Assessing the capacity of large-scale hydrologic-hydrodynamic models for mapping flood hazard in southern Brazil

Abstract

Mapping flood risk areas is important for disaster management at the local, regional, and national scales. The aim of this study was to evaluate the ability of large-scale models to obtain flood hazard maps. The models were compared to the estimates developed by the Brazilian Geological Survey (CPRM) for different return periods (RP). The floods were evaluated for the municipalities of Uruguaiana, Montenegro and São Sebastião do Caí in the Rio Grande do Sul state. It was shown that the flood mapping generated by MGB covers larger areas (greater than 1000 km2; Siqueira et al. 2018), with a lower cost of obtaining for large scales. The - Hit Rate of the regional and continental MGB model versions with the CPRM maps ranged from about 40% to 90% in different cities, and the Hit Rate between the regional model and the CPRM map increased with the increased return period floods. The continental model compatibility was similar for all analyzed RPs. Our results suggest the agreement in terms of Hit Rate of current large-scale hydrological-hydrodynamic models to assess flood hazard.

Sub-seasonal streamflow forecasts for hydropower dams in the Brazilian Eletrical Interconnected System

“…The MGB-SA model has been calibrated with over 600 in situ stations and verified with various remote sensing products. The MGB-SA (Siqueira et al, 2018) model is essentially the same modelling framework in terms of code and complexity of other MGB model traditional applications for smaller regions such as basins or states (Alves et al, 2022;Föeger et al, 2022;Possa et al, 2022;Fan et al, 2021;Fleischmann et al, 2021). MGB-SA is a distinct from previous applications due to the decisions on the river-reach spatial representation level of detail adopted (drainage initiation areas thresholds of 1000km 2 and 15 km-long river segments) and that the model is the basis for a continentalscale research agenda on comparative hydrology, land use, climate change and forecasting studies (e.g.…”

Forecasting annual maximum water level for the Negro River at Manaus using dynamical seasonal predictions

“…Catchment-based hydrologic-hydrodynamic models use discretization of the river networks to successfully simulate water levels in the Amazon basin (Fan et al, 2021), even with limited data for the river features and geometry (Paiva et al, 2011). The water level forecasts can have meaningful skill for Amazon river flow, even for seasonal timescales, by assimilating in situ and radar altimetry data (Paiva et al, 2013); can be used to map the flood hazards, which compare well against CPRM estimates for different flood return periods (Alves et al, 2022). However, the hydrological models used over the Amazon basin have errors due to the precipitation forcing and the riverfloodplain parameters used (de Paiva et al, 2013); the uncertainty from the model initial conditions degrades river flow forecast skill at seasonal timescales (Paiva et al, 2012).…”

Mapeamento das áreas suscetíveis a inundações na bacia hidrográfica do rio Tejipió, em Pernambuco, Brasil

“…Ozturk et al (2021) definem inundação como um fenômeno natural que ocorre quando um fluxo d'água excede a capacidade de sua seção transversal, atingindo a planície de inundação ou área de várzea, em consequência do excesso de chuvas ou degelo. Esse tipo de desastre natural é o mais frequente no mundo e representa consideráveis riscos à vida da população, causando, na maioria das vezes, perdas humanas e patrimoniais (Amaral & Ross, 2020;Alves et al, 2022;Silva et al, 2020;Tiwari et al, 2020). Assim, para Alves et al (2022), o conhecimento da dinâmica e extensão das áreas inundáveis se torna essencial para gestores e tomadores de decisão no tocante à gestão eficiente desse desastre.…”

“…Esse tipo de desastre natural é o mais frequente no mundo e representa consideráveis riscos à vida da população, causando, na maioria das vezes, perdas humanas e patrimoniais (Amaral & Ross, 2020;Alves et al, 2022;Silva et al, 2020;Tiwari et al, 2020). Assim, para Alves et al (2022), o conhecimento da dinâmica e extensão das áreas inundáveis se torna essencial para gestores e tomadores de decisão no tocante à gestão eficiente desse desastre.…”