20220714
19365000
1.0
FALSE
P100-P5_Maks_vanndybde_33N.tif
file://\\nsv2-nasuni-01\prosjekt\O10240\10240464-01\10240464-01-03 ARBEIDSOMRAADE\10240464-01 RIVASS\10240464-01-05 MODELLER\Results\SAMLET\P100-P5_Maks_vanndybde_33N.tif
Local Area Network
002
262013.155691
267566.155691
6626512.240647
6639624.240647
1
Projected
GCS_ETRS_1989
Linear Unit: Meter (1.000000)
ETRS_1989_UTM_Zone_33N
<ProjectedCoordinateSystem xsi:type='typens:ProjectedCoordinateSystem' xmlns:xsi='http://www.w3.org/2001/XMLSchema-instance' xmlns:xs='http://www.w3.org/2001/XMLSchema' xmlns:typens='http://www.esri.com/schemas/ArcGIS/2.9.0'><WKT>PROJCS["ETRS_1989_UTM_Zone_33N",GEOGCS["GCS_ETRS_1989",DATUM["D_ETRS_1989",SPHEROID["GRS_1980",6378137.0,298.257222101]],PRIMEM["Greenwich",0.0],UNIT["Degree",0.0174532925199433]],PROJECTION["Transverse_Mercator"],PARAMETER["False_Easting",500000.0],PARAMETER["False_Northing",0.0],PARAMETER["Central_Meridian",15.0],PARAMETER["Scale_Factor",0.9996],PARAMETER["Latitude_Of_Origin",0.0],UNIT["Meter",1.0],AUTHORITY["EPSG",25833]]</WKT><XOrigin>-5120900</XOrigin><YOrigin>-9998100</YOrigin><XYScale>450445547.3910538</XYScale><ZOrigin>-100000</ZOrigin><ZScale>10000</ZScale><MOrigin>-100000</MOrigin><MScale>10000</MScale><XYTolerance>0.001</XYTolerance><ZTolerance>0.001</ZTolerance><MTolerance>0.001</MTolerance><HighPrecision>true</HighPrecision><WKID>25833</WKID><LatestWKID>25833</LatestWKID></ProjectedCoordinateSystem>
32
FALSE
LZW
1
TIFF
TRUE
continuous
floating point
inf
Clip Resultater_Samlet\P100-P5\Maks_vanndyde\P100-P5_Maks_vanndybde_uten_clip.tif "599471.637734345 6620642.49111455 603885.158733054 6633399.78411094" "\\nsv2-nasuni-01\prosjekt\O10240\10240464-01\10240464-01-03 ARBEIDSOMRAADE\10240464-01 RIVASS\10240464-01-05 MODELLER\Results\SAMLET\P100-P5_Maks_vanndybde.tif" Resultater_Samlet\Klippefil_Samlet inf ClippingGeometry NO_MAINTAIN_EXTENT
ProjectRaster "\\nsv2-nasuni-01\prosjekt\O10240\10240464-01\10240464-01-03 ARBEIDSOMRAADE\10240464-01 RIVASS\10240464-01-05 MODELLER\Results\SAMLET\P100-P5_Maks_vanndybde.tif" "\\nsv2-nasuni-01\prosjekt\O10240\10240464-01\10240464-01-03 ARBEIDSOMRAADE\10240464-01 RIVASS\10240464-01-05 MODELLER\Results\SAMLET\P100-P5_Maks_vanndybde_33N.tif" PROJCS["ETRS_1989_UTM_Zone_33N",GEOGCS["GCS_ETRS_1989",DATUM["D_ETRS_1989",SPHEROID["GRS_1980",6378137.0,298.257222101]],PRIMEM["Greenwich",0.0],UNIT["Degree",0.0174532925199433]],PROJECTION["Transverse_Mercator"],PARAMETER["False_Easting",500000.0],PARAMETER["False_Northing",0.0],PARAMETER["Central_Meridian",15.0],PARAMETER["Scale_Factor",0.9996],PARAMETER["Latitude_Of_Origin",0.0],UNIT["Meter",1.0]] "Nearest neighbor" "1 1" # # PROJCS["ETRS_1989_UTM_Zone_32N",GEOGCS["GCS_ETRS_1989",DATUM["D_ETRS_1989",SPHEROID["GRS_1980",6378137.0,298.257222101]],PRIMEM["Greenwich",0.0],UNIT["Degree",0.0174532925199433]],PROJECTION["Transverse_Mercator"],PARAMETER["False_Easting",500000.0],PARAMETER["False_Northing",0.0],PARAMETER["Central_Meridian",9.0],PARAMETER["Scale_Factor",0.9996],PARAMETER["Latitude_Of_Origin",0.0],UNIT["Meter",1.0]] NO_VERTICAL
20220715
08321400
20220715
08321400
Microsoft Windows 10 Version 10.0 (Build 19042) ; Esri ArcGIS 12.9.0.32739
P100_P5_Maks_vanndybde_33N_2
1
10.753684
10.867022
59.828810
59.708237
Maks_D_P100-P5
Maks vanndybde ved et 100-årsregn minus 5-årsregn
Raster Dataset
dataset
EPSG
4.3(4.0.0)
2
1
0
262013.155691 6626512.240647
262013.155691 6639624.240647
267566.155691 6639624.240647
267566.155691 6626512.240647
264789.655691 6633068.240647
5553
1.000000
13112
1.000000
Band_1
1528169899229200.000000
0.000000
32
20220715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