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• 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]

Different conclusions have been drawn in literature as using reanalysis rainfall products to drive hydrological models for the simulation of streamflow, which warrant a need of further investigation before a generalised conclusion can be drawn. This paper assesses the utility of two widely used reanalysis rainfall datasets (WFD (developed by the WATCH project) and APHRODITE (Asian Precipitation–Highly-Resolved Observational Data Integration Towards Evaluation of the water resources)) against the gauged rainfall in terms of flood simulation in lumped (Xinanjiang Model) and distributed (SWAT Model) hydrological models in a tributary basin of Yangtze River with 94,660 km 2 drainage area in humid region of southern China. Differences in terms of rainfall accumulation, number of rainy days, spatial patterns of the rainfall amount and frequency distribution of the rain rates are evaluated. The APHRODITE product shows high consistency with the gauged rainfall while WFD data gives large errors in various statistical indices in the study region. Simulated discharges from the gauged and reanalysis rainfall data, respectively, are analysed and compared with the observed discharge in basin outlet over the period 1991–2005. The APHRODITE data show relatively high ability in modelling hydrological responses while the WFD data based models give large error in simulating the discharge. For the assessment of the high flows, both datasets exhibit some skills in flood prediction, however, APHRODITE data perform better than the WFD data when forcing into the lumped Xinanjiang Model than into the distributed SWAT Model in terms of flood duration, Probability of Detection, False Alarm Rate, regressions of annual peaks and partial duration series. [ABSTRACT FROM AUTHOR]

Summary Rain gauge networks are used to provide estimates of area average, spatial variability and point rainfalls at catchment scale and provide the most important input for hydrological models. Therefore, it is desired to design the optimal rain gauge networks with a minimal number of rain gauges to provide reliable data with both areal mean values and spatial–temporal variability. Based on a dense rain gauge network of 185 rain gauges in Xiangjiang River Basin, southern China, this study used an entropy theory based multi-criteria method which simultaneously considers the information derived from rainfall series, minimize the bias of areal mean rainfall as well as minimize the information overlapped by different gauges to resample the rain gauge networks with different gauge densities. The optimal networks were examined using two hydrological models: The lumped Xinanjiang Model and the distributed SWAT Model. The results indicate that the performances of the lumped model using different optimal networks are stable while the performances of the distributed model keep on improving as the number of rain gauges increases. The results reveal that the entropy theory based multi-criteria strategy provides an optimal design of rain gauge network which is of vital importance in regional hydrological study and water resources management. [ABSTRACT FROM AUTHOR]

Highlights: [•] This paper examines the impacts of rain gauge densities on runoff simulations. [•] Variation of rain gauge densities impacts the areal mean rainfall considerably. [•] The locations of rain gauges impact the model performances considerably. [•] Great impacts on model performances occurring when rain gauges fall below 0.4 station per 1000km2. [Copyright &y& Elsevier]

Summary: In this study a rigorous evaluation and comparison of the difference in water balance simulations resulted from using different downscaling techniques, GCMs and hydrological models is performed in upper Hanjiang basin in China. The study consists of the following steps: (1) the NCEP/NCAR reanalysis data for the period 1961–2000 are used to calibrate and validate the statistical downscaling techniques, i.e. SSVM (Smooth Support Vector Machine) and SDSM (Statistical Downscaling Model); (2) the A2 emission scenarios from CGCM3 and HadCM3 for the same period are used as input to the statistical downscaling models; and (3) the downscaled local scale climate scenarios are then used as the input to the Xin-anjiang and HBV hydrological models. The results show that: (1) for the same GCM, the simulated runoffs vary greatly when using rainfall provided by different statistical downscaling techniques as the input to the hydrological models; (2) although most widely used statistics in the literature for evaluation of statistical downscaling methods show SDSM has better performance than SSVM in downscaling rainfall except the Nash–Sutcliffe efficiency (NSC) and root mean square error-observations standard deviation ratio (RSR), the runoff simulation efficiency driven by SDSM rainfall is far lower than by SSVM; and (3) by comparing different statistics in rainfall and runoff simulation, it can be concluded that NSC and RSR between simulated and observed rainfall can be used as key statistics to evaluate the statistical downscaling models’ performance when downscaled precipitation scenarios are used as input for hydrological models. [Copyright &y& Elsevier]

Summary: In this study, we analyze the long streamflow series of three hydrological stations of the lower Pearl River basin and the streamflow ratio between Makou and Sanshui stations by using statistical techniques. Furthermore, we also attempt to address influences of precipitation and human activities (human-induced deepening of river channels) on streamflow ratio. The results indicate that: (1) the streamflow variations show remarkable relations with precipitation changes in West and East River basins, implying tremendous influences of climate changes on hydrological processes. Decreasing precipitation was observed in North River basin. However, the streamflow amount of the Sanshui station largely increased due to enlarged streamflow allocation from the West River to the North River; (2) increasing streamflow ratio of Sanshui/(Makou+Sanshui) is the result of morphological changes (downcut) of river channels in the upper Pearl River Delta. The fast downcut of river channels is mainly due to intensive sand mining. Larger magnitude of increase in streamflow ratio corresponds well to the higher intensity of in-channel sand dredging; (3) after late-1990s, decreasing precipitation of the Pearl River basin abates the streamflow amount and also the streamflow ratio. The influences of human activities and climate changes are varying in different time intervals and in different river basins. Due to tremendous impacts of increased streamflow ratio between Sanshui and Makou station, relations between streamflow and precipitation relations in the North River basin are not statistically good. This study helps to improve understandings of the causes underlying altered streamflow variations in the lower Pearl River basin and the hydrological alterations within the Pearl River Delta region. [Copyright &y& Elsevier]

Summary: Long monthly streamflow and sediment load series observed at the Datong station located in the lower Yangtze River basin were analyzed using the scanning t-test, F-test and coherency analysis techniques. The results indicated that: (1) different changing properties of the first and the second moments of the hydrological series on different time scales were observed, reflecting different driving factors influencing the hydrological processes of the lower Yangtze River basin; (2) a generally decreasing trend can be identified after the mid-1980s. Significant abrupt changes in sediment load were analyzed in the sediment load series. However, more complicated changing patterns can be observed in the changes in streamflow. Generally decreasing sediment load and increasing streamflow gave rise to anti-phase relations between sediment load and the streamflow on longer time scales. In-phase relations between sediment load and streamflow on shorter time scales may imply a considerable influence of the hydrological dynamics on sediment transport; and (3) human activities, particularly the construction of water storage reservoirs, exerted a massive influence on sediment load variations. Construction of a large amount of water reservoirs on the tributaries of the Yangtze River and the Gezhouba Dam on the mainstem of the Yangtze River seem to be the main factors responsible for abrupt changes in the sediment load. Construction of the Three Gorges Dam causes a sharp decrease and unstable variability in sediment load variations, which may pose new challenges for the ecological environment conservation and the deltaic management of the Yangtze Delta region. [Copyright &y& Elsevier]

Summary: Annual runoff and annual suspended sediment loads of hydrological gauging stations along the mainstream of the Yangtze River basin (Pingshan station, Yichang station, Hankou station and Datong station) and main tributaries (Beipei station in Jialingjiang River, Wulong station in Wujiang River and Huangzhuang station in Hanjiang River) were analyzed with the help of Mann–Kendall trend analysis and linear regression analysis. Research results indicate that (1) changing patterns of runoff and sediment loads are different in different parts of the Yangtze River basin. No significant trend is detected for annual runoff at all stations at >95% confidence level. Changes of sediment loads, however, demonstrate different pictures in the Yangtze River basin. The sediment loads are in increasing trend in Pingshan station- the most upstream station on the Yangtze River basin (this increasing trend is significant at >95% confidence level after about 1990), but are in decreasing trend at other stations (including stations in the tributaries studied in this paper). This decreasing trend becomes more obvious from Yichang station to Datong station. (2) Water reservoirs exerted more influences on changes of sediment loads than on runoff, which is the main reason for the decreasing trend of sediment loads found in most stations. (3) Influences of water reservoirs on sediment loads are more obvious in the tributaries than in the mainstream of the Yangtze River basin, while in the mainstream the variation patterns of sediment loads are determined by multiple factors. [Copyright &y& Elsevier]

• A framework was provided for analyzing change causes of runoff and sediment load. • Runoff change attribution analysis was incorporated in quantifying sediment change. • Dam construction obviously disturbed the evolution characteristics of sediment load. Under the background of climate variability and anthropogenic stresses, changes of the runoff and sediment processes have drawn increasing concerns during the past decades. Analyzing the evolution characteristics and causes of runoff and sediment load is critical for the sustainable development of water resource and terrestrial ecosystems of river basins. This study presents an integrated approach for analyzing the evolution characteristics of the annual runoff and sediment load, including the trend, periodicity, change point and their correlations in the Wujiang River basin (WRB) –a typical mountainous river system in southwest China, based on the observed data during the period 1970–2016. On this basis, the contributions of climate variability and human activities on runoff change were first distinguished by using a widely applied Budyko-based hydrological sensitivity method, and then the quantified result was further applied in the attribution analysis of river sediment load change. Research results revealed that the change patterns of runoff and sediment processes, such as the trend, periodicity and potential change point, as well as the relationships of precipitation-runoff and runoff-sediment load are quite different in the WRB. Quantitative assessment revealed that climate variability contributes to 71.5%–85.6% changes of mean annual runoff in 1985–1994, 1995–2004 and 2005–2016 with reference to the baseline period of 1970–1984. In contrary, human activities dominate the reduction of sediment load in the river, and the contribution rate ranged in 67.2%–107.5%. Variations of annual runoff in the WRB was the direct result of climate variability, while land use change played a secondary role. The construction of cascade hydropower reservoirs, particularly in the upper and middle reaches of the Wujiang River is fundamentally responsible for the significant decreasing and the weakened periodicity of sediment load as well as the changed runoff-sediment load correlation at Wulong station in recent years. However, this influence was not big enough to modify the regime of annual runoff. [ABSTRACT FROM AUTHOR]

• We proposed a new drought monitoring index; • We corroborated the applicability of this newly-proposed drought index; • We characterized spatial and temporal patterns of droughts across China. In this study, we proposed a new integrated remote sensing drought monitoring indices, i.e. Multiple Remote Sensing Drought Index integrated by Principal Component Analysis (PSDI), Multiple Remote Sensing Drought Index integrated by multiple linear regression (MRSDI) and Multiple Remote Sensing drought index integrated by gradient boosting method (GBMDI), based on the Precipitation Condition Index (PCI), Temperature Condition Index (TCI), Vegetation Condition Index (VCI), and Soil Moisture Condition Index (SMCI). The monitoring performance of PSDI, MRSDI and GBMDI was compared and verified based on the real-world observed droughts during 2002 to 2016. We also evidenced drought monitoring performance of the PSDI MRSDI and GBMDI by comparison between PSDI, MRSDI, GBMDI and SPEI, SPI and PDSI based on the in situ observed meteorological data. We found that the spatiotemporal characteristics of droughts monitored by the PSDI, MRSDI and GBMDI were generally in good agreement with those by the SPI and SPEI. The GBMDI performs better than PSDI and MRSDI in describing drought processes and spatial patterns of droughts of different drought intensities. Comparison between the real-world observed drought-affected croplands and those monitored by PSDI, MRSDI and GBMDI indicated better drought monitoring performance of GBMDI than PSDI and MRSDI in monitoring droughts across widespread drought-affected regions. Besides, the trend of GBMDI is in good agreement with standardized crop yield. Therefore GBMDI should be the first choice in drought monitoring practice. The GBMDI developed in this study can help to provide an alternative drought monitoring index for large-scale drought monitoring across China and also in other regions of the globe. [ABSTRACT FROM AUTHOR]

• Four hydrological models calibrated with four objective functions are compared. • The Granger Ramanathan average variant C (GRC) method performs the best. • Using more than nine ensemble members does not further improve performance. • Combinations of models and objective functions are better than the single model and objective. • Averaging outperforms the ensemble members except in low-flow simulations. The multi-model combination is a technique to improve the performances of hydrological streamflow simulations. An area that has not been investigated much is the performance dependence of combination techniques on the hydrological model calibration strategy and ensemble size. This study aims at investigating the joint effect of the hydrological models, calibration strategies and ensemble sizes on combination abilities for selecting the most appropriate multi-model combination method. The ensemble members were constructed by applying four hydrological models and four objective functions over 383 catchments in China. The ensemble members were combined by using nine commonly used methods, which are Equal Weights (EWA), Akaike Information Criterion (AICA), Bayes Information Criterion (BICA), Bates and Granger (BGA), Granger Ramanathan A, B, and C (GRA, GRB, and GRC), Bayesian Model Averaging (BMA) and Multi-model Super Ensemble (MMSE). The GRC is found as the best multi-model combination method for hydrological simulations. Adding ensemble members by either multiple hydrological models or calibration strategies could help to improve the simulation abilities. Specifically, the increase of ensemble members can obviously enhance the performance of multi-model combinations when the ensemble size is less than six, while only limited improvement is achieved when the ensemble size is more than nine. The combination of ensemble members with various calibration strategies is hard to compensate for the weakness of hydrological model structures. As well, the application of a single calibration strategy in ensemble members only emphasizes single discharge periods and neglects other important discharge periods. This study found that various models with different objective functions are more robust and efficient. The combination performs better than any individual model in terms of Nash–Sutcliffe efficiency (NSE) for approximately 70% catchments, but the multi-model combination is less efficient in terms of low-flow simulations. [ABSTRACT FROM AUTHOR]

Summary: Spatial rainfall is a key input to distributed hydrological models, and its precisions heavily affect the accuracy of stream flow predictions from a hydrological model. Traditional interpolation techniques which obtain the spatial rainfall distribution from rain gauge data have some limitations caused by data scarcity and bad quality, especially in developing countries or remote locations. Satellite-based precipitation products are expected to offer an alternative to ground-based rainfall estimates in the present and the foreseeable future. For this purpose, the quality and usefulness of satellite-based precipitation products need to be evaluated. The present study compares the difference of Tropical Rainfall Measuring Mission (TRMM) rainfall with rain gauges data at different time scales and evaluates the usefulness of the TRMM rainfall for hydrological processes simulation and water balance analysis at the Xinjiang catchment, located in the lower reaches of the Yangtze River in China. The results show at daily time step TRMM rainfall data are better at determining rain occurrence and mean values than at determining the rainfall extremes, and larger difference exists for the maximal daily and maximal 5-day rainfalls. At monthly time scale, good linear relationships between TRMM rainfall and rain gauges rainfall data are received with the determination coefficients (R 2) varying between 0.81 and 0.89 for the individual stations and 0.88 for areal average rainfall data, respectively. But the slope of regression line ranges between 0.74 for Yingtan and 0.94 for Yushan, indicating that the TRMM satellite is inclined to underestimate the monthly rainfall in this area. The simulation of daily hydrological processes shows that the Water Flow Model for Lake Catchment (WATLAC) model using conventional rain gauge data produces an overall good fit, but the simulation results using TRMM rainfall data are discontented. The evaluation results imply that the TRMM rainfall data are unsuited for daily stream flow simulation in this study area with desired precisions. However, good performance can be received using TRMM rainfall data for monthly stream flow simulations. The comparison of the simulated annual water balance components shows that the different rainfall data sources can change the volume value and proportion of water balance components to some extent, but it generally meets the need of practical use. [Copyright &y& Elsevier]

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]

• An evaluation method of model transferability is proposed. • The complex models (XAJ and HBV) have better spatial transferability than the simple model (GR4J). • The performance of models transferred from the wettest to the driest period is worse than that of models transferred from the driest to the wettest period. Changing conditions of the climate and underlying surface have altered the rainfall-runoff relationships in many basins, greatly increasing additional challenges in the applicability of hydrological models for studying the hydrological response to those potential changes. However, systematic and simultaneous testing and comparing of both temporal and spatial transferabilities of different hydrological models under changing conditions have not received enough attention. The present study investigates the potential differences between temporal and spatial transferabilities of different hydrological models under different climatic and underlying surface conditions, which are synthesized from two basins in Southern China with 50-year historical records (1966–2015). The transferability of five hydrological models, i.e., XAJ, HBV, SIMHYD, IHACRES and GR4J, is investigated under the synthesised changing conditions by using a new evaluation method, proposed in this study. The results show that: (1) the proposed evaluation method is proved to be effective in evaluating the transferability of the models; (2) for temporal transferability under stationary condition, the five models show similar performances, but for spatial transferability, the performances of complex models (XAJ and HBV) are better than that of the simple model (GR4J); (3) the difference in underlying surface conditions in the target basin affects spatial transferability of the models; (4) hydrological models have much better transferability from dry to wet period than otherwise. This study provides an insight to test temporal and spatial transferabilities of hydrological models in the context of changing climate and underlying surface conditions. [ABSTRACT FROM AUTHOR]

• Differences in the length of the forecast periods for different reservoirs in the system are considered. • The proposed two-stage risk analysis method is verified using Monte Carlo simulations. • The proposed real-time flood-control operation model can achieve dynamic control of reservoir storages during flood season. The real-time operation of multi-reservoir systems is a vital issue in the field of reservoir management. The uncertainty caused by inflow forecasting means that risk analysis is necessary for such real-time operation. However, differences in the length of the forecast periods for different reservoirs in the system are seldom considered in the risk analysis of multi-reservoir systems. This paper presents a two-stage flood risk analysis method of multi-reservoir systems that takes the differences in the length of forecast lead-times into consideration. The aim of the proposed method is to evaluate the uncertainty of the flood forecasting by dividing the operation horizon into the forecast lead-time and the beyond-forecast time period. The risk within the forecast lead-time is estimated by counting the frequency of failure numbers among all scenarios with the help of the scenario-based forecasts. The risk beyond the forecast time period is determined using reservoir flood routing with the design flood hydrographs, which are selected according to the differences in the length of the forecast periods between any two reservoirs. The proposed two-stage risk analysis method is verified using stochastic simulations method based on Monte Carlo sampling. A real-time flood control operation model is established by taking the proposed two-stage risk analysis method as a constraint. China's Ankang-Danjiangkou Cascade Reservoirs are considered as a case study, and the results indicate that the proposed real-time operation model can increase hydropower generation by 0.22 billion kWh during the summer flood season without increasing the flood risk in the multi-reservoir system. The proposed method enhances our understanding of risk management for the real-time flood control operations in multi-reservoir systems. [ABSTRACT FROM AUTHOR]

[Display omitted] • A cascade model chain suitable for the risk of future DWAA events is proposed. • Evolution characteristics of DWAA in the future period of YRB are revealed. • Severity of DWAA events in far-future is greater than that in the near-future. • Extreme precipitation events have strong synchronization with DWAA events. At present, many studies have investigated the evolution characteristics, frequency estimation techniques, and prediction of droughts and floods, but a comprehensive understanding of the spatiotemporal evolution law and joint risk of these two composite extreme events is still lacking. The synergistic effect of dry and wet abrupt alternation (DWAA) events has more significant consequences than a single drought and flood event, and these events have a considerable impact on agriculture, ecology, and economy. Furthermore, owing to the intensification of global warming and climate change, the potential synchronisation between DWAA events and extreme precipitation in the future requires special attention. Therefore, it is necessary to establish a reliable framework to analyse the evolution characteristics and risks of composite extreme events (DWAA). In this study, a cascade modelling chain comprising climate model downscaling, non-uniform bias-correction technique, and model integration was developed to study the spatiotemporal evolution characteristics and combined risk impact of DWAA events. The proposed methodology was applied to the Yellow River Basin (YRB) in the historical period (1960–2014, Hist) and the future period (2021–2060, FUT1; and 2061–2100, FUT2) under the Shared Socioeconomic Pathway (SSP) 3–7.0 and 5–8.5 scenarios. The results show that: (1) The constructed cascade modelling chain has high simulation accuracy for precipitation, and can be used to analyse future DWAA events. (2) Considering the precipitation characteristics under SSP3-7.0 and SSP5-8.5 scenarios, the precipitation increases slightly in the near future FUT1 (about 5–10%), and increases significantly in the far future FUT2 (about 10–70%). (3) Following either scenario, the frequency of DWAA events is expected to decrease in the future compared to that in the Hist, but the average intensity is expected to increase compared to that in the Hist. (4) The joint risk of the impact area and intensity of future DWAA events of FUT2 is greater than that of FUT1. [ABSTRACT FROM AUTHOR]

• The path analysis method is proposed to explain the interplay of drivers in soil moisture drought. • Precipitation deficits dominates the interannual variation of soil moisture drought while potential evapotranspiration impacts are magnified by drought deterioration. • Atmospheric movement and temperate indirectly affects soil moisture drought through precipitation and potential evapotranspiration. • Proportion of drought explanation decreased with increasing soil depth. Soil moisture drought, as one of the most important drought categories, is determined by both water supply (e.g., precipitation) and demand (e.g., potential evapotranspiration). To shed light on the underlying mechanisms driving soil moisture drought, the statistical multiple linear regression, machine learning, and modeling experiments methods have been pervasively used in early studies. However, these methods neglect the collinearity and interactions of climate variables, and thus cannot reflect the direct and indirect interaction of factors leading to soil moisture drought. To reveal the synergistic effects of water supply and demand on soil moisture drought, this study quantified the contributions of key drivers to the change of soil moisture drought by a path analysis method to exhibit the relationships between atmospheric movement state and soil moisture drought. Prior to applying the systematic path analysis model, we identified the spatial patterns of soil moisture droughts at different depths by using a state-of-art three-dimensional drought recognition method in China. Our results showed that precipitation deficits dominated the interannual variation of soil moisture drought while increasing potential evapotranspiration only had marginal intensification in drought. The response of soil moisture drought to potential evapotranspiration was magnified by drought deterioration, especially in basically severe drought conditions. The total column water vapor and the horizontal divergence of the vapor flux, as well as temperature, directly affected precipitation and potential evapotranspiration and led to soil moisture drought through various direct and indirect processes. This study highlighted that the interactions among precipitation, potential evapotranspiration, and atmospheric vapor movement state in space and time were important for understanding the drought development mechanisms. [ABSTRACT FROM AUTHOR]

The hydrologic design of nonstationary flood extremes is an emerging field that is essential for water resources management and hydrologic engineering design to cope with changing environment. This paper aims to investigate and compare the capability of four nonstationary hydrologic design strategies, including the expected number of exceedances (ENE), design life level (DLL), equivalent reliability (ER), and average design life level (ADLL), with the last three methods taking into consideration the design life of the project. The confidence intervals of the calculated design floods were also estimated using the nonstationary bootstrap approach. A comparison of these four methods was performed using the annual maximum flood series (AMFS) of the Weihe River basin, Jinghe River basin, and Assunpink Creek basin. The results indicated that ENE, ER and ADLL yielded the same or very similar design values and confidence intervals for both increasing and decreasing trends of AMFS considered. DLL also yields similar design values if the relationship between DLL and ER/ADLL return periods is considered. Both ER and ADLL are recommended for practical use as they have associated design floods with the design life period of projects and yield reasonable design quantiles and confidence intervals. Furthermore, by assuming that the design results using either a stationary or nonstationary hydrologic design strategy should have the same reliability, the ER method enables us to solve the nonstationary hydrologic design problems by adopting the stationary design reliability, thus bridging the gap between stationary and nonstationary design criteria. [ABSTRACT FROM AUTHOR]

• A method of regional non-stationary flood frequency analysis is proposed. • The index incorporates climatic and reservoir factors as covariates. • MRI is more suitable for non-stationary flood frequency analysis. • The prediction period of flood frequency analysis is extended. Global climate change and reservoir regulations can alter the natural flow of rivers. Influenced by these two drivers, flood sequences may no longer satisfy the assumption of stationary, thereby making it difficult to accurately analysis flood frequency and to design water conservancy projects. Therefore, it is of great significance to analyse the non-stationary frequency of flood sequences in a changing environment. In this study, we proposed a method for conducting nonstationary flood frequency analysis caused by cascade reservoirs as well as the low-frequency climate indices. The proposed non-stationary model 2, with the explanatory variables of climate indices and modified reservoir index (MRI), was compared with the traditional stationary model and the widely used non-stationary model 1 with time as the explanatory variable. The study was conducted at six hydrological stations in the main stream and tributaries of the upper reaches of the Yangtze River in China (considered as the Three Gorges Reservoir Area). The results of the generalized additive model for location, scale and shape (GAMLSS) showed that the Akaike information criterion and Bayesian information criterion values of the proposed non-stationary model method 2 are smaller than those of the two comparison models. When the low-frequency South Oscillation Index is high or the Arctic Oscillation and North Pacific Oscillation are low, the stationary model underestimates the design value of flood quantiles compared with the non-stationary model 2. Compared with the non-stationary model 1, the MRI and low-frequency climate indices as the explanatory variables in model 2 can better describe the non-stationary characteristics of flood frequency and amplitude. In addition, the non-stationary model considering external physical factors can provide better prediction of future design flood compared with two traditional models. [ABSTRACT FROM AUTHOR]

• Nonstationary models that incorporate climatic variables and reservoir index are constructed. • The nonstationary models provide a better fitting of nonstationary flood and low flow series. • Nonstationary frequency analysis of flood and low flow in upper of Huaihe River Basin are conducted. • Climatic and reservoir impacts are related to nonstationarity of flood and low flow respectively. Conventional water infrastructure designs for flood and low flows are usually based on the assumption of stationarity of extreme events. However, recent evidence suggests that the influences of climate variability and human activities have made the hypothesis of stationarity questionable. In this study, we used the generalized additive models for location, scale, and shape (GAMLSS) to construct a nonstationary model in which the parameters of the selected distributions were modelled as a function of climatic variables (i.e., climate indices and precipitation) and/or the reservoir index (RI). The nonstationary models were then used to analyse annual flood and low flow frequency at four hydrological stations in the upper reaches of the Huaihe River Basin, including Dapoling (DPL), Changtaiguan (CTG), Zhuganpu (ZGP), and Xixian (XX) stations. Annual floods were represented by the maximum daily streamflow in each year, and low flows were represented by the 95th quantile of the daily streamflow (Q 95) in each year. The change point and trend analysis revealed that the flood series of the ZGP station and the low flow series of the DPL and XX stations exhibited significant downward and upward trends (p < 0.1)), respectively. The low flow series of the ZGP station showed a significant change point in 1980 (p < 0.1). GAMLSS modelling results showed that, in comparison with stationary models, nonstationary models that included precipitation and the Arctic Oscillation climate index as covariates for the gamma distribution location parameter provided a superior description of the flood series at the four stations. Nonstationary models that incorporated precipitation and/or RI as covariates for the Weibull distribution parameters fit the low flow series better than stationary models at all stations. Furthermore, we found that nonstationary models outperformed stationary models in terms of flood frequency analysis, covering all flood observation points and capturing the generally decreasing trend in flood series, as well as a decrease in the scatter of estimated flood value magnitudes. For the low flow frequency analysis, the comparison results showed that the nonstationary and stationary models performed identically for the DPL, CTG, and XX stations, where no significant change point was detected. However, for the ZGP station, where a significant change point was detected, the nonstationary models performed better than the stationary models and could accurately capture the changes in the magnitude of the estimated low flow values before and after the change point. Overall, the proposed nonstationary model can serve as a tool for nonstationary frequency analysis of flood and low flow series under the influence of climate variability and reservoir regulations, thus providing a reference for regional water infrastructure design. [ABSTRACT FROM AUTHOR]

• Nonstationary models that incorporate climatic variables and reservoir index are constructed. • The nonstationary models provide a better fitting of nonstationary flood and low flow series. • Nonstationary frequency analysis of flood and low flow in upper of Huaihe River Basin are conducted. • Climatic and reservoir impacts are related to nonstationarity of flood and low flow respectively. Conventional water infrastructure designs for flood and low flows are usually based on the assumption of stationarity of extreme events. However, recent evidence suggests that the influences of climate variability and human activities have made the hypothesis of stationarity questionable. In this study, we used the generalized additive models for location, scale, and shape (GAMLSS) to construct a nonstationary model in which the parameters of the selected distributions were modelled as a function of climatic variables (i.e., climate indices and precipitation) and/or the reservoir index (RI). The nonstationary models were then used to analyse annual flood and low flow frequency at four hydrological stations in the upper reaches of the Huaihe River Basin, including Dapoling (DPL), Changtaiguan (CTG), Zhuganpu (ZGP), and Xixian (XX) stations. Annual floods were represented by the maximum daily streamflow in each year, and low flows were represented by the 95th quantile of the daily streamflow (Q 95) in each year. The change point and trend analysis revealed that the flood series of the ZGP station and the low flow series of the DPL and XX stations exhibited significant downward and upward trends (p < 0.1)), respectively. The low flow series of the ZGP station showed a significant change point in 1980 (p < 0.1). GAMLSS modelling results showed that, in comparison with stationary models, nonstationary models that included precipitation and the Arctic Oscillation climate index as covariates for the gamma distribution location parameter provided a superior description of the flood series at the four stations. Nonstationary models that incorporated precipitation and/or RI as covariates for the Weibull distribution parameters fit the low flow series better than stationary models at all stations. Furthermore, we found that nonstationary models outperformed stationary models in terms of flood frequency analysis, covering all flood observation points and capturing the generally decreasing trend in flood series, as well as a decrease in the scatter of estimated flood value magnitudes. For the low flow frequency analysis, the comparison results showed that the nonstationary and stationary models performed identically for the DPL, CTG, and XX stations, where no significant change point was detected. However, for the ZGP station, where a significant change point was detected, the nonstationary models performed better than the stationary models and could accurately capture the changes in the magnitude of the estimated low flow values before and after the change point. Overall, the proposed nonstationary model can serve as a tool for nonstationary frequency analysis of flood and low flow series under the influence of climate variability and reservoir regulations, thus providing a reference for regional water infrastructure design. [ABSTRACT FROM AUTHOR]

• We developed a modified drought severity index (MDSI) with a constrained optimization technique. • MDSI improves drought monitoring for forests and farmland. • Ecological restoration and cultivated land reclamation can help mitigate droughts, while urbanization can potentially intensify droughts. With advancement of remote sensing techniques, remote-sensing drought indices have been widely used for drought monitoring. However, the monitoring accuracy of a specific drought index regionally varies. Considering the deficiency of existing drought indices in reflecting vegetation growth, here we propose a Modified Drought Severity Index (MDSI) with local optimization method constrained by the inclusion of vegetation greenness, crop water shortage, canopy temperature, vegetation structure, and physiological status. We evaluated drought monitoring performance of MDSI across China, and detected high correlations between MDSI and soil moisture (SM), Standardized Precipitation Index at a 3-month scale (SPI-3), actual drought-affected areas (ADA), evidencing higher drought performance of MDSI when compared to 8 widely-used drought indices. Besides, MDSI performed better in monitoring agricultural drought. We found amplifying short-term drought intensity in the future. Ecological restoration and cultivated land reclamation can alleviate drought effects. However, urbanization can potentially intensify droughts. How to adapt human behavior to droughts is a challenging task. [ABSTRACT FROM AUTHOR]

Summary Homogenization and similarities of precipitation and flow regimes across China are thoroughly investigated using Gini coefficient analysis method and the Analysis Of Similarity (ANOSIM) technique, respectively based on daily precipitation data from 554 meteorological stations and monthly streamflow data from 370 hydrological stations covering the period of 1960–2000. The results indicate that: (1) Homogenization of precipitation regimes is increasing from the northwest to the southeast China. However, different spatial patterns of homogenization of flow regions are identified. Spatially, lower homogenization of flow regimes is detected in the northeast China and higher homogenization of flow regimes in the central and southeast China. Temporally, flow regimes during 1961–2000 are characterized mainly by increasing homogenization, and it is particularly true after 1980; (2) precipitation regimes during 1961–2000 are characterized by decreasing dissimilarities. Larger areas of China are characterized by decreasing dissimilarities of precipitation regimes during 1980–2000 when compared to those during 1961–1980, which should be due to increasing precipitation concentration and intensifying precipitation regimes in recent years; (3) distinctly dissimilar precipitation and flow regimes can be identified between geographically separate river basins. Interregional similarities of flow regimes are enhancing after 1980 when compared to those before 1980 though interregional similarities of precipitation regimes are not changed much; and (4) spatial mismatch is evident in terms of spatial range and changing degree of flow and precipitation regimes. Roughly spatial match can be observed between changes of flow and precipitation indicates and it is particularly the case for precipitation and flow changes in dry season such as winter in China. However, influences of human activities and precipitation changes on streamflow are varying as for specific river basins, such as the Yangtze and the Yellow River basins. Damming-induced fragmentation of river basins is the major cause behind higher homogenization of flow regimes. Thus, human interferences in hydrological processes via damming and construction of reservoirs greatly alter streamflow vs. precipitation relations. The results of this study provide theoretical and practical grounds for management and planning of water resources at basin or interbasin scales under the influences of human activities and climate changes. [ABSTRACT FROM AUTHOR]

• A novel Encoder-Decoder with an Exogenous input (EDE) structure is proposed. • Four models are evaluated and compared from different perspectives. • The EDE structure is more suitable for long lead-time flood forecasting. • The LSTM-EDE model improves the multi-step-ahead flood forecasting accuracy. Accurate and reliable multi-step-ahead flood forecasting is beneficial for reservoir operation and water resources management. The Encoder-Decoder (ED) that can tackle sequence-to-sequence problems is suitable for multi-step-ahead flood forecasting. This study proposes a novel ED with an exogenous input (EDE) structure for multi-step-ahead flood forecasting. The exogenous input can be the outputs of process-based hydrological models. This study constructs four multi-step-ahead flood forecasting approaches, including the Xinanjiang (XAJ) hydrological model, the single-output long short-term memory (LSTM) neural network with recursive strategies, the recursive ED combined with the LSTM neural network (LSTM-RED), and the LSTM-EDE models. The performance of these four models is evaluated and compared by the long-term 3 h hydrologic data series of the Lushui and Jianxi basins in China. The results show that the LSTM-RED model that integrates recursive strategies into the training process of neural networks is more advantageous than the LSTM model. The proposed LSTM-EDE model can overcome the exposure bias problem, simplify its model structure, increase the computational efficiency in the validation process, and improve the multi-step-ahead flood forecasting accuracy, as compared to the LSTM-RED model. [ABSTRACT FROM AUTHOR]

Summary The assumption of stationarity of annual peak flood (APF) records at 28 hydrological stations across the Pearl River basin, China, is tested. Abrupt changes in mean and variance are tested using the Pettitt technique and the Loess method. Trends of APFs are analyzed using the Mann–Kendall method and the Spearman technique. And then the stationarity of the APF series is further investigated by GAMLSS models and long-term persistence. Results indicate that: (1) abrupt changes in mean and variance have similar influences on the changing properties of APFs, such as stationarity. Abrupt changes in mean and variance are only field significant in the East River basin; (2) the change points have a considerable impact on the detection of trends, and these may be attributed to the fact that a abrupt increase or decrease in mean values will affect the trend variations. Besides, for the APF series being free of change points and trend, the GAMLSS models also corroborate stationarity of the APF series; (3) the nonstationarity in the Pearl River basin is mainly due to the existence of the change point. However, the APF series with change points in mean and/or variance are also characterized by long-term persistence, and thus it is infeasible to assert that the abrupt behaviors and/or trends of the APF series are the result of human activities or long-term persistence, especially in the East River basin. Results of this study will provide information for management of water resources and design of hydraulic facilities in the Pearl River basin in a changing environment. [ABSTRACT FROM AUTHOR]

• Machine learning assists hybrid model to promote flood forecasting and early warning. • Hybridizing MCQRNN with XAJ model for flood probability density forecasting. • XAJ-MCQRNN conquers overfitting and biased-prediction bottlenecks. • XAJ-MCQRNN improves accuracy and reliability of flood probability density forecasts. Making accurate and reliable probability density forecasts of flood processes is fundamentally challenging for machine learning techniques, especially when prediction targets are outside the range of training data. Conceptual hydrological models can reduce rainfall-runoff modelling errors with efficient quasi-physical mechanisms. The Monotone Composite Quantile Regression Neural Network (MCQRNN) is used for the first time to make probability density forecasts of flood processes and serves as a benchmark model, whereas it confronts the drawbacks of overfitting and biased-prediction. Here we propose an integrated model (i.e. XAJ-MCQRNN) that incorporates Xinanjiang conceptual model (XAJ) and MCQRNN to overcome the phenomena of error propagation and accumulation encountered in multi-step-ahead flood probability density forecasts. We consider flood forecasts as a function of rainfall factors and runoff data. The models are evaluated by long-term (2009–2015) 3-hour streamflow series of the Jianxi River catchment in China and rainfall products of the European Centre for Medium-Range Weather Forecasts. Results demonstrated that the proposed XAJ-MCQRNN model can not only outperform the MCQRNN model but also prominently enhance the accuracy and reliability of multi-step-ahead probability density forecasts of flood process. Regarding short-term forecasts in testing stages at four horizons, the XAJ-MCQRNN model achieved higher Nash-Sutcliffe Efficiency but lower Root Mean Square Error values, while improving Coverage Ratio and Relative Bandwidth values in comparison to the MCQRNN model. Consequently, the improvement can benefit the mitigation of the impacts associated with uncertainties of extreme flood and rainfall events as well as promote the accuracy and reliability of flood forecasting and early warning. [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]

Summary: This paper presents a method for regional frequency analysis and spatio-temporal pattern characterization of rainfall-extreme regimes (i.e. extremes, durations and timings) in the Pearl River Basin (PRB) using the well-known L-moments approach together with advanced statistical tests including stationarity test and serial correlation check, which are crucial to the valid use of L-moments for frequency analysis. Results indicate that: (1) the entire Pearl River Basin (40 sites) can be categorized into six regions by cluster analysis together with consideration of the topography and spatial patterns of mean precipitation in the basin. The results of goodness-of-fit measures indicate that the GNO, GLO, GEV, and PE3 distributions fit well for most of the basin for different HOM regions, but their performances are slightly different in term of curve fitting; (2) the estimated quantiles and their biases approximated by Monte Carlo simulation demonstrate that the results are reliable enough for the return periods of less than 100years; (3) excessive precipitation magnitude records are observed at Guilin region of Guangxi Province and Fogang region of Guangdong Province, which have sufficient climate conditions (e.g. precipitation and humidity) responsible for the frequently occurred flood disasters in the regions. In addition, the spatial variations of precipitation in different return periods (Return period=1, 10, 50years to 100years) increase from the upstream to downstream at the regional scale; (4) the seasonal patterns of precipitation extremes for different topographical regions are different. The major precipitation events of AM1R, AM3R, AM5R and AM7R in regions of low-elevation in lower (south-eastern) part of the basin occur mainly in May, June, July and August, while the main precipitation periods for the mountainous region upstream are June, July and August. Further analysis of the NCAR/NCEP reanalysis data indicates that the eastern Asian summer monsoon and typhoons (or hurricanes) are major metrological driving forces on the precipitation regimes. Additionally, topographical features (i.e. elevation, distance to the sea, and mountain’s influences) also exert different impacts on the spatial patterns of such regimes. To the best of our knowledge, this study is the first attempt to conduct a systematic regional frequency analysis on various annual precipitation extremes (based on consecutive 1-, 3-, 5-, 7-day averages) and to establish the possible links to climate pattern and topographical features in the Pearl River Basin and even in China. These findings are expected to contribute to exploring the complex spatio-temporal patterns of extreme rainfall in this basin in order to reveal the underlying linkages between precipitation and floods from a broad geographical perspective. [Copyright &y& Elsevier]

Summary: In this study, a GIS based simple and easily performed runoff routing approach based on travel time was developed to simulate storm runoff response process with consideration of spatial and temporal variability of runoff generation and flow routing through hillslope and river network. The watershed was discretized into grid cells, which were then classified into overland cells and channel cells through river network delineation from the DEM by use of GIS. The overland flow travel time of each overland cell was estimated by combining a steady state kinematic wave approximation with Manning’s equation, the channel flow travel time of each channel cell was estimated using Manning’s equation and the steady state continuity equation. The travel time from each grid cell to the watershed outlet is the sum of travel times of cells along the flow path. The direct runoff flow was determined by the sum of the volumetric flow rates from all contributing cells at each respective travel time for all time intervals. The approach was calibrated and verified to simulate eight storm runoff processes of Jiaokou Reservoir watershed, a sub-catchment of the Yongjiang River basin in southeast China using available topography, soil and land use data for the catchment. An average efficiency of 0.88 was obtained for the verification storms. Sensitivity analysis was conducted to investigate the effect of the area threshold of delineating river networks and parameter K relating channel velocity calculation on the predicted hydrograph at the basin outlet. The effects of different levels of grid size on the results were also studied, which showed that good results could be attained with a grid size of less than 200m in this study. [Copyright &y& Elsevier]

• A comprehensive framework for quantifying the climatic and anthropogenic influences on river hydrological health is proposed. • Human activities significantly aggravate the degradation of river hydrological health for the studied catchment. • Widespread artificial water withdrawal and reservoir operation are the two crucial human activities. • Adequate rainfall maintains the river hydrological health at a good status in the 1990 s. Climate change and human activities have together altered the river hydrological regime and consequently threatened the health of river ecosystems. Quantifying the impact of climate change and human activities on river hydrological health regimes is essential for water resource management and river ecology protection. Although previous studies have analysed the hydrologic alterations using some indicators, separating effects of climate change and human activities on river hydrological health is needed for developing adaptive measures to protect the ecosystem of river basins. In this study, a comprehensive assessment framework for quantifying climatic and anthropogenic influences on river hydrological health variation was proposed. The framework consists of the following steps: (1) the reconstruction of natural river streamflow using the variable infiltration capacity (VIC) hydrological model, (2) calculation of river hydrological health through the ecological flow threshold method, and (3) quantification of the impacts of climate change and human activities on river hydrological health using the 'observed–simulated' comparison approach. The semi-arid Laohahe Basin in northern China, which consists of three human-influenced catchments (Taipingzhuang, Chifeng, and Xinglongpo) and one natural catchment (Xiquan), was selected as the case study area. The case study demonstrated that the proposed procedure is efficient in quantifying climatic and anthropogenic influences on river hydrological health. The results revealed that the hydrological health level has significantly declined in the three human-influenced catchments for the human-influenced period (1980–2016), particularly in the 2000 s and 2010 s, where it degraded much more severely. Whereas, the relatively adequate rainfall in the 1990 s maintained the river hydrological health at a good status. The quantitative evaluation showed that human activities were the main driving factors for the hydrological health degradation during the whole human-influenced period, with contributions of 80.8%, 91.9%, and 86.0% for the Taipingzhuang, Chifeng, and Xinglongpo catchments, respectively. Widespread artificial water withdrawal and reservoir operation were the two crucial human activities that caused the degradation of river hydrological health for the studied catchment. The proposed procedure and findings of this study not only help in deeper understanding of the evolutionary characteristics and driving mechanisms of river hydrological health in a changing environment in general, but also provide scientific basis for local water resources management and river ecosystems protection. [ABSTRACT FROM AUTHOR]

• A dynamic three-stage blending scheme is developed to simultaneously correct precipitation occurrence and intensity. • A new daily 0.25° precipitation dataset over mainland China is produced by merging four multi-satellite/reanalysis products. • Different data merging methods are systematically evaluated in 238 catchments over China. • Our developed approach can facilitate hydrological modelling by substantially improving the KGE of simulated streamflow. Satellite-retrieved and atmospheric reanalysis precipitation can bridge the spatiotemporal gaps of in-situ gauging networks, but estimation biases can limit their reliable applications in hydrological monitoring and modelling. To correct precipitation occurrence and intensity simultaneously, this study develops a three-stage blending approach to integrate three multi-satellite precipitation datasets (IMERG Final, TMPA 3B42V7 and PERSIANN-CDR), the ERA5 atmospheric reanalysis product and a gauge dataset within a dynamic framework. Firstly, the systematic biases of the four members were individually corrected by combining the local intensity scaling and ratio bias correction methods. Then, the "state weights" used for determining wet/dry events were optimized by evaluating a score function of the four bias-corrected members. Thirdly, the "intensity weights" were optimized using the cuckoo search (CS) algorithm and the Bayesian Model Averaging (BMA) method, respectively. The three-stage blending approach produced dynamic weights varying both spatially and temporally, and the performance was thoroughly evaluated over mainland China. Results show that the three-stage dynamic scheme performs better than individual datasets and two-stage blending methods in terms of all eight statistical metrics, and the CS algorithm outperforms the BMA method in the third stage. By randomly sampling validation sites using K-fold experiments, the developed algorithm also demonstrates a superior performance in ungauged regions. After interpolating and normalizing blending parameters of all gauges to entire domain using ordinary kriging, a new blended precipitation dataset with a daily 0.25° scale was produced. Four hydrological models are forced by blended and primary precipitations in 238 catchments over China, further confirming that the developed approach can facilitate hydrological modelling demonstrated by improving the Kling-Gupta efficiency of simulated streamflow by 12–35%. [ABSTRACT FROM AUTHOR]

• An improved Budyko complementary relationship (IBCR) is developed. • The IBCR incorporates effects of both climate change and human activities on basin characteristics. • The IBCR is more flexible than the original relation in separating runoff change. Numerous efforts have been made to separate effects of climate change and human activities on long-term runoff change, including a commonly used method based on the Budyko complementary relationship (BCR) to deal with the non-closure problems often encountered in separating the observed runoff change into its contributed components. The BCR was derived by assuming the change of the Budyko parameter reflecting basin characteristics is related to human activities alone. However, some studies show that basin characteristics are related to both climate change and human activities in certain basins, where applying the BCR method could lead to wrong results. In the study, a more flexible form of the BCR, the improved Budyko complementary relationship (IBCR), is developed under the situation that basin characteristics may relate to climate change. A separation method based on the IBCR is also developed. In this IBCR method, the relationship of the basin characteristics parameter to climate and/or human factors is estimated by using a multi-year moving window. To identify the flexibility of the IBCR method relative to the BCR method, both methods were applied to the Weihe basin of China, whose basin characteristics have been found to be affected by both climate change and human activities. Compared with the IBCR method, the BCR method overestimates the climate effect, while underestimates the human effect on the runoff change in the Weihe basin. It is found that whether or not to incorporate the climate effect into the basin characteristics parameter has a significant impact on the runoff change separation results in Weihe basin. Overall, the IBCR method is more reasonable and flexible for separating the long-term runoff change into different components. [ABSTRACT FROM AUTHOR]

• A comprehensive approach for identifying drought termination characteristics is proposed. • Natural and human-influenced hydrological drought termination characteristics are identified. • Human activities significantly aggravate the drought termination duration, deficit and rate. • Human activities lead to shifts in drought termination seasonality. Although many previous studies have analysed the impacts of human activities on hydrological drought, studies that analysed these impacts from the perspective of drought termination, a critical re-wetting phase of hydrological drought, are limited. A deeper understanding on how human alter hydrological drought termination phase is essential for improving drought recovery prediction and performance of the drought early warning system. In this study, a comprehensive approach for identifying hydrological drought termination characteristics and quantifying the impact of human activities on drought termination was proposed. This approach, which combines the concept of drought termination (DT), an 'observed–simulated' comparison approach, and the variable threshold level method (TLM v), consists of the following steps: (1) reconstruction of natural streamflow using a hydrological model, (2) identification of hydrological drought termination characteristics using TLM v method and the concept of drought termination, and (3) quantification of human influence by comparison of the hydrological drought termination characteristics of human-influenced (observed) series and those of natural (simulated) series. The Laohahe basin, consists of four catchments (Xiquan, Xiaochengzi, Dianzi, and Taipingzhuang) in northern China, was evaluated using the proposed procedure. The study demonstrated that the proposed approach is efficient in quantifying human influence on hydrological drought termination phase. The results revealed that human activities have significant impacts on the hydrological drought termination phase in the Xiaochengzi, Dianzi, and Taipingzhuang catchments. All the average drought termination duration (DT dur), deficit (DT def), and rate (DT rate) in the human-influenced series of the three catchments (Xiaochengzi, Dianzi, and Taipingzhuang) increased in comparison to those in the natural series, with maximum increases of 230%, 865%, and 35%, respectively. The seasonality of the drought termination phase starts (DT start) and ends (DT end) for the three catchments exhibited obvious shifts due to human influence. The preferred seasons for DT start and DT end were shifted to summer and autumn, respectively. The proposed approach and findings of this study may help to gain a deeper understanding of how human activities alter hydrological drought termination severity (drought termination duration, deficit, and rate) and time (drought termination starts or ends). [ABSTRACT FROM AUTHOR]

• Multiple satellite precipitation products (SPPs) were merged with gauge observation. • A spatially downscaling process was performed prior to gauge-satellites precipitation fusion. • GWRR was established to overcome the collinearity problem in GWR. • The proposed merging scheme significantly improved the spatial resolution and accuracy of precipitation estimates. Merging gauge observation with a single original satellite-based precipitation product (SPP) is a common approach to generate spatial precipitation estimates. For the generation of high-quality precipitation maps, however, this common method has two drawbacks: (1) the spatial resolutions of original SPPs are still too coarse; and (2) a single SPP can't capture the spatial pattern of precipitation well. To overcome these drawbacks, a two-step scheme consisting of downscaling and fusion was proposed to merge gauge observation with multiple SPPs. In both downscaling and fusion steps, the geographically weighted ridge regression (GWRR) method, which is a combination of the geographically weighted regression (GWR) method and the ridge regression method, is proposed and implemented to generate improved spatial precipitation estimates by overcoming the collinearity problem of the pure GWR method. The proposed two-step merging scheme was applied to Xijiang Basin of China for deriving daily precipitation estimates from the data of both gauge observation and four near real-time SPPs (i.e., TMPA-3B42RT, CMORPH, PERSIANN and GSMaP_NRT) during the period of 2010–2017. The results showed that: (1) the collinearity problem caused by GWR was not serious in downscaling but serious enough to prevent GWR from being directly used in the fusion; and (2) the proposed two-step merging scheme significantly improved the spatial resolution and accuracy of precipitation estimates over the original SPPs. Comparisons also showed that, in the second step (fusion) of the merging scheme, the use of multiple SPPs provided more reliable spatial precipitation estimates than using a single SPP. [ABSTRACT FROM AUTHOR]

• A framework is proposed to investigate the drought hazard propagation. • Drought hazards are quantified by the most-likely scenarios and uncertainty envelope. • Drought hazards tend to ascend from meteorological to hydrological droughts. As a major weather-driven disaster, drought can be assessed from meteorological to hydrological aspects. Although the propagation from meteorological to hydrological droughts has received lots of attention in recent years, the hazard transferability in such a propagation process has been less investigated. In this study, we propose a framework with the incorporation of copulas and a drought hazard propagation ratio (DHPR) to examine the drought propagation process, particularly to investigate the accompanying hazard transferability. Three catchments with few human activities located in two major river basins of China (i.e., the Yangtze River basin and the Yellow River basin) with different hydro-climatic conditions are selected as case studies. First, the standardized precipitation evapotranspiration index (SPEI) and the standardized runoff index (SRI) are calculated to measure meteorological and hydrological droughts for the 1961–2014 period. Subsequently, the drought duration and severity are identified using the theory of run, and then the most-likely scenarios and the corresponding uncertainty ellipse based on copulas are incorporated to appraise meteorological and hydrological drought hazards. Finally, a novel concept of DHPR is proposed to evaluate the hazard transferability from meteorological to hydrological drought. The results show that (1) the drought propagation generally shows lengthened duration, amplified severity, and the time-delay phenomenon among these catchments; (2) drought hazards represented by the most-likely scenarios of duration and severity and the uncertainty ellipse tend to ascend based on the bivariate frequency analysis; and (3) the hazard transferability is stable from meteorological to hydrological droughts, as indicated by the almost unchanged DHPR ranging between 1 and 2 for the most-likely scenarios and varying between 2 and 4 for the uncertainty ellipse under different return periods. The above results imply firm and robust correlations between meteorological and hydrological drought hazards, which can provide a supplement for revealing the drought propagation mechanism and would benefit drought risk assessment. [ABSTRACT FROM AUTHOR]

• A sequential "slope-gully-river" structure is built in the WEP-SED. • The newly developed soil erosion model separates gully erosion process. • The gravitational erosion is simulated based on mechanical equilibrium principle. The Loess Plateau of China suffers from severe soil erosion. In the gullied rolling loess region, approximately half of the sediment derives from gully areas, which are the most prominent topographical features of this region. No existing soil erosion models are appropriate in accuracy or applicability for the Loess Plateau because existing models only consider slope and river erosion processes at the catchment scale. The absence of the gully erosion process in these models significantly limits their application to the Loess Plateau. Taking this issue into account, a three-process-based distributed soil erosion model, denoted by WEP-SED, is proposed to investigate soil erosion in this region based on the Water and Energy transfer Processes in Large river basins (WEP-L) model. In WEP-SED, a sequential "slope-gully-river" structure is built for the physically-based simulation of soil erosion and sediment yield. In this structure, soil erosion is integrated from six parts with successive transport relationships. The proposed model is applied to the upstream region of the Wenjiachuan hydrological station in the Kuye River basin. The simulated monthly average sediment transport rates from 1956 to 2010 at the Wenjiachuan hydrological station agree well with the observations, with a correlation coefficient of 0.76, an NSE of 0.58, and a relative error of −5.60%. Furthermore, the simulated average annual amount of gully erosion reaches 60.30% of the total soil erosion, reflecting the fact that the gully erosion is a serious problem and demonstrating that gully erosion must be considered separately in this area. [ABSTRACT FROM AUTHOR]