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1. Introduction The soil wetness condition is a very important factor to describe the regional soil water-storage capacity, which determines the relative magnitude of surface runoff from precipitation. Thus, for hydrological applications, especially flood simulation and early warning, the soil wetness condition is a very critical variable. However, it is difficult to measure the regional soil wetness condition by field measurements. A passive microwave radiometer at C or L band can provide soil
1. Introduction The soil wetness condition is a very important factor to describe the regional soil water-storage capacity, which determines the relative magnitude of surface runoff from precipitation. Thus, for hydrological applications, especially flood simulation and early warning, the soil wetness condition is a very critical variable. However, it is difficult to measure the regional soil wetness condition by field measurements. A passive microwave radiometer at C or L band can provide soil
found when k sat is multiplied by a factor of 10 and 100, respectively. The effects on soil moisture and evapotranspiration are opposite. In fact, an increase of soil saturated hydraulic conductivity leads to a decrease of SM and LE in consequence and owing to an increase in RET. A similar result is obtained if the Brooks and Corey index, which affects percolation, is multiplied by 2. The parameters that affect the representative equilibrium temperature most are the saturated hydraulic
found when k sat is multiplied by a factor of 10 and 100, respectively. The effects on soil moisture and evapotranspiration are opposite. In fact, an increase of soil saturated hydraulic conductivity leads to a decrease of SM and LE in consequence and owing to an increase in RET. A similar result is obtained if the Brooks and Corey index, which affects percolation, is multiplied by 2. The parameters that affect the representative equilibrium temperature most are the saturated hydraulic
of many recent research efforts ( McCabe et al. 2008 ) because of their potential to provide spatially continuous and temporally recurrent estimates over regional to global scales ( Alsdorf and Lettenmaier 2003 ). Precipitation is regularly retrieved from multisensor microwave and infrared data using a variety of techniques (e.g., Joyce et al. 2004 ). One of the recent datasets is the Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis (TMPA), which is designed to
of many recent research efforts ( McCabe et al. 2008 ) because of their potential to provide spatially continuous and temporally recurrent estimates over regional to global scales ( Alsdorf and Lettenmaier 2003 ). Precipitation is regularly retrieved from multisensor microwave and infrared data using a variety of techniques (e.g., Joyce et al. 2004 ). One of the recent datasets is the Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis (TMPA), which is designed to
: Attenuation of soil microwave emissivity by corn and soybeans at 1.4 and 5 GHz . IEEE Trans. Geosci. Remote Sens. , 28 , 978 – 980 . Jackson, T. J. , and Schmugge T. J. , 1991 : Vegetation effects on the microwave emission of soils . Remote Sens. Environ. , 36 , 203 – 212 . Jackson, T. J. , Vine D. M. L. , Hsu A. Y. , Oldak A. , Starks P. J. , Swift C. T. , Isham J. D. , and Haken M. , 1999 : Soil moisture mapping at regional scales using microwave radiometry: The southern
: Attenuation of soil microwave emissivity by corn and soybeans at 1.4 and 5 GHz . IEEE Trans. Geosci. Remote Sens. , 28 , 978 – 980 . Jackson, T. J. , and Schmugge T. J. , 1991 : Vegetation effects on the microwave emission of soils . Remote Sens. Environ. , 36 , 203 – 212 . Jackson, T. J. , Vine D. M. L. , Hsu A. Y. , Oldak A. , Starks P. J. , Swift C. T. , Isham J. D. , and Haken M. , 1999 : Soil moisture mapping at regional scales using microwave radiometry: The southern
over 2000 m, during winter and spring, and although the snowmelt season extends from April to June, the typically mild Mediterranean winters produce several accumulation–melting cycles before the final spring melting. Annual precipitation fluctuates widely and can range from 400 to 1500 mm, with a high spatial variability throughout the area due to topographic effects. The average temperature ranges from −5° to 5°C during the snow season, although minimum values of −20°C can be found at certain
over 2000 m, during winter and spring, and although the snowmelt season extends from April to June, the typically mild Mediterranean winters produce several accumulation–melting cycles before the final spring melting. Annual precipitation fluctuates widely and can range from 400 to 1500 mm, with a high spatial variability throughout the area due to topographic effects. The average temperature ranges from −5° to 5°C during the snow season, although minimum values of −20°C can be found at certain
and z 0h to calculate u * , θ * , and H cal from Eqs. (2a) – (2e) , 4) use u * and θ * to calculate kB −1 from kB −1 = ln( z 0m / z 0h ) according to each of the four z 0h schemes, and 5) repeat steps 2–4 until the cost function is minimized. c. Noah LSM The Noah LSM is widely used and forms the land component of the regional and global weather forecasting models at the National Centers for Environmental Prediction (NCEP) and of the Weather Research and Forecasting model (WRF
and z 0h to calculate u * , θ * , and H cal from Eqs. (2a) – (2e) , 4) use u * and θ * to calculate kB −1 from kB −1 = ln( z 0m / z 0h ) according to each of the four z 0h schemes, and 5) repeat steps 2–4 until the cost function is minimized. c. Noah LSM The Noah LSM is widely used and forms the land component of the regional and global weather forecasting models at the National Centers for Environmental Prediction (NCEP) and of the Weather Research and Forecasting model (WRF
