GIS Modelling for Assessing Flood Risk: A Case Study in Sumbermanjing Wetan District, Malang Regency, Indonesia - Listyo Yudha Irawan; Muhammad Nurul Huda; Irfan Helmi Pradana; Mohammad Tahir Mapa (Jurnal Pengelolaan Sumberdaya Alam dan Lingkungan (Journal of Natural Resources and Environmental Management), 2026)
Flood hazard in rural Indonesia is a constant issue, yet detailed risk assessments at the sub-district level are still lacking. This study creates a GIS-based multi-index framework to assess flood risk in Sumbermanjing Wetan District, Malang Regency. It combines hazard, vulnerability, and capacity dimensions using spatial and socioeconomic data. The hazard assessment looks at land systemcharacteristics, average rainfall over the past decade, land cover, and topographic features from a 12-meter resolution ALOS PALSAR DEM. Vulnerability and capacity are based on demographic and institutional indicators. We normalized variables and combined them into composite indices, resulting in a flood risk map for the entire district. Low hazard conditions cover 62.00% of the study area. Most of the vulnerability is moderate (68.48%), while 28.24% of the area is classified as high vulnerability. Medium capacity levels account for 64.30%, but they are unevenly spread throughout the region. Additionally, 32.11% of the district falls into the high-risk category. The highest-risk zones are found in four villages: Argotirto, Harjokuncaran, Tegalrejo, and Sitiarjo. The results indicate that socioeconomic factors and limited options for adaptation, rather than the severity of physical hazards, drive this risk. Therefore, efforts to reduce flooding in rural areas should focus on building social resilience rather than solely relying on physical infrastructure. Immediate steps in these four villages should include improving healthcare access, increasing emergency preparedness, and creating alternative income sources. Even though this study does not validate hydrodynamic modeling, the framework provides practical and clear guidance for prioritizing disaster management in data-limited areas.