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Water storage change in the Himalayas from the Gravity Recoveryand Climate Experiment (GRACE) and an empiricalclimate 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 withthousands of glaciers and high‐elevation snowfields. This is the source of water for the upperreaches of Asia’s main river systems, providing the livelihood for millions of people in thesubregion. Climate change is therefore critical for the Himalaya snow and glacier hydrology,the dependent ecosystems, and the people. Whereas temperature and precipitation arecommon indicators for climate change, snow and glacier dynamics are reliable precursors ofa warming or cooling climate. This study uses a simple empirical climate model (ECM) andthe Gravity Recovery and Climate Experiment (GRACE) satellite data to analyze waterstorage dynamics in the 5.072 × 106 km2 Himalayas and Tibetan Plateau region. About72 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 LandData Assimilation System (GLDAS) Noah land surface model and are validated with groundtruth data from 205 meteorological stations. Total water storage change derived from theGRACE 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 androot‐mean‐square error (RMSE) = 8.73 mm), suggesting that random errors in GRACE dataare 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 atboth 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 detectstorage signal but also that of the ECMmodel to characterize storage change in the snow andglacier hydrology. There is clear seasonality in the storage anomaly, with the highest insummer and lowest in winter. The corresponding storage change is delayed by a quarterof the year. The GRACE and ECM model indicate an overall negative storage trend of0.36 ± 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 negativestorage trend could be indicative of warming climate conditions in the region. Groundwaterabstraction (mainly for irrigation) in the southern plains, coupled with dwindling snowfallin the northern massifs, is a critical storage loss factor in the region. Invariably, storageloss in the Himalayan‐Tibetan Plateau region could have negative implications for thehydrology, 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 GravityRecovery 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 waterin high‐latitude regions [Mall et al., 2006; Casassa et al.,

2007; Fujita, 2008a; Qiu, 2008; Immerzeel et al., 2010]. Intotal, glaciers, ice sheets, and snowpacks store the equivalentof 68.8 m of global sea height [Raper and Braithwaite, 2006].About 70% of surface fresh waters are stored as glaciersand 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 meltcould mitigate or avert catastrophic droughts during weak,delayed, or failed precipitations, enhanced glacier meltcould also have devastating effects [Ren et al., 2003; Kumaret al., 2007]. Accelerated glacier melt may trigger rock‐ice

1Key Laboratory for Agricultural Water Resources, AgriculturalResources Research Center, Institute of Genetics and DevelopmentalBiology, Chinese Academy of Sciences, Shijiazhuang, China.

2Institute ofGeographical Sciences andNatural ResourcesResearch,Chinese Academy of Sciences, Beijing, China.

3College of Environment and Resources, Jilin University, Jilin, China.

Copyright 2011 by the American Geophysical Union.0043‐1397/11/2010WR010157

WATER RESOURCES RESEARCH, VOL. 47, W07521, doi:10.1029/2010WR010157, 2011

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http://dx.doi.org/10.1029/2010WR010157

avalanches, high‐debris flows, and floods [Ageta et al., 2000;Li et al., 2007]. On the other hand, enhanced glacier meltcould threaten long‐term water availability [Thayyen andGergan, 2009]. Acute water shortages would jeopardizeagroindustrial 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 gla-ciers are fast retreating [Tang and Li, 1992; China NationalCommittee on Climate Change, 2007]. While 82% of theTibetan 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 retreatwas in the last two decades [Liu et al., 2008]. Hydrologicalchanges on such scales could threaten glacier‐dependentlakes, 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 [Sanlavilleand Prieur, 2005; Mats et al., 2009; Immerzeel et al., 2010].There are reports of above global average warming in theregion [Du et al., 2004; Qiu, 2008]. Because of rising tem-peratures in the Himalayas, rain precipitation is increasing atthe expense of snow precipitation [Oerlemans and Reighert,2000; Fujita, 2008b]. Temperature is predicted to rise in 2100by 3.5°C–5.5°C in the Indian subcontinent and by 2.5°C–5°Cin 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] andindustrial and economic prosperity [Semwal et al., 2004].Using a mass balance approach, Schneeberger et al. [2003]predicted substantial glacier retreat in the next 50 yearsunder enhanced greenhouse gas emissions. Fujita [2008a]noted that the Himalaya glaciers are sensitive to precipita-tion seasonality. There are, however, limited studies on waterstorage dynamics in the Himalayas and Tibetan Plateauregion [Thapa, 1993;Muskett, 2008;Chambers, 2009]. Thereeven exist fewer studies relating the Gravity Recovery andClimate Experiment (GRACE) satellite gravity data withstorage 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 areused in spherical harmonic (Stokes) coefficients to measurethe Earth’s gravity field at an unprecedented accuracy [Tapleyet al., 2005; Chambers, 2009]. After adjusting for non-hydrologic effects, GRACE month‐to‐month gravity fieldsare inverted for vertically integrated terrestrial water storagechange [Wahr et al., 2004; Swenson and Wahr, 2006, 2007;Muskett, 2008; Moiwo et al., 2011]. In the Himalayas andTibetan Plateau region, hydrologic mass balance is dominatedby changes in glaciers, ice sheets, snowpacks, and permafrost.These sources of storage are sensitive to temperature andprecipitation, which are influenced by climate change andglobal 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 andlandslides once snow and glacier weight is gone are a possibledisastrous combination [Xu et al., 2007; Spencer, 2009]. Toaddress such threats, an empirical climate model (ECM) is

developed to estimate water storage change in the Himalayasand Tibetan Plateau region. The model, driven by the com-mon climate variables of temperature and precipitation, issimple and practicable. It would also enhance our under-standing about the effects of changes in climate on storage inthe snow and glacier hydrology of the Himalayan and TibetanPlateau. The results of the study will provide the basis forpolicy decisions and conservation strategies of the fragile,invaluable hydroecology. The logic and implementation ofthe ECM model are discussed in section 2.

2. Data and Method

2.1. Study Area

[9] The Himalayas and Tibetan Plateau cover, in variousproportions, 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 esti-mated area of 5.072 × 106 km2. It extends (in an arc shape)for 2500 km from west to east and 100–400 km from southto north [Muskett, 2008; Mats et al., 2009; Immerzeel et al.,2010]. Although over 80% of the area is in the TibetanPlateau, it also includes Karakoram, Hindu‐Kush, Pamir,Tien Shan, Inner and Outer Himalayas, and several othermountain ranges.[10] The geophysiographic features in this region are

complex and rugged [Bolch et al., 2008], with over 30 ofthe 110 massif peak elevations above 6000 m [Kaul, 1999;Macintosh, 2005]. The 70 km long Siachen Glacier (on theIndia‐Pakistan border) is the longest of the over 50,000 gla-ciers in the region [Kaul, 1999; Qiu, 2008]. The mountainsare characterized by deep snowcaps, glacial valleys andgorges, and rich ecobiodiversity [Saxena et al. 2005; Harris,2010]. The mountain valleys, depressions, and canyons pro-vide abundant storage for summer rainwater and meltwater[Ageta et al., 2000]. The tectonic glacial lakes occur at alti-tudes below 5000 m and generally diminish in size at higherelevations [Thayyen and Gergan, 2009]. Pangong Tso, thelargest endorheic lake in the region is ≈134 km long. Itspreads across the India‐China border at an average altitudeof 4350 m [Winiger et al., 2005].[11] Of the 519mm average annual precipitation, 72% falls

as summer monsoon rain and 28% as winter snow [Dulalet al., 2006]. Variation in diurnal temperature is large, witha long‐term average of 7°С. The coldest (in January) andhottest (in July) average monthly temperatures are 12°Сand 16°С, respectively [Shrestha et al., 2000]. Above‐16°Сsummer temperatures are not uncommon on the valley slopes[Dulal et al., 2006]. At elevations over 5000 m, above‐zerotemperatures only occur during the daytime [Thayyen andGergan, 2009]. Because of rising temperatures, snow pre-cipitation is increasingly shifting toward rain. Winds gener-ally strengthen at higher altitudes, reaching 120 km/h at6000 m [Burbank et al., 2003].[12] While the western phase of the Himalayas has a gen-

erally warm and humid monsoon climate, a typical moun-tain desert cold climate dominates on the northern slopesand Tibetan Plateau [Goswami et al., 2003]. As Asia’s mainsource of water, the Himalayas and Tibetan Plateau snow andglaciers greatly influence the hydrology, ecology, and live-lihoods of millions of people in the subcontinent [Burbank

MOIWO ET AL.: STORAGE CHANGE IN THE HIMALAYAS W07521W07521

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et al., 2003; Qiu, 2008; Thayyen and Gergan, 2009; Immerzeelet al., 2010].

2.2. Hydrologic Mass Balance

[13] Here storage change Dw [L/T ] is the differencebetween storage anomalies of two successive time steps. Astorage anomaly is the residual storage content w′ [L/T ] at agiven time with respect to the content at the reference epoch.Reference storagew′r [L/T ] is the mean storage within the timeinterval for which the temporal mean is computed. Hydro-logic mass balance in the Himalayas and Tibetan Plateaustudy area is mainly driven by storage in snow, glaciers, high‐altitude lakes, and permafrost. A glacier mass balancemethodcould therefore be used to estimate the storage change.Studies show that the climate variables with the most impacton 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 char-acteristics of temperature and precipitation, Oerlemans andReighert [2000] conducted glacier mass balance for differ-ent climatic regions of the world.[14] In this study, the method of Oerlemans and Reighert

[2000] is modified to estimate water storage change in theHimalayas and Tibetan Plateau region. To achieve this, it isassumed that the time derivative of the residual storageanomaly w′ [L/T ] with respect to the initial time of the waterstorage w [L/T ] is a nonlinear function of temperature T (K)and precipitation P [L]; i.e., w′ = f (T , P). A linearization ofthis function at the vicinity of the reference temperatureTr (K) and reference precipitation Pr [L] yields

D!′ ¼ CT T � Trð Þ þ CP P � Prð Þ; ð1Þ

with the residual storage change Dw′ = w′ − w′r [L/T ], ref-erence storage w′r = f (Tr, Pr) [L/T ], temperature coefficient

CT = ∂f/∂T [L/K], and precipitation coefficient CP = ∂f/∂P(dimensionless). The integration of the linearized relation-ship, after discretization, gives

D! ¼Xn

k¼1

CT ;k Tk � Tr;k� �þ CP;k Pk � Pr;k

� �� : ð2Þ

The index k is the time step (annual, seasonal, monthly, orother time scales) associated with the reference temperatureTr and precipitation Pr of the reference mass balance w′r(preferably zero). The temperature coefficient CT ,k [L/K] andprecipitation coefficient CP,k (dimensionless) in equation (2)are the so‐called climate sensitivity characteristics (CSC) ofthe mass balance [Fujita, 2008b]. The CT ,k and CP,k of themass balance are quantified as

CT ;k ¼ @!′

@Tk;CP;k ¼ @!′

@Pk: ð3Þ

This can be determined by any simple or complex degree‐dayor energy balance model that computes the cumulative massbalance 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 CSCvalue is calculated using the energy–mass balance modeldescribed by Fujita [2008b]. Because of the great size,apparently all glacier types exist in this region. This allowsthe use of average values and eliminates the need for differentsets of parameters for individual glaciers. The areas of theglaciers in the region are therefore added together and treatedas a seamless glacier. Most of the model input parameters(including glacier and ice extent, albedo, etc.) are derivedfrom Moderate Resolution Imaging Spectroradiometer(MODIS) and Landsat thematic mapper data [König et al.,2001]. The energy fraction of the model is balanced on gla-cier surface daily heat, radiation, sensible and latent turbulent

Figure 1. Map showing the geographic location of the study area, national boundaries, drainage, weathermonitoring stations, Mount Everest, and the ambient surface elevation.


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heat, and conductive heat into the glacier mass. The massfraction of the model is balanced on accumulated snow andrefrozen meltwater and on snowmelt and evaporation [Fujita,2008a, 2008b]. The model gains mass via solid precipitationand loses it via ablation. Conductive heat into the glaciersystem and the amount of water at the snow‐glacier interfaceare used to estimate meltwater refreeze [Fujita and Ageta,2000]. Snow surface albedo (which declines with glacierdepth and melt season duration) is calculated in relation tosurface snow density (which changes with compaction). Thispreserves climate feedback effects on the snow and glacierregimes. The model has been validated on selected glaciersin the Himalayas and Tibetan Plateau region. Further detailsof the model, including validation analyses, are documentedby 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 airtemperature within ±2 K. Next, systematic monthly pertur-bations 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 thisprocedure, 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 precipitationaffect their annual mass balances [Oerlemans and Reighert,2000]. The stability of the average Himalaya glacier systemis relatable to climate change and global warming, which aremainly driven by summer climate trends. Hence, a sensitivityindex (SI) that shows the average behavior of the glaciers

through the year is used to quantify perturbations in tem-perature (SITy) and precipitation (SIPy) as

SITy ¼ CT ;6 þ CT ;7 þ CT ;8

P12

k¼1CT ;k

; SIPy ¼ CP;6 þ CP;7 þ CP;8

P12

k¼1CP;k

; ð4Þ

where y is the year and 6, 7, and 8 denote June, July, andAugust, respectively. The SI is related to average yearlytemperatures and precipitation for 2003–2008 in Figure 2.The power fits in Figures 2a and 2c show strong correlationsbetween SI and temperature (R2 = 0.91) and precipitation(R2 = 0.86); all are significant at p < 0.01. Figure 2 suggeststhat snow and glacier vulnerability in the region increaseswith increasing temperature and precipitation.Of course, risingtemperatures favor rain precipitation (Figure 3), which inturn 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, thesteady state portion of water storage is highly correlated withstatic gravity fields. It is difficult to separate the steady stateportion of the water storage from the static gravity estimates.Hence, what GRACE provides is not total terrestrial watercontent but its anomaly field. Raw GRACE monthly solu-tions contain information about water storage anomaly inrelation to the reference storage. In other words, GRACE isindirectly sensitive to total water storage signal. In this study,water storage change estimated independently by the ECMmodel in equation (1) is compared with that derived from theGRACE satellite gravity data.

Figure 2. (a) Graphic illustration of climate sensitivity characteristics in the Himalayas and TibetanPlateau snow and glacier, along with (b) annual temperature sensitivity index, (c) annual precipitationsensitivity 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.


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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 dataproducts released by CSR. It constitutes significant improve-ments over earlier releases because of improvements in thebackground processing models [Chambers, 2009; Rodellet al., 2009]. Level‐2 products of the data are available inthe public domain at http://geoid.colorado.edu/grace/grace.php. Using user‐defined criteria, the site handles a series ofGRACE hydrology analysis. Retrievable end products arealso available in the form of maps, time series, and ASCIIfiles. CSR GRACE monthly solutions are processed onspherical harmonic (Stokes) coefficients and are thereforeprone to leakage. After removing a temporal mean, themonthly data are filtered for correlated north–south trendingerrors [Wahr et al., 2004; Swenson and Wahr, 2006, 2007,2009]. The data are further corrected for glacial isostaticadjustment [Paulson et al., 2007] and other nonhydrologiceffects [Muskett, 2008; Immerzeel et al., 2010]. This data sethas been used in hydrological studies in different agroclimaticregions 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 December2008) are used in the analysis. The 1° resolution GRACE dataare truncated to the 5.072 × 106 km2 study area and smoothedwith the Gaussian filter of 300 km half width. To minimizethe effect of geographic truncation [Mayer‐Guerr et al.,2007] on the analysis, a 1° buffer zone is maintained aroundthe study area. The fields are spatially averaged to create timeseries of total water storage anomalies. Total water storagechange 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 distributionfor realistic spatial representations. Satellite, statistical, andknowledge‐based numerical methods are therefore used tointerpolate, extrapolate, or predict spatial distributions ofclimate fields in such regions [Lindsay, 2005]. Thesemethodsoften take into account factors such as elevation, slope,gradient, aspect, curvature, geographic location, orographiceffectiveness, and other land physiographic elements, whichaffect the pattern (occurrence and distribution) of climatephenomena [Daly et al., 2008].

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 Himalayasand Tibetan Plateau study area.


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[22] The Mountain Climate Simulator (MTCLIM[Hungerford et al., 1989]) and Parameter‐elevation Regres-sion on Independent Slopes Model (PRISM [Daly et al.,1994; Gibson et al., 1997]) are specifically developed forinterpolating meteorological fields in complex terrains withlimited observations. As these models are, however, notfreely available, their applications outside the developerorganizations are limited. In this study, the temperature andprecipitation (snow and rain) data products of the GlobalLand Data Assimilation System (GLDAS) land surfacemodel (LSM) are used [Rodell et al., 2004]. About 72 monthsof the GLDAS Noah climate data, spanning for January 2003through December 2008, are used.[23] GLDAS uses satellite‐ and ground‐based data pro-

ducts to generate optimal fields of land surface states andfluxes 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 tosimulate subgrid variability. Soil and elevation parameters arebased on high‐resolution global data sets [Rodell et al., 2007].The baseline meteorological forcing data are produced by theNOAA Global Data Assimilation System (GDAS) atmo-spheric analysis system. The simulation, initialized for 1979,is performed on a 1° global grid and forced by bias correctionreanalysis 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 orPRISM model estimates. Despite this, the 72 month GLDASNoah data are validated with in situ measurements of tem-perature and precipitation from 205 meteorological stations(Figure 1). Linear correlation fits in Figures 2d and 2e showgood agreement between the GLDAS Noah data and tem-perature (R2 = 0.76, root‐mean‐square error (RMSE) =3.43°C) and precipitation (R2 = 0.84, RMSE = 43.61 mm); allare significant at p < 0.01.[25] The temperature and precipitation fields are afterward

spatially averaged (in much the same manner as the GRACEdata) and fed into the ECM model to estimate total waterstorage change in the study area. Next, the storage changederived from the ECM model is compared with that from theGRACE satellite gravity data. The GLDAS soil moisture dataare also used to isolate the contribution of groundwater tomass loss in the southern region of study area.

3. Results and Analysis

3.1. Uncertainty Analysis

[26] The GLDAS Noah monthly temperature and pre-cipitation data are validated with ground truth data from205 climate monitoring stations. The method described byStrassberg et al. [2007] is used to gauge uncertainties in theGLDAS Noah data. On the basis of the analysis, the RMSEdifference between the GLDASNoah and ground truth data is3.43°C (R2 = 76) for temperature and 43.61 mm (R2 = 0.84)for precipitation. Artificial disturbances (at the magnitude ofthe estimated uncertainties) are added to the ECM model toanalyze their propagation in the estimated water storage. Inprinciple, a disturbance at the beginning of the integrationaffects further evolutions of the balance. The model is initi-ated at the start of 1979 (the time GLDAS Noah data loggingstarted) and is run continuously for 30 years (1979–2008)with monthly temperature and precipitation forcing. Com-

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 measurementdata. This set of data is currently not in our repository for theHimalayas and Tibetan Plateau region. It is, however,important to note that the GRACE monthly solutions aredifferentiated prior to application in this study. In otherwords, the GRACE‐based water mass changes are consid-ered, not the monthly solutions (see sections 3.2–3.6). Fur-thermore, studies [e.g.,Rodell et al., 2009] show that GRACEsatellite gravity data reliably signal storage anomaly in theregion.Muskett [2008] and Immerzeel et al. [2010] have alsoshown that GRACE sufficiently detects storage change in theHimalayas and Tibetan Plateau region.

3.2. Monthly and Seasonal Storage Anomaly

[28] Time series of the monthly and seasonal anomaliesof snow, rain, precipitation, temperature, and GRACE totalwater storage are depicted in Figure 3. The negative (forsnow) and positive (for rain) linear trends (at the seasonalcycle) in Figures 3a and 3b suggest increasing rainfall at theexpense of snowfall in the study area. Overall, precipitation(snow plus rain) is increasing in the region, indicated by apositive linear trend for the seasonal cycle in Figure 3c.Temperature too is increasing (Figure 3d), which indicates awarming condition [see Fujita, 2008b; Thayyen and Gergan,2009]. The corresponding GRACE total water storage anom-aly shows an overall decline. The anomaly trends suggest thatthe increase in (rain) precipitation could be in response to therising temperatures.[29] The phases of the monthly and, more so, the seasonal

time series of precipitation, temperature, and GRACE trackone another. There is a near‐synchronized seasonality, withthe highest amplitudes in summer (June to August) and thelowest in winter (December to February). Whereas the lineartrends in the seasonal anomalies for GRACE and snowfall arenegative, those for rainfall and precipitation are positive. Thisshows that rain is increasingly becoming a dominant form ofprecipitation in the region.[30] Air temperature generally dictates the fraction of pre-

cipitation that falls as either low‐albedo rain or high‐albedosnow on glacier surfaces [Dulal et al., 2006]. Studies showthat under warming climate conditions, precipitation favorslow‐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 glaciermelt under decreasing snow precipitation. Low albedosincrease glacier surface exposure, which in turn acceleratesglacier melt even under stable temperature conditions [Fujitaand Ageta, 2000]. Warming climate conditions generallytrigger temperature rise, leading to less snow precipitationand high snow and glacier ablation and storage loss. Suchconditions 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, averagemonthly snow, rain, precipitation, and temperature anomalies


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for 2003–2008 are plotted in Figure 4. On the basis ofFigure 4a, snowfall increases from January through March,then decreases through August, before increasing againthrough December. On the other hand, rainfall, precipitation,temperature, and GRACE‐based storage anomalies havepositive trends for January through July and negative trendsthrough December [see also Fujita, 2008a, 2008b]. Becausetemperatures are highest in summer, snow and ice ablationlikely occurs during this period. This, coupled with thedeclining (high‐albedo) snowfall, induces storage loss in theregion.[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, storagechange generally increases from January through June, with aslight dip in March. It decreases through September beforegradually increasing through December [see Rodell et al.,2009]. The winter‐to‐spring rise in storage could be drivenby 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 inthe vast crayons, reservoirs, and (endorheic) lakes and tarnsin the region. Storage loss is noticeable either directly fromFigure 4e or as a negative storage change in Figure 4f. In bothcases, storage loss occurs between August and December.This could be mainly driven by groundwater depletion for

Figure 4. Time series of average monthly anomalies of (a) snowfall, (b) rainfall, (c) precipitation, (d) tem-perature, and (e) GRACE TWSA. (f) Comparison of average monthly total water storage change derivedfrom GRACE and the empirical climate model (ECM) for the period 2003–2008 in the Himalayas andTibetan Plateau study area.


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farm irrigation, especially in the southern half of the studyarea. [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 Plateauregion, 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 monthlystorage change from GRACE data and the ECM model(Figure 4f). This suggests that the temperature‐ and precipi-tation‐driven ECM model sufficiently characterizes storagedynamics in the study area.

3.4. Seasonal Storage Change

[33] This section illustrates the dependence of water stor-age in the Himalayas and Tibetan Plateau region on tempera-ture and precipitation as a time derivative of storage, notits function. Such analysis is not only informative but alsoefficient in terms of data and computational requirements.Furthermore, most studies involving GRACE hydrology arepresented as time derivative of storage, not its function. Thisapproach therefore allows direct comparisons of the resultshere with those of other studies.[34] Figures 5a–5c depict seasonal storage change in the

study area, separately computed from the GRACE, tem-perature (at CP,k = 0), and precipitation (at CT ,k = 0) data.Figure 5 shows the relative importance and suitability of theclimate variables of temperature and precipitation as indi-cators for storage change in the region. The storage change

(Figure 5) is delayed by a quarter of the year with respect tothe storage anomaly (Figure 3). The minima and maxima ofthe GRACE‐only, temperature‐only, and precipitation‐onlyestimated storage changes are in spring and autumn, respec-tively. However, the amplitudes (separate for the GRACE,temperature, and precipitation) are totally different. Thisindicates that temperature or precipitation variations alonedefinitely cannot explain water storage dynamics in the studyarea. However, the sum of the temperature and precipita-tion components almost completely explains the variationsin GRACE‐estimated storage change. The trends in the threestorage terms are negative, suggesting storage loss accelera-tion in the region [see Tang and Li, 1992; Hou et al., 2000;Muskett, 2008; Qiu, 2008; Rodell et al., 2009; Immerzeelet al., 2010].

3.5. Storage Change Comparison

[35] Figure 6a depicts a simple least squares sinusoidal fitfor the GRACE‐derived total water storage change. A goodagreement exists between the GRACE and sinusoidal curve,with R2 = 0.81 and RMSE = 8.73mm (significant at p < 0.01).The favorable agreement shows that random errors in GRACEdata are small. However, the test does not allow us to quantifysystematic errors in GRACE data. This second error type canbe quantified using ground‐based measurements of storagechange, data which are currently not in our database. Thesinusoidal fit is therefore assumed to give a measure ofGRACE’s ability to detect storage signal in the study area.In fact, GRACE is shown to reliably detect storage change in

Figure 5. Time series of seasonal water storage change separately derived from the (a) temperature,(b) precipitation, and (c) GRACEgravity data for 2003–2008 in theHimalayas and Tibetan Plateau study area.


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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 theGRACE‐ and ECM‐estimated storage change, with R2 = 0.83and RMSE = 7.64 mm. The phases and amplitudes of theestimated storage changes closely track each other. Thisfurther indicates that the ECM model characterizes storagedynamics in the study area. GRACE data are processed usingspherical harmonic (Stokes) coefficients and, as such, haveleakage problems. Like GRACE, uncertainties also exist inthe ECM model. To better represent the storage dynamics,therefore, the average of the GRACE‐ and ECM‐estimatedstorage changes is used. The long‐term storage change isnegative. It is on the order of 0.36 ± 0.03 mm/month or21.91 ± 1.95 km3/yr for the 5.072 × 106 km2 study area(significant at p < 0.1). This finding is consistent with earlierstudies in and around the Himalayas and Tibetan Plateauregion [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 GLDASNoah soil moisture data for the region south of the study area.It is readily noticeable that the amplitudes of the GRACEstorage change for this region are higher than those forthe entire study area. The amplitudes of the ECM plus soilmoisture storage change are also far lower than those of

GRACE. This suggests that storage change in the southernregion of the study area is mainly driven by groundwaterdepletion (see section 3.6 for details).

3.6. Annual Storage Distribution

[38] Figures 7a–7d depict average annual spatial distri-butions of snowfall, rainfall, precipitation, and temperaturefor 2003–2008. Snowfall (Figure 7a) is highest in the westcentral Tibetan Plateau, northeast Pakistan and northwestIndia. It is lowest in the regions east of the study area. Rainfall(Figure 7b) is high along the west–east southern flank andhighest in northeastern India. This is the rainward belt of theHimalayan massifs. Rainfall is lowest along the west–eastnorthern leeward region of the Tibetan Plateau. The precipi-tation distribution reflects the snow and rain distributionscombined. It is generally high along the west–east southernrainward 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 distri-bution (Figure 7d) depicts a subtle replica of the elevation.To some extent, it also moderately mimics the precipitationtrend. This shows the unique influence of elevation and pre-cipitation on temperature.[39] The precipitation distribution and, to a certain extent,

that of temperature are influenced by the westerlies andmonsoon winds [Ageta and Higuchi, 1984; Fujita, 2008a,2008b]. Mount Everest and the sister mountains along the

Figure 6. (a) Sinusoidal fit for GRACE‐derived total water storage change and comparisons of water stor-age changes fromGRACEwith the empirical climate model (ECM) (b) for the entire Himalayas and TibetanPlateau study area and (c) for the ECM plus GLDAS Noah soil moisture (SM) for the region south of thezero‐contour storage trend line in Figure 7c.


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southern arc are the rainward and rain shadow dividein the region. The ranges of snow, rain, and precipitation are28–997, 128–3116, and 132–3116 mm, respectively. Aver-age annual precipitation, and especially that of rain, is lowfor over two thirds of the study area. Temperature, with anaverage annual range of 17°C to +30°C, is also negative forover three quarters of the study area. The west–east rainwardbelt 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 mapsfor 2003–2008. Implicitly, the dynamics of the climate vari-ables of temperature and precipitation in the glacier‐drivenhydrology largely dictate the distribution pattern of theGRACEstorage trend map. The GRACE‐derived water storage trendshows a significant depletion in the south. This is especiallynoticeable in the border regions of Nepal, Bhutan, India,and Bangladesh; i.e., southeast of Mount Everest. Storagedepletion is also visible farther in the southeast, along theIndia‐Myanmar border region. A moderate depletion trendexists in the region northwest of Indian.[41] A negative storage trend exists in the high rain pre-

cipitation region along the west–east southern arc of the studyarea. The negative storage trend is a signal for groundwaterdepletion, especially in the southern half of the study area

[Muskett, 2008; Rodell et al., 2009; Wada et al., 2010]. Tojustify this point, the GLDAS Noah data are used to quantifysoil moisture storage change in the region south of the zero‐contour storage line in Figure 7e. The ECM model is alsoapplied to simulate snow and glacier storage change in thisregion. The sum of the ECM and soil moisture storagechanges is then subtracted from that of GRACE. This analysisisolates groundwater storage change in the region south ofthe zero‐contour storage line. In terms of water equivalentper area, the trend in the derived groundwater storage lossis ≈70% of the estimated storage loss in the region (seeFigure 6c). In terms of volume loss, however, it accountsfor <20% of the estimated long‐term storage loss in theHimalayas and Tibetan Plateau region. This is ascribed toirrigation farming, a critical storage depletion factor in thisregion [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 highin the headwater region of the Huang, Yangtze, and Mekongrivers. This region has relatively moderate snow, rain, andtemperature (Figures 7a–7c), conditions that generally favorstorage. This suggests that low‐temperature zones in theHimalayas and Tibetan Plateau are hydrologically more

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 fromGRACE monthly water storage maps for 2003–2008 in the Himalayas and Tibetan Plateau region.


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stable. These regions are less sensitive to the effects ofhuman activity, including climate change and globalwarming.

4. Discussion

[43] The foregoing analysis suggests that the climatevariables of temperature and precipitation are critical massbalance elements in the snow and glacier hydrology of theHimalayas and Tibetan Plateau [see Hou et al., 2000; Qiu,2008; Cogley et al., 2010; Immerzeel et al., 2010]. Temper-ature and rain precipitation are increasing with decreasingsnow precipitation (Figure 3). While snow and rain are neg-atively correlated (R = −0.57), temperature is rising on theaverage of 0.023°C/yr. Of course, temperature influences thefraction of precipitation that falls as either snow or rain [Dulalet al., 2006; Thapa, 1993; Fujita, 2008a]. Snowmelt andglacier melt and retreat accelerate with rising temperaturesand warming climate conditions. This has an overall effect ofstorage 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 neg-ative storage trend, an indicator for storage depletion in theHimalayas and Tibetan Plateau region. Consistent with thisstudy, several other studies have reported glacier retreator 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 evenrelated glacier retreat to rising temperatures and dwindlingsnowfall [Ageta and Higuchi, 1984; Fujita and Ageta, 2000;Hou et al., 2000; Fujita, 2008a, 2008b]. Using a snow-melt runoff model and the DTM‐1 GRACE gravity model,Immerzeel et al. [2010] noted a negative storage trend inthe Ganges Basin that was strong enough to conclusivelyshow ice storage decline in much of the Himalayas andTibetan Plateau region.[45] The negative (mass loss) trend in the southern part of

the study area is driven by groundwater pumping, particularlyfor farm irrigation [see Siebert et al., 2005;Mall et al., 2006;Muskett, 2008; Rodell et al., 2009; Wada et al., 2010]. Thewarming climate and subsequent snow and glacier meltwaterdischarge are additional factors of storage loss in the region[Qiu, 2008; Immerzeel et al., 2010]. The correspondencebetween these studies and ours suggests that the ECM modelreliably characterizes storage dynamics in the snow andglacier hydrology of the Himalayas.

5. Conclusions

[46] This study has used the GRACE and ECM model toestimate total water storage dynamics in the Himalayas andTibetan Plateau snow and glacier hydrology. About 72 con-secutive months of data, spanning from January 2003 throughDecember 2008, are used in the study. The GRACE data areverified with a sinusoidal model, and the ECM forcing dataare verified with in situ measurements of temperature andprecipitation. The GRACE‐estimated storage change isalso compared with that of the ECM model for the 5.072 ×106 km2 study area. All the correlation agreements are sta-tistically significant at p < 0.01. The favorable agreementssuggest that the ECMmodel sufficiently characterizes storagedynamics in the Himalayas and Tibetan Plateau snow and

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 precipi-tation, which are routinely measured by common weatherstations.[47] Rising temperatures in the region favor rain precipi-

tation, snowmelt and glacier melt, and storage depletion.Snowfall is highest in winter and lowest in summer, whilerainfall, precipitation, and temperature exhibit the reversebehavior (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 storageanomaly in the study area, as over 70% of the dominantsummer rain falls during this period. A considerable amountof the summer rainwater and meltwater is also stored in thevast reservoirs, endorheic lakes, and canyons, leading to highsummer storage anomalies. The corresponding storage changeis, however, lowest in spring and highest in autumn. This isbecause the storage change is delayed by a quarter of the year.[48] The GRACE and ECM model show an overall nega-

tive storage change in the Himalayas and Tibetan Plateaustudy area. Snow and glacier ablation and the subsequentstorage loss via discharge are mainly driven by rising tem-peratures and rain precipitation. Heavy groundwater irriga-tion, especially in the northwest regions of Pakistan and Indiaand in southern Nepal and Bhutan, is another mass loss factor.Storage loss in the far north and northeast regions of theTibetan Plateau is largely due to dwindling snowfall. Humanactivities (e.g., farm irrigation) and climate change (e.g.,temperature rise and global warming) are therefore the pos-sible causes of storage loss in the study area. Invariably,storage loss in the Himalayas and Tibetan Plateau regioncould have negative implications for the hydrology, depen-dent ecosystems, and livelihoods of millions of people.

[49] Acknowledgments. This study was funded by the InternationalCollaborative Project of the Ministry of Science and Technology(2009DFA21690), the KZCX1‐YW‐08‐03‐04 Innovative Project, andthe Hundred Talent Program of Chinese Academy of Sciences. Weduly acknowledge the valuable inputs of the reviewers, especially that ofPavel G. Ditmar.

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S. Han, J. P. Moiwo, and Y. Yang, Key Laboratory for AgriculturalWater Resources, Agricultural Resources Research Center, Institute ofGenetics and Developmental Biology, Chinese Academy of Sciences,286 Huaizhong Rd., Shijiazhuang 050021, China. ([email protected])W. Lu, College of Environment and Resources, Jilin University, Jilin

130021, China.F. Tao, Institute of Geographical Sciences and Natural Resources

Research, Chinese Academy of Sciences, 11 Datun Rd., Beijing 100101,China.


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