TOPMODEL Hydrometeorological Modeling with Rain Gauge Data Integrated by High-Resolution Satellite Estimates. A Case Study in MuriaéRiver Basin, Brazil

This study consists of hydrological simulations of the Muriaé river watershed with the topography-based hydrological model (TOPMODEL) and available stream gauge and rain measurements between 2009 and 2013 for two subbasins, namely Carangola and Patrocínio do Muriaé. The simulations were carried out with the Climate Prediction Center morphing method (CMORPH) precipitation estimates and rain gauge measurements integrated into CM- ORPH by the Statistical Objective Analysis Scheme (SOAS). TOPMODEL calibration was performed with the shuffled complex evolution (SCE-UA) method with Nash-Sutcliffe efficiency (NSE). The best overall results were obtained with CMORPH (NSE ~ 0.6) for both subbasins. The simulations with SOAS resulted in an NSE ~ 0.2. However, in an analysis of days with high- level stages, SOAS simulations resulted in a better hit rate (23%) compared to CMORPH (10%). CMORPH simulations underestimated the flows at the flood periods, which indicates the importance to use multi-sensor precipitation data. The results with TOPMODEL allow an estimate of future discharges, which allows for better planning of a flood warning system and discharge measurement schedule.

Hydrometeorological Modeling of Limpopo River Basin in Mozambique with TOPMODEL and Remote Sensing

The Limpopo River basin (LRB) is known for its vulnerability to floods, high rates of evapotranspiration, and droughts that cause significant losses to the local community. The present study aimed to perform simulations of flood events occurring in two Mozambican sub-basins of LRB, namely Chókwè and Xai-Xai from 2000 to 2015 with TOPography-based hydrological MODEL (TOPMODEL) and satellite remote sensing data. As input in TOPMODEL, data from two high-resolution global satellite-based precipitation products: Climate Prediction Center MORPHing technique (CMORPH) and Integrated Multi-Satellite Retrievals for the Global Precipitation Mission (GPM) algorithm (IMERG), 8-day MOD16 evapotranspiration product and surface runoff data estimated by Global Land Data Assimilation System (GLDAS) were used. The sensitivity tests of TOPMODEL parameters were applied using the Monte Carlo simulation. Calibration and validation of the model were performed by the Shuffled Complex Evolution (SCE-UA) method and were evaluated with the Kling-Gupta Efficiency (KGE) index. The results indicated that simulations with the GPM-IMERG (KGE: 0.59 and 0.65) tended to underestimate the stream flows, while with the CMORPH product the performance was much better (KGE: 0.66 and 0.77) in both sub-basins. Thus, TOPMODEL can help to develop flood monitoring systems from satellite remotely sensed data in similar regions of Mozambique.