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

Abstract

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 highlevel 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.

Hydrometeorology of Anthropogenic Erosion in the Metropolitan Region of São Paulo

“…This research aims to estimate precipitation volumes and identify the precipitation systems associated with them. It also seeks to demonstrate the reliability of the Climate Prediction Center Morphing Method (CMORPH) and the Statistical Objective Analysis method (SOAS) to determine the occurrence of rainfall and its volumes more broadly [11], as the CMORPH data are measured worldwide by satellite and you have the data for all of Brazil. Precipitation volumes were obtained using data from CMORPH, which integrated with the telemetric network of surface rain gauges of the National Water Agency (NWA) by SOAS, resulting in more reliable data.…”

Modelagem de vazões máximas e mínimas com base em aprendizado de máquina

“…A dinâmica da vazão em uma bacia hidrográfica também é influenciada pelo uso e a cobertura da terra, com destaque aos processos de urbanização e extensa implementação de cultivos agrícolas (JANIZADEH et al, 2021;SILVA;ALENCAR;ALMEIDA, 2020). A intensificação desses fatores pode modificar significativamente a frequência e magnitude de eventos extremos na bacia (LIU et al, 2017;PEREIRA FILHO;VEMADO, 2021;XU et al, 2014).…”

“…As alterações na ocorrência dos eventos de secas e de enchentes precisam ser investigadas, a fim de subsidiar as tomadas de decisões quanto aos impactos ambientais e sociais decorrentes de suas ocorrências. A bacia hidrográfica do rio Muriaé, localizada nos Estados de Minas Gerais e Rio de Janeiro, tem sido afetada expressivamente por eventos de seca e enchente, que causaram e causam diversos impactos em ambos os Estados (SALVIANO; PEREIRA FILHO; VEMADO, 2021).…”