Benchmarking of a Physically Based Hydrologic Model

Benchmarking of a Physically Based Hydrologic Model AbstractThe concepts of model benchmarking, model agility, and large-sample hydrology are becoming more prevalent in hydrologic and land surface modeling. As modeling systems become more sophisticated, these concepts have the ability to help improve modeling capabilities and understanding. In this paper, their utility is demonstrated with an application of the physically based Variable Infiltration Capacity model (VIC). The authors implement VIC for a sample of 531 basins across the contiguous United States, incrementally increase model agility, and perform comparisons to a benchmark. The use of a large-sample set allows for statistically robust comparisons and subcategorization across hydroclimate conditions. Our benchmark is a calibrated, time-stepping, conceptual hydrologic model. This model is constrained by physical relationships such as the water balance, and it complements purely statistical benchmarks due to the increased physical realism and permits physically motivated benchmarking using metrics that relate one variable to another (e.g., runoff ratio). The authors find that increasing model agility along the parameter dimension, as measured by the number of model parameters available for calibration, does increase model performance for calibration and validation periods relative to less agile implementations. However, as agility increases, transferability decreases, even for a complex model such as VIC. The benchmark outperforms VIC in even the most agile case when evaluated across the entire basin set. However, VIC meets or exceeds benchmark performance in basins with high runoff ratios (greater than ~0.8), highlighting the ability of large-sample comparative hydrology to identify hydroclimatic performance variations. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Journal of Hydrometeorology American Meteorological Society

Benchmarking of a Physically Based Hydrologic Model

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Publisher
American Meteorological Society
Copyright
Copyright © American Meteorological Society
ISSN
1525-7541
eISSN
1525-7541
D.O.I.
10.1175/JHM-D-16-0284.1
Publisher site
See Article on Publisher Site

Abstract

AbstractThe concepts of model benchmarking, model agility, and large-sample hydrology are becoming more prevalent in hydrologic and land surface modeling. As modeling systems become more sophisticated, these concepts have the ability to help improve modeling capabilities and understanding. In this paper, their utility is demonstrated with an application of the physically based Variable Infiltration Capacity model (VIC). The authors implement VIC for a sample of 531 basins across the contiguous United States, incrementally increase model agility, and perform comparisons to a benchmark. The use of a large-sample set allows for statistically robust comparisons and subcategorization across hydroclimate conditions. Our benchmark is a calibrated, time-stepping, conceptual hydrologic model. This model is constrained by physical relationships such as the water balance, and it complements purely statistical benchmarks due to the increased physical realism and permits physically motivated benchmarking using metrics that relate one variable to another (e.g., runoff ratio). The authors find that increasing model agility along the parameter dimension, as measured by the number of model parameters available for calibration, does increase model performance for calibration and validation periods relative to less agile implementations. However, as agility increases, transferability decreases, even for a complex model such as VIC. The benchmark outperforms VIC in even the most agile case when evaluated across the entire basin set. However, VIC meets or exceeds benchmark performance in basins with high runoff ratios (greater than ~0.8), highlighting the ability of large-sample comparative hydrology to identify hydroclimatic performance variations.

Journal

Journal of HydrometeorologyAmerican Meteorological Society

Published: Aug 6, 2017

References

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