Using GLEAM soil moisture to improve flash flood simulations at small to medium catchments with a two-layer rainfall-runoff model

Rainfall-runoff (RR) models play a critical role in water resource management and flood risk mitigations. Accurate depiction of soil moisture (SM) state in hydrological processes is very crucial for flash flood simulations with RR models. Satellite SM data offers a great opportunity to improve flood simulations by providing more accurate information about the SM state. However, how to make full use of satellite SM data to constrain flood model behavior is an important but tricky research issue, which is not fully solved. Here we propose a method to employ both satellite surface and root-zone soil moisture from the Global Land Evaporation Amsterdam Model (GLEAM) data to determine initial condition, a key parameter, using a two-layer RR model named as “MISDc-2L”. The flood simulations were performed at an hourly time step at small to medium catchments in China over 2010-2015. Results show that the MISDc-2L model satisfactorily simulates flash floods, and its performance varies with flood magnitude. Specifically, the model generally performs better for high-magnitude floods than medium and small ones. The GLEAM soil moisture data was found to be helpful to determine the initial conditions of the MISDc-2L model and thus substantially improved flood simulations. Furthermore, accounting for the different effects of surface SM and root-zone SM on the quantification of initial conditions clearly improves flood simulations. We conclude that satellite SM data is beneficial to flash flood simulations.