Sensitivity of meteorological-forcing resolution on hydrologic variables

Maina, Fadji Z.; Siirila-Woodburn, Erica R.; Vahmani, Pouya

doi:10.5194/hess-24-3451-2020

Title: Sensitivity of meteorological-forcing resolution on hydrologic variables

Journal Article · Thu Jul 09 00:00:00 EDT 2020 · Hydrology and Earth System Sciences (Online)

DOI:https://doi.org/10.5194/hess-24-3451-2020· OSTI ID:1659727

Maina, Fadji Z. ^[1]; Siirila-Woodburn, Erica R. ^[1]; Vahmani, Pouya ^[1]

Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)

Projecting the spatiotemporal changes in water resources under a no-analog future climate requires physically based integrated hydrologic models which simulate the transfer of water and energy across the earth's surface. These models show promise in the context of unprecedented climate extremes given their reliance on the underlying physics of the system as opposed to empirical relationships. However, these techniques are plagued by several sources of uncertainty, including the inaccuracy of input datasets such as meteorological forcing. These datasets, usually derived from climate models or satellite-based products, are typically only resolved on the order of tens to hundreds of kilometers, while hydrologic variables of interest (e.g., discharge and groundwater levels) require a resolution at much smaller scales. In this work, a high-resolution hydrologic model is forced with various resolutions of meteorological forcing (0.5 to 40.5 km) generated by a dynamical downscaling analysis from the regional climate model Weather Research and Forecasting (WRF). The Cosumnes watershed, which spans the Sierra Nevada and Central Valley interface of California (USA), exhibits semi-natural flow conditions due to its rare undammed river basin and is used here as a test bed to illustrate potential impacts of various resolutions of meteorological forcing on snow accumulation and snowmelt, surface runoff, infiltration, evapotranspiration, and groundwater levels. Results show that the errors in spatial distribution patterns impact land surface processes and can be delayed in time. Localized biases in groundwater levels can be as large as 5–10 m and 3 m in surface water. Most hydrologic variables reveal that biases are seasonally and spatially dependent, which can have serious implications for model calibration and ultimately water management decisions.

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Research Organization:: Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States)

Sponsoring Organization:: USDOE Office of Science (SC)

Grant/Contract Number:: AC02-05CH11231

OSTI ID:: 1659727

Journal Information:: Hydrology and Earth System Sciences (Online), Vol. 24, Issue 7; ISSN 1607-7938

Publisher:: European Geosciences Union (EGU)Copyright Statement

Country of Publication:: United States

Language:: English

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Cited by: 13 works

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