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The precipitation data from the meteorological stations provides the rainfall amount in mm on a monthly basis, measured over a time span of 8 to 18 years. The data provides the average monthly precipitation and we calculated the annual average. The data was then screened for errors and after removing invalid measurements a total of 107 valid points remained for the geospatial modelling and the validation of models. Given the influence of topography on precipitation as well as the Monsoon regime (i.e. rainfall patterns are seasonal and directional) we considered the following additional datasets: elevation, aspect and slope derived from 30m SRTM DEM, average January and July temperature, average January NDVI derived from the MODIS (MOD13Q1) data, average July NDVI derived from the MODIS (MOD13Q1) data. We considered the NDVI as potential additional dataset because the NDVI shows a fast response to precipitation (greening up), which might be more suitable to represent precipitation patterns related to the Monsoon regime compared to elevation. For similar reasons we included aspect and slope because there might be a certain directional pattern in the rainfall distribution. |
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The precipitation data from the meteorological stations provides the rainfall amount in mm on a monthly basis, measured over a time span of 8 to 18 years. The data provides the average monthly precipitation and we calculated the annual average. The data was then screened for errors and after removing invalid measurements a total of 107 valid points remained for the geospatial modelling and the validation of models. Given the influence of topography on precipitation as well as the Monsoon regime (i.e. rainfall patterns are seasonal and directional) we considered the following additional datasets: elevation, aspect and slope derived from 30m SRTM DEM, average January and July temperature, average January NDVI derived from the MODIS (MOD13Q1) data, average July NDVI derived from the MODIS (MOD13Q1) data. We considered the NDVI as potential additional dataset because the NDVI shows a fast response to precipitation (greening up), which might be more suitable to represent precipitation patterns related to the Monsoon regime compared to elevation. For similar reasons we included aspect and slope because there might be a certain directional pattern in the rainfall distribution. |
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Model: Climate Hazards Group: http://chg.ucsb.edu/data/ and Kaspar Hurni, Elias Hodel, University of Bern; www.cde.unibe.ch |
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50000 |
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<DIV STYLE="text-align:Left;"><DIV><DIV><P><SPAN STYLE="font-weight:bold;">etnachrpprec201</SPAN><SPAN STYLE="font-weight:bold;">7webm</SPAN><SPAN STYLE="font-weight:bold;">.tif</SPAN></P><P><SPAN STYLE="font-weight:bold;">etna: </SPAN><SPAN>National </SPAN><SPAN>D</SPAN><SPAN>ataset Ethiopia</SPAN></P><P><SPAN STYLE="font-weight:bold;">chrp: </SPAN><SPAN>CHIRPS Climate Hazards Group Infrared Precipitation</SPAN></P><P><SPAN STYLE="font-weight:bold;">prec: </SPAN><SPAN>precipitation</SPAN></P><P><SPAN STYLE="font-weight:bold;">201</SPAN><SPAN STYLE="font-weight:bold;">7</SPAN><SPAN STYLE="font-weight:bold;">: </SPAN><SPAN>Year of observation</SPAN></P><P><SPAN STYLE="font-weight:bold;">webm: </SPAN><SPAN>WebMercator projection</SPAN></P></DIV></DIV></DIV> |
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| title:
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etnachrpprec2017webm.tif |
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["Ethiopia","average","annual","rainfall","precipitation","climate","condition","risk","model"] |
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de-CH |
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50000000 |
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