![Page 1: Mapping the Thermal Climate of the HJ Andrews Experimental Forest, Oregon Jonathan Smith Dept. of Geosciences Oregon State University PHOTOS COURTESY OF](https://reader030.vdocuments.site/reader030/viewer/2022032606/56649eb65503460f94bc07c2/html5/thumbnails/1.jpg)
Mapping the Thermal Climate of the HJ Andrews Experimental Forest,
Oregon
Jonathan Smith
Dept. of Geosciences
Oregon State University PH
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Project goal
To create mean monthly maximum and minimum temperature maps of the HJ Andrews…
… taking into account as many environmental factors as possible affecting microclimates in forested, mountainous terrain
- Cloudiness- Topography- Vegetation
… and their effects on radiation regimes
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Why are temperature maps of the HJ Andrews needed?
• The HJ Andrews is part of the Long-Term Ecological Research (LTER) network and a major forest research site
• Spatial temperature distributions are needed for research and decision-making in studies involving:
- vegetation characteristics - animal habitats
- insect distributions - forest management
- hydrology (snow patterns, water balance)
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Why now?
• A complete 30-year dataset now available (1971-2000)
• Computer software now available to quantify environmental effects on microclimates (cloudiness,topography, vegetation cover)
• Opportunity to use a temperature spatial interpolation model well-suited to HJ Andrews geography
• Current GIS capabilities allowed us to spatially represent temperature distributions at high resolution
• Badly needed
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Large-scale influences on HJ Andrews temperature
• Proximity to Pacific Ocean
• Latitude
• Position relative to crest of Cascades
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Small-scale influences on HJ Andrews temperaturesTOPOGRAPHY
• elevation: determines temperature, precipitation regimes
• slope/aspect: determine radiation regime on a surface
• tree canopy: attenuates incoming shortwave radiation
blocks amount of visible sky
Highly variable topography and canopy cover at the HJ Andrews lead to complex microclimates!
• valleys/ridges: induce cold air drainage inversions thermal belts
block amount of visible sky (lower sky view factors)
VEGETATION
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Project is based on the assumption that:
• At a given elevation, radiation is a major determinant of temperature regimes
TMAX: incoming direct and diffuse shortwave solar radiation
TMIN: outgoing longwave terrestrial radiation
• in forested, mountainous terrain, radiation regimes are determined by cloudiness, topography, and tree canopy
• depict cloudiness and topographic effects of radiation on temperatures
• depict temperatures across the HJ Andrews with minimal canopy influence
… therefore the resulting maps:
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Why minimize canopy influence?
• To approximate standard siting conditions common in other station networks (NWS)
• To provide a universal ‘starting point’ for studies using these data as input
• To provide appropriate input for the many types of resource models that assume temperature data collected from forest clearings
• To provide stable datasets: vegetation changes from year to year, and if canopy is used to estimate temperatures, results will be temporally unstable
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• Open thermisters shielded above with PVC
• Digital data loggers
• MET sites: relatively open, (sometimes) flat terrain
thermister towers (1.5, 2.5, 3.5, 4.5m), many other sensors
• Other sites: highly variable canopies, slopes, aspectsair temperature sensor (~1.5m), other sensors
Climate station instrumentation and siting
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Initial adjustments to datasets• Any site not having at
least 3 years of data eliminated (62-19=43)
• Monthly means computed from daily TMAX, TMIN
• Data flagged in any way during initial processing discarded
• 1.5m sensors from MET sites used when possible
71 81 91 01
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1 Each short-term site paired with highest correlated long-term site (month-by-month)
2 Using long-term site data, calculated the difference between the mean TMAX and TMIN for the full period of record and the mean TMAX and TMIN for the short-term site’s period of record
3 Applied the above differences as overall adjustments to the short-term datasets
• To eliminate warm/cold temporal biases in dataset, short-term sites were adjusted to the full 30-year period:
Temporal adjustments to datasets
• 43 sites: 13 with at least 22.5 years (75%) of data (long-term)30 with less than 22.5 years (75%) of data (short-term)
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• Multiplied monthly radiation grids by cloud factors to get total daily radiation over area for each month
• Determined monthly cloud factors using UPLMET radiation data
1 – [UPLMET observed rad’n / IPW clear-sky rad’n]
• Used Image Processing Workbench (IPW) to create clear-sky radiation grids, using 50-meter DEM
Radiation adjustments to datasets
STEP 1: Determine monthly topo/cloud-sensitive radiation at each temperature site
JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC0.49 0.45 0.40 0.35 0.34 0.26 0.19 0.17 0.19 0.34 0.47 0.48
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• Explicitly accounted for cloudiness on topographic shading by adjusting ratio of direct to diffuse radiation:
- Used Bristow & Campbell’s (1985) equation relating direct and diffuse proportions of solar radiation to transmissivity
- Necessary to produce ‘sharp’ radiation regimes on clear days ( direct, diffuse), ‘flat’ radiation regimes on cloudy days ( direct, diffuse)
STEP 1: (continued)
• Picked out appropriate grid pixels to get estimated radiation at each temperature site
‘SHARP’ REGIME ‘FLAT’ REGIME
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• At each site, separated out proportion of radiation blocked by topography only:
• Used HemiView to calculate proportions of direct and diffuse radiation blocked by canopy and topography
• Took fisheye photos at each site
STEP 2: Determine monthly topo/cloud/canopy-sensitive radiation at each temperature site
• At each site, determined proportion of radiation blocked by canopy only:
• Reduced each sites’ topo/cloud-sensitive radiation by its proportion of radiation blocked by canopy only
1 - IPW topo/cloud-sensitive rad’n = IPW proportion of rad’n blocked IPW flat, open cloud-sensitive rad’n by topography only
HemiView proportion blocked _ IPW proportion blocked = proportion of rad’n blocked by canopy and topography by topography by canopy only
RS10 UPLMET OPEN
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• For TMAX: 1. Selected site pairs for comparison (7)
2. Plotted differences in monthly topo/cloud/canopy-sensitive radiation against
differences in monthly TMAX between site pairs, compute regression functions
STEP 3: Calculate regression functions to adjust temperatures to flat, open siting conditions
JAN: y = 1.17x (R² = 0.91)FEB: y = 0.73x (R² = 0.96)MAR: y = 0.45x (R² = 0.96)APR: y = 0.33x (R² = 0.99)MAY: y = 0.24x (R² = 0.82)JUN: y = 0.22x (R² = 0.84)JUL: y = 0.20x (R² = 0.74)AUG: y = 0.25x (R² = 0.82)SEP: y = 0.34x (R² = 0.87)OCT: y = 0.52x (R² = 0.91)NOV: y = 0.76x (R² = 0.92)DEC: y = 1.41x (R² = 0.95)
(y = difference in TMAX, x = difference in radiation)
3. Applied regression functions to each sites’ monthly TMAX to adjust temperatures ‘into the open’ (removing topo, vegetation effects)
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• For TMIN: 1. Selected site pairs for comparison (14)
2. Plotted differences in sky view factors against differences in monthly TMIN, and
compute regression functions:
JAN: y = -1.00x (R² = 0.49)FEB: y = -1.18x (R² = 0.33)MAR: y = -1.36x (R² = 0.58)APR: y = -1.01x (R² = 0.31)MAY: y = -1.51x (R² = 0.50)JUN: y = -1.80x (R² = 0.55)JUL: y = -3.41x (R² = 0.81)AUG: y = -3.46x (R² = 0.83)SEP: y = -3.25x (R² = 0.75)OCT: y = -2.07x (R² = 0.66)NOV: y = -1.56x (R² = 0.63)DEC: y = -1.14x (R² = 0.47)
(y = difference in TMIN, x = difference in sky view factors)
3. Applied regression functions to each sites’ monthly TMIN to adjust temperatures ‘into the open’ (removing topo, vegetation effects)
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• TMAX regression slopes varied throughout the year because of monthly variations in solar radiation:
• TMIN regression slopes varied throughout the year because of monthly variations in cloudiness:
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• ‘Parameter-elevation Regressions on Independent Slopes Model’ (PRISM) used to spatially interpolate TMAX, TMIN
• Uses a combination of geographic and statistical methods
• Elevation-based interpolator, using linear temperature-elevation regression functions, DEM, and point (station) data
• Accounts for elevation, inversion layers (2-layer atmosphere model)
Spatially interpolating temperatures across the HJ Andrews
* Results of different adjustment procedures removed effects of everything but elevation – PRISM’s strength is the elevation-temperature relationship
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• Station selection criteria:
- no stream sites
- no sites with obviously biased temperatures
- no sites with questionable locations
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• PRISM temperature-elevation plots showed inversions throughout the year
TMAX-Elevation Plot for January TMAX-Elevation Plot for July
TMIN-Elevation Plot for January TMIN-Elevation Plot for July
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• For TMAX:
1. (flat, open radiation grid – topographically-sensitive radiation grid) = radiation difference grid
2. For each pixel in radiation difference grid, applied TMAX/rad’n regression function to
get proper TMAX adjustment value
3. Added TMAX adjustment value to original PRISM TMAX grid
Applying topographic radiation/sky view factor effects to PRISM temperature grids
• For TMIN:
1. [flat, open grid (SVF=1) – topographically-sensitive SVF grid] = SVF difference grid
2. For each pixel in SVF difference grid, applied TMIN/SVF regression function to
get proper TMIN adjustment value
3. Added TMIN adjustment value to original PRISM TMIN grid
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a) PRISM September TMAX map showing no radiation effects
b) IPW September radiation map
c) PRISM September TMAX map showing radiation effects
d) Difference between a) and c)
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a) PRISM September TMIN map showing no SVF effects
b) IPW SVF map
c) PRISM September TMIN map showing SVF effects
d) Difference between a) and c)
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• Study quantified effects of cloudiness, topography, and vegetation on radiation/temperature regimes to spatially interpolate temperatures across the HJ Andrews
• Temperatures were modeled to minimize effects of vegetation
• Final temperature maps most sensitive to effects of elevation and topographic position (year-round valley inversions)
• TMAX was sensitive to shortwave radiation variations, especially in winter (due to low solar radiation load)
• TMIN was sensitive to sky view factor variations, especially during clear summer months (fewer clouds enhance radiative nighttime cooling)
• Study inventoried historical HJ Andrews temperature datasets, documented climate station sites (radiation regimes, canopy types, improved site locations)
Summary and conclusions
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• More research into the nature of cold-air drainage in the HJ Andrews
• More sites in under-represented areas
• Quantification of stream effects on temperatures needed
• Would be useful to test TMAX/radiation, TMIN/sky view factor functions elsewhere
• Additional climate station pairs at similar elevations with different canopy types would help fine-tune regression functions
Recommendations for future work
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• Some vegetation modification around climate stations seems justified to bring them up to NWS-standards
• Development of historical database of vegetation changes at each site
• Possible to create canopy-sensitive temperature maps by reintroducing vegetation effects with remotely-sensed canopy coverages