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

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

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

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

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

Summary: In this study, 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: Bayesian revision is widely used in hydrological model uncertainty assessment. With respect to model calibration, parameter estimation and analysis of uncertainty sources, various regression and probabilistic approaches have been used in different models calibrated for either daily or monthly time step. None of these applications however includes a comparison of uncertainty analysis in hydrological models with respect to the time periods, at which the models are operated. This study pursues a comprehensive inter-comparison and evaluation of uncertainty assessments by Bayesian revision using the Metropolis Hasting (MH) algorithm with the hydrological model WASMOD with daily and monthly time step. In the daily step model three likelihood functions are used in combination with Bayesian revision: (i) the AR (1) plus Normal time period independent model (Model 1), (ii) the AR (1) plus Multi-Normal model (Model 2), and (iii) the AR (1) plus Normal time period dependent model (Model 3). In addition an index called the percentage of observations bracketed by the Unit Confidence Interval (PUCI) was used for uncertainty evaluation. The results reveal that it is more important to consider the autocorrelation in daily WASMOD rather than monthly WASMOD. Firstly, the resulting goodness of fit of the daily model vs. observations as measured by the Nash–Sutcliffe efficiency value is comparable with that calculated by the optimization algorithm in monthly WASMOD. Secondly, the AR (1) model is not sufficiently adequate to estimate the distribution of residuals in daily WASMOD since PUCI shows that Model 2 outperforms Model 1. Furthermore, the maximum Nash–Sutcliffe efficiency value of Model 2 is the largest. Thirdly, Model 3 performs best over the entire flow range, while Model 2 outperforms Model 3 for high flows. This shows that additional statistical parameters reflect the statistical characters of the residuals more efficiently and accurately. Fourthly, by considering the difference in terms of application and computational efficiency it becomes evident that Model 3 performs best for daily WASMOD. Model 2 on the other hand is superior for daily time step WASMOD if the auto-correlation of parameters is considered. [Copyright &y& Elsevier]

Summary: Quantification of uncertainty of hydrological models has attracted much attention in hydrologic research in recent years. Many methods for quantification of uncertainty have been reported in the literature, of which GLUE and formal Bayesian method are the two most popular methods. There have been many discussions in the literature concerning differences between these two methods in theory (mathematics) and results, and this paper focuses on the computational efficiency and differences in their results, but not on philosophies and mathematical rigor that both methods rely on. By assessing parameter and modeling uncertainty of a simple conceptual water balance model (WASMOD) with the use of GLUE and formal Bayesian method, the paper evaluates differences in the results of the two methods and discusses the reasons for these differences. The main findings of the study are that: (1) the parameter posterior distributions generated by the Bayesian method are slightly less scattered than those by the GLUE method; (2) using a higher threshold value (>0.8) GLUE results in very similar estimates of parameter and model uncertainty as does the Bayesian method; and (3) GLUE is sensitive to the threshold value used to select behavioral parameter sets and lower threshold values resulting in a wider uncertainty interval of the posterior distribution of parameters, and a wider confidence interval of model uncertainty. More study is needed to generalize the findings of the present study. [Copyright &y& Elsevier]

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

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

Summary: This study investigated spatial and temporal patterns of trends of the precipitation maxima (defined as the annual/seasonal maximum precipitation) in the Yangtze River basin for 1960–2005 using Mann–Kendall trend test, and explored association of changing patterns of the precipitation maxima with large-scale circulation using NCEP/NCAR reanalysis data. The research results indicate changes of precipitation maxima from relative stable patterns to the significant increasing/decreasing trend in the middle 1970s. With respect to annual variability, the rainy days are decreasing and precipitation intensity is increasing, and significant increasing trend of precipitation intensity was detected in the middle and lower Yangtze River basin. Number of rain days with daily precipitation exceeding 95th and 99th percentiles and related precipitation intensities are in increasing tendency in summer. Large-scale atmospheric circulation analysis indicates decreasing strength of East Asian summer monsoon during 1975–2005 as compared to that during 1961–1974 and increasing geopotential height in the north China, South China Sea and west Pacific regions, all of which combine to negatively impact the northward propagation of the vapor flux. This circulation pattern will be beneficial for the longer stay of the Meiyu front in the Yangtze River basin, leading to more precipitation in the middle and lower Yangtze River basin in summer months. The significant increasing summer precipitation intensity and changing frequency in the rain/no-rain days in the middle and lower Yangtze River basin have potential to result in higher occurrence probability of flood and drought hazards in the region. [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: Annual runoff and annual suspended sediment loads of hydrological gauging stations along the mainstream of the Yangtze River basin (Pingshan station, Yichang station, Hankou station and Datong station) and main tributaries (Beipei station in Jialingjiang River, Wulong station in Wujiang River and Huangzhuang station in Hanjiang River) were analyzed with the help of Mann–Kendall trend analysis and linear regression analysis. Research results indicate that (1) changing patterns of runoff and sediment loads are different in different parts of the Yangtze River basin. No significant trend is detected for annual runoff at all stations at >95% confidence level. Changes of sediment loads, however, demonstrate different pictures in the Yangtze River basin. The sediment loads are in increasing trend in Pingshan station- the most upstream station on the Yangtze River basin (this increasing trend is significant at >95% confidence level after about 1990), but are in decreasing trend at other stations (including stations in the tributaries studied in this paper). This decreasing trend becomes more obvious from Yichang station to Datong station. (2) Water reservoirs exerted more influences on changes of sediment loads than on runoff, which is the main reason for the decreasing trend of sediment loads found in most stations. (3) Influences of water reservoirs on sediment loads are more obvious in the tributaries than in the mainstream of the Yangtze River basin, while in the mainstream the variation patterns of sediment loads are determined by multiple factors. [Copyright &y& Elsevier]

Summary: Sensitivity analysis is important in understanding the relative importance of climatic variables to the variation of reference evapotranspiration (ETref). In this study, a non-dimensional relative sensitivity coefficient was employed to predict responses of ETref to perturbations of four climatic variables in the Changjiang (Yangtze River) basin. ETref was estimated with the FAO-56 Penman–Monteith equation. A 41-year historical dataset of daily air temperature, wind speed, relative humidity and daily sunshine duration at 150 national meteorological observatory stations was used in the analysis. Results show that the response of ETref can be precisely predicted under perturbation of relative humidity or shortwave radiation by their sensitivity coefficients; the predictive power under perturbations of air temperature and wind speed depended on the magnitude of the perturbation, season and region. The prediction errors were much smaller than the seasonal and regional variation of their sensitivity coefficients. The sensitivity coefficient could also be used to predict the response of ETref to co-perturbation of several variables. The accuracy of the prediction increases from the lower to the upper region. Spatial variations of long-term average monthly and yearly sensitivity coefficients were obtained by interpolation of station estimates. In general, relative humidity was the most sensitive variable, followed by shortwave radiation, air temperature and wind speed. The actual rank of the four climatic variables in terms of their sensitivity varied with season and region. The large spatial variability of the sensitivity coefficients of all the climatic variables in the middle and lower regions of the basin was to a large extent determined by the distinct wind-speed patterns in those two regions. [Copyright &y& Elsevier]

Summary: In this study the Penman–Monteith reference evapotranspiration, pan evaporation measured by a 20cm pan, and pan coefficient, i.e., the ratio of Penman–Monteith evapotranspiration to pan evaporation, at 150 meteorological stations during 1960–2000 in the Changjiang (Yangtze River) catchment in China are calculated, compared and regionally mapped. Their spatial distributions and temporal variations are examined and the causes for the variations are discussed. The spatial distributions of temporal trends in the reference evapotranspiration as well as in the meteorological variables that determine evapotranspiration are analyzed. The contributions of various meteorological variables to the temporal trend detected in the reference evapotranspiration and pan evaporation are then determined. The results show that: (1) the spatial distributions of reference evapotranspiration and pan evaporation are roughly similar. Spatial correlation coefficients between the reference evapotranspiration and the pan evaporation are high for both the seasonal and annual values. The temporal correlation between the two estimates is higher in the lower (humid) region than in the upper (semi-arid) region. The spatial distribution pattern of the pan coefficient is significantly influenced by wind speed and relative humidity in the region. Higher values of the pan coefficient were found in the central area of the catchment with a relatively high humidity (as compared with the upper area) and a very low wind speed (as compared with other areas); (2) for the whole catchment, there is a significant decreasing trend in both the reference evapotranspiration and the pan evaporation, which is mainly caused by a significant decrease in the net total radiation and to a lesser extent by a significant decrease in the wind speed over the catchment. No temporal trend is detected for the pan coefficient; (3) sensitivity analysis shows that the reference evapotranspiration is most sensitive to the net total radiation, followed by relative humidity, air temperature and wind speed. [Copyright &y& Elsevier]

Abstract: Sensitivity of hydrological models to input data errors have been reported in the literature for particular models on a single or a few catchments. A more important issue, i.e. how model''s response to input data error changes as the catchment conditions change has not been addressed previously. This study investigates the seasonal and spatial effects of precipitation data errors on the performance of conceptual hydrological models. For this study, a monthly conceptual water balance model, NOPEX-6, was applied to 26 catchments in the Mälaren basin in Central Sweden. Both systematic and random errors were considered. For the systematic errors, 5–15% of mean monthly precipitation values were added to the original precipitation to form the corrupted input scenarios. Random values were generated by Monte Carlo simulation and were assumed to be (1) independent between months, and (2) distributed according to a Gaussian law of zero mean and constant standard deviation that were taken as 5, 10, 15, 20, and 25% of the mean monthly standard deviation of precipitation. The results show that the response of the model parameters and model performance depends, among others, on the type of the error, the magnitude of the error, physical characteristics of the catchment, and the season of the year. In particular, the model appears less sensitive to the random error than to the systematic error. The catchments with smaller values of runoff coefficients were more influenced by input data errors than were the catchments with higher values. Dry months were more sensitive to precipitation errors than were wet months. Recalibration of the model with erroneous data compensated in part for the data errors by altering the model parameters. [Copyright &y& Elsevier]

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

Highlights • The study shows the transferability of regionalization methods with changing climate. • The study compares the uncertainty from climate models and regionalization methods. • The difference between the regionalization methods tends to increase in the future. • In larger precipitation regions, climate models dominate the uncertainty. • Physical similarity method with parameter option produced the most robust result. Abstract Regionalization methods have been extensively discussed as the solution for runoff predictions in ungauged basins (PUB), especially during the PUB decade (2003–2012). At the same time, research topics relevant to climate change appear to be an essential and attractive field for hydrologists in recent decades, because the availability and quality of water resources are strongly affected by climate change. However, it is still unknown whether regionalization methods can be used to predict hydrological impacts of climate change for ungauged catchments or how much uncertainty of future predictions may result from the use of regionalization methods. Therefore, in this study, we investigate the transferability of regionalization methods (i.e. spatial proximity and physical similarity methods, regression method) under changing climate conditions and compare the uncertainty resulting from regionalization methods with that from using climate models. The investigation is based on 108 catchments in Norway, with large variability in climate conditions and geographic characteristics. The study applies a lumped conceptual rainfall-runoff model (WASMOD) with simple structure and six model parameters. Our result shows that (a) the differences in the predictions by the regionalization methods tend to increase in the future, (b) the physical similarity method with parameter option (i.e. the model parameters from the physically-similar donor catchments are first averaged and then used to run the model for the target catchment) shows higher transferability than other methods, (c) the uncertainty contributions from climate models and regionalization methods to future runoff prediction are basin dependent, and (d) the uncertainty of future runoff prediction due to regionalization methods can be higher than that from climate models in low precipitation areas. This study provides insight to the choice of regionalization methods under changing climate conditions and the role of regionalization methods to the uncertainty contributions in future runoff predictions. [ABSTRACT FROM AUTHOR]

Most lumped hydrological models use areal average precipitation data as model input. Though weather-radar-based and satellite-based precipitation estimation methods have been proposed in recent years, the rain gauge is still the most widely used precipitation-measuring tool. Optimal selection of rain gauge number and location will improve the accuracy of areal average precipitation estimations with minimum cost. In this study, the impacts of rain gauge density and distribution on lumped hydrological modelling uncertainty with different catchment sizes are analysed. To this end, the performances of a lumped hydrological model, the Xinanjiang model, in a densely gauged river basin, the Xiangjiang River basin, and its sub-basins under different gauge density and distribution are compared. First, seven levels of rain gauge density are defined. For each density level, several samples of different rain gauge distributions are randomly selected. Then, the areal average precipitation of each sample is estimated and used as input to the Xinanjiang model. Finally, the model is calibrated using the shuffled complex evolution (SCE-UA) algorithm, and model uncertainty is evaluated via the Bayesian method. The results show that 1) imperfect precipitation inputs measured by a sparse and irregular rain gauge network will lead to substantial uncertainty in model parameter estimation and flood simulation; 2) the impacts of imperfect precipitation estimates on model efficiency can be reduced to some extent through the adjustment of model parameters; 3) modelling uncertainty is reduced by increasing the rain gauge density or optimizing the rain gauge distribution pattern; and 4) the improvement in lumped model efficiency is no longer significant when the rain gauge density exceeds a certain threshold, but a further increase in rain gauge density will reduce model parameter uncertainty and the width of the runoff confidence interval. [ABSTRACT FROM AUTHOR]

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

Summary Climate change, rapid economic development and increase of the human population are considered as the major triggers of increasing challenges for water resources management. This proposed integrated optimal allocation model (IOAM) for complex adaptive system of water resources management is applied in Dongjiang River basin located in the Guangdong Province of China. The IOAM is calibrated and validated under baseline period 2010 year and future period 2011–2030 year, respectively. The simulation results indicate that the proposed model can make a trade-off between demand and supply for sustainable development of society, economy, ecology and environment and achieve adaptive management of water resources allocation. The optimal scheme derived by multi-objective evaluation is recommended for decision-makers in order to maximize the comprehensive benefits of water resources management. [ABSTRACT FROM AUTHOR]

Summary Due to the adaption, dynamic and multi-objective characteristics of complex water resources system, it is a considerable challenge to manage water resources in an efficient, equitable and sustainable way. An integrated optimal allocation model is proposed for complex adaptive system of water resources management. The model consists of three modules: (1) an agent-based module for revealing evolution mechanism of complex adaptive system using agent-based, system dynamic and non-dominated sorting genetic algorithm II methods, (2) an optimal module for deriving decision set of water resources allocation using multi-objective genetic algorithm, and (3) a multi-objective evaluation module for evaluating the efficiency of the optimal module and selecting the optimal water resources allocation scheme using project pursuit method. This study has provided a theoretical framework for adaptive allocation, dynamic allocation and multi-objective optimization for a complex adaptive system of water resources management. [ABSTRACT FROM AUTHOR]

Summary This study aims to provide model estimates of changes in hydrological elements, such as EvapoTranspiration (ET) and runoff, in Southern Africa in the near future until 2029. The climate change scenarios are projected by a high-resolution Regional Climate Model (RCM), RegCM4, which is the latest version of this model developed by the Abdus Salam International Centre for Theoretical Physics (ICTP). The hydrological projections are performed by using a large-scale hydrological model (WASMOD-D), which has been tested and customized on this region prior to this study. The results reveal that (1) the projected temperature shows an increasing tendency over Southern Africa in the near future, especially eastward of 25°E, while the precipitation changes are varying between different months and sub-regions; (2) an increase in runoff (and ET) was found in eastern part of Southern Africa, i.e. Southern Mozambique and Malawi, while a decrease was estimated across the driest region in a wide area encompassing Kalahari Desert, Namibia, southwest of South Africa and Angola; (3) the strongest climate change signals are found over humid tropical areas, i.e. north of Angola and Malawi and south of Dem Rep of Congo; and (4) large spatial and temporal variability of climate change signals is found in the near future over Southern Africa. This study presents the main results of work-package 2 (WP2) of the ‘Socioeconomic Consequences of Climate Change in Sub-equatorial Africa (SoCoCA)’ project, which is funded by the Research Council of Norway. [ABSTRACT FROM AUTHOR]

Summary Return period and risk of extreme hydrological events are critical considerations in water resources management. The stationarity assumption of extreme events for conducting hydrological frequency analysis to estimate return period and risk is now problematic due to climate change. Two different interpretations of return period, i.e. the expected waiting time (EWT) and expected number of exceedances (ENE), have already been proposed in literature to consider nonstationarity in return period and risk analysis by introducing the time-varying moment method into frequency analysis, under the assumption that the statistical parameters are functions only of time. This paper aimed at improving the characterization of nonstationary return period and risk under the ENE interpretation by employing meteorological covariates in the nonstationary frequency analysis. The advantage of the method is that the downscaled meteorological variables from the General Circulation Models (GCMs) can be used to calculate the nonstationary statistical parameters and exceedance probabilities for future years and thus the corresponding return period and risk. The traditional approach using time as the only covariate under both the EWT and ENE interpretations was also applied for comparison. Both approaches were applied to annual minimum monthly streamflow series of two stations in the Wei River, China, and gave estimates of nonstationary return period and risk that were significantly different from the stationary case. The nonstationary return period and risk under the ENE interpretation using meteorological covariates were found more reasonable and advisable than those of the EWT and ENE cases using time alone as covariate. It is concluded that return period and risk analysis of nonstationary low-flow series can be helpful to water resources management during dry seasons exacerbated by climate change. [ABSTRACT FROM AUTHOR]

• Soil moisture dynamics were compared between four typical types of land use and land cover in a humid mountainous watershed. • The bamboo forest had the largest variability in soil moisture content. • The cumulative infiltration increased but the infiltration rate decreased with the rainfall grade. • The runoff coefficient during representative rainfall events varied with the LULC. Identifying soil moisture dynamics is critical for understanding watershed hydrological processes. Soil moisture responses to rainfall vary with land use and land covers (LULCs) and thus influence rainfall-runoff processes, however, the knowledge about how different LULCs affect such processes was less revealed, especially in humid areas. In this study, we investigated the characteristics of soil moisture content (SMC) and rainfall-related soil moisture responses under four typical LULCs, i.e., waxberry forest, farmland, sloping farmland, and bamboo forest, based on in-situ observations in a humid mountainous watershed. We then used the HYDRUS-1D model to analyze the patterns of the rainfall-infiltration processes and quantified corresponding runoff generation to reveal the partitioning between infiltration and runoff at the event scale. We found significant differences of the average SMC at multiple depths for four LULCs. The deep soil (80 cm) had the largest SMC for the waxberry forest, farmland, and sloping farmland, while the surface soil (10 cm) was the wettest soil layer for the bamboo forest. During the representative rainstorms, although soil moisture responses exhibited depth gradients, the earlier responses at some deep layers indicated the occurrence of preferential flow. Generally, the cumulative infiltration increased while the infiltration rate decreased with the rainfall grade. Farmland and bamboo forest showed the most and the least infiltration, respectively. These results suggested that both the rainfall properties and LULCs had a strong impact on the soil moisture responses. Meanwhile, the estimated average runoff coefficients of four typical LULCs increased with rainfall grade, and the sloping farmland generated the most runoff during the representative rainfall events. Our findings provide new insights into soil moisture dynamics and infiltration regimes under different LULCs in humid areas, which can contribute to hydrological modeling at the field-scale and regional water resource management. [ABSTRACT FROM AUTHOR]

Summary Reservoirs are one of the most efficient infrastructures for integrated water resources development and management; and play a more and more important role in flood control and conservation. Optimal refill operation before the end of flood season is a valuable and effective approach to compromise the flood control, hydropower generation and comprehensive utilization of water resources of river basins. An integrated model consisting of a flood control risk analysis module, a utilization benefits analysis module and a multi-objective evaluation module was proposed in this study to derive joint optimal refill rules for cascade reservoirs. The Jinsha River and Three Gorges cascade reservoirs in the Changjiang River basin of China are selected for a case study. Sixty-one years of observed daily runoff data from 1950 to 2010 have been used to test the model. The results indicate that the proposed model can make an effective tradeoff between flood control and utilization benefits. Joint optimal synchronous and asynchronous refill rules can generate 3.25% and 2.78% more annual average hydropower, respectively and improve the fullness storage rate without increasing flood control risk comparing with the original designed operating rules. [ABSTRACT FROM AUTHOR]

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 In this study, the likelihood functions for uncertainty analysis of hydrological models are compared and improved through the following steps: (1) the equivalent relationship between the Nash–Sutcliffe Efficiency coefficient ( NSE ) and the likelihood function with Gaussian independent and identically distributed residuals is proved; (2) a new estimation method of the Box–Cox transformation (BC) parameter is developed to improve the effective elimination of the heteroscedasticity of model residuals; and (3) three likelihood functions— NSE , Generalized Error Distribution with BC (BC-GED) and Skew Generalized Error Distribution with BC (BC-SGED)—are applied for SWAT-WB-VSA (Soil and Water Assessment Tool – Water Balance – Variable Source Area) model calibration in the Baocun watershed, Eastern China. Performances of calibrated models are compared using the observed river discharges and groundwater levels. The result shows that the minimum variance constraint can effectively estimate the BC parameter. The form of the likelihood function significantly impacts on the calibrated parameters and the simulated results of high and low flow components. SWAT-WB-VSA with the NSE approach simulates flood well, but baseflow badly owing to the assumption of Gaussian error distribution, where the probability of the large error is low, but the small error around zero approximates equiprobability. By contrast, SWAT-WB-VSA with the BC-GED or BC-SGED approach mimics baseflow well, which is proved in the groundwater level simulation. The assumption of skewness of the error distribution may be unnecessary, because all the results of the BC-SGED approach are nearly the same as those of the BC-GED approach. [ABSTRACT FROM AUTHOR]

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

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

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

Summary: Quantification of uncertainty of hydrological models has attracted much attention in the recent hydrological literature. Different results and conclusions have been reported which result from the use of different methods with different assumptions. In particular, the disagreement between the Generalized Likelihood Uncertainty Estimation (GLUE) and the Bayesian methods for assessing the uncertainty in conceptual watershed modelling has been widely discussed. What has been mostly criticized is the subjective choice as regards the influence of threshold value, number of sample simulations, and likelihood function in the GLUE method. In this study the impact of threshold values and number of sample simulations on the uncertainty assessment of GLUE is systematically evaluated, and a comprehensive evaluation about the posterior distribution, parameter and total uncertainty estimated by GLUE and a formal Bayesian approach using the Metropolis Hasting (MH) algorithm are performed for two well-tested conceptual hydrological models (WASMOD and DTVGM) in an arid basin from North China. The results show that in the GLUE method, the posterior distribution of parameters and the 95% confidence interval of the simulated discharge are sensitive to the choice of the threshold value as measured by the acceptable samples rate (ASR). However, when the threshold value in the GLUE method is high enough (i.e., when the ASR value is smaller than 0.1%), the posterior distribution of parameters, the 95% confidence interval of simulated discharge and the percent of observations bracketed by the 95% confidence interval (P-95CI) for the GLUE method approach those values estimated by the Bayesian method for both hydrological models. Second, in the GLUE method, the insufficiency of number of sample simulations will influence the maximum Nash–Sutcliffe (MNS) efficiency value when ASR is fixed. However, as soon as the number of sample simulations increases to 2×104 for WASMOD and to 8×104 for the DTVGM model the influence of number of sample simulations on the model simulation results becomes of minor importance. Third, the uncertainty in simulated discharges resulting from parameter uncertainty is much smaller than that resulting from the model structure uncertainty for both hydrological models. Fourth, the goodness of model fit as measured by the maximum Nash–Sutcliffe efficiency value is nearly the same for the GLUE and the Bayesian methods for both hydrological models. Thus this study provides useful information on the uncertainty assessment of hydrological models. [Copyright &y& Elsevier]

Summary: The Danjiangkou reservoir lies in the upper Hanjiang basin and is the source of water for the middle route of the South-to-North Water Diversion Project (SNWDP) in China. Any significant change in the magnitude or timing of runoff from the Danjiangkou reservoir induced by changes in climatic variables would have significant implications for the economic prosperity of the area in the Hanjiang basin as well as for the South-to-North Water Diversion Project. In this paper the following issues are investigated: (1) Temporal trends of annual and seasonal precipitation and temperature from 1951 to 2003 in the Hanjiang basin are analyzed using the Mann–Kendall and the linear regression methods; spatial distributions of precipitation and temperature are interpolated by the inverse distance weighted interpolation method. (2) Temporal trends of runoff, precipitation and temperature from 1951 to 2003 in the Danjiangkou reservoir, an upper stream basin of the Hanjiang River, are further tested. (3) To assess the impact of climate change on water resources and predict the future runoff change in the Danjiangkou reservoir basin, a two-parameter water balance model is used to simulate the hydrological response for the climate change predicted by GCMs for the region for the period of 2021–2050. The results indicate that (1) at the α =0.05 significance level precipitation in the Hanjiang basin has no trend, but the temperature in the same region has significant upward trends in most parts of the Hanjiang basin. (2) The mean annual, spring, and winter runoffs in the Danjiangkou reservoir basin have decreasing trends. (3) The results simulated for the period 2021–2050 show that runoff of the Danjiangkou reservoir would increase in all the seasons, mainly in response to the predicted precipitation increase in the region. Sensitivity analysis shows that a 1°C and 2°C increase in temperature would reduce the mean annual runoff to about 3.5% and 7%, respectively. A decrease/increase of the mean monthly precipitation of 20% and 10% would decrease/increase the mean annual runoff to about 30% and 15%, respectively. The results of this study provide a scientific reference not only for assessing the impact of the climate change on water resources and the flood prevention in the Hanjiang basin, but also for dimensioning the middle route of the SNWDP in China. [Copyright &y& Elsevier]

Summary: Limitations in water quantity and quality are among the greatest social and economic problems facing mankind. However, difficulties in estimating the global long-term average runoff have led to differences of as much as 30% when integrated to the whole earth. Model estimates of runoff are especially uncertain for the 50% of the global land surface lacking consistent runoff data. In this study, we present the WASMOD-M global water-balance model, constructed to provide robust runoff estimates both for gauged and ungauged basins. WASMOD-M is a conceptual water-budgeting model with two state-variables and five tunable parameters. A simple parameter-value estimation procedure allowed “acceptable” parameter values to be identified both for the majority of gauged basins, and for most ungauged basins. Acceptable global simulations could be accomplished with continentally constant parameter values but at the cost of compensating errors on a basin scale. A “standard”, spatially-distributed parameter-value set was derived for a ”best” global simulation. Of the simulated 59132 0.5°×0.5° cells, 45% got “good” parameter values as a by-product of regionalisation, 41% from regionalisation, whereas 14% were given a default value set. This global set allowed simulation of the 1915–2000 world water balance. The simulation was in the same range as previously published model results and compilations of runoff measurements. Long-term average within-year runoff variations agreed well with previously published results for most of the studied runoff stations although WASMOD-M was only calibrated against long-term average runoff. Improvement of WASMOD-M and other global water-balance models should be simplified by a common definition of basin boundaries and areas, as well as runoff. Further, modelling progress will depend on improved global datasets of precipitation and runoff regulation. [Copyright &y& Elsevier]

Summary: Large differences in future climatic scenarios found when different global circulation models (GCMs) are employed have been extensively discussed in the scientific literature. However, differences in hydrological responses to the climatic scenarios resulting from the use of different hydrological models have received much less attention. Therefore, comparing and quantifying such differences are of particular importance for the water resources management of a catchment, a region, a continent, or even the globe. This study investigates potential impacts of human-induced climate change on the water availability in the Dongjiang basin, South China, using six monthly water balance models, namely the Thornthwaite–Mather (TM), Vrije Universitet Brussel (VUB), Xinanjiang (XAJ), Guo (GM), WatBal (WM), and Schaake (SM) models. The study utilizes 29-year long records of monthly streamflow and climate in the Dongjiang basin. The capability of the six models in simulating the present climate water balance components is first evaluated and the results of the models in simulating the impact of the postulated climate change are then analyzed and compared. The results of analysis reveal that (1) all six conceptual models have similar capabilities in reproducing historical water balance components; (2) greater differences in the model results occur when the models are used to simulate the hydrological impact of the postulated climate changes; and (3) a model without a threshold in soil moisture simulation results in greater changes in model-predicted soil moisture with respect to alternative climates than the models with a threshold soil moisture. The study provides insights into the plausible changes in basin hydrology due to climate change, that is, it shows that there can be significant implications for the investigation of response strategies for water supply and flood control due to climate change. [Copyright &y& Elsevier]

Abstract: Annual maximum streamflow and annual maximum water level and their variations exert most serious influences on human society. In this paper, temporal trends and frequency changes at three major stations of Yangtze River, i.e. Yichang, Hankou and Datong representing upper, middle and lower reaches, respectively, were detected with the help of parametric t-test, Mann–Kendall (MK) analysis and wavelet transform methods. The results show that: (1) there is a significant upward trend in streamflow at middle Yangtze River, indicating that flood hazard in the middle reach of the river, the flood rich region, will be more serious; (2) there is a consistent increase of water level from upper to lower reaches of the river which does not always coincide with the maximum streamflow variations; and (3) the periods of water level changes are decreasing over time, indicating the increasing occurrence frequency of annual maximum water level over time. This phenomenon is more obvious from upper Yangtze River to the lower Yangtze River. Human activities like destruction of vegetation, land reclamation and construction of levees reduced lake sizes and filled up the river bed, reducing the flood storage capacity of lakes and fluvial channel. These factors led to higher water level even some times the streamflow is small. Human should adjust his activity to enhance his adaptive capacity to flood hazard in the future. [Copyright &y& Elsevier]

Abstract: Most climate predictions show significant consequences globally and regionally, but many of its critical impacts will occur at sub-regional and local scales. Downscaling methods are, thus, needed to assess effects of large-scale atmospheric circulation on local parameters such as precipitation and runoff. This study aims at evaluating the analogue method (AM) as a benchmark method for precipitation downscaling in northern Europe. The predictors used in this study were daily and monthly gridded sea-level pressures from 1960 to 1997 in an area 45–75°N and 30°W–40°E with a resolution of 5×5° long-lat. Analogues for daily and monthly precipitation at seven precipitation stations in south-central Sweden were established with two techniques, principal-component analysis (PCA) and the Teweles-Wobus score (TWS). The results showed that AM downscaling on both daily and monthly basis was commonly generally much better than a random baseline but depended on the objective function used for assessment; PCA and TWS produced similar results in most cases but TWS was superior in simulating precipitation duration and intensity. Downscaling was improved when seasonality was included and when the SLP field was confined to those geographical areas that contributed most to precipitation in south-central Sweden. [Copyright &y& Elsevier]

• A robust input determination method was developed for monthly saltwater intrusion. • Combined use of MIC and r was applied to identify the relevant statistical inputs. • BMA provided more reliable forecast than individual member models. Mid- and long-term saltwater intrusion forecasts for estuaries are challenging due to a wide range of dynamic interactions and the limited amount of available data. This study proposes a tailor-made method for input determination for ensemble forecasts of monthly estuarine saltwater intrusion. The proposed method is based on determining the initial set of candidates by the combined use of Pearson's Coefficient (r) and Maximal Information Coefficient (MIC); and afterwards reducing the dimension of the input data sets by Principal Component Analysis (PCA). The current study uses Bayesian Model Averaging (BMA) method to combine the forecasting results of Random Forest (RF), Support Vector Machine (SVM) and Elman Neural Network (ENN) models to create an integrated forecast. The proposed modeling approach was tested and compared with seven alternative procedures to forecast the monthly saltwater intrusion at the Pearl River Delta (PRD). The results indicated that: (a) the monthly dynamics of saltwater intrusion are more sensitive to the long-term solar activities than the local wind force; (b) the valuable non-linear signals hidden in the related time series could be identified by the combined use of r and MIC; (c) dynamic statistics related to low runoff, high antecedent chlorinity, strong tidal force, and strong wind force are preferable over the monthly average values as model inputs; and (d) the proposed method achieved highest forecast accuracy with Nash-Sutcliffe coefficient (NSE) of 0.78. This study provides insights to the input determination for data-driven models of complex estuarine saltwater intrusion. [ABSTRACT FROM AUTHOR]

• Intelligent adaptation parameter method improves the hydrological model performance. • LightGBM builds flood characteristics-parameter intelligent adaptation relationship. • The XAJ-IAP model can provide accurate and reliable flood simulation. • LightGBM analysis reveals importance of flood characteristics and parameters. Traditional hydrological modeling methods use a set of parameters to simulate flood processes with complex causes and variable intensity, which can easily lead to parameter instability. To address the problem of parameter instability, this study proposes an approach integrating the hydrological model with Intelligent Adaptation Parameters (IAP), whose intelligent adaptation relationship is established by the light gradient-boosting machine (LightGBM) based on individual calibration parameters by each flood event and flood characteristics including flood-caused rainstorm information and initial soil moisture. A widely used hydrological model, Xin 'anjiang (XAJ) model, is chosen to be integrated with IAP (XAJ-IAP) in this study, which has a relatively complex structure and a total of 15 model parameters. The obtained findings demonstrate that: (1) recalibrating the sensitive runoff concentration and separation parameters with a single flood leads to a notable enhancement in simulation accuracy, while simultaneously considering the model's physical significance; (2) the XAJ overestimates large floods and underestimates small floods. Compared with the XAJ, the XAJ-IAP has a better rain-flood response relationship and simulation accuracy for floods of different magnitudes, solving the problem of parameter instability that exists in XAJ; and (3) evaluated in terms of information gain, sensitive parameters contribute the most to the establishment of the intelligent adaptation relationship in the LightGBM compared to flood-caused rainstorm information and initial soil moisture, indicating that sensitive parameters are the most important input features of the LightGBM. It can be concluded that the intelligent adaptation system can not only solve the problem of parameter instability that exists when traditional hydrological models simulate complex and changeable floods, but also further reveal the relationship between the model and floods. [ABSTRACT FROM AUTHOR]

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

• A new framework to cope with data scarcity problem is developed for lager scale flash flood risk mapping. • Analytic hierarchy process is improved considering the objective variability of data. • Data mining technique overcomes the difficulty in defining risk ranks' threshold. Flash floods are one of the most severe natural disasters throughout the world, and are responsible for sizeable social and economic losses, as well as countless injuries and death. Risk assessment, which identifies areas susceptible to flooding, has been shown to be an effective tool for managing and mitigating flash floods. The study aims to introduce the methods to determine the weights of the risk indices, and identify the different risk clusters. In this regard, we proposed a methodology for comprehensively assessing flash flood risk in a GIS environment, by the improved analytic hierarchy process (IAHP) method, and an integration of iterative self-organizing data (ISODATA) analysis and maximum likelihood (ISO-Maximum) clustering algorithm. The weight for each risk index is determined by the IAHP, which integrates the subjective characteristics with objective attributes of the assessment data. Based on the data mining technology, the integration of ISO-Maximum clustering algorithm derives a more reasonable classification. The Guangdong Province of China was selected for testing the proposed method's applicability, and we used a receiver operating characteristics (ROC) curve approach to validate the modeling of the flash-flood risk distribution. The validation against the historical flash flood data indicates a high reliability of this method for comprehensive flash flood risk assessment. In order to verify the proposed method's superiority, in addition, the technique for order performance by similarity to ideal solution (TOPSIS) and the weights-of-evidence (WE) methods are used for comparison with the IAHP and ISO-Maximum clustering algorithm method. Moreover, we analyzed and compared the regularity of flash floods in the rural and urban areas. This study not only provides a new approach for large-scale flash flood comprehensive risk assessment, but also assists researchers and local decision-makers in designing flash flood mitigation strategies. [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]

• Area and position indicators are created to assess extracted surface water path (SWP). • Theoretical SWP is deduced based on the formula of synthetic surfaces. • Artificial dispersion occurs in SWPs extracted by multiple flow direction algorithms. • Upscaling DEM resolution can decrease the value and position precisions. • Cross compensation decreases value precision but increases position precision. The surface water path (SWP) extracted from digital elevation model (DEM) by flow direction algorithms is widely employed to obtain a variety of topographic variables used in hydrological modeling. Accurate SWPs can facilitate understanding the underlying mechanisms of water movement on Earth's surface. However, the accuracy of extracted SWPs by different flow direction algorithms has not been systematically studied. In this work, two indicators are developed to measure the area and position errors of extracted SWPs relative to theoretical SWPs on four synthetic surfaces representing typical terrains of natural watersheds. Based on the formulas of the synthetic surfaces, theoretical true SWP can be derived for any grid cell on the DEM discretized from the synthetic surfaces. Several widely used flow direction algorithms including three single flow direction (SFD) algorithms (i.e. D8, Rho8 and D8-LTD approaches) and three multiple flow direction (MFD) algorithms (i.e. FDFM, MFD-md and D∞ approaches) are implemented to extract SWPs. Results suggest that significant distinctions can be detected in SWPs extracted by different flow direction algorithms. The SWPs extracted by SFD algorithms are always one-dimensional non-dispersive lines because SFD algorithms allow only one flow direction at each grid cell. In contrast, the SWPs extracted by MFD algorithms show excessive artificial dispersion. The average area error of extracted SWPs ranges from 16.3% to 75.2% on different synthetic surfaces and the minimum is obtained by FDFM approach for all synthetic surfaces. The average position error falls in the range of 46.0% to 161.4%. The maximum is gained by D8 or FDFM approach, and the minimum by D8-LTD or D∞ approach. The cross compensation of SWP area induced by artificial dispersion leads to relatively high area accuracy but relatively low position accuracy of MFD algorithms. In addition, increasing DEM resolution without capturing more topographic variability can decrease the area and position accuracy due to error accumulation from more steps of flow direction calculation. Our findings provide a beneficial insight into applying SWP-derived topographic variables to hydrological modeling. [ABSTRACT FROM AUTHOR]

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

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

• Two-component mixture distributions (TCMD) are used to model annual maximum floods. • Usage of flood timescale (FT) to classify distinct flood generating mechanisms. • Snowmelt-induced floods and rainfall-induced floods are identified in Norway. • The FT -based TCMD reduces uncertainty of design flood by 40% than traditional TCMD. The conventional flood frequency analysis typically assumes the annual maximum flood series (AMFS) result from a homogeneous flood population. However, actually AMFS are frequently generated by distinct flood generation mechanisms (FGMs), which are controlled by the interaction between different meteorological triggers (e.g., thunderstorms, typhoon, snowmelt) and properties of underlying surface (e.g., antecedent soil moisture and land-cover types). To consider the possibility of two FGMs in flood frequency analysis, researchers often use the two-component mixture distributions (TCMD) without explicitly linking each component distribution to a particular FGM. To improve the mixture distribution modeling in seasonally snow covered regions, an index called flood timescale (FT), defined as the ratio of the flood volume to peak value and chosen to reflect the relevant FGM, is employed to classify each flood into one of two types, i.e., the snowmelt-induced long-duration floods and the rainfall-induced short-duration floods, thus identifying the weighting coefficient of each component distribution beforehand. In applying the FT -based TCMD to model the AMFS of 34 watersheds in Norway, ten types of mixture distributions are considered. The design floods and associated confidence intervals are calculated using parametric bootstrap method. The results indicate that the FT -based TCMD model reduces the uncertainty in the estimation of design floods for high return periods by up to 40% with respect to the traditional TCMD. The improved predictive ability of the FT -based TCMD model is attributed to its explicit recognition of distinct generation mechanisms of floods, thereby being able to identify the weighting coefficient and FGM of each component distribution without optimization. [ABSTRACT FROM AUTHOR]

Abstract Editors of several journals in the field of hydrology met during the General Assembly of the European Geosciences Union—EGU in Vienna in April 2017. This event was a follow-up of similar meetings held in 2013 and 2015. These meetings enable the group of editors to review the current status of the journals and the publication process, and to share thoughts on future strategies. Journals were represented at the 2017 meeting by their editors, as shown in the list of authors. The main points on invigorating hydrological research through journal publications are communicated in this joint editorial published in the above journals. [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]

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