 and 120.8 % larger in scenario B-EGT60 compared to B-EGT30. These findings underscore the importance of flood scenarios and EGT on flood risk zones. The proposed risk zoning method can help government agencies in the JYR and major river regions worldwide with flood management and emergency strategy development. [ABSTRACT FROM AUTHOR]

• A stabilised SPH model in the u-w-p formulation for internal erosion is proposed. • A modified constitutive model is formulated for the erodible soils. • Extensions of a boundary treatment are proposed for the erosion-transport process. • A novel artificial damping term is established to mitigate numerical instabilities. • Groundwater flow, erosion-transport-deposition, and soil deformation are modelled. This paper presents a stabilised multi-phase smoothed particle hydrodynamics (SPH) model applicable to seepage-induced internal erosion and the resulting deformation in soils. Based on the continuum mixture theory, a new single-layer SPH model in the u - w - p formulation is derived for the mathematical description of the erodible porous material. A new modified constitutive model is formulated to account for the influence of erosion on the mechanical behaviour of soils. Extensions of a first-order consistent boundary condition as well as the diffusion algorithm for hydro-mechanical coupling in SPH are also proposed to accommodate the analysis of erosion-transport process. A novel viscous dissipation term is designed to mitigate the numerical instability commonly encountered in coupled problems. In contrast to existing stabilisation terms in the SPH literature, which operate on both the bulk component and shear component, this new stabilisation term offers the possibility to increase the dissipation of unphysical oscillation due to the shear deformation. The proposed method is validated through a series of benchmark tests. The results demonstrate the effectiveness of the proposed approach in addressing the coupled problem in porous materials involving multi-phase flow, phase interaction/transfer, and large deformation with enhanced accuracy, stability, and robustness. [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]

• Lake Urmia crisis, relevant consequences, and restoration plans are reviewed. • Restoration programs are scrutinized based on data and model availability. • Model-data interaction is highlighted for designing the lake restoration plans. • Massive data collection and integrated modeling are needed in the Lake Urmia Basin. Losing eight meters of water level over a 20-year period from 1996 to 2016 marked the Lake Urmia (LU) as one of the regional environmental crises. This condition has threatened biota life, intensified desertification around the lake, and raised social concerns by adversely impacting the inhabitants' health and economy. In 2013, the Urmia Lake Restoration National Committee (ULRNC) started implementing certain management practices to stop the drying trend of LU, resulted in the cease of water level drop and stabilization of LU condition in 2016. Nevertheless, the restoration actions have not yet raised the lake to the water level as planned by the roadmap. This paper aims to describe and to assess the LU restoration plans by underscoring the ULRNC achievements, challenges, and shortcomings. In particular, we discuss how the value of data and data-aided modeling has been underestimated by the LU restoration programs, leading to still existing puzzles about the lake interaction with the involving physical processes governing its dynamics. We show how the LU restoration timetable has not fulfilled the planned milestones as evidenced by the inability to capture the anticipated lake water levels, which is partly attributed to the lack of field data and dynamic modeling that could predict the lake response in a more reliable and conservative manner. The current restoration plans should also be revisited to ensure that any practice with the aim of reducing water consumption in the basin is not only environmentally sustainable but also feasible from the socioeconomic perspective. The insights provided by this paper attempt to underscore the value of field data collection for establishing a reliable conceptual model, and for executing pre- and post-monitoring of the lake so that the success or failure of the restoration actions taken by the policymakers can be appropriately evaluated. [ABSTRACT FROM AUTHOR]

• A novel insight on forecasting influent flow is developed by hybridizing ANFIS-GWO. • Three horizons: Very immediate short-term, mid-term, long-term as 5-min to 10-days. • Different input combinations based on Gamma Test analysis are surveyed in horizons. • Direct and recursive hybrid strategies for multi-step forecasting are provided. • Taylor diagrams, box-plots, ECDF used, ANFIS-GWO direct and recursive are superior. Accurate influent flow forecasting plays a significant role in management, operation, scheduling and utilization of the sewage treatment plants. In design and operate such plants, it is essential to measure and forecast the influent flow rate in wastewater plants. In this paper, the Very immediate-short-term to long-term influent flow rate are modeled and forecasted by a new developed hybrid model of ANFIS and Grey Wolf Optimizer (GWO). The objective of this study is the integration of GWO with ANFIS in forecasting multi-ahead influent flow rate. The forecast horizon of the model is from 5 min up to 10 days bases on Gamma Test (GT) feature selection of input combinations. As the parameters of ANFIS have effect on the forecasting accuracy, these parameters are adjusted and optimized by using Grey Wolf Optimizer (GWO). Then the choice of appropriate input parameters at different prediction horizons from Very immediate-short-term (5-min ahead) to long-term (10 days ahead) was discussed for influent forecasting. The statistical indices of RMSE, NSE, MAE, RAE, R2, d, CI and graphical evaluations such as scatter-plots with confidence bounds, error distributions, Taylor diagrams, box-plots and empirical cumulative distribution function (ECDF) were implemented for assessing the performance of all models in prediction horizons. Furthermore as another novelty in the present paper, recursive forecasting models based on previous forecasted values is used to improve the accuracy and applicability of ANFIS-GWO in recursive predictions. Our Results showed that: (1) the hybrid of ANFIS-GWO significantly improved the prediction accuracy. (2) ANFIS-GWO performs more efficiently than the ANFIS in almost all of the prediction horizons (ANFIS-GWO1: 5 min ahead; ANFIS-GWO11: 1–2 days ahead; ANFIS-GWO8: one week ahead). (3) The performance of models in influent flow forecasting is significantly influenced by the prediction horizon. The computational results confirmed that the ANFIS-GWO performs well in all of prediction horizons. Equally the true values and the trends are precisely forecasted by the ANFIS-GWO. Results of this novel study demonstrate that reliable estimates of influent flow rate from 5-min up to 10 days in advance can be achieved using the developed direct and recursive hybrid GWO models. [ABSTRACT FROM AUTHOR]

• Coupled groundwater flow and transport can be simulated using Cellular Automata. • Groundwater flow Cellular Automata shows good agreement to MODFLOW. • Pollution transport Cellular Automata shows good agreement to MT3DMS. • Weighted Cellular Automata approach enables mass conservation in transport modelling. • High potential of implementing additional transport mechanisms. High urbanization puts many groundwater resources at risk of quality deterioration. Analyzing all viable potential groundwater contamination scenarios for good decision making requires reliable tool. Coupling several complex models in integrated modelling can often fail to perform in reasonable time. Possible solution in that case could be usage of simplified models in order to speed up long-term continuous calculations and simulations. The paper presents the application of the Cellular Automata (CA) approach in modelling of the contaminant transport under unsteady groundwater conditions. It compares the results obtained using coupled CA models with well-known analytical solutions and standard methods used for pollution transport modelling in groundwater conditions, such as coupled MODFLOW and MT3DMS. Results obtained in this paper show that CA approach can be satisfactorily used for simulations of unsteady groundwater conditions, caused by surface-groundwater interaction, and pollution transport, especially in diffusion dominant cases, gaining the reduction of simulation time up to 10 times. [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]

• Common baseflow separation techniques often ignore physical processes. • This paper presents a separation technique that emphasises physical relevance. • The technique is demonstrated on a range of catchment conditions. • Two calibrated parameters facilitate a good fit to observed conditions. The satisfactory separation of baseflow from the other components of streamflow has long been a desirable but elusive goal. In the search for consistent and automated baseflow separation techniques, direct applicability to physical processes has been increasingly neglected. By way of contrast, this paper presents a continuous baseflow separation method that emphasises the physical relevance of the flow components, and demonstrates its performance. The proposed method combines the principle of an exponential master baseflow recession with a compatible smoothing function to link the segments of the master recession. Two calibrated parameters facilitate a good fit to a range of streamflow behaviours. [ABSTRACT FROM AUTHOR]

• The five widely used calculation models for the unfrozen water content of freezing soils are evaluated. • The parameters in the five models have different effects on the change process of the volumetric unfrozen water content. • The five models have great potential to calculate unfrozen water content of soils in field. • This paper would provide significant reference for the selection of unfrozen water content models. Evaluation of calculation models for the unfrozen water content of freezing soil is very important for numerical simulations. Based on the published 250 laboratory data and around 5 years field observed data, this paper evaluated the five widely used unfrozen water content models, i.e., the Zhang et al. (2017a) model, the Anderson and Tice (1972) model, the Michalowski (1993) model, the Mckenzie et al. (2007) model, and the Kozlowski (2007) model. The results show that the five models can all be used to calculate the unfrozen water content of laboratory soil samples. The Anderson and Tice (1972) model, the Michalowski (1993) model, and the Kozlowski (2007) model are descripted by gravimetric unfrozen water content. However, it is difficult to directly measure the gravimetric unfrozen water content during the freezing process. The Mckenzie et al. (2007) model and the Zhang et al. (2017a) model are descripted by volumetric unfrozen water content which can be directly measured during the freezing process. Besides, the parameters of the Zhang et al. (2017a) model are relatively simple, although the model slightly overestimates the volumetric unfrozen water content for some laboratory soils. Therefore, the Zhang et al. (2017a) model is more convenient for numerical simulations. Furthermore, all the models have great potential to calculate unfrozen water content of freezing soils in field observations. This paper would provide significant reference for the selection of unfrozen water content models in the description of hydro-thermo coupling process in cold regions. [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]