Authors: Jianhua Xu*, East China Normal University, Chong Wang, Shanghai University of Engineering Science, Jingping Zuo, East China Normal University
Topics: Water Resources and Hydrology, Climatology and Meteorology, Quantitative Methods
Keywords: Climatic-hydrological processes, Data-scarce mountain basin, Statistical downscaling, Multiscale simulation, Back propagation artificial neural network, Wavelet regression
Session Type: Poster
Start / End Time: 1:10 PM / 2:50 PM
Room: Lincoln 2, Marriott, Exhibition Level
Presentation File: No File Uploaded
Previous studies showed that the climatic processes drive the streamflow of the inland river in Northwest China. However, it is difficult to quantitatively assess the hydrological processes in the ungauged mountainous basins because of the scarce-data. Combining statistical downscaling (SD), back propagation artificial neural network (BPANN) and wavelet regression (WR), this research developed an integrated approach for multiscale simulating the climatic-hydrological processes in data-scarce mountain basins of Northwest China. To validate the approach, we also simulated the climatic-hydrological processes at different scales in two data-scarce mountain basins, the Aksu River basin and Kaidu River basin in Northwest China. The main results are as follows: (i) the stream flow is related with regional climatic change as well as atmosphere-ocean variability; (ii) the BPANN model well simulated the nonlinear relationship between the stream flow and temperature & precipitation at the monthly scale; and (iii) although the annual runoff (AR) appears fluctuations, there are significant correlations among AR, annual average temperature (AAT), annual precipitation (AP) and oscillation indices, which can be simulated by equations of WR at different scales of years.