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Methodology

The Solar suitability mapping framework was developed using a GIS-based Multi-Criteria Evaluation (MCE) technique. The multi-criteria evaluation was implemented by combining spatial information from a number of geospatial drivers for solar based irrigation. These data sourced from a number of national and international databases include solar irradiation, slope, groundwater levels, aquifer productivity, groundwater storage, groundwater sustainability, proximity to rivers, proximity to small dams and inland valleys, soil characteristics, crop and land suitability, population, roads and travel time to markets. First, a constraint layer was developed to differentiate areas that would be suitable for solar based irrigation from those that cannot be suitable under any condition. Protected zones, forested and urban areas, and areas with extreme with droughts identified as unsuitable for agricultural production were excluded.

The absolute values of each of the input drivers (solar irradiation, slope, groundwater levels, aquifer productivity, groundwater storage, proximity to rivers, inland valleys and reservoirs, soil texture, soil drainage, soil depth, soil organic carbon, soil available water content, distance to roads, population density and travel time to markets) were rescaled to a 1-5 scale and used to derive weighted combination maps for the suitable areas. Layer weights were determined and assigned using the Saaty Analytic Hierarchy process in a GIS environment. A nearest neighbour resampling scheme was used to resample each of the individual inputs to obtain final suitability areas at a 90 meter resolution.

Suitability for solar-powered irrigation was mapped when considering different water resources: only surface water, only groundwater and both surface and groundwater at various groundwater depths (0-7 m, 7-25 m, 25-50 m, 50 – 100 m, 100-250 m).

How were the legends generated?

Legends were generated using an equal interval class. The legends range from 20% probability in least suitable areas to 100% probability in highly suitable areas. Graduation is as below:

Least suitable 0 - 30% probability

Less suitable 30.1 - 60% probability

Moderately suitable 60.1 - 75% probability

Suitable 75.1 - 90% probability

Highly suitable 90.1% - 100% probability

How can I help improve solar suitability tool?

To improve predictions for your country or region, consider contributing/sharing validation data or additional data so that your data can be included in the framework. IWMI always respects the data policy of data providers and will not publically share any data unless written permission is given to do so. Agencies that contribute their data will be dully acknowledged.

References

Hengl, T., de Jesus, J.M., Heuvelink, G.B.M., Gonzalez, R.M., Kilibarda, M., Blagotic, A., Wei, S., Wright, M.N., Geng, X., Bauer-Marschallinger, B., Guevara, M.A., Vargas, R., MacMillan, R.A., Batjes, N.H., Leenaars, J.G.B., Ribeiro, E., Wheeler, I., Mantel, S.K., Kempen, B., 2017. SoilGrids250m: Global gridded soil information based on machine learning. PLoS Biol. 12, 729–736.

MacDonald, A.M., Bonsor, H.C., Dochartaigh, B.É.Ó., Taylor, R.G., 2012. Quantitative maps of groundwater resources in Africa. Environ. Res. Lett. 7.

Schmitter, P., Kibret, K.S., Lefore, N., Barron, J., 2018. Suitability mapping framework for solar photovoltaic pumps for smallholder farmers in sub-Saharan Africa. Appl. Geogr. 94, 41–57.