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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: With respect to model calibration, parameter estimation and analysis of uncertainty sources, various regression and probabilistic approaches are used in hydrological modeling. A family of Bayesian methods, which incorporates different sources of information into a single analysis through Bayes’ theorem, is widely used for uncertainty assessment. However, none of these approaches can well treat the impact of high flows in hydrological modeling. This study proposes a Bayesian modularization uncertainty assessment approach in which the highest streamflow observations are treated as suspect information that should not influence the inference of the main bulk of the model parameters. This study includes a comprehensive comparison and evaluation of uncertainty assessments by our new Bayesian modularization method and standard Bayesian methods using the Metropolis-Hastings (MH) algorithm with the daily hydrological model WASMOD. Three likelihood functions were used in combination with standard Bayesian method: the AR(1) plus Normal model independent of time (Model 1), the AR(1) plus Normal model dependent on time (Model 2) and the AR(1) plus Multi-normal model (Model 3). The results reveal that the Bayesian modularization method provides the most accurate streamflow estimates measured by the Nash–Sutcliffe efficiency and provide the best in uncertainty estimates for low, medium and entire flows compared to standard Bayesian methods. The study thus provides a new approach for reducing the impact of high flows on the discharge uncertainty assessment of hydrological models via Bayesian method. [Copyright &y& Elsevier]

Summary: In this study, different versions of autoregressive error models are evaluated as post-processors for probabilistic streamflow forecasts. The post-processors account for hydrologic uncertainties that are introduced by the precipitation–runoff model. The post-processors are evaluated with the discrete ranked probability score (DRPS), and a non-parametric bootstrap is applied to investigate the significance of differences in model performance. The results show that differences in performance between most model versions are significant. For these cases it is found that (1) error models with state dependent parameters perform better than those with constant parameters, (2) error models with an empirical distribution for the description of the standardized residuals perform better than those with a normal distribution, and (3) procedures that use a logarithmic transformation of the original streamflow values perform better than those that use a square root transformation. [ABSTRACT FROM AUTHOR]

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]

Abstract: Scaling behaviors of the long daily streamflow series of four hydrological stations (Longchuan (1952–2002), Heyuan (1951–2002), Lingxia (1953–2002) and Boluo (1953–2002)) in the mainstream East River, one of the tributaries of the Pearl River (Zhujiang River) basin, were analyzed using multifractal detrended fluctuation analysis (MF-DFA). The research results indicated that streamflow series of the East River basin are characterized by anti-persistence. MF-DFA technique showed similar scaling properties in the streamflow series of the East River basin on shorter time scales, indicating universal scaling properties over the East River basin. Different intercept values of the fitted lines of log–log curve of versus implied hydrological regulation of water reservoirs. Based on the numerical results, we suggested that regulation activities by water reservoirs could not impact the scaling properties of the streamflow series. The regulation activities by water reservoir only influenced the fluctuation magnitude. Therefore, we concluded that the streamflow variations were mainly the results of climate changes, and precipitation variations in particular. Strong dependence of generalized Hurst exponent on demonstrated multifractal behavior of streamflow series of the East River basin, showing ‘universal’ multifractal behavior of river runoffs. The results of this study may provide valuable information for prediction and assessment of water resources under impacts of climatic changes and human activities in the East River basin. [Copyright &y& Elsevier]

Summary: Variability and possible teleconnections between annual maximum streamflow from the lower, the middle and the upper Yangtze River basin and El Niño/Southern Oscillation (ENSO) are detected by continuous wavelet transform (CWT), cross-wavelet and wavelet coherence methods. The results show that: (1) different phase relations are found between annual maximum streamflow of the Yangtze River and El Niño/Southern Oscillation (ENSO) in the lower, the middle and the upper Yangtze River basin. In-phase relations are detected between annual maximum streamflow of the lower Yangtze River and anti-phase relations are found in the upper Yangtze River. But ambiguous phase relations occur in the middle Yangtze River, showing that the middle Yangtze River basin is a transition zone. Different climatic systems control the upper and the lower Yangtze River. The upper Yangtze River is mainly influenced by the Indian summer monsoon and the lower Yangtze is mainly influenced by the East Asian summer monsoon; (2) as for the individual stations, different phase relations are found in the longer and the shorter periods, respectively. In the longer periods, the annual maximum streamflow is more influenced by climatic variabilities, while in the shorter periods, it is influenced by other factors, e.g. human activities. The results of the study provide valuable information for improving the long-term forecasting of the streamflow using its relationship with ENSO and the Indian Monsoon. [Copyright &y& Elsevier]

Summary In recent years, the Poyang Lake basin is in a prolonged drought which has placed immense pressure on the water resources utilization. In this paper, we explore the spatial and temporal distributions of extreme droughts in the Poyang Lake basin by using the methods of SPI (Standardized Precipitation Index) and EOF (Empirical Orthogonal Function) for the period of 1956–2009, which are influenced by regional precipitation anomalies and river–lake interaction due to water impounding of the Three Gorges Dam (TGD). The results show that: (1) the Poyang Lake basin experienced six extreme droughts during the past 60 years, which lead to decreases in streamflow from five tributary rivers down to the Poyang Lake. The droughts in the 1960s and the 2000s were the most serious ones. However, there was an increasing trend of streamflow in the upper and middle Yangtze in the 1960s, and a decreasing trend appeared in the 2000s. The decline of streamflow in the upper Yangtze reaches has lowered the water level of lower Yangtze River which has caused more outflow from the Poyang Lake to the Yangtze River; (2) the operation of the Three Gorges Dam (TGD) has altered the seasonal pattern of flow regimes in the Poyang Lake and significantly reduced the water level in the lower Yangtze River during the TGD impounding period from late September to early November; and (3) the conjunction of extreme droughts in the Poyang Lake and the upper Yangtze reaches coincided with the impounding of the TGD is the main cause of the low water level in the Poyang Lake. Although the impact of the recent droughts in the Poyang Lake and upper Yangtze reaches has played a crucial role in the low water level of Poyang Lake, more attention should be paid to its sensitivity to the influence of the large dam-induced changes in the interaction between river and lake, particularly during impounding periods. [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]

Abstract: Long monthly streamflow series of three control hydrological stations of the Pearl River basin were analyzed by using the scanning t-test and the scanning F-test. Possible implications of the changing properties of streamflow variations for the water resource management of the Pearl River Delta are also discussed. The results indicated that: 1) more complicated changes were observed in terms of the second center moment when compared to the first original moment. More significant abrupt changes of the second center moment imply more sensitive response of streamflow stability to climate changes and human activities; 2) abrupt behaviors of the first (second) center moment of the streamflow variations tend to be more sensitive to climate changes and/or human activities in the larger river basin when compared to those in the smaller river basin. These phenomena are attributed to buffering functions of more storage space of longer river channel, and more complicated and longer runoff yield and concentration processes in the river basin of larger drainage area; 3) annual minimum streamflow of the Pearl River basin tends to be increasing. This will be helpful for better human mitigation of the salinity intrusion in dry seasons across the Pearl River Delta. Annual maximum streamflow, when compared to annual minimum streamflow, shows larger-magnitude variability reflected by larger standard deviation, implying unfavorable conditions for flood mitigation in the Pearl River Delta. The results of this paper are of scientific and practical merits for water resource management and sound human mitigation to water hazards across the Pearl River Delta, and also are a good case study for similar researches in other river deltas in the world under the changing environment. [ABSTRACT FROM AUTHOR]

• A systematic scheme to detect and verify abrupt shift of runoff was developed. • A model was proposed to forecast natural streamflow in data-poor areas. • Mechanism of abrupt shift in minor periods in human-impacted runoff was explored. Understanding the long-term variability of streamflow and its response to human activities in water-limited areas is essential for socio-hydrologic models' development. In this study, a framework for the detection and attribution of abrupt shift of minor periods in human-impacted streamflow is proposed. First, the most significant abrupt shift in human-impacted streamflow is detected using the Pettitt test and verified based on different statistical characteristics of streamflow series (trend, periodicity, and different quantiles) and main physical causes (main reservoirs operations). According to the breakpoint, the study period was divided into approximately natural sub-period and human-impacted sub-period. Interestingly, we found the "missing" of minor (2–4-year timescale) periods of the runoff records after abrupt shift points in the study cases. To investigate its mechanisms, we proposed an Improved Multivariate Fuzzy Mean Generating Function (IMFMGF) model to simulate the natural runoff in the human-impacted period and decomposed the observed runoff into natural runoff component and human-impacted changing runoff component. Then, the periodicity of these two components was compared based on Morlet wavelet analysis and Ensemble Empirical Mode Decomposition (EEMD). Results showed that the minor periods' wave crests and troughs of the above two components had excellent negative correspondence. The candidate mechanism is that the offsetting effects (i.e., the regular anthropogenic withdrawal or intake of water.) resulted in the disappearance of minor periods of the human-impacted observed, which can give more certain inputs into the prediction of non-stationary streamflow series. [ABSTRACT FROM AUTHOR]

Highlights • A progressive segmented optimization algorithm (PSOA) for calibration was proposed. • The PSOA calibrated time-variant parameters of Snowmelt Runoff Model effectively. • The PSOA outperformed the segmented optimization algorithm (SOA) Abstract To capture the temporal variability of parameters of hydrological models, the segmented optimization algorithm (SOA) is usually used which subdivides the calibration period into a number of sub-periods and seeks optimal parameters for each sub-period by optimizing the objective function based on the measured and estimated data in the same sub-period. In this paper, we developed a new method that is called a progressive segmented optimization algorithm (PSOA), which seeks optimal parameters by optimizing the objective function based on both the current and all the prior sub-periods. We applied and compared the SOA and PSOA algorithms to the Snowmelt Runoff Model (SRM) in simulating snow-melt streamflow for the Manasi River basin, northwest of China, during snowmelt seasons of 2001–2012. The study showed: (1) PSOA can effectively calibrate the time-variant model parameters while avoiding too much computational time caused by a significant increase of parameter dimensionality. (2) PSOA outperforms SOA for both single-snowmelt-season and multi-snowmelt-season simulations. (3) For single-snowmelt-season simulation, the length of the sub-period has an apparent effect on model performance, the shorter the sub-period is, the better the model performance will be, when the model is calibrated using the PSOA method. (4) For multi-snowmelt-season simulation, an over-short sub-period may cause overfitting problems in some cases such as the situation of taking Nash-Sutcliffe efficiency (NSE) as the objective function. A compromised length of sub-period and objective function may have to be chosen as a trade-off among evaluation criteria and between the importance of calibration and validation. [ABSTRACT FROM AUTHOR]

Climate change imposes profound influence on regional hydrological cycle and water security in many alpine regions worldwide. Investigating regional climate impacts using watershed scale hydrological models requires a large number of input data such as topography, meteorological and hydrological data. However, data scarcity in alpine regions seriously restricts evaluation of climate change impacts on water cycle using conventional approaches based on global or regional climate models, statistical downscaling methods and hydrological models. Therefore, this study is dedicated to development of a probabilistic model to replace the conventional approaches for streamflow projection. The probabilistic model was built upon an advanced Bayesian Neural Network (BNN) approach directly fed by the large-scale climate predictor variables and tested in a typical data sparse alpine region, the Kaidu River basin in Central Asia. Results show that BNN model performs better than the general methods across a number of statistical measures. The BNN method with flexible model structures by active indicator functions, which reduce the dependence on the initial specification for the input variables and the number of hidden units, can work well in a data limited region. Moreover, it can provide more reliable streamflow projections with a robust generalization ability. Forced by the latest bias-corrected GCM scenarios, streamflow projections for the 21st century under three RCP emission pathways were constructed and analyzed. Briefly, the proposed probabilistic projection approach could improve runoff predictive ability over conventional methods and provide better support to water resources planning and management under data limited conditions as well as enable a facilitated climate change impact analysis on runoff and water resources in alpine regions worldwide. [ABSTRACT FROM AUTHOR]

The Indicators of Hydrologic Alteration (IHA) based on 33 parameters in five dimensions (flow magnitude, timing, duration, frequency and change rate) have been widely used in evaluation of hydrologic alteration in river systems. Yet, inter-correlation seriously exists amongst those parameters, therefore constantly underestimates or overestimates actual hydrological changes. Toward the end, a new method (Representative-IHA, RIHA) is developed by removing repetitions based on Criteria Importance Through Intercriteria Correlation (CRITIC) algorithm. RIHA is testified in evaluating effects of future climate change on hydro-ecology in the Niger River of Africa. Future flows are projected using three watershed hydrological models forced by five general circulation models (GCMs) under three Representative Concentration Pathways (RCPs) scenarios. Results show that: (1) RIHA is able to eliminate self-correlations amongst IHA indicators and identify the dominant characteristics of hydrological alteration in the Upper Niger River, (2) March streamflow, September streamflow, December streamflow, 30-day annual maximum, low pluses duration and fall rates tends to increase over the period 2010–2099, while July streamflow and 90-day annual minimum streamflow shows decrease, (3) the Niger River will undergo moderate flow alteration under RCP8.5 in 2050s and 2080s and low alteration other scenarios, (4) future flow alteration may induce increase water temperatures, reduction dissolved oxygen and food resources. Consequently, aquatic biodiversity and fish community of Upper Niger River would become more vulnerable in the future. The new method enables more scientific evaluation for multi-dimensional hydrologic alteration under the context of climate change. [ABSTRACT FROM AUTHOR]

• The most suitable ecological streamflow was calculated via kernel density estimation method. • Ecological drought was identified using the variable threshold method based on ecological streamflow. • Impacts of climate variability and human activities on ecological drought were quantified. • Human activities are the dominant factor aggravating the ecological drought in the Weihe River Basin. Although various studies have investigated the impacts of climate variability and human activities on drought, researches specifically analysing the impact on ecological drought are still limited. A deep understanding of the climatic and anthropogenic effects on ecological drought processes is crucial for ecological regulation and management in the changing environments. In the present study, an integrated approach for comprehensive understanding and quantification of ecological drought in rivers was proposed which first applied the nonparametric kernel density estimation (KDE) method to calculate the most suitable ecological streamflow (MSES) for a river ecosystem. Then, the variable threshold level method based on the MSES for each month and the run theory method were applied to identify the ecological drought duration and deficit volumes. Finally, a quantification approach based on hydrological model simulation was proposed to attribute the impacts of climate variability and human activities on ecological drought. The proposed approach was applied on two catchments, Xianyang (XY) and Huaxian (HX) within the Weihe River Basin (WRB) in northern China. Comparison results obtained using the two empirical methods revealed that the MSES calculated using the KDE method was reasonable and can be used for ecological drought identification. The identification results showed that both the median and upper quartile values of the drought duration and deficit volumes during the disturbed period (1991–2017) were greater than those during the undisturbed period (1961–1990). Quantification results showed that human activities were the dominant factor aggravating ecological drought in the WRB after 1990. The contribution rates of climate variability and human activities toward ecological drought variations were 25.6% and 74.4%, respectively, for the XY station and 42.7% and 57.3%, respectively, for the HX station. Although the WRB was selected as a case study, the proposed approach can also be applied to other regions to provide scientific guidance for regional ecological management. [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]

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: 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 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]

• 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]

• 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]