Estimating the Routing Parameter of the Xin’anjiang Hydrological Model Based on Remote Sensing Data and Machine Learning

• Published in: Remote sensing (Basel, Switzerland) Vol. 14; no. 18; p. 4609
• Main Authors: Fang, Yuanhao, Huang, Yizhi, Qu, Bo, Zhang, Xingnan, Zhang, Tao, Xia, Dazhong
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.09.2022
• Subjects: Algorithms; Calibration; Catchments; China; Correlation coefficient; Correlation coefficients; data collection; Datasets; drainage; Drainage density; Hydrologic models; Hydrology; Learning algorithms; Machine learning; Mathematical functions; Meteorology; model parameters; Parameter estimation; Parameters; Partial differential equations; Precipitation; prediction; regionalization; Regression analysis; Remote sensing; remote sensing data; remotely sensed underlying surface characteristics; Robustness; Root-mean-square errors; Runoff; Stream flow; streams; Surface properties; Variables; watersheds; Xin’anjiang hydrological model; United States > US; China

The parameters of hydrological models should be determined before applying those models to estimate or predict hydrological processes. The Xin’anjiang (XAJ) hydrological model is widely used throughout China. Since the prediction in ungauged basins (PUB) era, the regionalization of the XAJ model par...