(2019). Using GIS Tools and Data for Hydrological Modelling in Arid Areas: A Literature Review. The Egyptian Journal of Environmental Change, 11(1), 5-27. doi: 10.21608/ejec.2019.96533
. "Using GIS Tools and Data for Hydrological Modelling in Arid Areas: A Literature Review". The Egyptian Journal of Environmental Change, 11, 1, 2019, 5-27. doi: 10.21608/ejec.2019.96533
(2019). 'Using GIS Tools and Data for Hydrological Modelling in Arid Areas: A Literature Review', The Egyptian Journal of Environmental Change, 11(1), pp. 5-27. doi: 10.21608/ejec.2019.96533
Using GIS Tools and Data for Hydrological Modelling in Arid Areas: A Literature Review. The Egyptian Journal of Environmental Change, 2019; 11(1): 5-27. doi: 10.21608/ejec.2019.96533
Using GIS Tools and Data for Hydrological Modelling in Arid Areas: A Literature Review
This study reviews a number of hydrological modelling studies in arid areas, including the use of spatial analysis in geographical information systems (GIS) and remote sensing (RS) packages. Specifically, it aims to review the most important GIS tools and spatial data used in modeling processes. We discuss the use of digital elevation model data, land-use/cover data, soil data, and rainfall data in terms of their impacts on applications of hydrological modelling. The review concentrates on commonly used tools, e.g., the soil and water assessment tool, the kinematic runoff and erosion model, the identification of unit hydrograph and component flows from rainfall, the evaporation and stream flow model, and the Hydrologic Engineering Center-Hydrologic Modelling System. The runoff curve number method is illustrated as an example of methods that are utilized in GIS-based hydrological models. Arid areas require more attention from researchers in order to overcome the obstacles to hydrological modelling identified in the study. For example, they should consider new methods for determining spatial rainfall and infiltration from transient flows that come from flash floods. Instead of using gauged data on arid areas, RS data can be used for simulating events. Observational data can also be improved by creating multiple measurement stations in affected areas.
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