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• 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 Budyko-type equations have begun to be widely adopted to separate the contributions of climate change and human activities to the variation of runoff over long-term periods by using the multi-year averages of hydrological variables. In this study, a two-step framework based on four single-parameter Budyko-type equations is proposed to separate the impacts of climate change and human activities on runoff. First, the relationship of the parameter w in each Budyko-type equation with climatic and human factors is built to reveal the time-variation process of w by using an 11-year moving window. Second, the impacts of climate change and human activities on runoff are separated by using both the decomposition method and the sensitivity method. This separating framework is applied to analyze the variation of the runoff during 1960–2009 in the Weihe River. It is found that the parameter w in each Budyko-type equation is significantly related to both factors of climate and human activities. The results from both the decomposition method and the sensitivity method show that climate change is the main driving factor to the decline in runoff of the Weihe River, while human activities are another important factor. In general, climate change affects runoff not only by changing the hydrological inputs (precipitation and potential evaporation) but also by altering the watershed characteristics as represented by the parameter w ; while the impacts of human activities on runoff are exerted mainly through the alteration of the watershed characteristics. [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]

Summary Changes in lake hydrological regimes and the associated impacts on water supplies and ecosystems are internationally recognized issues. During the past decade, the persistent dryness of Poyang Lake (the largest freshwater lake in China) has caused water supply and irrigation crises for the 12.4 million inhabitants of the region. There is conjecture as to whether this dryness is caused by climate variability and/or human activities. This study examines long-term datasets of catchment inflow and Lake outflow, and employs a physically-based hydrodynamic model to explore catchment and Yangtze River controls on the Lake’s hydrology. Lake water levels fell to their lowest during 2001–2010 relative to previous decades. The average Lake size and volume reduced by 154 km 2 and 11 × 10 8 m 3 during the same period, compared to those for the preceding period (1970–2000). Model simulations demonstrated that the drainage effect of the Yangtze River was the primary causal factor. Modeling also revealed that, compared to climate variability impacts on the Lake catchment, modifications to Yangtze River flows from the Three Gorges Dam have had a much greater impact on the seasonal (September–October) dryness of the Lake. Yangtze River effects are attenuated in the Lake with distance from the River, but nonetheless propagate some 100 km to the Lake’s upstream limit. Proposals to build additional dams in the upper Yangtze River and its tributaries are expected to impose significant challenges for the management of Poyang Lake. Hydraulic engineering to modify the flow regime between the Lake and the Yangtze River would somewhat resolve the seasonal dryness of the Lake, but will likely introduce other issues in terms of water quality and aquatic ecosystem health, requiring considerable further research. [ABSTRACT FROM AUTHOR]

• A photogrammetry-based method for river surface velocity estimation is proposed. • The general variational formulation is derived for the proposed method. • The proposed method has good performance in various surface velocity estimations. The ease of access to media resources and computational power has recently generated interest in using vision-based approaches for hydraulic monitoring. A key challenge for non-intrusive, image-based hydrology measurement methods is incorporating different hydraulic variables as prior knowledge with image information. We propose a photogrammetry-based method called L 1-Diffusion derived from the convection–diffusion equation commonly used in hydrodynamics with an additional regularization term to estimate the fluid motion field in the image plane, from which the free surface velocity can be further obtained using the photogrammetric projection relationship between the image plane and world coordinates. The inverse problem is used to discuss the relationship between the widely used space–time image velocimetry (STIV) and the proposed L 1-Diffusion. To validate the proposed method, unmanned aerial vehicle (UAV) images as well as in-situ acoustic Doppler current profiler (ADCP) experiments were carried out. Based on comparison results with the ADCP measurement and vision-based flow field estimation, the newly proposed L 1-Diffusion algorithm can accurately and efficiently estimate the free surface velocity of a river from the image sequences in a variety of scenarios. [ABSTRACT FROM AUTHOR]

• We propose a dynamic assessment of ecological vulnerabilityfrom three dimensions. • We compare ecological vulnerability across different ecosystems&different dimensions. • We explore the ecological response of the QTP through influencing factors analysis. At present, climate change has brought huge challenges to vegetation and ecosystems. As the Qinghai-Tibet Plateau (QTP) is a sensitive area of global climate change, the dynamic assessment of its ecological vulnerability is very important. In order to better quantify the relative size of the ecological vulnerability of the QTP, this study starts from the background characteristics and dynamic change process of the ecosystem, by fitting the vegetation index net primary productivity (NPP) and the temperature, precipitation and meteorological elements. The coefficients of autocorrelation multiple linear regression are used to construct an ecological vulnerability model from the three dimensions of "exposure-sensitivity-elasticity" to conduct a dynamic assessment of ecological vulnerability. Based on the evaluation results, from 2000 to 2015, the ecologically fragile areas were mainly distributed in the eastern and central areas of the QTP. The ecological fragility of the western region showed obvious discontinuities, with high and low vulnerabilities staggered. The three ecosystems of forest, grassland, and bare land have significant differences in their ecological vulnerability to climate change, showing a clear positive correlation in the three dimensions of exposure, sensitivity, and resilience, but in terms of the contribution rates of the three dimensions. The performance is relatively similar, and the relative relationship between the three dimensions is relatively balanced. The longitude and precipitation of the sample points have a greater impact on ecological vulnerability and its three dimensions, and the impact of precipitation on ecological vulnerability is more significant than that of temperature. This research provides theoretical support for plateau ecological conservation and ecological security under the influence of global climate. [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]

Highlights: [•] We examined the long-term hydro-climatic changes of the Poyang Lake catchment. [•] We investigated the relative impacts of climate change and human activities. [•] Relative effects varied among sub-catchments under different decades. [•] Increased catchment streamflow was mainly affected by climate variability. [•] Comprehensive effect of specific human activities led to a decrease of streamflow. [Copyright &y& Elsevier]

Summary: Although discharge estimations are central for water management and hydropower, there are few studies on the variability and uncertainty of their basis; deriving discharge from stage heights through the use of a rating curve that depends on riverbed geometry. A large fraction of the world’s river-discharge stations are presumably located in alluvial channels where riverbed characteristics may change over time because of erosion and sedimentation. This study was conducted to analyse and quantify the dynamic relationship between stage and discharge and to determine to what degree currently used methods are able to account for such variability. The study was carried out for six hydrometric stations in the upper Choluteca River basin, Honduras, where a set of unusually frequent stage–discharge data are available. The temporal variability and the uncertainty of the rating curve and its parameters were analysed through a Monte Carlo (MC) analysis on a moving window of data using the Generalised Likelihood Uncertainty Estimation (GLUE) methodology. Acceptable ranges for the values of the rating-curve parameters were determined from riverbed surveys at the six stations, and the sampling space was constrained according to those ranges, using three-dimensional alpha shapes. Temporal variability was analysed in three ways: (i) with annually updated rating curves (simulating Honduran practices), (ii) a rating curve for each time window, and (iii) a smoothed, continuous dynamic rating curve derived from the MC analysis. The temporal variability of the rating parameters translated into a high rating-curve variability. The variability could turn out as increasing or decreasing trends and/or cyclic behaviour. There was a tendency at all stations to a seasonal variability. The discharge at a given stage could vary by a factor of two or more. The quotient in discharge volumes estimated from dynamic and static rating curves varied between 0.5 and 1.5. The difference between discharge volumes derived from static and dynamic curves was largest for sub-daily ratings but stayed large also for monthly and yearly totals. The relative uncertainty was largest for low flows but it was considerable also for intermediate and large flows. The standard procedure of adjusting rating curves when calculated and observed discharge differ by more than 5% would have required continuously updated rating curves at the studied locations. We believe that these findings can be applicable to many other discharge stations around the globe. [Copyright &y& Elsevier]

Abstract: Projections of changes in climate extreme are important in assessing the potential impacts of climate change on social and natural systems. This article presents future projections of climate extremes in the Tibetan Plateau constructed from ensembles of coupled general circulation models (CGCMs) contributing to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC AR4), under a range of emission scenarios. The extremes of temperature and precipitation are described by seven indices, namely, the frost day (FD), percentage of nights when Tmin >90th percentile (TN90), consecutive dry days (CDD), annual count of days when precipitation >=10mm (R10), maximum 5-day precipitation total (R5D), simple daily intensity index (SDII), and annual total precipitation when precipitation >95th percentile (R95T). Results indicate that frost days decrease over the Tibetan Plateau in the 21st century. More frequent warm nights are also projected in the plateau. The increases of these temperature extremes under A2 and A1B scenarios are more pronounced than under B1. Heavy precipitation events for single days and pentads are projected to increase in their intensity over most parts of Tibetan Plateau. CDD, R10, R5D, R95T and SDII collectively suggest more extreme precipitation in the region (2011–2020). In addition, impacts of climate extremes changes on local water resources and fragile ecosystem are discussed as an extension of this article. The findings will be beneficial to project regional responses in this unique region to global climate change, and then to formulate regional strategies against the potential menaces of climate change. [Copyright &y& Elsevier]

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]

• Evaluated 14 regionalization methods with four hydrological models in 3444 catchments. • Keeping poorly calibrated gauged catchments is preferable in poorly gauged regions. • The rank of different regionalization methods is similar among different climatic regions and hydrological models. Over recent decades, hydrologists have proposed a variety of methods to predict discharge in ungauged catchments, and significant progress has been made in the field of hydrological model parameter regionalization. However, uncertainties from both hydrological models and regionalization methods make it a challenge to draw clear conclusions for some questions in regionalization (e.g., the best performing regionalization method, the optimal number of donor catchments, and the optimal efficiency threshold of donor catchments). In this study, for the first time, we made an attempt to address such questions in one paper through a comprehensive evaluation of model performance by using five regionalization methods with two weighting schemes, two averaging options, five efficiency thresholds, and four lumped hydrological models over a broad set of 3444 catchments under varying hydroclimatic conditions in North America. The results show that: (1) Spatial Proximity with the Inverse Distance Weighting method and the output average option (SPI-out) generally performs better than or comparable to other regionalization methods for different climate regions and hydrological models, while the global mean method performs the worst. (2) The rank of different regionalization methods is similar among different climatic regions and hydrological models. Compared to catchments with other climates, regionalization methods perform worst in the arid regions. (3) The selection of five donors is relatively efficient for distance/attributes-based regionalization approaches with the output averaging option disregarding the efficiency threshold. (4) The differences of median Kling-Gupta efficiency values among thresholds of "all", 0.6 and 0.7 are no more than 0.05 for each regionalization method. However, the regionalization performance significantly deteriorates from using efficiency thresholds of 0.7 to 0.9 due to the significant reduction of available donor catchments. Thus, the poorly calibrated catchments may need to be included in the regionalization process, especially when the number of catchments is insufficient. [ABSTRACT FROM AUTHOR]

• An improved Budyko framework was proposed to evaluate basin hydrologic resilience to adverse climate change; • All the16 basins except two on the Tibetan plateau have low hydrologic resilience; • Vegetation structure plays an important role in regulating the hydrologic resilience. Climate change has a significant impact on the runoff of basins in cold, dry areas. The quantification of regional ecohydrological responses to climate change such as warming and drought is essential for establishing proper water resource management schemes. We propose a simple and novel method based on the Budyko framework to evaluate the hydrologic resilience of 16 basins that conform the Asian Water Tower in the Tibetan Plateau (TP). Our method defines two metrics within the Budyko domain – tolerance (ψ) and plasticity (φ) – that characterize the hydrologic resilience of a basin. Based on an ecohydrological point of view, a basin is considered hydrologically resilient if ψ and φ are both greater than 1 or its φ is negative and ψ is greater than 1. Our results show that ψ varies between 0.27 and 0.74, with an average value of 0.45 and φ varies between 2 and 16.33, with an average value of 6.90, for 14 out of the 16 basins. Only two basins – Taohe and Datonghe – had negative φ (-11.67 and -8.11, respectively) and ψ greater than 1 (2.26 and 19.58, respectively), suggesting that these two are the only basins with a hydrologic resilience to climatic warming/drying in the TP. Within the non-resilient basins, we found vegetation to play a key role in the level of tolerance and plasticity indicating that basins with a larger vegetation cover display a lower capability to adapt to adverse climatic changes. Following these results, we call for afforestation efforts to be carefully considered in cold, dry areas. The proposed method and conclusions drawn by this study may help predict the hydrologic responses to future adverse climatic conditions. [ABSTRACT FROM AUTHOR]

• 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]

Summary: A distributed rainfall-runoff model was developed to predict storm runoff from humid forested catchments. The model is physically based and takes into account the saturation excess overland flow mechanism and preferential subsurface flow. The watershed is discretized into a number of square grids, which then are classified into overland flow and channel flow elements based on water flow properties. On the overland elements, Infiltration, overland flow and lateral subsurface flow are estimated, while on channel flow elements river flow routing is performed. Lateral subsurface flow is calculated using Darcy’s law and the continuity equation, whereas overland flow and channel flow are modeled using a one dimensional kinematic wave approximation to the St. Venant equations. The model governing equations are solved by an implicit finite difference scheme. While using process-based equations and physically meaningful parameters, the model still maintains a relatively simple structure. Most of the model parameters can be derived from digital elevation models (DEMs), digital soil and land use data, and the remainder of the parameters that are comparatively sensitive can be determined by model calibration. The model is tested using nine storm events in the Jiaokou watershed, a sub-basin of Yongjiang River in Zhejiang Province, China. Of these storms, one storm is used for calibrating the model parameters and the remaining eight storms are used to verify the model. When judged by the model efficiency coefficient (R 2), volume conversation index (VCI), absolute error of the time to peak (ΔT), and relative error of the peak flow rate (δP max), acceptable results are achieved. Sensitivity analysis shows that the model is sensitive to saturated hydraulic conductivity (Ks), Manning’s roughness coefficients (n) and the initial soil moisture content. [Copyright &y& Elsevier]

The rise of global mean temperature has aroused wide attention in scientific communities. To reduce the negative climate change impact, the United Union's Intergovernmental Panel on Climate Change (IPCC) set a goal to limit global warming to 1.5 °C relative to pre-industrial levels based on the previous 2.0 °C target in October 2018. To understand the necessity of more stringent emission reduction, this study investigates the impacts of additional 0.5 °C global warming from 1.5 to 2.0 °C on global extreme precipitation, and especially its socioeconomic consequences. The extreme precipitation is represented by extreme precipitation frequency (R95pF), extreme precipitation percentage (R95pT), and maximum one-day precipitation (RX1day) as indicators, calculated based on daily precipitation data extracted from 29 Coupled Model Inter-comparison Project Phase 5 (CMIP5) global climate models (GCMs) under two representative concentration pathways: RCP4.5 and RCP8.5. The exposures of economy and population to extreme precipitation events are also computed and compared for two warming levels by using the Shared Socioeconomic Pathways (SSPs). The results show that most regions in the world are likely to suffer from increasing extreme precipitation hazards in a warming climate, with ascending gross domestic product (GDP) and population being exposed to extreme dangers with an additional 0.5 °C warming. R95pT and RX1day are projected to increase overwhelmingly throughout all continents, directly leading to intensified precipitation extremes and flash floods. In middle and low latitudes, the annual total wet-day precipitation (PRCPTOT) shows a rich-get-richer trend and R95pF decreases, which will reinforce the intensified trend of the magnitude of extreme precipitation. The exposures of GDP and population in regions with extensive exposure to extreme precipitation events at the 1.5 °C warming increase more remarkably with the additional 0.5 °C warming. In particular, Asia and Africa show lager sensitivity to global warming than other regions. These findings could provide information for mitigation and adaptation policymaking. Unlabelled Image • Response of extreme precipitation was assessed for 1.5 and 2.0 °C warming levels. • Exposures of economy and population to extreme precipitation were investigated. • Dynamic socioeconomic scenarios are used, instead of a fixed socioeconomic scenario. • Most regions would suffer from increasing extreme precipitation hazards with warming. • Asia and Africa show lager sensitivity to global warming than other regions. [ABSTRACT FROM AUTHOR]

The Space-Time Image Velocimetry (STIV) is a time-averaged velocity measurement method, which takes river surface images as the analysis object, and detects the Main Orientation of Texture (MOT) in a generated Space-Time Image (STI) to obtain one-dimensional velocities on the water surface. The STIV has great potential in real-time monitoring of river flow owing to its high spatial resolution and low time complexity. However, the generated STI contains a lot of noise and interference texture, which is inevitable in practical applications. The practicality of the STIV is severely limited by the low-quality STI. To solve this problem, a denoising method based on the filtering technology is proposed and combined with different texture detection algorithms in this paper. The accuracy of this method is verified through a comparative field experiment with an impellor-style current meter. The experimental results show: (1) By using this new denoising method, the robustness and accuracy of the STIV are significantly improved no matter what kind of texture detection algorithm is adopted; (2) Among all the texture detection algorithms, the FFT-based STIV combined with the new denoising method performs best. The relative errors of the surface velocities are controlled within 6%, and the relative errors of the discharges are controlled within ±4%. • STIV utilizes the STIs to obtain 1-D flow but is limited by the low-quality STIs. • For enhancing the quality of the STI, a new denoising method based on the filtering technology was developed. • This denoising method was combined with different texture detection algorithms. • For the validation, a comparative field experiment with an impellor-style current meter was conducted. • By using this new denoising method, the robustness and accuracy of the STIV are significantly improved. [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]

In this paper, we comprehensively evaluated the utility of integrated long-term satellite-based precipitation and evapotranspiration products for drought monitoring over mainland China. The latest Integrated Multi-satelliteE Retrievals for Global Precipitation Measurement V06 three Runs precipitation products, i.e., the near real-time Early Run (IMERG-E) and Late Run (IMERG-L) and the post-real time Final Run (IMERG-F), and the Global Land Evaporation Amsterdam Model V3.3a (GLEAM) potential evapotranspiration (PET) products from 2001 to 2017 were considered. The accuracy of IMERG precipitation and GLEAM PET products was first evaluated against observed precipitation and Penman-Monteith method estimated PET, respectively, based on dense meteorological station network. The Standard Precipitation Evapotranspiration Index (SPEI) calculated based on IMERG precipitation and GLEAM PET products (SPEIs, including SPEI E , SPEI L and SPEI F corresponding to IMERG-E, IMERG-L and IMERG-F, respectively) were then validated by using SPEI calculated based on meteorological data (SPEIm) at multiple temporal-spatial scales. Finally, four typical drought events were selected to analyse the ability of SPEIs to characterize the temporal-spatial evolution of drought situations. The results showed that the IMERG-F presents much better performance than IMERG-E and IMERG-L in terms of higher CC and smaller BIAS and RMSE values over mainland China. The GLEAM PET well simulated the change trend of reference PET, but generally underestimated reference PET in Northwest China (NW), Xinjiang (XJ) and Qinghai–Tibet plateau (TP). In general, the performances of SPEIs over eastern China and Southwest China (SW) were significantly superior to their performances in the NW, XJ, and TP regions. Even though the SPEI F performed the best, the SPEI E and SPEI L also performed reasonably well in some specific regions. SPEIs can well capture the temporal process and reasonably reflect the spatial characteristics for four typical drought events. It is thus highlighted that the latest IMERG precipitation (especially for IMERG-F) and GLEAM PET products could be used as alternative data sources for comprehensive drought monitoring, on account of the water balance principle over mainland China, particularly in eastern China and SW China. The outcomes of this study will provide valuable references for drought monitoring by integration of multi-source remote-sensing datasets in the GPM era. • The utility of integrated IMERG precipitation and GLEAM PET for drought monitoring was evaluated over mainland China. • SPEIs based on IMERG precipitation and GLEAM PET were validated at multiple spatial and temporal scales over mainland China. • IMERG precipitation and GLEAM PET show great potential for drought monitoring over eastern China and Southwest China. • SPEIs can well capture the temporal process and reasonably reflect the spatial characteristics for typical drought events. [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]

• RSTI was developed to investigate the coupled effect of rainfall and reservoir. • Linear or nonlinear regression models were set up to simulate the sediment load. • Sediment load had a striking decreasing trend and a significant change-point. • Nonlinear regression had the better performance than linear regression model. • Coupled effect of rainfall and reservoir can better explain sediment load change. Sediment load is a critical issue in hydrologic process analysis and river basin management. Many studies have analyzed the impacts of rainfall or reservoir separately on the downstream sediment load; however, few researches investigated the coupled effect of rainfall and reservoir on sediment load change. In this study, a rainfall-augmented sediment trapping index (RSTI) that combines the impacts of effective rainfall and reservoir sediment trapping capacity was developed to attribute the sediment load change in the Wujiang River Basin (WRB) during the period of 1952–2017. Eight linear or nonlinear regression models were set up to investigate how to best utilize the proposed RSTI to reveal the coupled effect of rainfall and reservoir on the downstream sediment load of WRB. It is found that observed sediment load has a large decrease after 1984 when the Wujiangdu Reservoir was put into full operation while rainfall had only a slight change in the same period. The nonlinear regression model with RSTI as an explanatory variable (NSE = 0.837) has the best performance in simulating the observed sediment load series. These results might be helpful for the downstream sediment management under a changing environment. [ABSTRACT FROM AUTHOR]

• Propose a multi-objective method to raise mutual benefits of water-food-energy nexus. • Floating photovoltaic deployment on ponds drives up renewable energy output. • Hybrid hydro-floating photovoltaic power generation raises energy efficiency. • Energy-driven solutions improve water-food-energy nexus management. Hybrid hydropower and floating photovoltaic power generation has far-reaching effects on the intertwined water, food and energy (WFE) nexus, but the complementary operation is fundamentally challenging especially under high uncertainties of hydro-meteorological conditions. This study proposed an artificial intelligence-based WFE system-overarching solution driven by hybrid hydro-floating photovoltaic power generation for promoting nexus synergies. A multi-objective optimization model grounded upon the Grasshopper Optimization Algorithm was developed to simultaneously maximize hydro-floating photovoltaic power output, the ratio of water storage to reservoir capacity, and the ratio of water supply to water demand. The Shihmen Reservoir watershed and its WFE system in northern Taiwan constituted the case study. The results demonstrated that the proposed optimization model could significantly improve synergistic benefits of the WFE nexus by reaching 13%, 13.3% and 15.1% in water storage, food production and hydro-floating photovoltaic power output, respectively. The optimal tilt angles of floating photovoltaic installation would vary between −11.9° (Summer) and 44.3° (Winter). This study opens up new perspectives on green energy production expansion while stimulating WFE nexus synergies in support of policy-makers with feasible schemes on floating photovoltaic deployment in the interest of social sustainability. In consequence, new niches are exploited for floating photovoltaic deployment and give rise to impact mitigation concerning hydro-meteorological uncertainties on WFE nexus management. [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]

• The first study to characterize the multidimensional behaviors of unregulated floods. • A strong geographic cohesion identified in the multidimensional behaviors of floods. • The geographic cohesion partly explained by the relations between floods and ENSO. • The decreases in floods in southern Australia projected to continue in warmer future. Changes in peak magnitude, volume, frequency and duration of floods obtained from a peak-over-threshold sampling in 780 unregulated catchments show significant differences between northern and southern Australia over 1975–2012. Increases of the flood properties are mainly located in northern Australia, while decreases are mostly in southern Australia. These changes could be dominated by inter-annual and/or decadal variability of floods. The multidimensional behaviors of flood change across Australia can be described by three distinct groups (i.e. no changes, increases and decreases in all flood properties), showing strong geographic cohesion. The geographical consistency between the changing patterns of flood properties and spatial patterns of vapor transport anomalies during the El Niño-Southern Oscillation (ENSO) positive phase could partly explain the geographic cohesion of flood changes. In a warmer future, the observed decreases in floods in southern Australia are projected to continue with high model agreement, while only magnitude and volume of floods in northern Australia are projected to increase but with high uncertainties. The diametric changes in flood magnitude between northern and southern Australia are projected to be more evident in extreme (i.e. 50-year) floods than small (i.e. 5- and 20-year) floods. [ABSTRACT FROM AUTHOR]

• The study evaluated the dependence of regionalization methods on hydrological models. • The performance of regionalization methods assessed in varied climate conditions. • Output averaging option works better than parameter averaging option. • Difference between output and parameter options increases with model complexity. • Simpler models are not superior to complex models in regionalization study. Hydrological models have been widely used to predict runoff in regions with observed discharge data, and regionalization methods have been extensively discussed for providing runoff predictions in ungauged basins (PUB), especially during the PUB decade (2003–2012). Great progress has been achieved in the field of regionalization in previous studies, in which different hydrological models have been coupled with various regionalization methods. However, different conclusions have been drawn due to the use of different hydrological models, regionalization methods, and study regions. In this study, we assessed the performance of the five most widely used regionalization methods (spatial proximity with parameter averaging option (SP-par), spatial proximity with output averaging option (SP-out), physical similarity with parameter averaging option (Phy-par), physical similarity with output averaging option (Phy-out), and regression methods (PCR)) and four daily rainfall-runoff models (GR4J, WASMOD, HBV and XAJ, with 6, 8, 13, and 17 parameters, respectively) at the same time. Our aim was to evaluate how the performance of the regionalization methods depends on (a) the selection of hydrological models, (b) nonstationary climate conditions, and (c) different climatic regions. This investigation used data from 86 independent catchments evenly distributed throughout Norway, covering three different climate zones (oceanic, continental and polar tundra) according to the Köppen-Geiger classification. The results showed that (a) the SP-out and Phy-out methods performed better than the SP-par and Phy-par for all the hydrological models, and the regression method performed worst in most cases; (b) the difference between the parameter averaging option and the output averaging option is positively related to the number of hydrological model parameters, i.e. the greater the number of parameters, the larger the difference between the two options; (c) the XAJ model with the greatest number of parameters produced the best results in most cases, and models with fewer parameters tend to produce similar performance for the different regionalization methods; (d) models with more parameters displayed larger declines in performance than those with fewer parameters for nonstationary conditions; and (e) clear differences in the performance of the regionalization methods exist among the three climatic regions. This study provides insight into the relationship between the complexity of hydrological models and regionalization methods in cold and seasonally snow-covered regions. [ABSTRACT FROM AUTHOR]

• The method could attribute the impact of multiple human activities. • The method could attribute on the water cycle processes not only the streamflow. • The method uses the mean of multiple scenarios estimation for one factor. • The method ensures the sum of contribution of all impact factors equals 100%. • The principle of the new method is mathematical proved. The impacts of human activities on the natural water cycle have become increasingly severe due to population growth and economic development. Therefore, it is important to evaluate the contributions of different human activities (e.g., land-use change, industrial water use, and domestic water use) on different hydrological variables. However, existing attribution methods have shortcomings when attributing the impacts of different human activities; for instance, human activities may only be considered as one impact factor, and the total impact of all factors may not be guaranteed to equal 100% of the total change. A new approach, using multiple scenario simulations from a distributed model, was developed in this study to overcome these shortcomings. All potential contributions for one impact factor were calculated by fixing the other impact factors at different statuses, and the arithmetic mean of all potential contributions was presented as the final result. This method ensures that the sum of the contributions of all impact factors equals the total change in hydrological variables between two periods, which was demonstrated mathematically. A case study of the Wei River Basin was used to evaluate the proposed methodology, which attributed four impact factors (climate change, land-use change, industrial and domestic water use, and agricultural irrigation water use) to three hydrological variables (evapotranspiration, rainfall infiltration, and streamflow drainage into the Yellow River). The results showed that the new method could attribute the impacts of the four factors to the changes of the three annual average hydrological variables between the period 1956–1980 and the period 1981–2005; these four factors accounted for 373.3%, −33.3%, −133.3%, and −106.7% of the change in evapotranspiration, 129.8%, −2.2%, −3.5%, and −24.1% of the change in rainfall infiltration, and 75.2%, 4.4%, 13.4%, and 7.0% of the change in streamflow drainage into the Yellow River, respectively. Moreover, the sum of the relative contributions of the four impact factors to the three hydrological variables was equal to 100% at an annual time scale. To avoid the limitations of existing methods, we recommend adopting the newly proposed method for evaluating the impacts of climate change and multiple human activities on water cycle processes. [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]