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)