Search Results
1. Introduction Surface and subsurface flow routings (FR) are important hydrological processes that define the movement of water, nutrients, and sediments on the land surface ( Quinn et al. 1991 ; Orlandini and Moretti 2009 ; Camporese et al. 2010 ). Process-based and distributed hydrological models, such as Distributed Hydrology Soil Vegetation Model (DHSVM; Wigmosta et al. 1994 ) and Regional Hydro-Ecologic Simulation System (RHESSys; Band 1993 ; Tague and Band 2004 ), can
1. Introduction Surface and subsurface flow routings (FR) are important hydrological processes that define the movement of water, nutrients, and sediments on the land surface ( Quinn et al. 1991 ; Orlandini and Moretti 2009 ; Camporese et al. 2010 ). Process-based and distributed hydrological models, such as Distributed Hydrology Soil Vegetation Model (DHSVM; Wigmosta et al. 1994 ) and Regional Hydro-Ecologic Simulation System (RHESSys; Band 1993 ; Tague and Band 2004 ), can
watersheds in Canada. The third dataset was derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) satellite observation. The other three datasets were the outcome of three different LSMs, including the Community Land Model (CLM), the Ecological Assimilation of Land and Climate Observations (EALCO) model, and the Variable Infiltration Capacity model (VIC). The second objective is to characterize the ET climatology over this vast landmass and discuss the advantages, limitations
watersheds in Canada. The third dataset was derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) satellite observation. The other three datasets were the outcome of three different LSMs, including the Community Land Model (CLM), the Ecological Assimilation of Land and Climate Observations (EALCO) model, and the Variable Infiltration Capacity model (VIC). The second objective is to characterize the ET climatology over this vast landmass and discuss the advantages, limitations
identifying suitable ecological niches, dynamic models simulate vector abundance over time utilizing stage-dependent temperature-driven development and mortality with location-specific daily weather data. They successfully simulated observed WNV vector abundance ( Gong et al. 2011 ; Morin and Comrie 2010 ). We focus on two major WNV U.S. vectors, Cx. pipiens and Cx. tarsalis , expanding the Dynamic Mosquito Simulation Model (DyMSiM) from the original STELLA-based model for a southern U.S. WNV vector
identifying suitable ecological niches, dynamic models simulate vector abundance over time utilizing stage-dependent temperature-driven development and mortality with location-specific daily weather data. They successfully simulated observed WNV vector abundance ( Gong et al. 2011 ; Morin and Comrie 2010 ). We focus on two major WNV U.S. vectors, Cx. pipiens and Cx. tarsalis , expanding the Dynamic Mosquito Simulation Model (DyMSiM) from the original STELLA-based model for a southern U.S. WNV vector
coupled model is in better agreement with observations compared to the noninteractive mode. Eastman (1999) analyzed the effects of CO 2 and landscape change using a coupled plant and meteorological model [GEMTM and the Regional Atmospheric Modeling System (RAMS)]. All of these attempts, including the one conducted in this paper ( Lu 1999 ), demonstrate that both atmospheric and ecologic research communities are beginning to realize the importance of including two-way feedbacks between the
coupled model is in better agreement with observations compared to the noninteractive mode. Eastman (1999) analyzed the effects of CO 2 and landscape change using a coupled plant and meteorological model [GEMTM and the Regional Atmospheric Modeling System (RAMS)]. All of these attempts, including the one conducted in this paper ( Lu 1999 ), demonstrate that both atmospheric and ecologic research communities are beginning to realize the importance of including two-way feedbacks between the
; Daly 1984 ; Wooldridge et al. 1996 ). In light of the role that snow plays in influencing land and atmospheric processes, it is essential that local, regional, and global models used to simulate weather, climate, hydrologic, and ecologic interactions be capable of accurately describing the seasonal snow evolution. In recent years, significant strides have been made to represent snow cover better in climate models ( Verseghy 1991 ; Lynch-Stieglitz 1994 ; Marshall and Oglesby 1994 ; Marshall et
; Daly 1984 ; Wooldridge et al. 1996 ). In light of the role that snow plays in influencing land and atmospheric processes, it is essential that local, regional, and global models used to simulate weather, climate, hydrologic, and ecologic interactions be capable of accurately describing the seasonal snow evolution. In recent years, significant strides have been made to represent snow cover better in climate models ( Verseghy 1991 ; Lynch-Stieglitz 1994 ; Marshall and Oglesby 1994 ; Marshall et
this method would work well during dry summer months in the USSW, but it may be applicable during the wetter times of the year ( Thornton et al. 2000 ). Derivative elements useful in ecological modeling, such as potential evapotranspiration (PET), are most effectively interpolated by mapping the individual components involved (e.g., temperature, solar radiation, humidity, wind speed), and calculating the derived value from grid calculations, rather than interpolating directly. This approach allows
this method would work well during dry summer months in the USSW, but it may be applicable during the wetter times of the year ( Thornton et al. 2000 ). Derivative elements useful in ecological modeling, such as potential evapotranspiration (PET), are most effectively interpolated by mapping the individual components involved (e.g., temperature, solar radiation, humidity, wind speed), and calculating the derived value from grid calculations, rather than interpolating directly. This approach allows
1. Introduction The Regional Hydro-Ecological Simulation System (RHESSys) is a hydroecological model designed to simulate integrated water, carbon, and nutrient cycling and transport over spatially variable terrain at small (first-order streams) to medium (fourth- and fifth-order streams) scales. The model is structured as a spatially nested hierarchical representation of the landscape with a range of hydrological, microclimate, and ecosystem processes associated with specific landscape objects
1. Introduction The Regional Hydro-Ecological Simulation System (RHESSys) is a hydroecological model designed to simulate integrated water, carbon, and nutrient cycling and transport over spatially variable terrain at small (first-order streams) to medium (fourth- and fifth-order streams) scales. The model is structured as a spatially nested hierarchical representation of the landscape with a range of hydrological, microclimate, and ecosystem processes associated with specific landscape objects
, soil temperature, in addition to soil moisture, is gaining consideration as another important initial condition for both weather and climate models. Soil temperature anomalies at various depths also directly influence yield of agricultural crops such as corn, beans, and oats. Soil temperature can impact an array of ecological processes, in particular vegetation growth ( McMichael and Burke 1998 ), soil biological, and chemical activity ( Kirschbaum 1995 ). For agriculture, soil temperature affects
, soil temperature, in addition to soil moisture, is gaining consideration as another important initial condition for both weather and climate models. Soil temperature anomalies at various depths also directly influence yield of agricultural crops such as corn, beans, and oats. Soil temperature can impact an array of ecological processes, in particular vegetation growth ( McMichael and Burke 1998 ), soil biological, and chemical activity ( Kirschbaum 1995 ). For agriculture, soil temperature affects
processes governing its responses. The photosynthesis-driven stomatal conductance models have been widely used in ecological, hydrological, and climate studies for calculating energy, water, and carbon fluxes since it was first proposed by Ball et al. (1987) . This kind of model usually attributes the change of stomatal conductance to environmental factors such as water stress to the change in leaf photosynthesis, while model parameters are treated as constants. On the other hand, some investigators
processes governing its responses. The photosynthesis-driven stomatal conductance models have been widely used in ecological, hydrological, and climate studies for calculating energy, water, and carbon fluxes since it was first proposed by Ball et al. (1987) . This kind of model usually attributes the change of stomatal conductance to environmental factors such as water stress to the change in leaf photosynthesis, while model parameters are treated as constants. On the other hand, some investigators
profound implications for predicting how the terrestrial C cycle responds to climate change ( Nottingham et al. 2020 ; Schuur et al. 2015 ; Todd-Brown et al. 2013 ). Earth system models (ESMs) are an essential tool for understanding and predicting climate change ( Bonan and Doney 2018 ; Flato 2011 ). However, the uncertainty stemming from the terrestrial C cycle component strongly influences the trajectory of future climate change ( Bodman et al. 2013 ; Booth et al. 2012 ; Cox et al. 2000
profound implications for predicting how the terrestrial C cycle responds to climate change ( Nottingham et al. 2020 ; Schuur et al. 2015 ; Todd-Brown et al. 2013 ). Earth system models (ESMs) are an essential tool for understanding and predicting climate change ( Bonan and Doney 2018 ; Flato 2011 ). However, the uncertainty stemming from the terrestrial C cycle component strongly influences the trajectory of future climate change ( Bodman et al. 2013 ; Booth et al. 2012 ; Cox et al. 2000
