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Selection: Gochis:D [3 articles] 

Publications by author Gochis:D.
 

A gridded global data set of soil, intact regolith, and sedimentary deposit thicknesses for regional and global land surface modeling

  
Journal of Advances in Modeling Earth Systems, Vol. 8, No. 1. (March 2016), pp. 41-65, https://doi.org/10.1002/2015ms000526

Abstract

Earth's terrestrial near-subsurface environment can be divided into relatively porous layers of soil, intact regolith, and sedimentary deposits above unweathered bedrock. Variations in the thicknesses of these layers control the hydrologic and biogeochemical responses of landscapes. Currently, Earth System Models approximate the thickness of these relatively permeable layers above bedrock as uniform globally, despite the fact that their thicknesses vary systematically with topography, climate, and geology. To meet the need for more realistic input data for models, we developed a high-resolution ...

 

Increased evaporation following widespread tree mortality limits streamflow response

  
Water Resources Research, Vol. 50, No. 7. (1 July 2014), pp. 5395-5409, https://doi.org/10.1002/2013wr014994

Abstract

A North American epidemic of mountain pine beetle (MPB) has disturbed over 5 million ha of forest containing headwater catchments crucial to water resources. However, there are limited observations of MPB effects on partitioning of precipitation between vapor loss and streamflow, and to our knowledge these fluxes have not been observed simultaneously following disturbance. We combined eddy covariance vapor loss (V), catchment streamflow (Q), and stable isotope indicators of evaporation (E) to quantify hydrologic partitioning over 3 years in MPB-impacted and ...

 

A comparison of statistical and dynamical downscaling of winter precipitation over complex terrain

  
J. Climate In Journal of Climate, Vol. 25, No. 1. (19 July 2011), pp. 262-281, https://doi.org/10.1175/2011jcli4109.1

Abstract

Abstract Statistical downscaling is widely used to improve spatial and/or temporal distributions of meteorological variables from regional and global climate models. This downscaling is important because climate models are spatially coarse (50?200 km) and often misrepresent extremes in important meteorological variables, such as temperature and precipitation. However, these downscaling methods rely on current estimates of the spatial distributions of these variables and largely assume that the small-scale spatial distribution will not change significantly in a modified climate. In this study the ...

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