Calibration of IBIS against data from four primary forest sites in Amazonia Marcos Heil Costa, Hewlley M. A. Imbuzeiro, Gleidson C. B. Baleeiro, Humberto.

Slides:



Advertisements
Similar presentations
Seasonality of water and heat fluxes over a tropical forest in eastern Amazonia. Humberto R. da Rocha*, Leandro D.V.O. Pinto, Helber C. Freitas, Adelaine.
Advertisements

Continuous Measurements of Fluxes of Biogenic VOCs in the Amazon Basin L.V. Gatti 1, C. R. Trostdorf 1, A. M. Cordova 1, A. Yamazaki 1, C.A.B.Aquino 2,
J. Ogée – N. Viovy – P. Friedlingstein – P. Ciais G. Krinner – N. deNoblet J. Polcher (IPSL) Evaluation of the global biospheric model ORCHIDEE against.
1/21 EFFECTS OF CLIMATE CHANGE ON AGRICULTURE: THE SOY-AMEX EXPERIMENT Marcos Heil Costa Aristides Ribeiro DEA/UFV.
A thermodynamic model for estimating sea and lake ice thickness with optical satellite data Student presentation for GGS656 Sanmei Li April 17, 2012.
Seasonality & Interannual variability in photosynthetic metabolism of Amazon rainforests: insights from remote sensing Kamel Didan, Scott Saleska, Natalia.
A NEW LAND-LAKE SENSOR NETWORK FOR MEASURING GREENHOUSE GAS, WATER, AND ENERGY EXCHANGES: USE IN EDUCATION AND OUTREACH 1. Introduction Stepien, Carol.
Watershed Hydrology, a Hawaiian Prospective: Evapotranspiration Ali Fares, PhD Evaluation of Natural Resource Management, NREM 600 UHM-CTAHR-NREM.
Carbon dioxide fluxes across the Amazon basin: overview of 1999 and 2000 results for EU- LBA-flux tower sites Kabat, P. 1), Nobre, A D 2), Grace, J 3),
UC IRVINE: SCOTT MILLER, ED READ, CHRIS DOUGHTY, MIKE GOULDEN USP: HELBER FREITAS, HUMBERTO DA ROCHA Boat-Based Eddy Covariance Measurements of CO 2 Exchange.
Carbon flux at the scale up field of GLBRC. The Eddy Covariance cluster towers Terenzio Zenone 1 Jiquan Chen 1 Burkhard Wilske 1 and Mike Deal 1 Kevin.
03/06/2015 Modelling of regional CO2 balance Tiina Markkanen with Tuula Aalto, Tea Thum, Jouni Susiluoto and Niina Puttonen.
Energy Budget of the Earth- Atmosphere System. Energy Transfer Conduction -- direct molecular transfer Convection -- fluids; air or water –Sensible heat.
CNRM activities during CarboEurope Regional Experiment Strategy - forecasting support - wind profiler - Ceilometer - Radiosonde soundings - Surface flux.
Cross-spectral analysis on Net Ecosystem Exchange: Dominant timescale and correlations among key ecosystem variables over the Ameriflux Harvard forest.
AMMA-UK Kick-off meeting Jan 2005 WP1 Land surface and atmosphere interactions Chris Taylor Phil Harris.
A Sensitivity Analysis on Remote Sensing ET Algorithm— Remote Evapotranspiration Calculation (RET) Junming Wang, Ted. Sammis, Luke Simmons, David Miller,
MODELING OF COLD SEASON PROCESSES Snow Ablation and Accumulation Frozen Ground Processes.
The observed responses of ecosystem CO2 exchange to climate variation from diurnal to annual time scale in the northern America. C. Yi, K.J. Davis, The.
Plant physiological responses to precipitation in the Amazon forest, an isotopic approach Universidade de São Paulo: Jean Pierre Ometto; Luiz Martinelli;
Soil-Vegetation-Atmosphere Transfer (SVAT) Models
Paul R. Moorcroft David Medvigy, Stephen Wofsy, J. William Munger, M. Dietze Harvard University Developing a predictive science of the biosphere.
These notes are provided to help you pay attention IN class. If I notice poor attendance, fewer notes will begin to appear on these pages Snow Measuring.
SAHRA – NSF Center for Sustainability of semi-Arid Hydrology and Riparian Areas Intro Page Modeling Amazonian Carbon Release with Calibrated Soil-Vegetation-Atmosphere.
Changes and Feedbacks of Land-use and Land-cover under Global Change Mingjie Shi Physical Climatology Course, 387H The University of Texas at Austin, Austin,
Abstract Carbon Fluxes Across Four Land Use Types in New Hampshire Sean Z. Fogarty, Lucie C. Lepine, Andrew P. Ouimette — University of New Hampshire,
Long term weather and flux data: treatment of discontinuous data. Bart Kruijt, Wilma Jans, Cor Jacobs, Eddy Moors Loobos.
How Do Forests, Agriculture and Residential Neighborhoods Interact with Climate? Andrew Ouimette, Lucie Lepine, Mary Martin, Scott Ollinger Earth Systems.
Simulated Interactions of Soil Moisture, Drought Stress, and Regional Climate in the Amazon Basin Scott Denning 1, Jun Liu 1, Ian Baker 1, Maria Assun.
State-of-the-Art of the Simulation of Net Primary Production of Tropical Forest Ecosystems Marcos Heil Costa, Edson Luis Nunes, Monica C. A. Senna, Hewlley.
Kevin Czajkowski, Richard Becker, Changliang Shao Jiquan Chen, Carol Stepien, Thomas Bridgeman, Housen Chu 4/24/2014.
The PILPS-C1 experiment Results of the first phase of the project Complementary simulation to be done Proposition for the future.
New Funding for Ongoing Research at the FLONA Tapajos The USDA Forest Service Chief’s Emergency Fund has agreed to support ongoing research at the FLONA.
Lake Superior Eddy Covariance Stannard Rock Light PI: Peter Blanken, CU-Boulder Reported by Ankur Desai DO NOT DISTRIBUTE.
The Lake Biwa Project The field observation has been carried out under the Japanese hydro- meteorological project called "Lake Biwa Project". A core activity.
Deforestation and the Stream Flow of the Amazon River -- Land Surface Processes and Atmospheric Feedbacks Michael T. Coe1, Marcos Heil Costa2, and Britaldo.
DTerEST, DTerIdF PAGE 1 Task 1 : to assess the refreshing potential of a VGR Task 2 : to develop relevant indicators dedicated to VGR environmental impacts.
Biometeorology Lecture 2: Surface Energy Balance Professor Noah Molotch September 5, 2012.
How Do Forests, Agriculture and Residential Neighborhoods Interact with Climate? Andrew Ouimette, Lucie Lepine, Mary Martin, Scott Ollinger Earth Systems.
Graduate Course: Advanced Remote Sensing Data Analysis and Application A COMPARISON OF LATENT HEAT FLUXES OVER GLOBAL OCEANS FOR FOUR FLUX PRODUCTS Shu-Hsien.
Landscape-level (Eddy Covariance) Measurement of CO 2 and Other Fluxes Measuring Components of Solar Radiation Close-up of Eddy Covariance Flux Sensors.
Fig 1 –The top figure shows canopy PAR during the dry season of 2001 between AM. The bottom two figures show leaf level light and temperature in.
Surface conductance and evaporation from 1- km to continental scales using remote sensing Ray Leuning, Yonqiang Zhang, Amelie Rajaud, Helen Cleugh, Francis.
Ground-based energy flux measurements for calibration of the Advanced Thermal and Land Application Sensor (ATLAS) Eric Harmsen, Associate Professor Dept.
Results Time Study Site Measured data Alfalfa Numerical Analysis of Water and Heat Transport in Vegetated Soils Using HYDRUS-1D Masaru Sakai 1), Jirka.
AAAHHHHH!!!!. Climate Change Climate Physical properties of the troposphere of an area based on analysis of its weather records over a long period Two.
OEAS 604: Introduction to Physical Oceanography Surface heat balance and flux Chapters 2,3 – Knauss Chapter 5 – Talley et al. 1.
Mechanistic model for light-controlled phenology - its implication on the seasonality of water and carbon fluxes in the Amazon rainforests Yeonjoo Kim.
Estimating the Reduction in Photosynthesis from Sapflow Data in a Throughfall Exclusion Experiment. Rosie Fisher 1, Mathew Williams 1, Patrick Meir 1,
Effects of Intra-Biome Variations in the Tropical Rainforest Biophysical Parameters on the Fluxes Between the Surface and the Atmosphere Hewlley Acioli.
Energy balance closure at four forest sites in Wisconsin Nan Lu LEES Lab, University of Toledo 10/27/06.
Evaporation What is evaporation? How is evaporation measured?
Scott Saleska (U. of Arizona) Lucy Hutyra, Elizabeth Hammond-Pyle, Dan Curran, Bill Munger, Greg Santoni, Steve Wofsy (Harvard University) Kadson Oliveira.
Analysing seasonality of CO 2 on the basis of daily totals Bart Kruijt, Alessandro Araujo, Celso von Randow, Fabricio Zanchi, Renata Goncalvez, Jair Maia,
Arctic RIMS & WALE (Regional, Integrated Hydrological Monitoring System & Western Arctic Linkage Experiment) John Kimball FaithAnn Heinsch Steve Running.
A New Climatology of Surface Energy Budget for the Detection and Modeling of Water and Energy Cycle Change across Sub-seasonal to Decadal Timescales Jingfeng.
Hydrologic Losses - Evaporation Learning Objectives Be able to calculate Evaporation from a lake or reservoir using the following methods – Energy Balance.
Hydrologic Losses - Evaporation
Remote Sensing ET Algorithm— Remote Evapotranspiration Calculation (RET) Junming Wang,
Conghe Song Department of Geography University of North Carolina
Department of Hydrology and Water Resources
Marcos Heil Costa Universidade Federal de Viçosa
Global energy balance SPACE
Vinod Mahat, David G. Tarboton
Are forests helping in the fight against climate change?
Hydrologic Losses - Evaporation
GEWEX Radiation Panel Report
Spatial Variation in Corn and Soybean Fields in Central Iowa
Hydrologic Losses - Evaporation
MODELING AT NEIGHBORHOOD SCALE Sylvain Dupont and Jason Ching
Presentation transcript:

Calibration of IBIS against data from four primary forest sites in Amazonia Marcos Heil Costa, Hewlley M. A. Imbuzeiro, Gleidson C. B. Baleeiro, Humberto R. da Rocha, Antonio Manzi, Scott Saleska, Steve Wofsy, Lucy Hutira, Michael Goulden, Scott Miller

1. Introduction Land surface/ecosystem models, like IBIS, simulate the fluxes of energy, water and CO 2 between the surface and the atmosphere Traditionally in the field, these models are usually calibrated against data collected in one single site per ecosystem

Given the availability of LBA data for Amazonia, in this study, we calibrate IBIS against data from four primary rainforest sites in Amazonia? –Flona Tapajós km 67 –Flona Tapajós km 83 –Reserva do Cuieiras km 34 –Rebio Jaru The process of calibration aims at minimizing the RMSE for latent and sensible heat fluxes, as well as carbon fluxes

Model input variables –S in (solar incoming radiation) –L in (infrared incoming radiation) –T a (air temperature) –h a (air relative humidity) –u a (wind speed) –P (precipitation) Model validation –R n (net radiation) –H (sensible heat flux) –LE (latent heat flux) –NEE (net ecosystem exchange)

Data filtering Input data Turbulence threshold (u * 0 = 0.1 m/s) Energy balance closure (d = 0.4), on a daily basis

Parameters calibrated Fine root distribution (b 2 ) Maximum capacity of Rubisco enzyme (V max ) Angular coefficient related to stomatal conductance (m) Heat capacity of stems (C HS )

2. Initial results Flona Tapajós km 83

Flona Tapajós km 83 - Rn

Flona Tapajós km 83 - H

Flona Tapajós km 83 - LE

Flona Tapajós km 83 - NEE

Flona Tapajós km 83 Cumulative fluxes

Initial results Flona Tapajós km 67

Flona Tapajós km 67 - Rn

Flona Tapajós km 67 - H

Flona Tapajós km 67 - LE

Flona Tapajós km 67 - NEE

Flona Tapajós km 67 Cumulative fluxes

Initial results Reserva do Cuieiras km 34

Reserva do Cuieiras km 34 - Rn

Reserva do Cuieiras km 34 - H

Reserva do Cuieiras km 34 - LE

Reserva do Cuieiras km 34 - NEE

Reserva do Cuieiras km 34 Cumulative fluxes

Initial results Rebio Jaru

Rebio Jaru - Rn

Rebio Jaru - H

Rebio Jaru - LE

Rebio Jaru - NEE

Rebio Jaru Cumulative fluxes

Initial results – summary β2β2 V max ( mol m ­2 s ­1 ) mC HS (J m ­2 °C -1 ) km · 10 5 km · 10 5 km · 10 5 Rebio Jaru · 10 5

3. New results Flona Tapajós km 83 and km 67 only Same period for km 83, but with revised meteorological station and flux data Errors reported in water vapor at the initial time series initially used Used different period for km 67

Flona Tapajós km 83

3. New results (still preliminary) Flona Tapajós km 83

Flona Tapajós km 83 - Rn

Flona Tapajós km 83 - H

Flona Tapajós km 83 - LE

Flona Tapajós km 83 - NEE

Flona Tapajós km 83 Cumulative fluxes

New Results Flona Tapajós km 67

Flona Tapajós km 67 - Rn

Flona Tapajós km 67 - H

Flona Tapajós km 67 - LE

Flona Tapajós km 67 - NEE

Flona Tapajós km 67 Cumulative fluxes u* 0 = 0.2 m/s

New results – summary β2β2 V máx ( mol m ­2 s ­1 ) mC HS (J m ­2 °C -1 ) km · 10 5 km · 10 5 km · 10 5 km · 10 5 km · 10 5 Rebio Jaru · 10 5

4. Conclusions (1/2) Calibrated IBIS against data for 4 LBA primary forest sites Expected similar parameters for all sites Initial results shows 2 sets of parameters Errors in the meteorological or flux datasets reported in 2 sites New results (preliminary) indicate that 3 of the 4 sites show similar parameters Model results very sensitive to errors in input data

4. Conclusions (2/2) Model results across sites also sensitive to inconsistencies in input data across sites Inconsistencies in input data very likely, given the different design and operation of LBA sites Collaboration with field scientists essential to eliminate inconsistencies among datasets Representative parameters to all rainforest sites may depend on elimination of inconsistencies