1. Introduction What are short rotation coppices (SRCs)? Woody, - - PDF document

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1. Introduction What are short rotation coppices (SRCs)? Woody, - - PDF document

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2017-07-07 C ONVERTING W ASTE T O R ESOURCES: A DECISION-SUPPORT MODEL FOR WASTEWATER-IRRIGATED SHORT ROTATION CROPS (General Introduction) By Huy Nguyen 1,* , Evan Davies 2 , Miles Dyck 3 , Martin Blank 4 , Richard Krygier 4 1. Department of

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CONVERTING WASTE TO RESOURCES:

A DECISION-SUPPORT MODEL FOR WASTEWATER-IRRIGATED SHORT ROTATION CROPS (General Introduction)

By

Huy Nguyen1,*, Evan Davies2, Miles Dyck3, Martin Blank4, Richard Krygier4

1

  • 1. Department of Civil Engineering and Applied Mechanics, McGill University, Montreal, QC, Canada
  • 2. Department of Civil and Environmental, University of Alberta, Edmonton, AB, Canada
  • 3. Department of Renewable Resources, University of Alberta, Edmonton, AB, Canada
  • 4. Natural Resources Canada, Canadian Forest Service, Canadian Wood Fibre Centre, Edmonton, AB, Canada

*Corresponding author: huy.nguyen5@mail.mcgill.ca

Content

  • 1. Project Introduction
  • 2. Application of System Dynamics

to SRC System

  • 3. Model Description
  • 4. Model Uses & Applications
  • 5. Conclusions

2

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  • 1. Introduction
  • What are short rotation coppices (SRCs)?
  • Woody, perennial crops: willow, poplar,
  • High-yielding 7-10 T/ha/yr (North America)
  • Harvest: 3-4 year cycle, life cycle > 20 years (~7 coppices)
  • Environmentally friendly: permitting disposal of treated, nutrient-

rich, domestic wastewater and biosolids

  • Economically viable: providing a sustainable source of wood fibre for

biofuel and biochar production

3 http://www.treehugger.com/renewable-energy/biomass-can-only-offer-major-emission-reductions-if-best-practices-are-followed-new-uk-report-says.html

(Keoleian & Volk, 2005, Yemshanov & McKenney, 2008)

  • 1. Introduction

Biomass production, WWT, Irrigation, Bioenergy, Land-use, Environmental quality, Production Cost, SRWC policies

(Image source: EUBIA, n.y.)

Willow Poplar 3-year- rotation

4

  • Complex interactions and

feedbacks between the system components

(Caslin et al., 2011; Dimitriou et al., 2011; Langeveld et al., 2012; Weih, 2009)

  • The Big Picture: SRC as a “SYSTEM”
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  • 1. Introduction
  • Goals and Challenges:

The interactions and feedbacks within and between components are complex and hard to quantify

Image source: http://www.mberg.com.au/successful-tree-planting/ 5

Soil – Water – Climate – Plant growth & yield component

  • 1. Introduction
  • Goals and Challenges:

6

Development of a decision-support model to aid in long-term planning for environmentally and economically sustainable SRC plantations

Such a model can be used to:

▪ Simulate crop growth and inputs, their interaction with yield and end-uses ▪ Simulate soil water and solute transport ▪ Estimate the biomass energy content (biofuel), project economy ▪ Identify how alternative decisions affect system behaviour through the use of “what-if” scenarios ▪ Provide insights into the SRC plantation and management

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  • 1. Introduction

7

  • Part of our decision-support model:
  • 1. PLANT GROWTH & YIELD
  • 2. SOIL WATER

Content

  • 1. Project Introduction

2.Application of System Dynamics to SRC System

  • 3. Model Description
  • 4. Model Uses & Applications
  • 5. Conclusions

8

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  • 2. Application of SD to SRC system
  • What is “system dynamics”?

“A rigorous method of system description that facilitates feedback analysis – usually via a simulation model – of the effects of alternative system structure and control policies on system behavior”.

  • System dynamics tools

– Causal loop diagram (CLD), – Stock and flow diagram (SFD) and simulation model (Model)

(Simonovic & Davies, 2007)

9

CLD SFD & Model

(Sterman, 2000)

  • 2. Application of SD to SRC system

Causal Loop Diagram (CLD)

– Causality: (+), (-), delay – Feedback Processes – Feedback Loop: reinforcing, balancing

delay

(Sterman, 2000)

10

A B A B

(+)

A B A B

(-)

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  • 2. Application of SD to SRC system

Stock and Flow Diagram (SFD)

“Stocks are accumulations. They characterize the state of the system and generate the information upon which decisions and actions are based” Examples:

  • Stock: Bank account

balance, number of trees in plot, water in lake

  • Flow: income, timber

harvesting, stream flows

11

source sink

(Sterman, 2000)

  • 2. Application of SD to SRC system

Basic feedback processes of dynamic behaviour Combination of basic feedback processes

(Sterman, 2000)

Note: these behaviors are typically seen in numerical simulation models (based on SFDs), not in CLDs.

12

https://en.wikipedia.org/wiki/System_dynamics

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  • 2. Application of SD to SRC system

Stock & Flow Diagrams Decision-support Model Causal Loop Diagrams Literature Review

Four

study components How did we construct the systems model for SRC, ‘WISDOM’, or Willow System Dynamics Model ?

13

SRWCP

Land Use Biodiversity Agriculture Water Quality Soil Quality Biomass Production Bioenergy Climate Change GHG emission reduction Local Economy Producers /Farmers Govenrment Policies

  • n SRWC

SRC Growth Treated Municipal Waste Water Irrigation Effluents Nutrients Threats to Public Health, Aquatic Life Environmental Regulations Net Profits Employment + +

  • +

Energy Supply for facilities +

  • +

Job Creation

  • +

+ + + +

  • Heavy metal

extraction Selling Fuel/ Heat/Energy Waste Water Treatment Costs

  • Phyto-remediation

+ + Treatment Plants Irrigation

Salinity in rootzone + Nutrient leaching Soil nutrient Soil water content Nutrient uptake by SRC Water uptake by SRC + + + <SRC planting> +
  • Wat
deficie +
  • Decomposition
Soil Organic Matter +
  • Precipitation
+ + + + Weathering CO2 DOC Base cations Ca2+, Mg2+, K2+ BC uptake by SRC + + + +
  • +
  • +
+ + Mineralisation Water Extraction + <Salinity in rootzone>
  • Nitrificatrion
Immobilization Denitrification Litter + <SRC Foliage> + Nitrates NO3- Ammonium NH4+ + + + + + +
  • Fertilizer
Soil P + + +
  • <AET>
  • +
SRC Growth + <Sewage Sludge> + Respiration Photosynthesis <CO2> Radiation + + + + + + +
  • +
O2 + <SRC Root Biomass> <Drainage>
  • Soil K
+ + <Irrigation Req> <Irrigation Supply>

Entire system visualization

Sub-system visualization

Literature Review General CLD 7 Sub-system CLDs SFD & Model

DATA

  • 2. Application of SD to SRC system

14

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Content

  • 1. Project Introduction
  • 2. Application of System Dynamics

to SRC System

  • 3. Model Description
  • 4. Model Uses & Applications
  • 5. Conclusions

15 http://www.ceh.ac.uk/staffwebpages/images/willowebnm.jpg

  • 3. Model Description

16

7 interconnected components

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Based on 3-PG

  • 3. Model Description
  • Plant growth & yield:

Root allocation (Eta R) Root allocation - most limiting conds (Eta Rx) Root allocation - no limiting conds (Eta Rn) Degree of site limitation (Zeta) + Physilogical modifier (Phi) <f (soil water content)> + Stem allocation (Eta S)

  • Foliage to stem

allocation ratio (Pfs) Foliage allocation (Eta F)

  • +

Allometric scale factor (a p) DBH Pfs20 (Mature) Allometric power (n p) Pfs2 (Saplings)

  • <Site fertility rating

(FR)>

  • Total GPP

(Pg) Total NPP (Pn) Constant CarbonUE (Y=0.47) + Numbers of day in month (dm) Light use efficiency (epsilon) PAR + + <Convert g/m2 to T/ha> Convert total radiation into PAR(value=0.5) Light extinction coeff (k) Canopy LAI (L) (3-PG modul) + Fractional ground cover

  • f canopy (Beta gc)

+ Total solar radiation (Phi o) daily incident (monthly average) + Specific leaf area (Sigma f) <Convert T/ha to kg/m2> Critical canopy LAI (Lgc) + LAI / LAI critical + Stem biomass Foliage biomass Root biomass + + + + + <Stem biomass> Basal area Self thinning Stocking Mortality

  • +

+

  • Canopy quantum

efficiency (alpha C) + Diameter at breast height (DBH)

  • +

+ <Growth modifiers> + <f (VPD) (ignored: value=1)> <f (age)> + Maximum canopy quantum efficiency (alpha Cx) +

17

(Landsberg and Sands, 2011 ; Amichev et al., 2010, 2011, 2012; Headlee et al., 2012; Nair et al., 2012)

Based on 3-PG

  • 3. Model Description
  • Plant growth & yield:

Root allocation (Eta R) Root allocation - most limiting conds (Eta Rx) Root allocation - no limiting conds (Eta Rn) Degree of site limitation (Zeta) + Physilogical modifier (Phi) <f (soil water content)> + Stem allocation (Eta S)

  • Foliage to stem

allocation ratio (Pfs) Foliage allocation (Eta F)

  • +

Allometric scale factor (a p) DBH Pfs20 (Mature) Allometric power (n p) Pfs2 (Saplings)

  • <Site fertility rating

(FR)>

  • Total GPP

(Pg) Total NPP (Pn) Constant CarbonUE (Y=0.47) + Numbers of day in month (dm) Light use efficiency (epsilon) PAR + + <Convert g/m2 to T/ha> Convert total radiation into PAR(value=0.5) Light extinction coeff (k) Canopy LAI (L) (3-PG modul) + Fractional ground cover

  • f canopy (Beta gc)

+ Total solar radiation (Phi o) daily incident (monthly average) + Specific leaf area (Sigma f) <Convert T/ha to kg/m2> Critical canopy LAI (Lgc) + LAI / LAI critical + Stem biomass Foliage biomass Root biomass + + + + + <Stem biomass> Basal area Self thinning Stocking Mortality

  • +

+

  • Canopy quantum

efficiency (alpha C) + Diameter at breast height (DBH)

  • +

+ <Growth modifiers> + <f (VPD) (ignored: value=1)> <f (age)> + Maximum canopy quantum efficiency (alpha Cx) +

18

Growth modifiers <f (soil water content)> <f(temperature)> <f(salinity)> <f(soil nutrient)> <f (age)> <f (VPD) (ignored: value=1)>

Challenge: plant functional type

  • 3-PG: developed for evergreen trees
  • SRCs: deciduous trees

(Landsberg and Sands, 2011 ; Amichev et al., 2010, 2011, 2012; Headlee et al., 2012; Nair et al., 2012)

Solution:

  • Applied the principle of virtual leaf biomass
  • Frankfurt Biosphere Model (Ludeke et al., 1994)
  • Canadian Terrestrial Ecosystem Model (Arora and Boer, 2005)
  • Allows simulation of both deciduous and evergreen

trees

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Economic assessment

19 Total Expenditures Expenditures <TIME STEP> <Trigger (onset of harvest)> <Planting cost (yearly) (total)> <Trigger (once at beginning)> <Harvesting cost (N harvesters)> Total revenues Revenues <Revenues from selling biomass> <Field storage cost> <Subsequent process (Grinding) cost> <Highway transporting cost> <OST cost (N harvester)> <Fixed- and indirect costs (total)> <Trigger (yearly)> <Trigger (onset of harvest)>

  • Acc. Cash Flow

(total) Revenues (ACF) Expenditures (ACF)

  • Acc. Cash Flow

(per ha) <Project size> <Trigger (once at the end)> <Benefit from C sequestration> <Benefit from C offset> <Maintenance cost (yearly) (total)> <Soil prep.+establ. cost (yearly) (total)> <Irrigation system setup (total)> <Irrigation system operation cost (yearly) (total)> <Switch (irrigation system)> <Recultivating cost (yearly) (total)> HTDS PPMR

PGY sub-model

<Switch (C sequestered)> <Switch (C

  • ffset)>

HTDS sub-model Soil water sub-model C mitigation sub-model

Biomass production Moisture content Irrigation Economic sub-model boundary Economic sub-model boundary

  • 3. Model Description

(Buchholz and Volk, 2010; Marron et al., 2012)

Economic assessment

20 Total Expenditures Expenditures <TIME STEP> <Trigger (onset of harvest)> <Planting cost (yearly) (total)> <Trigger (once at beginning)> <Harvesting cost (N harvesters)> Total revenues Revenues <Revenues from selling biomass> <Field storage cost> <Subsequent process (Grinding) cost> <Highway transporting cost> <OST cost (N harvester)> <Fixed- and indirect costs (total)> <Trigger (yearly)> <Trigger (onset of harvest)>

  • Acc. Cash Flow

(total) Revenues (ACF) Expenditures (ACF)

  • Acc. Cash Flow

(per ha) <Project size> <Trigger (once at the end)> <Benefit from C sequestration> <Benefit from C offset> <Maintenance cost (yearly) (total)> <Soil prep.+establ. cost (yearly) (total)> <Irrigation system setup (total)> <Irrigation system operation cost (yearly) (total)> <Switch (irrigation system)> <Recultivating cost (yearly) (total)> HTDS PPMR

PGY sub-model

<Switch (C sequestered)> <Switch (C

  • ffset)>

HTDS sub-model Soil water sub-model C mitigation sub-model

Biomass production Moisture content Irrigation Economic sub-model boundary Economic sub-model boundary

  • 3. Model Description
  • Based mainly on Ecowillow (economic budgeting model)
  • What’s new:
  • Using process-chain for SRC costs
  • Accounting for potential trade of carbon credits
  • Accounting for irrigation system costs
  • Including more options in harvest-transport component
  • Connecting yield input to PGY sector

(Buchholz and Volk, 2010; Marron et al., 2012)

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21

KUP-Ernteplaner (SRC-Harvest-Planner)

Ecowillow

(Buchholz and Volk, 2010) (Marron et al., 2012)

Content

  • 1. Project Introduction
  • 2. Application of System Dynamics to

SRC System

  • 3. Model Description

4.Model Uses & Applications

  • 5. Conclusions

22

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  • 4. Model Uses & Applications

23

  • 4. Model Uses & Applications

24

  • Simulation of Whitecourt SRC system
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  • 4. Model Uses & Applications

25

Model Input: Set in EXCEL

(a) (b) (c) enter inputs change “switch” Parameter values

  • 4. Model Uses & Applications

26

Model Input: Set in EXCEL

  • 30
  • 15

15 30 50 100 150 200 2006 2007 2008 2009 2010 2011 2012 2013 T (oC), Rn (MJ/m2/d), U (km/hr) P (mm), H (%) Time (year) P H Tmax Tmin Tmean Rn U

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  • 4. Model Uses & Applications

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Model Output

  • 4. Model Uses & Applications

28

* SD model was tested using data from Whitecourt, Alberta, trial site

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  • How to deal with uncertainties ?

– Measured data (e.g., shoot biomass, irrigation, soil chemical, soil moisture) – Data from literature (i.e., parameter value ranges)

29

No Parameter Range Unit Note Source 1 Light use efficiency 1.06 – 2.22 1 – 1.38 Dmnl (LUE ~ above-ground biomass) Green et al. (2001) Cannel et al. (1987) (cited in Landsberg and Sands 2011) 2 Light extinction coefficient 0.555 – 0.917 Dmnl Green et al. (2001) 3 Canopy LAI 2 - 6 Dmnl 1st and 2nd seasons of 2nd coppice rotation Pellis et al. (2004) Afas et al. (2005) Laureysens et al., (2005) 4 Specific leaf area 16 – 21.1 16 – 23.5 m2/kg 1st GS 1st and 2nd GS Pellis et al. (2004) Afas et al. (2005)

Use as inputs -> uncertainties … Use for output validations

  • 4. Model Uses & Applications
  • How to deal with uncertainties ?
  • > Using Monte Carlo simulation

30

MC alpha Cx TS=025 50% 75% 95% 100% "Maximum canopy quantum efficiency (alpha cx)" 0.02 0.0175 0.015 0.0125 0.01 1 20.25 39.5 58.75 78 Time (Month) MC alpha Cx TS=025 50% 75% 95% 100% Shoot SRC Biomass 40 30 20 10
  • 1e-006 1

20.25 39.5 58.75 78 Time (Month)

MC beta gc (bare ground) MC beta gc MC alpha Cx TS=025 50% 75% 95% 100% "Beta gc (bare ground)" 0.06 0.045 0.03 0.015 0 1 20.25 39.5 58.75 78 Time (Month) MC beta gc (bare ground) MC beta gc MC alpha Cx TS=025 50% 75% 95% 100% Shoot SRC Biomass 20 15 10 5
  • 1e-006 1

20.25 39.5 58.75 78 Time (Month) MC virtual leaf - inital 50% 75% 95% 100% initial virtual leaf biomass 0.4 0.3 0.2 0.1 0 1 20.25 39.5 58.75 78 Time (Month) MC virtual leaf - inital 50% 75% 95% 100% Shoot SRC Biomass 20 15 10 5

  • 1e-006 1

20.25 39.5 58.75 78 Time (Month)

Very sensitive Sensitive Less sensitive

Maximum canopy quantum efficiency Initial virtual leaf biomass Fractional ground cover at bare ground

  • 4. Model Uses & Applications
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  • 4. Model Uses & Applications

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▪ Answer “what if?” questions, rather

than optimize policies ▪ Answers offer model users insights into various aspects of SRC plantation and management ▪ Example of “what-if”: Vary leaching fraction =0.2-0.5 (Gainer, 2012)

‘WISDOM’ as a DST

Irrigation Drainage Soil EC

  • 4. Model Uses & Applications

32

‘WISDOM’ as a DST

  • Whitecourt SRC plantation growth and yield: 3 scenarios

5 10 15 20 25 30 35 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 Shoot biomass (ODT/ha) Time (Year)

Shoot biomass of different scenarios (ODT/ha)

Opt Avg Pes Val Obs

No Scenario Yield case Net rad. Rn (MJ/m2/d) Air Temperature (oC) Comment Tmax Tmin Tmean 1 Optimistic max max min max mean Irrigation requirement case: No water, nutrient, and salinity stresses f(ϴr)=1, f(FR)=1, f(S)=1 2 Average avg avg avg avg avg 3 Pessimistic min min max min mean

(Based on 8 years of historical data)

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  • 4. Model Uses & Applications

33

‘WISDOM’ as a DST

  • Whitecourt SRC plantation economics

3 yield

scenarios

(opt, avg, pes)

3 harvester

  • ptions

(JF, HS, BB)

x

3 harvesting

speeds

(max, avg, min)

x

27

economic scenarios

=

Avg_JF_max Avg_JF_avg Avg_JF_min Avg_HS_max

…

Avg_HS_avg

x

JF

x

=

(Blank, 2012; Phillips, 2013)

34

  • 4. Model Uses & Applications

‘WISDOM’ as a DST

Whitecourt SRC plantation economics

Illustration:

9 scenarios

(Average yield x 3 harvesting

  • ptions

x 3 harvesting speeds) Cumulative cash flows Net present values Internal rate of returns

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Content

  • 1. Project Introduction
  • 2. Application of System Dynamics

to SRC System

  • 3. Model Description
  • 4. Model Uses & Applications
  • 5. Conclusions

35

http://www.ceh.ac.uk/staffwebpages/images/willowebnm.jpg

  • 5. Conclusions
  • Value of feedback-based systems modelling

methods

  • Simulate system behaviour, elucidate cause-and-effect

relationships

  • Represent SRC systems in a realistic comprehensive way
  • The application of WISDOM
  • Good simulation: R2= 0.98 for biomass production,

R2= 0.92 - tree height, and R2 = 0.90 - soil EC

  • Answering “what-if” questions
  • 3 scenarios for yield predictions
  • 27 scenarios for economy forecast

36

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37

Thank You!

Acknowledgement

huy.nguyen5@mail.mcgill.ca

Contact me