Long-term soil moisture variability from a new P-E
water budget method
Ning Zeng1,
2 Jin-Ho Yoon1, Annarita Mariotti2,3 and Sean Swenson 4
1Department of Atmospheric and Oceanic Science
University of Maryland, USA
2Earth System Science Interdisciplinary Center
University of Maryland, USA
3ENEA Climate section, Rome, Italy
4University of Colorado, USA
1Corresponding author address: Department of Atmospheric and Oceanic Science, University of Mary-land, College Park, MD 20742-2425, USA; email: [email protected]; http://www.atmos.umd.edu/˜ zeng
Abstract
Basin-scale soil moisture is traditionally estimated using either land-surface model
forced by observed meteorological variables or atmospheric moisture convergence from
atmospheric analysis and observed runoff. Interannual variability from such meth-
ods suffer from major uncertainties due to the sensitivity to small imperfections in
the land-surface model or the atmospheric analysis. Here we introduce a novel P-E
method in estimating basin-scale soil moisture, or more precisely apparent land water
storage (AWS). The key input variables are observed precipitation and runoff, and
reconstructed evaporation. We show the results for the tropics using the example of
the Amazon basin. The seasonal cycle of diagnosed soil moisture over the Amazon is
about 200mm, compares favorably with satellite estimate from the GRACE mission,
thus lending confidence both in this method and the usefulness of space gravity based
large-scale soil moisture estimate. This is about twice as large as estimates from sev-
eral traditional methods, suggesting that current models tend to under estimate the
soil moisture variability. The interannual variability in AWS in the Amazon is about
150mm, also consistent with GRACE data, but much larger than model results. We
also apply this P-E method to the midlatitude Mississippi basin and discuss the impact
of major 20th century droughts such as the dust bowl period on the long-term soil
moisture variability. The results suggest the existence of soil moisture memories on
decadal time scales, significantly longer than typically assumed seasonal timescales.
1
1 Introduction
Freshwater stored on the continents in the soil, at the surface or underground is fundamental for life
on land. These water reservoirs, especially soil moisture are also important in climate variability as
they provide potential feedback mechanisms for climate variability (e.g., Yeh et al. 1984; Delworth
and Manabe 1993; Zeng et al. 1999; Koster et al. 2004). Motivated partly by the prospect of
improving seasonal-interannual climate prediction using the knowledge of soil moisture state, there
has been significant interest in soil moisture variability in recent years.
Several methods have been used in obtaining soil moisture information including in situ
observations, satellite remote sensing, offline land-surface model simulations, land data assimilation
using both data and model, basin-scale budget analysis. Table 1 lists some of these methods and
examples as well as their main characteristics. Each of these method has its own advantages and
limitations. While there is a reasonable understanding of the climatological seasonal cycles of
all aspects of the hydrological cycle, there is a significant lack of knowledge on the interannual
variability of terrestrial hydrological variables including soil moisture.
A clarification of terminology is warranted here. While all other methods in Table 1 estimate
near surface soil moisture, the two budget approaches (Moisture convergence and P-E) and the
satellite gravity sensor measure total column water storage. This apparent land water storage
(AWS) includes moisture in the soil, but also surface and underground water, as well as water stored
in vegetation. In the case of satellite gravity, other factors such as change in biomass could also
contribute, but the water-related changes should dominate. Deep soil moisture and underground
water change may be of importance as discussed later, but given the lack of knowledge and a clear
definition of these differences in literature, we also use ‘soil moisture’ for general discussion, with
the understanding that apparent water storage AWS is a more precise term in the budget methods.
Here we present a new budget method in which basin-scale water storage is diagnosed using
observed precipitation and runoff. We present this method in section 2 and contrast it with the
more traditional method using atmospheric moisture convergence (Rasmusson 1968). We then
2
discuss the seasonal cycle and interannual variability from this method for the Amazon basin in
sections 3 and 4, and compare the results with the moisture convergence method and satellite
gravity based observations. In section 5, the long-term soil moisture variability is presented for
the Mississippi basin using streamflow measurement from Vicksburg. Potential limitations of the
method is discussed in section 6.
2 Methodology
2.1 The moisture convergence (MC) method
The traditional moisture convergence method (Rasmusson 1968; Roads et al. 1994; Zeng 1999;
Masuda et al. 2001) considers the atmosphere and land-surface over a drainage basin as one single
box, thus precipitation and evaporation vanish as interior fluxes for the total water budget. In this
method (Fig. 1), moisture convergence (C; the vertically integrated water vapor flux) and observed
streamflow at the river mouth (runoff integrated over the whole basin) are integrated to obtain the
change in atmosphere (W ) and soil water storage (S):
d(W + S)
dt= C − R (1)
Using the recent atmospheric reanalyses, this method appears to produce reasonable esti-
mates of the seasonal cycles over various basins around the world (Roads et al. 1994; Zeng 1999;
Masuda et al. 2001; Seneviratne et al. 2004). After some initial success using the NASA/DAO
reanalysis (Schubert et al. 1993) for the Amazon basin (Zeng 1999; Fig. 2c), we were hopeful reason-
ing that unlike the earlier analyses, a single reanalysis system is stable over time so that they may
capture the interannual variability well enough despite potential bias in the climatology. However,
when we recently applied this method to long-term soil moisture variability over the Amazon using
the longer NCEP/NCAR 50 year (Kalnay et al. 1996) and ECMWF 15 year reanalyses (ERA15;
Gibson et al. 1997), this method produced apparently unreasonable changes, in particular over
the earlier periods (Fig. 2a,b). Both the amplitudes are too large and the variability are often not
consistent with the known ENSO correlation in this region.
3
Further analysis (not shown) suggests that precipitation variability in these analyses are
often very different from the observations, and the associated moisture convergence variabilities
are not reliable enough for the purpose of soil moisture diagnosis. For instance, ERA15 shows
an apparent unrealistic difference around 1988 (Fig. 2b). Betts et al. (2005) also identified an
unexplained sudden jump around 1972-73 in the Amazon hydrological cycle in the newer ECMWF
40 year reanalysis. Part of the reason the moisture convergence is unreliable over the Amazon is
the sparse atmospheric sounding data available to constrain the model in such a remote tropical
region. Another potential problem is that the long-term changes in observational technique and
instrumentation are difficult to identify and correct. Such problems may be significantly alleviated if
the observed precipitation is used in place of moisture convergence because precipitation is generally
a better observed quantity over longer period of time, and this leads us to the P-E method.
2.2 The P-E method
In the P-E method, only the land surface is considered (Fig. 3). The water budget equation for the
land box is:
dS
dt= P − E − R (2)
where S is the apparent land water storage and P is precipitation, E is evapotranspiration.
In this method, precipitation and runoff (averaged over the basin) are from observations,
but no basin-scale evaporation observation is available. Instead, evaporation (in this work, we
use evaporation and evapotranspiration inter-exchangeably for simplicity) is calculated using a
land surface model driven by observed precipitation and other surface atmospheric variables such
as temperature, wind speed and radiation. Similar to the correction to moisture convergence
(Rasmusson 1968; see discussions of this technique in Zeng 1999), a constant correction is added
to E such that P − E∗− R (E∗ = E+correction) integrated over time is zero. As a result, the
diagnosed soil moisture has the same value at the beginning and the end of the integration. This
is equivalent to removing a linear trend (which is often unrealistically large as to mask out real
4
changes) in soil moisture. Thus only the relative changes within the integration period can be
inferred from such methods.
The above equation is then integrated to obtain soil water S as a function of time, which
can only be determined up to an integral constant S0. We choose S0 such that the minimum of
S is zero. Equation (2) is used to derive the soil water storage at a time step of one month. The
diagnosed quantity S is not only soil moisture, it also includes the changes in surface water such
as rivers and lakes within the basin, underground water, and storage of water in vegetation. We
thus term the quantity S apparent land water storage (AWS) as discussed above.
The observed precipitation for 1901-2000 from the Climate Research Unit of University of
East Anglia (CRU; New et al. 1999) was used in Eq.2. The precipitation and temperature from
the same dataset was also used in conjunction with the climatological values of surface wind and
vapor pressure, along with radiation from NCEP/NCAR reanalysis to drive an offline model SLand
(Simple-Land, Zeng et al. 2000) coupled to a dynamic vegetation model VEGAS (Zeng et al.
2005). We also used the precipitation data from Xie and Arkin (1996) based on gauge and satellite
measurements and the results are found similar to those using CRU precipitation, and here only
the results from the longer CRU dataset are shown. The Southern Oscillation Index (SOI) is used
as an index for the atmospheric variability over the tropical Pacific Ocean for comparison purpose
because the Amazon climate and hydrological variability are largely controlled by ENSO.
The monthly historical streamflow records for the Amazon River at Obidos, and for the
Xingu River at Altamira (3◦12′S) were used to reconstruct the Amazon basin runoff, following
the method used by Zeng (1999). In the following 3 sections, we present the seasonal cycle and
variability from 1970 to 1997 for the Amazon basin, and long-term variability from 1928 to 1998
for the Mississippi basin.
5
3 Seasonal cycle over the Amazon basin
A climatological seasonal cycle was derived as the average of the 28 year diagnosed apparent land
water storage. Figure 4 shows this seasonal cycle for the Amazon basin. The 3 MC method
analyses (Fig. 4a) have similar seasonal amplitude of 150mm, while the satellite gravity based
estimate from GRACE and the P-E method have an amplitude of about 300mm (Fig. 41b). The
two offline models, SLand (Zeng et al. 2000) and the CPC Leaky Bucket model (Fan and van den
Dool, 2004), also have an amplitude of about 150mm. Both GRACE and the P-E method give
a maximum in April-May and minimum in October-November after the drier boreal summer (the
basin averages tend to be dominated by the larger southern Amazon). The models and reanalyses
produced maximum and minimum somewhat earlier by 1-2 months.
To the extent that GRACE measurement can be considered as a good observation of the
basin scale water storage, the P-E method appears to capture this observed change. The reanalyses
and the two offline models thus tend to underestimate somewhat the seasonal cycle amplitude in
the Amazon. Given the uncertainties in all these methods and large interannual variability (below),
the seasonal cycle of Amazon water storage can be given as 250±100 mm. However, the two basin
budget methods (GRACE and P-E) include all the changes from surface to underground water,
thus providing an upper limit to the models which normally include only a fraction of the active
soil moisture as discussed further below.
4 Amazon interannual and decadal variability 1970-1997
Figure 5a shows the water input (P-E*) and the observed runoff R of the Amazon basin at monthly
resolution. There is a robust seasonal cycle over which P-E surpasses R during winter and spring,
when soil moisture is recharged. P-E is less than R from early summer to fall when soil moisture is
discharged (Fig.4b). Overall, R has a seasonal amplitude about factor of two smaller and a phase
lag of 3-4 months relative to P-E* (also see Zeng 1999). This reduced amplitude and phase lag is
6
typical because soil moisture is a damped and delayed response to the driving precipitation due to
its memory effect.
The diagnosed soil moisture shows large interannual to interdecadal variability on which the
seasonal cycle is superimposed. The long-term variability can be seen more clearly by filtering out
the seasonal cycle using a simple 12 month running mean (Fig.6). On multidecadal time scale,
there is a major recharge period from 1971 to 1985, followed by a discharge afterwards. The overall
amplitude over the 27 years is about 600 mm. It is worth noting that, the correction in E* (section
2.2) removes any long term trend so that over the whole analysis period there is no net gain in soil
water storage. Thus the lowest frequency change can only be viewed as relative, i.e., the decrease
in the latter half is only relative to the increase in the first half of the 27 years.
The large change of 600 mm in soil moisture is remarkable, as many land-surface models have
field capacity (the maximum change in soil moisture a model can produce) comparable or smaller
than this. Although we currently have no other means to validate the magnitude of such long-
term change, the general trend can be assessed because Amazon hydrological cycle is dominated by
ENSO related interannual variability. For instance, the period with largest recharge during 1974-75
corresponds to two major La Nina events before the 1976-77 decadal climate shift in the Pacific
Ocean, as indicated by the Southern Oscillation Index (SOI). In the other direction, the major
discharge period of early 1990s was caused by the protracted El Nino of 1991-1993.
A simple high-pass filter was applied to the diagnosed soil moisture in Fig. 6b to remove
the frequencies lower than 7 years. The remaining signal is mostly interannual (Fig. 7), showing
decreasing soil moisture during events such as 1982-83 El Nino. However, even in this case, the
major peaks reflect the lower frequency variations such as the two La Nina events around 1975 and
early 1990 El Nino. Also plotted in Fig. 7 are the model simulated soil moisture from SLand and
the Leaky Bucket model, and both show significantly smaller variability at about 1/2 to 1/3 of the
diagnosed amplitude on interannual time scales, while the decadal and longer-term variability is
even smaller (cf. Fig. 6b).
7
The advent of recent satellite gravity based measurement from NASA’s GRACE mission
provides an independent means to validate the interannual variability of the diagnosis method.
Figure 8 shows the GRACE measurement of Amazon water change for the two year period of April
2002 to May 2004. The first half of this period has a larger seasonal variation of about 400 mm,
while the second half has a smaller seasonal amplitude of about 250 mm (the seasonal amplitude
in Fig.4 is an estimate over the whole 2 yr period), following the El Nino of 2002-2003. Thus
the interannual change is about 150 mm, comparable to the amplitude of interannual variability
derived using the P-E method (Fig. 7). Since the 2002-2003 El Nino is a relatively small one, the
GRACE data indicates a potentially very large interannual variability in the land-surface water
storage, consistent with our diagnostic approach (with the caveat that the GRACE data has its
own uncertainties and the observation period is too short to actually verify the diagnosis approach).
Such large differences especially on longer time scales are striking. While the diagnosis may
overestimate the amplitude of these slow variations (section 6), the models appear to significantly
underestimate it. One contributor of the smaller model changes is that current land-surface models
typically only represent the water holding capacity of the top 1-2 meters of soil. Among the two
models, SLand has a field capacity of 500 mm, while the Leaky Bucket model has 750 mm. Thus
it is understandable that these models underestimate the multi-decadal change on the order of 600
mm. However, simple increase in model soil depth may not be sufficient if deep soil water can not
be utilized by vegetation (section 6).
5 Long-term variability in the Mississippi basin
It is also of great interest to see how the P-E method works for midlatitude regions. We have
applied the method to the Mississippi basin (Fig.9). The apparent water storage in the Mississippi
decreases by about 400 mm from the 1920s to the end of the 1930s (the Dust Bowl period), followed
by a recharge period in the 1940s. The drought in the 1950s plunged soil moisture to the lowest
level, and then recovered to high level during the following two decades of pluvial period (Seager et
8
al. 2005). Smaller drought events also left their impact on the soil moisture, such as 1988. Further
analysis (not shown) suggests that these changes are largely influenced by the west part of the
basin, in particular, the Great Plain region.
A long soil moisture memory is again implied here. For instance, the discharge during the
Dust Bowl period is somewhat larger than that of the 1950’s drought, as seen by the larger shaded
area with negative P-E*-R values for the 1930s in Fig.9a, yet soil moisture is at a minimum at the
end of the 1950’s drought. This is because the recharge in the 1940s is not sufficient to recover
the water loss in the 1930s. Thus the memory of the Dust Bowl period was not forgotten until
a few decades later! While uncertainties in the data and methodology may hamper the accuracy
of such detailed interpretation, the results suggest a longer soil moisture memory than typically
represented in models.
6 Discussions and conclusions
The large change of 500-600 mm in soil moisture over multidedadal timescales is remarkable, as
many land-surface models have field capacity (the maximum change in soil moisture a model can
produce) comparable or smaller than this. Theoretically speaking, the diagnosed AWS variability
is larger than or equal to any modeled soil moisture variability because the diagnosis includes all
the possible changes in the basin, including surface and underground water and water stored in
vegetation, while models typically only simulate soil moisture change in the top 1-2 meter. For
example, during flooding season, Amazon river expands into adjacent forest and the surface water
can account for about 10% of the seasonal soil moisture change. However, the major contributor
missing in simple land-surface models is likely the deeper soil moisture storage including ground
water. Such deep water storage is utilized by deep forest roots. In one instance, Nepstad et al.
(1994) found deep root water uptake down to 8 m below surface that sustained normal growth
during a prolonged dry period. Even deeper roots have been observed in many other regions
(Schenk and Jackson 2005).
9
More sophisticated models may include several soil layers which would increase the effective
filed capacity and thus the amplitude of variation. However, the lack of deep roots may prevent
the model to utilize deep water storage as efficiently as Nature does. Further analysis with these
models is obviously needed.
The large change on decadal to multidedadal timescales found by the current diagnosis ap-
proach suggest that current models may significantly underestimate such variations. An important
implication is that land-surface may have a memory beyond one year related to the change in the
apparent water storage1, significantly longer than the typically cited 1 month to 1 season.
Although the general ups and downs in the diagnosed AWS appear realistic, we can not
exclude the possibility that the method overestimates the magnitude of changes. The observed
precipitation and runoff might have different biases that change in time as precipitation and stream-
flow are measured differently and the techniques change in time. The sparse raingauge coverage
in the Amazon might be another source of potential bias. Even if the observations were perfect,
evapotranspiration is calculated using a model, forced by observed precipitation and temperature.
If the variability of evapotranspiration is underestimated, as may be the case because models tend
to underestimate long-term soil moisture variability and the variability in surface radiation is not
considered, then the P-E method would lead to overestimate of soil moisture variability. Since
model uses P as input (in a way similar to Eq.2), and the diagnostic approach also uses observed
R, our results suggest that model may overestimate runoff variability. To put it the other way, real
land surface seems to be able to hold up more water when it is wet, and releases more when it is
dry, while models’ runoff tend to be more sensitive to change in precipitation. It is worth noting
that the modeled variability may behave differently from modeled mean runoff which is not studied
here.
The P-E budget approach is very unique, and there has been a lack of independent means to
validate this method. Recent satellite gravity based measurement from NASA’s GRACE mission
1time scale can be estimated as capacity divided by flux. Assuming a field capacity of 1000 mm for the Amazon,then the time scale is 1000 mm / (5 mm d−1) = 200 days
10
provides such a means. Unlike numerical models, both methods yield only basin-scale total changes
because one is limited by the spatial resolution of measurement from space, and the other by the
fact that runoff data is only available for whole drainage basin. But both methods provide change in
the total water storage on land, thus they provide an upper limit for the numerical models or point
observation. The general consistency between the two completely independent methods suggest
that we may be on the right track in quantifying the interannual and longer-term variability in
land water storage.
Acknowledgments. We thank discussions with H. van den Dool, A. Robock, J. Wahr and P.
Dirmeyer. This research was supported by NSF grant ATM-0328286, NOAA grants NA04OAR4310091
and NA04OAR4310114.
11
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List of Figures
1 The moisture convergence method in diagnosing basin scale soil moisture variability.
The atmosphere and land are treated as one single box such that the total water
storage (atmosphere water W plus soil water storage S) can be diagnosed as a time
integral of moisture convergence (C; from atmospheric analysis) minus runoff (R;
observed). Precipitation (P) and evaporation (E) are not needed because they are
interior fluxes. Adopted from Zeng (1999). . . . . . . . . . . . . . . . . . . . . . . . 17
2 Diagnosed Amazon soil water storage using the moisture convergence method from
three reanalyses. The smooth curves are 12 month running means; and note the
different vertical scales, indicating large uncertainties. . . . . . . . . . . . . . . . . . 18
3 The P-E method in diagnosing basin scale soil water storage variability. Only the
water budget for the land-surface is needed. Soil water storage (S) is a time integral
of precipitation (P) minus evaporation (E) minus runoff (R). Precipitation and runoff
are from observations while evaporation needs to be reconstructed using model. . . 19
4 Diagnosed seasonal cycle of Amazon soil moisture a) using the moisture convergence
method with three reanalyses; b) using the P-E method with CRU rainfall, observed
runoff and evaporation reconstructed using the SLand model. For comparison, also
plotted in (b) are the seasonal cycles derived from two years of GRACE satellite
observation (Fig. 8), and simulated by two offline land surface models (SLand and
CPC Leaky Bucket) forced by observed atmospheric variables, and from the Global
Land Data Assimilation System (GLDAS). . . . . . . . . . . . . . . . . . . . . . . . 20
5 Variabilities of P-E* and R in mm d−1 (a) and the diagnosed Amazon soil moisture in
mm (b). When P-E* is greater than R, the soil water storage increases, undergoing
a recharge period (heavy shading); and when P-E* is less than R, the soil undergoes
discharge (light shading). There is a strong seasonal cycle, but also with large
interannual-interdecadal variabilities. . . . . . . . . . . . . . . . . . . . . . . . . . . . 21
6 As in Fig. 5, but with the seasonal cycle removed by a 12 month running mean. Also
plotted is the southern oscillation index (SOI; labeled on the right in mb). A major
recharge period occurred during two large La Nina events in 1974-75, and a major
discharge period was associated with the protracted El Nino of 1991-93. . . . . . . . 22
7 High frequency (less than 7 years) component of the diagnosed soil water storage,
compared to those simulated by two simple land surface models. The models capture
most of the variations but with significantly smaller amplitude. . . . . . . . . . . . . 23
15
8 Two years of observed changes in Amazon soil water storage (mm) from the GRACE
satellite gravity measurements. A much smaller seasonal cycle was seen following
the 2002-2003 El Nino compared to the year before. . . . . . . . . . . . . . . . . . . 24
9 As in Fig. 6, but for the Mississippi basin. The major discharge periods are the Dust
Bowl in the 1930s, drought in the 1950s. A major recharge took place during the
pluvial period from the 1960s to the 1990s. . . . . . . . . . . . . . . . . . . . . . . . 25
16
C
R
P E
atmo
land
W
S
Figure 1: The moisture convergence method in diagnosing basin scale soil moisture variability. Theatmosphere and land are treated as one single box such that the total water storage (atmospherewater W plus soil water storage S) can be diagnosed as a time integral of moisture convergence (C;from atmospheric analysis) minus runoff (R; observed). Precipitation (P) and evaporation (E) arenot needed because they are interior fluxes. Adopted from Zeng (1999).
17
Figure 2: Diagnosed Amazon soil water storage using the moisture convergence method from threereanalyses. The smooth curves are 12 month running means; and note the different vertical scales,indicating large uncertainties.
18
Figure 3: The P-E method in diagnosing basin scale soil water storage variability. Only the waterbudget for the land-surface is needed. Soil water storage (S) is a time integral of precipitation(P) minus evaporation (E) minus runoff (R). Precipitation and runoff are from observations whileevaporation needs to be reconstructed using model.
19
Figure 4: Diagnosed seasonal cycle of Amazon soil moisture a) using the moisture convergencemethod with three reanalyses; b) using the P-E method with CRU rainfall, observed runoff andevaporation reconstructed using the SLand model. For comparison, also plotted in (b) are theseasonal cycles derived from two years of GRACE satellite observation (Fig. 8), and simulated bytwo offline land surface models (SLand and CPC Leaky Bucket) forced by observed atmosphericvariables, and from the Global Land Data Assimilation System (GLDAS).
20
Figure 5: Variabilities of P-E* and R in mm d−1 (a) and the diagnosed Amazon soil moisture inmm (b). When P-E* is greater than R, the soil water storage increases, undergoing a rechargeperiod (heavy shading); and when P-E* is less than R, the soil undergoes discharge (light shading).There is a strong seasonal cycle, but also with large interannual-interdecadal variabilities.
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Figure 6: As in Fig. 5, but with the seasonal cycle removed by a 12 month running mean. Alsoplotted is the southern oscillation index (SOI; labeled on the right in mb). A major recharge periodoccurred during two large La Nina events in 1974-75, and a major discharge period was associatedwith the protracted El Nino of 1991-93.
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Figure 7: High frequency (less than 7 years) component of the diagnosed soil water storage, com-pared to those simulated by two simple land surface models. The models capture most of thevariations but with significantly smaller amplitude.
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Figure 8: Two years of observed changes in Amazon soil water storage (mm) from the GRACEsatellite gravity measurements. A much smaller seasonal cycle was seen following the 2002-2003 ElNino compared to the year before.
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Figure 9: As in Fig. 6, but for the Mississippi basin. The major discharge periods are the DustBowl in the 1930s, drought in the 1950s. A major recharge took place during the pluvial periodfrom the 1960s to the 1990s.
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Table 1: Methods commonly used to estimate soil moisture variability.
Method Examples Advantages Limitations Degree ofobservationalconstraint
In situ Global soil Direct Point data;moisture measurement limited coverage; Highdata banka near surface
Remote sensing Microwaveb High spatial and Surface; area HighMicrowave temporal resolution sparsely vegetated
Remote sensing GRACEc Independent; Low resolution; HighGravity total column only since 2002
Offline land model GSWPd; High spatial and Model dependent; Mediumforced by atmo vars PDSIe temporal resolution top soil layers
Data assimilation GLDASf High spatial and Model dependent; Med-HighModel+observation temporal resolution top soil layers
Basin-scale budget Rasmusson Long-term; Basin only; MediumMoisture convergence (1968) total column sensitive to quality
of atmospheric data
Basin-scale budget This Study Long-term; Basin only; Med-HighP-E total column some uncertainty in
evaporation estimate
a. Robock et al. (2000)b. Engman and Chauhan (1995)c. GRACE, Wahr and Swenson (2004)d. Global Soil Wetness Project (Dirmeyer et al. 1999)e. Palmer Drought Severity Index.f. Global Land Data Assimilation System (Rodell et al. 2004)
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