Conceptual hydrological model-guided SVR approach for monthly lake level reconstruction in the Tibetan Plateau

Abstract

Study region: Tibetan Plateau (TP) Study focus: Lakes in the TP that are subject to low human activity serve as an important indicator for quantitative assessment of regional climate change. However, previous studies have mainly focused on annual changes in lake area, level, and storage because of limited monitoring stations, and long-term lake level reconstruction at monthly resolution remains challenging. We propose a conceptual hydrological model-guided monthly lake level reconstruction (WBM-SVR) approach that incorporates water balance models (WBMs) and support vector regression (SVR), to improve the training and testing sets compared with SVR alone through consideration of the hydrological process. The WBM-SVR approach integrates WBMs to select input factors, sub-process control equations to quantify the contributions of input factors, empirical parameters to characterize catchment uniqueness, and SVR for water level modelling. New hydrological insights for the region: Physically guided WBM-SVR is more accurate than SVR in reconstructing monthly lake water levels. WBMs can quantify the hydrological process in the lake catchment area with efficient quasi-physical mechanisms and refine the input–output factors within lake hydrometeorology. The reconstructability, generalization capability, and transferability of WBM-SVR were validated for three different types of lakes (glacier-free inflow lake, glacier-free outflow lake, and glacier-fed inflow lake), and the reconstruction results indicate significant improvements in WBM-SVR compared with SVR. The WBM-SVR approach shows great promise for achieving monthly lake level reconstruction.

Keywords

• Data reconstruction method
• Theory-guided data science (TGDS)
• Machine learning (ML) model
• Conceptual hydrological model
• Lake level