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Comparison of Land Water Storage from Global Hydrologic and Land Surface Models and GRACE
Satellites
Bridget R. Scanlon
1Bureau of Economic Geology, Jackson School of Geosciences, Univ. of Texas at Austin, Austin, Texas, United States
Collaborators
Zizhan Zhang, Institute of Geodesy and Geophysics, Wuhan, China
Ashraf Rateb, Bur. Econ. Geol., Jackson School of Geosci., Univ. of Texas at Austin
Himanshu Save, Center for Space Research, Univ. of Texas at Austin
David N. Wiese, NASA Jet Propulsion Lab., Pasadena, California
Michael Croteau, Univ. of Colorado, Boulder, Colorado
David Lawrence, Univ. Corporation for Atmospheric Research, Boulder, Colorado
Zong Liang Yang, Dept. Geol. Sciences, Jackson School of Geosciences, Univ. of Texas at Austin
Hiroko Beaudong, Goddard Space Flight Center, Univ. of Maryland
Hannes Müller Schmied, Goethe University, Frankfurt, Germany
Rens van Beek, Utrecht University, Utrecht
Robert C. Reedy, Bur. Econ. Geol., Jackson School of Geosci., Univ. of Texas at Austin
Alex Sun, Bur. Econ. Geol., Jackson School of Geosci., Univ. of Texas at Austin
Outline
• Background1. GRACE satellites2. Global models
• Objectives
• Results1. Comparison of modeled water storage with GRACE
signals2. Evaluation of human intervention on water storage3. Impact of land water storage on global mean sea level
GRACEGravity Recovery and Climate Experiment
2002 - 2017GRACE Follow-On: May 2018
Satellites: 450 km above land surface220 km apart
Spatial resolution ~ 100,000 km2
~ 90 mins for circum-polar orbit
Monthly data1 gigaton mass change = 1 km3 of water
Only satellite to penetrate deep into subsurface
Total water storage changeSnow, surface water, soil moisture, and groundwater
http://grace.jpl.nasa.gov/mission/gravity-101/
How GRACE Works
GPS Ground Receiver
Mass Anomaly
GPS Satellites
Nominal Separation
GRACE: giant weighing scale in the skyGRACE satellites measure time variable gravityfield
GRACE satellitesare the instruments
How GRACE Works
GPS Ground Receiver
Mass Anomaly
GPS Satellites
Leading satellite approaches the anomaly andfeels a greater gravitational attraction:
Separation Velocity Increases
How GRACE Works
GPS Ground Receiver
Mass Anomaly
GPS Satellites
Trailing satellite approaches the anomaly andcatches up:
Separation Velocity Decreases
How GRACE Works
GPS Ground Receiver
Mass Anomaly
GPS Satellites
Leading satellite is no longer affected by the anomaly whilethe trailing satellite is being pulled backwards:
Separation Velocity Increases
How GRACE Works
GPS Ground Receiver
Mass Anomaly
GPS Satellites
Trailing satellite catches back up to the leading satelliteThe mass anomaly has been observed in the KBR data
How GRACE Works
• By measuring the distance between the two satellites, GRACE estimates changes in gravity
• Dominant control on temporal variations in gravity at monthly scales is water distribution
• GRACE estimates changes in Total Water Storage from the atmosphere to the Moho
• GRACE resolution is low and can only be applied to large scale basins (> ~100,000 km2)
• We can convert gravity change to equivalent water height or volume at Earth’s surface
• 1 gigaton mass change = 1 km3 of water
Global Models
Groundwater depletion (Wada et al., 2010)
Reservoir storage: decrease rate of sea level riseGW depletion, increase rate of sea level rise
We need models to make projections:• Impacts of climate change and human
intervention on water resources
UNESCO World Water Assessment ProgramGlobal assessments of water resources Wada et al., 2016
IPCC, impact of land water storage on sea level rise
SnWS, Snow water storageSWS: Surface water storage
SMS: soil moisture storage
GWS: groundwater storage
RESS
soil moisture
groundwater
SnWS
Global ModelsInput – Output = Change in Storage
Precip. – ET – Roff – Ext. = DTWSExt. human water extraction
GRACE (Change in Total Water Storage, DTWS)
SnWS from SNODAS or Global or National Land Data Assimilation System (GLDAS or NLDAS) land surface models (LSMs)SWS: reservoir storage monitored in some regions, altimetrySMS: estimated from land surface models (GLDAS or NLDAS)GLDAS: Land surface models: NOAH, MOSAIC, VIC, CLM, CLSMNLDAS: NOAH, MOSAIC, and VIC LSMs
RESS
soil moisture
groundwater
SnWS SnWS, Snow water storageSWS: Surface water storage
SMS: soil moisture storage
GWS: groundwater storage
Global Models
1. Global hydrologic models• Developed to assess water scarcity
– daily, water balance, – snow, surface water, soil moisture and groundwater, – human water use and reservoir management
2. Land Surface Models (LSMs)
– Lower boundary condition for global climate models– More physically based, including energy budget – Snow and soil moisture – No human water use or reservoir management
Model Attributes
MO
DEL
WG
HM
PC
R-
GLO
BW
B
VIC
NO
AH
-3.3
CLS
M-F
2.5
CLM
-4.0
Parameter GHWRMs Land Surface Models
Precip. WFDEI CMAP PGMFD CRU-NCEP
SWS x x x
SMS
GWS x x
Hum. Int. x x x x
SW rout. x x x
Soil lay. (no.)1 2 3 4 10 10
Soil (m) 2.0* 1.5 3.5 3.5 3.4 3.4
GHWRM: Global Hydrologic and Water Resource Models
Only WGHM is calibrated
Objectives
1. What is the relative contribution of different signals to the total signal?
2. How do modeled storage changes compare with those from GRACE?
3. What is the impact of human intervention on water storage?
4. What is the impact of land water storage trends on global mean sea level?
5. What is causing the differences between models and GRACE?
Objectives and Responses 1. What is the relative contribution of different signals to the total signal?
Seasonal signals dominant, 51 – 75% of total signal, trends ≤ 5% of total signal.
2. How do modeled storage changes compare with those from GRACE?
Models underestimate trends.
3. What is the impact of human intervention on water storage?
Human intervention reduces global water storage (56 – 86 km3/yr) but does not impact seasonal amplitudes at the basin scale.
4. What is the impact of land water storage trends on global mean sea level (GMSL)?
GRACE data indicate net increase in land water storage globally (71 – 82 km3/yr) which contributes negatively to GMSL whereas models indicate decreases in water storage (-450 to -12 km3/yr).
5. What is causing the differences between models and GRACE?
Lack of storage compartments and limited capacity
Uncertainties in fluxes
ModelsGHMs
GRACE
SphericalHarmonics
MasconsGHWRMs LSMs
WGHM PCR-GLOBWB
NOAH
MOSAIC
VIC
CLM
± Human water use
Total Water StorageAnomalies
CSR-GSHCSR-GSH.sf
CSR-MJPL-M.dsf
Time series decomposition
• Long-term trends• Seasonal• Interannual• Residual
ControlsBasin size, climate, irrigation
Total Water Storage
Anomalies
P – ET ± Q –EX – D = ΔTWS GRACECanopySWSSMS
SWS SWSSMSGWS
CanopySnowSWSSMSGWS
Total Water Storage Anomalies, 186 river basins, ~ 60% of land surface
Amazon
Ganges
Euphrates
Murray
Hai
Okavango
Arkansas
Scanlon et al., PNAS, 2018
1. What is the relative contribution of different signals to the total signal?Example: GRACE CSR-Mascons
Stotal = Slong-term + Sinterannual + Sseasonal + residuals
0
20
40
60
80
100
1 11 21 31 41 51 61 71 81 91 101 111 121 131 141 151 161 171 181
Perc
enta
ge
CSR-M Trend Inter-annual Seasonal Residual
Basin ID ranked by Basin Area
Trend: 4%; Interannual: 13%; Seasonal: 69%; Residual: 9%
1. Seasonal signal is dominant
• Seasonal signal as a percentage of total: • GRACE: seasonal 51% (JPL-M) to 69% (CSR-M) of total
• Models: seasonal 61% (CLM-4.0) to 75% (NOAH-3.3) of total
Objective
1. What is the relative contribution of different signals to the total signal?
2. How do modeled storage changes compare with those from GRACE?
3. What is the impact of human intervention on water storage?
4. What is the impact of land water storage trends on global mean sea level?
5. What is causing the differences between models and GRACE?
2. How do modeled land water storage trends compare with those from GRACE solutions?
Net water storage trends from 2002 - 2014
Scanlon et al., PNAS, 2018
GRACE: net land water storage trends: 71 – 82 km3/yr
Models: net land water Storage Trend: -450 to -12 km3/yr
-20
-15
-10
-5
0
5
10
km3 /
yr
-80
-60
-40
-20
0
20
40
60
km3/y
r
-20
-15
-10
-5
0
5
10
km3 /
yr
-6
-5
-4
-3
-2
-1
0
1
km3 /
yr
-3
0
3
6
9
12
15
km3 /
yr
Mississippi
Amazon
Okavango
Ganges
Hai
GRACE data GHM LSM
Models
MississippiGRACE Trends ( km3/yr; 2002 – 2014)
Amazon Okavango Ganges
Hai
2. Modeled vs GRACE Land Water Storage Trends
GRACE: CSR-M; JPL-M; CSRT-SH; Models: GHMs: WGHM, PCR-GLOBWB; LSMs: NOAH-3.3; MOSAIC, VIC, CLM-4.0; CLSM-F2.5
GRACE data GHM LSM
Models underestimate the rises in water storage during wet periods and the declines in water storage during dry periods and related to groundwater abstraction
GRACE rangeGRACE CSR-M
GRACE
WGHMPCR-GLOBWB
GHWRM
NOAH-3.3 MOSAIC VICCLM-4.0CLSM
LSM
Scanlon et al., PNAS, 2018
2. Model versus GRACE seasonal amplitudesGlobal models simulate seasonal amplitudes much better than trends
WGHM Global Hydrologic Model CLM-4.0 Land Surface Model
y = 0.65x + 36.4R² = 0.61
0
100
200
300
400
500
0 100 200 300 400 500
WG
HM
am
plit
ud
e (m
m)
GRACE mean amplitude (mm)
y = 0.90x + 12.21R² = 0.79
0
100
200
300
400
500
0 100 200 300 400 500
CLM
-4 a
mp
litu
de
(mm
)
GRACE mean amplitude (mm)
Objective
1. What is the relative contribution of different signals to the total signal?
2. How do modeled storage changes compare with those from GRACE?
3. What is the impact of human intervention on water storage?
4. What is the impact of land water storage trends on global mean sea level?
5. What is causing the differences between models and GRACE?
2. How does Human Intervention Impact Land Water Storage Trends?
Human intervention:Reservoir storageGroundwater abstractions
WGHM: net water storage change -56 km3/yr
Scanlon et al., PNAS, 2018
3. How does Human Intervention Impact Land Water Storage Trends?
WGHM: net storage change -56 km3/yr PCR-GLOBWB: net storage change -86 km3/yr
Relative Importance of Climate and Human Intervention in Water Storage in the Colorado River
GW depletion during drought ~ 80% of storage decline, attributed mostly to human water use.
Water storage depletion in U. Colorado Basin related to surfacewater storage and soil moisture + additional groundwater in lower Basin, mostly in response to climate variability.
Relative Importance of Climate and Human Intervention in Colorado Basin
Water storage depletion in U. Colorado Basin related to surface water storage and soil moisture+ additional groundwater in lower Basin, mostly in response to climate variability.
Scanlon et al., WRR, 2015
3. Comparison of Hydrographs in AMAs vs Irrigated Agricultural Areas without Conjunctive Use or Managed Aquifer Recharge
Active Management Areas Irrigated Ag Areas (no SW)
Managed Aquifer RechargeIrrigation sourced with surface water from Colorado
Basins with no access to surface waterGroundwater-fed irrigation: large depletion
Net Abstraction of Groundwater
mm/yr
Negative values: increase in groundwater storage as a result of surface water irrigationPositive values: groundwater-based irrigation Döll et al., 2012
3. What is the impact of human intervention on seasonal amplitudes?
Ran global hydrologic models with human intervention (WGHM, PCR-GLOBWB) and without human intervention (NHI: no human intervention). We see very little effect of human intervention (reservoir storage and water abstraction) on seasonal amplitudes at basin scale
Objective
1. What is the relative contribution of different signals to the total signal?
2. How do modeled storage changes compare with those from GRACE?
3. What is the impact of human intervention on water storage?
4. What is the impact of land water storage trends on global mean sea level?
5. What is causing the differences between models and GRACE?
4. What is the impact of land water storage on global mean sea level?
GRACE (H + C)
Human (M)
Climate (GRACE – Human)
GRACE: net trend71 – 82 km3/yr
4. How well can we estimate the net impact of land storage trends on global mean sea level (GMSL) change?
GRACE (H + C)
Human (M)
Climate (GRACE – Human)
GRACE: net trend71 to 82 km3/yr
Human intervention-56 to -86 km3/yr
4. How well can we estimate the net impact of land storage trends on global mean sea level (GMSL) change?
GRACE (H + C)
Human (M)
Climate (GRACE – Human)
Estimated climate contribution to land water storage = 2 times that from human intervention
GRACE: net trend71 to 82 km3/yr
Human intervention-56 to -86 km3/yr
Objective
1. What is the relative contribution of different signals to the total signal?
2. How do modeled storage changes compare with those from GRACE?
3. What is the impact of human intervention on water storage?
4. What is the impact of land water storage trends on global mean sea level?
5. What is causing the differences between models and GRACE?
5. What is causing differences between models and GRACE? 1. Storage capacity in models: storage compartments and capacity in compartments
a) LSMs: most do not include SWS or GWSb) Underestimation of seasonal amplitudes in tropical basins: lack of surface water
storage and overbank flooding in LSMsc) Storage capacity may not be sufficient to accommodate large declines and rises in
storage
2. Fluxes: Precip. – ET – Roff = DTWSIf models overestimate seasonal amplitude, this may be because they overestimate the input (P), underestimate the output (ET, Roff), or a combination of both.
• Variations in precipitation input • N high latitude basins: snow and frozen soil schemes • Arid basins: overestimation of ET
Path Forward:
• New GRACE Follow On mission launched in May 2018
• Global modeling should consider fluxes and storage
• Use multi-model ensembles rather than relying on single models
• Develop regional models to focus in different areas • Amazon, hotspots of human depletion, N high latitudes
• Calibrate models?
• Use single models and vary processes and parameters to isolate controls on differences
USGS Powell Research Study• Collaboration among USGS, NASA, and academia
• Evaluate global, national, and regional models with GRACE and flux data within the U.S.
Summary
1. What is the relative contribution of different signals to the total signal?
Seasonal signals are dominant, 51 – 75% of total signal, trends ≤ 5% of total signal.
2. How do modeled storage changes compare with those from GRACE?
Modeled storage trends underestimate those from GRACE whereas modeled seasonal amplitudes compare more favorably.
3. What is the impact of human intervention on water storage?
Human intervention results in net decline in global water storage (56 – 86 km3/yr) but does not impact seasonal amplitudes at the basin scale.
4. What is the impact of land water storage trends on global mean sea level (GMSL)?
GRACE data indicate net increase in land water storage globally (71 – 82 km3/yr) which contributes negatively to GMSL whereas models indicate decreases in water storage (-450 to -12 km3/yr).
5. What is causing the differences between models and GRACE?
Lack of storage compartments and limited capacity
Uncertainties in fluxes
GRACE Products
• Spherical Harmonics (SH) GRACE basin scale data
• Gridded SH GRACE product (Landerer and Swenson, 2012)
• Mascons data (CSR and JPL)
Landerer, F. W., and S. C. Swenson (2012), Accuracy of scaled GRACE terrestrial water storage estimates, Water Resources Research, 48.
Save, H., S. Bettadpur, and B. D. Tapley (2015), Evaluation of global equal-area mass grid solutions from GRACE, European Geosciences Union General Assembly, Vienna, Austria 2015.
Watkins, M. M., D. N. Wiese, D.-N. Yuan, C. Boening, and F. W. Landerer (2015), Improved methods for observing Earth's time variable mass distribution with GRACE using spherical cap mascons, Journal of Geophysical Research-Solid Earth, 120(4), 2648-2671.
Scanlon, B. R., Z. Zhang, H. Save, D. N. Wiese, F. W. Landerer, D. Long, L. Longuevergne, and J. Chen (2016), Global evaluation of new GRACE mascons products for hydrologic applications, Water Resour. Res. , 52(12), 9412-9429.
Processing Spherical Harmonic GRACE Data
• Raw GRACE KB range rate data, noisy
• It takes ~ 90 mins for satellite to complete circum-polar orbit, need 1 month to get global coverage
• Spherical Harmonics processing: Remove high frequency noise (truncation and filtering), also removes signal• Restore signal (scaling factors)
• Apply same processing to land surface model as GRACE to estimate a scaling factor: Land surface model soil moisture storage – Truncation + Filtering = LSMTF
• Scaling factor (dimensionless)= LSM/ LSMTF (≥ 0)
• Mascons: no scaling factor used for CSR GRACE data; NASA JPL use scaling factor to downscale 3 degree data to 1 degree data based on LSMs.
Recent Studies
• Model Intercomparison Projects (MIPs) based on fluxes (river discharge and evapotranspiration) have shown that global hydrologic models are even more variable than climate models (Schewe et al., PNAS, 2014) (InterSectoral Impact Model Intercomparison Project, ISIMIP)
• GRACE data show net increase in land water storage over past decade in response to climate variability (Reager et al., Science, 2016)
• GRACE satellites provide the big picture
Differences between Spherical Harmonics and Mascons• SH analysis: represents global mass change, cannot distinguish land
and ocean, leakage effects
• Mascons (mass concentration blocks), regional or global analysis, can delineate oceans and land• Higher signal/noise ratio than SH
• Can check for signal loss by doing post fit residuals against original GRACE range rate data
• All signal captured within GRACE resolution
• Some solutions use no models (Univ. Texas Center for Space Research) and others use models for downscaling 3 deg gridded data (NASA JPL mascons)