Tác giả

Do Xuan Hong 1 , Le Hoang Tu 2 , Nguyen Quang Binh 3 , Le Manh Hung 4,5 , Pham Thanh Hung 3

Đơn vị công tác

1 Faculty of Environment and Natural Resources, Nong Lam University – Ho Chi Minh City, Ho Chi Minh City, Vietnam; doxuanhong@hcmuaf.edu.vn
2 Research Center for Climate Change, Nong Lam University – Ho Chi Minh City, Ho Chi Minh City, Vietnam; tu.lehoang@hcmuaf.edu.vn
3 Faculty of Water Resources Engineering, The University of Danang - University of Science and Technology, Danang City, Vietnam; nqbinh@dut.udn.vn; pthung@dut.udn.vn
4 National Center for Water Resources Planning and Investigation, Hanoi, Vietnam; manhhung.le510@gmail.com
5 Department of Engineering Systems and Environment, University of Virginia, Charlottesville VA, USA
*Corresponding author: doxuanhong@hcmuaf.edu.vn; Tel.: +84–907433031

Tóm tắt

In recent years, new advances in remote sensing techniques have made Digital Elevation Models (DEMs) become popular elevation data sources for delineating catchment boundaries. This application of DEMs is particularly useful in water accounting and river basin management for Vietnam, of which the river network has very high drainage density and has been facing many pressures arising from recent economic advances. However, catchment delineated from DEMs is highly dependable to the quality of original data sources, leading to potential discrepancy in the shape as well as catchment area of the boundaries delineated from different DEMs over specific locations in Vietnam. This study comprehensively investigates this issue by analyzing the differences across catchment boundaries delineated from the most popular DEMs (i.e., HydroSHEDS, MERIT, and TanDEM–X). The impacts of these discrepancies (due to using different DEMs) on identifying areal rainfall from a gridded data product are assessed to highlight the importance of selecting DEM data sources that are suitable for specific study area.

Từ khóa

DEM; River basin; Water resources; Remote sensing; Catchment delineation

Trích dẫn bài báo

Hong, D.X.; Tu, L.H.; Binh, N.Q.; Hung, L.M.; Hung, P.T. The impact of digital elevation model data sources on identifying catchment boundary in Vietnam. Tạp chí Khí tượng Thủy văn 2022, EME4, 262-271.

Tài liệu tham khảo

1. Campbell, J.B.; Wynne, R.H. Introduction to remote sensing. Guilford Press, 2011.
2. Zhang, H.; Li, Z.; Saifullah, M.; Li, Q.; Li, X. Impact of DEM resolution and spatial scale: analysis of influence factors and parameters on physically based distributedmodel. Adv. Met. 2016, 8582041. https://doi.org/10.1155/2016/8582041.
3. Pham, H.T.; Marshall, L.; Johnson, F.; Sharma, A. A method for combining SRTM DEM and ASTER GDEM2 to improve topography estimation in regions without reference data. Remote Sens. Environ. 2018, 210, 229–241.
4. Smith, M.B.; Seo, D.J.; Koren, V.I. The distributed model intercomparison project (DMIP): motivation and experiment design. J. Hydro. 2004, 298(1–4), 4–26.
5. Beven, K. Changing ideas in hydrology–the case of physically–based models. J. Hydro. 1989, 105(1–2), 157–172.
6. Freeman, T.G. Calculating catchment area with divergent flow based on a regular grid. Comput. Geosci., 1991, 17(3), 413–422.
7. Le, H.M.; Corzo, G.; Medina, V.; Mercado, V.D.; Nguyen, B.L.; Solomatine, D.P. A Comparison of Spatio–Temporal Scale Between Multiscalar Drought Indices in the South Central Region of Vietnam. Spatiotemporal Analysis of Extreme Hydrological Events, 2019, 143–169.

8. Do, H.X.; Gudmundsson, L.; Leonard, M.; Westra, S. The Global Streamflow Indices and Metadata Archive (GSIM) – Part 1: The production of daily streamflow archive and metadata, Earth Syst. Sci. Data 2018, 10, 765–785. https://doi.org/10.5194/essd- 10-765-2018.
9. Garbrecht, J.; Martz, L.W. Digital elevation model issues in water resources modeling. In Hydrologic and hydraulic modeling support with geographic information systems, ESRI Press, Redlands, 2000, 1–28.
10. Li, J.; Wong, D.W. Effects of DEM sources on hydrologic applications. Comput. Environ. Urban Syst. 2010, 34(3), 251–261.
11. Lehner, B.; Verdin, K.; Jarvis, A. HydroSHEDS Technical Documentation, version 1.0. World Wildlife Fund US, Washington, DC, 2006, 1–27.
12. Farr, T.G.; Kobrick, M. Shuttle radar Topography mission produces a wealth of data eos. Trans. Am. Geophys. Union. 2000, 81, 583–585.
13. Yamazaki, D.; Ikeshima, Tawatari, D.; Yamaguchi, R.; O'Loughlin, T.; Neal, F.; Sampson, J.C.; Kanae, C.C.; Bates, S.P.D. A high accuracy map of global terrain elevations. Geophys. Res. Lett. 2017, 44, 5844–5853. doi: 10.1002/2017GL072874.
14. Yamazaki, D.; Ikeshima, D.; Sosa, J.; Bates, P.D.; Allen, G.H.; Pavelsky, T.M. MERIT Hydro: A High–Resolution Global Hydrography Map Based on Latest Topography Dataset. Water Resour. Res. 2019, 55(6), 5053–5073. https://doi.org/10.1029/2019WR024873.
15. Xu, F.; Dong, G.; Wang, Q.; Liu, L.; Yu, W.; Men, C.; Liu, R. Impacts of DEM uncertainties on critical source areas identification for non–point source pollution control based on SWAT model. J. Hydrol. 2016, 540, 355–367. https://doi.org/10.1016/j.jhydrol.2016.06.019.
16. Hawker, L.; Neal, J.; Bates, P. Accuracy assessment of the TanDEM–X 90 Digital Elevation Model for selected floodplain sites. Remote Sens. Environ., 2019, 232. https://doi.org/10.1016/j.rse.2019.111319
17. Nguyen–Xuan, T.; Ngo Duc, T.; Kamimera, H.; Trinh–Tuan, L.; Matsumoto, J.; Inoue, T.; Phan–Van, T. The Vietnam Gridded Precipitation (VnGP) dataset: Construction and validation. SOLA 2016, 12, 291−296. https://doi.org/10.2151/sola.2016-057.
18. Winchell, M.; Srinivasan, R.; Di Luzio, M.; Arnold, J. ARCSWAT 2.3. 4 interface for SWAT2005: User’s guide. Blackland Research Center: Texas Agricultural Experiment Station, Temple, 2009, pp. 465.

19. William, K.; Saunders, B.; David, R. A GIS assessment of nonpoint source Pollution in the San Antonio–Nueces Coastal Basin. Center for Research in Water Resources, The University of Texas at Austin, 1996.