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precipitation, but higher resolutions are needed to fully resolve the processes that influence precipitation in regions dominated by convection. Similarly, studies that were run at high spatial resolution were implemented over small domains for short time periods, yielding results that do not include downwind effects or are not applicable outside of the simulated periods. Models have been used to simulate irrigation with varying levels of complexity. Using a regional climate model, Kueppers et al. (2007
precipitation, but higher resolutions are needed to fully resolve the processes that influence precipitation in regions dominated by convection. Similarly, studies that were run at high spatial resolution were implemented over small domains for short time periods, yielding results that do not include downwind effects or are not applicable outside of the simulated periods. Models have been used to simulate irrigation with varying levels of complexity. Using a regional climate model, Kueppers et al. (2007
1. Introduction Seasonal greening of drought-deciduous ecosystems in semiarid regions significantly alters the surface energy balance, including evapotranspiration ET (e.g., Montaldo et al. 2005 ; Watts et al. 2007 ; Vivoni et al. 2008 ). Relatively little is known about the impacts of vegetation dynamics on the catchment response during the North American monsoon (NAM; July–September), despite quantification of these dynamics at regional scales ( Forzieri et al. 2011 ). Quantifying the
1. Introduction Seasonal greening of drought-deciduous ecosystems in semiarid regions significantly alters the surface energy balance, including evapotranspiration ET (e.g., Montaldo et al. 2005 ; Watts et al. 2007 ; Vivoni et al. 2008 ). Relatively little is known about the impacts of vegetation dynamics on the catchment response during the North American monsoon (NAM; July–September), despite quantification of these dynamics at regional scales ( Forzieri et al. 2011 ). Quantifying the
orographically enhanced precipitation downwind of irrigated areas in southern Spain. Segal et al. (1998) simulated slight continentwide increases in precipitation over North America using a regional climate model, while Sacks et al. (2009) simulated slight global precipitation increases over land due to irrigation. In Part I of this study ( Harding and Snyder 2012 , hereafter HS2012 ), precipitation increases of 1% from irrigation were simulated regionwide over the northern Great Plains and Midwest
orographically enhanced precipitation downwind of irrigated areas in southern Spain. Segal et al. (1998) simulated slight continentwide increases in precipitation over North America using a regional climate model, while Sacks et al. (2009) simulated slight global precipitation increases over land due to irrigation. In Part I of this study ( Harding and Snyder 2012 , hereafter HS2012 ), precipitation increases of 1% from irrigation were simulated regionwide over the northern Great Plains and Midwest
hydrologic and atmospheric model performance ( Betts 2004 , 2009 ). By linking the surface, PBL, and cloud processes, coupling encompasses complex cross-scale interactions that determine the climate state. Coupling strength varies on local (5–10 km) to regional scales (400 km) and temporally on daily to weekly time scales ( Betts 2004 ; Koster et al. 2003 ; Taylor and Ellis 2006 ), modulated by background synoptic weather (i.e., convergence/divergence, monsoons, and cloud fields) and larger scale (i
hydrologic and atmospheric model performance ( Betts 2004 , 2009 ). By linking the surface, PBL, and cloud processes, coupling encompasses complex cross-scale interactions that determine the climate state. Coupling strength varies on local (5–10 km) to regional scales (400 km) and temporally on daily to weekly time scales ( Betts 2004 ; Koster et al. 2003 ; Taylor and Ellis 2006 ), modulated by background synoptic weather (i.e., convergence/divergence, monsoons, and cloud fields) and larger scale (i
1. Introduction Soil moisture plays an important role in modifying the behavior of the atmosphere by its influence on land surface fluxes of moisture, energy, carbon, and trace gases [ Seneviratne et al. (2010) and references therein]. Of particular interest is the way in which the effects of these moisture and energy fluxes combine to create feedbacks on precipitation. Such feedbacks are complex because of their dependence on a variety of mechanisms. The scarcity of observations of soil
1. Introduction Soil moisture plays an important role in modifying the behavior of the atmosphere by its influence on land surface fluxes of moisture, energy, carbon, and trace gases [ Seneviratne et al. (2010) and references therein]. Of particular interest is the way in which the effects of these moisture and energy fluxes combine to create feedbacks on precipitation. Such feedbacks are complex because of their dependence on a variety of mechanisms. The scarcity of observations of soil
ensemble simulations, and therefore parsing out the reasons for differences in coupling strength amongst models (and inherent land surface and PBL physics) remains a difficult task. A companion effort has since been launched that focuses on local L–A coupling (LoCo; Santanello et al. 2009 , 2011 ) in coupled models by diagnosing land–PBL interactions at the process level using a regional, high-resolution test bed. The methodology and diagnostics developed in LoCo can be applied to any model or
ensemble simulations, and therefore parsing out the reasons for differences in coupling strength amongst models (and inherent land surface and PBL physics) remains a difficult task. A companion effort has since been launched that focuses on local L–A coupling (LoCo; Santanello et al. 2009 , 2011 ) in coupled models by diagnosing land–PBL interactions at the process level using a regional, high-resolution test bed. The methodology and diagnostics developed in LoCo can be applied to any model or
in the NAM region ( Brito-Castillo et al. 2003 ; Gochis et al. 2006 ). However, understanding the spatiotemporal variability of streamflow remains an elusive challenge for several reasons. First, most regional river basins, particularly within Mexico, remain ungauged (or poorly gauged) because of inadequate rainfall, weather, and/or streamflow records. Sparse data limit the ability to observe hydrologic variables of interest at the appropriate spatiotemporal scales for streamflow forecasting
in the NAM region ( Brito-Castillo et al. 2003 ; Gochis et al. 2006 ). However, understanding the spatiotemporal variability of streamflow remains an elusive challenge for several reasons. First, most regional river basins, particularly within Mexico, remain ungauged (or poorly gauged) because of inadequate rainfall, weather, and/or streamflow records. Sparse data limit the ability to observe hydrologic variables of interest at the appropriate spatiotemporal scales for streamflow forecasting
