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

This paper compares the original and bias corrected global gridded precipitation datasets, tropical rainfall measuring mission (TRMM) and water and global change forcing data (WFD) with gauged precipitation and evaluates the usefulness of gridded precipitation datasets for hydrological simulations using the distributed soil and water assessment tool (SWAT) and lumped Xinanjiang model in Xiangjiang River basin, Southern China. The results show that the differences in areal mean rainfalls of original TRMM and WFD datasets and gauged dataset are in acceptable limits of less than 10 %, while larger differences exist in maximal 5-day rainfalls, dry spells and Fréchet distance. The bias correction methods are able to significantly improve the biases in the mean values of TRMM and WFD datasets. The nonlinear bias correction method gives good results in correcting the standard deviations of TRMM/WFD data. The hydrological modelling results show that the WFD datasets perform relatively better than TRMM datasets even though the results are poor as compared with using gauged rainfall as input in daily step hydrological models. At monthly time step, both TRMM and WFD data produce acceptable model simulation results in terms of Nash-Sutcliffe efficiency ( E > 0.7 for original TRMM/WFD data and E > 0.8 for linearly corrected TRMM/WFD data) and relative error (| R| < 10 %). [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: 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]

Abstract: Predicting hydrological variables in ungauged catchments has been singled out as one of the major issues in the hydrological sciences. In this study, the conceptual rainfall-runoff model, HBV, was applied to Dongjiang basin and its 13 sub-basins for the purposes of examining the applicability of this well-known model in south China and exploring the possibility of transferring the calibrated parameter values to ungauged basins. For testing the applicability of the model in gauged basins, the model was calibrated for a period of 1978–1983 and validated for a period of 1984–1988. For testing the transferability of parameter values to ungauged basins, two parameter regionalization methods – proxy-basins and global mean – were investigated. The results showed that: 1) the HBV model worked well in the Dongjiang basin with the average indexes of agreement (D) and coefficient of efficiency (ME), respectively, equal to 0.79 and 0.82 in the calibration period, and 0.76 and 0.78 in the validation period; 2) transferring the parameter values from basins that passed the cross-basin test with higher ME values to the hypothetical ungauged catchments produced acceptable results with an average ME value equals to 0.72; 3) compared with the proxy-basin method of parameter estimation, the model produced equally good results for the global mean method with an average ME value equals to 0.74 when using simple arithmetic mean values. Neither the area weighted mean method nor the Thiessen polygon method produced regional parameter values could markedly improve the accuracy of modeling results. It was concluded that both regionalization methods could effectively estimate parameters for ungauged catchments in the Dongjiang basin, and similar model performances were obtained. [Copyright &y& Elsevier]

Currently used goodness-of-fit (GOF) indicators (i.e. efficiency criteria) are largely empirical and different GOF indicators emphasize different aspects of model performance; a thorough assessment of model skill may require the use of robust skill matrices. In this study, based on the maximum likelihood method, a statistical measure termed BC-GED error model is proposed, which firstly uses the Box-Cox (BC) transformation method to remove the heteroscedasticity of model residuals, and then employs the generalized error distribution (GED) with zero-mean to fit the distribution of model residuals after BC transformation. Various distance-based GOF indicators can be explicitly expressed by the BC-GED error model for different values of the BC transformation parameter λ and GED kurtosis coefficient β. Our study proves that (1) the shape of error distribution implied in the GOF indicators affects the model performance on high or low flow discharges because large error-power (β) value can cause low probability of large residuals and small β value will lead to high probability of zero value; (2) the mean absolute error could balance consideration of low and high flow value as its assumed error distribution (i.e. Laplace distribution, where β = 1) is the turning point of GED derivative at zero value. The results of a study performed in the Baocun watershed via comparison of the SWAT model-calibration results using six distance-based GOF indicators show that even though the formal BC-GED is theoretically reasonable, the calibrated model parameters do not always correspond to high performance of model-simulation results because of imperfection of the hydrologic model. However, the derived distance-based GOF indicators using the maximum likelihood method offer an easy way of choosing GOF indicators for different study purposes and developing multi-objective calibration strategies. [ABSTRACT FROM AUTHOR]

Understanding hydrological responses to rising levels of greenhouse gases are essential for climate and impact research. It is, however, often limited by a lack of long record of observational data to provide a basis for understanding the long-term behavior of the climate system. Integrating reconstructed data and (global climate and hydrological) model simulations will help us to better understand the variability of climate and hydrology over timescales ranging from decades to centuries. In this study, we proposed an integrated approach to study flood variability in the upper reach of the Yangtze River over the last millennium to the end of the 21st century. To accomplish this, we first drove hydrological models using the precipitation and temperature from four Global Climate Models (GCM), BCC-CSM1.1, MIROC, MRI-CGCM3, and CCSM4, to simulate daily discharge for the upper reach of the Yangtze River during the period of the last millennium (850–1849), historical period (1850–2005), and a future period (2006–2099). Then, we evaluated whether the modeled precipitation, temperature, and extreme discharge had statistical properties similar to those shown in the documented dry-wet periods, temperature anomalies, and paleoflood records. Finally, we explored the extreme discharge variability using model simulations. The results indicate that: (1) The MIROC-ESM model, differing from the other three GCM models, revealed positive temperature changes from the warm period (Medieval Climate Anomaly; MCA) to the cold period (Little Ice Age; LIA), while the temperature variability of the other models was similar to the records. (2) The BCC-CSM1.1 model performed better than the others regarding correlations between modeled precipitation and documented dry-wet periods. (3) Over most of the subbasins in the upper Yangtze River, the magnitude of extreme discharge in the BCC-CSM1.1 model results showed that there was a decrease from the MCA to the LIA period and an increase in the historical period relative to the cold period, while a future increase was projected by the four GCMs under the influence of climate change. [ABSTRACT FROM AUTHOR]

Hydrological models are often used for studying the hydrological effects of climate change; however, the stability of model performance and parameter values under changing climate conditions has seldom been evaluated and compared. In this study, three widely-used rainfall–runoff models, namely the SimHYD model, the HBV model and the Xin’anjiang model, are evaluated on two catchments subject to changing climate conditions. Evaluation is carried out with respect to the stability in their performance and parameter values in different calibration periods. The results show that (a) stability of model performance and parameter values depends on model structure as well as the climate of catchments, and the models with higher performance scores are more stable in changing conditions; (b) all the tested models perform better on a humid catchment than on an arid catchment; (c) parameter values are also more stable on a humid catchment than on an arid catchment; and (d) the differences in stability among models are somewhat larger in terms of model efficiency than in model parameter values. [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]

Climate and soil characteristics vary considerably around the Lake Victoria basin resulting in high spatial and temporal variability in catchment inflows. However, data for estimating the inflows are usually sparsely distributed and error-prone. Therefore, modelled estimates of the flows are highly uncertain, which could explain early difficulties in reproducing the lake water balance. The aim of this study was to improve the estimates of catchment flow to Lake Victoria. The WASMOD model was applied to the Nzoia River, one of the major tributaries to Lake Victoria. Uncertainty was assessed within the GLUE framework. During calibration, log-transformation was performed on both simulated and observed flows. The results showed that, despite its simple structure, WASMOD produces acceptable results for the basin. For a Nash-Sutcliffe efficiency (NS) threshold of 0.6, the percentage of observations bracketed by simulations (POBS) was 74%, the average relative interval length (ARIL) was 0.93, and the maximum NS value was 0.865. The residuals were shown to be homoscedastic, normally distributed and nearly independent. When the NS threshold was increased to 0.8, POBS decreased to 54% with an improvement of ARIL to 0.49, highlighting the effect of the subjective choice of likelihood threshold. Citation Kizza, M., Rodhe, A., Xu, C.-Y. & Ntale, H. K. (2011) Modelling catchment inflows into Lake Victoria: uncertainties in rainfall–runoff modelling for the Nzoia River. Hydrol. Sci. J. 56(7), 1210–1226. [ABSTRACT FROM PUBLISHER]

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]

• 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]

Recent events across many regions around the world have shown that short-term droughts (i.e., daily or weekly) with sudden occurrence can lead to huge losses to a wide array of environmental and societal sectors. However, the most commonly used drought indices can only identify drought at the monthly scale. Here, we introduced a daily scale drought index, that is, the standardized antecedent precipitation evapotranspiration index (SAPEI) that utilizes precipitation and potential evapotranspiration and also considers the effect of early water balance on dry/wet conditions on the current day. The robustness of SAPEI is first assessed through comparison with two typical monthly indices [Palmer drought severity index (PDSI) and standardized precipitation evapotranspiration index (SPEI)] and soil moisture, and then applied to tracking short-term droughts during 1961–2015 for the Pearl River basin in south China. It is demonstrated that SAPEI performs as well as SPEI/self-calibrating PDSI at the monthly scale but outperforms SPEI at the weekly scale. Moreover, SAPEI is capable of revealing daily drought conditions, fairly consistent with soil moisture changes. Results also show that many of the historical short-term droughts over the Pearl River basin have multiple peaks in terms of severity, affected area, and intensity. The daily scale SAPEI provides an effective way of exploring drought initiation, development, and decay, which could be conducive for decision-makers and stakeholders to make early and timely warnings. [ABSTRACT FROM AUTHOR]

Hydrological impact assessments are increasingly performed at fine spatial and temporal resolutions in order to resolve local-scale changes under a future climate. Apart from the uncertainty represented by different climate models, emission scenarios and post-processing methods, the local-scale internal variability of the climate can be a major source of uncertainty for hydrological projections. To assess the latter at the catchment scale, this paper presents a methodology which is particularly suitable for spatially distributed hydrological models. An ensemble of daily precipitation and daily mean temperature realizations on a high-resolution grid is simulated from stochastic weather generators (WGs) trained on historical data and equipped with climate change information obtained from a regional climate model. Based on the resulting simulated daily runoff data, the significance of changes in the runoff regime is assessed using a statistical hypothesis test, and the variability contributed by the two input variables is quantified using the analysis of variance (ANOVA). As a proof of concept, simulations on a 1-km grid over a period of 19 years are carried out for nine catchments in central Norway. Significant changes in runoff regimes are found, indicating that the trends introduced in the WGs are not overwhelmed by the local-scale internal variability. Variability in the runoff simulations varies substantially throughout the year; it is highest in periods with potential snowmelt and lowest during winter. Temperature is the dominant source of variability in the colder months (November–March) due to its influence on rainfall and snowmelt. High variability in May–June is contributed comparably by both temperature and precipitation. In summer and early autumn the runoff variability is precipitation dominated. The results are in line with findings in the literature where the runoff variability is driven by the large-scale internal climate variability. This indicates that ignoring the local-scale internal variability may yield an underestimation of the overall variability in runoff projections and projected changes. • Gridded climate input data simulated using stochastic weather generators. • Significance of changes in runoff regime assessed using a statistical test. • Runoff variability due to input variables quantified using ANOVA. • Results in line with findings based on large-scale internal climate variability. • Runoff projections need to consider local-scale internal variability of climate. [ABSTRACT FROM AUTHOR]

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

• A longer calibration period is required for catchments with nonstationary rainfall-runoff relationships to achieve stable simulations. • With the increase in the length of the interval between the calibration and transfer periods, the transferability of the model decreases gradually. • When forecasting runoff under nonstationary rainfall-runoff relationships, the closeness of the total rainfall amount between calibration and transfer periods is more important than the similarity in rainfall processes. Changing climatic conditions have changed the stationary rainfall-runoff relationships in many basins. In this context, the value of the model parameters will depend more on the selection of the calibration period, which directly affects the accuracy of runoff forecasting. However, systematic exploration and testing of the impact of calibration conditions on the transferability of hydrological models under stationary and nonstationary climatic conditions require more effort. The present study investigates the impact of four calibration conditions on model transferability, including the length of the calibration period, the length of the interval between the calibration and transfer periods, the difference in climate conditions as measured by the total rainfall amount between the calibration and transfer periods, and the difference in the similarity of rainfall processes between the calibration and transfer periods. Two catchments with stationary and nonstationary climatic conditions, and five models, including XAJ, HBV, IHACRES, SIMHYD and GR4J, are used in this study. The results show that (1) a longer calibration period is required for catchments with nonstationary rainfall-runoff relationships to achieve stable simulations; (2) with the increase in the length of the interval between the calibration and transfer periods, the transferability of the model decreases gradually, and the degree of reduction is greater for catchments with nonstationary climatic conditions and rainfall-runoff relationships; and (3) when forecasting future runoff under nonstationary rainfall-runoff relationships, the closeness of the total rainfall amount between the calibration and transfer periods is more important than the similarity in rainfall series between the calibration and transfer periods. This study provides insight into the impact of calibration conditions on the transferability of hydrological models in the context of climate change. [ABSTRACT FROM AUTHOR]

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

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

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

The projected impacts of climate change and variability on floods in the southern Africa has not been well studied despite the threat they pose to human life and property. In this study, the potential impacts of climate change on floods in the upper Kafue River basin, a major tributary of the Zambezi River in southern Africa, were investigated. Catchment hydrography was delineated using the Hydro1k at a spatial resolution of 1 km. The daily global hydrological model WASMOD-D model was calibrated and validated during 1971-1986 and 1987-2001 with the simple-split sample test and during 1971-1980 and 1981-1990 with the differential split sample test, against observed discharge at Machiya gauging station. Predicted discharge for 2021-2050 and 2071-2100 were obtained by forcing the calibrated WASMOD-D with outputs from three GCMs (ECHAM, CMCC3 and IPSL) under the IPCC's SRES A2 and B1 scenarios. The three GCMs derived daily discharges were combined by assigning a weight to each of them according to their skills to reproduce the daily discharge. The two calibration and validation tests suggested that model performance based on evaluation criteria including the Nash-Sutcliffe coefficient, Pearson's correlation coefficient (r), Percent Bias and R was satisfactory. Flood frequency analysis for the reference period (1960-1990) and two future time slices and climate change scenarios was performed using the peak over threshold analysis. The magnitude of flood peaks was shown to follow generalised Pareto distribution. The simulated floods in the scenario periods showed considerable departures from the reference period. In general, flood events increased during both scenario periods with 2021-2050 showing larger change. The approach in our study has a strong potential for similar assessments in other data scarce regions. [ABSTRACT FROM AUTHOR]

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

Grid-based distributed models have become popular for describing spatial hydrological processes. However, the influence of non-homogeneity within a grid on streamflow simulation was not adequately addressed in the literature. In this study, we investigated how the statistical characteristics of soil moisture storage within a grid impacts on streamflow simulations. The spatial variation of the topographic index, TI, within a grid was used to determine parameter B of the statistical curve of soil moisture storage in the Xinanjiang model. For comparison of influences of the non-homogeneity within a grid on streamflow simulation, two parameterization schemes of soil moisture storage capacity were developed: a grid-parameterization scheme for a distributed model and a catchment-averaged scheme for a semi-distributed model. The practicability and usefulness of the grid-parameterization method were evaluated through model comparisons. The two models were applied in Jiangwan experimental catchment Zhejiang Province, China. Streamflow discharge data at the catchment outlet from 1971 to 1986 at different temporal resolutions, e.g. 15 min and daily time step, were used for model calibration and validation. Statistical results for different grid scales demonstrated that the mean and variation of TI and B decline significantly as the grid scale increases. The simulated streamflow discharges of the two models were similar and the semi-distributed model outperformed the distributed model slightly when the streamflow at the outlet of the catchment was used as the only basis for comparison. In addition, a relatively larger bias in the predicted discharges between these two models was observed along with an abrupt increase of soil moisture saturation ratio. A further analysis of the simulated soil moisture content distribution revealed that the distributed model can provide a reasonable representation of the variable source area concept, which was justified to some extent by the field experiment data. Editor D. Koutsoyiannis Citation Liu, J.T., Chen, X., Wu, J.C., Zhang, X.N., Feng, D.Z. and Xu, C.-Y., 2012. Grid parameterization of a conceptual, distributed hydrological model through integration of a sub-grid topographic index: necessity and practicability. Hydrological Sciences Journal, 57 (2), 282–297. [ABSTRACT FROM PUBLISHER]

This study comprehensively evaluates eight satellite-based precipitation datasets in streamflow simulations on a monsoon-climate watershed in China. Two mutually independent datasets—one dense-gauge and one gauge-interpolated dataset—are used as references because commonly used gauge-interpolated datasets may be biased and unable to reflect the real performance of satellite-based precipitation due to sparse networks. The dense-gauge dataset includes a substantial number of gauges, which can better represent the spatial variability of precipitation. Eight satellite-based precipitation datasets include two raw satellite datasets, Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN) and Climate Prediction Center MORPHing raw satellite dataset (CMORPH RAW); four satellite-gauge datasets, Tropical Rainfall Measuring Mission 3B42 (TRMM), PERSIANN Climate Data Record (PERSIANN CDR), CMORPH bias-corrected (CMORPH CRT), and gauge blended datasets (CMORPH BLD); and two satellite-reanalysis-gauge datasets, Multi-Source Weighted-Ensemble Precipitation (MSWEP) and Climate Hazards Group InfraRed Precipitation with Stations (CHIRPS). The uncertainty related to hydrologic model physics is investigated using two different hydrological models. A set of statistical indices is utilized to comprehensively evaluate the precipitation datasets from different perspectives, including detection, systematic, random errors, and precision for simulating extreme precipitation. Results show that CMORPH BLD and MSWEP generally perform better than other datasets. In terms of hydrological simulations, all satellite-based datasets show significant dampening effects for the random error during the transformation process from precipitation to runoff; however, these effects cannot hold for the systematic error. Even though different hydrological models indeed introduce uncertainties to the simulated hydrological processes, the relative hydrological performance of the satellite-based datasets is consistent in both models. Namely, CMORPH BLD performs the best, which is followed by MSWEP, CMORPH CRT, and TRMM. PERSIANN CDR and CHIRPS perform moderately well, and two raw satellite datasets are not recommended as proxies of gauged observations for their worse performances. [ABSTRACT FROM AUTHOR]

The traditional calibration objective of hydrological models is to optimize streamflow simulations. To identify the value of satellite soil moisture data in calibrating hydrological models, a new objective of optimizing soil moisture simulations has been added to bring in satellite data. However, it leads to problems: (i) how to consider the trade-off between various objectives; (ii) how to consider the uncertainty these satellite data bring in. Among existing methods, the multi-objective Bayesian calibration framework has the potential to solve both problems but is more suitable for lumped models since it can only deal with constant variances (in time and space) of model residuals. In this study, to investigate the utilization of a soil moisture product from the Soil Moisture Active Passive (SMAP) satellite in calibrating a distributed hydrological model, the DEM (Digital Elevation Model) -based Distributed Rainfall-Runoff Model (DDRM), a multi-objective Bayesian hierarchical framework is employed in two humid catchments of southwestern China. This hierarchical framework is superior to the non-hierarchical framework when applied to distributed models since it considers the spatial and temporal residual heteroscedasticity of distributed model simulations. Taking the streamflow-based single objective calibration as the benchmark, results of adding satellite soil moisture data in calibration show that (i) there is less uncertainty in streamflow simulations and better performance of soil moisture simulations either in time and space; (ii) streamflow simulations are largely affected, while soil moisture simulations are slightly affected by weights of objectives. Overall, the introduction of satellite soil moisture data in addition to observed streamflow in calibration and putting more weights on the streamflow calibration objective lead to better hydrological performance. The multi-objective Bayesian hierarchical framework implemented here successfully provides insights into the value of satellite soil moisture data in distributed model calibration. [ABSTRACT FROM AUTHOR]