Ma Tengfei

Quantifying the spatial variability of rainfall and flow routing on flood response across scales

The different spatial patterns of hydrologic processes always lead to different flood responses, and this effect varies with environment and scale. In this paper, we evaluate the sensitivity of hydrologic response to the spatial variability of rainfall and flow routing by a variability framework, which explicitly expresses the main features of hydrology in terms of the spatial–temporal variability of rainfall, runoff and flow routing. For a more general conclusion, a stochastic rainfall generator is used as input of the variability framework. We perform the numerical experiments at the Yanduhe Basin and its 64 subcatchments, with area ranging over three orders of magnitude. The results suggest that the sensitivity of hydrologic response to the spatial variability of rainfall depends on the characteristics of rainfall and antecedent soil moisture. The contribution of spatial variability of rainfall reaches the peak in the case of relatively small rainfall event or antecedent dry condition. Influenced by the characteristics of local rainfall, the contribution of spatial variability increases with the subcatchment size at the Yanduhe Basin. The hillslope routing is dominant for slow runoff or small catchment. With the increasing of flow velocity and catchment size, the importance of channel routing significantly increases.

Application of variable selection in hydrological forecasting based on Partial Least Squares

In this paper, a variable selection method based on Partial Least Squares is discussed and used to establish the hydrological forecasting model. The Q2cum score is taken as objective function, while Partial Correlation Coefficient (PCC), Variable Importance of Projection (VIP) and Function of Fitting Error (FFE) are used to measure the importance of each regression factor in every step. As a case study, the Taihu daily water level forecasting model has been build. The results show the following: The partial least squares regression can effectively overcome the serious multicollinearity among factors, and the regression coefficient accords with the hydrological law; The Q2cum score is closely associated with the accuracy of the forecast period, effectively reflect forecast ability of the model; The FFE plays a better role in measuring the importance of factors than VIP and PCC; Based on FFE, by forcing the impact of the factors to be continuous, the forecast ability of the model declines, but the model accords with the hydrological law much better, and is more practical.