A Statistical Hydrological Model for Yangtze River Watershed Based on Stepwise Cluster Analysis

Feng Wang,Jinliang Xu,Ruixin Duan,Guoqing Wang,Jiayan Ren,Guohe Huang,Yongping Li,Jianyun Zhang

doi:10.3389/feart.2021.742331

Abstract

Streamflow prediction is one of the most important topics in operational hydrology. The responses of runoffs are different among watersheds due to the diversity of climatic conditions as well as watershed characteristics. In this study, a stepwise cluster analysis hydrological (SCAH) model is developed to reveal the nonlinear and dynamic rainfall-runoff relationship. The proposed approach is applied to predict the runoffs with regional climatic conditions in Yichang station, Hankou station, and Datong station over the Yangtze River Watershed, China. The main conclusions are: 1) the performances of SCAH in both deterministic and probabilistic modeling are notable.; 2) the SCAH is insensitive to the parameter p in SCAH with robust cluster-tree structure; 3) in terms of the case study in the Yangtze River watershed, it can be inferred that the water resource in the lower reaches of the Yangtze River is seriously affected by incoming water from the upper reaches according to the strong correlations. This study has indicated that the developed statistical hydrological model SCAH approach can characterize such hydrological processes complicated with nonlinear and dynamic relationships, and provide satisfactory predictions. Flexible data requirements, quick calibration, and reliable performances make SCAH an appealing tool in revealing rainfall-runoff relationships.

Highlights

Streamflow prediction is one of the most important topics in operational hydrology, which can provide valuable information for water resource allocation, hydropower generation, flood risk management, irrigation, and agricultural crop forecasting (Fan et al, 2015)
As one of the most densely populated and economically developed areas in China, the Yangtze River Basin has experienced a booming economy over the last decade and constituted over 40% of gross domestic product (GDP) (Chen et al, 2017)
The lower average simulation error in stepwise cluster analysis hydrological model (SCAH) can be observed through the lower Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE) values

Summary

INTRODUCTION

Streamflow prediction is one of the most important topics in operational hydrology, which can provide valuable information for water resource allocation, hydropower generation, flood risk management, irrigation, and agricultural crop forecasting (Fan et al, 2015). A number of statistic models such as multiple linear regression, autoregressive, and autoregressive integrated moving average cannot reflect nonlinear relationships between predictors (e.g., climatic factors) and responses (e.g., streamflow) (Solomatine and Ostfeld, 2008; Ordieres-Meré et al, 2020) It can hardly fit the observations very well with nonlinear relationships in the water cycle (Fan et al, 2020; Li et al, 2020). With the advantage of capturing discrete and nonlinear relationships between explanatory and response variables, stepwise cluster analysis has received much attention for environmental issues such as air quality prediction (Huang, 1992), process control (Huang et al, 2006), climate projections (Wang et al, 2013), stream flow prediction (Cheng et al, 2016; Zhuang et al, 2016), groundwater simulation (Han et al, 2016), and ecosystem analysis and prediction (Sun et al, 2018). The data has not been extended beyond 1990 in order to preserve the stationarity of the data, since rapid economic development and large-scale land uses have taken place in China since 1990

RESULT

CONCLUSION

Findings

DATA AVAILABILITY STATEMENT