25 October 2016 Impact of dynamically changing land cover on runoff process: the case of Iligan river basin
Author Affiliations +
Iligan river basin located in Northern Mindanao, Philippines covers 165.7 km2 of basin area. In December 2011, tropical storm Sendong (Washi) hit Iligan City, leaving a trail of wrecked infrastructures and about 490 persons reported dead. What transpired was a wake up call to mitigate future flood disasters. Fundamental to mitigation is understanding runoff behavior inside a basin considering that this is the main source of flooding. For this reason, the present study evaluated total runoff volume, peak discharge and lag time given land cover scenarios in four different years- 1973, 1989, 1998 and 2008. IFSAR and LIDAR DEM were integrated to generate the basin model in ArcGIS. HEC-HMS was used in simulating models for each scenario with Soil Conservation Service Curve Number (SCS CN) as the loss parameter method. Four simulation models of the runoff with varying CN values were established using RIDF as rainfall input with 5 year, 10 year, 25 year, 50 year and 100 year Rainfall Return Period (RRP). Total Runoff volume, peak discharge and lag time were progressively higher from 1973 to 2008 with 1989 land cover as exception where runoff parameters was its lowest. The total runoff volume, peak discharge and lag time is governed by vegetation type. When vegetation is characterized predominantly with woody perennials, runoff volume and peak time is lower. Conversely, when the presence of woody perennials is minimal, these parameters are higher. This study shows that an important way to mitigate flooding is to reduce surface runoff by maintaining vegetation predominantly composed of woody perennials.
Conference Presentation
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Stephanie Mae B. Salcedo, Stephanie Mae B. Salcedo, Peter D. Suson, Peter D. Suson, Alan E. Milano, Alan E. Milano, Ma. Teresa T. Ignacio, Ma. Teresa T. Ignacio, } "Impact of dynamically changing land cover on runoff process: the case of Iligan river basin", Proc. SPIE 9998, Remote Sensing for Agriculture, Ecosystems, and Hydrology XVIII, 99980V (25 October 2016); doi: 10.1117/12.2241273; https://doi.org/10.1117/12.2241273

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