This study aims to assess the results of runoff volume, peak flow and the lag time between peak rainfall and peak river discharge or peak flow when no proper land use management is done and another is when sound land use management is adopted. Hence, two (2) land cover/use scenarios were created. The first scenario is the Projected Land Cover. This scenario was created using a Trend Analysis function from MS Excel derived from the 1973, 1989, 1998, 2008 and 2010 land cover scenarios. The second scenario is the Desired Land Use wherein it makes use of slope as the basis in assigning the different land uses. Specifically, agriculture and built-up were assign to 0-18% slope, 18-30% slope for agroforestry, 30-50% for production forest and >50% for protection forest. Limitedly available LiDAR DEM strips was integrated into the IFSAR DEM to generate a detailed basin model and slope in GIS. The HEC-HMS was used for simulating runoff models. The Projected Land Cover has a higher total runoff volume and peak flow and shorter Lag time as compared to the Desired Land Use scenario in all the four (4) Rainfall Return Period these are 5 years, 25 years, 50 years and 100 years. The latter has twice as much forest vegetation that the projected land cover scenario, it has better forest cover quality and plus the presence of agroforestry. Such condition helps improve soil infiltration and thus reduces runoff volume and peak time. The study shows when land cover conditions are left by itself without any intervention, the impact of flood disaster is more likely to be magnified. The study also shows that flood disaster can be mitigated if the Desired Land Use scenario will be adopted as one of the course of action in flood disaster risk reduction management.
Alan E. Milano, Peter D. Suson, Stephanie Mae B. Salcedo, Jennifer G. Blasco, and Teresa T. Ignacio, "Land cover/use scenario building and its impact on runoff process inside the Iligan river basin," Proc. SPIE 9998, Remote Sensing for Agriculture, Ecosystems, and Hydrology XVIII, 999807 (Presented at SPIE Remote Sensing: September 26, 2016; Published: 25 October 2016); https://doi.org/10.1117/12.2241270.
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