P3 “Green infrastructure and Microclimate”Confirmation of PhD Candidature

Darien Pardiñas DíazSupervisors:

Jason Beringer, Nigel Tapper and Matthias Demuzere

Evaluating the cooling effectiveness of green infrastructure as a heat

mitigation strategy

• Motivation

• Knowledge gaps and research questions

• Research objectives and approach explained

• Summary

• Progress today and timetable

Structure

Motivation: The Problem

EHE

AnthropogenicHeat

CO, SO2, NOx, PM generation from fossil fuel combustion

Indoor Cooling

NOX and VOC → O3

 

Positive feedback

WARMER CLIMATE

(UHI)

ENERGYDEMAND

REDUCED HUMAN

HEALTH AND COMFORT

Urban planning policiesand regulations

RAPID & UNPLANNE

D URBANISATI

ON

AIRPOLLUTION

Urbanisation will continue in the next decades

Look for long-term solutions to minimise the negative impacts of

urbanisation in climate

Motivation: Solutions and Challenges

?Urban forestry have proven to be a cost-effective way to reduce urban temperatures

Challenges associated: Implementation of UHI MS demands

initiative and important investments Climate benefits of a particular MS are

difficult to quantify because they depends on many factors difficult to consider in depth

UCM, RS and GIS techniques can help

us to ensure that implementation

practices report MAX benefits AT the MIN

costs.

There are a range of technologies that can be applied to reduce the UHI intensity

Climatic benefits (cooling) of UHI MS based on vegetation depends on: Extent and scale of implementation Spatial arrangement of existing urban features Geographic zone and regional climate (rainfall, humidity,

temperatures, etc.) Vegetation is irrigated or not

-Results should not be extrapolated across scales or different cities -Climate knowledge has to be in correspondence with the spatial scale and scope of urban planning actions

Limitation of previous studies Time scales studied do not always satisfies the long-term climate

information that urban planners and policy-makers often demand Rough estimates of land surface changes are usually employed in

urban climate runs → Unrealistic MS

Knowledge gaps

How effective is the urban forestry as a heat mitigation strategy at local scale and how this effectiveness varies spatially and temporally in Australian cities?

1. How well can urban climate models simulate the observed climate? Can daily and seasonal climate be reproduced well at different densities of urbanisation? How sensitive is the model to prescribed vegetation cover parameters?

2. What is the current LULC of the urban landscape and what are the opportunities for implementation of urban forestry, considering urban physical constraints?

3. How much cooling can be achieved by extensive implementation of urban forestry as a heat MS? Is the urban forestry a viable alternative for cooling under different climatic conditions?

Assess how the cooling effectiveness varies among different seasons of the year and in EHE Assess the cooling effectiveness across periods of different rainfall regimens Compare the cooling effectiveness in two cities of different climate characteristics. Develop case studies in support of forestation programs (“Greening the West”)

Research Questions

Summary of the Research Approach

Melbourne &

Brisbane30 ye

ars @ 30 m

in

resolution

UCM/LSM validation, sensitivity

and selection

Surfa

ce

para

met

erisa

tion

K↓, L↓, Ta, Qa, Psurf, Ws, Rainfall

Current LC maps

T [°C]

Modified LC maps

Planning zones

T [°C] 

Cooling [°C]

Mitigation Strategies

  

Remote Sensing

OBJECTIVE 1

UC/LSM

UC/LSM

OBJECTIVE 2

OBJECTIVE 3

City-wide simulations

Atmospheric Forcing Model

outputs

OBJECTIVE 1

To evaluate the ability of existing models as a tool to assess cooling from heat mitigation strategies.

Urban climate models have strengths and weakness that need to be considered when employing them in particular urban climate problems

Objective 1: Validation sitesPreston Armadale Surrey Hills

Geometrical parameters:Building heightWall-to-plan area ratio ~ h/wRoof fractionRoughness lengthRadiation Parameters:Albedo for roof, wall and roadsEmissivity for roof, wall and roadsThermal parameters:Volumetric heat capacity of roof, walls and roads.Thermal conductivity of roof, walls and roads.

Vegetation Parameters:Natural surface fractions of trees and grassMonthly green vegetation fraction, LAI, roughness length and emissivityShortwave and NIR albedosMinimum stomatal resistanceRoot depths and distributionEtc.Soil parameters:Soil texture (% of clay and sand)Slope index

1-furb

furb

Objective 1: Simulation results (Preston)TEB_GARDEN vs. SLUCM_NOAH

Summer Autumn Winter Spring

Objective 1: Performance when Tmax > 35°C

  Sensible Heat (QH) [W/m2] Latent Heat (QE) [W/m2]  TEB_GARDEN TEB_ISBA SLUCM_NOAH TEB_GARDEN TEB_ISBA SLUCM_NOAH

σobs 111.6 111.6 111.6 66.4 66.4 66.4σmod 146.7 174.6 126.2 41.3 30.5 60.1MBE 19.2 34.9 -1.9 -15.4 -23.5 -1.2RMSE 52.7 80.4 39.8 42.3 50.5 34.9RMSES 35.4 66.8 8.6 35.6 47.5 15.3RMSEU 39.0 44.8 38.9 22.9 17.2 31.4R2 0.93 0.93 0.90 0.69 0.68 0.73

8 days, 206 flux samples selected

During daytimeIt seems that the performance of

SLUCM_NOAH is significantly better during daytime

The performance is similar in general but it varies across the seasons of the year and time of the day.

Systematic underestimation of QE in most seasons:

Surface parameters for vegetated surfaces could be improved (e.g. z0 in urban conditions etc.);

Although Melbourne was under Stage 1 water restriction (Coutts et at. 2007) no irrigation whatsoever was considered.

Patchy vegetation may transpires at a relative higher rate than a completely vegetated surface (Offerle et al. 2006). Vegetation is really patchy in Preston.

Objective 1. Preliminary remarks

Validate models in Armadale and Surrey Hills Sensitivity to vegetation parameters

(evapotranspiration)

Select the most appropriate model configuration to estimate cooling

OBJECTIVE 2

To obtain the current LC data suitable for climate modelling and to derive realistic UHI MS based on urban forestry

The spatial heterogeneity of the urban landscape requires very high resolution LC information to estimate the implementation opportunities of MS.

Objective 2: Derivation of LC fractions

High resolution land cover data (small area)

Multi-spectral Remote Sensing Imagery (Landsat TM)

900m

Objective 2: Accuracy of LC estimation

Site Cover Type Expert Classification

Manual Classification Average Landsat TM

classification

Armadale(37°51’S 145°1’E)

Trees 0.21 0.19 0.20 0.20Grass 0.18* 0.11 0.15 0.15

Impervious 0.61 0.70 0.67 0.65

Preston(37°43’S 145°0’E)

Trees 0.29 0.16 0.23 0.18Grass 0.11 0.2 0.15 0.18

Impervious 0.60 0.64 0.62 0.64

Surrey Hills(37°49’S 145°5’E)

Trees 0.27 0.31 0.29 0.34Grass 0.19 0.16 0.18 0.18

Impervious 0.52 0.54 0.53 0.48

  Tree cover Impervious cover Grass cover30m 60m 900m 30m 60m 900m 30m 60m 900m

Pearson’s r 0.788 0.794 0.968 0.827 0.830 0.989 0.742 0.744 0.926MAE 0.068 0.017 0.014 0.121 0.030 0.023 0.100 0.025 0.028MBE 0.000 0.000 -0.004 0.001 0.000 0.01 0.001 0.000 -0.006

In City of Melbourne (LGA)

In flux tower sites (radius 500m)

Objective 2. Derivation of MS based on vegetation

Current Land cover Maps

Modified Land cover Maps(Mitigation)

Analysis by planning zones to derive ‘realistic’ mitigation scenarios based on feasible increases of the amount of vegetation in urban areas

No. Planning zone classes Total cover fraction

Tree cover (%)

Grass cover (%)

Impervious cover (%)

1 Business zone 4.6 % 9.0 15.9 75.12 Industrial zone 10.4 % 13.1 19.6 67.33 Low density residential zone 4.0 % 36.0 32.4 31.6

4 Public parks / Recreational zones 7.3 % 24.1 40.3 35.6

5 Public use zones 1.7 % 15.6 27.6 56.86 Road zone 4.8 % 21.9 22.2 55.97 Rural use zone 13.4 % 35.4 37.5 27.18 Residential zone 48.5 % 24.1 22.4 53.59 Special use zones 4.3 % 15.6 36.0 48.4  Water bodies 1.0 % - - -

Objective 2. Derivation of mitigation scenarios

… …Original LC Modified LC

More vegetated

Least vegetated

Planning zone

pi

OBJECTIVE 3

To understand how the cooling from vegetation varies at different spatial and temporal scales.Run city-wide simulations in Melbourne and Brisbane. Process simulation outputs to answer the main research question

City-wide, long-term simulations of the current and improved urban climate can provide the data to understand how the cooling effectiveness varies across different spatial and temporal scales of the urban climate

Surface parameterisation (city wide simulations)

Current cover fractions

Water bod-ies

Impervious Deciduous

Evergreen Grass

Modified cover fractions

Water bod-ies

Impervious Deciduous

Evergreen Grass

Radiation and thermal parameter Units Symbol SourceLayers thickness for roof, wall and road [m] Δz i, i = 1:4 Defaults from Loridan et al. (2011)Albedo for roof, wall, and road [-] αroof, αwall, αroad Defaults from Loridan et al. (2011)Emissivity for roof, wall, and road [-] εroof, εwall, εroad Defaults from Loridan et al. (2011)Volumetric heat capacity for roof, wall and road [MJ/K/m3] croof, cwall, croad Defaults from Loridan et al. (2011)Thermal conductivity for roof, wall and road [W/K/m] λroof, λwall, λroad Defaults from Loridan et al. (2011)

Geometrical parameters      Mean building height [m] h From urban planning zones aggregationRoof width [m] r From urban planning zones aggregationRoad width [m] w From urban planning zones aggregationRoughness length for momentum [m] z0town From urban planning zones aggregation

Vegetation parameter Units Symbol SourceGreen fraction [fraction] σf (monthly) Time varying remote sensing NDVI and cover fractionsLeaf area Index [m2/m2] LAI (monthly) Profiles from literature adjusted to southern hemisphereRoughness length for momentum [m] z0 (monthly) Tree height by planning zones and seasonal considerations

Shortwave albedo [-] αveg (monthly) Literature reviewEmissivity [-] εveg (monthly) Literature reviewMinimum stomatal resistance [s/m] RSmin Literature review

Soil class [-] Soil Harmonized Wold Soil Database Slope class [-] Slope WRF default global datasetDeep soil temperature [K] Tbot WRF default global dataset

Cells of 900m

City-wide Atmospheric Forcing data

Melbourne Brisbane

K↓, L↓,Psfc, Tzref, Qzref,

Wspd,Rainfall

Select surface station that have 30m meteorological data in the domain of interest and fill single 30-min gaps

Gap filling using the nearest station in the period of interest Derive K↓, L↓ using cloud cover, T2m and Q2m (NARP parameterisation) Complement Rainfall with daily outputs from AWAP dataset

Adjustment of forcing Tair and Qair at the forcing height zref = 40 m form T2m and Q2m by an iterative process based on bias correction (Lemonsu 2009)

30 years @ 30 min

Urban forestry is an effective way to mitigate heat in urban areas but its effectiveness needs to be quantified in Australian cities

The cooling effectiveness of UHI MS depends of several spatial and temporal factors

UCM/LSM can help to quantify the cooling effectiveness of heat mitigation scenarios but their fit-to-purpose should be assured.

Modifications in the landscape as a result of UHI MS must be represented as accurately as possible considering urban physical constrains

Summary

Planning (Jun 2011-Apr 2012) Literature review (70%) Assimilation of models and set-up (100%) Data request (90%) Writing of the CoC report (100%)

Objective 1 (Dec 2011-Mar 2013) Validate of an UCM/LSM pair (80%) Assess the fit-to-purpose as a heat mitigation

strategy assessment tool (20%) Publish relevant results (0%)

Objective 2 (Dec 2011-Mar 2013) Derivation of current land cover (Melbourne

only) (95%) Derivation of heat mitigation scenarios (25%) Surface parameterisation for baseline and

scenarios (0%) Publish relevant results (20% -> ICUC8 Dublin

2012)

Objective 3 (Jun 2012-Dec 2013) Prepare forcing data to run city-scale climate

simulations in Melbourne and Brisbane (10%) Perform grid-based simulations with current

and modified landscapes for the domains of Melbourne and Brisbane (0%)

Analyse model outputs to respond research questions related (0%)

Run proposed neighbourhood cases (0%) Publication of results and thesis writing (0%)

Thesis revision and submission

(Jan 2014 – May 2014)

Progress today and time frames

THANK YOU!

Questions?

Objective 1. Urban Canopy / Land Surface Models

Q* + QF = QH + QE + ΔQS [W/m2]

Urban canopy model ≈ urban energy balance

fgardenfroadfroof

za

zT

zR

Ta

TS garden

TS wallTS wall

TS road

TS roof

Tcanyon

Ti bld

 

Tcell = furbTurb + (1 – furb)Tnature

Soil hydrology and thermodynamicsDirect evaporation from soil and canopyEvapotranspiration

Radiation trapping in the canyonHeat storage by the urban fabricAnthropogenic heat release, etc.

Objective 1: Parameters prescription

Monthly LAI [m2/ m2]αnir

[-]αvis

[-]

RSmin [s/m]Jan Feb Mar Apr Ma

y Jun Jul Aug

Sep Oct Nov Dec

Deciduous trees

4.2 4.8 5.6 4.4 2.4 1.8 1.5 1.2 1.1 2.2 3.1 3.5 0.25c 0.05c   100c

Evergreen tress 3.2a 3.2 3.2 3.2 3.2 3.2 3.2 3.2 3.2 3.2 3.2 3.2 0.45d 0.12d   250ac

Grass 1.0b 1.0b 1.0b 1.0b 1.0b 1.0b 1.0b 1.0b 1.0b 1.0b 1.0b 1.0b 0.3c 0.1c   40bc

a Value taken from Peel et al. (2005)b Values taken from Lynn et al. (2009)c Default values in ECOCLIMAP-II natural parameters for such cover type (Champeaux, Masson et al. 2005)d Average taken from Figure 3 in Lewis (2002) for Eucalyptus spp. and Acacia spp. spectral profile.

Australian native and exotic trees

Thermal parameters of urban materials

Most evergreen tree are native (Eucalyptus and Acacias spp.) (Frank 2006)

Models’ performance has been found to be similar in general The integrated approach (TEB_GARDEN) did not report any evident improvement.

Geometries of residential areas in Melbourne do not form well defined urban canyons

Systematic underestimation of QE in most seasons:

Surface parameters for vegetated surfaces could be improved (e.g. z0 in urban conditions etc.);

Although Melbourne was under Stage 1 water restriction (Coutts et at. 2007) no irrigation whatsoever was considered.

Patchy or sparse vegetation transpires at a relative higher rate than a completely vegetated surface (Offerle et al. 2006). Vegetation is really patchy in Preston.

Objective 1. Preliminary remarks

Validate models in Armadale and Surrey Hills Select the most appropriate model configuration for

cooling calculation

Objective 1: Model comparison (Preston)  TEB_GARDEN TEB_ISBA SLUCM_NOAH  K↑ L↑ QH QE K↑ L↑ QH QE K↑ L↑ QH QESummer (Nobs = 689)                        

σobs 51.8 40.4 110.8 61.6 51.8 40.4 110.8 61.6 51.8 40.4 110.8 61.6σmod 58.4 53.2 140.4 53.2 67.2 50.7 151.5 50.0 52.4 61.6 127.3 47.7MBE 4.7 4.2 17.4 -8.5 13.3 -0.2 20.6 -13.1 -0.1 6.9 3.7 -5.5RMSE 9.9 16.8 60.2 56.8 21.1 14.0 68.5 58.7 3.5 25.9 43.2 49.6RMSES 7.8 12.3 25.4 34.6 20.1 9.2 35.5 39.4 0.5 20.2 10.1 32.5RMSEU 6.1 11.4 54.5 45.1 6.4 10.6 58.6 43.5 3.4 16.2 42.0 37.4R2 0.99 0.95 0.85 0.28 0.99 0.96 0.85 0.24 1.00 0.93 0.89 0.38Autumn (Nobs =785)                        σobs 27.1 27.2 48.4 27.1 27.1 27.2 48.4 27.1 27.1 27.2 48.4 27.1σmod 28.4 34.6 56.1 23.6 34.3 34.2 58.4 26.2 26.9 37.3 61.4 17.3MBE 0.2 2.3 -0.7 -4.8 3.8 0.3 -3.4 -3.3 -0.5 -1.5 -1.2 -6.9RMSE 3.5 10.5 25.5 23.4 8.6 9.8 26.5 24.1 1.9 14.5 26.6 22.5RMSES 1.1 6.9 1.7 13.8 7.9 6.1 5.2 11.9 0.6 8.3 7.6 17.9RMSEU 3.3 8.0 25.5 18.9 3.5 7.6 26.0 20.9 1.8 11.8 25.5 13.7R2 0.99 0.95 0.79 0.36 0.99 0.95 0.80 0.36 1.00 0.90 0.83 0.38Winter (Nobs = 596)                        σobs 23.1 14.2 45.9 29.4 23.1 14.2 45.9 29.4 23.1 14.2 45.9 29.4σmod 24.2 19.8 47.9 29.8 29.4 19.1 46.0 33.3 23.1 23.7 53.7 17.6MBE 0.5 4.0 2.6 -2.6 4.1 2.2 -2.6 0.8 -0.1 0.3 3.7 -9.8RMSE 2.8 8.6 19.1 30.0 7.9 7.4 18.1 30.6 1.5 10.8 18.6 26.5RMSES 1.1 6.2 3.1 15.0 7.3 4.7 4.3 11.8 0.2 8.6 6.0 22.1RMSEU 2.6 6.0 18.8 25.9 2.9 5.7 17.6 28.2 1.4 6.6 17.6 14.7R2 0.99 0.91 0.85 0.24 0.99 0.91 0.85 0.28 1.0 0.92 0.89 0.3Spring (Nobs = 622)                        σobs 43.7 35.0 93.0 59.0 43.7 35.0 93.0 59.0 43.7 35.0 93.0 59.0σmod 48.0 43.4 105.6 50.7 55.8 41.8 103.7 55.4 43.5 51.0 101.5 43.7MBE 1.8 4.0 8.7 -14.6 7.9 0.8 1.7 -9.4 -0.8 5.1 6.2 -16.3RMSE 7.2 12.0 37.8 45.5 15.2 10.0 34.8 43.7 3.0 20.1 31.6 38.6RMSES 4.4 8.6 10.7 27.6 14.2 6.0 5.2 21.2 0.8 15.2 7.2 28.8RMSEU 5.7 8.3 36.3 36.2 5.4 8.0 34.4 38.2 2.9 13.1 30.8 25.7R2 0.99 0.96 0.88 0.49 0.99 0.96 0.89 0.52 1.00 0.93 0.91 0.65

For every urban planning zone class :

Calculate the cover fractions intersected with a grid of resolution X.

Given a function y(ftree, fgrass) that weighs the cooling obtainable from grass and trees fractions, sort the land cover composition by y.

Given a threshold of implementation () [0..1] obtain the land cover composition for every given class whose position in the sorted array divided by the number of samples is equal to .

Replace the existing land cover composition on every cell of which

Aggregate the modified cover fractions back to the urban climate model resolution.

Objective 2. Derivation of mitigation scenarios

y(ftree, fgrass) = βftree + fgrass

… …Original Modified

More vegetated

Least vegetated

Business

zone

Seasonal parameters of vegetation are important in simulations of long periods.

Deciduous species of occupy an significant percentage of the total urban forestry (Frank 2006)

Assume that evergreen trees and grass present similar properties during all seasons, then estimate

fexotic = α(NDVIleaf-on – NDVIleaf-off)

Better than assuming the same fraction

of deciduous trees city wide

Objective 2. Seasonal variability of vegetation parameters

Parameters with ambiguous definitions have to be prescribed (e.g. h/w)

Objective 1. Models limitations and data uncertainties

Validate models in Armadale and Surrey Hills

Make further analysis of performance to determine the causes of limitations (e.g. underrated QE)

Test other vegetation approaches (NOAH-MP Ball Berry)

Sensitivity analysis to vegetation parameters

Selection of the most appropriate model configuration for cooling calculation

Objective 1. Next steps

32/24

Surface ParameterisationGeometrical parameters:Mean Building height [m]Wall-to-plan area ratio [-] ~ h/wRoof fraction [-]Roughness length [m]Radiation Parameters:Albedo for roof, wall and roads [-] ~0.1 – 0.2Emissivity for roof, wall and roads [-] ~0.85 – 0.98Thermal parameters:Volumetric heat capacity of roof, walls and roads.Thermal conductivity of roof, walls and roads.Vegetation Parameters:Vegetation fractions of trees and grass[-]Monthly green vegetation fraction [-]Monthly LAI [m2/m2]Monthly roughness length [m]Monthly emissivity [-]Shortwave and NIR albedosMinimum stomatal resistance [s/m]Other curve-fitting parameters (RGL, HS, …)Soil parameters:Parameters derived from the soil texture

fimperv

fpervious

33

Preston Site (2004): Impervious fraction:

62% → 50% Tree fraction:

23% → 40% Grass fraction:

15% → 10%

Significantly dry summer (33mm in the period assessed)

Discussion of scales

Cooling effectiveness calculation