Application of the Regression-Augmented Regionalization Approach for BTOP Model in Ungauged Basins

Abstract

Ten years after the Predictions in Ungauged Basins (PUB) initiative was put forward, known as the post-PUB era (2013 onwards), reducing uncertainty in hydrological prediction in ungauged basins still receives considerable attention. This integration or optimization of the traditional regionalization approaches is an effective way to improve the river discharge simulation in the ungauged basins. In the Jialing River, southwest of China, the regression equations of hydrological model parameters and watershed characteristic factors were firstly established, based on the block-wise use of TOPMODEL (BTOP). This paper explored the application of twelve regionalization approaches that were combined with the spatial proximity, physical similarity, integration similarity, and regression-augmented approach in five ungauged target basins. The results showed that the spatial proximity approach performs best in the river discharge simulation of the studied basins, while the regression-augmented regionalization approach is satisfactory as well, indicating a good potential for the application in ungauged basins. However, for the regression-augmented approach, the number of watershed characteristic factors considered in the regression equation impacts the simulated effect, implying that the determination of optimal watershed characteristic factors set by the model parameter regression equation is a crux for the regression-augmented approach, and the regression strength may also be an influencing factor. These findings provide meaningful information to establish a parametric transfer equation, as well as references for the application in data-sparse regions for the BTOP model. Future research should address the classification of the donor basins under the spatial distance between the reference basin and the target basin, and build regression equations of model parameters adopted to regression-augmented regionalization in each classification group, to further explore this approach’s potential.

Integration of Hydrological Model and Time Series Model for Improving the Runoff Simulation: A Case Study on BTOP Model in Zhou River Basin, China

“…Leaf area index (LAI) data with a resolution of 0.05 degrees were adopted from the National Environmental Information Center (NCEI) [23]. All the above data sets were resampled to 1 km and input into the BTOP model for runoff simulation [24]. The BTOP model is a distributed watershed hydrological model based on a physical mechanism [25,26].…”

Ungauged Basin Flood Prediction Using Long Short-Term Memory and Unstructured Social Media Data

“…Recently, various studies using neural network models for flood prediction have been conducted [4][5][6][7][8][9][10][11][12][13][14][15][16][17][18][19][20][21]. An artificial neural network (ANN) model is a data-driven model that can make predictions rapidly, owing to fewer computational requirements than existing physical models.…”

“…Liu et al [19] used the Global Flood Awareness System (GloFAS) and ERA5-Land hydro-meteorological data with a piecewise random forest to produce more accurate hydrological simulation results. Furthermore, Xiao et al and Zhu et al [20,21] employed the BTOP model for ungauged basins, resulting in a notable increase in the Nash-Sutcliffe efficiency (NSE). However, deep learning models, such as LSTM models, surpass accuracy stochastic (e.g., autoregressive integrated moving average; ARIMA) and shallow learning models [22].…”

Enhancing Runoff Simulation Using BTOP-LSTM Hybrid Model in the Shinano River Basin

“…An accurate runoff simulation can provide a vital scientific basis for predicting flow velocity, mitigating short-term flood risks and managing long-term water resource systems [1][2][3][4][5][6]. However, due to the complex dynamic process of rainfall-runoff, which has high spatiotemporal variability and is influenced by various factors such as precipitation [7], terrain [8], and climate characteristics [9], accurately predicting runoff is a long-lasting challenge and remains one of the important topics in the study of surface hydrological processes [10][11][12].…”