# Presentation: 066128

## Testing Ecosystem Models with Field Data

**Example #1  BOREAS**
- 	(boreal spruce forest)
**Example #2 Walker Branch TDE**	  (temperate hardwood forest)
**Needs**

## Main Purpose or Deliverable

- Quantitative evaluation of accuracy/realism of ecosystem models
- Needed by policy makers to evaluate significance of assessments of impacts of environmental change
- Needed by modelers/scientists to identify critical gaps requiring research and model development

## BOREAS Model Evaluation Exercise

- BEPS (Jing Chen)
- BIOME-BGC (John Kimball)
- CLASS (Shusen Wang)
- Ecosys (Robert Grant)
- FORFLUX (Ned Nikolov)
- LoTEC (Tony King)
- NASA-CASA (Chris Potter)
- SPAM (Steve Frolking)
- TEM (Dave McGuire, Joy Clein)
- Data (Mike Goulden,Steve Wofsy)
- Summary (Jeff Amthor)

**BOREAS Model Evaluation Exercise**

## BOREAS Model Evaluation Exercise: Methods

- Nine models
- Model time steps: hourly (x3), daily (x5), monthly (x1)
- Field data:
- 3 years (1994-1996) of eddy covariance data (CO2 and water) at northern old black spruce site (Manitoba**)**
- Ground-based estimates of NPP, GPP, NEP, soil respiration
- Model parameterized for “most-productive” part of tower footprint

## BOREAS Model Evaluation Exercise: Conclusions

- Significant model-model differences were apparent
- Model-measurement agreement was sometimes good, sometimes poor
- Measurement uncertainty remained an open issue

## Walker Branch TDE Model Evaluation Exercise

**Participating models and modelers**
- BGC++	(Ray Hunt)
- BIOME-BGC (John Kimball and Peter Thornton)
- CANOAK (Kell Wilson)
- CLASS (Shusen Wang)
- Ecosys (Robert Grant)
- INTRASTAND (Paul Hanson)
- LaRS (Jeff Amthor)
- LINKAGES v2.1 (Stan Wullschleger)
- LoTEC (Anthony King)
- MAESTRA (Yiqi Luo and Dafeng Hui)
- NuCM (Dale Johnson)
- PnET II (Ge Sun and Steve McNulty)
- SPA (Anne Hartley and Matt Williams)

## TDE Model Evaluation Exercise

- Models represented a range of time steps and complexity

## TDE Model Evaluation Exercise

**TDE Model Evaluation Exercise**

## TDE Model Parameterization

- Multi-species composition
-  Temperate deciduous hardwood forest
-  Initial biomass from dbh-based allometry or direct measurements

- Physiological Characteristics
-  Foliar and stem CO2 flux from chamber observations
-  Soil respiration from chambers

## TDE Model Evaluation Data

- Field data for comparisons
- Periodic soil water content data from TDR (1993-2000)
- Continuous soil water content from CS615 probes (1998-2000)
- Half-hour eddy flux data for NEE and ET during dry and wet biweekly periods in 1998 and 1999, respectively
- Integrated annual eddy flux data for ET (1995-2000)
- Soil respiration from chambers (1993-1999) and eddy flux (1998-2000)
- Leaf production from litter baskets (1993-2000)
- Bole and branch growth from dbh-based allometry (1993-2000)
- Root growth from mini-rhizotron tubes and ingrowth cores (1994-2000)
- Derived data for comparisons
- Biometric estimates of NPP and NEP (1993-2000)
- Sapflow-based estimates of stand transpiration (1998-2000)

## TDE: Hourly NEE and T

- _x0008_

## Soil Water: Observed vs. Measured

- No model fully captured the dynamics of observed soil water content changes.

## Daily ET: Observed vs. Measured

- Over- and under-predictions by various models cancel out.

## TDE Carbon Cycle

- Mean model outputs predict NEP to range from 200 to 400 g C m–2 y-1 over the period from 1993 to 2000.
- Substantial disagreement exits among models for both NEP and NPP.
- Mean model outputs agree with biometric estimates, but not eddy covariance integrations.

## Soil Respiration: Observed vs. Measured

- Models and measurements show differential sensitivity to litter layer wetting and drying.

## Slide 16

## TDE Water Cycle

- Good agreement between most models and independent data sets for 1993 to 2000.

## TDE Precipitation Scenarios

- Models showed variable amounts of sensitivity to scenarios of precipitation change.

## Modeled Soil Water and ET during the severe drought of 1998

-  Most models captured the dynamic of soil drying associated with a severe late-season drought in 1998, but did not produce accurate predictions.
- Predictions of ET during soil drying were highly variable, but the quantitative level and dynamic pattern of the mean model result was in good agreement with the daily eddy covariance observations.

## TDE Model Exercise: Conclusions

- Models shouldn’t be applied to new situations without careful consideration. (e.g., models without a soil water budget should not be asked to answer questions related to drought).
- Good inter-model agreement for ET and water cycle components.
- Poor inter-model agreement for NEE and NPP.  Variation associated with GPP and respiration components.
- Averaging multiple model results yields output that is most similar to observed data. Reasons for this phenomenon are not clear .
- Model estimates of NEE showed either high or low sensitivity to precipitation change. Low sensitivity appears to be the correct answer for Walker Branch.

## Needs (1)

- Quantification of uncertainty in field data used to test model output
- NEP example:	‘True’ ecosystem rate 	100 units
- 				Field ‘measurement’	120 units
- 				Model output		110 units
- Model may appear to underestimate NEP, but it actually overestimates rate/state.
- If model has 10% uncertainty (random error), it is not different from reality, but the full uncertainty must be considered in model applications.

## Needs (2)

-  Quantification of uncertainty of driving variables and parameterization data
-  Environmental variables (e.g., air temperature) used to drive model processes
-  Site or regional ecological variables (e.g., fine root maintenance respiration requirements) used to parameterize models
-  Quantification of model response to input driving data and parameters (sensitivity analysis)

## Needs (3)

-  Expanded use of field experiments to test model responses to environmental change
-  FACE experiments (for CO2 responses)
-  Warming experiments (for temperature responses)
-  Experiments with multiple factor changes
-  New experimental designs for explicit testing of models?

## Needs (4)

- New support for model evaluation exercises
- Agencies must provide funding
- Participants must follow the rules (i.e., use “agreed-on” driving data and parameters)
- Model output must be compared to measurements of the same states/rates (not just similar states/rates, e.g., comparing modeled photosynthesis to measured canopy CO2 exchange)