Improvement and Evaluation of CLM5 Application in the Songhua River Basin Based on CaMa-Flood

Abstract

This paper optimized the hydrological postprocessing of CLM5 using CaMa-Flood, combining multi-source meteorological forcing datasets and a dynamically changing surface dataset containing 16 PFTs (plant functional types) to simulate the high-resolution runoff process in the SRB from 1996 to 2014, specifically by integrating discharge with flooded area. Additionally, we evaluated the spatiotemporal variations of precipitation data from meteorological forcing datasets and discharge to validate the accuracy of model improvements. Both the discharge and the flooded area simulated by the coupled model exhibit pronounced seasonality, accurately capturing the discharge increase during the warm season and the river recession process in the cold season, along with corresponding changes in the flooded area. This highlights the model’s capability for hydrological process monitoring. The simulated discharge shows a high correlation coefficient (0.65–0.80) with the observed discharge in the SRB, reaching a significance level of 0.01, and the Nash–Sutcliffe efficiency ranges from 0.66 to 0.78. Leveraging the offline coupling of CLM and CaMa-Flood, we present a method with a robust physical mechanism for monitoring and providing a more intuitive representation of hydrological events in the SRB.