Diagnosing water variations within the Amazon basin using satellite data

ISI Document Delivery No.: 868AI Times Cited: 6 Cited Reference Count: 61 Cited References: Adler RF, 2003, J HYDROMETEOROL, V4, P1147, DOI 10.1175/1525-7541(2003)0042.0.CO;2 AghaKouchak A, 2011, J HYDROMETEOROL, V12, P274, DOI 10.1175/2010JHM1298.1 AghaKouchak A, 2011, J GEOPHYS RES-ATMOS, V116, DOI 10.1029/2010JD014741 Alsdorf D, 2007, GEOPHYS RES LETT, V34, DOI 10.1029/2007GL029447 Alsdorf DE, 2003, SCIENCE, V301, P1491, DOI 10.1126/science.1089802 Alsdorf DE, 2003, ANN ASSOC AM GEOGR, V93, P55, DOI 10.1111/1467-8306.93105 Betts AK, 2010, J ADV MODEL EARTH SY, V2, DOI 10.3894/JAMES.2010.2.6 Bjerklie DM, 2003, J HYDROL, V278, P17, DOI 10.1016/S0022-1694(03)00129-X Bullock A, 2003, HYDROL EARTH SYST SC, V7, P358 Chen JL, 2010, WATER RESOUR RES, V46, DOI 10.1029/2010WR009383 Chen JL, 2007, J GEODESY, V81, P237, DOI 10.1007/s00190-006-0055-7 Coe M. T., 2002, Journal of Geophysical Research, V107, DOI 10.1029/2001JD000740 Costa MH, 1997, J GEOPHYS RES-ATMOS, V102, P23973, DOI 10.1029/97JD01865 da Rocha HR, 2004, ECOL APPL, V14, P22, DOI DOI 10.1890/02-6001 Dai A, 2002, J HYDROMETEOROL, V3, P660, DOI 10.1175/1525-7541(2002)0032.0.CO;2 da Rocha HR, 2009, J GEOPHYS RES-BIOGEO, V114, DOI 10.1029/2007JG000640 Decharme B, 2008, J GEOPHYS RES-ATMOS, V113, DOI 10.1029/2007JD009376 Villar JCE, 2009, J HYDROL, V375, P297, DOI 10.1016/j.jhydrol.2009.03.004 Fekete BM, 2002, GLOBAL BIOGEOCHEM CY, V16, DOI 10.1029/1999GB001254 Fisher JB, 2008, REMOTE SENS ENVIRON, V112, P901, DOI 10.1016/j.rse.2007.06.025 Frappart F, 2010, HYDROL EARTH SYST SC, V14, P2443, DOI 10.5194/hess-14-2443-2010 Frappart F, 2008, J GEOPHYS RES-ATMOS, V113, DOI 10.1029/2007JD009438 Getirana ACV, 2010, HYDROL PROCESS, V24, P3219, DOI 10.1002/hyp.7747 GIBSON JK, 1997, REANALYSIS FINAL REP, V1 Goulden M.L., 2004, ECOL APPL, V14, P42, DOI DOI 10.1890/02-6008 Gu GJ, 2007, J CLIMATE, V20, P4033, DOI 10.1175/JCLI4227.1 Hsu KL, 1997, J APPL METEOROL, V36, P1176, DOI 10.1175/1520-0450(1997)0362.0.CO;2 Huffman GJ, 2009, GEOPHYS RES LETT, V36, DOI 10.1029/2009GL040000 Huffman GJ, 2007, J HYDROMETEOROL, V8, P38, DOI 10.1175/JHM560.1 Jimenez C, 2011, J GEOPHYS RES-ATMOS, V116, DOI 10.1029/2010JD014545 Joyce RJ, 2004, J HYDROMETEOROL, V5, P487, DOI 10.1175/1525-7541(2004)0052.0.CO;2 Kanamitsu M, 2002, B AM METEOROL SOC, V83, P1631, DOI [10.1175/Bams-83-11-1631, 10.1175/BAMS-83-11-1631] LEOPOLDO PR, 1987, EXPERIENTIA, V43, P222, DOI 10.1007/BF01945545 Marengo JA, 2005, CLIM DYNAM, V24, P11, DOI 10.1007/s00382-004-0461-6 Marengo JA, 2008, J CLIMATE, V21, P495, DOI 10.1175/2007JCLI1600.1 Monteith J L, 1965, Symp Soc Exp Biol, V19, P205 Mu Q, 2007, REMOTE SENS ENVIRON, V111, P519, DOI 10.1016/j.rse.2007.04.015 Papa F, 2010, J GEOPHYS RES-ATMOS, V115, DOI 10.1029/2009JD012674 Papa F, 2008, SURV GEOPHYS, V29, P297, DOI 10.1007/s10712-008-9036-0 Papa F, 2008, GEOPHYS RES LETT, V35, DOI 10.1029/2008GL033857 Prigent C, 2007, J GEOPHYS RES-ATMOS, V112, DOI 10.1029/2006JD007847 Rabus B, 2003, ISPRS J PHOTOGRAMM, V57, P241, DOI 10.1016/S0924-2716(02)00124-7 RICHEY JE, 1986, WATER RESOUR RES, V22, P756, DOI 10.1029/WR022i005p00756 Roads J, 2002, J HYDROMETEOROL, V3, P227, DOI 10.1175/1525-7541(2002)0032.0.CO;2 Rodell M, 2004, GEOPHYS RES LETT, V31, DOI 10.1029/2004GL020873 Rodell M, 1999, WATER RESOUR RES, V35, P2705, DOI 10.1029/1999WR900141 Seo KW, 2005, J GEODESY, V78, P442, DOI 10.1007/s00190-004-0410-5 Sheffield J, 2010, J HYDROMETEOROL, V11, P253, DOI 10.1175/2009JHM1176.1 Sheffield J, 2009, GEOPHYS RES LETT, V36, DOI 10.1029/2009GL037338 Sorooshian S, 2011, B AM METEOROL SOC, V92, P1353, DOI 10.1175/2011BAMS3158.1 Swenson S, 2006, GEOPHYS RES LETT, V33, DOI 10.1029/2005GL025285 Syed TH, 2005, GEOPHYS RES LETT, V32, DOI 10.1029/2005GL024851 Syed TH, 2009, J HYDROMETEOROL, V10, P22, DOI 10.1175/2008JHM993.1 Tang QH, 2010, J HYDROMETEOROL, V11, P156, DOI 10.1175/2009JHM1152.1 Villar JCE, 2009, INT J CLIMATOL, V29, P1574, DOI 10.1002/joc.1791 Vorosmarty CJ, 1996, WATER RESOUR RES, V32, P3137, DOI 10.1029/96WR01333 Wahr J, 2004, GEOPHYS RES LETT, V31, DOI 10.1029/2004GL019779 Wang KC, 2010, J GEOPHYS RES-ATMOS, V115, DOI 10.1029/2009JD013671 Zeng N, 1999, J GEOPHYS RES-ATMOS, V104, P9097, DOI 10.1029/1998JD200088 Zeng N, 2008, ENVIRON RES LETT, V3, DOI 10.1088/1748-9326/3/1/014002 Zhang K, 2010, WATER RESOUR RES, V46, DOI 10.1029/2009WR008800 Azarderakhsh, Marzieh Rossow, William B. Papa, Fabrice Norouzi, Hamidreza Khanbilvardi, Reza Papa, Fabrice/D-3695-2009 Papa, Fabrice/0000-0001-6305-6253 NASA Energy and Water cycle Study [NNXD7AO90G]; NOAA-CREST [NA06OAR4810162] NASA Energy and Water cycle Study, grant NNXD7AO90G and NOAA-CREST, grant NA06OAR4810162, funding supported this research. The authors would like to thank Q. Mu, K. Zhang and J. Sheffield for sharing their evapotranspiration data sets and the science teams of the GPCP, CMORPH, PERSIANN and TRMM and GRACE for providing public data source. 6 AMER GEOPHYSICAL UNION WASHINGTON J GEOPHYS RES-ATMOSThe components of the Amazon water budget and their spatiotemporal variability are diagnosed using monthly averaged remote sensing-based data products for the period September 2002-December 2006. The large Amazon basin is divided into 14 smaller watersheds, and for each of these sub-basins, fresh water discharge is estimated from the water balance equation using satellite data products. The purpose of this study is to learn how to apply satellite data with global coverage over the large tropical regions; therefore several combinations of remote sensing estimates including total water storage changes, precipitation and evapotranspiration. The results are compared to gauge-based measurements and the best spatiotemporal agreement between estimated and observed runoff is within 1 mm/d for the combination of precipitation from the GPCP and the Montana evapotranspiration product. Mean annual precipitation, evapotranspiration and runoff for the whole basin are estimated to be 6.3, 2.27 and 3.02 mm/d respectively but also show large spatial and temporal variations at sub-basin scale. Using the most consistent data combination, the seasonal dynamics of the water budget within the Amazon system are examined. Agreement between satellite based and in situ runoff is improved when lag-times between sub-basins are included (RMSE from 0.98 to 0.61 mm/d). We estimate these lag times based on satellite-inferred inundation extents. The results reveal not only variations of the basin forcing but also the complex response of the inter-connected sub-basin (SB) water budgets. Inter-annual and inter-SB variation of the water components are investigated and show large anomalies in northwestern and eastern downstream SBs; aggregate behavior of the whole Amazon is more complex than can be represented by a simple integral of the forcing over the whole river system