1887

Abstract

Summary

Flooding events intensity and magnitude are expected due to climate change, increasing the probability of pluvial floods, with adverse social, economic and environmental impacts. Topographic-related indexes are important to identify morphological features. The Topographic Wetness Index (TWI) is one of such indexes that can help to identify water accumulation areas. However, TWI results depend on the digital elevation models resolution. This paper analyses TWI results obtained from two DEM with different resolutions (2.5m and 10m). The results of this work contribute to identifying with more accuracy the areas potentially affected by floods.

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/content/papers/10.3997/2214-4609.20215K2033
2021-11-17
2024-04-29
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References

  1. Ballerine, C.
    (2017). Topographic Wetness Index Urban Flooding Awareness Act Action Support. Will and DuPage Counties, Illinois. Prairie Research Institute. Contract Report 2017-02.
    [Google Scholar]
  2. Besnard, A.G., La Jeunesse, I., Pays, O., Secondi, J.
    (2013). Topographic wetness index predicts the occurrence of bird species in floodplains. DiversityDistrib, 19, 955–963. https://doir.org/10.1111/ddi.12047.
    https://doi.org/10.1111/ddi.12047 [Google Scholar]
  3. García-Rivero, A.E., Olivera, J., Sallinas, E., Yuli, R.A., Bulege, W.
    (2017). Use of Hydrogeomorphic Indexes in SAGA-GIS for the Characterization of Flooded Areas in Madre de Dios, Peru. Inter Jour Appl Enge Research, 12(19), 9078–9086.
    [Google Scholar]
  4. Hasan, A., Pilesjö, P., and Persson, A.
    (2012). On Generating Digital Elevation Models from LiDAR Data: Resolution versus Accuracy and Topographic Wetness Index Indices in Northern Peatlands, Taylor and Francis, London, UK.
    [Google Scholar]
  5. Mattivi, P., Franci, F., Lanbertini, A., Bitelli, G.
    (2019). TWI Computation: a comparison of different open source GISs. OGDSS 4. 6. https://doi.org/10.1186/s40965‑019‑0066‑y.
    https://doi.org/10.1186/s40965-019-0066-y [Google Scholar]
  6. Moore, I. D., Grayson, R. B., Ladson, A. R.
    (1991). Digital terrain modelling: A review of hydrological, geomorphological, and biological applications. Hydrol. Process., 5, 3–30, https://doi.org/10.1002/hyp.3360050103.
    https://doi.org/10.1002/hyp.3360050103 [Google Scholar]
  7. Pinto, L.V., Pereira, P., Gazdic, M., Ferreira, A., Ferreira, C.S.S.
    (2021). Effectiveness of NBS in storm water regulation within urban areas: Case study of Coimbra, Portugal. In FerreiraC.S.S, Kalantari, Z., Hartmann, T., Pereira, P. (ed.): Nature-Based Solutions for Flood Mitigation - Environmental and Socio-Economic Aspects. Springer NatureSwitzerland AG.
    [Google Scholar]
  8. Santos, M., Santos, J.A., Fragoso, M.
    (2017). Atmospheric driving mechanisms of flash floods in Portugal. International Journal of Climatology, 37, 671–680.
    [Google Scholar]
  9. Saulnier, G. M., Obled, C., Beven, K.
    (1997). Analytical compensation between DTM grid resolution and effective values of saturated hydraulic conductivity within the TOPMODEL framework. Hydrol. Process., 11, 1331–1346.
    [Google Scholar]
  10. Sørensen, R., Seibert, J.
    (2007). Effects of DEM resolution on the calculation of topographical indices: TWI and its components. J. Hydrol., 347, 79–89. http://doir.org/10.1016/j.jhydrol. 2007.09.001.
    https://doi.org/10.1016/j.jhydrol [Google Scholar]
  11. State of Illinois, Illinois Department of Natural Resources
    State of Illinois, Illinois Department of Natural Resources. (2015). Report for the Urban Flooding Awareness Act. https://www.dnr.illinois.gov/WaterResources/Documents/Final_UFAA_Report.pdf
    [Google Scholar]
  12. Wang, L., Liu, H.
    (2006). An efficient method for identifying and filling surface depressions in digital elevation models for hydrologic analysis and modelling. Int J Geogr Inf Sci., 20(2), 193–213.
    [Google Scholar]
  13. Wilson, J.P.
    (2018). Calculating land surface parameters. In: Environmental applications of digital terrain modeling, 53–149. https://doi.org/10.1002/9781118938188.ch3.
    https://doi.org/10.1002/9781118938188.ch3 [Google Scholar]
  14. Wolock, D.M., McCabe, G.J.
    (1995). Comparison of single and multiple flow direction algorithms for computing topographic parameters in TOPMODEL. Water Resources Research, 31(5), 1315–1324. http://onlinelibrary.wiley.com/doi/ 10.1029/95WR00471/full
    https://doi.org/10.1029/95WR00471/full [Google Scholar]
  15. Zhao, L., Liu, Y., Luo, Y.
    (2020). Assessing hydrological connectivity mitigated by reservoirs, vegetation cover, and climate in Yan River watershed on the Loess Plateau, Chine. The Network Approach. Water, 12, 1742. https://doir.org/10.3390/w12061742.
    https://doi.org/10.3390/w12061742 [Google Scholar]
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