Soil Moisture from the FLUXNET global network

Transcription

1 Soil Moisture from the FLUXNET global network Richard de Jeu Department of hydrology and Geo-environmental Sciences, VU University Amsterdam, the Netherlands In collaboration with Karin Rebel 1, Thomas Holmes 2, Philipe Ciais 3, Nicolas Viovy 3, Shilong Piao 3 and Han Dolman 4 1 Utrecht University,2 USDA, 3 LSCE and 4 VUA SMOS VRT WORKSHOP 2009

2 OUTLINE FLUXNET DATASET SATELLITE AND MODEL VALIDATION Satellite Soil Moisture (AMSR-E LPRM) Orchidee Global Dynamic Vegetation Model (GDVM) Results Discussion SUMMARY/CONCLUSIONS

3 FLUXNET FLUXNET ( ): a Global network of micrometeorological flux measurements At all sites they measure water, energy and CO 2 fluxes

4 FLUXNET An example of one FLUXNET site, Chokurdakh (Tundra site, Siberia)

5 FLUXNET Pros More than 250 sites with data over a large variety of land surfaces all over the world More than 100 sites with top soil moisture obs. (5-15 cm) Ancillary data available (meteo based) Data is online available ( Dataset is still growing (more sites with soil moisture) Cons Big variety in soil moisture data quality Additional soil moisture information is lacking Ancillary data available but often not complete Data policy is not clear (an approved proposal is needed) Often single measurements per fluxnet site

6 VALIDATION: So how can we use FLUXNET data in a validation exercise Example: Evaluation of ORCHIDEE (modeled) soil moisture using FLUXNET data and Satellite Soil Moisture Basic research question: What is the current status of the modeled soil moisture in ORCHIDEE and could we improve the performance of ORCHIDEE if we add additional soil moisture information to the model

7 VALIDATION: ORCHIDEE ORCHIDEE: Global Dynamic Vegetation Model (Krinner et al., 2005) State of the art model to study global biogeochemical processes Energy and water exchange model (SECHIBA) Two variable depth layers (GQSB and BQSB) Parameter HUMREL describes the available soil moisture within the root zone Model was run on a daily time step at 0.25 degree resolution for two years ( )

8 VALIDATION: Satellite Soil Moisture Soil moisture dataset is derived from AMSR-E C-band microwave observations using the the Land Parameter Retrieval Model (LPRM) 1,2,3 Describes surface soil moisture (~ 1-2 cm) ~ daily coverage ~ 0.25 degree resolution Online available at Limitations: Soil moisture quality is a function of vegetation density C-band soil moisture retrievals are more sensitive to vegetation density than L-band No values when the vegetation cover is too dense Microwave observations are sensitive to Radio Frequency Interference (RFI, i.e. radar systems close to airports etc.) No values when the soil is frozen and/or snow is on the ground 1. Owe et al.,ieee 2001; 3 De Jeu and Owe, IJRS Owe et al., JGR 2008

9 VALIDATION: Satellite Soil Moisture AMSR-E, July 2004 (C-band) (2002-Now) SM in m 3 m -3

10 VALIDATION: Satellite Soil Moisture Global distribution of the Vegetation Density (OD) for July 2004 STD soil moisture is function of Vegetation density (OD)

11 VALIDATION: Satellite Soil Moisture LPRM Soil Moisture has been validated extensively with in situ data over a large variety of land surfaces, including forested 1, agricultural 2,3,4,5, semi-arid 3,4,7,8 and temperate/cold regions 9 The estimated accuracy is about 0.06 m 3 m -3 for sparse to moderate vegetated regions 4 LPRM Soil Moisture is being used in a variety of research studies including climate modeling 5,10,11 drought 12 and runoff prediction13, 14 Dataset has been downloaded over 400 times in one year (from more than 20 different countries) 1 Rebel et al. AGU 2008; 2 Owe et al., JGR 2008; 3 De Jeu and Owe, IJRS 2003; 4 De Jeu et al. SurvGP 2008; 5 Rudiger et al. JHM 2009; 6 Gruhier et al. AGU 2008; 7 Draper et al., RSE 2009; 8 Wagner et al., HG 2007; 9 Champagne et al. AGU 2008; 10 Scipal et al., GRL 2008; 11 Liu et al., GRL 2007; 12 Loew et al., JGR 2009; 13 Liu et al MODSIM 2007; 14 Beck et al EGU 2009.

12 VALIDATION: FLUXNET site selection Data Quality Data Availability Problem Only 15 FLUXNET sites passed the test No. Site name Vegetation (IGBP Class) 1 Lethbridge, Canada 2 Gebesee, Germany 3 Las Majadas del Tietar, Spain 4 Vall d'alinya, Spain 5 Le Bray, France 6 Laqueuille, France 7 Bugacpuszta, Hungary 8 Matra, Hungary 9 Dripsey, Ireland 10 Mitra IV Tojal, Portugal 11 Pang/ Lambourne, UK 12 Lamont, Oklahoma, USA 13 Sylvania W. Area, Michigan, USA 14 Skukuza- Kruger N. Park, S. Africa Croplands Savannas E. need. forest Dec. br. forest Mixed forest Savannas Climate (Kop.) (P mm yr -1 ) Dfb (378) Cfb (492) Csa (528) Cfb (1064) Cfb (972) Cfb (1100) Cfb (500) Cfb (600) Cfb (1450) Csa (750) Cfb (800) Cfa (740) Dfb (896) Cwa (650) 15 Flagstaff, Arizona, USA E. need. forest Csb (540)

13 VALIDATION: ANALYSIS Step 2 Correlation Analysis Correlation coefficient map between satellite soil moisture (AMSR-E) and Orchidee soil moisture (HUMREL)

14 VALIDATION: ANALYSIS Correlation varies between 0.1 and 0.9 ORCHIDEE seems to do a better job But can we use this analysis? No. Site name Vegetation (IGBP Class) 1 Lethbridge, Canada 2 Gebesee, Germany 3 Las Majadas del Tietar, Spain 4 Vall d'alinya, Spain 5 Le Bray, France 6 Laqueuille, France 7 Bugacpuszta, Hungary 8 Matra, Hungary 9 Dripsey, Ireland 10 Mitra IV Tojal, Portugal 11 Pang/ Lambourne, UK 12 Lamont, Oklahoma, USA 13 Sylvania W. Area, Michigan, USA 14 Skukuza- Kruger N. Park, S. Africa 15 Flagstaff, Arizona, USA Croplands Savannas E. need. forest Dec. br. forest Mixed forest Savannas E. need. forest Correlation R AMSR-E Correlation R HUMREL

15 VALIDATION: RESULTS Step 3 Alternative approach: Analyses of temporal autocorrelation Signal is spatially more stable Holds information of temporal behavior of soil moisture Calculation of the lag-k autocorrelation (Wilks, 1995) on both the in situ and the soil moisture products 20 soil moisture stations from the REMUDHES site (Spain) Wagner et al HG 2007

16 VALIDATION: RESULTS Comparison of autocorrelation correlation length over 15 FLUXNET sites with shallow (5-15 cm) soil moisture observations Matra, Hungary, Climate: Cfb, P = 600 m yr -1 Vall d'alinya, Spain, Climate: Cfb, P = 1064 m yr -1 Autocorrelation graphs; lag-k autocorrelation vs the time lag (Wilks 1995) RED= in Situ, BLUE = AMSR-E, BLACK = ORCHIDEE Global Dynamic Vegetation Model (HUMREL)

17 VALIDATION: RESULTS Comparison of autocorrelation correlation length over 15 FLUXNET sites with shallow (5-15 cm) soil moisture observations No. Site name Vegetation (IGBP Class) 1 Lethbridge, Canada 2 Gebesee, Germany Croplands 3 Las Majadas del Tietar, Spain Savannas 4 Vall d'alinya, Spain 5 Le Bray, France E. need. forest 6 Laqueuille, France 7 Bugacpuszta, Hungary 8 Matra, Hungary 9 Dripsey, Ireland 10 Mitra IV Tojal, Portugal 11 Pang/ Lambourne, UK Dec. br. forest 12 Lamont, Oklahoma, USA 13 Sylvania W. Area, Michigan, USA Mixed forest 14 Skukuza- Kruger N. Park, S. Africa Savannas 15 Flagstaff, Arizona, USA E. need. forest Scatterplot of autocor. length lags where rk = 1/e. BLUE = AMSR-E, RED = ORCHIDEE (HUMREL) AMSR-E describes the temporal sm characteristics!!

18 VALIDATION: RESULTS autocor. length lag (rk = 1/e.) of AMSR-E Correlation Coefficient AMSR-E - HUMREL autocor. length lag (rk = 1/e.) of HUMREL A STRONG DIFFERENCE IN TEMPORAL DYNAMICS!!! Time lag (days)

19 VALIDATION: DISCUSSION Temporal autocorrelation analysis can be a useful and alternative tool to validate satellite data But is it a better tool than the traditional correlation coefficient analysis? It seems that satellite soil moisture gives you better information about the temporal characteristics

20 VALIDATION: SUMMARY/CONCLUSIONS Soil moisture from the FLUXNET global network Data quality is still quite low 15 out of 104 sites have > 2 year record However dataset is still growing Validation activities: Model vs Satellite High correlations in sparse to moderate vegetated regions But different temporal characteristics! Temporal Autocorrelation might be an interesting tool to validate data Satellite SM might be able to add better temporal information in models