Statistical parameters calculation for precipitation data/Input for WGN_USER/
After the precipitation data was checked for quality and the appropriate station selected the statistical parameters of precipitation data must be calculated before model set up. The statistical parameters for precipitation were calculated using the programme pcpSTAT and dew0.2. This programme calculates the statistical parameters of daily precipitation data used by the weather generator of the SWAT model.

Table: 4- 2 Stastical analysis of daily precipitation data of Debretabor (1997-2015 EC)

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PCP_MM: Average monthly precipitation
PCPSTD: Standard deviation
PCPSKW: Skew coefficient
PR_W1: Probability of a wet day following a dry day
PR_W2: Probability of a wet day following a wet day
PCPD: Average number of days of precipitate in month
Table: 4- 3 Average Daily Precipitation in Month of Debretabor

Table: 4- 4 Total Monthly precipitation of Debretabor (1997-2015EC)

Table: 4- 5 Average Daily Dew Point Temperate for Debretabor from (1997-2015 EC

tmp_max: Average Daily Maximum Temperature in Month (?)
tmp_min: Average Daily Minimum Temperature in Month(?)
hmd: Average Daily Humidity in Month (%)
dewpt: Average Daily Dew Point Temperature in Month (?)

Table: 4- 6 Stastical Analysis of Daily precipitate data of Bahirdar Station (1997-2015 EC)

PCP_MM: Average monthly precipitation
PCPSTD: Standard deviation
PCPSKW: Skew coefficient
PR_W1: Probability of a wet day following a dry day
PR_W2: Probability of a wet day following a wet day
PCPD: Average number of days of precipitate in month

Table: 4- 7 Average Daily Dew Point Temperate for Bahirdar from (1997-2015 EC)

tmp_max: Average Daily Maximum Temperature in Month (?)
tmp_min: Average Daily Minimum Temperature in Month(?)
hmd: Average Daily Humidity in Month (%)
dewpt: Average Daily Dew Point Temperature in Month (?)
Table: 4- 8 Average Monthly precipitation of Bahirdar Station (1997-2015 EC)

Table: 4- 9 Total Monthly Precipitation of Bahirdar Station (1997-2015 EC)

Land use/Land cover Data
Objective
To understand the general procedures of land cover/use classification from satellite images (Landsat 7)
To conduct land cover classification from the Landsat 7 ETM using Supervised classification
To understand how the image classification accuracy to assesses how well a classification worked
Understand how to interpret the usefulness of someone else’s classification
Satellite Image Data
Source for this exercise https://earthexplorer.usgs.gov/
Open https://earthexplorer.usgs.gov/and click on path/row option, then insert the Path/Row (169/52) finally click on “show”. Then the location is visible as below.
Insert the period of duration that we want the satellite image (search from 01/01/2007-06/30/20180) as above.
Click on “Data Sets” tab and select Landsat, chose in Landsat collection 1 level-2 of Landsat 7 ETM+C1 level-2, because here we can get download option. And check in “Additional Criteria” option the cloud cover is less than 10%. Finally click on “Result” option to get the satellite images that we want.
After clicking on the Result icon in previous procedure the satellite image will appear as indicated below. Check land cloud cover will be zero by clicking on MetaData Set icon.
Finally download will begin as indicated below click on Level-1 GeoTIFF Data Product (252. MB), because satellite image accuracy is depend on its MB. save the zipped data for further analysis in working directory
Image classification concepts and procedures in ERDAS IMAGIN 2014
Image classification techniques
1. Supervised classification: is a method of land cover type classification using the sample polygons from the known land cover types.
2. Unsupervised classification: is the type of land cover/use classification from the satellite image data when the user doesn’t know how many land cover types are present in the field.
Procedures for land cover classification in ERDAS IMAGIN 2014
Supervised classification
Fore our land cover classification we use the supervised classification method, because we can know or identify the land cover types by connecting ERDAS to Google Earth.
For the first time extract the archive Landsat image after downloading and layer stack will be done. During layer stacking the GeoTIFF file is not less 4 TIFF file but more than 4 is possible. To stack the layer follow as (Raster Spectral Layer Stack) after done it get the image as shown below.
Then as shown below the satellite image of Landsat 7 has noise line. To remove this noise line subset/clip the image by our vector watershed. To subset the images (import the satellite image and vector data – click on vector- click on drawing option and select “past from selected object” icon-click on subset and chip-create subset image-give the input image and output name- click on AOI and select viewer-OK). The image is subset,
To remove the noise line from satellite image (click on Raster- Spatial-Focal Analysis- give input subset image and output name with unsigned 8bit or 16bit- Ignore Zero State-use all value in computation-Ignore specified values (s)- OK!). After some steps the noise line will be removed as shown below.
Classification of land cover:- to classify the land cover by using Supervised classification method in ERDAS IMAGIN 2014 for our case (connect to Google Earth-Raster-Supervised-Signature Editor-Drawing-click on Draw polygon icon –create new signature from AOI (s) and rename the land cover type, either Agricultural, Urban, Water, forest. . – save the signature editor and again go back to Raster- supervised- supervised classification-give as the input raster file image (subset image), input signature(that we save the signature) and output name(as wish)- OK!). Finally the land cover is classified as follows.
Land Cover of 2007 satellite Image
The Ribb Dam watershed is characterized by high cultivation along the valleys of the main river and the tributaries. The agriculture and forest are the two dominant land uses in the watershed.
The main land use types are forest, Urban, Agriculture, Bare Land, Grass Land and water body. Agriculture occupies about 72.156% of the watershed area. Forest cover is about 3.896% and Grass land 23.56% of the total area of the watershed. The summary of the land use of Ribb watershed is given in the Table 1.1 below.
Table: 4- 10 Land Cover of 2007
VALUE CLASS_NAME CPNAME AREA (km^2) % of Area
1 Range-Grasses RNGE 158.7010 23.5624
2 Urban URBN 0.1238 0.01837
3 Bare land RNGB 1.5305 0.22723
4 Water WATR 0.9374 0.13917
7 Forest-Mixed FRST 26.2444 3.89652
8 Agricultural Land-Close-grown AGRC 485.9980 72.1563
Total 674.130788 100

Figure: 4. 10 2007 Land Cover on RIBB Upper Watershed
Land Cover Classification of 2018 Satellite Image
In this satellite land cover also the main land use types are forest, Urban, Agriculture, Bare Land, Grass Land and water body. Agriculture occupies about 85.94% of the watershed area. Forest cover is about 5.35% and Grass land 7.59% of the total area of the watershed. The summary of the land use of Ribb watershed is given in the Table below.
Table: 4- 11 Land use Land Cover of 2018 in Upper Ribb Dam Watershed
VALUE CLASS_NAME LAND_COVER AREA(km^2) % of Area
3 Water WATR 5.63 0.84
4 Urban URBN 0.93 0.14
5 Bare-land RNGB 0.93 0.14
14 Range-Grass RNGE 51.05 7.59
21 Forest-Mixed FRST 35.99 5.35
23 Agricultural land AGRC 577.94 85.94
Total 673.5 100.00
As we seen from the above two land cover there is big difference between them. Which indicates land use land cover change is occurred, based on this change base flow of Ribb and Sediment yield in Ribb dam reservoir is change.

Figure: 4. 11 Land use Land Cover of 2018 in Upper Ribb Dam Watershed

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