![]() Step 7: Assign colors and prepare map layouts # Use the below univar command with zones and -t flag to compute stats per land cover type Let us now find the Mean and median annual AETI and PCP per land cover type. Step 6: Find the AETI and PCP stats per Land cover type # Below command compute the surface area of each Land cover type in hectares. R.category map=L1_LCC_2018 rules=rules.txt sep=comma # A text file with category details are developed and provided to you as 'rules_lcc.txt' inside 'Base_layers' folder. # First let us assign category names to each land cover id as per WaPOR Catalog Let us now find the surface area of each land cover type in ULB as per WaPOR LCC map of 2018. Univariate statistics results of annual AETI 2017/18 R.series file=maps_pcp.txt out=PCP_annual_2017_18 method=sum # Assuming the file with map names is maps_pcp.txt # List all the PCP scaled maps in Grass mapset R.series file=maps_aeti.txt out=AETI_annual_2017_18 method=sum #D.legend grass gis code# below code aggregate the maps using the function "sum" # Assuming the file with map names is maps_aeti.txt # select the maps you want to aggregate and save it into a text file using notepad.(see below screenshot) Let us consider the crop year from October to September (next year).īelow code compute the total AETI and PCP for the year 2017/18. Let us now aggregate the AETI and PCP maps to compute annual maps covering a crop year. pattern=L1_AETI*|cat` doįor i in `g.list rast mapset=. # let us use a 'for' loop to apply scale factor to all the AETI maps in the mapset in one go.įor i in `g.list rast mapset=. # Apply a mask to restrict further analysis to ULB So we apply a mask, before applying scale factor. This time we want to restrict the data within Urmia lake basin boundary. Below code apply the scale to all the maps in one go using a for loop. # Change the pattern above to select sub samples of mapsīoth AETI and PCP needs to be scaled by a factor of 0.1 as per the WaPOR documentation before using for analysis. # below command list the raster maps AETI of year 2019 Now you want to just list the AETI data for the year 2019. R.import in=$ extent=region resolution=region # let us use a 'for' loop to import all the maps in to GRASS GIS mapset in one go. # Navigate (cd) to the '' folder provided to youĬd /path/to/Data_ULB/Wapor # change the path to actual path in your computer # Now let us import all the WaPOR data into the grass gis mapset. # We have imported all the vector files (Base_layers) in the previous section. Grass78 /mnt/d/grassdata/utm38n/ulb_wapor # Open the Grass GIS in 'utm38n/ulb_wapor' location/mapset Base layers - Boundaries of Urmia lake, Urmia lake basin, Miandoab irrigation schemeįor this exercise, we will use some GeoTIFF files obtained from FAO WaPOR and the shape filesįor this exercise we will use the location/mapset created in previous section: utm38n/ulb_wapor.Monthly PCP (October 2017 to September 2020) at 2500 m resolution.Monthly AETI (October 2017 to September 2020) at 250 m resolution.The main objective of this exercise is to explore monthly Actual EvapoTranspiration (AETI) and Precipitation (PCP) data from FAO WaPOR over Urmia lake basin, and extract Land cover based statistics for the crop year 2018/2019.Īll data provided cover the entire Urmia Lake Basin Exercise 3 - Processing climatic data from GLDASĮxercise 1 - Seasonal aggregation and statistical analysis Objectives Aim:.Exercise 2 - Spatio-temporal analysis of climatic data (optional).Exercise 1 - Seasonal aggregation and statistical analysis.Spatio-temporal analysis with GRASS GIS IHE Delft Institute for Water Education The Netherlands ![]()
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