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Accurate and reliable long-term forecasting plays an important role in water resources management and utilization. In this paper, a hybrid model called SVR-HUP is presented to predict long-term runoff and quantify the prediction uncertainty. The model is created based on three steps. First, appropriate predictors are selected according to the correlations between meteorological factors and runoff. Second, a support vector regression (SVR) model is structured and optimized based on the LibSVM toolbox and a genetic algorithm. Finally, using forecasted and observed runoff, a hydrologic uncertainty processor (HUP) based on a Bayesian framework is used to estimate the posterior probability distribution of the simulated values, and the associated uncertainty of prediction was quantitatively analyzed. Six precision evaluation indexes, including the correlation coefficient (CC), relative root mean square error (RRMSE), relative error (RE), mean absolute percentage error (MAPE), Nash-Sutcliffe efficiency (NSE), and qualification rate (QR), are used to measure the prediction accuracy. As a case study, the proposed approach is applied in the Han River basin, South Central China. Three types of SVR models are established to forecast the monthly, flood season and annual runoff volumes. The results indicate that SVR yields satisfactory accuracy and reliability at all three scales. In addition, the results suggest that the HUP cannot only quantify the uncertainty of prediction based on a confidence interval but also provide a more accurate single value prediction than the initial SVR forecasting result. Thus, the SVR-HUP model provides an alternative method for long-term runoff forecasting. [ABSTRACT FROM AUTHOR]

In the wake of global and regional climate change and heightened human activities, runoff from some rivers in the world, especially in the arid and semi-arid regions, has significantly decreased. To reveal the varying characteristics leading to the change in runoff, detecting the influencing factors has been important in recent scientific discussions for water resources management in drainage basins. In this paper, an investigation into attributing the runoff response to climate change and human activities were conducted in two catchments (Wushan and Shetang), situated in the upper reaches of Weihe River in China. Prior to the identification of the factors that influenced runoff changes, the Mann-Kendall test was adopted to identify the trends in hydro-climate series. Also, change-points in the annual runoff were detected through Pettitt's test and the precipitation-runoff double cumulative curve method. It is found that both catchments presented significant negative trend in annual runoff and the detected change-point in runoff occurs in 1993. Hence, the pre-change period and post-change period are defined before and after 1993, respectively. Then, runoff response to climate change and human activities was quantitatively evaluated on the basis of hydrologic sensitivity analysis and hydrologic model simulation. They provided similar estimates of the percentage change in mean annual runoff for the post-change period over the considered catchments. It is found that the decline in annual runoff over both catchments can be mainly attributed to the human activities, the reduction percentages due to human activities range from 59 to 77 %. The results of this study can provide a reference for the development, utilization and management of the regional water resources and ecological environment protection. [ABSTRACT FROM AUTHOR]

Summary: The hydrological cycle has been substantially influenced by climate change and human activities. It is therefore of utmost importance to analyze the impact of climate change on hydrology, particularly on a regional scale, in order to understand potential future changes of water resources and water-related disaster, and provide support for regional water management. However, during the evaluation of climate change impact on hydrology or water resources, large uncertainty exists. In this paper, the Soil Water Assessment Tool (SWAT) model is used to investigate the potential impact of climate change on hydrology of the upper reaches of Qiantang River Basin, East China, for the future period 2011–2100. The uncertainty is considered by employing upgraded reliability ensemble averaged GCM climate projections under three emission scenarios A1B, A2 and B1 for three different stages of the future period. These projections are downscaled and used in the hydrological model. Impact of climate change on precipitation, potential evapotranspiraton and river runoff is then investigated. The model calibration and validation outcomes show reasonable performance of the SWAT model. The final results suggest that annual river runoff will likely decrease almost under all emission scenarios and time stages of the future period. Particularly, at Jinhua Station, substantial decrease of annual river runoff can be noticed, indicating less water resource possibly available for the region in future. Simulated monthly patterns show that the largest decrease will likely occur in winter while increases will occur in summer, implying possible more water-related disasters in this region. However, it is also noticed that the change signs/amount could be different under different emission scenarios and time stages, indicating large uncertainty involved in the impact analysis. [ABSTRACT FROM AUTHOR]

Variability and availability of water resources under changing environment in a regional scale have been hot topics in recent years, due to the vulnerability of water resources associated with social and economic development. In this paper, four subbasins in the Dongjiang basin with a significant land use change were selected as case study. Runoffs of the four subbasins were simulated using the SCS monthly model to identify the quantitative impacts of land use and climate change. The results showed that (1), in the Dongjiang basin, temperature increased significantly, evaporation and sunlight decreased strongly, while precipitation showed a nonsignificant increase; (2) since the 1980s, land uses in the Dongjiang basin have experienced a significant change with a prominent increase inurbanareas, amoderate increase in farmlands, anda greatdecrease inforest areas; (3) the SCSmonthlymodel performed well in the four subbasins giving that the more significant land use change in each subbasin, the more runoff change correspondingly; (4) overall, runoff change was contributed half and half by climate change and human activities, respectively, in all the subbasins, in which about 20%~30% change was contributed by land use change. [ABSTRACT FROM AUTHOR]

• The rainfall-runoff relationship has changed under climate change and human activities in the TRH region. • The impacts climate indicators of rainfall-runoff ratio in the sub-basins of TRH region were different. • The contribution rates of human activity in the YRB were a little higher than that in the YARB. Identifying the impact of climate change and human activities on rainfall-runoff relationship in the Three-River Headwaters (TRH) region is essential for water source conservation, ecological security, and biodiversity maintenance of China and East Asia. In this paper, the runoff-rainfall ratio was used as the index of rainfall-runoff relationship. Different methods were employed to identify the impact indicators and quantitative assessment of rainfall-runoff relationship from 1957 to 2016. The results are the following: (1) the rainfall-runoff relationship has changed in the source regions of Yangtze River basin (YARB) and Yellow River (YRB), and runoff-rainfall ratio has increased by 2.9% and decreased by 3.2%, respectively; (2) the main climate indicators of runoff-rainfall ratio were rainfall, potential evaporation and sunshine hours in the YARB, and rainfall, potential evaporation and temperature in the YRB; (3) the contribution rates of human activity in the YRB were a little higher than that in the YARB, and the contribution rates of climate change and human activity were 90.9% and 9.1% in the YARB and 42.9% and 57.1% in the YRB. The results can provide invaluable information for managing water resources and taking ecological protection measures. [ABSTRACT FROM AUTHOR]