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was taken from meteorological stations within the regions analyzed for this study. Maps indicating areas of extreme wetness and drought were collected for the study period. Drought maps were taken from the Canadian portion of the North American Drought Monitor (NADM). This dataset is a categorical representation of drought conditions based on expert interpretation of many drought indicator datasets, including meteorological drought indicators, modeled soil moisture, satellite vegetation condition
was taken from meteorological stations within the regions analyzed for this study. Maps indicating areas of extreme wetness and drought were collected for the study period. Drought maps were taken from the Canadian portion of the North American Drought Monitor (NADM). This dataset is a categorical representation of drought conditions based on expert interpretation of many drought indicator datasets, including meteorological drought indicators, modeled soil moisture, satellite vegetation condition
around an external land surface modeling system that provides the temporally and spatially varying first guess of the land surface state. This external or offline approach is used in the North American Land Data Assimilation System (NLDAS; Mitchell et al. 2004 ) and the Global Land Data Assimilation System (GLDAS; Rodell et al. 2004 ), as well as the surface externalized (SURFEX) module at Météo-France ( Masson et al. 2013 ). The advantage of using the offline approach is that the land surface
around an external land surface modeling system that provides the temporally and spatially varying first guess of the land surface state. This external or offline approach is used in the North American Land Data Assimilation System (NLDAS; Mitchell et al. 2004 ) and the Global Land Data Assimilation System (GLDAS; Rodell et al. 2004 ), as well as the surface externalized (SURFEX) module at Météo-France ( Masson et al. 2013 ). The advantage of using the offline approach is that the land surface
of interest (e.g., precipitation, soil moisture, and runoff) from average conditions ( Keyantash and Dracup 2002 ). Root-zone soil moisture percentile–based drought indices are often used to monitor agricultural drought ( Mo 2008 ), as done in the North American Land Data Assimilation System (NLDAS) experimental drought monitor ( Xia et al. 2014 ; Sheffield et al. 2012 ). Though the standard practice is to use such indices to measure droughts, they can also be used to quantify wetter
of interest (e.g., precipitation, soil moisture, and runoff) from average conditions ( Keyantash and Dracup 2002 ). Root-zone soil moisture percentile–based drought indices are often used to monitor agricultural drought ( Mo 2008 ), as done in the North American Land Data Assimilation System (NLDAS) experimental drought monitor ( Xia et al. 2014 ; Sheffield et al. 2012 ). Though the standard practice is to use such indices to measure droughts, they can also be used to quantify wetter
al. 1975b ). AMC ’74 was intended only as an interim until what is considered the second-generation Army Mobility Model (AMM-75) could be released ( Jurkat et al. 1975a , b ). The North Atlantic Treaty Organization (NATO) adopted AMM-75 in 1977 and changed the name to the NATO Reference Mobility Model (NRMM; Fatherree 2006 ). The three main user groups for NRMM are vehicle developers, vehicle procurement, and vehicle users ( Haley et al. 1979 ; Ahlvin and Haley 1992 ). For a given vehicle, NRMM
al. 1975b ). AMC ’74 was intended only as an interim until what is considered the second-generation Army Mobility Model (AMM-75) could be released ( Jurkat et al. 1975a , b ). The North Atlantic Treaty Organization (NATO) adopted AMM-75 in 1977 and changed the name to the NATO Reference Mobility Model (NRMM; Fatherree 2006 ). The three main user groups for NRMM are vehicle developers, vehicle procurement, and vehicle users ( Haley et al. 1979 ; Ahlvin and Haley 1992 ). For a given vehicle, NRMM
) ; the 1° × 1° (nonprecipitation) forcing in that dataset is applied uniformly across the 64 ⅛° × ⅛° cells contained within. Simulations HRP and LRP differ only in the nature of the precipitation forcing. In simulation HRP, the precipitation is taken from the ⅛° × ⅛° observations-based North American Land Data Assimilation System (NLDAS) dataset ( Xia et al. 2012 ), whereas simulation LRP uses the same dataset, but with a twist: the 64 ⅛° × ⅛° values of a given hour's precipitation in a given 1° × 1
) ; the 1° × 1° (nonprecipitation) forcing in that dataset is applied uniformly across the 64 ⅛° × ⅛° cells contained within. Simulations HRP and LRP differ only in the nature of the precipitation forcing. In simulation HRP, the precipitation is taken from the ⅛° × ⅛° observations-based North American Land Data Assimilation System (NLDAS) dataset ( Xia et al. 2012 ), whereas simulation LRP uses the same dataset, but with a twist: the 64 ⅛° × ⅛° values of a given hour's precipitation in a given 1° × 1
