Evaluating the satellite-derived DEM accuracy with rain-on-grid modeling for flood hydrograph prediction of Katulampa Watershed, Indonesia

References

• Azizian, A., and Brocca, L., 2020. Determining the best remotely sensed DEM for flood inundation mapping in data sparse regions. International Journal of Remote Sensing, 41 (5), 1884–1906.
   Web of Science ®Google Scholar
• Badan Informasi, Geospasial, 2018. DEMNAS: Seamless Digital Elevation Model (DEM) and Batimetri Nasional [online]. Available from: https://tanahair.indonesia.go.id/demnas/#/ [Accessed: February 2023].
   Google Scholar
• Badan Nasional Penanggulangan Bencana, 2018. Data bencana Indonesia 2017. Jakarta: Pusat Data, Informasi dan Humas. ISBN: 978-602-5693-04-5.
   Google Scholar
• Beven, K.J., et al., 2018. Epistemic uncertainties and natural hazard risk assessment – part 1: A review of different natural hazard areas. Natural Hazards and Earth System Sciences, 18 (10), 2741–2768.
   Web of Science ®Google Scholar
• Bhola, P.K., Leandro, J., and Disse, M., 2019. Reducing uncertainties in flood inundation outputs of a two-dimensional hydrodynamic model by constraining roughness. Natural Hazards and Earth System Sciences, 19 (7), 1445–1457.
   Web of Science ®Google Scholar
• Chow, V.T., 1959. Open channel hydraulics. In: Open channel hydraulics. New York, NY: McGraw-Hill.
   Google Scholar
• Chymyrov, A., 2021. Comparison of different DEMs for hydrological studies in the mountainous areas. The Egyptian Journal of Remote Sensing and Space Science, 24, 587–594.
   Web of Science ®Google Scholar
• Consortium for Spatial Information (CGIAR-CSI), 2004. SRTM 90 m DEM Digital Elevation Database [online]. Available from: https://srtm.csi.cgiar.org/ [Accessed February 2023].
   Google Scholar
• Costabile, P., et al., 2020. Performances of the new HEC-RAS version 5 for 2-D hydrodynamic-based rainfall-runoff simulations at basin scale: comparison with a state-of-the art model. Water, 12 (9), 2326.
   Web of Science ®Google Scholar
• David, A., and Schmalz, B., 2020. Flood hazard analysis in small catchments: comparison of hydrological and hydrodynamic approaches by the use of direct rainfall. Journal of Flood Risk Management, 13 (4), 1–26.
   Web of Science ®Google Scholar
• German Aerospace Centre (DLR), 2006. TanDEM: Science Service System [online]. Available from: https://tandemx-science.dlr.de/ [Accessed February 2023].
   Google Scholar
• Ginting, S., 2022. Analisis hidrologi untuk perencanaan sistem polder di DKI Jakarta. Bentang: Jurnal Teoritis dan Terapan Bidang Rekayasa Sipil, 10 (1), 29–38.
   Google Scholar
• Ginting, B.M., and Mundani, R.-P., 2019. Parallel flood simulations for wet–dry problems using dynamic load balancing concept. Journal of Computing in Civil Engineering, 33 (3), 1–18.
   Web of Science ®Google Scholar
• Godara, N., Bruland, O., and Alfredsen, K., 2023. Simulation of flash flood peaks in a small and steep catchment using rain-on-grid technique. Journal of Flood Risk Management, 16 (3), e12898.
   Google Scholar
• Hall, J., 2015. Direct rainfall flood modelling: The good, the bad and the ugly. Australian Journal of Water Resources, 19 (1), 74–85.
   Web of Science ®Google Scholar
• Hariri, S., et al., 2022. A balanced watershed decomposition method for rain-on-grid simulations in HEC-RAS. Journal of Hydroinformatics, 24 (2), 315–332.
   Web of Science ®Google Scholar
• Hope, A.S. and Schulze, R.E., 1982. Improved estimates of stormflow volumes using the SCS curve number method. In: V. P. Singh, ed. Rainfall-runoff relationships. Littleton: Water Resources, 419–431.
   Google Scholar
• The Japan Aerospace Exploration Agency (JAXA), 2006. ALOS: Advanced Land Observing Satellite [online]. Available from: https://www.eorc.jaxa.jp/ALOS/en/index_e.htm [Accessed: February 2023].
   Google Scholar
• Jarihani, A.A., et al., 2015. Satellite-derived digital elevation model (DEM) selection, preparation and correction for hydrodynamic modelling in large, low-gradient and data-sparse catchments. Journal of Hydrology, 524, 489–506.
   Web of Science ®Google Scholar
• Lyne, V., and Hollick, M., 1979. Stochastic time-variable rainfall-runoff modelling. In: Proceedings of the hydrology and water resources symposium. Perth: Institution of Engineers National Conference Publication. 89–92.
   Google Scholar
• Mireille, N.M., et al., 2019. Analysis of land use change and its impact on the hydrology of kakia and esamburmbur sub-watersheds of narok county, Kenya. Hydrology, 6 (4), 86.
   Web of Science ®Google Scholar
• Mishra, S.K., and Singh, V.P., 2006. A relook at NEH-4 curve number data and antecedent moisture condition criteria. Hydrological Processes, 20 (13), 2755–2768.
   Web of Science ®Google Scholar
• Munoth, P., and Goyal, R., 2019. Effects of DEM source, spatial resolution and drainage area threshold values on hydrological modeling. Water Resources Management, 33, 3303–3319.
   Web of Science ®Google Scholar
• Muthusamy, M., et al., 2021. Understanding the effects of digital elevation model resolution in urban fluvial flood modelling. Journal of Hydrology, 596, 126088.
   Web of Science ®Google Scholar
• NASA, 1997. Global Precipitation Measurement: TRMM [online]. Available from: https://gpm.nasa.gov/missions/trmm [Accessed: February 2023].
   Google Scholar
• Saksena, S., and Merwade, V., 2015. Incorporating the effect of DEM resolution and accuracy for improved flood inundation mapping. Journal of Hydrology, 530, 180–194.
   Web of Science ®Google Scholar
• Salami, W., et al., 2017. Runoff hydrographs using snyder and SCS synthetic unit hydrograph methods: A case study of south west Nigeria. Journal of Ecological Engineering, 18 (1), 25–34.
   Web of Science ®Google Scholar
• Schulze, R.E., 1982. The use of soil moisture budgeting to improve stormflow estimates by the SCS curve number method. Pietermaritzburg: University of Natal, Department of Agricultural Engineering, Report 15.
   Google Scholar
• Senjaya, T., et al., 2020. Application of TRMM in the hydrological analysis of upper bengawan solo river basin. Journal of the Civil Engineering Forum, 6 (3), 309.
   Google Scholar
• Shustikova, I., et al., 2019. Comparing 2D capabilities of HEC-RAS and LISFLOOD-FP on complex topography. Hydrological Sciences Journal, 64 (14), 1769–1782.
   Web of Science ®Google Scholar
• Singh, P.K., Mishra, S.K., and Jain, M.K., 2014. A review of the synthetic unit hydrograph: from the empirical UH to advanced geomorphological methods. Hydrological Sciences Journal, 59, 239–261.
   Web of Science ®Google Scholar
• Tesema, T.A., 2021. Impact of identical digital elevation model resolution and sources on morphometric parameters of tena watershed, Ethiopia. Heliyon, 7, 1–9.
   Web of Science ®Google Scholar
• Tügel, F., et al., 2022. Applicability of literature values for green–ampt parameters to account for infiltration in hydrodynamic rainfall–runoff simulations in ungauged basins. Environmental Modeling & Assessment, 27, 205–231.
   Web of Science ®Google Scholar
• Uuemaa, E., et al., 2020. Vertical accuracy of freely available global digital elevation models (ASTER, AW3D30, MERIT, TanDEM-X, SRTM, and NASADEM). Remote Sensing, 12 (21), 3482.
   Web of Science ®Google Scholar
• Yamazaki, D., 2019. MERIT-Hydro: global hydrography datasets [online]. Institute of Industrial Sciences, Tokyo University. Available from: http://hydro.iis.u-tokyo.ac.jp/~yamadai/MERIT_Hydro [Accessed: February 2023].
   Google Scholar
• Yamazaki, D., et al., 2019. MERIT hydro: A high-resolution global hydrography map based on latest topography dataset. Water Resources Research, 55 (6), 5053–5073.
   Web of Science ®Google Scholar
• Zeiger, S.J., and Hubbart, J.A., 2021. Measuring and modeling event-based environmental flows: An assessment of HEC-RAS 2D rain-on-grid simulations. Journal of Environmental Management, 285, 112125.
   PubMed Web of Science ®Google Scholar