environmental modelling

University of Calgary

PRISM: University of Calgary's Digital Repository

Schulich School of Engineering Schulich School of Engineering Research & Publications

2018

Environmental Modelling

Hassan, Quazi K.

http://hdl.handle.net/1880/106247

lecture

https://creativecommons.org/licenses/by-nc-sa/4.0

©2018 Quazi K. Hassan, under a Creative Commons Attribution-NonCommercial-ShareAlike

license: https://creativecommons.org/licenses/by-nc-sa/4.0/

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Suggested citation: Hassan, Q.K. 2018. Lecture note on: Modelling of potential species distribution, In Environmental Modelling, Calgary, AB, Canada.

Except otherwise noted, ©2018 Quazi K. Hassan, under a Creative Commons Attribution-NonCommercial-ShareAlike license: https://creativecommons.org/licenses/by-nc-sa/4.0/

Environmental Modelling(ENGO 583/ENEN 635)

Lecture Noteon:

Modelling of Potential Species Distribution

Dept. of Geomatics Engineering; and Centre for Environmental Engineering Research and Education

Schulich School of EngineeringUniversity of Calgary

Review of Last Topics

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Topics of Discussion:Modelling of Potential Species Distribution

o Introduction to modelling of potential species distribution

o Solar radiation on planto Solar radiation modelling and its validationo Temperature modellingo Soil water content modelling

o Generation of species-specific environmental modellingo A case study on modelling potential species distribution

Modelling Species-specific Potential Species Distribution:Climatic Variables

o Solar radiation

o Temperature

o Water in soil

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Photosynthetically active radiation (PAR):

PAR » 45% Global radiation

Absorbed PAR (APAR) = f (PAR)

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).

Solar Radiation on Plants (2)

Net

CO

2up

take

(µm

olm

-2s-

1 )

APA

R (µ

mol

m-2

s-1 )

Local time (hr)180014001000600

12

10

8

6

4

2

0

-2

1200

1000

800

600

400

200

0

CO2 fluxesAPAR fluxes

(Adapted from Hassan et al., 2006 ©2006 CASI; where the publisher allows Hassan as being an author to reproduce for non-commercial purposes)

4



Solar Radiation Modelling and Its Validation (1)

§ The measured PAR values are obtained from two sites in the Province of New Brunswick.

§ It reveals that there is a strong relation (i.e., r2»96%)between the measured and modelled PAR values.

(Adopted from Hassan et al., 2006 ©2006 CASI; where the publisher allows Hassan as being an author to reproduce for non-commercial purposes)

Daily PAR(in µmol m-2 s-1)

Water bodies/cloud

Low

High

Solar Radiation Modelling and Its Validation (2)

§ Ideally the slope and intercept should be 1 and 0 respectively.

§ A slope of 0.9 indicates it is close to the 1:1 line.

§ An intercept of 85.52 indicates there is a positive bias in the modelled values.

What does the slope and intercept of the regression analysis reveal?

(Adopted from Hassan et al., 2006 ©2006 CASI; where the publisher allows Hassan as being an author to reproduce for non-commercial purposes)

5



Temperature (1)

Growing Degree Days, GDD = Ta – Tbase

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.

(Adopted from ©Kauppi et al., 2014 licensed under CC BY)

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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o The air temperature measurements from weather stations are good to estimate growing degree days (GDD) at point-level.

o However, to capture the spatial dynamics, we may need to use surface temperature estimates from remote sensing data.

o Thus, we need to convert the remote sensing-based surface temperatures to the equivalent of air temperatures.

o In order to do that, the first step is to look into the relation between the daily mean air and surface temperatures.

o And then how the instantaneous remote sensing-based surface temperature can be converted into daily mean values.

Temperature (3)

Temperature:Relation between Daily Mean Air and Surface Temperature

Dai

ly m

ean

surf

ace

tem

pera

ture

(in

K)

Daily mean air temperature (in K)300290280270260250240

300

290

280

270

260

250

240

1:1 line

o Both daily mean surface and air temperatures acquired over a forested site are compared.

o The position of 1:1 line shows the strong relation between the two.

(Adopted from ©Hassan and Bourque, 2009 licensed under CC BY)

7



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

305

290

275

260

245

230

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]

(Adopted from Hassan et al., 2007a; ©2007 SPIE that permits Hassan as being an author to prepare derivative publications)

Temperature:Converting Instantaneous Surface Temperature to Daily Mean Values

(a) N

EProvince of NB

Province of PEI

Province of NS

GDD (degree-days)

1800+1600 - 18001400 - 16001200 - 14001000 - 1200800 - 1000

1

1

2**

3

35

6

7

4

** The range is between 1200-1500 degree-days

(b)

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

(Adopted from Hassan et al., 2007a; ©2007 SPIE that permits Hassan as being an author to prepare derivative publications)

Temperature:Spatial Dynamics of Growing Degree Days and Its Validation

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Soil Water Content (1)

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.

(Adopted from Hassan et al., 2007b; ©2007 by MDPI that permits the reproduction for non-commercial purposes)

Soil Water Content (2)

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.


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Maximum transpiration

NDVI

qS

Dry edge, qdry1

Maximum evaporation

TVWI = 1

TVWI = 0

Wet edge

No transpiration

Vegetation index

Pote

ntia

l sur

face

tem

pera

ture

TVWI

qwet

qdry

(NDVI, qS)

Dry edge, qdry2

No evaporationqdry1 = a1

qdry2 = a2+b1*NDVI

Soil Water Content:Temperature-vegetation Wetness Index (TVWI) (1)

wetdry

sdryTVWIq-q

q-q=


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).


Soil Water Content:Temperature-vegetation Wetness Index (TVWI) (2)

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Long-term Average Temperature-Vegetation Wetness Index (TVWI) (1)

1.00.80.60.40.20.0

180000

150000

120000

90000

60000

30000

0

Freq

uenc

y

TVWI

(b)

WET model-derived SWC

High

Low

(c)

Province of NS

Province of NB

Province of PEI

(a)

(Adopted from Hassan et al., 2007b; ©2007 by MDPI thatpermits the reproduction for non-commercial purposes)

High > 50%

Low <30%

TVWI-values

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%).



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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.

Long

-term

ave

rage

TVW

I

WET model-derived SWC(in % saturation)

(a)

0.900.750.600.450.30

0.40

0.38

0.36

0.34

0.32

0.30

Y = 0.279 + 0.073 Xr2 = 95.7%

Regression line

(b)

Freq

uenc

y

0.900.750.600.450.30

4000

3000

2000

1000

0

WET model-derived SWC(in % saturation)

<0.5%



o A comparison of mean values of TVWI with values of “% of saturation” for the same pixels and region provided reasonable agreement (r2 = 95.7%).

o Values of “% of saturation” < 0.45 were not included in the comparison as:

§ the mean TVWI values did not show any clear pattern with modelled wetness values (Fig. a; previous slide), and

§ the amount of data involved was fairly small, comprising < 0.5% of total available data points (Fig. b; previous slide).



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Spatio-temporal Variations of TVWI

VSW

C (i

n m

3 m

-3) /

TVW

I

Period no.

2019181716151413121110987654321

1.0

0.8

0.6

0.4

0.2

0.0

2019181716151413121110987654321

1.0

0.8

0.6

0.4

0.2

0.0

2019181716151413121110987654321

1.0

0.8

0.6

0.4

0.2

0.0

2003

2004

2005

Period

VSWC

TVWI

Mean values of VSWC

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).


PSD = Á (GDD) * Á (TVWI) * Á (PAR)

§ PSD = Potential Species Distribution (species-specific)

§ GDD = Growing Degree Day

§ TVWI = Temperature-Vegetation Wetness Index

§ PAR = Photosynthetically Active Radiation

Modelling Species-specific Potential Species Distribution

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(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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Balsam Fir-specific Habitat Suitability

o Spatial distribution of modelled habitat suitability.

o Balsam fir-dominated stands (light gray polygons) from current forest cover map are draped over a portion of the study area.

(i)

(ii)

Very high (0.75+)High (0.63-0.75)Medium (0.50-0.63)Low (0.25-0.50)Very low (0.00-0.25)

Habitat Suitability


Balsam Fir-specific Habitat Suitability

Stand bFcontent (%) Number of stands (n)

% of stands by HS range

0.0-0.5(Very low-to-low)

0.5-1.0(Medium-to-very high)

50% 11035 6.7% 93.3%60% 9470 7.3% 92.7%70% 4927 9.1% 90.9%80% 1771 11.8% 88.2%90% 886 14.1% 85.9%100% 31 16.1% 83.9%

50-100% 28120 7.9% 92.1%

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 (%).


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o Important for selecting the most appropriate tree-species for a given site.

o To be useful for planning bio-diversity for ecologically sustainable forest management practices.

Species-specific Potential Species Distribution:Application Areas


References

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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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?

Sample Review Questions

environmental modelling

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