Synergistic Effects of High‐Resolution Factors for Improving Soil Moisture Simulations Over China

Abstract

AbstractUnderstanding contributions of advanced land surface models and high‐resolution model inputs (e.g., meteorological forcings and soil parameters) to root‐zone soil moisture (RSM) simulations provides critical implications for both model and data development. Previous works have investigated influences of these factors separately, without considering the interdependence between multiple factors (e.g., positive impacts of high‐resolution forcings may be reduced by coarse‐resolution parameters). To date, how to quantify this interdependence and its relative importance remain to be investigated. Here, we propose a framework to quantify independent and interdependent/synergistic effects of forcings, parameters and models on high‐resolution RSM modeling using ensemble simulations. Forty‐eight RSM simulations with different high‐resolution factors superior in both spatial resolution and data accuracy are performed over China during 2013–2017, and observations from 1,553 stations across different climate zones are used to conduct evaluation. Results show that, the increase in Kling‐Gupta efficiencies (KGEs) after combining different high‐resolution factors are larger than the sum of that using individual factors. Such synergistic effects dominate the improvement of high‐resolution modeling at national and regional scales, and contribute to consistent improvements of simulations of RSM's mean state and variability. At station scale, although independent effect increases over western China, synergistic effect contributes 42%–60% to the improved KGEs over eastern China. The positive effects of an individual high‐resolution factor on RSM modeling could be reduced by 25%–80% without synergistic effects, indicating that the synergistic developments of models, meteorological forcings and soil parameters can facilitate high‐resolution RSM modeling more efficiently than only focusing on a single factor.