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1] Satellite gravity measurements from the Gravity Recovery and Climate Experiment (GRACE) provide new quantitative measures of the 2005 extreme drought event in the Amazon river basin, regarded as the worst in over a century. GRACE measures a significant decrease in terrestrial water storage (TWS) in the central Amazon basin in the summer of 2005, relative to the average of the 5 other summer periods in the GRACE era. In contrast, data-assimilating climate and land surface models significantly underestimate the drought intensity. GRACE measurements are consistent with accumulated precipitation data from satellite remote sensing and are also supported by in situ water-level data from river gauge stations. This study demonstrates the unique potential of satellite gravity measurements in monitoring large-scale severe drought and flooding events and in evaluating advanced climate and land surface models., 2005 drought event in the Amazon River basin as measured by GRACE and estimated by climate models, J. Geophys. Res., 114, B05404, doi:10.1029/2008JB006056.
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2005 drought event in the Amazon River basin as measured
by GRACE and estimated by climate models
J. L. Chen,
C. R. Wilson,
B. D. Tapley,
Z. L. Yang,
and G. Y. Niu
Received 1 September 2008; revised 8 January 2009; accepted 2 March 2009; published 8 May 2009.
[1]Satellite gravity measurements from the Gravity Recovery and Climate Experiment
(GRACE) provide new quantitative measures of the 2005 extreme drought event in the
Amazon river basin, regarded as the worst in over a century. GRACE measures a
significant decrease in terrestrial water storage (TWS) in the central Amazon basin in the
summer of 2005, relative to the average of the 5 other summer periods in the GRACE era.
In contrast, data-assimilating climate and land surface models significantly underestimate
the drought intensity. GRACE measurements are consistent with accumulated
precipitation data from satellite remote sensing and are also supported by in situ
water-level data from river gauge stations. This study demonstrates the unique potential of
satellite gravity measurements in monitoring large-scale severe drought and flooding
events and in evaluating advanced climate and land surface models.
Citation: Chen, J. L., C. R. Wilson, B. D. Tapley, Z. L. Yang, and G. Y. Niu (2009), 2005 drought event in the Amazon River basin
as measured by GRACE and estimated by climate models, J. Geophys. Res.,114 , B05404, doi:10.1029/2008JB006056.
1. Introduction
[2] In the summer of 2005, the Amazon basin experi-
enced an extreme drought. Many areas, especially in the
west and south, suffered the worst drought in over a century,
leading to official declarations of ‘‘public calamity’’, forest
fires, crop losses, and economic havoc [Rohter, 2005]. The
event appears connected both to the 2002–03 El Nin˜o and
to abnormal warming of the northern tropical Atlantic,
which was up to two degrees warmer than average [Zeng
et al., 2008a]. This paper compares measures of this event
taken from satellite gravity observations and from data-
assimilating hydrologic models.
[3] Understanding and quantification of drought occur-
rence, extent, and intensity is limited by conventional data
resources. Numerical climate models are valuable in ana-
lyzing and diagnosing climate variability, but quantifying
and simulating abnormal events such as droughts remains a
major modeling challenge. Prediction is an even greater
challenge. Conventional observations, especially in situ
meteorological and hydrological samples, are limited in
both space and time. Furthermore, model representations
of dynamical connections between boundary conditions and
extreme climate events tend to be poor.
[4] Terrestrial water storage (TWS) change, a major
component of the global water cycle, includes changes in
water stored in soil, as snow over land, and in ground water
reservoirs. TWS change reflects accumulated precipitation,
evapotranspiration, and surface and subsurface runoff with-
in a given area or basin. TWS change provides a good
measure of abnormal climate conditions such as drought,
and is valuable for agriculture and other water uses. How-
ever, TWS change is difficult to quantify because of limited
fundamental observations (ground water, soil moisture,
precipitation, evapotranspiration, snow water equivalent,
and others) at basin or smaller scales. Numerical model
estimates are useful but exhibit limited accuracy [e.g.,
Matsuyama et al., 1995]. Remote sensing data (such as
TRMM satellite precipitation data) and in situ measure-
ments (such as river level and discharge from gauge
stations) are valuable assets in estimating TWS changes
[e.g., Crowley et al., 2007]. Unfortunately, in situ measure-
ments alone are not sufficient, both because they tend to be
point measurements, and because other hydrological param-
eters (e.g., evapotranspiration) must be estimated separately
to determine TWS change.
[5] The Gravity Recovery and Climate Experiment
(GRACE) is the first dedicated satellite gravity mission,
jointly sponsored by NASA and the German Aerospace
Center (DLR). Launched in March 2002, GRACE has been
measuring Earth gravity change with unprecedented accu-
racy [Tapley et al., 2004] for over 6 years. Early GRACE
time-variable gravity observations showed an accuracy of
1.5 cm of equivalent water thickness change at about
1000-km spatial scale [Wahr et al., 2004]. Various studies
applied early GRACE results to a variety of problems
including TWS change [e.g., Wahr et al., 2004], polar ice
sheets mass balance [e.g., Velicogna and Wahr, 2006; Chen
et al., 2006], and oceanic mass change [e.g., Chambers et
al., 2004; Lombard et al., 2007].
[6] In early 2007, reprocessed GRACE release-04 (RL04)
time-variable gravity fields with improved background
geophysical models and data processing techniques were
released [Bettadpur, 2007a]. RL04 shows significantly
JOURNAL OF GEOPHYSICAL RESEARCH, VOL. 114, B05404, doi:10.1029/2008JB006056, 2009
Center for Space Research, University of Texas at Austin, Austin,
Texas, USA.
Department of Geological Sciences, University of Texas at Austin,
Austin, Texas, USA.
Copyright 2009 by the American Geophysical Union.
B05404 1of9
improved data quality and spatial resolution, now better
than 500 km [e.g., Chen et al., 2007], enabling the study of
a much wider class of problems than before. Improved
quality and spatial resolution of RL04 and optimized data
processing and filtering techniques [e.g., Swenson and
Wahr, 2006] provide new opportunities for quantification
of TWS changes, better monitoring of the global water
cycle, and related understanding of droughts and floods.
[7] In this study, we examine TWS change in the Amazon
basin using RL04 time-variable gravity fields and predic-
tions from major climate and land surface models, including
the National Oceanic and Atmospheric Administration
(NOAA) National Centers for Environmental Prediction
(NCEP) reanalysis II climate model and the global land
data assimilation system (GLDAS) [Rodell et al., 2004].
The goal is to demonstrate the capability of GRACE to
Figure 1. GRACE-averaged August and September water storage changes (in centimeters of water) in
South America in (a) 2002, (b) 2003, (c) 2004, (d) 2005, (e) 2006, and (f) 2007. A 2-step filtering scheme
(P4M6 and 500-km Gaussian smoothing) is applied, as described in the text.
observe and quantify the severe 2005 Amazon drought
event, and to compare GRACE results with climate model
descriptions of the same event.
2. Data Processing
2.1. TWS Changes From GRACE RL04
[8] Our GRACE time series includes 65 approximately
monthly gravity solutions, for the period April 2002 to
December 2007. Spherical harmonic (SH) coefficients (to
degree and order 60) are used to compute monthly mass
change fields on a 1°1°grid. Swenson and Wahr [2006]
showed that GRACE longitudinal stripe noise is associated
with correlations among even or odd degree SH coefficients
at a given order. Here we apply a two-step filter as in
previous studies [Chen et al., 2007]. The first step (called
P4M6) removes this correlated noise by fitting and sub-
tracting a fourth-order polynomial to even and odd coeffi-
cient pairs at SH orders 6 and above. The second step
involves smoothing with a 500-km Gaussian filter [Jekeli,
1981]. After filtering, the mean of the 65-point time series
at each grid point is removed to obtain a time series of
gravity field variations, expressed as equivalent surface mass
variations in cm of water.
[9] Atmospheric and oceanic mass changes have been
largely removed from RL04 using numerical model predic-
tions [Bettadpur, 2007b], so that variations over time scales
of months to years should reflect primarily unmodeled
effects such as TWS change, snow/ice mass changes
(including polar ice sheets and mountain glaciers), plus
other geophysical signals such as postglacial rebound
(PGR) and coseismic and postseismic deformation. Over
the Amazon basin, surface mass variations are expected to
be dominantly due to near-surface water storage changes.
Given this, the major errors in GRACE time series over the
Amazon arise from spatial leakage associated with a finite
range of SH terms, attenuation due to filtering, residual
atmospheric signals, and GRACE measurement errors.
2.2. TWS Changes From Climate and Land Surface
2.2.1. NCEP Reanalysis II Model Estimates
[10] (NCEP/DOE AMIP) II Reanalysis-II was developed
at NCEP and the Department of Energy (DOE) from the
widely used NCEP/NCAR Reanalysis [Kalnay et al., 1996].
NCEP reanalysis II (simply called NCEP in the subsequent
discussion) improves upon earlier results by correcting
errors and refining parameterizations of physical processes.
Soil moisture (volume percentage) and snow fields (cm of
water equivalent) are monthly averages from January 1948
to present, on a Gaussian grid (1.904°latitude by
1.875°longitude). Soil moisture is modeled for an upper
layer of 10 cm and a lower layer of 190 cm thickness.
Because NCEP does not model deeper ground water stor-
age, TWS change at each grid point is the sum of two soil
water layers plus snow water.
2.2.2. GLDAS Model Estimates
[11] GLDAS was developed jointly at the National
Aeronautics and Space Administration Goddard Space
Flight Center and NOAA NCEP [Rodell et al., 2004].
GLDAS parameterizes, forces, and constrains sophisticated
land surface models with ground and satellite products with
the goal of estimating land surface states (e.g., soil mois-
ture and temperature) and fluxes (e.g., evapotranspiration).
In this particular simulation, GLDAS drove the Noah land
surface model [Ek et al., 2003], with observed precipitation
Figure 2. Differences of mean August/September TWS changes (in centimeters of water) in South
America in 2005 relative to mean August/September TWS changes of other years in the period 2002
2007: (a) from GRACE, (b) from NCEP, and (c) from GLDAS. GRACE displays a strong 2005 TWS
deficit, while NCEP and GLDAS do not.
and solar radiation included as inputs. Monthly averaged
soil moisture (2 m column depth) and snow water equiv-
alent are available from 1979 to present, and TWS varia-
tion at each grid point is the sum of soil and snow water.
Greenland and Antarctica are excluded because the model
omits ice sheet physics. Ground water is also not modeled
2.2.3. TWS Changes From NCEP and GLDAS
[12] A fair comparison with GRACE observations
requires that NCEP and GLDAS fields be spatially filtered
in a similar way. To accomplish this, GLDAS and NCEP
TWS gridded fields were represented in a SH expansion to
degree and order 100, and the same two step filter described
earlier (to remove correlated noise and smooth RL04) was
applied. SH coefficients were truncated above degree and
order 60, and additionally SH coefficients for degree 0
(imposing global mass conservation) and degree 1 (remov-
ing geocenter motion) were set to zero. Finally, GLDAS and
NCEP SH representations were evaluated on a global 1°
3. Results
3.1. GRACE and Climate Models Estimates
[13] Average August and September [(August + Septem-
ber)/2] TWS changes are used to measure the 2005 Amazon
drought. The drought is at its maximum during these two
months, and GRACE observations are available for August
and September starting in 2002. Figure 1 shows GRACE
(August/September) TWS change relative to the mean in
South America for 2002 through 2007. In 2005 (Figure 1d),
a minimum in TWS is evident (in the region circled by a
gray curve). The Orinoco basin (to the north of the Amazon)
shows a significantly wetter 2007 (Figure 1f).
[14] Figure 2a displays the difference between the 2005
(August/September) average and the mean (August/September)
Figure 3. (a) Comparison of TWS changes in the central Amazon basin (average within the red box
[2°S–7°S,294°E 299°E] marked in Figure 2a) from GRACE (blue curve), NCEP (red curve), and
GLDAS (green curve). (b) Comparison of nonseasonal TWS changes in central Amazon basin from
GRACE (blue curve), NCEP (red curve), and GLDAS (green curve).
determined from the other 5 years. This indicates a
GRACE-observed deficiency around 8 9 cm basin wide
for 2005, or approximately 515 km
of water (integrated
over the entire Amazon basin). Figures 2b and 2c show
2005 (August/September) anomalies relative to the other 5
years from NCEP and GLDAS, respectively. NCEP anoma-
lies for 2005 are near zero, while those from GLDAS are
larger, though smaller than the tens of cm seen in Figure 2a.
[15] We examine in more detail a 5°5°region (Figure 2a,
red box) near the center of the 2005 drought region evident in
Figure 2a. Average TWStime series (average equivalent water
height) for this entire region are calculated from GRACE,
NCEP and GLDAS, and compared in Figure 3a. GRACE
shows a strong seasonal signal with peak-to-peak amplitudes
up to 70 cm, with additional nonseasonal variability. Near
the end of 2005, GRACE TWS diminishes (relative to other
years), indicating drought. Both NCEP and GLDAS show
strong seasonal variation, but with magnitudes about one
half of GRACE. Both show significantly less nonseasonal
[16] Annual and semiannual sinusoids were fit by un-
weighted least squares and removed from each of the three
time series, with results shown in Figure 3b. Additionally,
we removed from the GRACE series a 161-day S2 tide alias
[Knudsen, 2003] determined by least squares. The S2 alias
amplitude (in this GRACE time series) is 1 cm, as com-
pared to 25 cm for the annual component. In Figure 3b,
GRACE shows a steady TWS decrease of about 14 cm,
starting in March/April 2005 through August/September,
marking a clear anomaly. NCEP and GLDAS series each
show some TWS decrease in the same period, but the
change does not appear unusual in the context of variations
over the full 6 year time series.
[17] Estimates of GRACE noise (error bars in Figure 3b) are
determined from root-mean-square (RMS) variability over
tropical oceans (20°S–20°N), a region in which true mass
variability (GRACE signal) is probably near zero, as bara-
tropic ocean mass changes have been removed in GRACE
dealiasing process [Bettadpur, 2007b]. Therefore residuals
over the ocean could approximately represent residual errors
of GRACE data (plus some unmodeled baroclinic ocean
signals) [Wa hr e t a l., 2004]. Another estimate of GRACE
error [Wahr e t a l., 2006] is based on RMS residual of GRACE
variability over land after subtracting seasonal sinusoids and
smoothing, though this overestimates GRACE errors when
there is significant nonseasonal (nonsinusoidal) variability, as
evident in Figure 3b. However, after removing longer-period
interannual variability from GRACE data, the two error bar
estimates are similar (2.4 cm from ocean residuals versus
2.9 cm from land residuals).
3.2. Other Observational Evidences
[18] To validate GRACE-observed significant TWS de-
crease in during the 2005 Amazon drought, we analyze
precipitation data from the Global Precipitation Climatology
Project (GPCP) [Adler et al., 2003], for the GRACE period.
Figure 4. (a) Accumulated precipitation in the central Amazon region (marked by the red box in Figure 2a)
during June to September based on GPCP data. (b) Accumulated precipitation in the central Amazon
region during December to March based on GPCP data.
We compute accumulated precipitation totals in the central
Amazon region (Figure 2a, red box) in the 4 summer
months (i.e., the sum of the 4 months’ totals in June through
September) for each of the 6 years from 2002 to 2007, and
show the results in Figure 4a. The selection of summing
June through September is based on the consideration that
GRACE data show that the 2005 Amazon drought reached
the peak (with minimum TWS) in August/September, and
TWS change could reflect accumulated precipitation
changes of a few months prior to the drought. The use of
4 months summation is arbitrary. We test the calculation in
different cases (including using 3 and 6 months) and all
show the least amount of accumulated precipitation in 2005,
while the 4 months summation appears to show the most
distinctive drop of accumulated summer precipitation in
2005. Similar 4-monthly totals for the same region, but for
December to March are presented in Figure 4b. Clearly, in
the summer 2005, the central Amazon region recorded
significantly less precipitation (up to 14 cm less) than
any other year (during the period 2002 to 2008), consistent
with GRACE observations, as well as reports of the 2005
drought severity cited earlier.
[19] GRACE time series (Figures 3a and 3b) indicate the
2005 drought had ended by the end of 2005 (or beginning
of 2006), and the central Amazon region actually experi-
enced a wetter (as compared to other years) winter season in
early 2006. This is also consistent with GPCP precipitation
data (Figure 4b), as the December–March precipitation total
(in the central Amazon region) in 2005 is significantly less
(up to 30 cm) than other years.
[20] To further verify GRACE measurements, we exam-
ine daily water-level data of 4 selected river gauges in the
Amazon basin (see Figure 5). Among the 4 river gauge
stations, Itapeua and Jatuarana are located close to the
center of the TWS decrease observed by GRACE, and
Parintins and Obidos are spread on the down stream side.
To better focus on nonperiodic variations, we remove
annual and semiannual variations from the 4 water-level
time series (at the 4 gauges marked in Figure 5) using
unweighted least squares fit, and show the nonseasonal
water-level time series in Figure 6.
[21] Each of the 4 gauges show a clear drop of water level
in the summer 2005, bottomed in around August and
September, while Itapeua shows the largest water level
decrease of up to 4.8 m (with respect to the 6-year
temporal mean). In situ water-level data at these 4 river
gauge stations provide additional verification of the signif-
icant central Amazon TWS decrease in 2005 as observed by
Figure 5. Location map of 4 river gauge stations (Itapeua,
Jatuarana, Parintins, and Obidos) in the Amazon basin,
superimposed by GRACE-observed TWS decrease in
August/September 2005 (same as in Figure 2a).
Figure 6. Nonseasonal daily water-level change at 4 selected river gauge stations marked in Figure 5.
Annual and semiannual variations have been removed from these time series using unweighted least
squares fit.
GRACE, although quantitative comparisons (between
GRACE and in situ river gauge data) are difficult because
of different representations of the two different quantities.
We see a gradual decease in magnitudes of the water-level
drops among the 4 gauges, from 4.8 m at Itapeua, 4.4 m
at Jatuarana, 3 m at Parintins, to 2 m at Obidos (Figure 6).
This is consistent with the spatial TWS variation feature
observed by GRACE (Figure 5). In situ water-level data
also indicate that the 2005 Amazon drought had ended by
the end of 2005 and by February or March 2006, the central
Amazon region was actually wetter than normal (see Figure 6),
also consistent with GRACE observations and GPCP pre-
cipitation data.
4. Discussion and Conclusions
[22] GRACE RL04 time series clearly indicate a signif-
icant TWS deficit accompanying the 2005 Amazon
drought, on the order of 14 cm of water equivalent in
the central Amazon. GRACE time series indicate the
drought peaked in the period August to September 2005,
and was relieved by the beginning of 2006, consistent with
independent precipitation observations and in situ water-
level measurements.
[23] NCEP and GLDAS land surface models significantly
underestimate TWS change in the central Amazon relative
to GRACE. These two models show only half the seasonal
variability of GRACE observations, and both lack signifi-
cantly diminished TWS associated with the 2005 drought.
Unfortunately, there are no in situ TWS measurements to
directly validate GRACE estimates. GPCP precipitation
data are helpful for understanding TWS changes, but are
not directly comparable to GRACE observations because
they do not account for other elements affecting TWS.
GPCP data do show diminished precipitation during the
summer of 2005, consistent with GRACE time series.
Figure 7. (a) Comparison of GLDAS and LadWorld estimated TWS changes in the Amazon basin
(averaged over the entire basin). A 500-km Gaussian smoothing is applied to both data sets. (b) Comparison
of GLDAS and LadWorld estimated TWS changes in the central Amazon area (average within the red box
marked in Figure 2a).
Considering these limitations, this study demonstrates the
unique strength of GRACE observations in monitoring
droughts and floods via associated large spatial scale
TWS changes. Additionally, GRACE observations provide
independent measures of TWS for calibrating, evaluating,
and improving climate and land surface models.
[24] Both the GRACE estimate of the 2005 TWS deficit
(Figure 3b) and the discrepancy between GRACE and
model estimates of seasonal variability (Figure 3a) greatly
exceed estimated errors. Spatial leakage errors (due to
filtering and limited SH range) are not likely causes of
differences, given that similar filtering has been applied to
all data sets. Lack of a ground water component in NCEP
and GLDAS may partially account for differences between
GRACE and model estimates, but is probably not a major
cause [Niu et al., 2007].
[25] To further prove this speculation, we show in Figure 7
the comparison of GLDAS TWS estimates and similar
estimates from the LadWorld land surface model, which
includes the groundwater component [Milly and Shmakin,
2002]. LadWorld TWS data (from the Fraser version,
see information at
ladworld.htm for details) represent the sum of soil (of the
top 6 m), water equivalent snow, and groundwater, and
follow the same data processing (e.g., smoothing and
truncation) as used in GLDAS data. For Amazon basin-
wide average, GLDAS and LadWorld show very similar
TWS estimates, even though groundwater is modeled in
LadWorld. However, in the central Amazon area (marked in
Figure 2a), LadWorld indeed show notably larger TWS
variability than GLDAS (and the differences are consider-
ably larger when no smoothing is applied). The increased
TWS variability of LadWorld estimates is still significantly
smaller than GRACE observations (at both seasonal and
nonseasonal time scales), suggesting that groundwater is
important but may not be the major contributor to models’
underestimation of TWS changes in Amazon.
[26] A recent study [Zeng et al., 2008b] has compared
GRACE estimated TWS change in the Amazon basin with a
few models’ estimates and some other estimates based on
water conservation equation using modeled moisture con-
vergence minus observed runoff (MCR) or using observed
precipitation minus observed runoff and modeled evapo-
transpiration (PER), and indicates that the PER estimate
shows significantly larger seasonal variability (in the
Amazon basin) than both models and MCR estimates and
agrees well with GRACE data. This appears to indicate that,
given the parameterizations of current land surface models,
the more ‘traditional’ PER method can better depict TWS
changes, at least in the Amazon basin.
[27] Model and GRACE estimates are more similar in
other major basins than in the Amazon (e.g., the La Plata
to the south), although model TWS changes are consis-
tently smaller than GRACE estimates in the La Plata as
well. Because the Amazon is the largest river basin in the
world, estimated TWS is likely to be least affected by
GRACE’s spatial resolution limitations. On the other hand,
neither model appears to do a good job of describing TWS
variability in this basin. One reason may be that most
macrohydrological models (including NCEP, GLDAS, and
LadWorld) do not consider horizontal transport of water,
implying instantaneous runoff. In very large basins, such
as the Amazon, precipitation may actually be retained for
some time (in lakes, wet lands, and shallow reservoirs),
leading to underestimation of TWS change. This suggests
that proper modeling of the terrestrial water cycle within
the Amazon will likely require additional consideration of
complexity of its river systems, vegetation, soils, and
[28]Acknowledgments. The authors are grateful to Anny Cazenave
and an anonymous reviewer for their insightful comments, which led to
improved presentation of the results. This research was supported by
NASA’s Solid Earth and Natural Hazards and GRACE Science Program
(NNG04GF10G, NNG04G060G, NNG04GF22G, and NNG04GP70). The
authors would like to thank Flavio Vaz for providing the river gauges water-
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Zeng, N., J. H. Yoon, and A. Mariotti (2008b), Variability of basin-scale
terrestrial water storage from a P-E-R water budget method: The Amazon
and the Mississippi, J. Clim.,15, 248 – 265.
J. L. Chen, B. D. Tapley, and C. R. Wilson, Center for Space Research,
University of Texas at Austin, 3925 W. Braker Lane, Suite 200, Austin, TX
78759-5321, USA. (
G. Y. Niu and Z. L. Yang, Department of Geological Sciences, University
of Texas at Austin, 1 University Station C1100, Austin, TX 78712-0254,
... Australia is one of the most fire-prone regions in the world, and its wildfire situation is complex [2,3]. The 2019/2020 Australian wildfire have applied the GRACE solutions to detect and quantify regional hydrological drought, for example, in the Amazon River basin [29,30], southwest China [31], Australia [32], and United States [33]. Due to the great success of the GRACE mission, its follow-on (GRACE-FO) satellites were launched in May 2018. ...
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With the frequent occurrence of extreme climates around the world, the frequency of regional wildfires is also on the rise, which poses a serious threat to the safety of human life, property, and regional ecosystems. To investigate the role of extreme climates in the occurrence and spread of wildfires, we combined precipitation, evapotranspiration, soil moisture (SM), maximum temperature (MT), relative humidity, plant canopy water, vapor pressure deficit, and a combined hydrological drought index based on six Gravity Recovery and Climate Experiment (GRACE) and its follow-on (GRACE-FO) products to study the relationship between climate change and wildfires across Australia between 2003 and 2020. The results show that Australia’s wildfires are mainly concentrated in the northern region, with a small number being distributed along the southeastern coast. The high burned months are September (2.5941 × 106 ha), October (4.9939 × 106 ha), and November (3.8781 × 106 ha), while the years with a larger burned area are 2011 (79.95 × 106 ha) and 2012 (78.33 × 106 ha) during the study period. On a seasonal scale, the terrestrial water storage change and the hydrometeorological factors have the strong correlations with burned area, while for only the drought index, SM and MT are strongly related to burned area on an interannual scale. By comparing the data between the high burned and normal years, the impact of droughts on wildfires is achieved through two aspects: (1) the creation of a dry atmospheric environment, and (2) the accumulation of natural combustibles. Extreme climates affect wildfires through the occurrence of droughts. Among them, the El Niño–Southern Oscillation has the greatest impact on drought in Australia, followed by the Pacific Decadal Oscillation and the Indian Ocean Dipole (correlation coefficients are −0.33, −0.31, and −0.23, respectively), but there is little difference among the three. The proposed hydrological drought index in our study has the potential to provide an early warning of regional wildfires. Our results have a certain reference significance for comprehensively understanding the impact mechanism of extreme climates on regional wildfires and for establishing an early warning system for regional wildfires.
... The environment has been threatened by human-induced hazards like air pollution (Yang et al. 2022), water pollution (Dehkordi et al. 2022), and soil pollution (Boente et al. 2022), and natural hazards such as floods (Sharafati and Zahabiyoun 2014), earthquakes (Bommer 2022), and droughts (Ullah et al. 2022). However, the spatial and temporal extent and the persistence of drought effects are more significant than those of other phenomena and natural disasters (Chen et al. 2009;Shahid and Hazarika 2010). ...
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Drought directly impacts the human economy and society, so a proper understanding of its spatiotemporal characteristics in different time scales and return periods can be effective in its evaluation and risk warning. In this research, the spatiotemporal variation of drought characteristics in 70 investigated stations in Iran during 1981–2020 was examined, evaluated, and compared. The Standardized Precipitation Index (SPI) and Standardized Precipitation Evapotranspiration Index (SPEI) have been used on time scales of 1, 3, 6, 9, 12, and 24 months to calculate the meteorological drought. Drought characteristics have been calculated through the run theory method, and the correlation between these characteristics has been checked. Statistical distribution functions have been used to calculate drought characteristics for the 10-, 20-, 50-, and 100-year return periods. Results show that the duration, severity, and peak of the drought in rainy areas increase as the return period increases. The drought features obtained from the SPI and SPEI show that the average value of severity obtained based on the SPI (43.5) is higher than that of the SPEI (40.9) while the average values of the peak are 3.9 and 2.6 for SPI and SPEI, respectively. Extreme drought was identified in 1990 in all regions of Iran. The highest severity in the current study is from 1999 to 2003. At the end of this period, Iran faced wet years. These results are evident on all time scales. The results obtained in this study can identify drought-prone regions and the beneficial use of water resources in the region.
... Monitoring drought was recognized early in the mission as a practical application of GRACE data, though multiple years of observations were needed to develop a baseline for quantifying wet or dry extremes [170][171][172][173] . GRACE data assimilation was the foundation for the first routinely delivered GRACE-based soil moisture and groundwater drought/wetness indicator maps for the contiguous United States 174 . ...
Satellite observations of the time-variable gravity field revolutionized the monitoring of large-scale water storage changes, beginning with the 2002 launch of the Gravity Recovery and Climate Experiment (GRACE) mission. Most hydrologists were sceptical of the satellite gravimetry approach at first, but validation studies assuaged their concerns and high-profile, GRACE-based groundwater depletion studies caused an explosion of interest. The importance of GRACE observations for hydrologic and cryospheric science became so great that GRACE Follow-On (GRACE-FO) jumped the National Aeronautics and Space Administration’s Earth science mission queue and launched in 2018. A third mass change mission is currently under development. Here, we review key milestones in satellite gravimetry’s progression from the fringes of hydrology to being a staple of large-scale water cycle and water resources studies and the sole source of observations of what is now an ‘essential climate variable’, terrestrial water storage. The story of satellite gravimetry’s progression from the fringes of hydrology to being a staple of large-scale water cycle and water resources science and the sole source of global observations of terrestrial water storage now an ‘essential climate variable’.
... Remote Sens. 2022,14, 6012 ...
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Approximately 3.5 million people in Nicaragua have experienced food insecurity due to the El Niño-Southern Oscillation (ENSO)-induced drought from 2014 to 2016. It is essential to study terrestrial water storage component (TWSC) changes and their responses to ENSO to prevent the water crisis in Nicaragua influenced by ENSO. In this paper, we investigate the TWSC changes in Nicaragua and its sub-basins derived from the Gravity Recovery and Climate Experiment (GRACE)’s temporal gravity field, hydrological model, and water level data, and then determine the connection between the TWSC and ENSO from April 2002 to April 2021 by time series analysis. The research results show that: (1) The estimated TWSC changes in Nicaragua are in good agreement with the variation of precipitation and evaporation, and precipitation is the main cause of TWSC variation. (2) According to the cross-correlation analysis, there is a significant negative peak correlation between the interannual TWSC and ENSO in western Nicaragua, especially for interannual soil moisture (−0.80). The difference in peak correlation between the western and eastern sub-basins may be due to the topographic hindrance of the ENSO-inspired precipitation process. (3) The cross-wavelet analysis indicates that the resonance periods between TWSC and ENSO are primarily 2 and 4 years. These resonance periods are related to the two ENSO modes (the central Pacific (CP) mode with a quasi-2-year period and the eastern Pacific (EP) mode with a quasi-4-year period). Furthermore, their resonance phase variation may be due to the transition to ENSO mode. This study revealed the relationship between ENSO and TWSC in Nicaragua, which can provide a certain reference for water resources regulation.
... GRACE has also provided a unique opportunity to monitor TWS changes in the Amazon Basin, including interannual and long-term changes (see, for example, Refs. [1][2][3][4][5][6][7][8], but the GRACE mission ended in June 2017. ...
... Uno de los principales estudios fue el ciclo de carga y recarga del agua almacenada en la cuenca del Amazonas y su relación con los eventos del fenómeno El Niño Oscilación Sur (ENSO) en el Pacífico tropical. [5], [6]. Los datos GRACE en sinergia con diferentes datos y modelos hidrológicos han sido utilizados para comprender las variaciones anuales y estacionales del agua subterránea y su relación con eventos de sequías e inundaciones [7], [8], [9], [10]. ...
... The combination of TWS (Terrestrial Water Storage) and VPD (Vapor Pressure Deficit) offers the chance to achieve the goal. TWS represents the column integrated water stored on land including canopy (despite this portion being negligible), surface water, soil, groundwater and snow, and has been proved to be good at large-scale hydrological drought monitoring (Yirdaw et al., 2008, Chen et al., 2009, Frappart et al., 2012, Long et al., 2013, Zhao et al., 2017, Sinha et al., 2019. VPD, defined as the difference between saturated and actual atmospheric vapor pressures for a given temperature, directly represents the water required for the atmosphere to reach saturation (atmospheric "thirsty"). ...
Drought is known as a complex natural phenomenon because of its multifaceted effect on the environment. Composite drought indices have been considered a useful tool to capture the compound drought impacts, however, few of them are capable of operating at multi-timescales. Moreover, the vast of composite drought indices fail to depict the whole water deficit on terrestrial because of the ignoring of groundwater. In this study, we linearly combined two satellite observed variables, TWS (Terrestrial Water Storage) and VPD (Vapor Pressure Deficit), to develop a new index WDDI (Water Deficit Drought Index). It can provide the global composite drought conditions at multi-timescales by different weight combinations. The global multiscalar SPEI (Standardized Precipitation Evapotranspiration Index) from 2002 to 2011 and 2012 to 2017 were selected to train and assess the weights respectively. Weights assessment results showed that SPEI and WDDI reached moderate correlations (r > 0.4) over the globe, especially stronger in the summer season (0.5< r < 0.88). Good agreements between WDDI and scPDSI (self-calibrated Palmer Drought Severity Index), SPEI were found in the global historical drought area statistics, especially in area statistics of severe or worse drought conditions. WDDI at short and long timescales were found to be sensitive to drought signals in the meteorological and hydrological system respectively as it produced moderate or stronger correlations with VsmCI (Volumetric soil moisture Condition Index) and RCI (Runoff Condition Index) in most parts of the world. The application of WDDI in the specific drought events (the 2010 Russian drought and the 2012-2016 California drought) further reinforced the above inferences and revealed the unique superiority of considering groundwater deficit in hydrological drought monitoring. The new proposed multiscalar WDDI could help practitioners better identify the drought conditions and understand the drought propagation mechanism from the meteorological to the hydrological system.
Conference Paper
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The Middle East is one of the regions that has suffered significant effects from climate changes. However, the intensity of these effects varies from one area to another. Located in Kurdistan region, Hawraman is an elevate area that receives one of the largest precipitation rates within the Tigris river basin. In the present research, hydrological quantitative changes in Hawraman during the last two decades have been studied utilizing satellite gravimetry data. The use of a filter optimization method has allowed the extraction of useful information for the small-scale area of Hawraman. The principal cause of signal spatial leakage is identified as the ZerivSar Lake in the north of Hawraman; however, its impact during the studied period has been estimated negligible. The results show that the 20-year declining trend of water mass in Hawraman include lower slopes than the most of its neighboring regions in the Middle East.
The extreme change of water storage in the Yangtze River Basin (YRB) have a significant impact on identifying the characteristics of drought events in the basin. To quantify the historical hydrological drought characteristics, we put forward new framework to reconstruct the pre-2003 total water storage anomaly (TWSA) through the nonlinear autoregressive with exogenous input (NARX) model. The NARX model is developed by the Gravity Recovery and Climate Experiment (GRACE) based TWSA and the hydrometeorological data after removing the trend and seasonal signals from 2003 to 2017, then the full pre-2003 reconstructed TWSA signals were obtained by synthesizing hydrometeorological data driven NARX model results from 1979 to 2002 and GRACE-estimated seasonal cycle. We combined the reconstructed TWSA with GRACE observed TWSA to characterize the historical hydrological drought events (onset, end, duration, magnitude, intensity, and recovery) in the YRB. The results show that the drought-related extreme anomalies in total water storage can be captured successfully. From 1979 to 2017, 23 hydrological drought events were identified in the YRB with an average recovery time of 4.7 months. The longest drought lasted 28 months spanning from July 2006 to October 2008. The exceptional drought occurred in September 2011 reached to the largest deficit with a magnitude of -48.5 mm and minimum drought severity index (DSI) of -2.3. Comparing to the period of 1979-1999, the frequency, duration, and average recovery time of drought events increased significantly since 2000 in the YRB. Furthermore, we found that the duration and average recovery time of the drought events have an exponential relationship with the severity, which could help us to estimate the potential recovery time when drought events occur and predict water resources dynamic in the future.
Technical Report
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The glacial aquifer system groundwater availability study seeks to quantify (1) the status of groundwater resources in the glacial aquifer system, (2) how these resources have changed over time, and (3) likely system response to future changes in anthropogenic and environmental conditions.
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In an approach termed the PER method, where the key input variables are observed precipitation P and runoff R and estimated evaporation, the authors apply the basin water budget equation to diagnose the long-term variability of the total terrestrial water storage (TWS). Unlike the typical offline land surface model estimate where only atmospheric variables are used as input, the direct use of observed runoff in the PER method imposes an important constraint on the diagnosed TWS. Although there is a lack of basin- scale observations of evaporation, the tendency of E to have significantly less variability than the difference between precipitation and runoff (P R) minimizes the uncertainties originating from estimated evapo- ration. Compared to the more traditional method using atmospheric moisture convergence (MC) minus R (MCR method), the use of observed precipitation in the PER method is expected to lead to general improvement, especially in regions where atmospheric radiosonde data are too sparse to constrain the atmospheric model analyzed MC, such as in the remote tropics. TWS was diagnosed using the PER method for the Amazon (1970-2006) and the Mississippi basin (1928-2006) and compared with the MCR method, land surface model and reanalyses, and NASA's Gravity Recovery and Climate Experiment (GRACE) satellite gravity data. The seasonal cycle of diagnosed TWS over the Amazon is about 300 mm. The interannual TWS variability in these two basins is 100-200 mm, but multidecadal changes can be as large as 600-800 mm. Major droughts, such as the Dust Bowl period, had large impacts, with water storage depleted by 500 mm over a decade. Within the short period 2003-06 when GRACE data were available, PER and GRACE show good agreement both for seasonal cycle and inter- annual variability, providing potential to cross validate each other. In contrast, land surface model results are significantly smaller than PER and GRACE, especially toward longer time scales. While the authors currently lack independent means to verify these long-term changes, simple error analysis using three precipitation datasets and three evaporation estimates suggest that the multidecadal amplitude can be uncertain up to a factor of 2, while the agreement is high on interannual time scales. The large TWS variability implies the remarkable capacity of land surface in storing and taking up water that may be underrepresented in models. The results also suggest the existence of water storage memories on multiyear time scales, significantly longer than typically assumed seasonal time scales associated with surface soil moisture.
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We present the impact tests that preceded the most recent operational upgrades to the land surface model used in the National Centers for Environmental Prediction (NCEP) mesoscale Eta model, whose operational domain includes North America. These improvements consist of changes to the “Noah” land surface model (LSM) physics, most notable in the area of cold season processes. Results indicate improved performance in forecasting low-level temperature and humidity, with improvements to (or without affecting) the overall performance of the Eta model quantitative precipitation scores and upper air verification statistics. Remaining issues that directly affect the Noah LSM performance in the Eta model include physical parameterizations of radiation and clouds, which affect the amount of available energy at the surface, and stable boundary layer and surface layer processes, which affect surface turbulent heat fluxes and ultimately the surface energy budget.
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The Global Precipitation Climatology Project (GPCP) Version-2 Monthly Precipitation Analysis is described. This globally complete, monthly analysis of surface precipitation at 2.5 8 latitude 3 2.58 longitude resolution is available from January 1979 to the present. It is a merged analysis that incorporates precipitation estimates from low-orbit satellite microwave data, geosynchronous-orbit satellite infrared data, and surface rain gauge obser- vations. The merging approach utilizes the higher accuracy of the low-orbit microwave observations to calibrate, or adjust, the more frequent geosynchronous infrared observations. The dataset is extended back into the prem- icrowave era (before mid-1987) by using infrared-only observations calibrated to the microwave-based analysis of the later years. The combined satellite-based product is adjusted by the rain gauge analysis. The dataset archive also contains the individual input fields, a combined satellite estimate, and error estimates for each field. This monthly analysis is the foundation for the GPCP suite of products, including those at finer temporal resolution. The 23-yr GPCP climatology is characterized, along with time and space variations of precipitation.
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The GRACE mission is designed to track changes in the Earth's gravity field for a period of five years. Launched in March 2002, the two GRACE satellites have collected nearly two years of data. A span of data available during the Commissioning Phase was used to obtain initial gravity models. The gravity models developed with this data are more than an order of magnitude better at the long and mid wavelengths than previous models. The error estimates indicate a 2-cm accuracy uniformly over the land and ocean regions, a consequence of the highly accurate, global and homogenous nature of the GRACE data. These early results are a strong affirmation of the GRACE mission concept.
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A Global Land Data Assimilation System (GLDAS) has been developed. Its purpose is to ingest satellite- and ground-based observational data products, using advanced land surface modeling and data assimilation techniques, in order to generate optimal fields of land surface states and fluxes. GLDAS is unique in that it is an uncoupled land surface modeling system that drives multiple models, integrates a huge quantity of observation-based data, runs globally at high resolution (0.25degrees), and produces results in near-real time (typically within 48 h of the present). GLDAS is also a test bed for innovative modeling and assimilation capabilities. A vegetation-based "tiling" approach is used to simulate subgrid-scale variability, with a 1-km global vegetation dataset as its basis. Soil and elevation parameters are based on high-resolution global datasets. Observation-based precipitation and downward radiation and output fields from the best available global coupled atmospheric data assimilation systems are employed as forcing data. The high-quality, global land surface fields provided by GLDAS will be used to initialize weather and climate prediction models and will promote various hydrometeorological studies and applications. The ongoing GLDAS archive (started in 2001) of modeled and observed, global, surface meteorological data, parameter maps, and output is publicly available.
A diagnostic study was conducted to evaluate the applicability of ECMWF's global analysis data to the quantitative analysis of the interannual variability of the water budget of the Mississippi river basin. Vertically integrated vapor flux convergence over the entire basin was compared to monthly precipitation minus evapotranspiration (P-E) from 1985 to 1988. Evapotranspiration was calculated from Thornthwaite's formulation. Comparison with the annual run-off near the river mouth was also made for the years 1985 to 1992. The absolute values of the monthly vapor flux convergence were found to be less than the values of P-E from September 1986 to the end of 1988. Natural variability was insufficient to explain the discrepancy between P-E and the vapor flux convergence. We believe changes in the 4DDA system were largely responsible for the discrepancy. We also believe the initialization used in the ECMWF analysis weakens the divergent part of the wind field. With respect to the study conducted with the presently available ECMWF data, the interannual variability of the water budget should not be discussed quantitatively, even for the Mississippi river basin, where many radiosonde stations are in operation.
Monthly estimates of the Earth's gravitational field from the GRACE mission are used to construct a time-series of global mean ocean mass variations between August 2002 and December 2003. This time-series is compared to a mean climatology determined from satellite altimeter measurements of global mean sea level corrected for the steric variation. The GRACE observations show a seasonal exchange of water mass with the continents of the same magnitude (∼8.5 mm) and phase (maximum in early- to mid-October) as the steric-corrected altimetry. This is one of the first direct validations over the ocean of the primary GRACE science mission to measure time-variable transports of water mass in the Earth system, and it suggests that GRACE data can be used to measure non-steric mean sea level variations which is important for climate change studies.