Height Above Nearest Drainage (HAND) as a Model for Rapid Flood Inundation Mapping Based on Remote Sensing and Geographic Information Systems in the Kapuas Sintang Sub Watershed


Ajun Purwanto , Paiman






Vol. 9 No. 8 (2023): August


Geographical information system, Height above nearest drainage, Inundation, Model, Rapid flood, Remote sensing

Research Articles


How to Cite

Purwanto, A. ., & Paiman. (2023). Height Above Nearest Drainage (HAND) as a Model for Rapid Flood Inundation Mapping Based on Remote Sensing and Geographic Information Systems in the Kapuas Sintang Sub Watershed . Jurnal Penelitian Pendidikan IPA, 9(8), 5899–5905. https://doi.org/10.29303/jppipa.v9i8.3037


Download data is not yet available.


Metrics Loading ...


This study aims to map the flood inundation and the extent of the inundation in the study area using the HAND model. The data used in this study is DEM. The DEM is used to generate a hydrologic framework, including flow accumulation, drainage network, flow direction, elevation, and flow distance. The method used in this study is the HAND descriptor. The analysis in this study used spatial hydrological analysis and hypsometric analysis using zonal statistical tables in ArcGIS. Based on the results of the analysis of height above the nearest drainage it is known that the Kapuas Sintang sub-watershed has five classes of inundation, namely very high inundation, high inundation, moderate inundation, low inundation, and no inundation. Very high, high, and moderate inundation classes are spread over three sub-districts, namely Sintang, Dedai, and Tempunak sections. Sintang District has the widest distribution, followed by Dedai District and Tempunak District is the narrowest. Prediction of inundation area and flood area with HAND can be used to improve the new mapping model without involving additional data sources. The HAND model is a nice and simple tool that is useful for inundation studies as well as in inundation area prediction.


Arrighi, C., Pregnolato, M., Dawson, R. J., & Castelli, F. (2019). Preparedness against mobility disruption by floods. Science of the Total Environment, 654, 1010–1022. https://doi.org/10.1016/j.scitotenv.2018.11.191

Bhatt, C. M., Rao, G. S., Diwakar, P. G., & Dadhwal, V. K. (2017). Development of flood inundation extent libraries over a range of potential flood levels: a practical framework for quick flood response. Geomatics, Natural Hazards and Risk, 8(2), 384–401. https://doi.org/10.1080/19475705.2016.1220025

Bhola, P. K., Leandro, J., & Disse, M. (2018). Framework for offline flood inundation forecasts for two-dimensional hydrodynamic models. Geosciences, 8(9), 346. https://doi.org/10.3390/geosciences8090346

Buto, S. G., & Anderson, R. D. (2020). NHDPlus high resolution (NHDPlus HR)---A hydrography framework for the nation. US Geological Survey. https://doi.org/10.3133/fs20203033

Cipta, H. (2021). Banjir Kabupaten Sintang. Retrieved from https://regional.kompas.com/read/2021/11/24/105938978/kondisi-terkini-di-sintang-banjir-berangsur-surut?page=all. Accessed 30 November 2022.

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. https://doi.org/10.5327/Z21769478892

Gianotti, A. G. S., Warner, B., & Milman, A. (2018). Flood concerns and impacts on rural landowners: An empirical study of the Deerfield watershed, MA (USA). Environmental Science & Policy, 79, 94–102. https://doi.org/10.1016/j.envsci.2017.10.007

Hlodversdottir, A. O., Bjornsson, B., Andradottir, H. O., Eliasson, J., & Crochet, P. (2015). Assessment of flood hazard in a combined sewer system in Reykjavik city centre. Water Science and Technology, 71(10), 1471–1477. https://doi.org/10.2166/wst.2015.119

Hu, A., & Demir, I. (2021). Real-time flood mapping on client-side web systems using hand model. Hydrology. https://doi.org/10.3390/hydrology8020065

Johnson, J. M., Munasinghe, D., Eyelade, D., & Cohen, S. (2019). An integrated evaluation of the national water model (NWM)–Height above nearest drainage (HAND) flood mapping methodology. Natural Hazards and Earth System Sciences, 19(11), 2405-2420. https://doi.org/10.5194/nhess-19-2405-2019

Lamichhane, N., & Sharma, S. (2017). Development of flood warning system and flood inundation mapping using field survey and LiDAR data for the Grand River near the city of Painesville, Ohio. Hydrology. https://doi.org/10.3390/hydrology4020024

Lee, J., Perera, D., Glickman, T., & Taing, L. (2020). Water-related disasters and their health impacts: A global review. Progress in Disaster Science, 8, 100123. https://doi.org/10.1016/j.pdisas.2020.100123

Li, Z., Mount, J., & Demir, I. (2020a). Evaluation of model parameters of HAND model for real-time flood inundation mapping: iowa case study. https://doi.org/10.31223/osf.io/hqpzg

Li, Z., Mount, J., & Demir, I. (2020b). Model Parameter Evaluation and Improvement for Real-Time Flood Inundation Mapping Using HAND Model: Iowa Case Study. Retrieved from https://eartharxiv.org/hqpzg/https://doi.org/10.31223/OSF.IO/HQPZG

Liu, Y. Y., Maidment, D. R., Tarboton, D. G., Zheng, X., & ... (2016). A CyberGIS approach to generating high-resolution height above nearest drainage (HAND) raster for national flood mapping. https://doi.org/10.13140/RG.2.2.24234.41925/1

McGrath, H., Bourgon, J. F., Proulx-Bourque, J. S., Nastev, M., & ... (2018). A comparison of simplified conceptual models for rapid web-based flood inundation mapping. Natural Hazards. https://doi.org/10.1007/s11069-018-3331-y

Morris, J., Beedell, J., & Hess, T. M. (2016). Mobilising flood risk management services from rural land: principles and practice. Journal of Flood Risk Management, 9(1), 50–68. https://doi.org/10.1111/jfr3.12110

Mosavi, A., Ozturk, P., & Chau, K. (2018). Flood prediction using machine learning models: Literature review. Water, 10(11), 1536. https://doi.org/10.3390/w10111536

Nobre, A. D., Cuartas, L. A., Momo, M. R., Severo, D. L., Pinheiro, A., & Nobre, C. A. (2016). HAND contour: a new proxy predictor of inundation extent. Hydrological Processes, 30(2), 320–333. https://doi.org/10.1002/hyp.10581

Rosser, J. F., Leibovici, D. G., & Jackson, M. J. (2017). Rapid flood inundation mapping using social media, remote sensing and topographic data. In Natural Hazards. Springer. https://doi.org/10.1007/s11069-017-2755-0

Sayama, T., Tatebe, Y., Iwami, Y., & Tanaka, S. (2015). Hydrologic sensitivity of flood runoff and inundation: 2011 Thailand floods in the Chao Phraya River basin. Natural Hazards and Earth System Sciences, 15(7), 1617–1630. https://doi.org/10.5194/nhess-15-1617-2015

Seo, B.-C., Keem, M., Hammond, R., Demir, I., & Krajewski, W. F. (2019). A pilot infrastructure for searching rainfall metadata and generating rainfall product using the big data of NEXRAD. Environmental Modelling & Software, 117, 69–75. https://doi.org/10.1016/j.envsoft.2019.03.008

Sermet, Y., & Demir, I. (2019a). Flood action VR: a virtual reality framework for disaster awareness and emergency response training. In ACM SIGGRAPH 2019 Posters (pp. 1–2). https://doi.org/10.1145/3306214.3338550

Sermet, Y., & Demir, I. (2019b). Towards an information centric flood ontology for information management and communication. Earth Science Informatics, 12(4), 541–551. https://doi.org/10.1007/s12145-019-00398-9

Sermet, Y., Demir, I., & Muste, M. (2020). A serious gaming framework for decision support on hydrological hazards. Science of The Total Environment, 728, 138895. https://doi.org/10.1016/j.scitotenv.2020.138895

Sermet, Y., Villanueva, P., Sit, M. A., & Demir, I. (2020). Crowdsourced approaches for stage measurements at ungauged locations using smartphones. Hydrological Sciences Journal, 65(5), 813–822. https://doi.org/10.1080/02626667.2019.1659508

Sholihah, Q., Kuncoro, W., Wahyuni, S., Suwandi, S. P., & Feditasari, E. D. (2020). The analysis of the causes of flood disasters and their impacts in the perspective of environmental The analysis of the causes of flood disasters and their impacts in the perspective of environmental law. Environmental Science Journal. https://doi.org/10.1088/1755-1315/437/1/012056

Singh, Y. K., Dutta, U., Prabhu, T. S. M., Prabu, I., Mhatre, J., Khare, M., Srivastava, S., & Dutta, S. (2017). Flood response system—A case study. Hydrology, 4(2), 30. https://doi.org/10.3390/hydrology4020030

Tadesse, Y. B., & Fröhle, P. (2020). Modelling of flood inundation due to levee breaches: sensitivity of flood inundation against breach process parameters. Water, 12(12), 3566. https://doi.org/10.3390/w12123566

Tariq, A., Yan, J., Ghaffar, B., Qin, S., Mousa, B. G., Sharifi, A., Huq, M. E., & Aslam, M. (2022). Flash Flood Susceptibility Assessment and Zonation by Integrating Analytic Hierarchy Process and Frequency Ratio Model with Diverse Spatial Data. In Water (Vol. 14, Issue 19). https://doi.org/10.3390/w14193069

Teng, J., Jakeman, A. J., Vaze, J., Croke, B. F. W., Dutta, D., & Kim, S. (2017). Flood inundation modelling: A review of methods, recent advances and uncertainty analysis. Environmental Modelling & Software, 90, 201–216. https://doi.org/10.1016/j.envsoft.2017.01.006

Tewari, A., Kshemkalyani, V., Kukreja, H., Menon, P., & Thomas, R. (2021, May). Application of LSTMs and HAND in rapid flood inundation mapping. In 2021 5th International Conference on Intelligent Computing and Control Systems (ICICCS) (pp. 515-521). IEEE. https://doi.org/10.1109/ICICCS51141.2021.9432332

Wuysang, J. H. (2022). Sepekan Banjir di Kabupaten Sintang. Retrieved from https://www.republika.co.id/berita/rjqk6y314/sepekan-banjir-kabupaten-sintang.

Xiang, Z., & Demir, I. (2020). Distributed long-term hourly streamflow predictions using deep learning–A case study for State of Iowa. Environmental Modelling & Software, 131, 104761. https://doi.org/10.1016/j.envsoft.2020.104761

Xu, H., Windsor, M., Muste, M., & Demir, I. (2020). A web-based decision support system for collaborative mitigation of multiple water-related hazards using serious gaming. Journal of Environmental Management, 255, 109887. https://doi.org/10.1016/j.jenvman.2019.109887

Yildirim, E., & Demir, I. (2019). An integrated web framework for HAZUS-MH flood loss estimation analysis. Natural Hazards, 99(1), 275–286. https://doi.org/10.1007/s11069-019-03738-6

Yildirim, E., & Demir, I. (2021). An integrated flood risk assessment and mitigation framework: A case study for middle Cedar River Basin, Iowa, US. International Journal of Disaster Risk Reduction, 56, 102113. https://doi.org/10.1016/j.ijdrr.2021.102113

Zhou, Q., Leng, G., Su, J., & Ren, Y. (2019). Comparison of urbanization and climate change impacts on urban flood volumes: Importance of urban planning and drainage adaptation. Science of the Total Environment, 658, 24–33. https://doi.org/10.1016/j.scitotenv.2018.12.184

Author Biographies

Ajun Purwanto, IKIP PGRI Pontianak

Paiman, IKIP PGRI Pontianak


Copyright (c) 2023 Ajun Purwanto, Paiman

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

Authors who publish with Jurnal Penelitian Pendidikan IPA, agree to the following terms:

  1. Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution 4.0 International License (CC-BY License). This license allows authors to use all articles, data sets, graphics, and appendices in data mining applications, search engines, web sites, blogs, and other platforms by providing an appropriate reference. The journal allows the author(s) to hold the copyright without restrictions and will retain publishing rights without restrictions.
  2. Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in Jurnal Penelitian Pendidikan IPA.
  3. Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).