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1. Introduction A land surface model (LSM) is one of the components of a climate model. It computes the momentum, heat, and gas exchange between the underlying surface and the atmosphere. The roughness length (RL) and zero-plane displacement (ZPD) are the external parameters in LSMs to describe the frictional effect of the underlying surface. Sensitive studies have shown that the RL has a strong impact on the modeling results ( Sud and Smith 1985 ; Sud et al. 1988 ), particularly at regional
1. Introduction A land surface model (LSM) is one of the components of a climate model. It computes the momentum, heat, and gas exchange between the underlying surface and the atmosphere. The roughness length (RL) and zero-plane displacement (ZPD) are the external parameters in LSMs to describe the frictional effect of the underlying surface. Sensitive studies have shown that the RL has a strong impact on the modeling results ( Sud and Smith 1985 ; Sud et al. 1988 ), particularly at regional
hydrological models (GHMs) (see a comprehensive overview by Haddeland et al. 2011 ), land surface hydrologic processes are often treated in a rather conceptual way and surface energy balance critical to the evaporation process is not considered. In contrast, the land surface models (LSMs) used for climate modeling studies solve both water and energy balances. Since land surface hydrological processes exert profound influence on the overlying atmosphere ( Shukla and Mintz 1982 ; Koster et al. 2004 ), LSMs
hydrological models (GHMs) (see a comprehensive overview by Haddeland et al. 2011 ), land surface hydrologic processes are often treated in a rather conceptual way and surface energy balance critical to the evaporation process is not considered. In contrast, the land surface models (LSMs) used for climate modeling studies solve both water and energy balances. Since land surface hydrological processes exert profound influence on the overlying atmosphere ( Shukla and Mintz 1982 ; Koster et al. 2004 ), LSMs
1. Introduction Land surface models (LSMs) are the components of global climate models (GCMs) that simulate land surface processes, such as the absorption and partitioning of radiation, moisture, and carbon. They are typically provided with meteorological conditions as inputs (from a boundary layer atmospheric model) and produce outputs that include latent and sensible heat fluxes, CO 2 fluxes, reflected solar and emitted longwave radiation, surface runoff, and deep soil drainage. They have
1. Introduction Land surface models (LSMs) are the components of global climate models (GCMs) that simulate land surface processes, such as the absorption and partitioning of radiation, moisture, and carbon. They are typically provided with meteorological conditions as inputs (from a boundary layer atmospheric model) and produce outputs that include latent and sensible heat fluxes, CO 2 fluxes, reflected solar and emitted longwave radiation, surface runoff, and deep soil drainage. They have
methane emissions and global carbon storage and cycling. Plant decomposition, plant carbon fixation, and methane production and function are highly dependent on water table and soil temperature ( Zhang et al. 2002 ). Soil–vegetation–atmosphere transfer models (SVAT) offer significant capabilities to predict wetland water dynamics and soil temperature and moisture states. By constraining both the water and the energy balance, SVATs are routinely used to model land surface fluxes, root zone soil water
methane emissions and global carbon storage and cycling. Plant decomposition, plant carbon fixation, and methane production and function are highly dependent on water table and soil temperature ( Zhang et al. 2002 ). Soil–vegetation–atmosphere transfer models (SVAT) offer significant capabilities to predict wetland water dynamics and soil temperature and moisture states. By constraining both the water and the energy balance, SVATs are routinely used to model land surface fluxes, root zone soil water
1. Introduction To simulate runoff and surface energy and moisture fluxes, land surface models (LSMs) require an appropriate description of soil water content. Most LSMs calculate soil water content by numerically solving the Richards equation: where is the soil volumetric water content, is soil water pressure head ( , where is capillary potential, ), is the hydraulic conductivity, and is a sink term accounting for the effect of plant root uptake. To simulate soil water content
1. Introduction To simulate runoff and surface energy and moisture fluxes, land surface models (LSMs) require an appropriate description of soil water content. Most LSMs calculate soil water content by numerically solving the Richards equation: where is the soil volumetric water content, is soil water pressure head ( , where is capillary potential, ), is the hydraulic conductivity, and is a sink term accounting for the effect of plant root uptake. To simulate soil water content
suboptimal ( Dirmeyer et al. 2018 ). The necessary observational datasets for validation are only recently becoming available; datasets that combine collocated measurements of land surface states, surface fluxes, near-surface meteorology, and properties of the atmospheric column. Early field campaigns (e.g., Sellers et al. 1992 , 1995 ; Famiglietti et al. 1999 ; Jackson and Hsu 2001 ; Andreae et al. 2002 ) provided observations that helped advance theory and model parameterization development, but
suboptimal ( Dirmeyer et al. 2018 ). The necessary observational datasets for validation are only recently becoming available; datasets that combine collocated measurements of land surface states, surface fluxes, near-surface meteorology, and properties of the atmospheric column. Early field campaigns (e.g., Sellers et al. 1992 , 1995 ; Famiglietti et al. 1999 ; Jackson and Hsu 2001 ; Andreae et al. 2002 ) provided observations that helped advance theory and model parameterization development, but
observational studies agree that irrigation application reduces temperature and increases humidity via the repartitioning of latent heat flux and sensible heat flux ( Moore and Rojstaczer 2002 ; Adegoke et al. 2003 , 2007 ; Douglas et al. 2006 ; Bonfils and Lobell 2007 ; DeAngelis et al. 2010 ; Kueppers and Snyder 2012 ; Jiang et al. 2014 ). The ability of a numerical model to reproduce irrigation’s modifications to the surface energy balance is therefore essential for studies of land-use change
observational studies agree that irrigation application reduces temperature and increases humidity via the repartitioning of latent heat flux and sensible heat flux ( Moore and Rojstaczer 2002 ; Adegoke et al. 2003 , 2007 ; Douglas et al. 2006 ; Bonfils and Lobell 2007 ; DeAngelis et al. 2010 ; Kueppers and Snyder 2012 ; Jiang et al. 2014 ). The ability of a numerical model to reproduce irrigation’s modifications to the surface energy balance is therefore essential for studies of land-use change
1. Introduction Given the pressing demand for local climate change information by resource management and impact assessments, both global and regional climate models have incorporated increasingly sophisticated physics representations run at higher resolutions. As a key coupled component, land surface models (LSMs) have also evolved from simple bucket to comprehensive dynamic hydrology–ecosystem representations ( Gochis et al. 2004 ). Most current LSMs, however, still contain various defective
1. Introduction Given the pressing demand for local climate change information by resource management and impact assessments, both global and regional climate models have incorporated increasingly sophisticated physics representations run at higher resolutions. As a key coupled component, land surface models (LSMs) have also evolved from simple bucket to comprehensive dynamic hydrology–ecosystem representations ( Gochis et al. 2004 ). Most current LSMs, however, still contain various defective
hydroclimatic regions: radiation limited in the north and soil moisture limited in the south ( Teuling et al. 2009 ). However, the location and extent of the related transition zone is uncertain. The role of these land surface processes in heat waves and warm temperature extremes is reflected in atmospheric models. For example, the simulation of features such as the strength, location, and duration of the 2003 heat wave is influenced by soil moisture state ( Bisselink et al. 2011 ; Ferranti and Viterbo
hydroclimatic regions: radiation limited in the north and soil moisture limited in the south ( Teuling et al. 2009 ). However, the location and extent of the related transition zone is uncertain. The role of these land surface processes in heat waves and warm temperature extremes is reflected in atmospheric models. For example, the simulation of features such as the strength, location, and duration of the 2003 heat wave is influenced by soil moisture state ( Bisselink et al. 2011 ; Ferranti and Viterbo
1. Introduction Mesoscale regional climate models (RCMs) are recognized as an essential tool to address scientific issues concerning climate variability, changes, and impacts at regional to local scales. As the model resolution increases, land surface models (LSMs) coupled with RCMs need to incorporate more comprehensive physical processes and their nonlinear interactions. This has been the trend in recent developments ( Stieglitz et al. 1997 ; Chen and Kumar 2001 ; Warrach et al. 2002 ; Niu
1. Introduction Mesoscale regional climate models (RCMs) are recognized as an essential tool to address scientific issues concerning climate variability, changes, and impacts at regional to local scales. As the model resolution increases, land surface models (LSMs) coupled with RCMs need to incorporate more comprehensive physical processes and their nonlinear interactions. This has been the trend in recent developments ( Stieglitz et al. 1997 ; Chen and Kumar 2001 ; Warrach et al. 2002 ; Niu
