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• 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]

• • 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]

• 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]

• 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]

• 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]

• 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 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]

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 ensemble Kalman filter (EnKF) is recognized as a powerful data assimilation technique that generates an ensemble of model variables through stochastic perturbations of forcing data and observations. However, relatively little guidance exists with regard to the proper specification of the magnitude of the perturbation and the ensemble size, posing a significant challenge in optimally implementing the EnKF. This paper presents a robust data assimilation system (RDAS), in which a multi-factorial design of the EnKF experiments is first proposed for hydrologic ensemble predictions. A multi-way analysis of variance is then used to examine potential interactions among factors affecting the EnKF experiments, achieving optimality of the RDAS with maximized performance of hydrologic predictions. The RDAS is applied to the Xiangxi River watershed which is the most representative watershed in China’s Three Gorges Reservoir region to demonstrate its validity and applicability. Results reveal that the pairwise interaction between perturbed precipitation and streamflow observations has the most significant impact on the performance of the EnKF system, and their interactions vary dynamically across different settings of the ensemble size and the evapotranspiration perturbation. In addition, the interactions among experimental factors vary greatly in magnitude and direction depending on different statistical metrics for model evaluation including the Nash–Sutcliffe efficiency and the Box–Cox transformed root-mean-square error. It is thus necessary to test various evaluation metrics in order to enhance the robustness of hydrologic prediction systems. [ABSTRACT FROM AUTHOR]

This paper examines the multi-scale streamflow variability responses to precipitation over 16 headwater catchments in the Pearl River basin, South China. The long-term daily streamflow data (1952–2000), obtained using a macro-scale hydrological model, the Variable Infiltration Capacity (VIC) model, and a routing scheme, are studied. Temporal features of streamflow variability at 10 different timescales, ranging from 6 days to 8.4 years, are revealed with the Haar wavelet transform. The principal component analysis (PCA) is performed to categorize the headwater catchments with the coherent modes of multi-scale wavelet spectra. The results indicate that three distinct modes, with different variability distributions at small timescales and seasonal scales, can explain 95% of the streamflow variability. A large majority of the catchments (i.e. 12 out of 16) exhibit consistent mode feature on multi-scale variability throughout three sub-periods (1952–1968, 1969–1984, and 1985–2000). The multi-scale streamflow variability responses to precipitation are identified to be associated with the regional flood and drought tendency over the headwater catchments in southern China. [ABSTRACT FROM AUTHOR]

• The objectivity verification experiment of the dynamic system response curve (DSRC) method was designed based on multiple hydrological models. • The experiment in Sunshui River and Shiquan catchments presented that the use of DSRC-corrected rainfall series of each hydrological model showed improved simulation accuracy for other hydrological models. • The hypothesis that the DSRC method is objective is not rejected based on the results of the experiment. Error correction methods are widely used to improve the performance of streamflow modeling. The Dynamic system response curve (DSRC) is a newly developed error correction method that utilizes error feedback correction to obtain updated variables and thus enhances model accuracy. While the DSRC method is simple and practical, its objectivity has not been systematically demonstrated. To address this issue, the paper proposes an experiment to verify its objectivity. First, three hydrological models (Xinanjiang (XAJ), Hydrologiska Byråns Vattenavdelning (HBV) and vertically hybrid yielding (VHY)) were calibrated and three corrected areal mean rainfall series were obtained by using the DSRC-based models (XAJ-DSRC, HBV-DSRC and VHY-DSRC). The corrected rainfall series from one hydrological model was then used as input to other hydrological models, and its performance was compared to those of using the original rainfall series by five performance measures (Nash-Sutcliffe efficiency coefficient (NSE), root mean squared error (RMSE), percent bias (PBIAS), mean absolute error (MAE) and correlation coefficient (R)). Finally, this objectivity verification experiment was applied to different model combinations for further demonstration. Case studies of the Sunshui River and Shiquan catchments in China showed that when the DSRC-corrected rainfall series from either hydrological model was applied to the original or other hydrological models, an increase in NSE and R values and a decrease in RMSE and MAE values were observed. The absolute values of PBIAS were within 10%. Accordingly, the hypothesis that the DSRC method is objective is not rejected based on the results of the experiment. [ABSTRACT FROM AUTHOR]

This paper presents a Bayesian approach using Metropolis–Hastings Markov Chain Monte Carlo algorithm and applies this method for daily river flow rate forecast and uncertainty quantification for Zhujiachuan River using data collected from Qiaotoubao Gage Station and other 13 gage stations in Zhujiachuan watershed in China. The proposed method is also compared with the conventional maximum likelihood estimation (MLE) for parameter estimation and quantification of associated uncertainties. While the Bayesian method performs similarly in estimating the mean value of daily flow rate, it performs over the conventional MLE method on uncertainty quantification, providing relatively narrower reliable interval than the MLE confidence interval and thus more precise estimation by using the related information from regional gage stations. The Bayesian MCMC method might be more favorable in the uncertainty analysis and risk management. [ABSTRACT FROM AUTHOR]

Basin response to precipitation variability by terrestrial hydrological processes may be manifested at different timescales and vary from one basin to another. A quantitative grasp of natural variability modulation is desirable for characterizing basin tendency or vulnerability on floods and droughts; quantification is not always straightforward, however. Using a macro-scale hydrological model, the Variable Infiltration Capacity (VIC) model, the hydrological time series (runoff, soil moisture, and evapotranspiration) were obtained for the 10 sub-basins over the Pearl River basin in South China for the period 1952–2000. A continuous wavelet transform was used to identify the characteristics of the temporal scales of basin-featured extreme events and related multiple-scale variabilities. By use of wavelet coherence analysis and rank correlation method, the cross correlations of monthly anomalies of hydrological processes were examined. The results showed that the runoff correlated most with, and the evapotranspiration had less response to, the precipitation variability. The analyzed results revealed that the attenuation effect of runoff in response to precipitation variability was mainly within the timescale of 2 yr in the Pearl River basin. The soil moisture variability can extend precipitation variation along with timescales. With understanding of the variability features of the terrestrial hydrological processes, this paper showed one geomorphic index, a basin form factor (FF), can represent the wavelet-based indices for high- and low-frequency responses to precipitation variability. It was found that, for a sub-basin with a higher FF value, precipitation variability can be represented more by runoff variability at less than 2-yr timescales; such a sub-basin is apt to have serious floods. With a lower FF value, a sub-basin has longer memory of soil moisture, leading to severe droughts during dry periods. This finding would be valuable to evaluate the tendency on occurrence of floods and droughts in the Pearl River basin, a humid region, which may be transferable to other basins in the globe. [ABSTRACT FROM AUTHOR]

Hydrological forecasting is complicated by flow regime alterations in a coupled socio-hydrologic system, encountering increasingly non-stationary, nonlinear and irregular changes, which make decision support difficult for future water resources management. Currently, many hybrid data-driven models, based on the decomposition–prediction-reconstruction principle, have been developed to improve the ability to make predictions of annual streamflow. However, there exist many problems that require further investigation, the chief among which is the direction of trend components decomposed from annual streamflow series and is always difficult to ascertain. In this paper, a hybrid data-driven model was proposed to capture this issue, which combined empirical mode decomposition (EMD), radial basis function neural networks (RBFNN), and external forces (EF) variable, also called the CEREF model. The hybrid model employed EMD for decomposition and RBFNN for intrinsic mode function (IMF) forecasting, and determined future trend component directions by regression with EF as basin water demand representing the social component in the socio-hydrologic system. The Wuding River basin was considered for the case study, and two standard statistical measures, root mean squared error (RMSE) and mean absolute error (MAE), were used to evaluate the performance of CEREF model and compare with other models: the autoregressive (AR), RBFNN and EMD–RBFNN. Results indicated that the CEREF model had lower RMSE and MAE statistics, 42.8% and 7.6%, respectively, than did other models, and provided a superior alternative for forecasting annual runoff in the Wuding River basin. Moreover, the CEREF model can enlarge the effective intervals of streamflow forecasting compared to the EMD–RBFNN model by introducing the water demand planned by the government department to improve long-term prediction accuracy. In addition, we considered the high-frequency component, a frequent subject of concern in EMD-based forecasting, and results showed that removing high-frequency component is an effective measure to improve forecasting precision and is suggested for use with the CEREF model for better performance. Finally, the study concluded that the CEREF model can be used to forecast non-stationary annual streamflow change as a co-evolution of hydrologic and social systems with better accuracy. Also, the modification about removing high-frequency can further improve the performance of the CEREF model. It should be noted that the CEREF model is beneficial for data-driven hydrologic forecasting in complex socio-hydrologic systems, and as a simple data-driven socio-hydrologic forecasting model, deserves more attention. [ABSTRACT FROM AUTHOR]

• A theoretical framework for the relationship between WEF and poverty is proposed. • Four development types of YRB are classified. • The Hu Huanyong Line is demonstrated "dividing line between the rich and the poor". • A scheme for the quantification of poverty is provided. Under the double pressure of ecological vulnerability and economic development, natural resources and ecological environment have become the key constraints to the development of the Yellow River Basin (YRB). This paper proposed a theoretical framework for the relationship between water-energy-food (WEF) and poverty. An improved coupling coordination degree model and the geographical detector were used to evaluate the spatio-temporal coupling between WEF and poverty and its influencing factors in YRB. The results suggest that the levels of coupling coordination increase to different degrees in YRB and its provinces from 2000 to 2019, and the coupling types are characterized by pyramidal-shaped distribution. The regions of YRB are divided into four development types: water-driven type, energy-driven type, food-driven type, and economy-driven type. Spatially, a dominant factor zone is formed with a multi-polar core. Furthermore, some practical suggestions are put forward to promote the effective utilization of water resources and high-quality development according to specific regional situations in YRB. This study will promote coupling coordination and high-quality development in YRB, providing a reference for the coordination of the human-land relationship and regional sustainable development. [ABSTRACT FROM AUTHOR]

Summary Investigation of drought structure in terms of drought onset, termination, and their transition periods as well as drought duration helps to gain a better understanding of drought regime and to establish a reliable drought early warning system. In this study, a Nonparametric Multivariate Standardized Drought Index (NMSDI) combining the information of precipitation and streamflow was introduced to investigate the spatial and temporal characteristics of drought structure in the Yellow River basin (YRB). Furthermore, the correlations between the El Niño-Southern Oscillation (ENSO) events and NMSDI variations were explored using the cross wavelet technique. The results showed that (1) The variations of NMSDI were consistent with those of 6-month SPI (Standardized Precipitation Index) and SSFI (Standardized Streamflow Index), indicating that the proposed nonparametric multivariate drought index was reliable and effective in characterizing droughts. (2) The preferred seasons of drought onset were spring and summer, and winter was the preferred season of drought recovery in the YRB. The long-term average drought duration in the whole basin was nearly 5.8 months, which was clearly longer than the average drought onset and termination transition periods. (3) Overall, the drought structure in terms of drought duration, onset and termination transition periods in the YRB remained stable, and no appreciable change trend was found. (4) ENSO events exhibited a statistically negative correlation with NMSDI variations, suggesting that they showed strong impacts on drought evolutions in the YRB. Although the YRB was selected as a case study in this paper, the approach/indicator can be applied in other regions as well. [ABSTRACT FROM AUTHOR]

• An integrated sensitivity analysis (SA) system named GDISCs is proposed. • GDISCs can screen sensitive parameters (SPs) robustly. • The KGE' performs better than NSE and RMSE in parameter SA. • Optimizing SPs greatly accelerates the convergence with satisfied optimized results. Parameter optimization is mandatory in many numerical modeling studies of hydrology sciences. However, optimizing all parameters (APs) is highly inefficient, including the equifinality phenomenon. This paper proposes an integrated sensitivity analysis system consisting of, multiple Global sensitivity analysis methods, Design of experiment algorithms, Indicators, and Sample size design Combinations (GDISCs), for screening the sensitive parameters (SPs) robustly and reducing parameter optimization burden efficaciously. In this study, four design of experiment (DoE) algorithms are applied to collect samples with varying sizes, and the corresponding three indicator values. We evaluate the effectiveness and efficiency of one qualitative and two quantitative global sensitivity analysis (GSA) methods by identifying the appropriate DoE algorithm and the sufficient sample size. To apply the GDISCs, the Block-wise use of the TOPMODEL (BTOP) model is set up for the hydrological simulation in the upper Min River Basin, China. The results show that the GDISCs system robustly screens out three SPs of the BTOP model, and the DoE algorithms significantly impact the effectiveness and efficiency of the GSA methods. The SA performance with applying the modified Kling–Gupta efficiency (KGE') as the indicator is more robust than the root mean squared error (RMSE) and the Nash-Sutcliffe efficiency (NSE). Compared to the traditional APs optimization, the optimal values are identified quickly through almost 1/2 or even fewer model runs and duration for the SPs optimization, with a very close accuracy (the maximum NSE, KGE' and relative bias (RBias) differences are 0.137, 0.037 and 2.70%, and the minimum are 0.002, 0.001 and 0.20%). Optimizing SPs also has a noticeable improvement for the default parameter values, and the maximum differences of NSE, KGE' and RBias reach 0.764, 0.321, and −20.80%. In summary, the GDISCs system is especially applicable for obtaining the robust SA results to considerably improve model calibration efficiency and reduce model computational burden while ensuring satisfactory model simulation performance. [ABSTRACT FROM AUTHOR]

• Optimal GAMLSS models were selected by meteorological and anthropogenic covariates. • A nonstationary SRI was proposed for a coupled human-water system. • NSRI excels in accuracy and recall, showing an advantage in drought recognition. Significant nonstationarity of hydrological sequences, driven by the coupled influence of climate change and anthropogenic activities, has challenged traditional hydrological drought analysis under the stationarity assumption in some regions, and has questioned regional hydrological drought analysis under the changing environment. To evaluate hydrological drought characteristics, the novelty of this paper is the proposition of a GAMLSS (Generalized Additive Models for Location, Scale, and Shape)-based nonstationary standardized runoff index (NSRI), considering climate change and anthropogenic influence, for investigating the hydrological drought regime in the Wuding River basin. To that end, nonstationary test methods were performed on hydrological sequences, and data was segmented into three parts according to the results of the Pettitt test. A nonstationary probability distribution was fitted to runoff data with meteorological covariates (precipitation, temperature) and anthropogenic covariates (water consumption for social development demand, and water consumption triggered by water impounding by check dams). After selecting an optimal combination of covariates, the NSRI was calculated utilizing the GAMLSS model. A newly constructed evaluation threshold was proposed, based on the traditional SRI threshold. The performance of SRI and NSRI were compared at the Dingjiagou station in the Wuding River basin. Results of comparison between index identification and recorded drought events demonstrated that the NSRI better performed in drought event identification. Moreover, the NSRI identified more frequent severe droughts and extreme droughts. Therefore, the proposed NSRI provided a more accurate basis for the identification of drought events, which can offer valuable information for drought planning, preparedness, and mitigation. [ABSTRACT FROM AUTHOR]

Summary Yanhe watershed, as a typical and experimental district of Soil and Water Conservation District, has long been plagued by soil erosion due to severe human disturbances. Exploring the relationship between watershed landscape pattern and hydrological processes can find effective ways to solve soil erosion problems. At first, with remote sensing and GIS (Geographic Information System) technology and based on SWAT model, this paper analyzed and simulated ecological hydrological processes in Yanhe watershed. It is on subbasin scale that the runoff and sediment yields were simulated monthly in Yanhe watershed using SWAT model. Secondly, it quantified landscape pattern with landscape indices. The seven landscape indices at the landscape level were selected with principal component factor analysis, including Disjunct Core Area Density (DCAD), Radius of gyration (GYRATE_SD), Patch Cohesion Index (COHESION), Shannon’s diversity index (SHEI), Total Core Area (TCA), Perimeter-Area Fractal dimension index (PAFRAC), Interspersion and Juxtaposition Index (IJI), etc. Thirdly, a new composite landscape index was constructed on the basis of eco-hydrological processes, which was closely related to soil erosion. The results are as follows: (1) Coupled analysis on the relationship of landscape indices and annual runoff as well as annual sediment yields in each subbasin, the correlation coefficient of seven selected landscape indices and runoff is very small, no passing all significant tests. But the correlation between sediment yields and the indices except for TCA and IJI is significant, and the absolute value of the correlation coefficient is between 0.3 and 0.5. (2) According to the “source-sink” theory of soil erosion, Slope-HRUs landscape index (SHLI) was built and can reflect the relationship between landscape pattern and soil erosion processes to a certain extent. The coupling relationship between Slope-HRUs landscape index (SHLI) and annual sediment yields in each subbasin is very clear, and correlation coefficient is −0.6, which is significantly negatively correlated. [ABSTRACT FROM AUTHOR]

Summary The climate elasticity of runoff is an important indicator that is used to quantify the relationship between changes in runoff and changes in climate variables. It is a function of both climate and catchment characteristics. Recently, Yang and Yang (2011) proposed an analytical derivation of climate elasticity (YY2011), in which a parameter n was used to represent the impact of the catchment characteristics. In China, both climate and catchment characteristics have large spatial variations. To understand the spatial variation of hydrologic response to climate change, this paper divided China into 210 catchments, further calculated the parameter n , and then estimated the climate elasticity and evaluated the contribution of climate change to runoff for each catchment. The results show that n ranges from 0.4 to 3.8 (with a mean of 1.3 and a standard deviation of 0.6), which has a logarithmic relationship with catchment slope; the precipitation elasticity ranges from 1.1 to 4.8 (with a mean of 1.9 and a standard deviation of 0.6), which shows a large regional variation, smaller values (1.1–2.0) mainly appearing in Southern China, the Songhua River basin and the Northwest, and larger values (2.1–4.8) mainly appearing in the Hai River basin, the Liao River basin and the Yellow River basin. In addition, climate contribution to runoff exhibits a large regional variation, the largest positive values (1.1–3.1%/a) occurring in the Northwest, the largest negative values (−1.0 to −0.5%/a) occurring in the Hai River basin and the middle reach of the Yellow River basin. In theory, the YY2011 method is a first-order approximation. The approximation underestimates the precipitation ( P ) contribution to runoff when P increases and overestimates that when P decreases, and the relative error has a median of ∼3% and a maximum of ∼20% when 10% precipitations change in those catchments of China. [ABSTRACT FROM AUTHOR]

• An integrated postprocessing framework for hydrometeorological ensemble is developed. • The canonical event considered observed precipitation improves the forecast accuracy. • The streamflow simulation can benefit from the seamless hydrologic prediction. • The Bayesian method improves the forecast effect than a single hydrological model. Flood forecasting is a challenging task with major concerns that include forecast uncertainty and lead time. This paper aims to comprehensively address the data correction issue for hydrological models by developing an integrated postprocessing framework for hydrometeorological ensemble forecasts based on three types of ensemble precipitation forecasts (ECMWF, GEFS, and CFSv2). This framework mainly comprises the Ensemble Preprocessor (EPP) and the Bayesian model averaging (BMA) scheme: (1) the EPP eliminates various deviations from the ensemble precipitation forecasts, with the canonical event model extracting useful predictive information and extending the lead time by linking the forecasts seamlessly, and (2) the BMA scheme develops more skillful and reliable probabilistic hydrological forecasts from ensemble streamflow forecasts generated by three hydrological models (XAJ, VIC and DTVGM) driven by the postprocessed ensemble precipitation forecasts. The Ganjiang River Basin was selected as a case study to examine the capacity and efficiency of our developed framework. We found that the performance of the integrated postprocessing of hydrometeorological ensemble forecasts benefitted from a reasonable design of canonical events, especially when the observed precipitation was factored into the canonical events. Streamflow forecasts with increased lead times and accuracy were obtained by employing postprocessed precipitation forecasts instead of raw forecasts as the input for the hydrological models. We also found that the BMA scheme further improved the forecast effect by generating more skillful and reliable forecasts than a single hydrological model, which was verified by a series of statistical indices and uncertainty analysis. Our results underscore the importance of considering both input and output for hydrological models in hydrological forecasting. [ABSTRACT FROM AUTHOR]

• There are two different types of knickpoint within the Layue basin. • Damming events usually correspond to a knickpoint and the abrupt change of k sn. • The relief increases by 35.3–214.3% in the process of knickpoint spreading. • The retreat rate of 4 k knickpoint group in the Layue basin is 10.8–51.7 km/Ma. • Natural damming events obviously affect the river profile within 104 year. The longitudinal profile of rivers in active orogenic belts records the competitive process occurring between erosion and uplift, and the knickpoint, as a unique sign, can support this. A series of distinctive knickpoints are distributed at different heights in the basin of the Layue River, which is a tributary located on the northwest side of the Yurlung Tsangpo Basin, China. By combining the stream power erosion model and field investigation, this paper explores the relationship among dams, knickpoints and the normalized steepness index (k sn). In addition to sporadic damming-type knickpoints, a 4 k knickpoint group located in the basin has attracted attention. Using the slope-area analysis method and the integration analysis method, paleo profile reconstruction of 14 tributaries and trunks was carried out to estimate the resulting paleo relief and net uplift. The results show that the topographic relief increases by 35.3–214.3% during the process in which the knickpoint entered into the basin and spread, which is the result of the growth and retreat of the knickpoint. Using the celerity model for the 4 k knickpoint group, 83.3% of the model knickpoints are less than 5 km away from the observed knickpoints, which indicates that the model is suitable for application in the Layue River Basin. And the migration rate of the knickpoint in the Layue Basin is 10.8–51.7 km/Ma at an initial setup time of 1.25–6 Ma. [ABSTRACT FROM AUTHOR]

• An observation-based natural and human-impacted catchment comparison method is proposed. • Human influence are quantified based on gridded socio-economic and land use data. • Climatic and anthropogenic influences on drought propagation are investigated. • Climate change accelerates the response of hydrological drought to meteorological drought. • Human activities delay the propagation from meteorological to hydrological drought. It is crucial to investigate how a precipitation deficit is transformed into hydrological drought and how climate change and human activities affect this transformation process, which is helpful to gain a deep understanding of drought propagation process in this changing environment. This study proposed an observation-based natural and human-impacted catchment comparison method to assess the impacts of climate change and human activities on propagation from meteorological drought to hydrological drought. The method mainly consists of the following three steps: (1) selection of natural catchments through analysis of trends and change points of hydro-meteorological data, as well as statistics analysis of human influence based on land use and socio-economic indicators data sets; (2) calculation of drought propagation characteristics (e.g., drought severity, duration, and propagation time) based on run theory and the Pearson correlation coefficient; and (3) comparison of drought propagation characteristics of natural catchments between undisturbed and disturbed periods to identify the impacts of climate change on drought propagation, and comparison of the propagation characteristics between natural and human-impacted catchments during the disturbed period to investigate human influence on drought propagation. The Laohahe basin (with eleven sub-catchments), located in northern China, was evaluated via the proposed procedure, and standardized precipitation index (SPI) and standardized runoff index (SRI) were used to characterize meteorological and hydrological droughts, respectively. The results demonstrate that the proposed method is suitable tool for distinguishing natural and human-impacted catchments, and separating the impacts of climate change and human activities on drought propagation. Furthermore, the comparison results of different schemes show that climate change accelerates the propagation from meteorological drought to hydrological drought in the Laohahe basin, shortening it by approximately 3 months. Human activities, however, disturb and then delay the natural propagation from meteorological drought to hydrological drought, retarding it by 11–12 months. Although the Laohahe basin was selected as a case study in this paper, the proposed method can be applied in other regions as well to improve drought prediction and water resources management. [ABSTRACT FROM AUTHOR]

[Display omitted] • The Variation Coefficient-Shapley Value Method is proposed. • Several typical allocation methods are compared with the proposed method. • The PTD index is adopted to quantitatively evaluate the stability of the allocation results. Maximizing the benefits of cascade hydropower stations will inevitably reduce the benefits of some upstream hydropower stations. In order to promote the joint operation and mutual cooperation between cascade hydropower stations, the scientific and fair allocation of compensation benefits between cascade hydropower stations is required. In this paper, a Variation Coefficient-Shapley Value Method for the compensation benefits allocation of multi-owner cascade hydropower stations is proposed, by combining individual characteristic weight coefficients with the Shapley value of each hydropower station. The Propensity to Disrupt (PTD) method is adopted to quantitatively evaluate the stability of the allocation results. Then an exemplary cascade of three hydropower stations on the Nanhe River in China is used as a case to calculate the compensation benefits via the proposed Variation Coefficient-Shapley Value Method and compared with other typical allocation methods. The results show that the leading hydropower station can get more compensation benefits as well as the second and third stage hydropower stations get reasonable compensation benefits. The stability of the cascade alliance in the compensation benefits allocation of the proposed method surpasses that of the other methods. This indicates the ability of the proposed method to both consider the individual marginal contribution and individual characteristics of each hydropower station when participating in joint operation. The proposed method proves to achieve a fair and rational compensation benefits allocation among the cascade hydropower stations and can potentially improve the utilization efficiency of water resources in the river basin. [ABSTRACT FROM AUTHOR]

• Isochrone and SWMM model are combined to evaluate effect of sponge city construction. • Sponge city infrastructure reduce outflow volume and peak flow. • Sponge city infrastructure perform best against storms with smaller return periods. Sponge city construction in China is integrated with stormwater management infrastructure, which is based on the concept of low impact development, such that the urban flooding and nonpoint storm pollution are mitigated. We hypothesize that an analytic framework based on isochrone and SWMM models can be used to simulate and compare the rainfall-runoff process before and after sponge city construction and thus assess the performance of the sponge city infrastructure on controlling stormwater outflows. In this paper, the analytical framework driven by design storms (with return periods of 2, 5, 20, and 30-year) was developed and tested in the catchment in Fengxi New City, China. The modeling results were then analyzed with indicators like peak flow rate, outflow volume, rainfall-runoff ratio, and stormwater stored due to sponge city construction. Outflow hydrographs and volume curve analyses showed that the sponge city infrastructure significantly mitigates the risk of stormwater overflows in the catchment and dramatically relieves the pressure on the flood control system of its downstream areas by a firm limit of the peak flow rate. Also, the performance generally decreases as rainstorms become less frequent and more intense. We anticipate that the proposed framework provides a practical approach to quantifying the sponge city's response to rainfalls under complex models. [ABSTRACT FROM AUTHOR]

• Six fallow schemes with a two-year-three-crop system are designed as scenarios. • Water balance and crop yields are simulated in detail by a modified SWAT model. • The water-food-energy nexus is evaluated with 17 indices to assess fallow strategies. • The patterns of fallow schemes are optimized in terms of the water-food-energy nexus. • The optimized schemes can stop groundwater decline, maximize yield, and save energy. The increasing dependency on groundwater, especially in irrigated regions, has highlighted the notable place of groundwater resources within the water-food-energy nexus (WFEN). This role is particularly relevant in the Haihe River basin of China, a globally representative area that is experiencing rapid aquifer depletion. The winter wheat (Triticum aestivum L.) fallow strategy may have the potential to limit withdrawal in this region. Based on information from multiple sources, this paper proposed six kinds of fallow schemes—under the same triple-cropping system consisting of winter wheat and summer maize (Zea mays L.) followed by fallow and summer maize in two years (WW–SM/F–SM) but with different irrigation schemes—as scenarios to conduct detailed simulation by a modified Soil and Water Assessment Tool (SWAT) model. Then, the water balance components of the shallow aquifer and soil profile (2 m) under different scenarios were analyzed to quantify the variations in hydrological processes caused by changes in cropping system and pumping intensity. Furthermore, through 17 indices that could quantitatively describe the changes related to the WFEN, the effects of seasonal fallow schemes on shallow groundwater drawdown mitigation, grain yield reduction, and energy consumption savings were evaluated. Based on these evaluation outcomes, linear programming was used to optimize the fallow schemes at the subbasin scale. As a result, to satisfy the constraint of stopping groundwater drawdown as well as improving water and energy productivities, the minimum reduction in the annual average winter wheat yield would be 55% compared with the basic scenario, while the summer maize yield would remain basically stable. Under the optimized fallow scheme pattern, 66% of the well-irrigated cropland should adopt the WW–SM/F–SM system with two irrigation applications for winter wheat and a rain-fed scheme for summer maize; additionally, 24% of the well-irrigated cropland should adopt the WW–SM/F–SM system with one irrigation application for winter wheat and a rain-fed scheme for summer maize, and the recommended fallow schemes for the other 10% of well-irrigated cropland varied spatially. Compared to the basic scenario, the optimized fallow scheme pattern could decrease shallow groundwater exploitation by 36.5 × 108 m3 a−1 (i.e., to realize shallow groundwater equilibrium), reduce the diesel consumption of agricultural machines and electricity consumption of pumping wells by 32% and 90%, respectively, and save energy costs by approximately 873 yuan ha−1. These results could provide a quantitative reference for policy-making in this watershed and serve as a typical case for similar areas that wish to implement fallow strategies to achieve groundwater sustainability. [ABSTRACT FROM AUTHOR]

• Establishes the random forest classification model for identifying effective reservoirs. • Different floods are employed as training samples to explore the model stability. • The expected total cost with cross-validation is adopted to evaluate the classification accuracy. • The average increase of expected total cost with cross-validation is proposed to assess criteria importance. In the real-time operation of a flood control system, identifying effective reservoirs accurately and adaptively is the premise of establishing a multi-reservoir real-time flood control hybrid operation (MRFCHO) model. The existing effective reservoir intelligent reasoning (ERIR) method is based on the inference rules, which causes the identification results to be greatly influenced by the training samples. This paper establishes a random forest classification (RFC) model for identifying effective reservoirs to solve this problem. The performance of the RFC model is evaluated in terms of model stability and model accuracy, and compared with the ERIR model and other machine learning (ML) models. First, different flood samples are used to establish the models to verify the model stability. Then, the expected total cost under cross-validation is taken as the index to evaluate the classification accuracy. Finally, the average increase of expected total cost with cross-validation is proposed to evaluate the criteria importance and analyze the sensitive factors that affect the classification accuracy of the RFC model. The proposed method is applied to a multi-reservoir system in the Huaihe River basin in China. The results indicate that the RFC model has the characteristic of high classification accuracy, low sensitivity to flood samples and high stability. It displays more dominance in the dynamic identification of effective reservoirs compared to other models. [ABSTRACT FROM AUTHOR]

• A physically-based, national-scale hydrological model in China was developed with 19,406 sub-watersheds and 81,687 contour belts. • The research incorporated new mechanisms on soil moisture movement in special vadose zone and strengthened the elevation effect of meteorological and vegetation variables. • The results can achieve a detailed description of the spatio-temporal variation of hydrological variables in large-scale region with diverse climate, topography and underlying surface conditions. Expansion of a detailed hydrological modelling from catchment- to national- or continental-scale is of important theoretical and practical significance, yet faces many challenges. We developed a high-resolution, physically-based hydrological model named WEP-CN (Water and Energy transfer Processes in China) for China. Different climatic and hydrological conditions and geological structures as well as their impact on infiltration and runoff were examined and incorporated in WEP-CN. The study area (9.6 million km2) was divided into 19,406 sub-watersheds and 81,687 contour belts. The computation units, model input, and model structure and parameters in WEP-CN were improved to obtain a well-defined simulation area, a more reliable input as well as a detailed description of soil moisture movement in several special vadose zones such as karst development, swelling and frozen soil. Continuous simulations of various natural hydrological processes were conducted for 62 years from 1956 to 2017. We demonstrated the efficacy of our model by comparing simulated and statistical monthly streamflow at 203 hydrological stations across the country. For the validation period of 1981–2000, the Nash-Sutcliff Efficiency (NSE) was found to be larger than 0.7 at 80% of the stations, and the absolute value of relative error (RE) was less than 10% at 95% of the stations. Our result highlights the benefit of incorporating new mechanisms on the special vadose zone water movement and accounts for the impact of elevation change on meteorological and vegetation variables. This paper can serve as a reference for large-scale hydrological simulation with diverse climate, topography and underlying surface conditions. [ABSTRACT FROM AUTHOR]

• The seasonal cycles of simulated runoff show good agreement with the UNH/GRDC composite runoff. • VIC-CN05.1 generally reproduces the monthly time series of latent heat fluxes well at flux tower sites in China. • The monthly modeled 0–10 cm soil moisture anomalies are highly correlated to in situ measurements. • The spatial patterns of simulated snow variables are generally similar to observations in northeastern and northwestern China. • Except for the northwest basin, the simulated monthly TWSC match well with that from the GRACE. This paper describes a daily 0.25° × 0.25° terrestrial hydrological dataset (VIC-CN05.1) for China during 1961–2017. The dataset is simulated by the latest variable infiltration capacity (VIC) model and driven by pure station-based atmospheric forcings and high-resolution soil parameters based on field surveys. Multiple observational (in situ and remote sensing) products and five statistical metrics, including biases, relative error (RE), root mean square error (RMSE), Nash-Sutcliffe efficiency coefficient (NSE), and correlation coefficient (R), are used to validate the model outputs. The results indicate that the seasonal cycles of simulated total runoff and composite runoff from the University of New Hampshire and Global Runoff Data Centre (UNH/GRDC) show good agreement in the Yangtze, southeast, and Pearl River basins, with positive REs ≤ 7% and NSEs ≥ 0.87. At the Changbaishan, Haibei Shrubland, and Qianyanzhou flux towers, VIC-CN05.1 reproduces the monthly latent heat fluxes well during 2003–2005. The monthly modeled 0–10 cm soil moisture anomalies are highly correlated to in situ measurements (438 stations) during 2003–2016, with mean R = 0.80. The spatial patterns of simulated and observed snow depth, snow water equivalent (SWE), and snow cover in winter are generally consistent in the northeastern and northwestern China. Except for the northwest basin, the simulated monthly terrestrial water storage changes in most river basins (particularly the Yangtze, Huai, and Yellow River basins) match well with observations based on the Gravity Recovery and Climate Experiment (GRACE) during 2003–2016, with positive NSEs and mean R = 0.41. This terrestrial hydrology dataset is valuable for hydroclimatic research in China, such as model evaluations and long-term trend analyses. [ABSTRACT FROM AUTHOR]

• Spatiotemporal characteristics and source attribution of TP in Lancang River Basin are investigated by SWAT model. • The existing dams intercept much more phosphorus than those dams under construction. • The effect of dam construction in China on the lower Lancang-Mekong River Basin has been estimated. Nutrients are critical to the development of plant and animal life in the river ecological system. In recent years, more attentions have arisen on the impact of building hydropower dams to transborder nutrient flux in the Lancang-Mekong River Basin (LMRB). In this paper, phosphorus was taken as an example, which is considered as one of the most significant factors to limit biological activities in the river. The Soil and Water Assessment Tool (SWAT) was used to access the multisource loading pattern of Total Phosphorus (TP) under no-dam scenario, and various types of dam construction scenarios analyses were performed to investigate potential changes in the transborder TP flux from Lancang River Basin (LRB, Chinese part of LMRB) to lower LMRB (other parts of LMRB). The results showed that the Total Phosphorus load in the LRB is about 27,079 tons/year, of which 85% TP is collected from July to October. The annual TP export at the outlet of the LRB is 14,112 tons/year under no-dam scenario, which accounts for about 4.2% of the total TP loads in the lower LMRB. With seven to twenty-one dams included in experiments, TP flux at the outlet of the LRB decreased 50–59%, while the delivered ratio for the lower LMRB decreased 2.1–2.5%. As a summary, building dams has a significant impact on the TP transport pattern in the Chinese part of LMRB, but very limited effect in lower LMRB according to the modeling results, especially for the Mekong delta. Moreover, the existing dams (seven dams) seem to intercept much more phosphorus than those dams that are under construction. [ABSTRACT FROM AUTHOR]

• Assessment of NBLs on regional scales was conducted based on the knowledges of geology, hydrogeology and anthropogenic activities. • A large drainage basin was divided into 10 GEUs according to the characteristics of hydrogeological structures and local groundwater flow systems. • Iterative 2-δ technique, Calculated distribution function and Grubbs' test were used to assess the NBLs of various GEUs. • The outcrop of Tertiary rocks, the long-term irrigation and the rapid urbanization had important influences on NBLs. Natural background level (NBL) is a prerequisite for identifying groundwater pollution and for the evaluation of pollution control effectiveness. However, due to regional differences of natural geogenic, hydrogeological, biological and atmospheric processes, together with long-term intense anthropogenic activities, it is no longer reasonable to assess NBL as a single value without including human impact. This paper shows the importance of regional division based on the knowledge of geology, hydrogeology and anthropogenic activities on the assessment of NBLs. In the study, the Guanzhong Basin, situated in central China with an area of 20,035 km2 was divided into ten groundwater environmental units (GEUs) based on hydrogeological structures and groundwater flow systems. A total of 509 shallow groundwater samples were collected throughout the basin. Based on samples from each GEU, NBLs of Na+, Ca2+, Mg2+, HCO 3 −, SO 4 2−, Cl−, F−, Cr6+, TH and TDS were evaluated by using iterative 2-δ technique (2-δ), calculated distribution function (CDF) and Grubbs' test. The results indicate the distinct characteristics of NBLs for the various GEUs: NBLs of Na+, SO 4 2−, Cl−, HCO 3 −, F− and TDS north of the Wei River obviously exceeded those in the south; NBLs of most ions for GEU-V and GEU-X were highest north and south of the Wei River, respectively. Through detailed analyses of the natural and anthropogenic influences on NBLs, we found that the outcrops of Tertiary rocks in the Li mountains determine the highest NBLs of chemical components for GEU-X south of the Wei River; long-term irrigation is responsible for the highest NBLs of TDS, Na+, Mg2+, HCO 3 −, SO 4 2− and Cl− for GEU-V; rapid urbanization in Xi'an (GEU-VIII) slows down the accumulation of chemical components (e.g. TDS) in shallow groundwater by reducing evaporation and mixing of shallow water with deep water through ground fissures. Assessment of NBLs on a regional scale lays the basis for identification of groundwater pollution and is helpful for the reasonable management and effective protection of groundwater resources. [ABSTRACT FROM AUTHOR]

• A Bayesian information-theoretic approach for disclosing design flood uncertainty. • How the parameter uncertainty is propagated to bivariate design flood is uncovered. • Uncertainty in design flood is enormous and increases along with return period. • Parameters of marginal distributions contribute most to design flood uncertainty. Copula-based statistical model has been extensively used to characterize the joint probability behaviors of extreme hydro-climatic design events, such as bivariate design floods. However, parameter uncertainty, not desirably but inevitably, accompanies the copula-based design flood estimation (CDFE) model which makes the inference results of design flood ambiguous. In this paper, we develop a Bayesian information-theoretic (BIT) approach to disclose design flood uncertainty arising from parameter uncertainty in CDFE model and furthermore examine how the parameter uncertainty is propagated to design flood estimation. Two catchments located in the Yellow River Basin, China, are selected as study regions. The research we have done indicates that design flood uncertainty is considerably large under parameter uncertainty. Worse still, the uncertainty in design flood increases along with the return period, thus making the design flood inference more ambiguous. Specifically, shape and scale parameters of marginal distributions have the largest contribution to the design flood uncertainty, followed by copula parameter, finally the location parameter of marginal distribution. Furthermore, the interaction between some parameters has dominant effect on design flood uncertainty compared with the individual effect of these parameters. These findings will enable better understanding the design flood uncertainty under parameter uncertainty. Moreover, the proposed BIT approach also offers a promising technical reference for design estimation of other types of extreme hydrological events, not limited to the design flood. [ABSTRACT FROM AUTHOR]

• Our objective in this paper is to construct a decision-making framework for river ecological base flow considering the total economic value of economic service functions and ecological service functions of rivers system. • This framework can provide a valuable reference for decision- making the ecological base flow of river in the water shortage area of northwest China. • This conceptual framework can provide a quantitative basis for water management in water shortages area. Understanding how much water should be left in rivers is a hot topic around the world. Here, we proposed a decision-making framework for ecological base flow of rivers considering the total economic value of economic service functions and ecological service functions of rivers system. This framework mainly includes three key steps. Firstly, we analyzed the ecological services functions of rivers system (i.e., water used for the ecological base flow of rivers) and economic service functions (i.e., water used for economic sectors) and explained their relationship. Secondly, we calculate the economic values of economic service functions and ecological services functions of rivers system, respectively. Finally, we determined the recommended ecological base flow of rivers by reaching the maximum value in the change process of the total economic value of the ecological and economic services functions of rivers system. We applied this framework to determine the river ecological base flow in the Baoji Section of Weihe River, which is the largest tributary of the Yellow River, Northwest China. The calculations results revealed that the recommended ecological base flow of rivers is 35.02% of the natural runoff in Baoji section of Weihe River. This work can provide a valuable reference for decision-making the ecological base flow of river in the water shortage area of Northwest China. [ABSTRACT FROM AUTHOR]

• A new framework based on "feature decomposition-learning reconstruction" was developed for daily runoff forecast. • IPSO algorithm was used to optimize the parameters of DBN model. • PACF was applied to determine the most effective input variables. • VMD-DBN-IPSO model outperforms other comparison models, and it shows consistency in peak flow forecast. Accurate and reliable short-term runoff prediction is of great significance to the management of water resources optimization and reservoir flood operation. In order to improve the accuracy of short-term runoff forecasting, a hybrid model-based "feature decomposition-learning reconstruction" named VMD-DBN-IPSO was proposed. In this paper, variational mode decomposition (VMD) is first used to decompose the original daily runoff series into a set of sub-sequence for improving the frequency resolution. Partial autocorrelation function (PACF) is then applied to determine the input variables of each sub-sequence. The improved particle swarm optimization (IPSO) algorithm is combined with the deep belief network (DBN) model to predict each sub-sequences and finally reconstruct the ensemble forecasting result. Three quantitative evaluation indicators, mean absolute error (MAE), root mean square error (RMSE) and Nash-Sutcliffe efficiency coefficient (NSE), were used to evaluate and compare the established models using the historical daily runoff data (1/1/1988-31/12/2017) at Yangxian and Ankang hydrological station in the Han River Basin of China. Meanwhile, a comparative analysis of the performance of VMD-DBN-IPSO model under different forecast periods (1-, 3-, 5- and 7-day lead time) was performed. In addition, the prediction ability of peak runoff of the VMD-DBN-IPSO model is further verified by analyzing the 10 peak flows during the testing data-series. The results indicate that the VMD-DBN-IPSO model can always achieve the best performance in the training and testing stage, and has good stability and representativeness, the NSE coefficient remains above 0.8, and the prediction error of peak flow is within 20%. It is a preferred data-driven tool for forecasting daily runoff. [ABSTRACT FROM AUTHOR]