A Water Allocation Decision‐Support Model and Tool for Predictions in Ungauged Basins in Northeast British Columbia, Canada

Pressures on water resources due to changing climate, increasing demands, and enhanced recognition of environmental flow needs result in the need for hydrology information to support informed water allocation decisions. However, the absence of hydrometric measurements and limited access to hydrology...

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Bibliographic Details
Published in:Journal of the American Water Resources Association 2018-06, Vol.54 (3), p.676-693
Main Authors: Chapman, Allan R., Kerr, Ben, Wilford, David
Format: Article
Language:English
Subjects:
Allocations
Annual
Annual runoff
Basins
Climate
Climate change
computational methods
Decision making
decision‐support systems
Hydrology
Median (statistics)
Modelling
Monthly
Northeast Water Tool
Runoff
Statistical analysis
Support systems
surface water hydrology
Water allocation
Water balance
Water resources
Water supply
Watersheds
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Summary:Pressures on water resources due to changing climate, increasing demands, and enhanced recognition of environmental flow needs result in the need for hydrology information to support informed water allocation decisions. However, the absence of hydrometric measurements and limited access to hydrology information in many areas impairs water allocation decision‐making. This paper describes a water balance‐based modeling approach and an innovative web‐based decision‐support hydrology tool developed to address this need. Using high‐resolution climate, vegetation, and watershed data, a simple gridded water balance model, adjusted to account for locational variability, was developed and calibrated against gauged watersheds, to model mean annual runoff. Mean monthly runoff was modeled empirically, using multivariate regression. The modeled annual runoff results are within 20% of the observed mean annual discharge for 78% of the calibration watersheds, with a mean absolute error of 16%. Modeled monthly runoff corresponds well to observed monthly runoff, with a median Nash–Sutcliffe statistic of 0.92 and a median Spearman rank correlation statistic of 0.98. Monthly and annual flow estimates produced from the model are incorporated into a map‐ and watershed‐based decision‐support system referred to as the Northeast Water Tool, to provide critical information to decision makers and others on natural water supply, existing allocations, and the needs of the environment.
ISSN:1093-474X
1752-1688
DOI:10.1111/1752-1688.12643