Machine Learning Improvement of Streamflow Simulation by Utilizing Remote Sensing Data and Potential Application in Guiding Reservoir Operation

Sustainability

Yu Hui¹  Ziyue Zeng²  Lei Gu³  Youjiang Shen⁴  Lele Deng⁴  Shaokun He⁴  Jing Tian⁴  Zhen Liao⁴  Jiabo Yin⁴ 

[1] Changjiang Institute of Survey, Planning, Design and Research, Wuhan 430015, China;Changjiang River Scientific Research Institute, Wuhan 430015, China;School of Civil and Hydraulic Engineering, Huazhong University of Science and Technology, Wuhan 430074, China;State Key Laboratory of Water Resources and Hydropower Engineering Science, Wuhan University, Wuhan 430072, China;

关键词: ungauged basin;    machine learning;    streamflow simulation;    satellite precipitation;    atmospheric reanalysis;   

DOI  :  10.3390/su13073645

来源: DOAJ

【 摘 要 】

Hydro-meteorological datasets are key components for understanding physical hydrological processes, but the scarcity of observational data hinders their potential application in poorly gauged regions. Satellite-retrieved and atmospheric reanalysis products exhibit considerable advantages in filling the spatial gaps in in-situ gauging networks and are thus forced to drive the physically lumped hydrological models for long-term streamflow simulation in data-sparse regions. As machine learning (ML)-based techniques can capture the relationship between different elements, they may have potential in further exploring meteorological predictors and hydrological responses. To examine the application prospects of a physically constrained ML algorithm using earth observation data, we used a short-series hydrological observation of the Hanjiang River basin in China as a case study. In this study, the prevalent modèle du Génie Rural à 9 paramètres Journalier (GR4J-9) hydrological model was used to initially simulate streamflow, and then, the simulated series and remote sensing data were used to train the long short-term memory (LSTM) method. The results demonstrated that the advanced GR4J9–LSTM model chain effectively improves the performance of the streamflow simulation by using more remote sensing data related to the hydrological response variables. Additionally, we derived a reservoir operation model by feeding the LSTM-based simulation outputs, which further revealed the potential application of our proposed technique.

【 授权许可】

Unknown