Study of Hydrological Simulation on the Basis of Digitized Basin

Zhang, Xingnan; Xia, Dazhong; Jing, Li-Yang; Bakir, Mohammad

doi:10.1061/(asce)1084-0699(2008)13:5(317)

Cited by 1 publication

(2 citation statements)

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“…Previous studies regarding the estimation of CS mainly relied on conventional regression or even linear regression approaches, and these studies identified catchment area and slope as dominant factors affecting CS [22,46]. Our results showed that the drainage density, stream source density and catchment area were three dominant factors while the slope was less important.…”

Section: Implications Of This Studymentioning

confidence: 57%

“…Some other studies have tried to tackle the regionalization of the parameters using linear regression models or artificial neural networks [29,45]. Specifically, Lu [22], Zhang et al [46] tried to estimate CS using a few catchments by a conventional regression method. However, other studies raised concerns regarding the performance of hydrological models at ungauged links [47].…”

Section: Introductionmentioning

confidence: 99%

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

Fang

Huang²,

et al. 2022

Remote Sensing

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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.

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Section: Implications Of This Studymentioning

confidence: 57%