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
• ISSN: 2072-4292; 2072-4292
• DOI: 10.3390/rs14184609

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 parameters has been a subject of intense focus; nevertheless, while many efforts have targeted parameters related to runoff yield using in-site data sets, classic regression has predominantly been applied. In this paper, we employed remotely sensed underlying surface data and a machine learning approach to establish models for estimating the runoff routing parameter, namely, CS, of the XAJ model. The study was conducted on 114 catchments from the Catchment Attributes and MEteorology for Large-sample Studies (CAMELS) data set, and the relationships between CS and various underlying surface characteristics were explored by a gradient-boosted regression tree (GBRT). The results showed that the drainage density, stream source density and area of the catchment were the three major factors with the most significant impact on CS. The best correlation coefficient (r), root mean square error (RMSE) and mean absolute error (MAE) between the GBRT-estimated and calibrated CS were 0.96, 0.06 and 0.04, respectively, verifying the good performance of GBRT in estimating CS. Although bias was noted between the GBRT-estimated and calibrated CS, runoff simulations using the GBRT-estimated CS could still achieve results comparable to those using the calibrated CS. Further validations based on two catchments in China confirmed the overall robustness and accuracy of simulating runoff processes using the GBRT-estimated CS. Our results confirm the following hypotheses: (1) with the help of large sample of catchments and associated remote sensing data, the ML-based approach can capture the nonstationary and nonlinear relationships between CS and the underlying surface characteristics and (2) CS estimated by ML from large samples has a robustness that can guarantee the overall performance of the XAJ mode. This study advances the methodology for quantitatively estimating the XAJ model parameters and can be extended to other parameters or other models.