LandType Annual Area Change Forecasts

[sbassett]

Sohl et al. under four different climate scenarios.

Which Scenario: A1B

Due to mismatch in landcover types, will calculate change in area between years for the native LULC datase (250m) and then will crosswalk later once the change is modeled.

Percent change will be applied to areas from 30-m fabric to calculate the change in m2 annually.

[sbassett]

@aj1s It seems like this issue and #32 are chained. Once you have the annual changes in Sohl classes, lets work together to crosswalk them into annual estimates by landcat.

[aj1s]

Revised to include changes forecast from 2015 to 2016:

https://github.com/TNC-NMFO/NWLAND/blob/nwland_dev/raw_data/NWLAND_Area_Changes_2015_to_2050.csv

[sbassett]

I know we (AJ and SB) discussed which scenarios modeled by Sohl are applicable to our RCP 4.5 and RCP 8.5 climate scenarios. I can't remember the outcome, but I do remember using this page for reference when making the decision. https://www.globalchange.gov/browse/multimedia/emissions-concentrations-and-temperature-projections

[sbassett]

Sohl SRESs: B1,B2,A1B, and A2. https://www.sciencebase.gov/catalog/item/5b96c2f9e4b0702d0e826f6d

SRES B1 ~ RCP 4.5 SRES A1fi is best for RCP 8.5, SRES A2 is best for 8.5 of the SRESs modeled by Sohl.

[sbassett]

Because of issue #51, will parameterize the model with projected future land use changes, but tell the model it's getting [historical] landcover changes. Annual values are processed to produce an average annual change through time, so instead of producing annual estimates will produce annual portion of average change estimates [interpolating from two years of data].

[aj1s]

Flagged for renewed self-assignment to @aj1s

[sbassett]

Sohl reclassification: 0 = NODATA 1 — Water = Water 2 — Developed = Developed_all 3 — DISTURBED = Forest 4 — DISTURBED = Forest 5 — DISTURBED = Forest 6 — Mining = Developed 7 — Barren = Barren 8 — Deciduous Forest = Forest 9 — Evergreen Forest = Forest 10 — Mixed Forest = Forest 12 — Shrubland = Shrubland 13 — Cropland = Cultivated 14 — Hay/Pasture = Cultivated 15 — Herbaceous Wetland = Meadow 16 — Woody Wetland = Meadow 17 — Ice/Snow = Ice

[sbassett]

Lack of desert class in Sohl means we can't use them as a direct crosswalk. Will need to mask out developed_all and cultivated in the baseline landcat and create landcats updated to have 2100 4.5 and 2100 8.5 scenarios.

Aaron uploading the Sohl crosswalk here: ...\Box\USCA Project (CO and NM)\Model Development\Data\Sohl

• [ ] Check assumption that urban and cultivated expand.
• [ ] Ensure each baseline landcat is present in the 2100 scenarios

[sbassett]

Beginning with the baseline landcats

• Owner: K:\Projects\USCA_NCS_2020\Data\Proc\LandCategory\Landcat_2021_10_04_parts\Landcat_2021_10_04_OwnCode.tif
• Vegetation Type: K:\Projects\USCA_NCS_2020\Data\Proc\LandCategory\Landcat_2021_10_04_parts\Landcat_2021_10_04_VegTypeID.tif
• Region (County): K:\Projects\USCA_NCS_2020\Data\Proc\LandCategory\Landcat_2021_10_04_parts\Landcat_2021_10_04_RegionCode.tif

Modify veg to reflect projected changes.

[sbassett]

Create forecast vegetation layers: Con(("Sohl_CONM_A2_y2100.tif" == 2) | ("Sohl_CONM_A2_y2100.tif" == 6) , 160, Con(("Sohl_CONM_A2_y2100.tif" == 6) | ("Sohl_CONM_A2_y2100.tif"==13) , 130, "Landcat_2021_10_04_VegTypeID.tif")) = K:\Projects\USCA_NCS_2020\Data\Proc\LandCategory\ClimateProjections\VegType_2100_A2.tif

K:\Projects\USCA_NCS_2020\Data\Proc\LandCategory\ClimateProjections\VegType_2100_B1.tif

[sbassett]

Combine all layers into landcats representing 2100 scenarios. K:\Projects\USCA_NCS_2020\Data\Proc\LandCategory\ClimateProjections\landcat_2100_RCP85.tif Failing with errors 010158 and 010067