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[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] • 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]

• 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]

• 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]

• • 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 new method was proposed to merge multiple satellite-based precipitation products (SPPs) • The method estimates grid scale errors of SPPs based on water budget closure. • The method was verified by comparing with original SPPs and three merging methods. • The performance of SPPs was slightly reduced when enforcing water budget closure. Merging multiple satellite/reanalysis-based precipitation products (SPPs) is a critical way to improve the accuracy of spatial precipitation (P) estimation. In this paper, a new method was proposed to merge multiple SPPs by considering rain gauge-based observations and water budget closure. Based on the errors of SPPs at gauged regions, the proposed method first estimates the mesh-based errors of SPPs as the intermediate variable using the error in water budget closure and then calculates the respective merging weights of SPPs in data merging. The performance of this proposed method was verified by comparing its results with the input of original SPPs and the data merged by five existing merging methods in mainland China at mesh scale and in nine major basins. Two scenarios were designed for the period from 2003.02 to 2016.12 at a monthly temporal resolution − with and without water budget closure constraints. Seven popular SPPs were considered, including GPM IMERG, PERSIANN, GSMaP, CHIRPS, ERA5, CFSV2, and TerraClimate. The results showed that the proposed method improved the P estimates in terms of CC, RMSE, MAE, PBIAS and KGE in both scenarios, and that closing the terrestrial water budget would slightly reduce the accuracy of SPPs against observations. The reduction percentage is about 5% in term of CC and KGE, and 12% in term of RMSE. This study is the first attempt to merge SPPs and gauge-based rain observation with consideration of water budget closure. It provides not only an effective and robust tool for the merging of multiple SPPs, but also important insights into the further development of SPP merging methods by considering water budget closure. [ABSTRACT FROM AUTHOR]

• Transition zones are the hotspots of bioclimatic changes under global warming. • Global warming extends growing season lengths but increases heat and water stresses. • Sustainable grasslands in China shrink and shift northwestward with global warming. Bioclimatic response to global warming is a key linkage between anthropogenic climate change and ecosystem sustainability, particularly for the impact-prone climate transition zones. This paper examines changes in heat-water conditions from 2006 to 2100 across China, using the fifth generation climate reanalysis from the European Centre for Medium-Range Weather Forecasts (ERA5) and bias-adjusted climate projections by the Coupled Model Intercomparison Project Phase 5 (CMIP5). We then assess the effects of these changes on habitat suitability for responsive ecosystems using a species distribution model. The results reveal significant changes in bioclimatic environments across dry–wet climate transition zones under the present-day climate and the RCP8.5 scenario. By the end of the 21st century, local growing season length (GSL) could extend by 60 days and 30 % of the GSL could be under heat stress. Water conditions worsen overall in both the air and the soil, despite some improvements in the middle parts of transition zones. With altered heat-water conditions, sustainable grasslands shift northwestward, with a reduction of approximately 131 million km2 (46.6 % relative to 2006–2010) in total area. These changes primarily arise from alterations in winter and spring heat-water conditions. Our findings highlight the climate transition zones as hotspots of ecosystem vulnerability demanding particular attention in impact assessment and adaptation studies. [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]

• A detailed model is formulated to optimize the reservoir operation. • The trade-offs among social, economic and ecological benefits are presented. • Operational strategies are compared for achieving the comprehensive benefits. Balancing the multiple purposes of reservoir operation, including the social, economic, and ecological aspects, has become one of the most complicated problems facing the managers and operators worldwide. This paper formulates a detailed mathematical model to optimize the reservoir operation during the flood season, considering the three major operational purposes, i.e. flood control (social purpose), energy production (economic purpose), and management of sediment transport (ecological purpose). The entire formulations conform to a nonlinear programming (NLP) model, which can be solved effectively and efficiently by the Multi-Start Solver from the LINGO software through equivalent transformation. This model is applied to the Three Gorges Project (TGP) in China, which is the world's largest and most complex hydraulic engineering in operation. Three typical flood hydrographs (i.e. dry, normal, and wet scenarios) are selected as model inputs under the three strategies (i.e. design operating rules, increasing the flood control water level (FCWL), and impounding in advance). The trade-offs among these three major operational purposes are quantitatively analyzed. Moreover, phosphorus (P), one of the sediment-associated environmental factors, is introduced for exploring its responses to various scenarios and strategies. Results indicate that a higher fore-bay water level leads to a greater energy production, a smaller flood peak but less sediment and P transports to the downstream. Under the design operating rules, the maximum energy production of the TGP can reach 560, 620, and 699 × 108 KWh, respectively, in dry, normal, and wet scenarios, corresponding to a flood peak of 25000–27500 m3/s, a sediment load of 1517–1999 × 104 t, and a P load of 3152–3740 × 104 kg to the downstream. Whereas, the maximum sediment loads are 2796, 4640, and 5377 × 104 t, and the maximum P loads are 4021, 5345, and 5784 × 104 kg, respectively, in dry, normal, and wet scenarios, corresponding to an energy production of 500–588 × 108 KWh, and a flood peak of 38600–52500 m3/s to the downstream. In terms of achieving the comprehensive benefits of the TGP, impounding in advance is a better choice than both the design operating rules and increasing the FCWL, for it can increase the energy production and the probability of full refill with almost no increase in flood risk to both the dam and the middle and lower reaches of the Yangtze River as well as smaller reduction in the sediment and P transports. The formulated detailed mathematical model is a general one that is applicable to a variety of reservoirs owning multiple operational purposes. Moreover, the achievements gained from this paper will provide important references for the managers and operators. [ABSTRACT FROM AUTHOR]

• A spatial random forest-based downscaling and merging method (SRF-DM) is proposed. • SRF-DM performs better than the original satellite-based precipitation products. • SRF-DM is more accurate than the classical machine learning-based methods. • Spatial autocorrelation is the most important variable. • SRF-DM has a good performance even when the gauge density is low. Publicly available satellite precipitation products (SPPs) are essential inputs for hydrological models. However, the existing SPPs suffer from coarse resolutions and large uncertainties, which greatly hinder their widespread applications. Thus, to fully utilize the unique characteristics of each individual SPP, a spatial random forest (SRF)-based downscaling and merging (SRF-DM) method is proposed to fuse multiple SPPs and gauge observations in this paper. The proposed method incorporates spatial autocorrelation information between precipitation observations through a covariate. This covariate is estimated using ordinary kriging interpolation on spatial neighbors that have a high correlation with the target location. The proposed method was used to generate 1-km daily precipitation data using the datasets from five satellite precipitation products (IMERG, GSMaP, CHIRPS, MORPH, and PERSIANN) and gauge observations in the period from January 1, 2015 to December 31, 2019 over Sichuan province, China. The performance of SRF-DM was compared with the original SPPs, XGBoost-based downscaling and merging (XGB-DM), two double-layer machine learning (ML) methods, namely RF-RF and RF-artificial neural network (RF-ANN), and three single ML methods, namely RF, ANN, and RF-based Merging Procedure (RF-MEP). The experimental results demonstrate the following findings: (i) SRF-DM is more accurate than the original SPPs across different temporal scales (i.e., daily, monthly, and seasonal) in terms of statistical and categorical accuracy measures, (ii) SRF-DM outperforms the six ML-based methods in estimating high precipitation and capturing spatial precipitation patterns, (iii) the spatial autocorrelation between neighboring precipitation data is the most significant factor in both downscaling and merging, and (iv) the performance of SRF-DM is influenced by gauge density, but it still yields good results even with a limited number of gauges. On the whole, SRF-DM is considered an effective tool for generating daily precipitation products with high accuracy and resolution, which is primarily attributed to its satisfactory performance in regions with diverse precipitation patterns and topography. [ABSTRACT FROM AUTHOR]

• The counter-demand method effectively simulates water diversion and distribution. • Successfully resolve the simulation of water extraction-conveyance-use in the Karez. • The average annual groundwater overexploitation was 173 million m3 in the ALB. • Excessive extraction of groundwater poses a threat to nature vegetation survival. The long-standing, irrational development and exploitation of water resources in the Aiding Lake Basin (ALB), coupled with excessive groundwater extraction, have led to the gradual depletion of Aiding Lake, the disappearance of Karez wells, the depletion of spring water, and the deterioration of the ecological environment, all of which are serious threats to the economy and survival and welfare of inhabitants. To address the complexities associated with the staggered distribution of natural rivers and artificial canals within the agricultural irrigated region of the ALB, an inverse computation method to gauge canal water requirements was used in building an intelligent simulation of water diversion and conveyance, incorporating different water use methodologies for complex canal systems within the irrigated region. Additionally, this paper characterizes the interaction between the catchment and conveyance section of the Karez wells and the underground aquifer based on the seasonal river module to simulate the entire flow process of the Karez well system, from water extraction and conveyance to groundwater recharge by infiltration. This study presents a coupled simulation model of seasonal river–lake-groundwater interactions in the ALB, where groundwater is simulated under both steady and unsteady flows. The findings indicate that from 2013 to 2022, the annual average groundwater recharge within the ALB was approximately 1.048 billion m3, while the discharge was 1.221 billion m3, with an annual average groundwater overexploitation of 173 million m3. Furthermore, the long-term overexploitation of groundwater has had a direct impact on the ecological environment, resulting in a survival crisis for natural vegetation except in the upper vegetative zone of Aiding Lake, which has good groundwater recharge conditions. [ABSTRACT FROM AUTHOR]

[Display omitted] • The drought pattern in China is closely related to the basin distribution and multiple teleconnection factors. • The intensity and frequency of drought in China exhibited an increasing trend. • The ENSO-AO had important positive effects in the major basins in China. • Multiple wavelet coherence helps identify the processes controlling drought changes. Due to increasing global warming and the frequent occurrence of severe drought risk in recent decades, many studies have investigated drought in China. However, the spatiotemporal heterogeneities of droughts are complicated, and a comprehensive analysis of the driving forces has not been thoroughly explored. In this paper, we studied the temporal and spatial patterns of drought in China from 1960 to 2018 and disclosed its relationship with multiple teleconnection factors on different time scales. The results show that (1) Drought in China has exhibited a fluctuating increasing trend over the past 59 years, with a standardized precipitation–evapotranspiration index (SPEI) rate of change of –0.12/10 a, and the most severe droughts occurred during 2000–2010. (2) There was a significant abrupt change in China's meteorological droughts induced by climate change around 1997. (3) The trend in China was characterized by drought in spring and autumn and humidification in winter, while the trend in the Southwest River Basin was characterized by drought. (4) A combination of factors can best explain the drought variations from basin to basin, and the El Niño-Southern Oscillation (ENSO)-Arctic Oscillation (AO) was most commonly the best predictor. The use of multiple wavelet coherence helps to identify the dominant drought change processes. [ABSTRACT FROM AUTHOR]

• A framework is proposed to analyze compound floods with multiple driving factors. • Local rainfall has a larger impact on the flooding levels of the GBA, China. • Flooding area is about 50 km2 and 1.3 m flooded depth for the worst scenario. • Findings have important implications to GBA's flooding control and management. Low-lying coastal cities are vulnerable to compound floods caused by many factors including river flows, tides and local rainfall. Many previous studies focus on the impacts of rainfall and tidal levels (two driving factors) on estuaries or regions near the main single river, while research about the three influencing factors on the floods for complex urban river networks remain relatively scarce. This paper proposes a framework to simulate the coastal compound flooding process, with a series of scenarios designed to explore the main flooding drivers and their worst possible combination. The approach is used to quantify the induced flooding consequences, in which a two-dimensional model is employed to conduct a case study for a city with complex river networks. The Qianshan River basin in Zhuhai City, located in the Guangdong-Hong Kong-Macao Greater Bay Area (GBA) of China is used as the case study. Application results show that: (i) the occurrence and properties of flooding in this coastal area are jointly affected by upstream river flows, downstream tidal levels and local rainfall intensity with different contribution levels, (ii) compared to tidal levels, local rainfall intensity has a larger impact on the flooding levels of the study region, and (iii) the worst scenario where a 50-year return period upstream river flow co-occurring with high tide and a 100-year return period rainfall can produce a flooding area up to 50 km2 for the study region, with an average of 1.3 m flooded depth. The proposed framework with its findings offers an in-depth understanding and quantification of coastal urban flood characteristics within the GBA of China. [ABSTRACT FROM AUTHOR]

• Shale artificial fracture core models were developed with rough fracture surfaces. • Mineral hydration impairment on artificial fractures conductivity was revealed. • Mechanism of conductivity impairment were investigated from various aspects. • Hydration inhibitor increase fracture conductivity by 3.4 and 2.6 times, respectively. • Other factors impair fracture conductivity and implications were discussed. The shale gas fields are mainly in mountainous areas in China. Surface water is the major source of hydraulic fracturing fluid to develop shale gas. The clay mineral is generally rich in the shale gas reservoir. When encountered with low salinity surface water, the clay minerals are easy to hydrate in the process of fracturing and well shut-in. The hydration of clay minerals would result in the expansion, dispersion, and migration of mineral particles from the fracture surface, changing the structure and space of fractures, thereby affecting fracture conductivity. In this paper, shale artificial fracture core models were developed with rough fracture surfaces. With proppant-supported and self-supported fracture core models, the impact of hydration time, net pressure, and dislocation distance of fracture surfaces on the artificial conductivity were evaluated systematically. The results show that the conductivity of proppant-supported and self-supported fractures decreases by 85.3–95.1% under hydration. The turning point of conductivity is 8.0 hrs. and 4.3 hrs., respectively. At the initial stage of production, the artificial fracture could be protected better if the net pressure on the fracture is maintained from small to large. At the same time, the initial net pressure on fracture should be less than the proppant crushing pressure. Certain dislocation distances of self-supported fractures would augment the conductivity by 6.4–12.8 times. After hydration, illite and montmorillonite in the rock surface expanded and dispersed when encountered with water. The fracture surface strength decreases by 29.8%, while the surface roughness decreases by 46.1–51.6%, and some proppants were crushed. Then the fracture conductivity space was reduced. By adding KCl into the fracturing fluid, hydration can be effectively inhibited, which can increase the proppant-supported and self-supported fracture conductivity by 2.5–3.4 times, compared with fracturing fluid prepared with surface water. This study provides a theoretical basis for the optimization of shale gas reservoir fracturing technology and production strategy. [ABSTRACT FROM AUTHOR]

• Geodetector model offers a promising method for downscaling variables selection. • GWR is suitable for downscaling before calibrateing satellite using gauge observations. • Newly proposed GWR_Bi-LSTM framework outperformed the comparative. • Calibrated precipitation demonstrated better accuracy than the original IMERG. Accurate precipitation datasets with a high spatiotemporal resolution are crucial for regional hydrological simulation and water resources management. Although various global satellite precipitation products (SPPs) are available, they cannot be directly applied to small-scale research due to their low spatial resolution and significant bias. To solve these problems, based on the consideration of spatiotemporal relationships, this paper presented a comprehensive downscaling-calibrating framework that consists of spatial downscaling procedures and calibrating processes. Specifically, a geographically weighted regression (GWR) model, coupled with the environmental variables selected by the Geodetector model, was applied to generate uncorrected daily precipitation data with high-spatial resolution (1 km × 1 km). Then, the preliminary downscaled daily precipitation was calibrated using a bidirectional long short-term memory (Bi-LSTM) network. The proposed framework was applied to downscale and calibrate the Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (IMERG) product of the Jiulong River Basin in southeast China from 2010 to 2016. The results indicate that: (1) the spatial downscaling procedures improved the spatial resolution of the SPPs while maintaining its accuracy; (2) the calibrating processes can effectively correct the bias of preliminary downscaled daily precipitation. Meanwhile, the accuracy evaluation showed that the proposed GWR_Bi-LSTM framework outperformed the comparative methods, and the generated precipitation dataset exhibited a better performance under various evaluation conditions. Generally, based on the proposed framework, gridded daily precipitation data with high quality and spatial resolution can be generated for hydrological and meteorological research. [ABSTRACT FROM AUTHOR]

• Calculation unit division induced soil spatial aggregation in SWAT model. • Effect of soil spatial aggregation on simulation was separated and quantified.Soil spatial aggregation caused a significant increase in annual runoff and sediment load. • The effect of soil spatial aggregation on sediment load was greater than on runoff. • Optimal sub-watershed division scheme was discussed. Distributed hydrological model divides watersheds into distinct calculation units to simulate hydrological processes. Within the model, different calculation unit division schemes induce input elements, such as soil and land use, to certain degrees of generalization, thus affecting the simulation result. Analyzing the effect of the calculation unit division on the simulation result is an important methodology to reduce uncertainty and improve the simulation accuracy of the model. Previous researchers have focused on the comprehensive effect of underlying surface generalization (such as soil, land use, and terrain generalization) caused by the calculation unit division on the simulation results, while it did not separate the effect of the generalization of the individual input factor on the simulation result. In this paper, the Dongjiang headwater region in southern China was selected as the study area. The single type of land use and slope were input into the Soil and Water Assessment Tool (SWAT) model to fix the effects of land use and terrain caused by calculation unit division on the simulation results. Eleven calculation unit division schemes were used to analyze the impact of soil spatial aggregation on runoff and sediment load simulations. The calculation unit division schemes included 11 sub-watershed division schemes and their hydrological response unit (HRU) thresholds were set at 8 %. The drainage area thresholds of the 11 sub-watershed division schemes were 30 ha, 60 ha, 120 ha, 240 ha, 480 ha, 1000 ha, 1600 ha, 2200 ha, 2800 ha, 3400 ha and 4000 ha. The results demonstrated that the soil spatial distribution showed an obvious aggregation with an increase in the drainage area threshold in the SWAT model. Of these, the Krasnozem soil type which covers the largest area in the watershed, increased significantly with an increase in the drainage area threshold. However, the areas of all the other soil types decreased significantly. The Lioth soil type covers the smallest area in the watershed decreased and even disappeared when the threshold in the SWAT model was greater than 1000 ha. Furthermore, soil spatial aggregation caused a significant increase in the annual runoff and sediment load (P < 0.05), with the sediment load (Re = 29.78 %) increasing at a greater rate compared to runoff (Re = 1.74 %). Increased soil aggregation significantly increased the sediment loads in the maximum 1-day, the continuous maximum 5-day, the continuous maximum 7-day, and the sediment load in flood processes (P < 0.001). Refining the calculation unit division scheme will cause a significant increase in the number of calculation units. Due to the computer processing power limitations, we cannot further refine the calculation unit division scheme. Based on our current operational calculation unit division scheme, we did not identify the optimal division scheme in the study region. These results can provide reference information for future research into hydrological model uncertainty and can improve the SWAT model simulation accuracy. [ABSTRACT FROM AUTHOR]

• The NPPRI, MCR and SWAT models are superimposed in space to identify the critical source areas. • On the basis of analyzing the influence of BMPs on the landscape characteristics of watershed, the response relationship between NPS and landscape pattern are further revealed. • Based on landscape pattern theory and information entropy method, the best combination management measures for watersheds are proposed and evaluated. The deterioration of ecological environment caused by non-point source pollution (NPS) has seriously threatened human health and socioeconomic development. Effective identification of critical source areas and selecting appropriate best management practices (BMPs) for watersheds are the keys to controlling NPS. This paper, using Hanjiang River basin, the vital water source for the South-to-North Water Division, China, as a case study, presents a method for accurate critical source areas dentification according to the spatial distribution characteristics of underlying surface and pollution load by coupling NPS risk index (NPPRI), Minimum Cumulative Resistance (MCR) Model, and Soil and Water Assessment Tool (SWAT). In this research, ten single and three combined scenarios are set up in the critical source areas, including three non-engineering management practices, two engineering management practices, and five ecological buffer zones. Finally, the effect of BMPs for non-point source pollution is assessed by using landscape pattern theory and information entropy method. Furthermore, the influences of the five ecological buffer zones on the characteristics of landscape in watershed and the response relationship between the landscape pattern and NPS are analyzed. The following findings are obtained: (1) The critical source areas were mainly located in the south-central and southeastern part of the study area, accounting for 35.9 % of the total watershed area, and yielding 54.2 % and 60.7 % of the total nitrogen(TN) and total phosphorus(TP) loads in the watershed; (2) All ecological buffer zones play a positive role in NPS control with an average reduction rate of 32.1 % for TN and 46.7 % for TP, and the control effectiveness for TP are better than that for TN; (3) The deployment of the five ecological buffer zones have a significant impact on the interspersion juxtaposition index (IJI), the connectivity of the "sink" landscape is enhanced, the diversity of the "source" landscape is reduced and the "sink" landscape's ability of interception is boosted; (4) The calculation result of information entropy shows the highest attribute value of practices in the ecological buffer zones is Buffer 200 m. Overall, the findings serve as a reference for NPS control and water environment management in the watershed and strengthen the feasibility of implementing of BMPs in the watershed. [ABSTRACT FROM AUTHOR]

[Display omitted] • A network modeling framework suitable for various types of heterogeneous risk factors in urban floods has been proposed. • Simulation of the cascading effects of heterogeneous hazard-bearing bodies in the city from a functional availability perspective under continuous flood attack. • The heterogeneous hazard-bearing bodies are interdependent and networked, which causes flood damage to spread more quickly and widely. • The greater degree and more propagating hazard-bearing bodies are key factors influencing the spread of flood risk and the cascading failure of functionalities in urban areas. Increasing urban flooding posed serious threat to urban safety and sustainable development. The heterogeneity of hazard-bearing bodies is one of the important reasons for the variability of cascading failure scenarios in floods. Most previous studies have focused on failure propagation risks among specific types of hazard-bearing bodies, making it impossible to conduct a systematic analysis of the damage caused by persistent flooding in cities. In contrast to conventional graph representations, the heterogeneous hazard-bearing bodies network (HHBBN) based on heterogeneous functional graph theory was constructed in this paper and used to capture the relevant associations of urban resources in a flood. The HHBBN takes into account heterogeneity such as sensitivity and service diffusivity, and functionalities related to hazard-bearing bodies are represented as nodes, with service relationships and geographical associations depicted as edges. To further study the performance change of HHBBN in urban areas during rainstorm flooding, the cascading failure of HHBBN under continuous disturbance was also simulated. The proposed network modeling and cascading failure simulation methods were tested in Futian District of Shenzhen City, China. The case results demonstrate that the heterogeneous hazard-bearing bodies are interdependent and networked, which causes flood damage to spread more quickly and widely. Furthermore, the larger degree and more propagating hazard-bearing bodies are the key factors affecting the cascading failure of heterogeneous hazard-bearing bodies. [ABSTRACT FROM AUTHOR]

• The effect of time delay phenomenon in complex groundwater systems is stressed when spring discharge prediction methods based on deep learning are applied. • A delay-aware spring discharge prediction (DSDP) model is developed. • DSDP model outperforms baselines on a real karst spring and can predict the general trend of spring discharge. As an essential component of the hydrological cycle at both regional and global scales, karst aquifers store large amounts of water, serving as the main source of fresh water supply in many areas. Karst springs provide a natural drainage pathway for karst groundwater systems to participate in the hydrological cycle. Accurate prediction of karst spring discharge is of great value to the long-term planning and management of karst water resources. The mainstream methods proposed have achieved high prediction accuracy by introducing deep learning technology. However, these methods ignore the delay time of spring water discharge to water supply or drainage, so there is still room for improvement in prediction accuracy. To address this issue, this paper proposes a delay-aware spring discharge prediction (DSDP) framework that introduces the delay time to predict spring discharge. Specifically, the framework consists of three modules, including a time calibration module, a variation estimation module and a discharge prediction module. The time calibration module identifies the delay time of spring discharge to each element to calibrate the observations. And the variation estimation module utilizes a neural network to infer spring discharge variations. Finally, the discharge prediction module predicts spring discharge with derived discharge variations. This study validates the performance of the proposed method by using spring discharge data from Jinci Spring, a typical karst spring in China. The experimental results show that the proposed method outperforms baselines, with significant decreases in both MAE and RSM. The study will provide evidence for the precise management of Jinci Spring, and provide a methodological reference for the discharge prediction of other springs. [ABSTRACT FROM AUTHOR]

• The dam-break induced cascading hazard chain leading to a catastrophic event. • This paper used a typical outburst debris flow to describe the catastrophe process. • The flow amplification effect was analyzed based on the obstructive coefficient. • The movement process of outburst debris flow was simulated and validated. • Different scenario simulations were performed under the new mitigation projects. Strong earthquakes can induce large amounts of loose solid materials, forming landslide dams and blocking narrow flow channels. Under extreme rainfall conditions, instability can lead to an outburst of debris flow once the landslide dam has formed it. On July 10, 2013, a volume of 4.5 × 104 m3 of debris flow occurred in the Yangling gully, Wenchuan County, China. This event can be categorized as an outburst of the debris flow that might seriously threaten valley residents, existing sediment transport countermeasure works, and road infrastructures. This paper describes a typical outburst of debris flow, calculates its obstructive coefficient, simulates the process under various recurrence intervals, and analyzes the efficacy of a new mitigation project. Related research results have laid the foundation for further study of the risk assessment and early warning of debris flow due to landslide dam failure. In this paper, an accurate computational model for the obstructive coefficient, closely related to the flow amplification effect, is established based on a dam break's physical experiments. Combined with the numerical calculation method, the movement process of the Yangling gully was simulated under the old artificial dam and the flow channel engineering conditions. By comparing the measured and the simulated accumulation area, the verification results illustrate that the accumulation area considering the obstructive coefficient model is closer to the measured value. Based on the obstructive coefficient computational model, the results illustrate the outburst of debris flow discharge for various recurrence intervals (50, 100, and 200 years respectively) to perform scenario simulations of Yangling gully under the new mitigation project conditions. [ABSTRACT FROM AUTHOR]

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

• We summarized and discussed about antimony pollution in water environment and its sources. • Research on toxicity of antimony was documented. • Occurrence and biogeochemistry of antimony in water environment were discussed. Antimony, the compounds of which exhibit toxicity, finds extensive usage in industrial processes for flame retardants, plastics, batteries, electronic components, and mechanical bearings. Consequently, mining and industrial activities contribute to the continuous migration of antimony into aquatic environments, influencing the Earth's biogeochemical cycle. Given China's status as the primary global exporter of antimony, the country confronts the challenge of water pollution arising from intensive antimony mining operations. This paper focuses on the latest advances in research concerning antimony toxicity, identifies potential sources of antimony water pollution, and explores its migration pathways and biogeochemistry. Moreover, the study provides estimations of antimony quantities in different compartments. Finally, we discuss the prospects for enhancing our comprehension of antimony's biogeochemical behavior. [ABSTRACT FROM AUTHOR]

• Graph neural networks (GNN) model is established to simulate spring discharge. • The model is applied to Niangziguan Springs, the max karst spring in North China. • Three types of graph structures of GNN are constructed, and we find the best one. • After comparing ChebNet and graph convolutional network, we find ChebNet is better. Over past decades, extensive groundwater development in karst aquifers has led to significant declines in groundwater levels and spring discharges. While the recent implementation of sustainable karst aquifer development policies appears to have lessened the declining trend of spring discharge, many aspects of karst aquifers remain to be explored. This paper applied two spectral-based graph neural networks (GNN) models of ChebNet and graph convolutional networks (GCN) for karst hydrological processes by considering the correlations among precipitation, spring discharge, and human impacts. This approach captures the spatial dependence of precipitation infiltration, groundwater propagation in heterogeneous karst aquifer and spring discharge from bared karst aquifer. This study proposed three graph structures (complete graph, information flow graph, and association graph of groundwater flow field) to depict relations between precipitation and spring discharge in the GNN models in Niangziguan Springs, China. The results show that the association graph of groundwater flow field is the optimal graph structure of GNN models. Based on the optimal structure, we investigated precipitation-driven-discharge models using ChebNet and GCN to predict the discharge. The results show that the high-order ChebNet is more adaptable to simulate the karst hydrological processes with nonlinear and nonstationary behaviors than that GCN. Moreover, the study confirms that the groundwater sustainable development policy has achieved a prominent contribution 2 m3/s in water conservancy in Niangziguan Springs catchment. [ABSTRACT FROM AUTHOR]

• ADP is proposed to avoid the redundant computation in DP's recursive equation. • LSTM-based response surface model reduces intensive power output calculations. • ADP uses less time to provide satisfying results for hydropower reservoir operation. Dynamic programming (DP) is a classical method developed to address the multi-stage hydropower reservoir operation problem, but still suffers from the serious dimensionality problem where the computational burden increases exponentially with the number of state variables. To improve the DP performance, this paper proposes a LSTM-based approximate dynamic programming (ADP) method for complex hydropower reservoir operation optimization. In ADP, the long short-term memory (LSTM) is treated as the response surface model to reduce redundant computations of power outputs in DP's recursive equation, making obvious improvements in the execution efficiency. To fully assess its feasibility, the ADP method is used to find the scheduling schemes of a real-world reservoir system in China. Simulation results show that compared with the standard DP method, ADP effectively reduces the execution time while guarantee the solution quality in different cases. In the 1000-state and wet-year scenario, the ADP method achieves approximately 86.7% and 85.8% reductions in DP's computation time for Longyangxia and Laxiwa reservoir with the goal of maximizing power generation. Thus, the LSTM-based response surface model is an effective tool to improve the DP performance in the hydropower reservoir operation problem. [ABSTRACT FROM AUTHOR]

[Display omitted] • An improved COPK method is proposed for the covered karst groundwater vulnerability assessment, which is modified from the widely used COP method according to the unique hydrogeological features in northern China. • The COPK method is used in the Baotu Spring basin in Jinan City as a typical representative. • Most of the study area exhibits moderate to low karst groundwater vulnerability. • The nitrate concentration in karst groundwater demonstrated that the improved COPK method has much better performance than the original COP method. Karst in China is characterized by a wide distribution, diverse forms, well-developed features, and excellent preservation, making it a representative example on a global scale. The semi-arid karst in northern China, widely distributed in many provinces, has unique hydrogeological features, including covered karst, weak karst development, thick aquifers, highly interconnected fissure network, and numerous karst springs, making it difficult to directly apply the COP method which is widely used in karst groundwater vulnerability assessment in this area. Additionally, the quality of the karst groundwater in many northern China cities is facing a threat in recent years, making it urgent to evaluate the vulnerability of the karst groundwater. This study aims to develop an improved COPK method for the covered karst groundwater vulnerability assessment modified from the widely used COP method, and to use it in the Baotu Spring basin in Jinan City, as a typical representative of the covered karst area in northern China. The assessment result by the improved COPK method shows that most of the study area exhibits moderate to low karst groundwater vulnerability, and the area with very high to high vulnerability is situated in the Middle Ordovician-Upper Cambrian Chaomidian Formation limestone exposed area. The above results by the improved COPK method are more consistent with the hydrogeological conditions than those by the original COP method. A comparison of nitrate contamination map to the groundwater vulnerability map reveals the more efficient performance of the improved COPK method compared to the original COP method in vulnerability assessment of the typical covered karst areas in northern China. This paper can provide a reference for karst groundwater vulnerability assessment of covered karst areas in northern China, as well as other countries and regions with similar hydrogeological conditions, which can provide valuable support for groundwater pollution control efforts. [ABSTRACT FROM AUTHOR]

[Display omitted] • A novel framework is proposed to assess the risks of algal blooms in lakes. • Vine Copula was used to integrate the quantity-distribution of algal blooms. • The framework was used effectively in risk assessment of Chaohu Lake, China. • The framework can handle the problem of spatio-temporal dynamics in risk assessment. Algal blooms threaten water safety and quality, particularly in lakes with vulnerable environment and advanced urbanization. Due to low water exchange rate and multiple human activity influences, algal blooms in inland lakes display highly dynamic spatio-temporal distributions. This paper proposes a comprehensive framework for risk assessment of algal bloom coupling quantity and distribution based on remote sensing observations. The framework comprises algal bloom recognition, spatial zonation, quantity-distribution multivariate joint distribution construction, and risk assessment. The framework was applied to China's giant shallow and algae-prone lake, Chaohu Lake, to assess the annual algal bloom risk from 2000 to 2021. The results showed that this proposed method could achieve a more comprehensive and objective risk assessment by coupling the quantity and distribution of algal blooms. Furthermore, the internal dependence of algal bloom fluctuation in different zones was explicitly presented. This framework promotes systematic integration of algal bloom risk assessment, taking into account its highly spatio-temporal dynamic feature. It creates a quantitative approach to gain insights into the evolution of quantity and distribution in various environmental assessments. [ABSTRACT FROM AUTHOR]

• Refined land use classification and hydrological characterization approach. • Propose a vertical LID construction method to increase flow pathways between LIDs. • Outdoor experiments on individual and combined LIDs for method validation. • Establish a runoff connection flux to connect and transfer LID overflows. • Better guide LID allocation in a multi-dimensional spatial scale. The techniques and modeling methods of Low Impact Development (LID, a more decentralized approach that uses or mimics natural processes to mange stormwater), has been extensively studied due to their positive impacts on urban flooding and water environment. Traditional LID modeling has been typically allocated on a plane layout based on rough land use classification and hydrological characterization. As the LIDs cannot be combined in the same space due to modeling restrictions, the LID effects on runoff control are often underestimated. This paper proposed a vertical LID model construction method based on a refined land use classification and improved hydrological characterization. By establishing a flow connection flux in the LID controller calculations, the overflows of different LIDs can be interconnected and transferred for further infiltration, storage and conveyance. Results showed that the refined approach provided more accurate spatial allocation guidance for LIDs, and the obtained catchment outflow was reduced by 25%-34% in comparison to conventional land use classification in a case study in northern China. The vertical construction method allowed a multi-dimensional LID allocation in space and function and achieved a higher control effect on total outflow volume and peak value. The combined LIDs could cope with the rainfall (i.e., no outflow from the LID plots) within the 50-year event, and the total outflow volume was 13–26% lower than the one from the plane layout method. [ABSTRACT FROM AUTHOR]

• Data-driven sub-robust optimization method is used. • Consideration of soil salinization control systemic risk issues. • Comparison of simulated and measured values in 5 years. • Simulation-Optimization approach is used to solve the problem. • Sustainable water-resources management measures are suggested. How to rationally optimize the allocation of surface water and groundwater resources in saline-alkali areas is a difficult problem in current model optimization. Based on an interval two-stage stochastic programming (ITSP) method, this paper considers the system risk involved in saline-alkali land treatment, follows a robust optimization method, and constructs an interval two-stage data-driven subrobust optimization (ITDRO) model, which can optimize the distribution of groundwater and surface water for different crops and realize the combination of wells and canals through the joint dispatch of surface water and groundwater. This paper takes the Urad Irrigation Area as the research object, and the obtained results provide water distribution objectives under situations of multiple sources of water, multiple crops, and different robust coefficients. After the successful calibration and verification of the SaltMod model, the water distribution target is taken as the main input parameter and substituted into the simulation model based on the principle of water and salt balance. The output includes soil salinity, drainage salinity, groundwater salinity (GWS), groundwater table (GWT) depth, and drainage. The results show that the integrated ITDRO and SaltMod model can control the groundwater level below the critical depth under the premise of satisfying crop irrigation, reducing the groundwater level by approximately 0.75 m and the GWS in the irrigation area by approximately 0.68 g/L, which can provide a reference for the prevention and control of soil salinization and waterlogging. This approach is suitable for arid and semiarid regions that face similar problems. [ABSTRACT FROM AUTHOR]

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

• The proposed model improves the forecast skill for sub-daily precipitation. • The model was improved by treating precipitation forecasts as censored data. • The proposed model achieves similar forecast skill with an EMOS model. Statistical post-processing methods have been applied in hydrometeorological forecasting to correct the bias and spread error in raw forecasts. Among various post-processing methods, the meta-Gaussian distribution model (MGD) is one of the early successful methods for post-processing of precipitation forecasts and has been applied in the National Weather Service's Hydrologic Ensemble Forecast System (HEFS), together with the mix-type meta-Gaussian distribution model (MMGD). However, recent studies have shown that the original MGD cannot yield reliable forecasts especially for sub-daily precipitation forecasts (e.g., 6-hourly). In this paper, we improved the MGD model by applying the censored maximum likelihood estimation (CMLE) method. We conducted experiments using GEFS reforecasts in Huai river basin in China to evaluate its performance. The results show that the proposed method performs better than the original MGD for sub-daily precipitation forecasts. The proposed method also achieves similar forecast skill with the state-of-the-art censored, shifted Gamma distribution-based ensemble MOS (CSGD-EMOS) if both use ensemble mean as the only predictor. The results indicate that the proposed CMLE-MGD can be useful for further applications such as flood forecasting that needs forecasts of high temporal resolution. [ABSTRACT FROM AUTHOR]

• A test method was improved for hydraulic properties determination of fault zones. • Flow in the fault zone was divided into three flow phases. • The threshold of injection pressure for flow transition to nonlinear was obtained. • Temporal change in permeability of the fault zone induced by injections was studied. Coal remains the largest contributor to China's energy structure. However, coal production has frequently been threatened by groundwater inrush incidents induced by fault zones. The variations in the hydraulic properties of fault zones under high water pressure in relation to groundwater inrush risk are poorly understood. This paper presents high-pressure fluid injection tests used to investigate the variations in hydraulic properties of a fault zone at depth. The analysis of experimental results shows that the injection rate and hydraulic conductivity increase as nonlinear functions of injection pressure. The evolution process induced by high water pressure can be divided into three flow phases, including a Darcy flow phase, a flow transition phase and an unstable flow phase. Two thresholds of injection pressure for the onset of nonlinear flow and the onset of substantial fracture dilation and hydraulic fracturing were identified. The hydraulic conductivity of the fault zone is closely related to the water pressure, with an evident increase in permeability resulting from fracture extension and enhancement of fracture connectivity, which were attributed to water injection. Finally, a conceptual model was developed to demonstrate the textural and hydraulic evolution of the fault zone. [ABSTRACT FROM AUTHOR]

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

• Parameter uncertainty varies over time scales due to land use change (LUC). • The calibrated daily runoff model is deficient for the extreme flow changes. • Model structure impacts the extreme runoff direction change for moderate LUC. Hydrological modeling has provided key insights into the mechanisms of land-use change impacts on runoff. However, the uncertainty of this impact is poorly understood. This paper examines the uncertainty originated from hydrological models' parameters and structure in simulating hydrological responses to land use change on daily, monthly and annual time scales. Two hydrological models, SWAT and HSPF, were applied to simulate the runoff response in the Xitiaoxi basin of eastern China. The effects of three land use scenarios (in 1985, 2002 and 2014) were analyzed with respect to the expansion of urban areas and reduction of cropland and forests. The changes in streamflow with multi-timescales between two models were compared at the whole basin and the sub-basin scales. The parameter uncertainty was estimated based on a sequential uncertainty analysis method (SUFI-2). The results indicate that the two models (SWAT and HSPF) could reproduce the observed daily and seasonal flows well, but they cannot accurately reconstruct the extreme flows (such as annual 7-day minimum discharge, R_7dMIN). The effect of parameter uncertainty on streamflow varies over time scales. The simulated annual and monthly runoff change values show large discrepancies but with same trends. The simulated changes for annual maximum discharge (R_1dMAX) and R_7dMIN often show different signs. The average width of the predictive interval in the relative change of different flow characteristics exceeded 100% of the average relative changes for both models. HSPF tends to present larger relative changes for the annual and monthly runoffs compared to SWAT. Moreover, the model choice could affect the direction change in R_1dMAX and R_7dMIN. However, when land use change was significant to a certain degree, both models simulated increased R_1dMAX and decreased R_7dMIN, but with different significance levels. The study suggests that the model structure represents an additional uncertainty, which should be accounted for in the land-use change impact assessment. [ABSTRACT FROM AUTHOR]

• MCDM and machine learning models were compared for flood modelling. • Results show that machine learning is more potential for flood modelling. • Out of machine learning methods, NB outperforms NBT. • Out of MCDM methods, SAW and VIKOR outperform TOPSIS. Floods around the world are having devastating effects on human life and property. In this paper, three Multi-Criteria Decision-Making (MCDM) analysis techniques (VIKOR, TOPSIS and SAW), along with two machine learning methods (NBT and NB), were tested for their ability to model flood susceptibility in one of China's most flood-prone areas, the Ningdu Catchment. Twelve flood conditioning factors were used as input parameters: Normalized Difference Vegetation Index (NDVI), lithology, land use, distance from river, curvature, altitude, Stream Transport Index (STI), Topographic Wetness Index (TWI), Stream Power Index (SPI), soil type, slope and rainfall. The predictive capacity of the models was evaluated and validated using the Area Under the Receiver Operating Characteristic curve (AUC). While all models showed a strong flood prediction capability (AUC > 0.95), the NBT model performed best (AUC = 0.98), suggesting that, among the models studied, the NBT model is a promising tool for the assessment of flood-prone areas and can allow for proper planning and management of flood hazards. [ABSTRACT FROM AUTHOR]

• Phosphorus contamination on surface sediment at a confluence is severe. • P variability is primarily affected by floods and temperature. • A model is proposed for P variability on sediment. • The present field observation supplement database on confluence dynamics. River confluences are key features of a river network, and their effects on contaminant transport and transformation need to be understood to control the contamination of a river network. This paper investigates the dynamic characteristics of phosphorus (P) contamination on the surface sediment at a river confluence of the Huai River, China, where water and sediment contamination is severe. The results highlight that P contamination of the surface sediment at a river confluence is more severe than that of the main stream of the river network to which the river confluence belongs. Floods have critical impact on the transport of sediment and its surface texture. In the annual water cycle the initial small flood can convey much more contaminated sediment and pollute the downstream river, whereas the subsequent large flood leads to the coarsening of the surface sediment and decrease of the total P (TP) content on the surface sediment (TPS). Water temperature is one of the most important factors affecting the temporal variability of the TPS at the river confluence. Higher P amounts are adsorbed on the surface sediment during lower water temperatures in winter and spring. Calcium bound P (Ca-P) is a relatively stable fraction of the sedimentary P during the dynamic processes of contamination of the surface sediment, and the nonapatite P gets released from the surface sediment during the summer. Effects of P concentration of the surface water and that of pH on TPS variability are not discernable in the current study. These results will be useful for controlling phosphorus contamination at river confluences and in river networks. [ABSTRACT FROM AUTHOR]

• Effects of interpolation methods on rainfall data scarcity were compared. • Impacts of of data scarcity on model uncertainty were investigated. • The effects of the different imputed data sets on H/NPS results were quantified. • The EMB algorithm was proved to be superior to the traditional methods. • The threshold effect and monitoring during high-flow periods were highlighted. Rainfall data scarcity has caused enormous problems in hydrologic and non-point pollution (H/NPS) predictions, as rainfall data represent the key input to watershed models. In this study, the effects of different imputation methods such as the data augmentation (DA) and the expectation maximization with bootstrap (EMB) algorithms on rainfall data scarcity were compared. The effects of different data scarcity rates and periods on model performance and prediction uncertainty were then quantified. Finally, the effects of different imputed data sets on H/NPS results were evaluated with the soil and water assessment tool (SWAT). A real case study in the Daning River watershed, Three Gorges Reservoir Region, China, was evaluated. The results indicated that rainfall data scarcity during low flow periods would result in poorer model performance and larger prediction uncertainty, especially to some minimum values, and the time when the maximum values are more susceptible is the rainfall data scarcity during high flow periods. The repair of rainfall data and the H/NPS model performance obtained by the EMB algorithm are superior to the traditional DA and weather generator performances. This advantage of the EMB algorithm would be more definitive if a specific threshold of data scarcity is reached. It is noted that even if the best algorithm is used, the imputed value is always lower than the peak observed value. This paper reports important implications for the choice of imputation methods and the use of H/NPS models for solving data scarcity problems for watershed studies. [ABSTRACT FROM AUTHOR]

• A new framework is developed for detailed flash flood risk mapping in ungauged basin. • Performance of the developed model is assessed with −0.004 m of BIAS for water level. • Flood inundation parameters and socioeconomic features are considered in the model. Flash floods are among the most devastating natural hazards but the analyses of flash floods are hindered by the lack of hydro-meteorological data. In particular, data issues are critical in ungauged basins. This paper presents an investigation of flash flood modeling using coupled hydrological and hydrodynamic models. Specifically, the TOPography-based hydrological MODEL (TOPMODEL) is employed to obtain the flood hydrograph and the coupled 1D-2D hydrodynamic model MIKEFLOOD is used to simulate floodplain inundation. The methods are tested in the Pajiang River, a mountainous basin in South China. The results show that the TOPMODEL effectively simulates the flood hydrograph and the Nash–Sutcliffe coefficients of the flow rate are larger than 0.80 for both calibration and validation. Also, MIKEFLOOD effectively simulates the flood inundation extent and flooding depth and the BIAS for simulated water level is −0.004 m. Further, using socioeconomic and historical flood disaster datasets, areas with high flash flood risk are identified. A flash flood risk assessment shows a clear trend of change in the flood hazard and the spatial heterogeneity of the flood vulnerability is significant. A sensitivity analysis of the flood vulnerability to the socioeconomic indicators indicates that cultivated land is the most sensitive indicators, and the population and the construction areas have similar influences. Overall, the coupled hydrological and hydrodynamic models prove effective and can be used in practical applications for flash flood risk mapping. [ABSTRACT FROM AUTHOR]

• Water level in response to periodic loadings in two closely wells were compared. • Two wells show different response characteristics to different periodic loadings. • Permeability and noise perturbations lead to the different response sensitivity. Groundwater well can be used as a seismograph or as strainmeter, since the well water level is sensitive to seismic waves and other crustal strains through mechanical coupling between rock and groundwater. The use of the groundwater level in monitoring tectonic strain, however, is hindered by the enigmatic response discrepancy among wells (different responses may be recorded by even closely clustered wells). Therefore, understanding the factors that control the sensitivity of well-aquifer systems in response to different periodic loadings is important. In this paper, we analyzed two closely located wells that show no response to seismic waves and respond differently to earth tides and barometric pressure. We discuss these wells' sensitivity of response to periodic loadings by using the spectrum, barometric response function, tidal response, and slug test methods. Our results show that the different response characteristics are due to the differences in permeability and confinement of the well-aquifer systems. Well-aquifer systems with a low permeability cannot record coseismic responses, even if they can record clear tidal and barometric signals. In addition, periodic loading signals in shallow aquifers will be easily masked by the interference from the surface, for example rainfall or pumping, which diminishes the signal-to-noise ratio of the tectonic activity. Thus, the hydrogeological and tectonic settings are the two key factors that should be considered in choosing a suitable monitoring well location for tectonic strain monitoring. [ABSTRACT FROM AUTHOR]

Summary: In this paper, the trends of the annual streamflow and precipitation and cross correlations between them were analyzed in nine large river basins of China during 1956–2005. The results indicate that: (1) the annual mean streamflow decreases in arid and semi-arid regions of north China; however, increasing trends occur in south and Southwest China; (2) the annual streamflow and precipitation exhibit reasonable correlation in nine large river basins except those located in inland areas. The annual streamflow over most areas of China is fed by precipitation; however, the decline in streamflow is faster than the decreases of precipitation since 1970s in the arid and semi-arid regions of north China. The relationship between the annual precipitation and streamflow presents a non-stationary state in north China. This non-stationary relationship is strongly influenced by both human activities and precipitation changes; (3) a significant increase of water use might be the major factor responsible for the steeper decline in streamflow than in precipitation in Haihe River, Yellow River and Songliao River basins in north China. In inland river areas, increase of water use and actual evapotranspiration might result in decline in streamflow although precipitation has an increase tendency. This paper sheds light on the non-stationary relationship between annual precipitation and streamflow and possible underlying causes, which will be helpful for a better understanding of the changes of precipitation and streamflow in China at large scale and in other regions of the world. [Copyright &y& Elsevier]

Summary: The rapid economic development has caused heavy pollution in the Pearl River Delta Region (PRDR) and neighboring regions. In order to investigate the polluted characteristics, a coupled modelling approach has been deployed and the study area in the Pear River Delta is divided into two modelling systems: a river network that is simulated by a 1D hydrodynamic and water quality model, and an estuary that is modelled by a 3D model. The 1D river network model is coupled with the 3D estuary model based on the mass conservation criteria. The hydrodynamic (HD) model is based on 1D hydrostatic shallow water equations, and is solved by the Alternating Direction Implicit Approach. The water quality (WQ) model component is based on advection–diffusion equations. Some physical and bio-chemical processes are included in the WQ model to address the water pollution problems by different pollution substances, in particular, to the chemical oxygen demand (COD). The water quality data of the main rivers from 2004 to 2007 were collected, processed and evaluated. Because no detailed water quality data in the river is available, the COD source pollution intensity in the water quality model is taking the pollutant load from the confluence of the main five outlets of the Pearl River Estuary (PRE) for 1996–2007 as surrogate. The results show that the pollutants from the PRDR have a large impact on the quality of the Hong Kong seawater and much more serious in the wet season with high river discharge. An insight of the flow and transportation mechanism in the river and estuary system has been obtained through the paper, and a reliable basis to control water pollution of the Pearl River Delta is provided in this paper. [Copyright &y& Elsevier]

[Display omitted] • China's water footprint (WF) under the SSP-RCP scenario was predicted dynamically. • A new attempt to combine the system dynamic (SD) model with SSP-RCP was made. • The grey WF decreases significantly under all scenarios during the forecast year. • The agricultural WF is the main contributor to China's water footprint. • SSP1-2.6 is the best scenario to alleviate water shortages and water pollution. Under the dual influence of socioeconomic development and climate change, water resources in China are under increasing stress. It is of great significance to comprehensively explore the changing trend of China's water footprint (WF) in the future, clarify the water resource challenges that China will face, and alleviate water shortage and water pollution problems. This paper uses System Dynamics (SD) to build a simulation model of China's WF, calculate China's WF from 2000 to 2019, and for the first time, simulate and optimize China's WF from 2020 to 2050 under the SSP-RCP scenario matrix composed of Shared Socioeconomic Pathways (SSPs) and Representative Concentration Pathways (RCPs). The results are as follows. (1) From 2000 to 2019, China's WF increased to 2009 and began to decline. The main contributors are grey WF and agricultural WF. (2) During 2020–2050, China's total WF under the SSP1-2.6 scenario will always be at the lowest; compared with other scenarios, the simulation result of China's total WF under the SSP2-4.5 scenario is the closest to the actual situation in China in 2020, 2021 and 2022. China's total WF under the SSP4-6.0 scenario is close to that under the SSP2-4.5 scenario, but the numerical variation characteristics of the WF composition are different. China's total WF under the SSP3-7.0 and SSP5-8.5 scenarios is high. (3) Based on the changes in WF and water resources supply/demand ratio under different scenarios, the SSP1-2.6 scenario is the best scenario to mitigate a future water shortage and water pollution in China. The research results can help decision-makers formulate relevant management policies and socioeconomic development models for water resource utilization and provide decision support for alleviating water shortages and water pollution in China. [ABSTRACT FROM AUTHOR]

• A combined opto-electronic sensing approach was proposed for soil multivariables. • Loess thermo-hydraulic behaviors in freezing-thawing were deciphered by field data. • The laboratory-obtained soil freezing point is applicable to field investigations. • Importance of in-situ soil-freezing characteristic curve acquisition is emphasized. • Frozen soil matric suction considering ice is the driving force of water migration. Frost damage in mid-latitude loess regions is mainly caused by the coupled soil water and heat transfer during freezing-thawing (FT) processes. In this paper, to better understand the thermo-hydraulic (TH) behaviors of seasonally frozen loess, field monitoring and assessment were performed on the Loess Plateau, China with a quasi-distributed opto-electronic sensing system. This system successfully captured the spatiotemporal variations in multivariables of seasonally frozen soil in near real-time, including in-situ soil temperature, unfrozen water content, and ice content. The monitoring datasets collected during the 2020/2021 winter offer unique insight into the TH behaviors of freezing point (FP) depression, in-situ soil-freezing characteristic curve (SFCC), and cryosuction. The findings highlight the importance of acquiring SFCCs under natural boundary conditions and confirm the applicability of laboratory-obtained FP in determining in-situ soil freezing depth. Furthermore, the study reveals that soil matric suction, considering the presence of ice, is the primary driving force of water migration during FT. Finally, errors in field monitoring are discussed, which arise from two aspects: the special features of loess and imperfections in monitoring schemes. This assessment of the coupled TH responses to climate changes in loess offers valuable theoretical guidance for optimizing engineering construction schemes and mitigating geohazards in mid-latitude loess regions. [ABSTRACT FROM AUTHOR]

• A new hydrological-water quality model for humid and semi-humid catchments in China is presented. • Both horizontal and vertical heterogeneity are considered in the hydrological process and the water quality process modelling. • Model uncertainty and the sensitivity of model parameters are investigated. Non-point source (NPS) pollutants may cause water quality deterioration and eutrophication, posing a significant threat to aquatic systems. Understanding and modelling surface and sub-surface hydrological processes and nutrient cycles are therefore essential for water resources management and pollution control. This paper presents a new hydrological-water quality model for application in humid and semi-humid catchments in China, considering both hydrological processes, and nutrient transport and transformation. The model divides the soil into three layers, and each layer is implemented with individual computational procedures for soil wetness and nutrient process. Nutrient transport is driven by hydrological processes and follows the same pathways as water flow in the model: surface runoff, infiltration, and outflow from individual soil layers. River channels are described separately with routines to account for the turnover of nutrients. Model parameters are selected according to literature/open-sourced data or estimated from soil texture or land use types. The model is applied to simulate the hydrological processes and phosphorus transport in the Tongyang River Basin in China and the model performance is confirmed by comparing the predicted discharge and total phosphorus concentrations with measured data at the catchment outlets of Tonghe River and Yanghe River over 3 years. Uncertainty analysis has been further carried out using the GLUE method to demonstrate sensitivity of the simulation results to the selection of model parameters. After model calibration, the predicted results are found to compare well with field observations in terms of flow discharge and total phosphorus concentration. From sensitivity analysis, it is found that the recession coefficient of channel system (CS) and the pollutant recession coefficient of surface runoff (p KS) are the most influential hydrological and water quality parameters that control the arrival time and duration of flood peak and nutrient concentration peak, respectively. [ABSTRACT FROM AUTHOR]

• An assimilation scheme with error estimation on a distributed model is developed. • MAP provides effective error parameter estimation for EnKF of a distributed model. • Tests under different forcing show its potential to overcome poor rainfall forcing. • Tests in catchments with different features show mixed results. For flood simulation in small- and medium-sized catchments, discharge observations may be used to update model states of a distributed hydrological model to improve performance. The ensemble Kalman filter (EnKF) has been widely used for hydrological assimilation due to its relative simplicity and robustness. An advantage of the EnKF is that it is easy to include different sources of uncertainty, therefore the choice of error model is crucial for the application of the EnKF assimilation. This paper describes an EnKF assimilation scheme for estimating error models using the maximum a posteriori estimation method (MAP). We test this scheme in two small and medium-sized catchments in China with different characteristics, and in addition compared the performance differences under two kinds of rainfall forcing. We show that MAP is beneficial in specifying error models and providing reliable ensemble spread. The assimilation scheme can effectively ameliorate the degradation of distributed hydrological model performance due to uncalibrated model parameters and/or poor quality of input data. [ABSTRACT FROM AUTHOR]

• A water balance method is applied to quantize uncertainty in lake operation. • Quantized uncertainty is used as the objective function. • A universal model for lake operation that tackles uncertainty effects is proposed. • An integrated algorithm is employed to compute the operation model. • It can obtain the optimized operation plan with adaptability to human factors. Due to the complex water system and diversified management requirements, the optimal water resources operation of the rivers-connected lake has always been a difficulty in current researches. Based on the discussion of characteristics of the rivers-connected lake, this paper quantified the forecasting uncertainty in the operation process, and set the quantified uncertainty as the objective function to establish the water resources optimal operation model of rivers-connected lake. The human factors were coupled to the optimal water resources operation in the model, in order to obtain the optimal plan of lake water resources operation with human adaptability. Hongze Lake was taken as an example for the analysis of water resources optimization operation plan of rivers-connected lake. Also, an integrated algorithm including the normal boundary crossing method, the primal–dual interior-point method, and the Non-dominated Sorting Genetic Algorithm-II (NSGA-II) was proposed to solve the lake operation model. The results proved that the optimization calculation of multi-objective operation under the uncertainty conditions of Hongze Lake can be obtained by the proposed model and algorithm. Uncertainties related to forecasts of lake operation can be effectively reduced by the proposed model, which, thus, implied the obtained operation plan in this paper is more adaptive to changing environment than the existing other plans. The research results can provide technical methods for the lake water resources operation with plentiful and diverse objectives of China and other countries. [ABSTRACT FROM AUTHOR]

Hydrological and non-point source pollution (H/NPS) predictions in ungagged basins have become the key problem for watershed studies, especially for those large-scale catchments. However, few studies have explored the comprehensive impacts of rainfall data scarcity on H/NPS predictions. This study focused on: 1) the effects of rainfall spatial scarcity (by removing 11%–67% of stations based on their locations) on the H/NPS results; and 2) the impacts of rainfall temporal scarcity (10%–60% data scarcity in time series); and 3) the development of a new evaluation method that incorporates information entropy. A case study was undertaken using the Soil and Water Assessment Tool (SWAT) in a typical watershed in China. The results of this study highlighted the importance of critical-site rainfall stations that often showed greater influences and cross-tributary impacts on the H/NPS simulations. Higher missing rates above a certain threshold as well as missing locations during the wet periods resulted in poorer simulation results. Compared to traditional indicators, information entropy could serve as a good substitute because it reflects the distribution of spatial variability and the development of temporal heterogeneity. This paper reports important implications for the application of Distributed Hydrological Models and Semi-distributed Hydrological Models, as well as for the optimal design of rainfall gauges among large basins. [ABSTRACT FROM AUTHOR]

Extreme precipitation can cause flooding and may result in great economic losses and deaths. The return level is a commonly used measure of extreme precipitation events and is required for hydrological engineer designs, including those of sewerage systems, dams, reservoirs and bridges. In this paper, we propose a two-step method to estimate the return level and its uncertainty for a study region. In the first step, we use the generalized extreme value distribution, the L-moment method and the stationary bootstrap to estimate the return level and its uncertainty at the site with observations. In the second step, a spatial model incorporating the heterogeneous measurement errors and covariates is trained to estimate return levels at sites with no observations and to improve the estimates at sites with limited information. The proposed method is applied to the daily rainfall data from 273 weather stations in the Haihe river basin of North China. We compare the proposed method with two alternatives: the first one is based on the ordinary Kriging method without measurement error, and the second one smooths the estimated location and scale parameters of the generalized extreme value distribution by the universal Kriging method. Results show that the proposed method outperforms its counterparts. We also propose a novel approach to assess the two-step method by comparing it with the at-site estimation method with a series of reduced length of observations. Estimates of the 2-, 5-, 10-, 20-, 50- and 100-year return level maps and the corresponding uncertainties are provided for the Haihe river basin, and a comparison with those released by the Hydrology Bureau of Ministry of Water Resources of China is made. [ABSTRACT FROM AUTHOR]

The utilization of ecosystem services in flood management is challenged by the complexity of human-nature interactions and practical implementation barriers towards more ecosystem-based solutions, such as riverine urban areas or technical infrastructure. This paper analyses how flood management has dealt with trade-offs between ecosystem services and practical constrains towards more ecosystem-based solutions. To this end, we study the evolution of flood management in four case studies in the Dutch and German Rhine, the Hungarian Tisza, and the Chinese Yangtze basins during the last decades, focusing on the development and implementation of institutions and their link to ecosystem services. The complexity of human-nature interactions is addressed by exploring the impacts on ecosystem services through the lens of three management paradigms: (1) the control paradigm, (2) the ecosystem-based paradigm, and (3) the stakeholder involvement paradigm. Case study data from expert interviews and a literature search were structured using a database approach prior to qualitative interpretation. Results show the growing importance of the ecosystem-based and stakeholder involvement paradigms which has led to the consideration of a range of regulating and cultural ecosystem services that had previously been neglected. We detected a trend in flood management practice towards the combination of the different paradigms under the umbrella of integrated flood management, which aims at finding the most suitable solution depending on the respective regional conditions. [ABSTRACT FROM AUTHOR]

The Yangtze River is one of the largest and most important rivers in the world. Over the past several decades, the natural sediment regime of the Yangtze River has been altered by the construction of dams. This paper uses multi-scale entropy analysis to ascertain the impacts of large dams on the complexity of high-frequency suspended sediment dynamics in the Yangtze River system, especially after impoundment of the Three Gorges Dam (TGD). In this study, the complexity of sediment dynamics is quantified by framing it within the context of entropy analysis of time series. Data on daily sediment loads for four stations located in the mainstem are analyzed for the past 60 years. The results indicate that dam construction has reduced the complexity of short-term (1–30 days) variation in sediment dynamics near the structures, but that complexity has actually increased farther downstream. This spatial pattern seems to reflect a filtering effect of the dams on the on the temporal pattern of sediment loads as well as decreased longitudinal connectivity of sediment transfer through the river system, resulting in downstream enhancement of the influence of local sediment inputs by tributaries on sediment dynamics. The TGD has had a substantial impact on the complexity of sediment series in the mainstem of the Yangtze River, especially after it became fully operational. This enhanced impact is attributed to the high trapping efficiency of this dam and its associated large reservoir. The sediment dynamics “signal” becomes more spatially variable after dam construction. This study demonstrates the spatial influence of dams on the high-frequency temporal complexity of sediment regimes and provides valuable information that can be used to guide environmental conservation of the Yangtze River. [ABSTRACT FROM AUTHOR]