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

• 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]

• 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]

• 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]

• 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]

• 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 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]

• 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]

• Basic factors affecting deep learning models for streamflow forecasting. • Hybrid deep learning models for streamflow forecasting. • Input and hyperparameter optimization of deep learning models. • Limitation and prospects in streamflow forecasting. Deep learning has emerged as a powerful tool for streamflow forecasting and its applications have garnered significant interest in the hydrological community. Despite the publication of several review articles on machine learning applications in streamflow forecasting, no review paper has yet focused explicitly on deep learning and its hybrid forms. This paper starts with some characteristics of deep learning models to provide a quick view of deep learning. Next, the configurations and characteristics of hybrid deep learning models, which is a hybridization of modeling techniques with deep learning, are discussed. Another vital role while implementing deep learning modeling is the methods applied for input and hyperparameter optimization. Finally, the limitations encountered in streamflow forecasting using deep learning models and recommendations for further research are outlined. This review covers related studies from 2017 to 2023 to provide the most recent snapshot of deep learning modeling applications in streamflow forecasting. These efforts are expected to contribute to the advancement of streamflow forecasting, potentially enabling more informed decision-making in water resource management. [ABSTRACT FROM AUTHOR]

• • The concept of hydraulic radius can be reinterpreted as a measure of large-scale turbulent flow structure. • • Hydraulic radius as proxy for large-scale eddies enables new framework for bed shear stress model and unified scaling of scour depth. • • Hydraulic radius formulation provides insight into characterizing large-scale flow structure by geometrical boundary conditions. Hydraulic radius has long been customarily used as a characteristic length to assess the cross-sectional geometry and efficiency of channels and conduits. This review paper revisits the concept of the hydraulic radius in light of its extensive applications in recent studies, with the aim of piecing together a state-of-the-art understanding of its physical underpinnings. The original definition of the hydraulic radius is presented as the ratio of cross-sectional flow area to wetted parameter, which inherently integrates the properties of the flow domain and the contacting surface. The use of hydraulic radius as a versatile characteristic length in classical Manning's formula and the Reynolds number underscores its significance in relation to flow resistance. Through a thorough review of its diverse applications, this paper provides a unified understanding of the physical meaning of hydraulic radius, highlighting its interpretation as a measure of large-scale eddies within a given flow domain. This new insight can be justified beyond uniform flows, as evidenced by its use in addressing sediment transport issues in complex vegetated flows, based on turbulence phenomenological theory. Furthermore, the original formulation of the hydraulic radius provides insights into how large-scale flow structure can be quantified with geometrical boundary characteristics, leading to a promising framework for scour prediction based on large-scale vortex size. This paper highlights the versatility and utility of hydraulic radius across various fields of fluid mechanics and sediment transport research, and proposes its potential for future studies as a proxy for large-scale flow structures. [ABSTRACT FROM AUTHOR]

• A FERM was established to integrate the risk of various dimensional conflict sources. • Different dimensional conflicts show diverse changed trends in the MRB. • The water conflict risk will remain at a medium degree in the MRB. • Stakeholders having a shared future is necessary to solve water conflicts. Improving the reliability of conflict evaluation and risk prediction methods used in transboundary water-sharing problems is an important research gap that needs to be addressed. This paper constructed a novel and comprehensive water conflict risk assessment framework that takes into account the multi-dimensional attributes of water resources. The reliability of each evaluation index is represented based on the triangular fuzzy number which can address the ambiguity and uncertainty. The fuzzy evidence reasoning model (FERM) was constructed to comprehensively calculate the risk level of water conflict in the transboundary river basin. The GM (1,1) model was used to predict the indicators for dynamic forecasting of the water conflict risk, and the water sharing problem in the Mekong River Basin (MRB) was selected as the case study to validate the method proposed in this paper. The results showed: (1) Except for Myanmar, the risk of water conflicts in other countries has decreased and will continue to decrease in the future. (2) The water quantity conflict risk in Laos and Thailand is increasing. (3) There is little change in the risk of water ecological conflict in riparian countries. (4) The risk of socio-economic conflict in Cambodia rises from high grade to very high grade, while the risk of economic and social conflict in other countries will decline. (5) From 1992 to 2027, water conflict in riparian countries have been on the decline. The comparison of the TWAP's (Transboundary Waters Assessment Programme) assessment and prediction of water conflict risk shows that the research results of the two are close to each other. Therefore, the proposed model could help to assess and forecast the water conflict risk in transboundary rivers. This assists riparian countries to take effective preventive measures to avoid water conflicts. [ABSTRACT FROM AUTHOR]

Hydraulic models in urban flood analysis play a decisive role, as they obtain information about flood extension, flow patterns on the surface and in the pipe network comes at cost of time consuming model set up and computing time. Dual drainage models do have the advantage of more accurate results as they consider bidirectional interaction between surface and sewers. This can be crucial as for certain rainfall events the sewer system can have a decisive impact. The aim of this paper is to modify 2D surface models and consider the performance of the sewers according to design standards for sewer systems, without the actual coupling process. Therefore a time varying approach, as inflow capacity into the sewers, was designed which reduces the rainfall depth before surface run off occurs. For a one-hour storm event, the novel approach indicates a higher performance (about 13 %) of the sewer system in the first half of the rainfall event compared to using a constant drainage removal rate. The presented approach in this paper will be tested in further research. • Considering performance of sewers in 2D surface models increases quality of results. • Accuracy of dual drainage models comes at costs of model set-up and computing time. • Novel Approach to modify 2D-surface models according to design standards of sewers. [ABSTRACT FROM AUTHOR]

• Driving factors of groundwater level variation have been evaluated and ranked. • Attention-GRU model shows the best performance on groundwater level prediction. • Ecological water supplement shows variance effects in the North China Plain. • Specific yield updates exhibit a significant influence during specific period. • Groundwater storage variation presents different features in the North China Plain. The continuous ecological river supplement has enhanced the recharge of groundwater in the North China Plain, and alleviated the depletion of groundwater resources. In the study of ecological river supplement, efficient quantitative prediction of groundwater level (GWL) and groundwater storage (GWS) is a crucial issue. To predict GWL and estimate the variation of GWS in the North China Plain, this paper uses the attention-gated recurrent unit (Attention-GRU) model as a primary method and coefficient of determination (R2) and root mean square error (RMSE) as the indices to evaluate model performance. Compared to traditional recursive neural network (RNN) and long short-term memory (LSTM) model, Attention-GRU model demonstrated an outstanding performance, as the RMSE is respectively 0.5 m and 2.28 m in the training and testing periods, and the R2 is 0.87 and 0.68. With the usage of the convolution neural network (CNN) model, the specific yield field was continuously updated from 2018 to 2021. The average annual variation of GWS estimated by this method is respectively −15.19 × 104 m3, −13.29 × 104 m3, 2.55×104 m3 and 100.05×104 m3 from 2018 to 2021 in the North China Plain. Besides, the semiannual GWS variation, calculated using the updated specific yield, is −59.56 × 104 m3 in 2022. Furthermore, the influence of the reference time length and the driving factors were evaluated. Results show that the precipitation, evaporation and human activities are the most important temporal features influencing the changes in GWS, and the changes in different areas have varying local characteristics. In the piedmont plain, river ecological recharge played a crucial role for groundwater storage recovery. When it moved to the coastal plain, the contribution rate of recharge decreased from 23% to 11%. In addition, the optimal reference time length used in Attention-GRU model varies in different hydrogeology divisions, and the specific yield variation would result in substantial differences for the GWS estimation under different climate conditions and human activities. The systematic framework proposed in this paper is effective for evaluating and predicting GWL and GWS, and thereby able to support the management and development of groundwater, and provide a guidance for ecological river supplement. [ABSTRACT FROM AUTHOR]

• A fractal model for the soil–water characteristic curve of Hunan clay was derived. • A fractal model for nonlinear seepage of saturated clay was established. • The model allows for easy computation and prediction of continuous permeation. • The prediction remains accurate even in soil with high dry density. It is crucial to investigate nonlinear seepage phenomena in low-permeability soils. Currently, empirical formulas account for the majority of nonlinear seepage model formulations. Some theoretical prediction models have been proposed; however, they tend to be computationally complex, and their predictive performance must be enhanced. Considered an indirect indicator, the soil–water characteristic curve reflects the permeation channels of soil pores of varying diameters. A nonlinear seepage model for saturated clay has been devised, taking into account the initial hydraulic gradient of microscopic seepage channels. This paper derives and solves the fractal dimension of the soil–water characteristic curve fractal model and establishes a new nonlinear seepage fractal model for saturated clay under various hydraulic gradients. The new seepage fractal model proposed in this paper is used to predict the seepage velocity of saturated Hunan clay under various hydraulic gradients and is compared to the results of its nonlinear seepage test. Overall, the results indicate that the predicted values of the seepage fractal model presented in this paper closely match the experimental values, and the accuracy of the prediction of the seepage velocity of saturated clay with a higher dry density exceeds that of present theoretical models. [ABSTRACT FROM AUTHOR]

• A type of model selection and combination strategy is proposed incorporating 16 different physically based models with LSTM technology. • A grouped model performance evaluation metric was proposed, which not only considers the performance of individual models, but also comprehensively evaluates the performance of a model group formed by multiple models. • It is found that the F score metric may not be suitable for evaluating model selection. Accurate prediction of runoff is an important foundation for optimizing water resource allocation and reservoir scheduling operations. However, due to its complex characteristics such as time-varying and non-stationary, accurate prediction of the runoff process is very difficult. This paper proposes a novel approach for runoff forecasting by combining the physical mechanism models with the Long Short-Term Memory network (LSTM) method. Utilizing the simulation and description capabilities of physically based models, as well as the powerful nonlinear analysis provided by big data methods, a type of model selection and combination strategy is proposed incorporating 16 different physically based models with LSTM technology. Additionally, to accommodate the comprehensive analysis and evaluation of multi-model forecasting performance, this paper also proposed a comprehensive evaluation metric for runoff forecasting considering the characteristics of group models. The results of the case study demonstrate that this strategy can obtain model combinations suitable for different watershed characteristics and effectively improve the forecast accuracy of multiple models. Model combinations sorted by validation period RMSE and R 2 should be a superior choice. When evaluating the runoff forecasting accuracy of obtained optimal model combination during the calibration period, the average reduction of Root Mean Square Error (RMSE) is 39.62%, and the average increase of Nash-Sutcliffe coefficient R 2 is 7.49%. During the validation period, the average reduction of RMSE is 62.68% and the average increase of R 2 is 24.24%. When the model combinations sorted by validation period F score, the obtained model combination increased RMSE by 22.7% and decreased R 2 by 7.3% comparing to the model combination selected by RMSE and R 2. It is indicated that the F score may not be suitable for evaluating model selection. The method proposed in this article can effectively improve the overall forecasting performance of a single forecasting model and has good practical value for promotion and application. [ABSTRACT FROM AUTHOR]

• • Development of a research framework for evaluating streamflow discharge and salinity variation in an estuary with multiple river outlets. • • Investigation of the impacts of freshwater discharge on the vertical and longitudinal distribution and spatial variation of salinity due to anthropogenic impacts. • • Identification of the key factors in determining salinity variation during a rainy season with the different scenarios of freshwater discharge. Estuarine salinity is one of the key factors affecting coastal ecosystems. However, due to impenetrable situation of mixing streamflow from river basins and seawater currents, our capability of evaluating salinity variation is far behind requirement in preventing coastal environment. Thus, this paper presents an integrated study on salinity variation in the Pearl River Estuary (PRE) in South China in the rainy season since the estuary would experience more salinity variation at the time. The PRE has a complicated water body with eight river outlets, four ones in the east and the others in the west. The paper develops a new research framework to evaluate the impacts of streamflow on the salinity variation, including adjusting diversion ratios of freshwater discharges to the estuary and using two numerical models for simulating basin hydrologic processes and estuary salinity variations. With the salinity observation from a field cruise in the PRE in July 2017 and the newly-developed framework, this study validates the numerical simulation results, detects the salinity variation under different scenarios of freshwater discharge, and discloses the environmental factors related to salinity variation. Specifically, the study shows the distinct characteristics of salinity variation in association with freshwater discharge and environmental factors, including seawater current velocity and topography in the east (a funnel-shaped bay consists of four narrow deep channels), and west (a sub-estuary of the Modaomen waterway) part of the estuary. The results indicate that salinity in the east part is more sensitive to streamflow variation with a farther freshwater-saltwater divide (about 25 km) away from the outlets than that in the west part (14 km). The estuary area affected by salinity variation due to different streamflow diversion ratios is more significant in the east (1,266 km2) which is 7.6 times larger than that in the west (166 km2). However, the values of salinity change due to different discharges are greater in the west (more than 2.0 psu in 34% of the affected area) than those in the east (all less than 2.0 psu). Moreover, the study shows that the seawater salinity variation in the east is greatly influenced by the distance to the outlet, freshwater discharge and seawater depth; while in the west, the salinity variation is largely determined by seawater current velocity. [ABSTRACT FROM AUTHOR]

• A novel method based on species diversity theory would assess preferential flow. • Effects of soil properties on the proportion of dye-stained areas (PDA) were studied. • Soil clay content and sand content primarily control PDA in upper soil layers. • Soil drainage capacity and bulk density primarily control PDA in lower soil layers. • Random forest algorithm could provide accurate and stable predictions for PDA. The preferential flow path development is potentially the result of spatial variations in soil properties with soil depth. However, visualizing the evolution of the preferential flow path with soil depth remains a challenge. This paper presents dye tracer and species diversity theory methods for characterizing preferential flow paths. Field dye tracer experiments were performed at three sites (tree, bush, and grass) in the Yellow River Delta wetland and dye distribution diversity indices (Simpson index (D s), Shannon-Wiener index (H), Margalef index (D m), and Pielou index (E)) were applied to verify their availability for preferential flow assessment. The results showed that the uniformity of the shallow-infiltrated dye at the tree site, non-uniformity of the shallow-infiltrated dye at the bush site, and deep dye infiltration at the grass site were the three typical infiltration types. The average proportion of dye-stained areas (PDA) gradually decreased with increasing soil depth. The quantitative effects of soil properties on PDA changes were profound, indicating that soil clay content at 0–10 cm depth, soil sand content at 10–20 cm depth, soil drainage capacity at 20–30 cm depth, and soil bulk density at 30–40 cm depth were the most predictive factors controlling PDA changes. Our results also showed that dye-stained patches with extremely high and high dye concentrations were the most distributed; D s , H , D m , and E were the highest at the tree site and E was the diversity index with the greatest importance for PDA change. The findings reveal the soil properties controlling the formation of preferential flow paths, which will improve our understanding of water resource management in the vadose zones of coastal wetlands. [ABSTRACT FROM AUTHOR]

[Display omitted] • Pumping test is conducted to study the building settlement during pit dewatering. • Building settlement is observed lagging behind the water level decline. • We reveal the reasons for the lagging settlement based on numerical analysis. Dewatering in a multi-layered aquifer-aquitard system often causes groundwater drawdowns in multiple aquifers, which together lead to obvious settlement of adjacent pile-supported building. This paper documents an in-situ pumping test conducted in a multi-aquifer system. It indicates that an adjacent pile-supported building settled continuously after water level was stabilized. To clarify the possible reasons for this phenomenon, a series of numerical models were established, validated and employed to explore the load transfer behaviour between pile and multi-aquifers during different dewatering conditions. Results indicate that the appearances of several aquitards in the multi-aquifer system significantly block the hydraulic connection between different aquifers, leading to non-synchronized developments of groundwater drawdown and pile shaft resistance at different soil layers during pit dewatering, which eventually causes the lagging settlement of the building. In practical pit dewatering in multi-aquifer system, engineers should appropriately increase the monitoring time for the deformation of adjacent pile-supported buildings for avoiding missing the maximum settlement. [ABSTRACT FROM AUTHOR]

• A procedure for transforming a DFN into a complex network by considering the intersection relationships among fractures is provided; • A novel method based on complex-network-based metrics for estimating the REV of a DFN is proposed; • An empirical formula for estimating the EPC using complex-network-based metrics is established; • The proposed estimation methods are validated through numerical experiments. The determination of the representative elementary volume (REV) and equivalent permeability coefficient (EPC) of fractured rock masses is crucial for studying the hydraulic behaviors of engineering rock masses. The complex network originated from graph theory and represents a rapidly developing interdisciplinary field in the 21st century. In this paper, we propose a novel method that utilizes complex-network-based metrics to estimate the REV and EPC of fractured rock masses. In this method, fractures and their intersections are treated as nodes and edges of a complex network, respectively. We propose a transformation procedure of a discrete fracture network (DFN) into a complex network, and the corresponding algorithms are conducted in MATLAB. Subsequently, we obtain a complex network, and several complex-network-based metrics, such as average degree, average clustering coefficient, and graph density, are calculated using Gephi software. We employ the average degree to estimate the REV size of DFN models, while the coefficient of variation is used to quantitatively assess changes in the average degree of multiple random models. Furthermore, we establish an empirical formula utilizing the average degree, average clustering coefficient, and graph density to estimate the EPC of a large-scale DFN model based on the EPC of a small-scale DFN model. To verify the developed method, we conduct numerical experiments using the MAFIC (Matrix/Fracture Interaction Code) solver within the FracMan software. This enabled us to calculate the EPC, which was then used to determine the REV. The results demonstrate that: (a) the REV size estimated by the average degree aligns with the REV size obtained through numerical simulations for both isotropic and anisotropic DFN models, and (b) the error rates between the average EPC calculated by the empirical formula and that obtained by numerical simulations are within 10%. Thus, the proposed seepage parameter estimation methods are deemed valid. [ABSTRACT FROM AUTHOR]

• The Stacking-Hybrid model is proposed by combining process-driven and data-driven models, considering multiple sources of uncertainty. • A series of comparison models are applied to verify the effectiveness of the proposed model in terms of deterministic, interval and probabilistic forecasts. • The Stacking-hybrid model has promising applications in real-time flood forecasting at an hourly scale. Accurate and reliable incoming flood forecasting is an important prerequisite for flood warning, flood risk analysis and reservoir flood control operation. This paper proposes a hybrid model for real-time flood forecasting that couples process-driven hydrological models (HMs) with data-driven models (DDMs). The generic hybrid model framework adds DDMs as the post-processing procedure for residual correction to the original results of HMs, and considers multiple uncertainties in input data, parameter and model structure simultaneously. The performance of the hybrid model is evaluated comprehensively in terms of deterministic forecast accuracy, interval forecast reliability, and the reliability and sharpness of probabilistic forecast. Taking the multireservoir system at the east Pi River as a study case, the results indicate that: (1) Compared to the benchmark model (ensemble XAJ model), the hybrid model with additional residual analysis show a significant improvement. The average continuous ranked probability score (CRPS) metric values calculated by the Stacking-Hybrid model improved by 71.5 %, 67.0 % and 38.1 % in the three data samples. Furthermore, the adaptability of the Stacking-Hybrid model for residual correction during short-duration intense rainfall events has been validated, with the relative error of the peak discharge improved to within ± 10 %. (2) The Stacking-Hybrid model, which also takes into account structure uncertainty, is able to better exploit the combined advantages and improve the stability of the model performance compared to those that only apply a single DDM. (3) When the number of iterations within the BOA reaches 300, the parameter optimization process is capable to search for the hyperparameters that bring out the best performance of the DDMs. (4) When the ensemble size reaches 200, the uncertainty of HM parameters can be fully defined, and the consumed computational resources can be controlled within an acceptable bound while ensuring stable model performance. Overall, the hybrid model that takes into account multiple sources of uncertainty generates both interval and probabilistic forecast in addition to deterministic forecast, which can provide richer risk information for subsequent flood warning and reservoir operation, making the flood prevention decisions more reliable and scientific. [ABSTRACT FROM AUTHOR]

• Analytical results to flow in unconfined aquifer induced by a decaying conductivity. • Exact solutions for drainage into penetrating channels at the aquifer boundaries. • Analytical transit time for calculating particle travel from water table to boundary. • Analytical inverse model to estimate decay parameter by solving a nonlinear algebraic equation. This paper presents some analytical results related to groundwater flow through an unconfined horizontal aquifer induced by areal recharge on the top and a decaying hydraulic conductivity. Under steady-state conditions, and in the context of Boussinesq equation, exact analytical solutions describing drainage into one or two penetrating channels at the aquifer boundaries have been derived in closed forms. The analytical expressions have been used to investigate the existence of water divide inside the unconfined aquifer. Based on the derived analytical solutions, a closed-form analytical formula is developed for calculating groundwater transit time within Dupuit-type flow system. The analytical transit time expression is simply expressed in terms of common mathematical functions. Analytical results are verified against a numerical model and show excellent agreements. The analytical transit time is validated against a numerical one obtained from a two-dimensional model representing a rectangular unconfined aquifer. Based on the explicit closed-form solution, a fully analytical inverse problem to estimate the decay parameter is proposed using a least-square objective function. The solution of the inverse problem simply corresponds to the solution of a nonlinear algebraic equation obtained from the definition of the objective function and the form of the analytical solution. [ABSTRACT FROM AUTHOR]

• Drought in Play facilitates comprehension of drought planning concepts and principles. • Fun, engaging, and accessible approach to promote equitable water resources management. • Playful approach to grasp interplay between hydrological and social dynamics. • Simulate impacts of planned and unplanned actions in drought crises. • Facilitate consideration of multiple uses when developing drought plans. Drought management requires a proactive approach and active involvement of stakeholders to enhance effectiveness and equity. However, there are challenges such as power imbalances and knowledge disparities. This paper proposes an innovative methodological tool to facilitate the understanding of key concepts, such as drought states and reservoir zoning, alerts for changes in drought state, risk predisposition, breaking down hierarchical structures of teaching and learning, contributing to more horizontal and participatory processes. We present "Drought in Play" from its creation and design to its application in two water systems in the state of Ceará, located in the Northeast region of Brazil. The game encourages active participation in the creation of a proactive drought plan for water systems and stimulates critical thought and proactive actions regarding drought and water resources management. The game was designed for a multidisciplinary team and tested with researchers that know the drought plans methodology and the challenges to build an effective drought plan. "Drought in Play" includes game elements (cards, boardgame) and simulations. We explore the participants' perceptions when using this tool to facilitate stakeholder participation in building a proactive plan to deal with drought and to enable water users to collectively manage a reservoir that is a common resource to all of them. The results suggest that "Drought in Play" can be used as a tool for socio-hydrological modeling, because it combines hydrological elements with social aspects such as decision-making, cooperation, and conflicts among participants. The game has potential to facilitate sustainable water resources management, enhance social learning, and provide a knowledge framework to address complex environmental challenges. It can be easily adapted to other regions with similar situations without requiring further modifications. [ABSTRACT FROM AUTHOR]

[Display omitted] • Complete phase diagram of pore solution at icing stage was presented. • Freezing point of saline soils was estimated with equivalent solute concentration. • Calculation method for eutectic temperature of saline soils was proposed. • Criteria for judging saline soils with one or two freezing stages was proposed. • SFCC shape index varying with soil matrix and initial solute concentration was found. Soil freezing characteristic curve (SFCC) describes the relationship between unfrozen water and subzero temperature, which is of significance for the simulation of heat, water, salt migration in cold regions. There are two phase transition stages in some saline soils, which is often explained by the phase equilibrium of bulk solution. However, the second phase transition and chemical characteristic of solute are ignored in the current coupling numerical models for freezing-thawing soils. The newest SFCC methods considering abovementioned two items are not applicable to numerical models due to complex calculation or irregularly changeable parameters. To solve those problems, a simple model was proposed in this paper. Firstly, the complete phase diagram of pore solution at icing stage was speculated from published experimental SFCCs of saline soils. Then, the temperatures of saline soils at freezing and eutectic points were quantitative expressed with phase diagram of bulk solution and SFCC of nonsaline soil, and the criteria determining whether single icing stage or icing-eutectic stage occurs in saline soils was proposed. A synthetic index was used to describe the effects of soil matrix and initial solute concentration on the exponent. Only three constant parameters were introduced to reflect the soil matrix effect on phase transition points of pore solution and the influence of chemical characteristic of solute on pore water freezing process. Finally, the model was validated by the experimental data of saline soils and showed good results and ease of use compared to a widely used theorical model for saline soil with single freezing stage and other two models both considering two phase transitions. The work provides a deeper view of freezing process in saline soils and promote the improvement of numerical simulation effect for heat, water and salt migration in seasonal freezing-thawing areas. [ABSTRACT FROM AUTHOR]

• A MGWR-based SMAP soil moisture product downscaling method is proposed. • The method considers the scale differences in the impact of surface variables on soil moisture. • Field validation shows higher accuracy from the downscaled SM than the SMAP SM itself. • This proposed downscaling method performs better than the traditional GWR and MW methods in the semiarid area. Passive microwave remote sensing missions such as Soil Moisture Active Passive (SMAP) are widely used for global soil moisture (SM) estimation. But the low spatial resolution of passive microwave SM products limits their applications at regional and local scales (normally 1–10 km). Using high-resolution auxiliary data to downscale passive microwave SM products is the primary way to obtain high-resolution SM data. However, the existing SM downscaling methods do not entirely account for the scale differences in the impact of various variables on the distribution of SM. To solve this problem, this paper introduced MGWR (Multiscale Geographically Weighted Regression) as a novel way to analyze the scale differences of normalized difference vegetation index (NDVI) and land surface temperature (LST) on the spatial pattern of SM. Accordingly, a new spatial downscaling algorithm for SMAP SM products is proposed and compared with the geographically weighted regression (GWR) algorithm and moving window algorithm (MW). The algorithm was applied to data taken during the SMAP Validation Experiment 2016 (SMAPVEX16), and the downscaled SM evaluated with in situ SM and aircraft observations. These results show that: 1) the SM downscaling conversion function, based on the MGWR, effectively reveals how different surface parameters relate to SM, with LST influencing SM globally and NDVI affecting it locally, 2) compared with GWR and MW, the 1 km SM obtained by the MGWR-based downscaling method showed better spatial agreement with airborne SM in the semiarid region with the ubRMSE decreasing by 0.023 m3/m3 and 0.026 m3/m3 for GWR and MW respectively, 3) when evaluating the 1 km SM with in situ SM in the semiarid region, the SM obtained through MGWR exhibited lower ubRMSE values as compared to both GWR and MW. Specifically, the median ubRMSE values for MGWR, GWR, and MW were 0.043 m3/m3, 0.053 m3/m3, and 0.094 m3/m3, respectively. In conclusion, this study demonstrates that MGWR can effectively improve the accuracy of downscaled SM in semiarid regions. [ABSTRACT FROM AUTHOR]

• Out-of-sample prediction of extreme precipitation using nonstationary models. • Significant trends in data of extreme precipitation are insufficient to warrant the selection of a nonstationary model. • Large prediction uncertainty reduces the support for nonstationary models of extreme precipitation. • (Non)stationary model selection should explicitly account for model complexity. Knowledge of the recurrence intervals of precipitation extremes is vital for infrastructure design, risk assessment, and insurance planning. However, trends and shifts in rainfall patterns globally pose challenges to the application of extreme value analysis (EVA) which relies critically on the assumption of stationarity. In this paper, we explore: (1) the suitability of nonstationary (NS) models in the presence of statistically significant trends, and (2) their potential in modeling out-of-sample data to improve frequency analysis of extreme precipitation. We analyze the benefits of using a nonstationary Generalized Extreme Value (GEV) model for annual extreme precipitation records from Southern Brazil. The location of the GEV distribution is allowed to change with time. The unknown GEV model parameters are estimated using Bayesian techniques coupled with Markov chain Monte Carlo simulation. Next, we use GAME sampling to compute the evidence (and their ratios, the so-called Bayes factors) for stationary and nonstationary models of annual maximum precipitation. Our results show that the presence of a statistically significant trend in annual maximum precipitation alone does not justify the use of a NS model. The location parameter of the GEV distribution must also be well defined, otherwise, stationary models of annual maximum precipitation receive more support by the data. These findings reiterate the importance of accounting for GEV model parameters and predictive uncertainty in frequency analysis and hypothesis testing of annual maximum precipitation data records. Furthermore, a meaningful EVA demands detailed knowledge about the origin and persistence of observed changes. [ABSTRACT FROM AUTHOR]

[Display omitted] • PINN correctly solves different types of forward diffusive wave problem. • The technique of time division improves the PINN performance. • A new neural network structure enhances the representation capability of PINN. • The rainfall pattern is correctly learned in the inverse diffusive wave problem. The diffusive wave model (DWM), a nonlinear second-order simplified form of the shallow water equation, has been widely used in hydraulic, hydrologic and irrigation engineering. Solution of the forward problem of the DWM can be utilized to predict evolution in water levels and discharge. Solution of its inverse problem allows for the identification of crucial parameters (such as Manning coefficient, rainfall intensity, etc.) based on observations. This paper applied the physics-informed neural network (PINN) with novel improvements to solve the DWM for both forward and inverse problems. In the forward problem, compared to traditional numerical methods, PINN was able to predict the evolution at any location. In the inverse problem, PINN provided a simple and efficient solution process. In order to overcome the gradient explosion in the training process caused by the characteristics of the DWM, the stop-gradient technique was adopted to train the neural network. To improve the estimation of DWM parameters, the concept of time division was developed, and a new network structure was proposed. To verify the effectiveness of PINN and its improved algorithm for DWM, seven examples were simulated. The PINN solutions for forward problems were compared with the results obtained by classical numerical methods, while the correct rainfall pattern was identified for the inverse problem. [ABSTRACT FROM AUTHOR]