* Corresponding Author
Use of HAND Model for Estimating Flood-Prone in Serawai Basins Base on Remote Sensing and Sistem Information Geography
https://doi.org/10.22146/ijg.89225 Ajun Purwanto(1*), Dony Andrasmoro(2), Eviliyanto Eviliyanto(3)
(1) Department of Geography Education , IKIP PGRI Pontianak
(2) Department of Geography Education , IKIP PGRI Pontianak
(3) Department of Geography Education , IKIP PGRI Pontianak
(*) Corresponding Author
Abstract
A river basin's flood-prone mapping is essential for managing flood risks, developing mitigation plans, and developing flood forecasting and warning systems, among other things. This research uses the HAND model to estimate the level of flood-prone and its distribution in watersheds. The method used is survey and image interpretation. The data used is DEM imagery with a resolution of 10 meters. Data analysis uses spatial analysis, which includes elevation, hydrological analysis, fill, flow accumulation, flow direction, flow distance, and minus statistical analysis. The results showed that the Serawai watershed has five classes: very prone, prone, moderate, not prone, and very not prone. The very prone class has an area of 112,213.82 ha (65.41%), including Tontang, Sedaha, Nanga Serawai, Begori, Nanga Lekawai, Surga, Buntut Ponte, and Nanga Segulang village. The prone class has an area of 29,356.65 ha (17.14%), spread across the village of part of Beurgea, part of Nanga Segulang, Nanga Jelundung, and part of Tontang village. The moderate level has an area of 18,971.52 ha (11.08%), spread across Tontang, part of Nanga Jelundung, and part of Baras Nabun village. The area with a not-prone is 7,996.20 ha (4.67%), spread across Baras Nabun and parts of Nanga Jelundung village. For areas that are very not prone, they have an area of 3,004.20 (1.75%), spread over parts of the villages of Sedaha, parts of Baras Nabun, and Nanga Jelundung. Based on the research results, it can be concluded that the HAND Model is an effective and easy-to-use model for estimating flood-prone areas.
Keywords
Full Text:
PDFReferences
Alemayehu, Z. (2007). Modeling of Flood hazard management for forecasting and emergency response of ‘Koka’area within Awash River basin using remote sensing and GIS method. Addis Ababa University.
Althuwaynee, O. F., Pradhan, B., & Lee, S. (2012). Computers & Geosciences Application of an evidential belief function model in landslide susceptibility mapping. Computers and Geosciences, 44, 120–135. https://doi.org/10.1016/j.cageo.2012.03.003
Barnes, R., Lehman, C., & Mulla, D. (2014). An efficient assignment of drainage direction over flat surfaces in raster digital elevation models. Computers & Geosciences, 62, 128–135.
Basri, H., & Chandra, S. Y. (2021). Assessment of infiltration rate in the Lawe Menggamat Sub-Watershed, Aceh Province, Indonesia. IOP Conference Series: Earth and Environmental Science, 667(1), 12069.
Bui, D. T., Khosravi, K., Shahabi, H., & Daggupati, P. (2019). Flood Spatial Modeling in Northern Iran Using Remote Sensing and GIS : A Comparison between Evidential Belief Functions and Its Ensemble with a Multivariate Logistic Regression Model.
Burayu, D. G. (2022). Identification of Groundwater Potential Zones Using AHP, GIS and RS Integration: A Case Study of Didessa Sub-Basin, Western Ethiopia. Remote Sensing of Land, 6 (1), 1–15.
Cai, S., Fan, J., & Yang, W. (2021). Flooding Risk Assessment and Analysis Based on GIS and the TFN-AHP Method: A Case Study of Chongqing, China. Atmosphere, 12(5), 623.
Cardoso, C. A., Dias, H. C. T., Soares, C. P. B., & Martins, S. V. (2006). Caracterização morfométrica da bacia hidrográfica do rio Debossan, Nova Friburgo, RJ. Revista Árvore, 30, 241–248.
Cendrero, A., Remondo, J., Beylich, A. A., Cienciala, P., Forte, L. M., Golosov, V. N., Gusarov, A. V, Kijowska-Strugała, M., Laute, K., & Li, D. (2022). Denudation and geomorphic change in the Anthropocene; a global overview. Earth-Science Reviews, 233, 104186.
Choubin, B., Moradi, E., Golshan, M., Adamowski, J., Sajedi-Hosseini, F., & Mosavi, A. (2019). An ensemble prediction of flood susceptibility using multivariate discriminant analysis, classification and regression trees, and support vector machines. Science of the Total Environment, 651, 2087–2096.
Coumou, D., & Rahmstorf, S. (2012). A decade of weather extremes. Nature Climate Change, 2(7), 491–496.
Dantas, A. A. R., & Paz, A. R. (2021). Use of HAND terrain descriptor for estimating flood-prone areas in river basins. Brazilian Journal of Environmental Sciences (Online), 56(3), 501–516.
Desalegn, H., & Mulu, A. (2021). Flood vulnerability assessment using GIS at Fetam watershed, upper Abbay basin, Ethiopia. Heliyon, 7(1), e05865.
Ekmekcioğlu, Ö., Koc, K., & Özger, M. (2021). Stakeholder perceptions in flood risk assessment: A hybrid fuzzy AHP-TOPSIS approach for Istanbul, Turkey. International Journal of Disaster Risk Reduction, 60, 102327.
Fan, F. M., Collischonn, W., Sorribas, M. V., & Pontes, P. R. M. (2013). Sobre o início da rede de drenagem definida a partir dos modelos digitais de elevação. Rbrh: Revista Brasileira de Recursos Hídricos. Porto Alegre, RS. Vol. 18, n. 3 (Jul./Set. 2013), p. 241-257.
Forner, A., Vilana, R., Bianchi, L., Rodríguez-Lope, C., Reig, M., García-Criado, M. Á., Rimola, J., Solé, M., Ayuso, C., & Bru, C. (2015). Lack of arterial hypervascularity at contrast-enhanced ultrasound should not define the priority for diagnostic work-up of nodules< 2 cm. Journal of Hepatology, 62(1), 150–155.
Gentile, N., Lee, E. S., Osterhaus, W., Altomonte, S., Amorim, C. N. D., Ciampi, G., Garcia-Hansen, V., Maskarenj, M., Scorpio, M., & Sibilio, S. (2022). Evaluation of integrated daylighting and electric lighting design projects: Lessons learned from international case studies. Energy and Buildings, 268, 112191.
Geographic, N. (2019). Floods. Available online:https://www.nationalgeographic.com/environment/ natural-disasters/floods/.
Gigović, L., Pamučar, D., Bajić, Z., & Drobnjak, S. (2017). Application of GIS-interval rough AHP methodology for flood hazard mapping in urban areas. Water, 9(6), 360.
Goerl, R. F., Michel, G. P., & Kobiyama, M. (2017). Mapeamento de áreas susceptíveis a inundação com o modelo HAND e análise do seu desempenho em diferentes resoluções espaciais. Revista Brasileira de Cartografia, 69(1).
Gorczyca, G., Tylingo, R., Szweda, P., Augustin, E., Sadowska, M., & Milewski, S. (2014). Preparation and characterization of genipin cross-linked porous chitosan–collagen–gelatin scaffolds using chitosan–CO2 solution. Carbohydrate Polymers, 102, 901–911.
Grayson, R. B., & Ladson, A. R. (1991). DIGITAL TERRAIN MODELLING : A REVIEW OF HYDROLOGICAL , GEOMORPHOLOGICAL , AND BIOLOGICAL APPLICATIONS. 5(September 1990), 3–30.
Hagos, Y. G., Andualem, T. G., Yibeltal, M., & Mengie, M. A. (2022). Flood hazard assessment and mapping using GIS integrated with multi-criteria decision analysis in upper Awash River basin, Ethiopia. Applied Water Science, 12(7), 1–18.
Haq, M., Akhtar, M., Muhammad, S., Paras, S., & Rahmatullah, J. (2012). Techniques of remote sensing and GIS for flood monitoring and damage assessment: a case study of Sindh province, Pakistan. The Egyptian Journal of Remote Sensing and Space Science, 15(2), 135–141.
Hu, A., & Demir, I. (2021). Real-time flood mapping on client-side web systems using hand model. Hydrology. https://www.mdpi.com/1067816
Huong, H. T. L., & Pathirana, A. (2013). Urbanization and climate change impacts on future urban flooding in Can Tho city, Vietnam. Hydrology and Earth System Sciences, 17(1), 379.
Jenson, S. K., & Domingue, J. O. (1988). Extracting topographic structure from digital elevation data for geographic information system analysis. Photogrammetric Engineering and Remote Sensing, 54(11), 1593–1600.
Jha, A., Lamond, J., Bloch, R., Bhattacharya, N., Lopez, A., Papachristodoulou, N., Bird, A., Proverbs, D., Davies, J., & Barker, R. (2011). Five feet high and rising: cities and flooding in the 21st century. World Bank Policy Research Working Paper, 5648.
Kia, M. B., Pirasteh, S., Pradhan, B., Mahmud, A. R., Nor, W., Sulaiman, A., & Moradi, A. (2012). An artificial neural network model for flood simulation using GIS : Johor River Basin , Malaysia. 251–264. https://doi.org/10.1007/s12665-011-1504-z
Komolafe, A. A., Awe, B. S., Olorunfemi, I. E., & Oguntunde, P. G. (2020). Modelling flood-prone area and vulnerability using integration of multi-criteria analysis and HAND model in the Ogun River Basin, Nigeria. Hydrological Sciences Journal, 65(10), 1766–1783.
Kumar, A., Sharma, S., Goyal, N., Singh, A., Cheng, X., & Singh, P. (2021). Secure and energy-efficient smart building architecture with emerging technology IoT. Computer Communications, 176, 207–217.
Lindsay, J. B. (2016). The practice of DEM stream burning revisited. Earth Surface Processes and Landforms, 41(5), 658–668.
Liu, J., Gao, G., Wang, S., Jiao, L., Wu, X., & Fu, B. (2018). The effects of vegetation on runoff and soil loss: Multidimensional structure analysis and scale characteristics. Journal of Geographical Sciences, 28(1), 59–78.
Liu, Y., Cui, Z., Huang, Z., López-Vicente, M., & Wu, G.-L. (2019). Influence of soil moisture and plant roots on the soil infiltration capacity at different stages in arid grasslands of China. Catena, 182, 104147.
Mark, D. M. (1984). Part 4: mathematical, algorithmic and data structure issues: automated detection of drainage networks from digital elevation models. Cartographica: The International Journal for Geographic Information and Geovisualization, 21(2–3), 168–178.
Mehr, A. D., & Akdegirmen, O. (2021). Estimation of urban imperviousness and its impacts on flashfloods in Gazipaşa, Turkey. Knowledge-Based Engineering and Sciences, 2(1), 9–17.
Mojaddadi, H., Pradhan, B., Nampak, H., Ahmad, N., & Halim, A. (2017). Ensemble machine-learning-based geospatial approach for flood risk assessment using multi-sensor remote-sensing data and GIS. 5705(March). https://doi.org/10.1080/19475705.2017.1294113
Momo, M. R., Pinheiro, A., Severo, D. L., Cuartas, L. A., & Nobre, A. D. (2016). Desempenho do modelo HAND no mapeamento de áreas suscetíveis à inundação usando dados de alta resolução espacial. RBRH, 21, 200–208.
Mukherjee, F., & Singh, D. (2020). Detecting flood-prone areas in Harris County: A GIS-based analysis. GeoJournal. https://doi.org/10.1007/s10708-019-09984-2
Mumbai, G. (2020). Frequency ratio and fuzzy gamma operator models in GIS : a case study of Urban flood susceptibility zonation mapping using evidential belief function, frequency ratio, and fuzzy gamma operator models in GIS : a case study of Greater Mumbai, Maharashtra, Geocarto International, 0(0), 1–26. https://doi.org/10.1080/10106049.2020.1730448
Nahib, I., Ambarwulan, W., Rahadiati, A., Munajati, S. L., Prihanto, Y., Suryanta, J., Turmudi, T., & Nuswantoro, A. C. (2021). Assessment of the impacts of climate and LULC changes on the water yield in the Citarum River Basin, West Java Province, Indonesia. Sustainability, 13(7), 3919.
Nobre, A. D., Cuartas, L. A., Hodnett, M., Rennó, C. D., Rodrigues, G., Silveira, A., & Saleska, S. (2011). Height Above the Nearest Drainage–a hydrologically relevant new terrain model. Journal of Hydrology, 404(1–2), 13–29.
Ozkan, S. P., & Tarhan, C. (2016). Detection of flood hazard in urban areas using GIS: Izmir case. Procedia Technology, 22, 373–381.
Prokop, P., Wiejaczka, Ł., Sarkar, S., Bryndal, T., Bucała-Hrabia, A., Kroczak, R., Soja, R., & Płaczkowska, E. (2020). Morphological and sedimentological responses of small stream channels to extreme rainfall and land use in the Darjeeling Himalayas. Catena, 188, 104444.
Purwanto, A., & Andrasmoro, D. (2023). Identification of Groundwater Potential Zones Using Remote Sensing and GIS Technique: A Case Study of the Ketungau Basin in Sintang, West Kalimantan. The Indonesian Journal of Geography, 55(2), 221–234.
Purwanto, A., Rustam, R., Andrasmoro, D., & Eviliyanto, E. (2022). Flood Risk Mapping Using GIS and Multi-Criteria Analysis at Nanga Pinoh West Kalimantan Area. Indonesian Journal of Geography, 54(3).
Rączkowska, Z., Bucała-Hrabia, A., & Kędzia, S. (2024). Triggers of major floods and controls on their geomorphological effects in high-mountain streams (Tatra Mountains, Poland). CATENA, 239, 107933.
Rimba, A. B., Setiawati, M. D., Sambah, A. B., & Miura, F. (2017). Physical flood vulnerability mapping applying geospatial techniques in Okazaki City, Aichi Prefecture, Japan. Urban Science, 1(1), 7.
Rincón, D., Khan, U. T., & Armenakis, C. (2018). Flood risk mapping using GIS and multi-criteria analysis: A greater Toronto area case study. Geosciences, 8(8), 275.
Romanescu, G., Hapciuc, O. E., Minea, I., & Iosub, M. (2018). Flood vulnerability assessment in the mountain–plateau transition zone: a case study of Marginea village (Romania). Journal of Flood Risk Management, 11, S502–S513.
Rozalis, S., Morin, E., Yair, Y., & Price, C. (2010). Flash flood prediction using an uncalibrated hydrological model and radar rainfall data in a Mediterranean watershed under changing hydrological conditions. Journal of Hydrology, 394(1–2), 245–255.
Sahoo, S., Dhar, A., Debsarkar, A., & Kar, A. (2018). Impact of water demand on hydrological regime under climate and LULC change scenarios. Environmental Earth Sciences, 77(9), 341. https://doi.org/10.1007/s12665-018-7531-2
Sajil Kumar, P. J., Elango, L., & Schneider, M. (2022). GIS and AHP Based Groundwater Potential Zones Delineation in Chennai River Basin (CRB), India. Sustainability, 14(3), 1830.
Santos, E. D. S., Pinheiro, H. S. K., & Gallo Junior, H. (2021). Height above the nearest drainage to predict flooding areas in são Luiz do Paraitinga, são Paulo. Floresta e Ambiente, 28, e20200070.
Sarmah, T., Das, S., Narendr, A., & Aithal, B. H. (2020). Assessing human vulnerability to urban flood hazard using the analytic hierarchy process and geographic information system. International Journal of Disaster Risk Reduction, 50, 101659.
Silalahi, F. A., Zainabun, Z., & Basri, H. (2019). Kajian Sifat Fisika Tanah pada Lahan Budidaya Sub DAS Krueng Jreu Kabupaten Aceh Besar. Jurnal Ilmiah Mahasiswa Pertanian, 4(2), 457–463.
Singh, A. P., Arya, A. K., & Singh, D. Sen. (2020). Morphometric analysis of Ghaghara River Basin, India, using SRTM data and GIS. Journal of the Geological Society of India, 95(2), 169–178.
Skilodimou, H. D., Bathrellos, G. D., Chousianitis, K., Youssef, A. M., & Pradhan, B. (2019). Multi-hazard assessment modeling via multi-criteria analysis and GIS: a case study. Environmental Earth Sciences, 78(2), 47.
Speckhann, G. A., Chaffe, P. L. B., & ... (2018). Flood hazard mapping in Southern Brazil: a combination of flow frequency analysis and the HAND model. Hydrological …. https://doi.org/10.1080/02626667.2017.1409896
Sugianto, S., Deli, A., Miswar, E., Rusdi, M., & Irham, M. (2022). The effect of land use and land cover changes on flood occurrence in Teunom Watershed, Aceh Jaya. Land, 11(8), 1271.
Suryadi, U. E., & Riduansyah, B. (2021). LAJU INFILTRASI PADA BEBERAPA PENGGUNAAN LAHAN DI DESA PAK MAYAM KECAMATAN NGABANG KABUPATEN LANDAK. Jurnal Sains Mahasiswa Pertanian, 10(1).
Ullah, K., & Zhang, J. (2020). GIS-based flood hazard mapping using relative frequency ratio method: A case study of Panjkora River Basin, eastern Hindu Kush, Pakistan. Plos One, 15(3), e0229153.
Wondim, Y. K. (2016). Flood hazard and risk assessment using GIS and remote sensing in lower Awash sub-basin, Ethiopia. Journal of Environment and Earth Science, 6(9), 69–86.
Wu, T., Li, J., Li, T., Sivakumar, B., Zhang, G., & Wang, G. (2019). High-efficient extraction of drainage networks from digital elevation models constrained by enhanced flow enforcement from known river maps. Geomorphology, 340, 184–201.
Zhang, N., Luo, Y.-J., Chen, X.-Y., Li, Q., Jing, Y.-C., Wang, X., & Feng, C.-H. (2018). Understanding the effects of composition and configuration of land covers on surface runoff in a highly urbanized area. Ecological Engineering, 125, 11–25.
Zhou, Q., Su, J., Arnbjerg-Nielsen, K., Ren, Y., Luo, J., Ye, Z., & Feng, J. (2021). A GIS-Based Hydrological Modeling Approach for Rapid Urban Flood Hazard Assessment. Water, 13(11), 1483.
Zwenzner, H., & Voigt, S. (2009). Improved estimation of flood parameters by combining space-based SAR data with very high-resolution digital elevation data. Hydrology and Earth System Sciences, 13(5), 567–576.
DOI: https://doi.org/10.22146/ijg.89225
Article Metrics
Abstract views : 279
| 
views : 37
Refbacks
- There are currently no refbacks.
Copyright (c) 2024 Ajun Purwanto, Dony Andrasmoro, Eviliyanto Eviliyanto
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
Accredited Journal, Based on Decree of the Minister of Research, Technology and Higher Education, Republic of Indonesia Number 225/E/KPT/2022, Vol 54 No 1 the Year 2022 - Vol 58 No 2 the Year 2026 (accreditation certificate download)
ISSN 2354-9114 (online), ISSN 0024-9521 (print)