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Validation of annual variations in satellite
based transpiration and soil moisture using
tree ring data
De Jeu RAM1, Miralles DG1, Van Marle MJE1, Dorigo WA2, Wagner W2, and Liu YY3
1VU
University Amsterdam, FALW, Dept. Hydrology and GeoEnvironmental Sciences, De Boelelaan 1085,
1081 HV Amsterdam, the Netherlands,Richard.de.jeu@falw.vu.nl, 2Institute for Photogrammetry and
Remote Sensing, Vienna University of Technology, Vienna, Austria, 3Climate Change Research Centre,
University of New South Wales, Sydney, Australia
Abstract: Recently, a multi-decadal dataset of satellite based soil moisture and the
evaporative flux have been developed. This dataset is based on different satellite
observations and has been validated extensively with in situ observations and
models. However, these validation studies have always been applied on short
periods but never evaluated long term variations (> 10 years). This study aims on
the use of tree ring data for annual variation validation. As a first test case, the Los
Lobos Creek site in central California was selected with a 21 year record (19842004) of tree ring width data from 27 blue oak trees (Quercus Douglasii). The
dominant tree ring signal was retrieved by applying an empirical orthogonal
Function (EOF) analysis on the normalized tree ring widths resulting in one
dominant signal that described the temporal behaviour of the 27 trees for 80%.
This signal was then compared to soil moisture and transpiration rates from the
satellite based Global Land surface Evaporation: the Amsterdam Methodology
(GLEAM) Model. The analysis revealed a strong relationship between annual tree
ring width and transpiration in June (R=0.79) and soil moisture in April (R=0.82).
These first preliminary results give us confidence in the reliability of the annual
variations of the different products. Both the soil moisture and transpiration fluxes
capture the extreme years as given by the tree ring width. Although this is still
based on one site we believe that data from the tree ring data bank can have a
strong potential as a validation tool for long record satellite datasets. However,
further research on other sites is still necessary to confirm the consistency of such
an approach.
Keywords: ‘Evaporation; soil moisture; tree ring data; satellite data’.
1
INTRODUCTION
The hydrological cycle is expected to accelerate as temperatures rise and the capacity of the air
to carry moisture increases (Dolman and De Jeu, 2010). This acceleration may lead to
increased global precipitation, faster evaporation, and a general exacerbation of extreme
hydrologic anomalies, floods, and droughts (Easterling, et al., 2000; Meehl, et al., 2000; Milly, et
al., 2002; Wentz, et al., 2007; Ziegler, et al., 2003). As shown by the IPCC such long-term,
large-scale, human-induced changes to global water and energy cycles could interact with
natural variability (e.g., the El Nino–Southern Oscillation (ENSO) phenomenon) on daily to
decadal timescales and may adversely affect social/economic well-being in many regions of the
world (Ziegler, et al., 2003).
Datasets from satellites could be used to study the impact of climate, but till now they are often
considered to be too short to evaluate the effect of climate change on hydrology. Ziegler et al.
(2003) estimated that data lengths longer than 30 years are needed to detect the acceleration of
the hydrological cycle. With the merging of different historical satellite datasets it slowly become
possible to reach the required time record.
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In the last years several attempts have been made to build long term evaporation and soil
moisture datasets. Liu et al., (2009, 2011) developed an approach to merge both active and
passive microwave satellite soil moisture data into one 30+ year soil moisture record and
Miralles et al.(2011a and b) build a methodology to create a 24 year daily record of evaporation
fluxes, including interception, snow sublimation, bare soil evaporation and transpiration.
These datasets have been validated extensively with in situ observations and models, but never
on a multi-decadal time scale. However, validation at this time scale is needed to quantify both
the quality and usefulness of these datasets for climate studies.
In this study we want to analyse a possibility to validate a long term satellite based dataset
using tree ring data.
Tree ring data is being used for many purposes, but mainly for the reconstruction of
temperatures and climates in the past (Briffa, 2000). Tree rings chronologies are sensitive to
climate variations and are successfully used to reconstruct evaporation, precipitation and
drought fluctuations (Kagawa et al., 2003; Case and MacDonald, 1995; Jacoby and D’Arrigo,
1995) and temperature at the higher latitudes (Briffa et al., 2004). The preservation of climate
records in tree ring data makes it potentially suitable for independent satellite validation.
In this study, a tree ring site in central California (Griffin, 2005) is selected to validate satellite
based records of transpiration and soil moisture. The results from this study are described
below.
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2.1
METHODS
Study Area
Tree ring data is obtained from the International Tree-Ring Data bank. Tree ring data is free
available and maintained by the NOAA Paleoclimatology Program and World Data Center for
Paleoclimatology. The data bank host more than 2000 sites over six continents (see
http://www.ncdc.noaa.gov/paleo/treering). For this study we used tree rings from 27 different
blue oak trees (Quercus Douglasii) from the Los Lobos Creek site, located in western California
(34.922 ºN and 119.2432 ºW) (Griffin, 2005). The region has a mild climate with temperatures
round 16 degrees in winter time and about 25 degrees in summer time. The temporal variation
of precipitation is also high with dominant rain events in winter and a lack of rainfall in the
summer period (See Figure 1).
Figure 1: Yearly average patterns of temperature and precipitation for the1984-2004 period.
The collected tree ring width data was normalized and detrended. An Empirical Orthogonal
Function Analysis (EOF) was used to determine the most dominant temporal signal over the 27
trees. EOF is a commonly used statistical method to extract dominant signals from tree ring
datasets (Briffa et al., 2002). In our case we derived 5 modes from the analysis on all the 27
trees, but only the first mode, describing 80 % of the temporal signal, was used for further
analysis.
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2.2
GLEAM data
In our analysis we made use of data from the Global Land-surface Evaporation: the Amsterdam
Methodology (GLEAM) model. GLEAM is driven by large set of remote sensing observations
from different satellites (Miralles et al., 2011a) and produces daily estimates of actual
evaporation (and its different components, including tall canopy transpiration). GLEAM is based
on the Priestley and Taylor (PT) evaporation formula in combination with the Gash analytical
model of forest rainfall interception (Gash, 1979; Valente et al.,1997) and uses a soil water
module to calculate the soil water stress. The soil water module consists of a multilayer bucket
model driven by precipitation and calculating soil moisture for different layers within the rootzone. Satellite-measured surface soil moisture is used and assimilated in the first layer of the
profile by means of a Kalman Filter. A more detailed description on GLEAM can be found in
(Miralles et al., 2011a and b). In this study we will make use of the assimilated soil moisture and
tall canopy transpiration of GLEAM version 1A. This dataset spans a period from 1984-2007
and produces daily data at a 0.25 degree scale. Data of the study area is extracted from this
global dataset and compared with the tree ring width data
3
RESULTS
The relationship between transpiration and soil moisture and tree ring width was analysed.
Spearman ranked correlation coefficients between annual ring width and monthly average
transpiration and soil moisture over the 1984 to 2004 period were calculated and plotted in
Figure 2. The transpiration graph shows a strong tree growth response from October till October
with a peak in June (R=0.74). The soil moisture graph also shows high correlations with tree
ring width but the peak is a bit earlier in April (R=0.82). This means that tree growth is highly
dependent on transpiration and soil moisture and first benefits from available water in early
spring and then later on from high transpiration rates.
Figure 2: Spearman ranked correlation coefficients of monthly average values of transpiration and soil
moisture with yearly ring width of the Q. Douglasii (blue oak) for the1984-2004 period.
Figure 3 shows the actual time series of the average June transpiration and April soil moisture
versus the first EOF mode of the normalized trees. This graph shows a strong similarity
between productive tree growth years and high transpiration rates, especially for the years
1993, 1995 and 1998. The years 1986 and 1988 were less clear, although they are very well
visible in the soil moisture rates. Apparently for these years the growth was more dominated by
water availability. The less productive years 1990, 1994, 1997 and 2002 showed both low soil
moisture values and low transpiration rates
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Figure 3: Time series of normalized dominant tree ring width as obtained by the first EOF mode (dashed
line) vs average June transpiration rates and April soil moisture values (solid lines).
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DISCUSSION AND CONCLUSIONS
In this study the potential use of tree ring width data for validation purposes of multi-decadal
satellite based datasets is presented. A site in California was selected as a first test case.
Twenty seven trees were analysed and compared to satellite based transpiration and soil
moisture as derived from the GLEAM model. Similar patterns were found in both the tree ring
data and the transpiration and soil moisture datasets. Productive years corresponded with high
transpiration rates and high soil moisture values and the opposite was found for less productive
years.
From this long term validation exercise it became clear that the GLEAM model products for this
site had the same inter annual information as the independent tree ring dataset.
This study gives us confidence that tree ring data could be used to validate annual variability in
long term satellite data sets, especially for regions where the tree-growth response is highly
dependent on the weather conditions.
Although this is still based on one site we believe that data from the tree ring data bank can be
used as an alternative validation tool for multi-decadal satellite based datasets. However,
further research on other sites is still necessary to confirm the consistency of this approach
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