Search Results
surface model used in this study to simulate soil moisture is the JULES model—a widely used tiled model of subgrid heterogeneity that simulates water and energy fluxes between a vertical profile of soil layers, vegetation, and the atmosphere ( Best et al. 2011 ). The JULES model uses meteorological forcing data, surface land cover data, soil properties data, and values for prognostic variables. The soil properties data were derived from the Digital Atlas of Australian Soils ( McKenzie et al. 2000
surface model used in this study to simulate soil moisture is the JULES model—a widely used tiled model of subgrid heterogeneity that simulates water and energy fluxes between a vertical profile of soil layers, vegetation, and the atmosphere ( Best et al. 2011 ). The JULES model uses meteorological forcing data, surface land cover data, soil properties data, and values for prognostic variables. The soil properties data were derived from the Digital Atlas of Australian Soils ( McKenzie et al. 2000
the error to the physics of the model ( Reichle 2008 ). One way to quantify this model error is by including the uncertainties in both the forcing of the data and the model formulation ( Reichle 2008 ). In this study, a representative value of the variability was employed to perturb the forcing parameters. Considering that the time resolution of the meteorological data was 5 min and according to the time step of the modeling, 1 h, the standard deviation of the data was calculated every hour. The
the error to the physics of the model ( Reichle 2008 ). One way to quantify this model error is by including the uncertainties in both the forcing of the data and the model formulation ( Reichle 2008 ). In this study, a representative value of the variability was employed to perturb the forcing parameters. Considering that the time resolution of the meteorological data was 5 min and according to the time step of the modeling, 1 h, the standard deviation of the data was calculated every hour. The
explicitly the horizontal variability of soil moisture within a given surface element, which leads to conceptually improved treatments of subsurface moisture dynamics, evaporation, and runoff ( Koster et al. 2000 ). For this study CLSM is set up on the 36-km SMAP EASE grid and spun up for 18 years prior to the SMOS observation period using surface meteorological forcing data at ½° × ⅔° spatial and hourly temporal resolution from the Modern-Era Retrospective Analysis for Research and Applications (MERRA
explicitly the horizontal variability of soil moisture within a given surface element, which leads to conceptually improved treatments of subsurface moisture dynamics, evaporation, and runoff ( Koster et al. 2000 ). For this study CLSM is set up on the 36-km SMAP EASE grid and spun up for 18 years prior to the SMOS observation period using surface meteorological forcing data at ½° × ⅔° spatial and hourly temporal resolution from the Modern-Era Retrospective Analysis for Research and Applications (MERRA
arid and semiarid areas have been affected by a number of nonclimatic forcings, such as heavy groundwater abstraction for irrigation purpose. These often resulted in lowering of the groundwater table, reflecting a loss of aquifer storage ( Green et al. 2011 ). If groundwater abstraction exceeds the net groundwater recharge over prolonged periods, persistent groundwater depletion occurs ( Gleeson et al. 2010 ). For such cases, Wada et al. (2012) explicitly use the term “nonrenewable groundwater
arid and semiarid areas have been affected by a number of nonclimatic forcings, such as heavy groundwater abstraction for irrigation purpose. These often resulted in lowering of the groundwater table, reflecting a loss of aquifer storage ( Green et al. 2011 ). If groundwater abstraction exceeds the net groundwater recharge over prolonged periods, persistent groundwater depletion occurs ( Gleeson et al. 2010 ). For such cases, Wada et al. (2012) explicitly use the term “nonrenewable groundwater
, with a north–south distance of about 100 km and an east–west distance of about 80 km. The lake connects to the Yangtze River to the north through a funnel-shaped area, with a wide mouth to the south and narrow head to the north. Thus, in the lake area, a north–south wind blows on most days. Only in July and August does the monsoon from the Indian Ocean force a south–north wind. The connecting funnel area yields a higher wind speed in the narrow head to the north and the wind speed decreases from
, with a north–south distance of about 100 km and an east–west distance of about 80 km. The lake connects to the Yangtze River to the north through a funnel-shaped area, with a wide mouth to the south and narrow head to the north. Thus, in the lake area, a north–south wind blows on most days. Only in July and August does the monsoon from the Indian Ocean force a south–north wind. The connecting funnel area yields a higher wind speed in the narrow head to the north and the wind speed decreases from
