Department of Hydrology and Water Resources

Slides:



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

Land surface in climate models Parameterization of surface fluxes Bart van den Hurk (KNMI/IMAU)
Seasonal dynamics of soil, litter, and ecosystem respiratory carbon dioxide fluxes as indicated by stable isotope analyses Jean Ometto, Luiz Martinelli,
Land Surface Evaporation 1. Key research issues 2. What we learnt from OASIS 3. Land surface evaporation using remote sensing 4. Data requirements Helen.
A thermodynamic model for estimating sea and lake ice thickness with optical satellite data Student presentation for GGS656 Sanmei Li April 17, 2012.
Evergreen tree dynamics in tropical savanna
1 CODATA 2006 October 23-25, 2006, Beijing Cryospheric Data Assimilation An Integrated Approach for Generating Consistent Cryosphere Data Set Xin Li World.
Hydraulic redistribution in Amazonian trees Rafael S. Oliveira 1, Todd E. Dawson 1, Stephen S. Burgess 1,2 Daniel C. Nepstad 3 1 University of California,
Satellite Remote Sensing and Applications in Hydrometeorology Xubin Zeng Dept of Atmospheric Sciences University of Arizona Tucson, AZ
UNSTABLE, DRI and Water Cycling Ronald Stewart McGill University.
MODIS Science Team Meeting - 18 – 20 May Routine Mapping of Land-surface Carbon, Water and Energy Fluxes at Field to Regional Scales by Fusing Multi-scale.
03/06/2015 Modelling of regional CO2 balance Tiina Markkanen with Tuula Aalto, Tea Thum, Jouni Susiluoto and Niina Puttonen.
The Global Carbon Cycle Humans Atmosphere /yr Ocean 38,000 Land 2000 ~90 ~120 7 GtC/yr ~90 About half the CO 2 released by humans is absorbed by.
VIC Model Status Blowing Snow and Lake Algorithms Princeton Meeting December 4, 2006.
4. Testing the LAI model To accurately fit a model to a large data set, as in the case of the global-scale space-borne LAI data, there is a need for an.
PREFER 1 st Annual Review Meeting, 5-6 Dec 2013, Milano-Italy PREFER WP3.1 - Information Support to Preparedness/Prevention Phase Product: “Daily Fire.
CSIRO LAND and WATER Estimation of Spatial Actual Evapotranspiration to Close Water Balance in Irrigation Systems 1- Key Research Issues 2- Evapotranspiration.
Remote Sensing Data Assimilation for a Prognostic Phenology Model How to define global-scale empirical parameters? Reto Stöckli 1,2
Optimising ORCHIDEE simulations at tropical sites Hans Verbeeck LSM/FLUXNET meeting June 2008, Edinburgh LSCE, Laboratoire des Sciences du Climat et de.
Coupled Climate Models OCEAN-ATMOSPHEREINTERACTIONS.
The role of vegetation-climate interaction on Africa under climate change - literature review seminar Minchao Wu Supervisor: Markku Rummukainen, Guy Schurgers.
JULES: Joint UK Land Environment Simulator A community land surface scheme.
NWS Calibration Workshop, LMRFC March, 2009 Slide 1 Analysis of Evaporation Basic Calibration Workshop March 10-13, 2009 LMRFC.
ORCHIDEE-Dev : January 8th, 2013 Theme #1 Water cycle, river flows, water quality and interactions with biosphere under future climate Réservoir souterrain.
Translation to the New TCO Panel Beverly Law Prof. Global Change Forest Science Science Chair, AmeriFlux Network Oregon State University.
A. Henderson-Sellers, M. Fischer, K. McGuffie and D. Noone Australian Nuclear Science & Technology Organisation, University of Technology, Sydney and University.
Land Surface Processes in Global Climate Models (1)
Coupling of the Common Land Model (CLM) to RegCM in a Simulation over East Asia Allison Steiner, Bill Chameides, Bob Dickinson Georgia Institute of Technology.
Land Surface Hydrology Research Group Civil and Environmental Engineering University of Washington Land Surface Hydrology Research Group Civil and Environmental.
How are Land Properties in a Climate Model Coupled through the Boundary Layer to Affect the Amazon Hydrological Cycle? Robert Earl Dickinson, Georgia Institute.
OVERVIEW OF SATELLITE BASED PRODUCTS FOR GLOBAL ET Matthew McCabe, Carlos Jimenez, Bill Rossow, Sonia Seneviratne, Eric Wood and numerous data providers.
Andes-Amazon Project: Hydrology Model-Data Intercomparison Brad Christoffersen Nov. 08, 2010 Moore Foundation.
Validation (WP 4) Eddy Moors, Herbert ter Maat, Cor Jacobs.
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.
Integration of biosphere and atmosphere observations Yingping Wang 1, Gabriel Abramowitz 1, Rachel Law 1, Bernard Pak 1, Cathy Trudinger 1, Ian Enting.
Evapotranspiration Partitioning in Land Surface Models By: Ben Livneh.
7/24/02 MODIS Science Meeting Seasonal Variability Studies Across the Amazon Basin with MODIS Vegetation Indices Alfredo Huete 1, Kamel Didan 1, Piyachat.
LMWG progress towards CLM4 –Soil hydrology CLM3.5 major improvement over CLM3 (partitioning of ET into transpiration, soil evap, canopy evap; seasonal.
Application of the ORCHIDEE global vegetation model to evaluate biomass and soil carbon stocks of Qinghai-Tibetan grasslands Tan Kun.
Investigating Land-Atmosphere CO 2 Exchange with a Coupled Biosphere-Atmosphere Model: SiB3-RAMS K.D. Corbin, A.S. Denning, I. Baker, N. Parazoo, A. Schuh,
Biases in land surface models Yingping Wang CSIRO Marine and Atmospheric Research.
Evapotranspiration Eric Peterson GEO Hydrology.
Hydro-Thermo Dynamic Model: HTDM-1.0
Mechanistic model for light-controlled phenology - its implication on the seasonality of water and carbon fluxes in the Amazon rainforests Yeonjoo Kim.
Xiangming Xiao Institute for the Study of Earth, Oceans and Space University of New Hampshire, USA The third LBA Science Conference, July 27, 2004, Brasilia,
Initial Results from the Diurnal Land/Atmosphere Coupling Experiment (DICE) Weizhong Zheng, Michael Ek, Ruiyu Sun, Jongil Han, Jiarui Dong and Helin Wei.
SiSPAT-Isotope model Better estimates of E and T Jessie Cable Postdoc - IARC.
Effects of Intra-Biome Variations in the Tropical Rainforest Biophysical Parameters on the Fluxes Between the Surface and the Atmosphere Hewlley Acioli.
Fundamental Dynamics of the Permafrost Carbon Feedback Schaefer, Kevin 1, Tingjun Zhang 1, Lori Bruhwiler 2, and Andrew Barrett 1 1 National Snow and Ice.
1 Xiaoyan Jiang, Guo-Yue Niu and Zong-Liang Yang The Jackson School of Geosciences The University of Texas at Austin 03/20/2007 Feedback between the atmosphere,
References: 1)Ganguly, S., Samanta, A., Schull, M. A., Shabanov, N. V., Milesi, C., Nemani, R. R., Knyazikhin, Y., and Myneni, R. B., Generating vegetation.
LSM Hind Cast for the Terrestrial Arctic Drainage System Theodore J. Bohn 1, Dennis P. Lettenmaier 1, Mark C. Serreze 2, and Andrew G. Slater 2 1 Department.
SimSphere SVAT model SimSphere is available for free from Aberystwyth University, UK:
Using vegetation indices (NDVI) to study vegetation
Koichi Sakaguchi and Xubin Zeng
A Brief Introduction to CRU, GHCN, NCEP2, CAM3.5
Terrestrial-atmosphere (1)
Lisbon, Portugal 8-10 March 2006
Marcos Heil Costa Universidade Federal de Viçosa
Ecosystem Demography model version 2 (ED2)
Infiltration and unsaturated flow (Mays p )
Jili Qu Department of Environmental and Architectural College
Introduction to Land Information System (LIS)
Land surface analysis over India using High Resolution Land Data Assimilation System (HRLDAS) H P Nayak and M Mandal Centre for Oceans, Rivers, Atmosphere.
Jianmin Zhang1, Timothy J. Griffis1 and John M. Baker2
NMMB-DUST developments
LBA-MIP: Motivation and Overview
RC Izaurralde – JGCRI With contributions from NJ Rosenberg – JGCRI
J.T. Kiehl National Center for Atmospheric Research
Presentation transcript:

Department of Hydrology and Water Resources Assessing the limiting factors of CO2 exchange in the Amazon rainforest during the dry season using the Simple Biosphere Model 3 Rafael Rosolem Department of Hydrology and Water Resources May 6th, 2008

Motivation Saleska et al. 2003 (Science) Myneni et al. 2007 (PNAS)

Motivation Nemani et al. 2003 (Science)

Hypothesis How to solve this issue? - No water stress is observed during the dry season because deep roots can still extract water from the soil; Dry season = less cloud development; as a result more incoming solar energy to the vegetation which enhances photosynthesis. How to solve this issue? Vegetation phenology Soil-water dynamics Available water depth Stockli et al. (in preparation)

Objective Identify the mechanisms associated with the dry season “green-up” of the Amazon rainforest using a widely known land surface parameterization scheme; - Understand the soil-water dynamics component of the model;

Site location Located at FLONA (“Floresta Nacional”) Tapajós (lat/lon 3.01 S / 54.58 W); Vegetation: tropical humid forest on a broad flat plateau; The soil is mainly clay with some patches of sandy soil

Simple Biosphere 3 (SiB3) – Colorado State University New Features: - Prognostic T, e, CO2, other tracers in “canopy air space”; - 10-layer soil (T and w), with adjustable water extraction profile (roots); - Snowpack of 0-5 layers; - Mixed canopy physiology (e.g., savanna); and - Stable isotope fractionation of CO2. Adapted from Dr. Denning slides

Model configuration Forcing data: Temp. (oC), q (g kg-1), U (m s-1), Press. (hPa), Sdown (W m-2), Ldown (W m-2), and Prec. (mm hr-1): 1 hr timestep available from Jan/2001- Dec/2003; Highly parameterized scheme (36 parameters + 8 time-varying inputs); Soil and veg. type dependent parameters: SiB2 look-up tables applied to UMD vegetation and FAO soil maps; biophysical parameters and time-varying inputs: global version of the 1982-2001 European Fourier-Adjusted and Interpolated NDVI (EFAI-NDVI): 20 year long NASA/NOAA AVHRR Pathfinder NDVI data (10-day temporal and 0.1 degree spatial resolution) at http://www.iac.ethz.ch/staff/stockli/efaindvi_page/efaindvi.html; No model calibration and validation experiments: LARGE SOURCE OF UNCERTAINTY!!! 30-yr spin-up has been applied to stabilized soil moisture and carbon quantities in the model;

Simulation experiments Control Saturation Saturation - Control

Results: Monthly sat con

Results: Diurnal – Dry season con

Results: Diurnal and seasonal variation of NEE

Results: Cumulative NEE con sat - con

Conclusions Control experiment fails to simulate the energy partion (H and LE); Simulation is clearly improved in the dry season when water is available at the bottom layers: energy partition is improved!!! Saturation experiment: available energy becomes the only limiting factor in the Amazon rainforest (consistent with past studies); Model calibration MUST be performed (e.g, diurnal pattern of gorund heat flux); At the end of the 3 years, the saturation simulation uptakes approximately 30% more carbon (0.25 g m-2 s-1) than the control experiment; IMPORTANT: This study is just a QUICK FIX to the issue of Amazon “green- up”; the best approach is actually to incorporate a powerful phenology prognostic model and develop a rooting dynamics formulation to account for water extraction under stress conditions;