Description of 3-PG. Peter Sands. CSIRO Forestry and Forest Products and CRC for Sustainable Production Forestry

Similar documents
OCN 401. Photosynthesis

Spatial Heterogeneity of Ecosystem Fluxes over Tropical Savanna in the Late Dry Season

TREE WATER USE PATTERNS ARE MAINLY DRIVEN BY ENVIRONMENTAL VARIABLES AND TREE STRUCTURAL PARAMETERS IN HUMID TEMPERATE DECIDUOUS HARDWOOD FORESTS

GEOG415 Mid-term Exam 110 minute February 27, 2003

DETAILED DESCRIPTION OF CENW 3.1

Carbon Input to Ecosystems

Nutrient Cycling in Land Vegetation and Soils

Lecture notes: Interception and evapotranspiration

Changes in biomass allocation buffer low CO2 effects on tree growth during the last glaciation

Ecosystems. 1. Population Interactions 2. Energy Flow 3. Material Cycle

Mycorrhizal Fungi. Symbiotic relationship with plants -- form sheath around fine roots and extend hyphae into soil and sometimes into root cells

Soil Water Atmosphere Plant (SWAP) Model: I. INTRODUCTION AND THEORETICAL BACKGROUND

Simulating Carbon and Water Balances in the Southern Boreal Forest. Omer Yetemen, Alan Barr, Andrew Ireson, Andy Black, Joe Melton

Estimating Vegetation Primary Production in the Heihe River Basin of China with Multi- Source and Multi-Scale Data

Nutrient Cycling in Land Vegetation and Soils

TREES. Functions, structure, physiology

Evapotranspiration. Rabi H. Mohtar ABE 325

Supplement of Upside-down fluxes Down Under: CO 2 net sink in winter and net source in summer in a temperate evergreen broadleaf forest

CLIMATOLOGICAL REPORT 2002

PreLES an empirical model for daily GPP, evapotranspiration and soil water in a forest stand

The role of soil moisture in influencing climate and terrestrial ecosystem processes

Lecture 3A: Interception

Zachary Holden - US Forest Service Region 1, Missoula MT Alan Swanson University of Montana Dept. of Geography David Affleck University of Montana

Temperature and light as ecological factors for plants

Range Cattle Research and Education Center January CLIMATOLOGICAL REPORT 2012 Range Cattle Research and Education Center.

Evapotranspiration. Here, liquid water on surfaces or in the very thin surface layer of the soil that evaporates directly to the atmosphere

LECTURE 13: RUE (Radiation Use Efficiency)

Lungs of the Planet with Dr. Michael Heithaus

Lungs of the Planet. 1. Based on the equations above, describe how the processes of photosynthesis and cellular respiration relate to each other.

12 SWAT USER S MANUAL, VERSION 98.1

Contents. 1. Evaporation

Range Cattle Research and Education Center January CLIMATOLOGICAL REPORT 2016 Range Cattle Research and Education Center.

Lecture 8: Snow Hydrology

LEAF AND CANOPY PHOTOSYNTHESIS MODELS FOR COCKSFOOT (DACTYLIS GLOMERATA L.) GROWN IN A SILVOPASTORAL SYSTEM

2. Irrigation. Key words: right amount at right time What if it s too little too late? Too much too often?

Dynamik der Biosphäre. Exogene Dynamik, Biosphärenmodelle

Model description. Photosynthesis

Supplementary Figures

Savannah River Site Mixed Waste Management Facility Southwest Plume Tritium Phytoremediation

Benefits of NT over CT. Water conservation in the NT benefits from reduced ET and runoff, and increased infiltration.

Introduction to ecosystem modelling (continued)

Ecosystem-Climate Interactions

Effects of rising temperatures and [CO 2 ] on physiology of tropical forests

Remote sensing of the terrestrial ecosystem for climate change studies

GOTILWA+ An integrated model of forest growth

2.4. Model Outputs Result Chart Growth Weather Water Yield trend Results Single year Results Individual run Across-run summary

Lecture 6: Precipitation Averages and Interception

Understanding how vines deal with heat and water deficit

Evaluation of a MODIS Triangle-based Algorithm for Improving ET Estimates in the Northern Sierra Nevada Mountain Range

Radiation transfer in vegetation canopies Part I plants architecture

Assimilation of satellite fapar data within the ORCHIDEE biosphere model and its impacts on land surface carbon and energy fluxes

Promoting Rainwater Harvesting in Caribbean Small Island Developing States Water Availability Mapping for Grenada Preliminary findings

EVAPORATION GEOG 405. Tom Giambelluca

Gapfilling of EC fluxes

The remote sensed status and trend of ecosystem Services over Three-River Headwaters, Qing-Tibet Plateau, China, in

Breeding for Drought Resistance in Cacao Paul Hadley

ONE DIMENSIONAL CLIMATE MODEL

Data Analysis and Modeling with Stable Isotope Ratios. Chun-Ta Lai San Diego State University June 2008

Variability of Reference Evapotranspiration Across Nebraska

Rangeland Carbon Fluxes in the Northern Great Plains

Evapotranspiration. Andy Black. CCRN Processes Workshop, Hamilton, ON, Sept Importance of evapotranspiration (E)

Global Biogeography. Natural Vegetation. Structure and Life-Forms of Plants. Terrestrial Ecosystems-The Biomes

APPL ICATION OF SOME ACTUAL EVAPOTRANSPIRATION MODELS IN SIMULATION OF SOIL MOISTURE

Carbon Assimilation and Its Variation among Plant Communities

Vegetation Remote Sensing

Carbon Budget of Ecosystem in Changbai Mountain Natural Reserve

Forest growth and species distribution in a changing climate

METR 130: Lecture 2 - Surface Energy Balance - Surface Moisture Balance. Spring Semester 2011 February 8, 10 & 14, 2011

Weathering is the process that breaks down rock and other substances at Earth s surface

Eric. W. Harmsen 1, John Mecikalski 2, Pedro Tosado Cruz 1 Ariel Mercado Vargas 1

ROBUST EVAPORATION ESTIMATES: RESULTS FROM A COLLABORATIVE PROJECT

Meteorology. Chapter 15 Worksheet 1

Appendix B. Evaluation of Water Use for Zuni Tribe PPIW Appendix B AMEC Earth and Environmental

in this web service Cambridge University Press

Evapotranspiration: Theory and Applications

Forestry and Forest Products

SUPPLEMENTARY INFORMATION

5B.1 DEVELOPING A REFERENCE CROP EVAPOTRANSPIRATION CLIMATOLOGY FOR THE SOUTHEASTERN UNITED STATES USING THE FAO PENMAN-MONTEITH ESTIMATION TECHNIQUE

Review of ET o calculation methods and software

Module 11: Meteorology Topic 3 Content: Climate Zones Notes

Biosphere Organization

Validation of MODIS Data for Localized Spatio- Temporal Evapotranspiration Mapping

Plant Ecophysiology in a Restoration Context

Thermal Crop Water Stress Indices

Hydrologic cycles Land Woods Hole, July 12-13, 2004

Physical Aspects of Surface Energy Balance and Earth Observation Systems in Agricultural Practice

What other observations are needed in addition to Fs for a robust GPP estimate?

Assimilating terrestrial remote sensing data into carbon models: Some issues

Appendix A. Stemflow

Simulating energy and carbon fluxes over winter wheat using coupled land surface and terrestrial ecosystem models

Environmental Plant Physiology Photosynthesis - Aging. Department of Plant and Soil Sciences

Photosynthesis - Aging Leaf Level. Environmental Plant Physiology Photosynthesis - Aging. Department of Plant and Soil Sciences

Modelling Fuel Moisture Under Climate Change

Plant Water Stress Frequency and Periodicity in Western North Dakota

Evaluation of Methodologies to Estimate Reference Evapotranspiration in Florida

LECTURE 07: CROP GROWTH ANALYSIS

HydroUmcal Interactions Between Atmosphere, Soil and Vernation (Proceedings of the Vienna Symposium, August 1991). IAHS Publ. no. 204,1991.

1 BASIC CONCEPTS AND MODELS

POTENTIAL EVAPOTRANSPIRATION AND DRYNESS / DROUGHT PHENOMENA IN COVURLUI FIELD AND BRATEŞ FLOODPLAIN

Eric. W. Harmsen 1, John Mecikalski 2, Vanessa Acaron 3 and Jayson Maldonado 3

Transcription:

Description of 3-PG Peter Sands CSIRO Forestry and Forest Products and CRC for Sustainable Production Forestry 1

What is 3-PG? Simple, process-based model to predict growth and development of even-aged stands. Uses basic mean-monthly climatic data, and simple site factors and soil descriptors. Generates: foliage, woody tissue and root biomass, conventional stand attributes (volume, BA, stocking), soil water content and water usage. Runs on monthly time step. Parameterised using stand-level data. 2

Main Components of 3-PG Production of biomass Based on environmental modification of light use efficiency and constant ratio of NPP to GPP. Biomass partitioning Affected by growing conditions and tree size. Stem morality Based on self-thinning rule. Soil water balance A single soil layer model with evapotranspiration determined from Penman-Monteith equation. Stand properties Determined from biomass pools and assumptions about specific leaf area, branchbark fraction, and wood density. 3

Structure and Causal Influences Interception Water balance Rain Soil water Conductance Evaporation branch & bark ratio Volume growth stem volume VPD Root turnover FR PhysMod Canopy production Tav PAR Intercepted PAR GPP NPP Respiration LAI SLA Root Foliage biomas biomass wood density Biomass partitioning Above ground woody biomass p FS DBH Litterfall Stem mass Mortality Stem mortality Meaning of elements Input data State variable Internal variable Fixed parameter Explicitly time-dependent parameter Physical flow with rate Causal influence: = causes increase = causes decrease = has optimum Stocking 4

Biomass Production and Intercepted Solar Radiation Observation shows: above-ground production linearly related to intercepted radiation gross production proportional to intercepted radiation. Slope of these relationships is light use efficiency ε (g DM MJ -1 ). This finding is the basis for many simple models. Above-ground production (t ha -1 yr -1 ) Above-ground production (t ha -1 yr -1 ) 16 14 12 10 8 6 4 2 0 16 14 12 10 8 6 4 2 0 E. globulus age x nutrition Gippsland, Vic. Assorted species four s ites Esperance, Tas. y = 4.3673x - 1.4366 R 2 = 0.9873 0 1 2 3 4 Intercepted radiation (GJ m -2 yr -1 ) y = 4.2908x - 0.6211 R 2 = 0.9839 0 1 2 3 4 Intercepted radiation (GJ m -2 yr -1 ) 5

Light use efficiency Light Use Efficiency is affected by climatic factors (e.g. temperature) and site factors (e.g. soil-water status) varies seasonally, but annual values more stable. This concept forms basis of many simple models, e.g. GrowEst PlantGro 3-PG grasslands; based on T & soil water; multiplicative many crops; many environmental & site factors; law of the minimum trees; based on T, VPD, soil water & nutrition; mainly multiplicative 6

Calculating Intercepted Radiation Beer s law determines light transmitted through canopy. Thus radiation intercepted by the canopy is: = (1 kl ) int 0 Q e Q 100 Note diminishing returns from high leaf area indices Intercepted radiation (%) 80 60 40 20 0 0 1 2 3 4 5 6 Canopy LAI 7

Gross Canopy Production Putting these together, total gross production by the canopy is P = α (1 e kl ) Q g C where canopy quantum efficiency α C (mol mol -1 ) or light use efficiency ε, g DM MJ -1 : measure efficiency of conversion of solar radiation into biomass depend on environmental and site factors. 0 8

Net Canopy Production Respiration assumed to be constant fraction of gross canopy production. Thus net canopy production is: P n = YP g kl = α Y(1 e ) Q C 0 where Y 0.47. This is a contentious assumption, which greatly simplifies treatment of respiration. 9

Growth Modifiers in 3-PG Each environmental factor is represented by a growth modifier = function of factor which varies between 0 (total limitation) and 1 (no limitation). Factor Vapor pressure deficit Soil water Temperature Frost Site nutrition Stand age Modifier f VPD (D) f SW (θ) f T (T av ) f F (d f ) f N (FR) f age (t) Parameters k D θ max,c θ,n θ T min, T opt, T max k F f N0 n age, r age 10

Effects on Canopy Production All modifiers affect canopy production: α = f f f min{ f, f } f α C T F N VPD SW age Cx where α Cx is maximum canopy quantum efficiency. In 3-PG the combination of modifiers ϕ = min{ fvpd, fsw } fage also affects canopy conductance, and is called PhysMod in the program. 11

Temperature Growth Modifier f T (T a ) f T ( T ) a T T T T a min max a = Topt T min Tmax T opt ( T T ) ( T T ) max opt opt min where 1.0 T a = mean monthly daily temperature T min = minimum temperature for growth T opt = optimum temperature for growth T max = maximum temperature for growth Temperature modifier (f T) 0.8 0.6 0.4 0.2 T min = 7.5, T opt = 15, T max = 35 0.0 5 10 15 20 25 30 Mean temperature 12

Frost Growth Modifier f F (d F ) f ( d ) = 1 k ( d /30) F F F F where d F = number of frost days in month k F = number of days of production lost for each day of frost. Frost modifier (ff) 1.0 0.8 0.6 0.4 0.2 0.0 k F = 1 0 5 10 15 20 25 30 Days of frost in month 13

Soil-water Growth Modifier f SW (θ) f SW ( θ ) 1 = 1 (1 θ / θ )/ [ c ] x θ n θ where θ = current available soil water θ x = maximum available soil water c θ = relative water deficit for 50% reduction. n θ = power determining shape of soil water response. Soil water growth modifier (f SW) 1.0 0.8 0.6 0.4 Sand Sandy-loam 0.2 Clay-loam Clay 0.0 0.0 0.2 0.4 0.6 0.8 1.0 Relative available soil water 14

VPD Growth Modifier f VPD (d) k D f ( D) = e D VPD 1.0 where D = current vapor pressure deficit k D = strength of VPD response. VPD growth modifier (f VPD) 0.8 0.6 0.4 0.2 k D = 0.05 0.0 0 2 4 6 8 10 Vapor pressure deficit (mbar) 15

Age-related Growth Modifier f age (t) f age () t 1 = n 1 ( t/ r t ) age age x where t t x = current stand age = likely maximum stand age r age = relative stand age for 50% growth reduction n age = power determining strength of growth reduction Age-related modifier 1.0 0.8 0.6 0.4 0.2 n age = 4, r age = 0.95 0.0 0.0 0.2 0.4 0.6 0.8 1.0 Relative stand age 16

Structure and Causal Influences Interception Water balance Rain Soil water Conductance Evaporation branch & bark ratio Volume growth stem volume VPD Root turnover FR PhysMod Canopy production Tav PAR Intercepted PAR GPP NPP Respiration LAI SLA Root Foliage biomas biomass wood density Biomass partitioning Above ground woody biomass p FS DBH Litterfall Stem mass Mortality Stem mortality Meaning of elements Input data State variable Internal variable Fixed parameter Explicitly time-dependent parameter Physical flow with rate Causal influence: = causes increase = causes decrease = has optimum Stocking 17

Biomass Partitioning NPP is partitioned into biomass pools (t DM ha -1 ): foliage (W F ), above-ground woody tissue (W S ) roots (W R ) Partitioning rates (η F, η R and η S ) depend on growth conditions and stand DBH. Litter-fall (γ F ) and root-turnover (γ R ) also taken into account. Thus: W = η P γ W F F n F F W = η P γ W R R n R R W = η P S S n 18

Root Partitioning Partitioning to roots affected by growth conditions through ϕ (PhysMod) and by soil fertility: where η R m = m 0 (1-m 0 )FR = η Rx = root partitioning under very poor conditions η Rn = root partitioning under optimal conditions ηrxη Rn ηrn ( ηrx ηrn) m ϕ Root partitioning 1.0 0.8 0.6 0.4 0.2 0.0 ηrx = 0.8 ηrn = 0.23 0.0 0.2 0.4 0.6 0.8 1.0 Growth conditions 19

Foliage and Stem Partitioning Above-ground partitioning based on foliage:stem partitioning ratio n p = η / η = ab FS F S B is diameter at breast height determined from an allometric relationship between stem mass and B a, b are coefficients determined from p FS at B = 2 and 20 cm. Then 1ηR η =, η = 1 p S F FS S FS p η 20

Above-ground Partitioning v Tree-size Increasing DBH decreases foliage partitioning and increases stem partitioning. Graphs show response when p FS (2) = 1, p FS (20) = 0.25, η R = 0.4 1.2 0.6 Ratio of foliage:shoot partitioning 1.0 0.8 0.6 0.4 0.2 0.0 Above-ground partitioning 0.5 0.4 0.3 0.2 0.1 0.0 Stem Foliage 0 10 20 30 0 10 20 30 Stem diameter Stem diameter 21

Root-turnover and Litter-fall Root-turnover is a constant fraction of root biomass (γ R = 0.015 month -1 ). Litter-fall is age-dependent fraction of foliage biomass: γ Fxγ 0 1 F γ Fx γ F ( t) =, k = ln 1 kt γ ( γ γ ) e γ F0 Fx F0 t γ F F0 where γ F0 = litter-fall rate at age 0 γ Fx = maximum litter-fall rate t γf = age when γ F =½(γ F0 γ Fx ) Litter-fall rate/month 0.03 0.02 0.01 0.00 γf0 = 0.002 γ Fx = 0.027 t γ F = 12 0 1 2 3 4 Stand age (years) 22

Stem Mortality Based on self-thinning where average stem weight for current stocking w Sx (N) w Sx0 (1000/N) 3/2 (kg/tree) and w Sx0 = max. stem weight at 1000 trees ha -1. If at 1 then w S w Sx (N) no mortality If at 2 then w S >w Sx (N) self-thin to 2 N' = 1000( w / w ) W =γ Sx0 S ( N N') W N 2/3 S S S where γ S 0.2 is a parameter. Average stem mass (kg tree -1 ) w S w Sx (N) self-thinning line N' 2' N Stocking (trees ha -1 ) 2 1 23

Structure and Causal Influences Interception Water balance Rain Soil water Conductance Evaporation branch & bark ratio Volume growth stem volume VPD Root turnover FR PhysMod Canopy production Tav PAR Intercepted PAR GPP NPP Respiration LAI SLA Root Foliage biomas biomass wood density Biomass partitioning Above ground woody biomass p FS DBH Litterfall Stem mass Mortality Stem mortality Meaning of elements Input data State variable Internal variable Fixed parameter Explicitly time-dependent parameter Physical flow with rate Causal influence: = causes increase = causes decrease = has optimum Stocking 24

Soil Water Balance Soil water balance model based on single soil layer. Uses monthly time steps. Inputs: rainfall and irrigation Losses are: interception = fixed % of rainfall evapotranspiration determined using Penman-Monteith equation excess over field capacity lost as run off 25

Evapotranspiration Evapotranspiration is calculated using the Penman- Monteith equation. Directly affected by VPD and radiation. Canopy conductance: determined by LAI affected by growth conditions VPD, soil water and age. Boundary layer conductance: is assumed constant (0.2 m s -1 ) 26

Calculation of Stomatal Conductance Canopy conductance is affected by VPD, soil water and stand age through ϕ (= PhysMod in 3-PG code), and increases with canopy LAI: g C = g Cx ϕ min{l/l gc, 1} where ϕ = min{f VPD, f SW } f age g Cx = maximum conductance L gc = LAI at max. conductance Canopy conductance (m s -1 ) g Cx L gc Canopy leaf area index (L) 27

Structure and Causal Influences Interception Water balance Rain Soil water Conductance Evaporation branch & bark ratio Volume growth stem volume VPD Root turnover FR PhysMod Canopy production Tav PAR Intercepted PAR GPP NPP Respiration LAI SLA Root Foliage biomas biomass wood density Biomass partitioning Above ground woody biomass p FS DBH Litterfall Stem mass Mortality Stem mortality Meaning of elements Input data State variable Internal variable Fixed parameter Explicitly time-dependent parameter Physical flow with rate Causal influence: = causes increase = causes decrease = has optimum Stocking 28