MERIT DEM Performs Better for Hydrodynamic Flood Model in Amazon Basin

Abstract:

Continental-scale Hydrodynamic flood modelling is essential to understand the global hydrological cycle and other interdisciplinary research work. Digital Elevation Model (DEM) is one of the primary inputs for physical-based river flood model and can affect the modelling accuracy to a large extent. Understanding "one DEM is better than the other, considering multiple uncertainties in flood simulations" can be evaluated using Hydrodynamic flood model. This insight would guide towards accurately predicting the flood by reducing the errors. MERIT DEM is a high accuracy DEM developed by removing the absolute bias, stripe noise, speckle noise, and tree height bias using multiple satellite data sets and filtering technique. Comparison of MERIT DEM with SRTM DEM explicitly using Flood Hydrodynamic Model is needed to know the improvement in flood forecast accuracy due to MERIT DEM.

Comparison of multiple variables for the best parameter set is needed to evaluate the performance of the two DEMs. Catchment-based Macro-scale Floodplain (CaMa-Flood) over Amazon basin evaluated with the best set of parameters for respective DEMs for Amazon basin. Discharge, Water Surface Elevation (WSE) and Flooded Area output from the CaMa-Flood using two DEMs compared to see the overall performance of the Model for individual DEMs. CaMa-Flood performs better at 70-80% of the observation locations for discharge using MERIT DEM compared to SRTM DEM in terms of Nash–Sutcliffe efficiency (NSE). Similarly, WSE and Flooded area in terms of Root Mean Square Error (RMSE) and Flood extent based on performance index predicted more accurately using MERIT DEM. The CaMa-Flood perform accurately for all the variables at a time using MERIT DEM whereas the same is not valid for SRTM DEM. The overall result shows the significant improvement in the performance of Hydrodynamic Flood Model using the MERIT DEM in Amazon basin.