Modelagem hidrológica de eventos de enchentes na bacia do rio Ibirapuitã, Alegrete-RS: simulações com o modelo HEC-HMS

Abstract

This study addresses the issue of floods in Alegrete-RS, a city susceptible to extreme weather events resulting from heavy precipitation, due to its location in a flood-prone region. To monitor river levels and flows, satellite-based tracking techniques and data from relevant entities are proposed. In order to verify these events, a simulation is proposed to analyze floods, using climate indices and modeling with HEC-HMS software, covering a 40-year period from 1982 to 2021. The methodology includes the use of SCS models like the CN to estimate excess precipitation, and the unit hydrograph model to calculate flood hydrographs. Exploring various aspects within the Rio Ibirapuitã basin involves using point data from ANA stations, grid data from BR-DWGD, as well as different soil types, land use, and terrain characteristics. This understanding of hydrological dynamics in the region provides essential insights for sustainable water resource planning and management through effective public policies, incorporating mitigation practices and adaptation to global climate change. Simulations were conducted for the entire historical analysis period, categorizing flows by date of occurrence of major floods, with temporal variations of two, four, and six months. The implemented climatology highlights different precipitation regimes, with an average precipitation threshold of 166.3 mm over the 40-year analysis period, potentially leading to increased river levels after runoff. Another factor examined is soil infiltration, which varies depending on the method used; SCS determines that temporal variation should be short for better modeling. The results presented corroborate that simulations for the years 1991, 1992, and 1998 show better NS and R2 indices, differing from 2009 and 2019, which exhibit satisfactory R2 values but disparate NS results. Thus, each event occurs differently, varying with rainfall patterns within each time interval in the basin region.