Modeling Mangrove Ecosystem Carbon Budgets Using Eddy Covariance and Satellite based Products

Transcription

1 Modeling Mangrove Ecosystem Carbon Budgets Using Eddy Covariance and Satellite based Products Jordan G Barr 1, Vic Engel 1, Jose D Fuentes 2, Greg Okin 3 1 South Florida Natural Resource Center, Everglades National Park, Homestead, FL Department of Meteorology, Pennsylvania State University, University Park, Pennsylvania Department of Geography, University of California Los Angeles, Los Angeles, California 90095

2 Talk Overview Introduction ti Motivation why should mangrove production be quantified? Stresses on mangrove carbon production Tower-based EC measurements and GPP estimates. Description of productivity drivers Selection of a satellite index or indices Climate drivers in mangrove ecosystems The mangrove vegetation photosynthesis model framework and response functions Results Comparison with the MODIS (MOD17A2) product What do the shapes of the response functions tell us? Conclusions and implications Direction and future work Acknowledgements

3 Motivation modeling gross primary productivity (GPP) 1. Tower-based eddy covariance (EC) GPP is limited it spatially to 1-3 3km 2, while models can be applied across regions (>100 km 2 ) having a welldefined vegetation structure. 2. A MODIS GPP product (MOD17A2) exists, BUT it has not been validated using ground based mangrove productivity estimates. 3. EC instrumentation suffers down time during major disturbance events such as hurricane Wilma. This results in substantial temporal gaps in GPP data sets. 4. We can use models to quantify productivity changes resulting from any variety of scenarios such as changes in water management practices and sea level rise. 5. Mangrove productivity is linked to sediment accretion rates. Long term sustainability depends on sediment accretion rates meeting or exceeding the rate of sea level rise.

4 Soil accretion and sea level rise? Height abo ove MSL (m) Height above MSL 12 month average Best-fit line Sea level rise at Key West, FL Average rate of sea level l rise: 2.31 mm per year Peat accretion must at least match sea level rise. Without upstream carbon inputs, accretion is solely dependent on production If peat collapse occurs, mangrove lands may be permanently lost

5 Hurricane disturbance Everglades National Park Wilma (October 2005) makes landfall as a Category y3 storm. Direct hit to our site!

6 SRS6, Shark River tower site Site location and mangrove zone Tower site s fetch

7 Eddy covariance (EC) derived gross primary production (GPP) Step 1 EC-system measures vertical wind speed and CO 2 concentration at high (10 Hz) frequency. Step 2 In house software converts data to net ecosystem exchange (NEE) of CO 2 over 30-minute intervals. Step 3 NEE is gap-filled to produce a continuous record of fluxes. Step 4 Nighttime ecosystem respiration (R E ) response to air temperature (T A ) is determined, and the relationship is used to estimate daytime R E. St 5 GPP d t i d NEE + R Step 5 - GPP determined as NEE + R E, and nighttime GPP = 0. GPP is summed over appropriate intervals (e.g., 8-days)

8 Forest recovery following hurricane Wilma

9 Satellite based drivers of productivity NDVI & EVI NDVI & EVI both indicate disturbance (Oct 05) and subsequent recovery by NDVI Some seasonality, but exhibits too many spikes. EVI Seasonality is not visible, and signal is noisy. exhibits too many spikes. However. EVI is reduced by ~0.05 comparing with predisturbance values.

10 Satellite based drivers of productivity Greenness (GV) Greenness properties 1. A proxy for green vegetation cover (not absolute GV cover) 2. Comparable to NDVI 3. Based on relative spectral mixture analysis (RSMA) 4. Less sensitive to changes in surface/soil reflectance than NDVI

11 Satellite based drivers of productivity Albedo Albedo characteristics Albedo characteristics 1. Serves as a proxy for changes in leaf area index (LAI) 2. Filtered albedo includes only periods where the solar elevation angle was between 35 o and 50 o. 3. Only the tower-based albedo shows seasonal changes in vegetation structure. 4. MODIS albedo does indicate disturbance from the hurricane.

12 Climate drivers of productivity

13 Modeling framework Met dt data Satellite dt data EC GPP (tower) Parameter list 1. Set bounds 2. Random values VPM model 1. Optimization 2. 5 fold cross validation RMSE i < RMSE MIN? YES 1. Constrained minimization with 8 parameters Output 2. Objective function is the mean squared error 1. parameter list (MSE) of daily GPP. 2. RMSE 3. Model selected based on minimum cross validation error (RMSE) 4. The minimization generally reached a plateau after <30 optimizations. Each optimization began with randomly chosen parameter values.

14 Vegetation photosynthesis model (VPM) Light-use efficiency approach to estimate gross primary production (GPP): GPP = g FAPAR PAR PAR is photosynthetically active irradiance (MJ) per day (or longer) FAPAR is the fraction of PAR absorbed by the vegetation canopy ε g is the light use efficiency (LUE, mol C (mol photons) -1 ) FAPAR sigmoid pgv 0 pgv1 GV ratio Where, GV ratio GV - GV GV - GV max min min GV is the green vegetation index GV min, GV max are minimum and maximum GV values over some time interval p GV0 and p GV1 are constants determined during optimization

15 Vegetation photosynthesis model (VPM) Light-use efficiency, ε g, is controlled by apparent quantum yield, ε 0, and the functional response to the local climate g 0 fta f salinity Air temperature response (Raich et al., 1991) - f T A Tmin T Tmax T T T T T A TA = T 2 A min Salinity response - f A sigmoid p max salinity sal0 sal1 p A salinity ε 0 responds to seasonal changes in leaf area index (LAI), but seasonal LAI values are not available. Albedo serves as a proxy for LAI. p p albedo 0,max sigmoid alb 0 alb 1 0 opt

16 Results Modeled and EC derived GPP

17 Results MODIS (MOD17A2) and EC derived GPP

18 Results MODIS and EC derived GPP MODIS and VPM GPP are 85% and 97% of EC estimates respectively MODIS and VPM GPP are 85% and 97% of EC estimates, respectively. During , MODIS GPP was 13% lower than VPM GPP.

19 Results salinity forcing on GPP 1. Modeled GPP declines ~linearly with increasing salinity 2. Result is consistent with overall pattern of decreasing PAR-use efficiency (Σ GPP/ Σ PAR) with increasing salinity

20 Results GV and T A forcing on GPP Greenness response Air temperature response 1. After hurricane Wilma, the fraction of PAR absorbed by green vegetation (FAPAR) was reduced 20% to 30%. 2. Seasonally dependent effect of warming climate During cool dry season periods, increasing temperatures contribute to increased productivity During clear sky wet season periods, increasing temperatures contribute to decreased productivity

21 Conclusions The green vegetation ti (GV) index determined d from relative spectral mixture analysis (RSMA) is a key driver of mangrove productivity. GV co-varies with GPP seasonal trends GV provides better forest structural information that NDVI and EVI, which only capture the hurricane disturbance signal. A vegetation photosynthesis model (VPM), specific to mangrove forests, successfully reproduces seasonal and annual (97% of EC GPP, on average) productivity. The MODIS GPP product is approximately the same magnitude (85%, on average) compared to EC GPP. However, the MODIS product does not capture productivity seasonality. Our VPM model represents a first step in determining mangrove Our VPM model represents a first step in determining mangrove carbon budgets for areas larger than the tower footprint (>3 km 2 ).

22 Direction and future work Model GPP and ecosystem respiration across salinity mixing zones What drivers link mangrove forests of differing heights and biomass? Expand VPM models using EC-derived GPP from other vegetation types in Expand VPM models using EC-derived GPP from other vegetation types in the Everglades Marsh site (Steve Oberbauer and Jessica Schedlbauer) Big Cypress sites (Barclay Shoemaker) Water depth added as a key driver of productivity

23 MODIS GPP 8 day interval (day 145, 2000)

24 Spatial variability Known spatial variability in height and biomass quantified above ground carbon storage Need to determine spatial variability in rate of carbon production (GPP) and rate of change in carbon storage (NEP) S. Wdowinski University of Miami

25 Acknowledgements Critical Ecosystem Studies Initiative (CESI, J ) Everglades National Park Department of Energy (DOE), National Institute for Climate Change Research (NICCR) The National Science Foundation Florida Coastal Everglades Long Term Ecological Research Program (FCE LTER) Jones Everglades Research Fund Gordon Anderson, United States Geological Survey (USGS)

26 Extra slides

27 Hurricane disturbance Everglades National Park before after T.J. Smith III, USGS

28 Hurricane disturbance Everglades National Park Marine sediment deposition Shark River EC tower site Tree mortality correlated with soil loss! Whelan et al., 2009 T.J. Smith III (USGS)

29 Study Objectives Build and test t a vegetation ti photosynthesis th model (VPM) using meteorological and satellite-based drivers affecting mangrove physiological functioning. Generate model estimates of gross primary productivity (GPP) at the Shark River tower site to understand variability in production resulting from 1) natural inter-annual climate variability, and 2) disturbance from Hurricane Wilma. Develop the modeling framework for scaling productivity estimates for the mangroves along the entire west coast of Florida. management implications of changing climate, SLR, and changes in freshwater flow?

30 Net Ecosystem Exchange Environmental controls Shark River, Everglades N.P. Salinity effects on NEE (contours) are most apparent at high PAR & high h air temperatures Light use efficiency decreases with salinity