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University of Calgary
PRISM: University of Calgary's Digital Repository
Schulich School of Engineering Schulich School of Engineering Research & Publications
o PAR is a function of:§ latitude, solar declination angle, solar angle, and cloud condition§ at micro-level topography (i.e., slope and aspect)
o APAR depends on vegetation conditions that can be estimated from normalized difference vegetation index (NDVI: a function of the reflections from red and near infrared spectrum of the solar radiation)
Solar Radiation on Plants (1)
o Net CO2 uptake is the accumulation of carbon into the plants, in other words, it indicates the plant growth.
o The long-term average diurnal patterns for net CO2 uptake and absorbed PAR show that both are: § increasing between 6:00-14:00, § decreasing between 14:00-19:00; and§ following each other closely.
o Plant growth is directly proportional to APAR (i.e., solar radiation).
o Temperature is a spatially-dynamic climatic variable that plays vital roles in influencing plant growth and development by directly affecting plant functions, such as,
§ Evapotranspiration§ Photosynthesis§ Plant respiration, and § In-plant water and nutrient movement
o Growing degree days (GDD) is a simple temperature-based index that determines the potential plant growth.
o The most widely practiced and standard protocol for estimating GDD is to use daily meanair temperature (𝑻𝒂) acquired at approximately 1.5-2 m above grassed surfaces. (Hassan et al., 2007)
o Tbase is the base temperature » 5 oC; below which vegetation ceases to be biologically active.
o The new regression (red) referring to 2006–11 is based on data from the 15 regions in Finland. The black dot on the red line shows the area-weighted average of all 15 regions.
o The old regression (blue) as published in Kauppi and Posch(1985) was based on 19 data points as recorded (in Finland) in the mid-20th century.
Temperature (2)
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Suggested citation: Hassan, Q.K. 2018. Lecture note on: Modelling of potential species distribution, In Environmental Modelling, Calgary, AB, Canada.
o Comparisons are performed between the time period of remote sensing-based surface temperature (i.e., between 10:30am – 12:00 pm for MODIS) and daily mean values.
o These are conducted over a number of locations across Canada (i.e., New Brunswick, Quebec, Ontario, and Saskatchewan).
o For all the sites, it reveals strong relations (see the r2-values in the graph).
Dai
ly m
ean
grou
nd-b
ased
sur
face
te
mpe
ratu
re (K
)
Average ground-based surface temperature between 10:30 am – 12:00 pm (K)
NB: Y = 26.75 + 0.90 X (r2 = 98.4%)
QC: Y = 13.12 + 0.94 X (r2 = 97.4%)
ON: Y = 17.54 + 0.93 X (r2 = 98.1%)
SK: Y = 24.43 + 0.90 X (r2 = 97.8%)
Average: Y = 17.73 + 0.93 X (r2 = 97.5%)
305290275260245230
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Average regression line
NB [46.472° N, 67.100° W] QC [49.69247° N, 74.34204° W]
x ON [48.21738° N, 82.15553° W]+ SK [53.91634° N, 104.69203° W]
1: Highlands2: Northern Uplands3: Central Uplands4: Fundy Coastal5: Valley Lowlands6: Eastern Lowlands7: Grand Lake Lowlands
Ecoregions
GDD from 1951-1980 period MO
DIS
-der
ived
long
-term
ave
rage
d G
DD
(c)
5, 67
4
3
1
2
800
1200
1600
2000
800 1200 1600 2000
1:1 line(a) modelled growing degree days(b) generalized growing degree days from air temperature (c) comparison between (a) and (b), shows good agreements
Soil water content is a measure of the total amount of water, including thewater vapor, contained within a soil column above the ground water table.It is critical to understand the followings:
§ Drought and water scarcity modeling§ Agriculture crop production and food security§ Soil erosion and runoff modelling§ Forest productivity§ Forest fire§ Insect outbreaks§ Forest harvest planning, among others.
o The soil water content measurements at point locations are the best possible data set.
o However, to capture the spatial dynamics, we may need to use either hydrological models or predict from remote sensing data.
o In this case, remote sensing-based products of normalized difference vegetation index (NDVI: a measure of vegetation greenness) and surface temperature are employed.
o The reason behind using remote sensing is that it is very difficult to have reasonable measure of vegetation (as it also plays vital role in water balance) in the framework of hydrological models.
o The potential surface temperature (qS: a terrain corrected surface temperature) and NDVI were used to model TVWI.
o A trapezoidal shape was generally observed when qS and NDVI data were plotted.
o The dry edge (qdry; the line where qS is the highest in relation to NDVI) represents the case where water is not available for evapotranspiration, and as a result, TVWI possesses the lowest value (~0.0). The dry edge (qdry) is determined as a linear fit of the highest qS in relation to NDVI.
o In contrast, the wet edge (qwet; the line where qS is the lowest in relation to NDVI) represents the case where water is freely available for evapotranspiration, i.e., TVWI is the highest (~1.0).
o A map of long-term averages of TVWI produced by averaging the 58 TVWI images. TVWI values fell mostly in the range of 20-60% with an average of 40%.
o The areas along the coastlines and wider river channels showed higher TVWI (i.e., > 50%) due to their proximity to water and low elevation relative to exposed water.
o Some high relief areas, such as in northwestern New Brunswick and in the eastern part of Nova Scotia, had comparatively lower TVWI values than other regions in the Maritime Provinces (20-32%).
A well-known hydrological model (i.e., WET model; Moore et al., 1993) was employed. It was calculated based on inputs of long-term average values of precipitation, soil infiltration capacity,
solar energy input and exchange, flow accumulation, and surface run-off rates.
Mean values of TVWIo In general, a larger spatial variability was
observed between ground-based volumetric soil water content (VSWC) and derived TVWI.
o This was expected that given VSWC was acquired at point locations, while TVWI values were obtained by averaging individual pixel values within an 1 km ´ 1 km area centered over the point of measurement.
o The mean values represented by the red and blue closed symbols, however, were consistently close (within ~ ±25% or better).
(Adopted from Bourque, Hassan, and Swift, 2010. Modelled potential species distribution under current and projected climates for the Acadian Forest Region of Nova Scotia, Canada [For Department of Natural Resources & Energy, Nova Scotia], 46p.)
Species-specific Environmental Response Functions
PSD = Á (GDD) * Á (TVWI) * Á (PAR)
o The “potential species distribution (PSD)” modelling framework has been applied to balsam fir-dominant forested regions in the Province of New Brunswick. The reasons are:
§ It is the most commercially important tree-species.
§ It occupies ~19% of total forest in the eastern Canadian province of New Brunswick.
Species-specific Potential Species Distribution
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Suggested citation: Hassan, Q.K. 2018. Lecture note on: Modelling of potential species distribution, In Environmental Modelling, Calgary, AB, Canada.
Overlay-analysis summary of high balsam fir-content stands in relation to modelled HS; n is the number of stands falling in each stand-balsam fir-content category (%).
o Hassan, Q.K., and Bourque, C.P.-A. Potential species distribution based on the integration of biophysical variables derived with remote sensing and process-based models, Remote Sensing, 2009, 1: 393-407.
o Hassan, Q.K., Bourque, C.P.-A., and Meng, F.-R. Estimation of daytime net ecosystem CO2 exchange over balsam fir forests in eastern Canada: combining averaged tower-based flux measurements with remotely sensed MODIS data. Can. J. Remote Sens. 2006, 32: 405-416.
o Hassan, Q.K., Bourque, C.P.-A., Meng, F.-R., and Cox, R.M.. A wetness index using terrain-corrected surface temperature and normalized difference vegetation index: an evaluation of its use in a humid forest-dominated region of eastern Canada. Sensors, 2007b, 7:2028-2048.
o Hassan, Q.K., Bourque, C.P.-A., Meng, F.-R., and Richards, W. Spatial mapping of growing degree days: an application of MODIS-based surface temperatures and enhanced vegetation index. J. Applied Remote Sens. 2007a, 1: 013511.
o Kauppi, P., and Posch, M. Sensitivity of boreal forests to possible climatic warming. Climatic Change, 1985, 7: 45–54.
o Kauppi, P.E., Posch, M., and Pirinen, P. Large impacts of climatic warming on growth of boreal forests since 1960. PLoS ONE, 2014, 9: e111340.
o Moore, I.D., Norton, T.W., and Williams, J.E. Modelling environmental heterogeneity in forested landscapes. J. Hydrology, 1993, 150: 717–747.
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Suggested citation: Hassan, Q.K. 2018. Lecture note on: Modelling of potential species distribution, In Environmental Modelling, Calgary, AB, Canada.
o What are the most important climatic variables influencing the quality of a given site to support optimal plant growth? Discuss their specific roles in plant growth.
o How the climatic regimes (i.e., temperature, solar radiation, and water content in the soil) could be modelled.
o What are the variables that influence the photosynthetically active radiation (PAR) and absorbed PAR (APAR)?
o Draw a diagram to illustrate the interactions between temperature and vegetation for modelling surface wetness condition, and discuss the mechanisms.
o Draw the species-specific environmental response functions.
o What are the applications of species-specific potential species distribution?