Modelling actual and potential wind erosion risk by using readily available data on weather elements and GIS.
1, Mohammadali Saremi Naeini2, Goswin Heckrath3, Nikolaus J. Kuhn4
1Physical Geography and Environmental Change, University of Basel (firstname.lastname@example.org), Basel, Switzerland, 2Natural Resources & Desert Studies Faculty, Yazd University, Yazd, Iran, 3Department of Agroecology, Aarhus University, Aarhus, Denmark, 4Institute for Urban and Landscape Studies, University of Basel, Basel, Switzerland
Wind erosion is globally recognized as one of the most important processes leading to soil degradation, especially in arid and semi-arid regions. Many of the threatened regions of the world are located in remote areas where data availability maybe scarce and quality is often poor. Since highly sophisticated wind erosion models require a lot of information for a wide spectrum of input parameters, such as soil moisture, they are usually not applicable in these regions, unless the required database is obtained through very expensive field monitoring campaigns. ‘Simpler' models, on the other hand, commonly oversimplify the risk assessment by just focusing on readily available parameters. According to the complexity of the wind erosion process, the main aim of this research was to design a practical GIS-based model to predict potential and actual wind erosion risk based on spatial distribution of a limited number of key parameters, which can easily be accessed even in regions with low data availability.
Three aspects distinguish the proposed model from other basic wind erosion risk assessment models:
1) Separation of wet- and dry-times is taken into account for the wind velocity distribution analysis to include the effect of soil moisture in the erosion model;
2) The impact of climate change is considered for factors that are used in the model;
3) Running the model for given return periods based on extreme wind velocity analysis.
The change detection analysis of wind erosivity maps with and without implementation of wet- and dry-times revealed a significant overestimation, if the conventional approach was used. It is, therefore, strongly recommended for wind erosion risk assessments to associate more importance to winds that occur during dry times of the year. In conclusion, the simple user interface and the limited amount of required data are the main reasons why this model seems to fit very nicely into the gap between scientific wind erosion models and 'simple' erosivity or erodibility prediction models, which are often used for applied wind erosion risk assessments.