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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]

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]

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]

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]