Download Water storage change in the Himalayas from the Gravity Recovery

WATER RESOURCES RESEARCH, VOL. 47, W07521, doi:10.1029/2010WR010157, 2011
Water storage change in the Himalayas from the Gravity Recovery
and Climate Experiment (GRACE) and an empirical
climate model
Juana Paul Moiwo,1,2 Yonghui Yang,1 Fulu Tao,2 Wenxi Lu,3 and Shumin Han1
Received 25 October 2010; revised 2 March 2011; accepted 21 March 2011; published 13 July 2011.
[1] The Himalayas and Tibetan Plateau harbor hundreds of mountain lakes along with
thousands of glaciers and high‐elevation snowfields. This is the source of water for the upper
reaches of Asia’s main river systems, providing the livelihood for millions of people in the
subregion. Climate change is therefore critical for the Himalaya snow and glacier hydrology,
the dependent ecosystems, and the people. Whereas temperature and precipitation are
common indicators for climate change, snow and glacier dynamics are reliable precursors of
a warming or cooling climate. This study uses a simple empirical climate model (ECM) and
the Gravity Recovery and Climate Experiment (GRACE) satellite data to analyze water
storage dynamics in the 5.072 × 106 km2 Himalayas and Tibetan Plateau region. About
72 consecutive months (January 2003 through December 2008) of data are used in the study.
The temperature and precipitation (snow plus rain) data are acquired from the Global Land
Data Assimilation System (GLDAS) Noah land surface model and are validated with ground
truth data from 205 meteorological stations. Total water storage change derived from the
GRACE gravity data is fitted with a simple sinusoidal least squares regression model.
A favorable agreement exists between the GRACE and sinusoidal curve (R2 = 0.81 and
root‐mean‐square error (RMSE) = 8.73 mm), suggesting that random errors in GRACE data
are small. However, the sinusoidal fit does not quantify systematic errors in GRACE data.
Agreements between the GRACE‐ and ECM‐estimated storage changes are also favorable at
both monthly (R2 = 0.93, RMSE = 5.46 mm) and seasonal (R2 = 0.83, RMSE = 7.64 mm)
cycles. The agreements (significant at p < 0.01) indicate not only GRACE’s ability to detect
storage signal but also that of the ECM model to characterize storage change in the snow and
glacier hydrology. There is clear seasonality in the storage anomaly, with the highest in
summer and lowest in winter. The corresponding storage change is delayed by a quarter
of the year. The GRACE and ECM model indicate an overall negative storage trend of
0.36 ± 0.03 mm/month or 21.91 ± 1.95 km3/yr for the study area (significant at p < 0.1).
Given that snow and glaciers are particularly sensitive to temperature change, the negative
storage trend could be indicative of warming climate conditions in the region. Groundwater
abstraction (mainly for irrigation) in the southern plains, coupled with dwindling snowfall
in the northern massifs, is a critical storage loss factor in the region. Invariably, storage
loss in the Himalayan‐Tibetan Plateau region could have negative implications for the
hydrology, dependent ecosystems, and livelihoods of millions of people.
Citation: Moiwo, J. P., Y. Yang, F. Tao, W. Lu, and S. Han (2011), Water storage change in the Himalayas from the Gravity
Recovery and Climate Experiment (GRACE) and an empirical climate model, Water Resour. Res., 47, W07521,
doi:10.1029/2010WR010157.
1. Introduction
[2] Mountain snow and glaciers are major sources of water
in high‐latitude regions [Mall et al., 2006; Casassa et al.,
1
Key Laboratory for Agricultural Water Resources, Agricultural
Resources Research Center, Institute of Genetics and Developmental
Biology, Chinese Academy of Sciences, Shijiazhuang, China.
2
Institute of Geographical Sciences and Natural Resources Research,
Chinese Academy of Sciences, Beijing, China.
3
College of Environment and Resources, Jilin University, Jilin, China.
Copyright 2011 by the American Geophysical Union.
0043‐1397/11/2010WR010157
2007; Fujita, 2008a; Qiu, 2008; Immerzeel et al., 2010]. In
total, glaciers, ice sheets, and snowpacks store the equivalent
of 68.8 m of global sea height [Raper and Braithwaite, 2006].
About 70% of surface fresh waters are stored as glaciers
and ice sheets, mostly occurring at the poles [Casassa et al.,
2007].
[3] Estimates of cumulative glacier lengths show that
globally, glaciers are retreating on the average of 0.25 m/yr
[Hoelzle et al., 2003]. Whereas snowmelt and glacier melt
could mitigate or avert catastrophic droughts during weak,
delayed, or failed precipitations, enhanced glacier melt
could also have devastating effects [Ren et al., 2003; Kumar
et al., 2007]. Accelerated glacier melt may trigger rock‐ice
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avalanches, high‐debris flows, and floods [Ageta et al., 2000;
Li et al., 2007]. On the other hand, enhanced glacier melt
could threaten long‐term water availability [Thayyen and
Gergan, 2009]. Acute water shortages would jeopardize
agroindustrial systems, the backbone of modern societies
[Immerzeel et al., 2010].
[4] Despite its strategic importance as Asia’s main source
of water, the Himalayan and Tibetan Plateau snow and glaciers are fast retreating [Tang and Li, 1992; China National
Committee on Climate Change, 2007]. While 82% of the
Tibetan glaciers have retreated in the past half century, 10%
of its permafrost has degraded in the past decade alone [Qiu,
2008]. In fact, over 64.3% of the estimated glacier retreat
was in the last two decades [Liu et al., 2008]. Hydrological
changes on such scales could threaten glacier‐dependent
lakes, rivers, and wetlands in the region and beyond.
[5] Climate change is critical for the long‐term stability of
the Himalayas and Tibetan Plateau hydroecology [Sanlaville
and Prieur, 2005; Mats et al., 2009; Immerzeel et al., 2010].
There are reports of above global average warming in the
region [Du et al., 2004; Qiu, 2008]. Because of rising temperatures in the Himalayas, rain precipitation is increasing at
the expense of snow precipitation [Oerlemans and Reighert,
2000; Fujita, 2008b]. Temperature is predicted to rise in 2100
by 3.5°C–5.5°C in the Indian subcontinent and by 2.5°C–5°C
in the Tibetan Plateau [Macintosh, 2005; Kumar et al., 2007].
[6] Storage depletion in the ecologically fragile Himalayas
and Tibetan Plateau region is attributed, in part, to water‐
intensive farming [Saxena et al., 2005; Harris, 2010] and
industrial and economic prosperity [Semwal et al., 2004].
Using a mass balance approach, Schneeberger et al. [2003]
predicted substantial glacier retreat in the next 50 years
under enhanced greenhouse gas emissions. Fujita [2008a]
noted that the Himalaya glaciers are sensitive to precipitation seasonality. There are, however, limited studies on water
storage dynamics in the Himalayas and Tibetan Plateau
region [Thapa, 1993; Muskett, 2008; Chambers, 2009]. There
even exist fewer studies relating the Gravity Recovery and
Climate Experiment (GRACE) satellite gravity data with
storage change in the region [Muskett, 2008; Rodell et al., 2009;
Immerzeel et al., 2010].
[7] The GRACE mission, launched in May 2002, consists
of twin co‐orbiting satellites in tandem [Swenson et al., 2006;
Rodell et al., 2009]. The intersatellite distance variations are
used in spherical harmonic (Stokes) coefficients to measure
the Earth’s gravity field at an unprecedented accuracy [Tapley
et al., 2005; Chambers, 2009]. After adjusting for nonhydrologic effects, GRACE month‐to‐month gravity fields
are inverted for vertically integrated terrestrial water storage
change [Wahr et al., 2004; Swenson and Wahr, 2006, 2007;
Muskett, 2008; Moiwo et al., 2011]. In the Himalayas and
Tibetan Plateau region, hydrologic mass balance is dominated
by changes in glaciers, ice sheets, snowpacks, and permafrost.
These sources of storage are sensitive to temperature and
precipitation, which are influenced by climate change and
global warming [Feng et al., 1998; Du et al., 2004; Fujita,
2008a; Qiu, 2008; Mats et al., 2009; Immerzeel et al., 2010].
[8] Permafrost loss, monsoon disruption, sea level rise
caused by terrestrial ice loss, and increased earthquakes and
landslides once snow and glacier weight is gone are a possible
disastrous combination [Xu et al., 2007; Spencer, 2009]. To
address such threats, an empirical climate model (ECM) is
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developed to estimate water storage change in the Himalayas
and Tibetan Plateau region. The model, driven by the common climate variables of temperature and precipitation, is
simple and practicable. It would also enhance our understanding about the effects of changes in climate on storage in
the snow and glacier hydrology of the Himalayan and Tibetan
Plateau. The results of the study will provide the basis for
policy decisions and conservation strategies of the fragile,
invaluable hydroecology. The logic and implementation of
the ECM model are discussed in section 2.
2. Data and Method
2.1. Study Area
[9] The Himalayas and Tibetan Plateau cover, in various
proportions, the countries of Pakistan, Afghanistan, Tajikistan,
China, India, Nepal, Myanmar, Bhutan, and Bangladesh
(Figure 1). The study area is located between 25.59°N–
40.27°N and 69.12°E–105.54°E (Figure 1) and has an estimated area of 5.072 × 106 km2. It extends (in an arc shape)
for 2500 km from west to east and 100–400 km from south
to north [Muskett, 2008; Mats et al., 2009; Immerzeel et al.,
2010]. Although over 80% of the area is in the Tibetan
Plateau, it also includes Karakoram, Hindu‐Kush, Pamir,
Tien Shan, Inner and Outer Himalayas, and several other
mountain ranges.
[10] The geophysiographic features in this region are
complex and rugged [Bolch et al., 2008], with over 30 of
the 110 massif peak elevations above 6000 m [Kaul, 1999;
Macintosh, 2005]. The 70 km long Siachen Glacier (on the
India‐Pakistan border) is the longest of the over 50,000 glaciers in the region [Kaul, 1999; Qiu, 2008]. The mountains
are characterized by deep snowcaps, glacial valleys and
gorges, and rich ecobiodiversity [Saxena et al. 2005; Harris,
2010]. The mountain valleys, depressions, and canyons provide abundant storage for summer rainwater and meltwater
[Ageta et al., 2000]. The tectonic glacial lakes occur at altitudes below 5000 m and generally diminish in size at higher
elevations [Thayyen and Gergan, 2009]. Pangong Tso, the
largest endorheic lake in the region is ≈134 km long. It
spreads across the India‐China border at an average altitude
of 4350 m [Winiger et al., 2005].
[11] Of the 519 mm average annual precipitation, 72% falls
as summer monsoon rain and 28% as winter snow [Dulal
et al., 2006]. Variation in diurnal temperature is large, with
a long‐term average of 7°С. The coldest (in January) and
hottest (in July) average monthly temperatures are 12°С
and 16°С, respectively [Shrestha et al., 2000]. Above‐16°С
summer temperatures are not uncommon on the valley slopes
[Dulal et al., 2006]. At elevations over 5000 m, above‐zero
temperatures only occur during the daytime [Thayyen and
Gergan, 2009]. Because of rising temperatures, snow precipitation is increasingly shifting toward rain. Winds generally strengthen at higher altitudes, reaching 120 km/h at
6000 m [Burbank et al., 2003].
[12] While the western phase of the Himalayas has a generally warm and humid monsoon climate, a typical mountain desert cold climate dominates on the northern slopes
and Tibetan Plateau [Goswami et al., 2003]. As Asia’s main
source of water, the Himalayas and Tibetan Plateau snow and
glaciers greatly influence the hydrology, ecology, and livelihoods of millions of people in the subcontinent [Burbank
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Figure 1. Map showing the geographic location of the study area, national boundaries, drainage, weather
monitoring stations, Mount Everest, and the ambient surface elevation.
et al., 2003; Qiu, 2008; Thayyen and Gergan, 2009; Immerzeel
et al., 2010].
2.2. Hydrologic Mass Balance
[13] Here storage change Dw [L/T ] is the difference
between storage anomalies of two successive time steps. A
storage anomaly is the residual storage content w′ [L/T] at a
given time with respect to the content at the reference epoch.
Reference storage w′r [L/T ] is the mean storage within the time
interval for which the temporal mean is computed. Hydrologic mass balance in the Himalayas and Tibetan Plateau
study area is mainly driven by storage in snow, glaciers, high‐
altitude lakes, and permafrost. A glacier mass balance method
could therefore be used to estimate the storage change.
Studies show that the climate variables with the most impact
on glacier dynamics are temperature and precipitation [Meier,
1984; Oerlemans and Fortuin, 1992; Dyurgerov and Meier,
1997; Raper and Braithwaite, 2006; Kaser et al., 2006;
Fujita, 2008b; Qiu, 2008]. Using seasonal sensitivity characteristics of temperature and precipitation, Oerlemans and
Reighert [2000] conducted glacier mass balance for different climatic regions of the world.
[14] In this study, the method of Oerlemans and Reighert
[2000] is modified to estimate water storage change in the
Himalayas and Tibetan Plateau region. To achieve this, it is
assumed that the time derivative of the residual storage
anomaly w′ [L/T ] with respect to the initial time of the water
storage w [L/T] is a nonlinear function of temperature T (K)
and precipitation P [L]; i.e., w′ = f (T , P). A linearization of
this function at the vicinity of the reference temperature
T r (K) and reference precipitation Pr [L] yields
D!′ ¼ CT ðT Tr Þ þ CP ð P Pr Þ;
ð1Þ
with the residual storage change Dw′ = w′ − w′r [L/T ], reference storage w′r = f(T r, Pr) [L/T ], temperature coefficient
CT = ∂f/∂T [L/K], and precipitation coefficient CP = ∂f/∂P
(dimensionless). The integration of the linearized relationship, after discretization, gives
D! ¼
n X
CT;k Tk Tr;k þ CP;k Pk Pr;k :
ð2Þ
k¼1
The index k is the time step (annual, seasonal, monthly, or
other time scales) associated with the reference temperature
T r and precipitation Pr of the reference mass balance w′r
(preferably zero). The temperature coefficient CT ,k [L/K] and
precipitation coefficient CP,k (dimensionless) in equation (2)
are the so‐called climate sensitivity characteristics (CSC) of
the mass balance [Fujita, 2008b]. The CT ,k and CP,k of the
mass balance are quantified as
CT ;k ¼
@!′
@!′
; CP;k ¼
:
@Tk
@Pk
ð3Þ
This can be determined by any simple or complex degree‐day
or energy balance model that computes the cumulative mass
balance through the year [Oerlemans and Reighert, 2000].
[15] As this study focuses on the entire snow and glacier
system of the Himalayas and Tibetan Plateau, a bulk CSC
value is calculated using the energy–mass balance model
described by Fujita [2008b]. Because of the great size,
apparently all glacier types exist in this region. This allows
the use of average values and eliminates the need for different
sets of parameters for individual glaciers. The areas of the
glaciers in the region are therefore added together and treated
as a seamless glacier. Most of the model input parameters
(including glacier and ice extent, albedo, etc.) are derived
from Moderate Resolution Imaging Spectroradiometer
(MODIS) and Landsat thematic mapper data [König et al.,
2001]. The energy fraction of the model is balanced on glacier surface daily heat, radiation, sensible and latent turbulent
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Figure 2. (a) Graphic illustration of climate sensitivity characteristics in the Himalayas and Tibetan
Plateau snow and glacier, along with (b) annual temperature sensitivity index, (c) annual precipitation
sensitivity index, (d) average annual field‐measured versus GLDAS Noah‐estimated temperature, and
(e) average annual field‐measured versus GLDAS Noah‐estimated precipitation for 2003–2008.
heat, and conductive heat into the glacier mass. The mass
fraction of the model is balanced on accumulated snow and
refrozen meltwater and on snowmelt and evaporation [Fujita,
2008a, 2008b]. The model gains mass via solid precipitation
and loses it via ablation. Conductive heat into the glacier
system and the amount of water at the snow‐glacier interface
are used to estimate meltwater refreeze [Fujita and Ageta,
2000]. Snow surface albedo (which declines with glacier
depth and melt season duration) is calculated in relation to
surface snow density (which changes with compaction). This
preserves climate feedback effects on the snow and glacier
regimes. The model has been validated on selected glaciers
in the Himalayas and Tibetan Plateau region. Further details
of the model, including validation analyses, are documented
by Fujita and Ageta [2000], Fujita et al. [2007], and Fujita
[2008a, 2008b].
[16] In calculating CSC, the calibrated model is rerun for
the reference state Dw′ = 0 by adjusting annual mean air
temperature within ±2 K. Next, systematic monthly perturbations in temperature (±0.5 K) and in precipitation (±10%)
are induced to derive the CT ,k and CP,k in equation (3),
respectively. Further details, including the rationale of this
procedure, are explained by Oerlemans and Reighert [2000].
The results of the CSC analysis are depicted in Figure 2a.
[17] One distinctive feature of glaciers is the length of
period for which perturbations in temperature or precipitation
affect their annual mass balances [Oerlemans and Reighert,
2000]. The stability of the average Himalaya glacier system
is relatable to climate change and global warming, which are
mainly driven by summer climate trends. Hence, a sensitivity
index (SI) that shows the average behavior of the glaciers
through the year is used to quantify perturbations in temperature (SITy) and precipitation (SIPy) as
SITy ¼
CT;6 þ CT ;7 þ CT ;8
CP;6 þ CP;7 þ CP;8
; SIPy ¼
;
12
12
P
P
CT ;k
CP;k
k¼1
ð4Þ
k¼1
where y is the year and 6, 7, and 8 denote June, July, and
August, respectively. The SI is related to average yearly
temperatures and precipitation for 2003–2008 in Figure 2.
The power fits in Figures 2a and 2c show strong correlations
between SI and temperature (R2 = 0.91) and precipitation
(R2 = 0.86); all are significant at p < 0.01. Figure 2 suggests
that snow and glacier vulnerability in the region increases
with increasing temperature and precipitation. Of course, rising
temperatures favor rain precipitation (Figure 3), which in
turn increases glacier vulnerability [Oerlemans and Reighert,
2000].
[18] The CSC so computed is used in the ECM model to
estimate storage change in the study area. In GRACE, the
steady state portion of water storage is highly correlated with
static gravity fields. It is difficult to separate the steady state
portion of the water storage from the static gravity estimates.
Hence, what GRACE provides is not total terrestrial water
content but its anomaly field. Raw GRACE monthly solutions contain information about water storage anomaly in
relation to the reference storage. In other words, GRACE is
indirectly sensitive to total water storage signal. In this study,
water storage change estimated independently by the ECM
model in equation (1) is compared with that derived from the
GRACE satellite gravity data.
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Figure 3. Time series of monthly and seasonal anomalies in (a) snowfall, (b) rainfall, (c) precipitation,
(d) temperature, and (e) GRACE total water storage (TWSA) for the period 2003–2008 in the Himalayas
and Tibetan Plateau study area.
2.3. Data Acquisition and Processing
2.3.1. GRACE Data
[19] GRACE data product release‐04 (RL‐04), from the
Center for Space Research (CSR) at the University of Texas,
is used in this study. This is the latest version of GRACE data
products released by CSR. It constitutes significant improvements over earlier releases because of improvements in the
background processing models [Chambers, 2009; Rodell
et al., 2009]. Level‐2 products of the data are available in
the public domain at http://geoid.colorado.edu/grace/grace.
php. Using user‐defined criteria, the site handles a series of
GRACE hydrology analysis. Retrievable end products are
also available in the form of maps, time series, and ASCII
files. CSR GRACE monthly solutions are processed on
spherical harmonic (Stokes) coefficients and are therefore
prone to leakage. After removing a temporal mean, the
monthly data are filtered for correlated north–south trending
errors [Wahr et al., 2004; Swenson and Wahr, 2006, 2007,
2009]. The data are further corrected for glacial isostatic
adjustment [Paulson et al., 2007] and other nonhydrologic
effects [Muskett, 2008; Immerzeel et al., 2010]. This data set
has been used in hydrological studies in different agroclimatic
regions of the world [Swenson et al., 2008; Swenson and
Wahr, 2007, 2009; Rodell et al., 2007, 2009; Moiwo et al.,
2009, 2011].
[20] Because data are missing for June 2003, about
71 months of GRACE data (January 2003 through December
2008) are used in the analysis. The 1° resolution GRACE data
are truncated to the 5.072 × 106 km2 study area and smoothed
with the Gaussian filter of 300 km half width. To minimize
the effect of geographic truncation [Mayer‐Guerr et al.,
2007] on the analysis, a 1° buffer zone is maintained around
the study area. The fields are spatially averaged to create time
series of total water storage anomalies. Total water storage
change is then calculated from the anomaly data.
2.3.2. Climate Data
[21] In especially complex mountain terrains, climate data
are not always available in sufficient density and distribution
for realistic spatial representations. Satellite, statistical, and
knowledge‐based numerical methods are therefore used to
interpolate, extrapolate, or predict spatial distributions of
climate fields in such regions [Lindsay, 2005]. These methods
often take into account factors such as elevation, slope,
gradient, aspect, curvature, geographic location, orographic
effectiveness, and other land physiographic elements, which
affect the pattern (occurrence and distribution) of climate
phenomena [Daly et al., 2008].
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[22] The Mountain Climate Simulator (MTCLIM
[Hungerford et al., 1989]) and Parameter‐elevation Regression on Independent Slopes Model (PRISM [Daly et al.,
1994; Gibson et al., 1997]) are specifically developed for
interpolating meteorological fields in complex terrains with
limited observations. As these models are, however, not
freely available, their applications outside the developer
organizations are limited. In this study, the temperature and
precipitation (snow and rain) data products of the Global
Land Data Assimilation System (GLDAS) land surface
model (LSM) are used [Rodell et al., 2004]. About 72 months
of the GLDAS Noah climate data, spanning for January 2003
through December 2008, are used.
[23] GLDAS uses satellite‐ and ground‐based data products to generate optimal fields of land surface states and
fluxes such as snow, rain, temperature, evapotranspiration,
and ground heat flux. A vegetation‐based tiling approach
(with a 1 km global vegetation data set as the basis) is used to
simulate subgrid variability. Soil and elevation parameters are
based on high‐resolution global data sets [Rodell et al., 2007].
The baseline meteorological forcing data are produced by the
NOAA Global Data Assimilation System (GDAS) atmospheric analysis system. The simulation, initialized for 1979,
is performed on a 1° global grid and forced by bias correction
reanalysis products of Berg et al. [2005] prior to 2000.
[24] The reliability of GLDAS data products [Rodell et al.,
2007] is therefore commensurate to that of the MTCLIM or
PRISM model estimates. Despite this, the 72 month GLDAS
Noah data are validated with in situ measurements of temperature and precipitation from 205 meteorological stations
(Figure 1). Linear correlation fits in Figures 2d and 2e show
good agreement between the GLDAS Noah data and temperature (R2 = 0.76, root‐mean‐square error (RMSE) =
3.43°C) and precipitation (R2 = 0.84, RMSE = 43.61 mm); all
are significant at p < 0.01.
[25] The temperature and precipitation fields are afterward
spatially averaged (in much the same manner as the GRACE
data) and fed into the ECM model to estimate total water
storage change in the study area. Next, the storage change
derived from the ECM model is compared with that from the
GRACE satellite gravity data. The GLDAS soil moisture data
are also used to isolate the contribution of groundwater to
mass loss in the southern region of study area.
3. Results and Analysis
3.1. Uncertainty Analysis
[26] The GLDAS Noah monthly temperature and precipitation data are validated with ground truth data from
205 climate monitoring stations. The method described by
Strassberg et al. [2007] is used to gauge uncertainties in the
GLDAS Noah data. On the basis of the analysis, the RMSE
difference between the GLDAS Noah and ground truth data is
3.43°C (R2 = 76) for temperature and 43.61 mm (R2 = 0.84)
for precipitation. Artificial disturbances (at the magnitude of
the estimated uncertainties) are added to the ECM model to
analyze their propagation in the estimated water storage. In
principle, a disturbance at the beginning of the integration
affects further evolutions of the balance. The model is initiated at the start of 1979 (the time GLDAS Noah data logging
started) and is run continuously for 30 years (1979–2008)
with monthly temperature and precipitation forcing. Com-
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parison at seasonal cycle shows a good agreement, with R2 =
0.81 and RMSE = 7.82 mm.
[27] As an important condition, GRACE data application
requires validation with in situ hydrological measurement
data. This set of data is currently not in our repository for the
Himalayas and Tibetan Plateau region. It is, however,
important to note that the GRACE monthly solutions are
differentiated prior to application in this study. In other
words, the GRACE‐based water mass changes are considered, not the monthly solutions (see sections 3.2–3.6). Furthermore, studies [e.g., Rodell et al., 2009] show that GRACE
satellite gravity data reliably signal storage anomaly in the
region. Muskett [2008] and Immerzeel et al. [2010] have also
shown that GRACE sufficiently detects storage change in the
Himalayas and Tibetan Plateau region.
3.2. Monthly and Seasonal Storage Anomaly
[28] Time series of the monthly and seasonal anomalies
of snow, rain, precipitation, temperature, and GRACE total
water storage are depicted in Figure 3. The negative (for
snow) and positive (for rain) linear trends (at the seasonal
cycle) in Figures 3a and 3b suggest increasing rainfall at the
expense of snowfall in the study area. Overall, precipitation
(snow plus rain) is increasing in the region, indicated by a
positive linear trend for the seasonal cycle in Figure 3c.
Temperature too is increasing (Figure 3d), which indicates a
warming condition [see Fujita, 2008b; Thayyen and Gergan,
2009]. The corresponding GRACE total water storage anomaly shows an overall decline. The anomaly trends suggest that
the increase in (rain) precipitation could be in response to the
rising temperatures.
[29] The phases of the monthly and, more so, the seasonal
time series of precipitation, temperature, and GRACE track
one another. There is a near‐synchronized seasonality, with
the highest amplitudes in summer (June to August) and the
lowest in winter (December to February). Whereas the linear
trends in the seasonal anomalies for GRACE and snowfall are
negative, those for rainfall and precipitation are positive. This
shows that rain is increasingly becoming a dominant form of
precipitation in the region.
[30] Air temperature generally dictates the fraction of precipitation that falls as either low‐albedo rain or high‐albedo
snow on glacier surfaces [Dulal et al., 2006]. Studies show
that under warming climate conditions, precipitation favors
low‐albedo rain, not high‐albedo snow [Thapa, 1993; Fujita,
2008a]. Because of accelerated absorption of solar radiation,
glacier melt increases in the absence of high‐albedo snow
[Ren et al., 2003; Fujita, 2008a]. In fact, Higuchi et al. [1982]
and Fujita [2008b] noted increasing snowmelt and glacier
melt under decreasing snow precipitation. Low albedos
increase glacier surface exposure, which in turn accelerates
glacier melt even under stable temperature conditions [Fujita
and Ageta, 2000]. Warming climate conditions generally
trigger temperature rise, leading to less snow precipitation
and high snow and glacier ablation and storage loss. Such
conditions could negatively impact the hydrology, ecology,
and livelihoods in the Himalayas and Tibetan Plateau region
[Muskett, 2008; Qiu, 2008; Immerzeel et al., 2010].
3.3. Average Monthly Storage Anomaly
[31] Along with GRACE total water storage, average
monthly snow, rain, precipitation, and temperature anomalies
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Figure 4. Time series of average monthly anomalies of (a) snowfall, (b) rainfall, (c) precipitation, (d) temperature, and (e) GRACE TWSA. (f) Comparison of average monthly total water storage change derived
from GRACE and the empirical climate model (ECM) for the period 2003–2008 in the Himalayas and
Tibetan Plateau study area.
for 2003–2008 are plotted in Figure 4. On the basis of
Figure 4a, snowfall increases from January through March,
then decreases through August, before increasing again
through December. On the other hand, rainfall, precipitation,
temperature, and GRACE‐based storage anomalies have
positive trends for January through July and negative trends
through December [see also Fujita, 2008a, 2008b]. Because
temperatures are highest in summer, snow and ice ablation
likely occurs during this period. This, coupled with the
declining (high‐albedo) snowfall, induces storage loss in the
region.
[32] Figure 4f illustrates a comparison of the average
monthly total water storage change derived separately from
GRACE data and the ECM model. In the study area, storage
change generally increases from January through June, with a
slight dip in March. It decreases through September before
gradually increasing through December [see Rodell et al.,
2009]. The winter‐to‐spring rise in storage could be driven
by snow accumulation during this period [Ren et al., 2003;
Siebert et al., 2005; Qiu, 2008; Thayyen and Gergan, 2009].
Then high summer storage could be due to water storage in
the vast crayons, reservoirs, and (endorheic) lakes and tarns
in the region. Storage loss is noticeable either directly from
Figure 4e or as a negative storage change in Figure 4f. In both
cases, storage loss occurs between August and December.
This could be mainly driven by groundwater depletion for
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Figure 5. Time series of seasonal water storage change separately derived from the (a) temperature,
(b) precipitation, and (c) GRACE gravity data for 2003–2008 in the Himalayas and Tibetan Plateau study area.
farm irrigation, especially in the southern half of the study
area. [Sarwar and Bastiaanssen, 2001; Singh and Bengtsson,
2004; Mall et al., 2006; Kumar et al., 2007; Muskett, 2008;
Rodell et al., 2009; Immerzeel et al., 2010; Wada et al., 2010].
Dwindling snowfall, particularly in the Tibetan Plateau
region, could be another important driver of storage loss
[Qiu, 2008]. There is strong correlation (R2 = 0.93, RMSE =
5.46 mm, significant at p < 0.01) between average monthly
storage change from GRACE data and the ECM model
(Figure 4f). This suggests that the temperature‐ and precipitation‐driven ECM model sufficiently characterizes storage
dynamics in the study area.
3.4. Seasonal Storage Change
[33] This section illustrates the dependence of water storage in the Himalayas and Tibetan Plateau region on temperature and precipitation as a time derivative of storage, not
its function. Such analysis is not only informative but also
efficient in terms of data and computational requirements.
Furthermore, most studies involving GRACE hydrology are
presented as time derivative of storage, not its function. This
approach therefore allows direct comparisons of the results
here with those of other studies.
[34] Figures 5a–5c depict seasonal storage change in the
study area, separately computed from the GRACE, temperature (at CP,k = 0), and precipitation (at CT ,k = 0) data.
Figure 5 shows the relative importance and suitability of the
climate variables of temperature and precipitation as indicators for storage change in the region. The storage change
(Figure 5) is delayed by a quarter of the year with respect to
the storage anomaly (Figure 3). The minima and maxima of
the GRACE‐only, temperature‐only, and precipitation‐only
estimated storage changes are in spring and autumn, respectively. However, the amplitudes (separate for the GRACE,
temperature, and precipitation) are totally different. This
indicates that temperature or precipitation variations alone
definitely cannot explain water storage dynamics in the study
area. However, the sum of the temperature and precipitation components almost completely explains the variations
in GRACE‐estimated storage change. The trends in the three
storage terms are negative, suggesting storage loss acceleration in the region [see Tang and Li, 1992; Hou et al., 2000;
Muskett, 2008; Qiu, 2008; Rodell et al., 2009; Immerzeel
et al., 2010].
3.5. Storage Change Comparison
[35] Figure 6a depicts a simple least squares sinusoidal fit
for the GRACE‐derived total water storage change. A good
agreement exists between the GRACE and sinusoidal curve,
with R2 = 0.81 and RMSE = 8.73 mm (significant at p < 0.01).
The favorable agreement shows that random errors in GRACE
data are small. However, the test does not allow us to quantify
systematic errors in GRACE data. This second error type can
be quantified using ground‐based measurements of storage
change, data which are currently not in our database. The
sinusoidal fit is therefore assumed to give a measure of
GRACE’s ability to detect storage signal in the study area.
In fact, GRACE is shown to reliably detect storage change in
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Figure 6. (a) Sinusoidal fit for GRACE‐derived total water storage change and comparisons of water storage changes from GRACE with the empirical climate model (ECM) (b) for the entire Himalayas and Tibetan
Plateau study area and (c) for the ECM plus GLDAS Noah soil moisture (SM) for the region south of the
zero‐contour storage trend line in Figure 7c.
the Himalayas and the surrounding regions [Muskett, 2008;
Rodell et al., 2009; Immerzeel et al., 2010].
[36] Figure 6b is a comparison of total water storage
change estimated from GRACE data and the ECM model.
There is a near‐synchronized annular seasonality in the
GRACE‐ and ECM‐estimated storage change, with R2 = 0.83
and RMSE = 7.64 mm. The phases and amplitudes of the
estimated storage changes closely track each other. This
further indicates that the ECM model characterizes storage
dynamics in the study area. GRACE data are processed using
spherical harmonic (Stokes) coefficients and, as such, have
leakage problems. Like GRACE, uncertainties also exist in
the ECM model. To better represent the storage dynamics,
therefore, the average of the GRACE‐ and ECM‐estimated
storage changes is used. The long‐term storage change is
negative. It is on the order of 0.36 ± 0.03 mm/month or
21.91 ± 1.95 km3/yr for the 5.072 × 106 km2 study area
(significant at p < 0.1). This finding is consistent with earlier
studies in and around the Himalayas and Tibetan Plateau
region [Muskett, 2008; Qiu, 2008; Rodell et al., 2009;
Immerzeel et al., 2010; Cogley et al., 2010; Wada et al., 2010].
[37] Figure 6c compares GRACE‐derived total water
storage change with that obtained from ECM plus GLDAS
Noah soil moisture data for the region south of the study area.
It is readily noticeable that the amplitudes of the GRACE
storage change for this region are higher than those for
the entire study area. The amplitudes of the ECM plus soil
moisture storage change are also far lower than those of
GRACE. This suggests that storage change in the southern
region of the study area is mainly driven by groundwater
depletion (see section 3.6 for details).
3.6. Annual Storage Distribution
[38] Figures 7a–7d depict average annual spatial distributions of snowfall, rainfall, precipitation, and temperature
for 2003–2008. Snowfall (Figure 7a) is highest in the west
central Tibetan Plateau, northeast Pakistan and northwest
India. It is lowest in the regions east of the study area. Rainfall
(Figure 7b) is high along the west–east southern flank and
highest in northeastern India. This is the rainward belt of the
Himalayan massifs. Rainfall is lowest along the west–east
northern leeward region of the Tibetan Plateau. The precipitation distribution reflects the snow and rain distributions
combined. It is generally high along the west–east southern
rainward regions and low in the northern rain shadow areas.
The only exception is the west central Tibetan Plateau region,
where snowfall is high (Figure 7c). The temperature distribution (Figure 7d) depicts a subtle replica of the elevation.
To some extent, it also moderately mimics the precipitation
trend. This shows the unique influence of elevation and precipitation on temperature.
[39] The precipitation distribution and, to a certain extent,
that of temperature are influenced by the westerlies and
monsoon winds [Ageta and Higuchi, 1984; Fujita, 2008a,
2008b]. Mount Everest and the sister mountains along the
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Figure 7. Spatial distributions of the amplitude of anomalies in (a) average annual snowfall, (b) rainfall,
(c) precipitation, and (d) temperature along with (e) a map of linear trend in total water storage derived from
GRACE monthly water storage maps for 2003–2008 in the Himalayas and Tibetan Plateau region.
southern arc are the rainward and rain shadow divide
in the region. The ranges of snow, rain, and precipitation are
28–997, 128–3116, and 132–3116 mm, respectively. Average annual precipitation, and especially that of rain, is low
for over two thirds of the study area. Temperature, with an
average annual range of 17°C to +30°C, is also negative for
over three quarters of the study area. The west–east rainward
belt to the south is the highest temperature region (Figure 7d).
[40] Figure 7e illustrates a map of linear trend in total water
storage derived from GRACE monthly water storage maps
for 2003–2008. Implicitly, the dynamics of the climate variables of temperature and precipitation in the glacier‐driven
hydrology largely dictate the distribution pattern of the GRACE
storage trend map. The GRACE‐derived water storage trend
shows a significant depletion in the south. This is especially
noticeable in the border regions of Nepal, Bhutan, India,
and Bangladesh; i.e., southeast of Mount Everest. Storage
depletion is also visible farther in the southeast, along the
India‐Myanmar border region. A moderate depletion trend
exists in the region northwest of Indian.
[41] A negative storage trend exists in the high rain precipitation region along the west–east southern arc of the study
area. The negative storage trend is a signal for groundwater
depletion, especially in the southern half of the study area
[Muskett, 2008; Rodell et al., 2009; Wada et al., 2010]. To
justify this point, the GLDAS Noah data are used to quantify
soil moisture storage change in the region south of the zero‐
contour storage line in Figure 7e. The ECM model is also
applied to simulate snow and glacier storage change in this
region. The sum of the ECM and soil moisture storage
changes is then subtracted from that of GRACE. This analysis
isolates groundwater storage change in the region south of
the zero‐contour storage line. In terms of water equivalent
per area, the trend in the derived groundwater storage loss
is ≈70% of the estimated storage loss in the region (see
Figure 6c). In terms of volume loss, however, it accounts
for <20% of the estimated long‐term storage loss in the
Himalayas and Tibetan Plateau region. This is ascribed to
irrigation farming, a critical storage depletion factor in this
region [Sarwar and Bastiaanssen, 2001; Siebert et al., 2005;
Mall et al., 2006; Kumar et al., 2007; Cogley et al., 2010].
[42] On the basis of the GRACE map, storage trend is high
in the high‐snow regions northeast of Pakistan. It is also high
in the headwater region of the Huang, Yangtze, and Mekong
rivers. This region has relatively moderate snow, rain, and
temperature (Figures 7a–7c), conditions that generally favor
storage. This suggests that low‐temperature zones in the
Himalayas and Tibetan Plateau are hydrologically more
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stable. These regions are less sensitive to the effects of
human activity, including climate change and global
warming.
4. Discussion
[43] The foregoing analysis suggests that the climate
variables of temperature and precipitation are critical mass
balance elements in the snow and glacier hydrology of the
Himalayas and Tibetan Plateau [see Hou et al., 2000; Qiu,
2008; Cogley et al., 2010; Immerzeel et al., 2010]. Temperature and rain precipitation are increasing with decreasing
snow precipitation (Figure 3). While snow and rain are negatively correlated (R = −0.57), temperature is rising on the
average of 0.023°C/yr. Of course, temperature influences the
fraction of precipitation that falls as either snow or rain [Dulal
et al., 2006; Thapa, 1993; Fujita, 2008a]. Snowmelt and
glacier melt and retreat accelerate with rising temperatures
and warming climate conditions. This has an overall effect of
storage loss.
[44] GRACE detects storage signal in the Himalayas
and Tibetan Plateau region [Muskett, 2008; Immerzeel et al.,
2010]. The GRACE and ECM models show an overall negative storage trend, an indicator for storage depletion in the
Himalayas and Tibetan Plateau region. Consistent with this
study, several other studies have reported glacier retreat
or storage loss in the Himalayas and Tibetan Plateau region
[Higuchi et al., 1982; Yamada et al., 1992; Kaser et al., 2006;
Qiu, 2008; Cogley et al., 2010]. Some studies have even
related glacier retreat to rising temperatures and dwindling
snowfall [Ageta and Higuchi, 1984; Fujita and Ageta, 2000;
Hou et al., 2000; Fujita, 2008a, 2008b]. Using a snowmelt runoff model and the DTM‐1 GRACE gravity model,
Immerzeel et al. [2010] noted a negative storage trend in
the Ganges Basin that was strong enough to conclusively
show ice storage decline in much of the Himalayas and
Tibetan Plateau region.
[45] The negative (mass loss) trend in the southern part of
the study area is driven by groundwater pumping, particularly
for farm irrigation [see Siebert et al., 2005; Mall et al., 2006;
Muskett, 2008; Rodell et al., 2009; Wada et al., 2010]. The
warming climate and subsequent snow and glacier meltwater
discharge are additional factors of storage loss in the region
[Qiu, 2008; Immerzeel et al., 2010]. The correspondence
between these studies and ours suggests that the ECM model
reliably characterizes storage dynamics in the snow and
glacier hydrology of the Himalayas.
5. Conclusions
[46] This study has used the GRACE and ECM model to
estimate total water storage dynamics in the Himalayas and
Tibetan Plateau snow and glacier hydrology. About 72 consecutive months of data, spanning from January 2003 through
December 2008, are used in the study. The GRACE data are
verified with a sinusoidal model, and the ECM forcing data
are verified with in situ measurements of temperature and
precipitation. The GRACE‐estimated storage change is
also compared with that of the ECM model for the 5.072 ×
106 km2 study area. All the correlation agreements are statistically significant at p < 0.01. The favorable agreements
suggest that the ECM model sufficiently characterizes storage
dynamics in the Himalayas and Tibetan Plateau snow and
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glacier hydrology. The ECM model is simple, practicable,
and readily transferable to other snow and glacier regions.
The main drivers of the model are temperature and precipitation, which are routinely measured by common weather
stations.
[47] Rising temperatures in the region favor rain precipitation, snowmelt and glacier melt, and storage depletion.
Snowfall is highest in winter and lowest in summer, while
rainfall, precipitation, and temperature exhibit the reverse
behavior (Figure 4). Direct snow and glacier accumulation
(storage gain) occurs in winter, and ablation (storage loss)
occurs in summer. There is, however, a high summer storage
anomaly in the study area, as over 70% of the dominant
summer rain falls during this period. A considerable amount
of the summer rainwater and meltwater is also stored in the
vast reservoirs, endorheic lakes, and canyons, leading to high
summer storage anomalies. The corresponding storage change
is, however, lowest in spring and highest in autumn. This is
because the storage change is delayed by a quarter of the year.
[48] The GRACE and ECM model show an overall negative storage change in the Himalayas and Tibetan Plateau
study area. Snow and glacier ablation and the subsequent
storage loss via discharge are mainly driven by rising temperatures and rain precipitation. Heavy groundwater irrigation, especially in the northwest regions of Pakistan and India
and in southern Nepal and Bhutan, is another mass loss factor.
Storage loss in the far north and northeast regions of the
Tibetan Plateau is largely due to dwindling snowfall. Human
activities (e.g., farm irrigation) and climate change (e.g.,
temperature rise and global warming) are therefore the possible causes of storage loss in the study area. Invariably,
storage loss in the Himalayas and Tibetan Plateau region
could have negative implications for the hydrology, dependent ecosystems, and livelihoods of millions of people.
[49] Acknowledgments. This study was funded by the International
Collaborative Project of the Ministry of Science and Technology
(2009DFA21690), the KZCX1‐YW‐08‐03‐04 Innovative Project, and
the Hundred Talent Program of Chinese Academy of Sciences. We
duly acknowledge the valuable inputs of the reviewers, especially that of
Pavel G. Ditmar.
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