<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">H Liu</style></author><author><style face="normal" font="default" size="100%">G. Brown</style></author><author><style face="normal" font="default" size="100%">J. Craig</style></author><author><style face="normal" font="default" size="100%">B. Tolson</style></author><author><style face="normal" font="default" size="100%">A. Newman</style></author><author><style face="normal" font="default" size="100%">A. Wood</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Discretization strategies for distributed models of mountainous watersheds</style></title><secondary-title><style face="normal" font="default" size="100%">AGU Fall Meeting Abstracts</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2019</style></year></dates><volume><style face="normal" font="default" size="100%">2019</style></volume><pages><style face="normal" font="default" size="100%">H42B-04</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;
	Distributed hydrological models are based on the discretization of spatial information into homogeneous computational units (eg, sub-catchment, grid). Watershed discretization attempts to capture the important spatial variability which controls the hydrological response of a basin, while simultaneously minimizing model complexity. This process may result in a level of information loss relative to the raw data (eg, DEM information) and thus the structure uncertainty of a hydrological model. To justify a discretization scheme, it is important to understand the impacts of the associated information loss.
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