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Alys Thomas Hydrology and Climate Research Group Dept. of Earth System Science University of California, Irvine 2013 AMS Annual Meeting, Austin Using the G.R.A.C.E. Satellites’ Water Storage Anomaly Dataset to Identify Regional Scale Drought Conditions
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Objective cm eqv. water height
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The GRACE mission provides monthly, global maps of Earth's gravity and how it changes as the mass distribution shifts Large scale gravity changes attributed to mass movement of water on and beneath the surface Helps us understand how water moves from the land to the ocean and back again The G ravity R ecovery A nd C limate E xperiment DATASET Release 5 Land data from Uni. Texas Center for Space Research (CSR) Monthly time steps: March 2003- August 2012 Mean is removed to produce terrestrial water storage anomalies (TWSA) on 1° grids Units: CM of equivalent water thickness Courtesy of CSR & JPL, http://www.csr.utexas.edu/grace/gallery/
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Back to the basics: Once variable (i.e. rainfall, streamflow, water storage, etc.) crosses a given threshold, it enters into a dry/drought period The length of time it stays under and deviates from that threshold give us the severity of the drought event Characterizing drought with GRACE data Mishra et al. (2009) Yevjevich (1967) Keyantash and Dracup (2002) Examples:
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Amazon River Basin Hydrologically-defined Area: ~6,100,000 km 2 Two well-documented meteorological droughts during GRACE period (2005 & 2010) High Plains Region (U.S.) Not hydrologically defined Area: ~2,000,000 km 2 Two documented droughts during GRACE period (Severest drought late 2010 to present) Study Areas
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Processing of GRACE data: Global 1° gridded GRACE product downloaded Scale factors (provided) and interpolation of missing months Monthly climatology (TWSA) calculated using ~9 years of TWSA data GRACE anomalies converted from ‘cm’ to ‘km’ of eqv. water height Masks for study areas used to calculate the spatial averages Regional average storage anomalies (km) were then multiplied by the region’s area (km 2 ) resulting in: regional-average water storage volume change (km 3 ) Methodology
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Storage Temporal Characteristics: AMAZON
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Storage Temporal Characteristics: HIGH PLAINS
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Determining Drought Severity Assign a dryness threshold (zero) based on storage values with monthly climatology removed Negative = dry/drought Positive = wet/flood Water storage with the climatology removed:
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Drought Magnitude & Duration: AMAZON
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Drought Severity: AMAZON -2406 -3874 -1293 -3458 (DEFICIT)
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Drought Magnitude & Duration: HIGH PLAINS
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Drought Severity: HIGH PLAINS -622 -2323 (DEFICIT) -3827
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Average volume of water missing from region per drought event: Amazon: ① Dec 2004 – Dec 2005 drought ~ 298 km 3 Severest mo. (Aug2005): 499 km 3 ② Feb 2010 – Mar 2011 drought ~ 247 km 3 Severest mo. (Oct2010): 463 km 3 Drought: Impacts on Water Storage Southern Plains: ① Oct 2010 – Aug 2012 drought ~ 166 km 3 Severest mo. (Jun2012): 348 km 3
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Summary THIS IS WHAT GRACE SAW... A. Monthly observations of terrestrial water storage variability from GRACE satellites reveal extended periods of relatively dry conditions: A. Amazon River Basin in 2005 and 2010 B. Southern Plains region in 2006 and 2010-present B. Analyzing water storage anomalies without their 9-year monthly climatology allow us to define a dryness threshold for each region C. We can then determine GRACE-identified drought magnitude, duration, and severity in units of volume of water “missing” from the system during drought and how much is needed to “recover” D. Future work: A. Spatial characteristics within these regions B. Linking “missing” water storage numbers with water management C. Application to other regions
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Funding: NASA Graduate Student Research Project Many thanks to: Jay Famiglietti, J.T. Reager, Matt Rodell, Bailing Li, Caroline de Linage, Hiroko Beaudoing Contact Alys Thomas: thomasac@uci.edu Keyantash, J. A., and J. A. Dracup, 2002: The quantification of drought: An evaluation of drought indices. Bull. Amer. Meteor. Soc., 83, 1167–1180 Landerer F.W. and S. C. Swenson, Accuracy of scaled GRACE terrestrial water storage estimates. Water Resources Research 2012. Mishra, K., V. P. Singh, and V. R. Desai, Drought characterization: a probabilistic approach, Stoch Environ Res Risk Assess (2009) 23:41–55, DOI 10.1007/s00477-007-0194-2. Swenson S.C, D. P. Chambers, and J. Wahr: Estimating geocenter variations from a combination of GRACE and ocean model output. J Geophys. Res.- Solid Earth, Vol 113, Issue: B8, Article B08410. 2008. Hydrospheric and Biospheric Sciences
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