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Global Data for Watershed Modeling: The Case of Data Scarcity Areas

  • Abdelhamid FadilEmail author
  • Abdelali El Bouchti
Chapter
  • 274 Downloads
Part of the Advances in Science, Technology & Innovation book series (ASTI)

Abstract

Data availability is a main element in determining the watershed modeling success. This factor becomes more critical in the case of using spatial models that require space-time distributed data. In developing countries, the implementation of such approaches is often hampered by data scarcity. To deal with this situation, the use of global data captured by earth observation satellites is considered as a major issue. This work aims to show the utility of using this type of information in data scarcity areas, especially for spatial modeling of large watersheds. To estimate the global data contribution, an analysis was performed by comparing them with local measured data. The study focuses on data representing the watershed state (morphological properties) and the input variables of hydrological models (climate data). The quality assessment of these data is calculated through statistical indicators. The comparison of global data grids with local observations shows the utility of using some of these grids for watershed modeling and especially for the state parameters such as topography. For climate parameters, the comparison is very appropriate for the minimum and maximum temperature, and moderate for the humidity and solar radiation but low or very low for rainfall and wind speed data. This work reveals that if global data derived from satellites are an alternative and very promising solution to overcome data scarcity in some areas, they still need to be enhanced to make them more efficient and accurate, especially for climate data.

Keywords

Global data Data scarcity Spatial data Climate data GIS Remote sensing Modeling Watershed Morocco 

References

  1. 1.
    Sivapalan M (2003) Process complexity at hillslope scale, process simplicity at the watershed scale: is there a connection? Hydrol Process 17:1037–1041.  https://doi.org/10.1002/hyp.5109CrossRefGoogle Scholar
  2. 2.
    Roche P-A, Miquel J, Gaume E (2012) Quantitative hydrology: processes, models and decision support (Hydrologie quantitative: Processus, modèles et aide à la décision). Springer, FranceGoogle Scholar
  3. 3.
    Hingray B, Picouet C, Musy A (2014) Hydrology: a science for engineers. CRC PressGoogle Scholar
  4. 4.
    Singh VP, Frevert DK (2006) Watershed models. Taylor and Francis Group, USAGoogle Scholar
  5. 5.
    Abbaspour KC, Vaghefi SA, Srinivasan R (2017) A guideline for successful calibration and uncertainty analysis for soil and water assessment: a review of papers from the 2016 international SWAT conference. Water (Switzerland) 10.  https://doi.org/10.3390/w10010006CrossRefGoogle Scholar
  6. 6.
    Musy A, Higy C (2004) Hydrology, A science of nature (Hydrologie, Une science de la nature). Presses polytechniques et universitaires romandesGoogle Scholar
  7. 7.
    Singh VP, Frevert DK (2002) Mathematical models of large watershed hydrology. Water Resource Publications, ColoradoGoogle Scholar
  8. 8.
    Chow VT, Maidment DR, Mays LW (1988) Applied hydrology. McGraw-Hill, USAGoogle Scholar
  9. 9.
    Singh VP (1995) Computer models of watershed hydrology. Water Resources Publications, Colorado, USAGoogle Scholar
  10. 10.
    Ambroise B (1998) Dynamics of the water cycle in a watershed (La Dynamique du cycle de l’eau dans un bassin versant). Edition HGA, Bucharest, RomaniaGoogle Scholar
  11. 11.
    Wheater H (2007) Hydrological modelling in arid and semi-arid areas—an introduction. In: Wheater H, Sorooshian S, Sharma KD (eds) Hydrological modelling in arid and semi-arid areas. Cambridge University Press, p 222Google Scholar
  12. 12.
    Jajarmizadeh M, Harun S, Salarpour M (2012) A review on theoretical consideration and types of models in hydrology. J Environ Sci Technol 5:249–261CrossRefGoogle Scholar
  13. 13.
    Gayathri KD, Ganasri BP, Dwarakish GS (2015) A review on hydrological models. Aquat Procedia 4:1001–1007.  https://doi.org/10.1016/j.aqpro.2015.02.126CrossRefGoogle Scholar
  14. 14.
    Lin S, Jing C, Chaplot V et al (2010) Effect of DEM resolution on SWAT outputs of runoff, sediment and nutrients. Hydrol Earth Syst Sci Discuss 7:4411–4435CrossRefGoogle Scholar
  15. 15.
    Pillot B, Muselli M, Poggi P et al (2016) Development and validation of a new efficient SRTM DEM-based horizon model combined with optimization and error prediction methods. Sol Energy 129:101–115.  https://doi.org/10.1016/j.solener.2016.01.058CrossRefGoogle Scholar
  16. 16.
    Rodríguez E, Morris CS, Belz JE et al (2005) An assessment of the SRTM topographic products, Technical report JPL D-31639. Jet Propulsion Laboratory, Pasadena, California, USAGoogle Scholar
  17. 17.
    Sharma A, Tiwari KN (2014) A comparative appraisal of hydrological behavior of SRTM DEM at catchment level. J Hydrol 519:1394–1404.  https://doi.org/10.1016/j.jhydrol.2014.08.062CrossRefGoogle Scholar
  18. 18.
    Gamache M (2004) Free and low cost data sets for international mountain cartography. Paper presented at the Workshop of the Commission on Mountain Cartography in the International Cartographic Association. Vall de Nuria, SpainGoogle Scholar
  19. 19.
    Hirt C, Filmer MS, Featherstone WE (2010) Comparison and validation of the recent freely-available ASTER- GDEM ver1, SRTM ver4. 1 and GEODATA DEM-9S ver3 digital elevation models over Australia. Aust J Earth Sci 57:337–347.  https://doi.org/10.1080/08120091003677553.ComparisonCrossRefGoogle Scholar
  20. 20.
    Team AGV (2009) ASTER global DEM validation summary report. METI & NASA, 28pGoogle Scholar
  21. 21.
    Nikolakopoulos KG, Kamaratakis EK, Chrysoulakis N (2006) SRTM vs ASTER elevation products. Comparison for two regions in Crete, Greece. Int J Remote Sens 27:4819–4838.  https://doi.org/10.1080/01431160600835853CrossRefGoogle Scholar
  22. 22.
    Kervyn M, Ernst GGJ, Goossens R, Jacobs P (2008) Mapping volcano topography with remote sensing: ASTER vs. SRTM. Int J Remote Sens 29:6515–6538.  https://doi.org/10.1080/01431160802167949CrossRefGoogle Scholar
  23. 23.
    Fujita K, Suzuki R, Nuimura T, Sakai A (2008) Performance of ASTER and SRTM DEMs, and their potential for assessing glacial lakes in the Lunana region, Bhutan Himalaya. J Glaciol 54:220–228.  https://doi.org/10.3189/002214308784886162CrossRefGoogle Scholar
  24. 24.
    Harcourt P (2015) Vertical accuracy assessment of SRTM3 V2. 1 and aster GDEM V2 using GPS control points for surveying & geo-informatics applications—case study of Rivers State, Nigeria 6:81–89Google Scholar
  25. 25.
    Elkhrachy I (2016) Vertical accuracy assessment for SRTM and ASTER digital elevation models: a case study of Najran city, Saudi Arabia. Ain Shams Eng J 2:1–11.  https://doi.org/10.1016/j.asej.2017.01.007CrossRefGoogle Scholar
  26. 26.
    Szabó G, Singh SK, Szabó S (2015) Slope angle and aspect as influencing factors on the accuracy of the SRTM and the ASTER GDEM databases. Phys Chem Earth 83–84:137–145.  https://doi.org/10.1016/j.pce.2015.06.003CrossRefGoogle Scholar
  27. 27.
    Su Y, Guo Q (2014) A practical method for SRTM DEM correction over vegetated mountain areas. ISPRS J Photogramm Remote Sens 87:216–228.  https://doi.org/10.1016/j.isprsjprs.2013.11.009CrossRefGoogle Scholar
  28. 28.
    Dembélé M, Zwart SJ (2016) Evaluation and comparison of satellite-based rainfall products in Burkina Faso, West Africa. Int J Remote Sens 37:3995–4014.  https://doi.org/10.1080/01431161.2016.1207258CrossRefGoogle Scholar
  29. 29.
    Huffman GJ, Bolvin DT, Nelkin EJ et al (2007) The TRMM Multisatellite Precipitation Analysis (TMPA): quasi-global, multiyear, combined-sensor precipitation estimates at fine scales. J Hydrometeorol 8:38–55.  https://doi.org/10.1175/JHM560.1CrossRefGoogle Scholar
  30. 30.
    Novella NS, Thiaw WM (2013) African rainfall climatology version 2 for famine early warning systems. J Appl Meteorol Climatol 52:588–606.  https://doi.org/10.1175/JAMC-D-11-0238.1CrossRefGoogle Scholar
  31. 31.
    Zhao L, Xia J, Xu C et al (2013) Evapotranspiration estimation methods in hydrological models. J Geogr Sci 23:359–369.  https://doi.org/10.1007/s11442-013-1015-9CrossRefGoogle Scholar
  32. 32.
    Fuka DR, Walter MT, Macalister C et al (2014) Using the climate forecast system reanalysis as weather input data for watershed models. Hydrol Process 28:5613–5623.  https://doi.org/10.1002/hyp.10073CrossRefGoogle Scholar
  33. 33.
    Seyoum S, MacAlister C, Fuka D (2011) Global climate data for local applications e.g. CFSR for SWATGoogle Scholar
  34. 34.
    Tohme RA, Holmberg SD, Bressmann T et al (2007) The NCEP climate forecast system reanalysis Suranjana. Japanese Account Today 2:1–146.  https://doi.org/10.1007/s13398-014-0173-7.2CrossRefGoogle Scholar
  35. 35.
    Yuan X, Wood EF, Luo L, Pan M (2011) A first look at Climate Forecast System version 2 (CFSv2) for hydrological seasonal prediction. Geophys Res Lett 38:1–7.  https://doi.org/10.1029/2011GL047792CrossRefGoogle Scholar
  36. 36.
    Gafurov A, Götzinger J, Bárdossy A (2006) Hydrological modelling for meso-scale catchments using globally available data. Hydrol Earth Syst Sci Discuss 3:2209–2242.  https://doi.org/10.5194/hessd-3-2209-2006CrossRefGoogle Scholar
  37. 37.
    Ha LT, Bastiaanssen WGM, van Griensven A et al (2018) Calibration of spatially distributed hydrological processes and model parameters in SWAT using remote sensing data and an auto-calibration procedure: a case study in a Vietnamese river basin. Water (Switzerland) 10.  https://doi.org/10.3390/w10020212CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Laboratory of Systems Engineering (LaGes)Hassania School of Public WorksCasablancaMorocco
  2. 2.Institute for Forecasting and Futuristics (I2F)CasablancaMorocco

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