Interpretable baseflow segmentation and prediction based on numerical experiments and deep learning

Qiying Yu

doi:10.1016/j.jenvman.2024.121089

Interpretable baseflow segmentation and prediction based on numerical experiments and deep learning

2024-06-01

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DOI：10.1016/j.jenvman.2024.121089

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Baseflow is a crucial water source in the inland river basins of high -cold mountainous region, playing a significant role in maintaining runoff stability. It is challenging to select the most suitable baseflow separation method in data-scarce high -cold mountainous region and to evaluate effects of climate factors and underlying surface changes on baseflow variability and seasonal distribution characteristics. Here we attempt to address how meteorological factors and underlying surface changes affect baseflow using the Grey Wolf Optimizer Digital Filter Method (GWO-DFM) for rapid baseflow separation and the Long Short-Term Memory (LSTM) neural network model for baseflow prediction, clarifying interpretability of the LSTM model in baseflow forecasting. The proposed method was successfully implemented using a 63-year time series (1958 - 2020) of flow data from the Tai Lan River (TLR) basin in the high -cold mountainous region, along with 21 years of ERA5-land meteorological data and MODIS data (2000 - 2020). The results indicate that: (1) GWO-DFM can rapidly identify the optimal filtering parameters. It employs the arithmetic average of three methods, namely Chapman, ChapmanMaxwell and Eckhardt filter, as the best baseflow separation approach for the TLR basin. Additionally, the baseflow significantly increases after the second mutation of the baseflow rate. (2) Baseflow sources are mainly influenced by precipitation infiltration, glacier frozen soil layers, and seasonal ponding. (3) Solar radiation, temperature, precipitation, and NDVI are the primary factors influencing baseflow changes, with Nash-Sutcliffe efficiency coefficients exceeding 0.78 in both the LSTM model training and prediction periods. (4) Changes in baseflow are most influenced by solar radiation, temperature, and NDVI. This study systematically analyzes the changes in baseflow and response mechanisms in high -cold mountainous region, contributing to the management of water resources in mountainous basins under changing environmental conditions.

Keywords:

Baseflow prediction

Grey wolf optimization digital filter

HYSEP

Baseflow separation

LSTM-SHAP

Interpretable baseflow segmentation and prediction based on numerical experiments and deep learning

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