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dc.contributor.authorLiu, Hongli
dc.contributor.authorTolson, Bryan A.
dc.contributor.authorCraig, James R.
dc.contributor.authorShafii, Mahyar 18:55:31 (GMT) 18:55:31 (GMT)
dc.descriptionThe final publication is available at Elsevier via © 2016. This manuscript version is made available under the CC-BY-NC-ND 4.0 license
dc.description.abstractWatershed spatial discretization is an important step in developing a distributed hydrologic model. A key difficulty in the spatial discretization process is maintaining a balance between the aggregation-induced information loss and the increase in computational burden caused by the inclusion of additional computational units. Objective identification of an appropriate discretization scheme still remains a challenge, in part because of the lack of quantitative measures for assessing discretization quality, particularly prior to simulation. This study proposes a priori discretization error metrics to quantify the information loss of any candidate discretization scheme without having to run and calibrate a hydrologic model. These error metrics are applicable to multi-variable and multi-site discretization evaluation and provide directly interpretable information to the hydrologic modeler about discretization quality. The first metric, a sub basin error metric, quantifies the routing information loss from discretization, and the second, a hydrological response unit (HRU) error metric, improves upon existing a priori metrics by quantifying the information loss due to changes in land cover or soil type property aggregation. The metrics are straightforward to understand and easy to recode. Informed by the error metrics, a two-step discretization decision-making approach is proposed with the advantage of reducing extreme errors and meeting the user-specified discretization error targets. The metrics and decision-making approach are applied to the discretization of the Grand River watershed in Ontario, Canada. Results show that information loss increases as discretization gets coarser. Moreover, results help to explain the modeling difficulties associated with smaller upstream subbasins since the worst discretization errors and highest error variability appear in smaller upstream areas instead of larger downstream drainage areas. Hydrologic modeling experiments under candidate discretization schemes validate the strong correlation between the proposed discretization error metrics and hydrologic simulation responses. Discretization decision-making results show that the common and convenient approach of making uniform discretization decisions across the watershed performs worse than the proposed non-uniform discretization approach in terms of preserving spatial heterogeneity under the same computational cost.en
dc.description.sponsorshipNSERC Canadian FloodNet granten
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International*
dc.subjectA priori spatial discretization error metricsen
dc.subjectDistributed hydrologic modelingen
dc.subjectSpatial heterogeneityen
dc.subjectInformation lossen
dc.subjectRouting errorsen
dc.subjectDiscretization decision-makingen
dc.titleA priori discretization error metrics for distributed hydrologic modeling applicationsen
dcterms.bibliographicCitationLiu, H., Tolson, B. A., Craig, J. R., & Shafii, M. (2016). A priori discretization error metrics for distributed hydrologic modeling applications. Journal of Hydrology, 543, 873–891.
uws.contributor.affiliation1Faculty of Scienceen
uws.contributor.affiliation2Earth and Environmental Sciencesen

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