Abstract
The increasing availability and reliability of satellite remote sensing products [e.g., precipitation (P), evapotranspiration (ET), and the total water storage change (TWSC)] make it feasible to estimate the global terrestrial water budget at fine spatial resolution. In this study, we start from a reference water budget dataset that combines all available data sources, including satellite remote sensing, land surface model (LSM) and reanalysis, and investigate the roles of different non-satellite remote sensing products in closing the terrestrial water budget through a sensitivity analysis by removing/replacing one or more categories of products during the budget estimation. We also study the differences made by various satellite products for the same budget variable. We find that the gradual removal of non-satellite data sources will generally worsen the closure errors in the budget estimates, and remote sensing retrievals of P, ET, and TWSC together with runoff (R) from LSM give the worst closure errors. The gauge-corrected satellite precipitation helps to improve the budget closure (4.2–9 % non-closure errors of annual mean precipitation) against using the non-gauge-corrected precipitation (7.6–10.4 % non-closure errors). At last, a data assimilation technique, the constrained Kalman filter, is applied to enforce the water balance, and it is found that the satellite remote sensing products, though with worst closure, yield comparable budget estimates in the constrained system to the reference data. Overall, this study provides a first comparison between the water budget closure using the satellite remote sensing products and a full combination of remote sensing, LSM, and reanalysis products on a quasi-global basis. This study showcases the capability and potential of the satellite remote sensing in closing the terrestrial water budget at fine spatial resolution if properly constrained.
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References
Adler RF, Kidd C, Petty G, Morissey M, Goodman HM (2001) Intercomparison of global precipitation products: the third Precipitation Intercomparison Project (PIP-3). Bull Am Meteorol Soc 82:1377–1396
Bytheway JL, Kummerow CD (2013) Inferring the uncertainty of satellite precipitation estimates in data-sparse regions over land. J Geophys Res Atmos 118:9524–9533. doi:10.1002/jgrd.50607
Dirmeyer PA, Gao X, Zhao M, Guo Z, Oki T, Hanasaki N (2006) GSWP-2: multimodel analysis and implications for our perception of the land surface. Bull Am Meteorol Soc 87:1381–1397. doi:10.1175/BAMS-87-10-1381
Durand M, Fu L-L, Lettenmaier DP, Alsdorf DE, Rodriguez E, Esteban-Fernandez D (2010) The surface water and ocean topography mission: observing terrestrial surface water and oceanic submesoscale eddies. Proc IEEE 98:766–779
Famiglietti JS (2014) The global groundwater crisis. Nat Clim Change 4:945–948. doi:10.1038/nclimate2425
Fisher JB, Tu KP, Baldocchi DD (2008) Global estimates of the land–atmosphere water flux based on monthly AVHRR and ISLSCP-II data, validated at 16 FLUXNET sites. Remote Sens Environ 112:901–919
Funk CC et al (2014) A quasi-global precipitation time series for drought monitoring. US Geol Surv Data Ser. doi:10.3133/ds832
Gao H, Tang Q, Ferguson CR, Wood EF, Lettenmaier DP (2010) Estimating the water budget of major US river basins via remote sensing. Int J Remote Sens 31:3955–3978
Haddeland I et al (2011) Multimodel estimate of the global terrestrial water balance: setup and first results. J Hydrometeorol 12:869–884
Hong Y, Gochis D, Cheng J-T, Hsu K-L, Sorooshian S (2007) Evaluation of PERSIANN-CCS rainfall measurement using the NAME event rain gauge network. J Hydrometeorol 8:469–482
Huffman GJ et al (2007) The TRMM multisatellite precipitation analysis (TMPA): quasi-global, multiyear, combined-sensor precipitation estimates at fine scales. J Hydrometeorol 8:38–55
Huffman GJ, Adler RF, Bolvin DT, Nelkin EJ (2010) The TRMM multi-satellite precipitation analysis (TMPA). Springer, Satellite rainfall applications for surface hydrology, pp 3–22
Jacob T, Wahr J, Pfeffer WT, Swenson S (2012) Recent contributions of glaciers and ice caps to sea level rise. Nature 482:514–518
Joyce RJ, Janowiak JE, Arkin PA, Xie P (2004) CMORPH: a method that produces global precipitation estimates from passive microwave and infrared data at high spatial and temporal resolution. J Hydrometeorol 5:487–503
Landerer FW, Swenson SC (2012) Accuracy of scaled GRACE terrestrial water storage estimates. Water Resour Res 48(4)
Luo L, Wood EF, Pan M (2007) Bayesian merging of multiple climate model forecasts for seasonal hydrological predictions. J Geophys Res Atmos 1984–2012:112
Miralles DG, Holmes TRH, De Jeu RAM, Gash JH, Meesters AGCA, Dolman AJ (2011) Global land-surface evaporation estimated from satellite-based observations. Hydrol Earth Syst Sci 15:453–469. doi:10.5194/hess-15-453-2011
Mu Q, Heinsch FA, Zhao M, Running SW (2007) Development of a global evapotranspiration algorithm based on MODIS and global meteorology data. Remote Sens Environ 111:519–536
Oki T, Musiake K, Matsuyama H, Masuda K (1995) Global atmospheric water balance and runoff from large river basins. Hydrol Process 9:655–678
Paiva RCD, Durand MT, Hossain F (2015) Spatiotemporal interpolation of discharge across a river network by using synthetic SWOT satellite data. Water Resour Res 51:430–449
Pan M, Sahoo AK, Troy TJ, Vinukollu RK, Sheffield J, Wood EF (2012) Multisource estimation of long-term terrestrial water budget for major global river basins. J Clim 25:3191–3206
Pavelsky TM, Durand MT, Andreadis KM, Beighley RE, Paiva RCD, Allen GH, Miller ZF (2014) Assessing the potential global extent of SWOT river discharge observations. J Hydrol 519:1516–1525
Rienecker MM et al (2011) MERRA: NASA’s modern-era retrospective analysis for research and applications. J Clim 24:3624–3648. doi:10.1175/JCLI-D-11-00015.1
Rodell M et al (2015) The observed state of the water cycle in the early 21st century. J Clim. doi:10.1175/JCLI-D-14-00555.1
Sahoo AK, Pan M, Troy TJ, Vinukollu RK, Sheffield J, Wood EF (2011) Reconciling the global terrestrial water budget using satellite remote sensing. Remote Sens Environ 115:1850–1865
Sakumura C, Bettadpur S, Bruinsma S (2014) Ensemble prediction and intercomparison analysis of GRACE time-variable gravity field models. Geophys Res Lett 41:1389–1397
Schneider U, Becker A, Finger P, Meyer-Christoffer A, Ziese M, Rudolf B (2014) GPCC’s new land surface precipitation climatology based on quality-controlled in situ data and its role in quantifying the global water cycle. Theor Appl Climatol 115:15–40. doi:10.1007/s00704-013-0860-x
Sheffield J, Wood EF (2007) Characteristics of global and regional drought, 1950–2000: analysis of soil moisture data from off-line simulation of the terrestrial hydrologic cycle. J Geophys Res Atmos 112:D17115. doi:10.1029/2006JD008288
Sheffield J, Goteti G, Wood EF (2006) Development of a 50-year high-resolution global dataset of meteorological forcings for land surface modeling. J Clim 19:3088–3111
Sheffield J, Ferguson CR, Troy TJ, Wood EF, McCabe MF (2009) Closing the terrestrial water budget from satellite remote sensing. Geophys Res Lett 36(7)
Simmons A, Uppala S, Dee D, Kobayashi S (2006) ERA-interim: new ECMWF reanalysis products from 1989 onwards. ECMWF Newsl 110:26–35
Tapley BD, Bettadpur S, Ries JC, Thompson PF, Watkins MM (2004) GRACE measurements of mass variability in the Earth system. Science 305:503–505
Thomas AC, Reager JT, Famiglietti JS, Rodell M (2014) A GRACE-based water storage deficit approach for hydrological drought characterization. Geophys Res Lett 41:1537–1545
Tian Y, Peters-Lidard CD (2010) A global map of uncertainties in satellite-based precipitation measurements. Geophys Res Lett 37(24)
Trenberth KE, Smith L, Qian T, Dai A, Fasullo J (2007) Estimates of the global water budget and its annual cycle using observational and model data. J Hydrometeorol 8:758–769
Troy TJ, Sheffield J, Wood EF (2011) Estimation of the terrestrial water budget over northern eurasia through the use of multiple data sources. J Clim 24:3272–3293. doi:10.1175/2011JCLI3936.1
Vinukollu RK, Meynadier R, Sheffield J, Wood EF (2011) Multi-model, multi-sensor estimates of global evapotranspiration: climatology, uncertainties and trends. Hydrol Process 25:3993–4010. doi:10.1002/hyp.8393
Wahr J, Swenson S, Zlotnicki V, Velicogna I (2004) Time-variable gravity from GRACE: first results. Geophys Res Lett 31(11)
Wang S, Huang J, Li J, Rivera A, McKenney DW, Sheffield J (2014) Assessment of water budget for sixteen large drainage basins in Canada. J Hydrol 512:1–15
Weedon GP et al (2011) Creation of the WATCH forcing data and its use to assess global and regional reference crop evaporation over land during the twentieth century. J Hydrometeorol 12:823–848
Acknowledgments
This paper is part of the special issue of the ISSI workshop on remote sensing and water resources. This work is supported by NASA research grants NNX08AN40A “Developing Consistent Earth System Data Records for the Global Terrestrial Water Cycle” and NNX09AK35G “Development and diagnostic analysis of a multi-decadal global evaporation product for NEWS”
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Zhang, Y., Pan, M. & Wood, E.F. On Creating Global Gridded Terrestrial Water Budget Estimates from Satellite Remote Sensing. Surv Geophys 37, 249–268 (2016). https://doi.org/10.1007/s10712-015-9354-y
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DOI: https://doi.org/10.1007/s10712-015-9354-y