Keywords

hydrology, water budget, cycle, remote sensing, modeling, water balance, selection

Location

Colorado State University

Start Date

26-6-2018 5:00 PM

End Date

26-6-2018 7:00 PM

Abstract

Many modeled and reanalysis products estimate water balance components, such as precipitation and evapotranspiration, across a wide variety of spatiotemporal scales. When selecting datasets for use in model simulation and calibration, modelers are faced with the dilemma of selecting a product to use that best fits their needs in terms of accuracy, reliability, and usability. In this study, we review methodologies that can be used to 1) inter-compare products for a water budget component across temporal and spatial scales, 2) evaluate region-specific product metrics based on observational data, 3) identify outlying or unexpected values, and 4) describe product functionality in terms of scale, resolution, and ease of usability. We do not seek to identify a “best” product in terms of accuracy, but rather suggest methods that modelers can use to select the product that best fits their unique model requirements, whether the desired product is to be used as forcing data or to set range limits for calibration.

Stream and Session

A6: Innovation in Continental Scale Modelling for Decision-making, Research, and Education

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Jun 26th, 5:00 PM Jun 26th, 7:00 PM

New hydrologic model performance evaluation methodologies

Colorado State University

Many modeled and reanalysis products estimate water balance components, such as precipitation and evapotranspiration, across a wide variety of spatiotemporal scales. When selecting datasets for use in model simulation and calibration, modelers are faced with the dilemma of selecting a product to use that best fits their needs in terms of accuracy, reliability, and usability. In this study, we review methodologies that can be used to 1) inter-compare products for a water budget component across temporal and spatial scales, 2) evaluate region-specific product metrics based on observational data, 3) identify outlying or unexpected values, and 4) describe product functionality in terms of scale, resolution, and ease of usability. We do not seek to identify a “best” product in terms of accuracy, but rather suggest methods that modelers can use to select the product that best fits their unique model requirements, whether the desired product is to be used as forcing data or to set range limits for calibration.