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the failure of energy balance closure (e.g., Wilson et al. 2002 ) hinder the use of the residual method from the assessment of annual E WC . Alternatively, physically based E WC models have been used and validated with the observed interception rainfall in various climate and vegetation types ( Rutter et al. 1975 ; Gash 1979 ; Link et al. 2004 ). The Rutter-type models, in particular, have been widely adopted for E WC algorithms in many hydrological models and land surface models (LSMs) (e
the failure of energy balance closure (e.g., Wilson et al. 2002 ) hinder the use of the residual method from the assessment of annual E WC . Alternatively, physically based E WC models have been used and validated with the observed interception rainfall in various climate and vegetation types ( Rutter et al. 1975 ; Gash 1979 ; Link et al. 2004 ). The Rutter-type models, in particular, have been widely adopted for E WC algorithms in many hydrological models and land surface models (LSMs) (e
Biosphere model (SiB) (GLCC.S; Sellers et al. 1986 ) and GLCC.I ( Loveland et al. 2000 ) were produced with the same satellite data, algorithm, and other factors, except for the land classification legend. Snow and ice were classified with a 12-month maximum value normalized difference vegetation index (NDVI) composite, which is less than the threshold values that depend on continental characteristics. Wetland was classified with an unsupervised cluster analysis that inputs the temporal NDVI composite
Biosphere model (SiB) (GLCC.S; Sellers et al. 1986 ) and GLCC.I ( Loveland et al. 2000 ) were produced with the same satellite data, algorithm, and other factors, except for the land classification legend. Snow and ice were classified with a 12-month maximum value normalized difference vegetation index (NDVI) composite, which is less than the threshold values that depend on continental characteristics. Wetland was classified with an unsupervised cluster analysis that inputs the temporal NDVI composite
, and Shige et al. (2009) have used them in a global estimation algorithm showing good skills at high latitudes where normally other algorithms based on lower frequencies fail. The reader is referred to Levizzani et al. (2007) for a review of the basic principles of satellite precipitation estimation methods. The Advanced Microwave Sounding Unit-B (AMSU-B) on board the National Oceanic and Atmospheric Administration polar-orbiting satellites is a PMW sensor based on the high frequency and high
, and Shige et al. (2009) have used them in a global estimation algorithm showing good skills at high latitudes where normally other algorithms based on lower frequencies fail. The reader is referred to Levizzani et al. (2007) for a review of the basic principles of satellite precipitation estimation methods. The Advanced Microwave Sounding Unit-B (AMSU-B) on board the National Oceanic and Atmospheric Administration polar-orbiting satellites is a PMW sensor based on the high frequency and high
significant positive correlation. Price (1977) attempted to develop maps of the thermal inertia distribution using daily maximum and minimum radiative temperatures of the earth’s surface measured by satellites and other meteorological data. Subsequently, remote sensing researchers have proposed various algorithms for mapping the thermal inertia distribution mainly using the radiative surface temperature (“surface temperature” hereafter) (e.g., Price 1985 ; Xue and Cracknell 1995 ; Sobrino and El
significant positive correlation. Price (1977) attempted to develop maps of the thermal inertia distribution using daily maximum and minimum radiative temperatures of the earth’s surface measured by satellites and other meteorological data. Subsequently, remote sensing researchers have proposed various algorithms for mapping the thermal inertia distribution mainly using the radiative surface temperature (“surface temperature” hereafter) (e.g., Price 1985 ; Xue and Cracknell 1995 ; Sobrino and El
algorithms for the retrieval of large-scale fluctuations in subsurface hydrology from satellite gravity measurements ( Rodell and Famiglietti 1999 , 2002 ) as part of global ocean mass assessment ( Cazenave et al. 2001 ), crustal motion projections ( Mangiarotti et al. 2001 ), and as a land surface analysis used for comparison and validation in other studies (e.g., Georgakakos and Smith 2001 ; Nakaegawa et al. 2003 ). In summary, the pilot phase of GSWP proved the viability of producing global land
algorithms for the retrieval of large-scale fluctuations in subsurface hydrology from satellite gravity measurements ( Rodell and Famiglietti 1999 , 2002 ) as part of global ocean mass assessment ( Cazenave et al. 2001 ), crustal motion projections ( Mangiarotti et al. 2001 ), and as a land surface analysis used for comparison and validation in other studies (e.g., Georgakakos and Smith 2001 ; Nakaegawa et al. 2003 ). In summary, the pilot phase of GSWP proved the viability of producing global land
other drivers of changes in ecosystems and the water cycle (not accounted for in this study) can be also important: (i) feedbacks between vegetation change and fires, as changes in fire regimes may affect ecosystems and the climate-induced conversion of forests to grasslands can modify fire regimes ( Lewis 2006 ) (MAPSS contains a simplified fire algorithm); (ii) hurricane and extreme events, which can be modified by climate change and have impacts on the structure of forests (i.e., stem density and
other drivers of changes in ecosystems and the water cycle (not accounted for in this study) can be also important: (i) feedbacks between vegetation change and fires, as changes in fire regimes may affect ecosystems and the climate-induced conversion of forests to grasslands can modify fire regimes ( Lewis 2006 ) (MAPSS contains a simplified fire algorithm); (ii) hurricane and extreme events, which can be modified by climate change and have impacts on the structure of forests (i.e., stem density and
. Amer. Meteor. Soc. , 78 , 5 – 20 . Huffman, G. J. , and Coauthors , 2007 : The TRMM Multisatellite Precipitation Analysis (TMPA): Quasi-global, multiyear, combined-sensor precipitation estimates at fine scales . J. Hydrometeor. , 8 , 38 – 55 . Jackson, T. J. , 1993 : Measuring surface soil moisture using passive microwave remote sensing . Hydrol. Processes , 7 , 139 – 152 . Jackson, T. J. , Hurkmans R. , Hsu A. , and Cosh M. H. , 2004 : Soil moisture algorithm validation
. Amer. Meteor. Soc. , 78 , 5 – 20 . Huffman, G. J. , and Coauthors , 2007 : The TRMM Multisatellite Precipitation Analysis (TMPA): Quasi-global, multiyear, combined-sensor precipitation estimates at fine scales . J. Hydrometeor. , 8 , 38 – 55 . Jackson, T. J. , 1993 : Measuring surface soil moisture using passive microwave remote sensing . Hydrol. Processes , 7 , 139 – 152 . Jackson, T. J. , Hurkmans R. , Hsu A. , and Cosh M. H. , 2004 : Soil moisture algorithm validation
