methods for applying geospatial tools for climatic...
TRANSCRIPT
Valerie Deeter Environmental Engineering, UC Berkeley
Acknowledgements Professor Sally Thompson, Professor John Radke, Beki McElavin
Limitations and Conclusion Models can be limited by the resolution of the input raster data. Using a
DEM with 50 m cell resolution with 13 km cell resolution required interpolation
which did not take into account complexities of terrain. Therefore, data analysis
results may be limited by the raster input that has the lowest resolution.
Interpolation methods also need to be considered especially over complex
terrain. The chosen interpolation scheme, IDW, may have not been the best
choice. Variation of solar radiation values because of annual averaging can
potentially result in values not realistic for the growing season. This is
exacerbated in analysis of steep narrow terrain which can result in near zero
values. So typical values for solar radiation and other climatic parameters should
be used to verify the reasonability of model output. Field data may also be
necessary to make adjustments to the generated solar radiation rasters.
Given the limitations, geospatial analysis using climatic and hydrological
models can still provide a valuable resource for forecasting the habitability of
plant species for climate change mitigation. Furthermore, GIS is proven itself to
be a powerful tool in predicting this habitability for management choices under
climate change scenarios.
Aim and Methods Outline This project’s aim is to use ArcGIS spatial tools for analysis of climatic raster data
to determine correlations between climatic parameters and six tree species in
the Kings Canyon and Sequoia National Parks.
Considered tree species: White fir, Red fir, Ponderosa pine, Jeffrey pine, Foxtail
pine and Lodgepole pine.
Raster Method Outline:
1) Slope and Aspect Analysis
• Generate slope and aspect surface from DEM 1/3 ARC sec obtained
from the USGS National Viewer. (cell size adjusted to 50 meters)
2) Raster Interpolation
• Download temperature and AET rasters from Cal-Adapt.org
(cell size 13 km).
• Generate rasters by point extraction and inverse distance weighted
(IDW) interpolation (cell size adjusted to 50 m).
3) Solar Radiation Analysis
• Generate Solar Radiation raster from DEM with 50 meter cell size.
4) Raster Calculations and Reclassifications
• Reclassify slope and aspect value to join to text descriptions (i.e. ‘N’ or
‘steep’).
• Generate emissivity values by reclassifying the temperature raster.
• Generate Longwave out and Shortwave out radiation rasters from the
Solar Radiation, temperature and emissivity rasters.
• Generate Net Solar Radiation by summing radiation inputs and outputs.
• Generate PET raster from Net Solar Radiation raster with the Priestly-
Taylor equation.
• Generate D raster by subtracting PET from AET rasters.
5) Extraction and Exportation
• Extract AET and D raster values by random points generated for six
trees species.
• Export attributes tables from tree points.
• Plot data for various aspects, slopes and tree species.
Introduction It almost indisputable that climate change will have lasting effects on the
natural environment. Species may relocate due to intolerance or inability to adapt
to climate induced stressors. For plant species, lack of mobility may result in
extinction if adaption is infeasible. Studies have shown that plant habitability of a
region can be determined by hydrological and climate models. It is well known
that plants utilize most water from soil by transpiration (95%) and the remaining
for photosynthesis (5%). The rate of transpiration is dependent on the water
availability and on sufficient energy for water vaporization. Two water balance
and energy parameters meaningful to determine the viability of vegetation are
evapotranspiration (ET) and water deficit (D). Potential evapotranspiration (PET)
is the amount of evaporation that would occur if a sufficient water source were
available. Actual evapotranspiration (AET) is the actual amount of evaporation
given watershed climate and storage conditions. Climatic water deficit (D) is the
evaporative demand not met by available water. Therefore, D = PET - AET.
Correlations between D and AET for specific vegetation can be used to forecast
changes in the watershed due to climate change. Geospatial analysis may
improve previous methods by enhancing analysis with solar radiation,
interpolation, slope and aspect tools to cover broad regions.
Methods
Methods Results
Various tree species plotted over DEM raster (left) and Temperature Raster (right) with
original 13 km cell temperature raster, point extraction and IDW interpolation.
Methods for Applying Geospatial Tools for Climatic Analysis
of Tree Species in the Sierra Nevada
Cal-Adapt.org Solar Radiation raster (left) versus ArcGis Solar Radiation raster (right)
Legend
SEKI_Park_Boundary
netr1_98yr
ValueHigh : 105.92
Low : 74.8104
srin_wm2_98yr
ValueHigh : 528.238
Low : 0.0076836
250
300
350
400
450
500
550
600
0 50 100 150 200 250 300
An
nu
al A
ctu
al
Evap
otr
an
spir
ati
on
(m
m)
Annual Deficit (mm)
Lod
Jef
FOX
PON
RED
WHT
Flow Chart
ArcGIS generated a larger range of radiation values when compared with the
Cal-Adapt raster data. Unfortuately, a large quantity of near zero radiation
values from ArcGIS resulted in negative deficits (D), which is unrealistic. Cal-
Adapt data provided narrower, nonzero range of values which was better for
analysis but lacked high resolution from the complex terrain. Field data is
necessary to verify dubious data. The Cal-Adapt data was chosen for the final
analysis since no obvious solution for correcting the questionable ArcGIS solar
radiation was determined.
Legend
SEKI_Park_Boundary
Flat (-1)
North (0-22.5)
Northeast (22.5-67.5)
East (67.5-112.5)
Southeast (112.5-157.5)
South (157.5-202.5)
Southwest (202.5-247.5)
West (247.5-292.5)
Northwest (292.5-337.5)
North (337.5-360)
Legend
SEKI_Park_Boundary
slope_prk50m
<VALUE>
0 - 12
12.1 - 20
20.1 - 35
35.1 - 76.9
Slope and Aspect Analysis
Legend
WhiteFir
RedFir
PonderosaP
LodgepoleP
JefferyP
FoxtailP
SEKI_Park_Boundary
parkdem_50m
ValueHigh : 110270
Low : 420.051
Legend
temp98
temp98_50m
ValueHigh : 15.3478
Low : -1.605
tairflx_98UTM.tif
ValueHigh : 25.1221
Low : -4.36354
250
300
350
400
450
500
550
600
0 100 200 300
An
nu
al A
ctu
al E
vap
otr
ansp
irat
ion
(m
m)
Annual Deficit (mm)
Foxtail Pine - Slope Plot
Flat
Gradual
Semi-Steep
Steep
250
300
350
400
450
500
550
600
0 100 200 300
An
nu
al A
ctu
al E
vap
otr
ansp
irat
ion
(m
m)
Annual Deficit (mm)
Foxtail Pine - Aspect Plot
East
North
West
South
Trends in plot indicate preferential ranges of D and AET for tree species. There
appears to be no definitive boundaries between tree species as determined from
previous studies. Linear trends in data may a result of interpolation methods or
lack of high resolution raster generated from complex terrain. It is likely that
input of high resolution solar radiation data (properly adjusted for near zero
values) would produce better output.
Clients US Forest Service, US National Parks, US Department of Agriculture
Slope Classifications:
Flat (0-12⁰), Gradual (12.1-20⁰), Semi-Steep (20.1-35⁰) and Steep (35⁰+)
Solar Radiation Analysis
Solar Radiation Analysis
(All maps shown are projected in NAD 1983 UTM Zone 11N)
Foxtail Pine plots show so no obvious preference for slope or aspect variations.
(Only one species shown for due to limited space.)
Plotted results versus D and AET plot from previous study by Nathan Stephenson, Biological
Resources Division, USGS, 1998 Figure 6( lower right)