20220608
09003300
1.0
FALSE
P200_Maks_vannhastighet_33N.tif
file://\\nsv2-nasuni-01\prosjekt\O10240\10240464-01\10240464-01-03 ARBEIDSOMRAADE\10240464-01 RIVASS\10240464-01-05 MODELLER\Results\SAMLET\P200_Maks_vannhastighet_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
3.4e+38
MosaicToNewRaster 'Resultater_Samlet\P200\Maks _vannhastighet\Opsjon_10_200aar_WS_Clip.tif';'Resultater_Samlet\P200\Maks _vannhastighet\Spmax_Op2-4_P200_Kbekk.tif';'Resultater_Samlet\P200\Maks _vannhastighet\Spmax_Op2-4_P200.tif';'Resultater_Samlet\P200\Maks _vannhastighet\Spmax_P200_Ops1+9_clip.tif';'Resultater_Samlet\P200\Maks _vannhastighet\Vmax_Ops6-7_P200_clip.tif';'Resultater_Samlet\P200\Maks _vannhastighet\Vmax_P200_Op8.tif' "C:\Users\eld\OneDrive - Multiconsult\Skrivebord\Kastes\Merge_NF" P200_vmaks.tif 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]] "32 bit float" 1 1 First First
ProjectRaster P200_vmaks.tif "\\nsv2-nasuni-01\prosjekt\O10240\10240464-01\10240464-01-03 ARBEIDSOMRAADE\10240464-01 RIVASS\10240464-01-05 MODELLER\Results\SAMLET\P200_Maks_vannhastighet_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
20220608
09081600
20220608
09081600
Microsoft Windows 10 Version 10.0 (Build 19042) ; Esri ArcGIS 12.9.0.32739
P200_Maks_vannhastighet_33N_1
1
10.753684
10.867022
59.828810
59.708237
P200_maksH
Maksimal vannhastighet ved et 200-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
3429970.000000
0.000000
32
20220608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