The effects of land use change and precipitation change on direct runoff in Wei River watershed, China.

The principles and degrees to which land use change and climate change affect direct runoff generation are distinctive. In this paper, based on the MODIS data of land use in 1992 and 2003, the impacts of land use and climate change are explored using the Soil Conservation Service Curve Number (SCS-CN) method under two defined scenarios. In the first scenario, the precipitation is assumed to be constant, and thus the consequence of land use change could be evaluated. In the second scenario, the condition of land use is assumed to be constant, so the influence only induced by climate change could be assessed. Combining the conclusions of two scenarios, the effects of land use and climate change on direct runoff volume can be separated. At last, it is concluded: for the study basin, the land use types which have the greatest effect on direct runoff generation are agricultural land and water body. For the big sub basins, the effect of land use change is generally larger than that of climate change; for middle and small sub basins, most of them suffer more from land use change than from climate change.

Uncertainty analysis of coupling multiple hydrologic models and multiple objective functions in Han River, China.

Three different hydrological models are chosen to simulate rainfall-runoff relationships under each of three objective functions including mean squared errors of squared transformed flows, squared root transformed flows and logarithmic transformed flows; thus nine individual models are constructed. By weighted averaging over these nine models, the method of Bayesian model averaging (BMA) was used to provide both the mean value and the uncertainty intervals of flow prediction. Three kinds of uncertainty information can be generated: the uncertainty of individual member model's predictions; the total uncertainty of BMA mean prediction; the between-model and within-model uncertainties in the BMA scheme. Based on the estimated results in this study, the coupling of multiple models with multiple objective functions in general offers better results for both the mean prediction and the uncertainty intervals for the runoffs in a selected basin in Han River, China, than the individual models.