Hydrologic Modeling Analysis from Land Use Scenario Changes in Quebrada Seca and Bermudez Watershed

Authors

  • Matías Adrián Chaves Herrera School of Agricultural Engineering, University of Costa Rica.
  • Alejandra Rojas González School of Agricultural Engineering, University of Costa Rica.
  • José Pablo Rojas School of Agricultural Engineering, University of Costa Rica.
  • José Francisco Aguilar Pereira School of Agricultural Engineering, University of Costa Rica.

Abstract

During the last few years, the expansion of urban cover in the Quebrada Seca-Bermudez watershed has caused a series of floods that have damaged houses, bridges and other important infrastructure of the area. Hence local governments need a more precise description of these extreme rainfall events through reliable data and modeling. This study quantifies the discharge at several points in the Bermudez´s River watershed, based on 3 different storm durations and five different scenarios: three scenarios from previous years (2001, 2008 and 2012) and 2 forecasted scenarios for the year 2020 (one according to the projected urban growth and the other one based on local urban regulations). Land cover variations were determined using Lansat 7 ETM+ images. Both supervised and unsupervised classifications were applied to the satellite images and 6 common classes were obtained: forest, crops, pasture, urban, bare soil and industrial. The Curve Number was assigned based on this information and the soil data with a 1:20 000 scale resolution. A digital elevation model (DEM) with a 30 meters resolution was used to calculate the watershed parameters. Rainfall data over a period of almost 15 years from three meteorological stations were analyzed in order to obtain 2-, 5-, 10- and 25-year return periods. Discharge for all the scenarios was calculated with HEC-HMS program in order to evaluate the changes of urban growth. The results showed a rate of impervious cover of 27% for scenario 1 and 55% for scenario 2. The flow discharge increase for the year 2020 is expected to be between 1% to 14.9% for scenario 1.

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Section

World Conference on Computers in Agriculture, San Jose, Costa Rica, 2014