Utilizing Open Access Spatial Data for Flood Risk Mapping: A Case Study in the Upper Solo Watershed

Indonesia is experiencing a rise in natural disasters due to its geographical position within a tropical region, with the Upper Solo River watershed exhibiting a heightened risk of flooding. This region has already suffered numerous floods due to excessive precipitation and insufficient drainage. Susceptibility, hazard, and risk studies have been conducted to investigate this phenomenon but have been limited to specific regions within the catchment area. This study aims to construct a GIS-based flood risk model using Open-Access Spatial Data (OASD) based on diverse physical characteristics, urbanization levels, and population. We used several OASD, including SRTM, Sentinel 2 MSI, GPM v6, NASA-USDA Enhanced SMAP Global Soil Moisture Data, GHS-SMOD R2023A - Global Human Settlement Layers, and GHSL: Global Population Surfaces 1975-2030 (P2023A). The model integrates the risk parameters to identify flood risk using a weighted overlay in ArcGIS. The results demonstrate spatial heterogeneity in flood risk throughout the watershed. The result also reveals that Surakarta City, with a high proportion of its area in the 'High' (57.3%) and 'Very High' (29.54%) risk categories, is at the highest risk of flooding within the watershed. The study enhances understanding of this topic by comprehensively evaluating flood hazards, vulnerabilities, and risks. It highlights the significance of utilizing low-cost OASD to improve flood preparedness and response strategies.

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Publication Details

Journal
Geoplanning Journal of Geomatics and Planning
Published
2024-11-29
DOI
https://doi.org/10.14710/geoplanning.11.2.189-204
Citations
3
Primary Topic
Data Mining and Machine Learning Applications
Type
article
Field-Weighted Citation Impact
2.34

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article

Utilizing Open Access Spatial Data for Flood Risk Mapping: A Case Study in the Upper Solo Watershed

Choirul Amin, Vidya Nahdhiyatul Fikriyah, Farha Sattar, Muhammad Nawaz et al.
3 citations
Geoplanning Journal of Geomatics and Planning
Data Mining and Machine Learning Applications
2.34
article

Utilizing Open Access Spatial Data for Flood Risk Mapping: A Case Study in the Upper Solo Watershed

Choirul Amin, Vidya Nahdhiyatul Fikriyah, Farha Sattar, Muhammad Nawaz, Arif Rohman, Hamim Zaky Hadibasyir, ‪Kuswaji Dwi Priyono, Aynaz Lotfata, Efri Roziaty, Danardono Danardono, Heni Masruroh, Jumadi Jumadi
article en
3 citations

Abstract

Indonesia is experiencing a rise in natural disasters due to its geographical position within a tropical region, with the Upper Solo River watershed exhibiting a heightened risk of flooding. This region has already suffered numerous floods due to excessive precipitation and insufficient drainage. Susceptibility, hazard, and risk studies have been conducted to investigate this phenomenon but have been limited to specific regions within the catchment area. This study aims to construct a GIS-based flood risk model using Open-Access Spatial Data (OASD) based on diverse physical characteristics, urbanization levels, and population. We used several OASD, including SRTM, Sentinel 2 MSI, GPM v6, NASA-USDA Enhanced SMAP Global Soil Moisture Data, GHS-SMOD R2023A - Global Human Settlement Layers, and GHSL: Global Population Surfaces 1975-2030 (P2023A). The model integrates the risk parameters to identify flood risk using a weighted overlay in ArcGIS. The results demonstrate spatial heterogeneity in flood risk throughout the watershed. The result also reveals that Surakarta City, with a high proportion of its area in the 'High' (57.3%) and 'Very High' (29.54%) risk categories, is at the highest risk of flooding within the watershed. The study enhances understanding of this topic by comprehensively evaluating flood hazards, vulnerabilities, and risks. It highlights the significance of utilizing low-cost OASD to improve flood preparedness and response strategies.

Geoplanning Journal of Geomatics and PlanningVol. 11(2)
State University of Malang (ID), National University of Singapore (SG), Charles Darwin University (AU), Muhammadiyah University of Surakarta (ID), Sumatera Institute of Technology (ID), University of California, Davis (US), University of Twente (NL)
Direktorat Jenderal Pendidikan Tinggi
Sustainable cities and communities
Openalex Percentile: Top 9%
Data Mining and Machine Learning Applications
2.34
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