Abstract
Abstract. 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.
Highlights
Understanding water and energy fluxes across the earth’s critical zone, a region spanning from bedrock to vegetation canopy, is important to assess the impacts of climate change on water resources
The precision, and the uncertainty reduction of hydrologic models are extensively discussed in the literature, more attention is given to the physical representation of the phenomena occurring in the hydrological systems (Beven, 1993; Beven and Binley, 1992; Liu and Gupta, 2007), the reduction of uncertainties related to the hydrodynamic parameters (Gilbert et al, 2016; Janetti et al, 2019; Maina and Guadagnini, 2018; Srivastava et al, 2014), and the numerical resolution of the mathematical equations governing the physics of the environment
A possible explanation for this difference is the resolution of the physics-based model used in this study compared to that of Rasmussen and coauthors, the integrated hydrologic model we used in addition to the climate model, or differences stemming from watershed locations of the two studies
Summary
Understanding water and energy fluxes across the earth’s critical zone, a region spanning from bedrock to vegetation canopy, is important to assess the impacts of climate change on water resources. Integrated hydrologic models, solving water–energy interactions and transfers, across the lower atmosphere, the land surface, and the subsurface, allow for analyzing water resources in both time and space and projecting into a no-analog future where empirical models are no longer valid. With the advancement of computing power, these highfidelity, high-resolution models are becoming widely used (e.g., MIKE-SHE of Abbott et al, 1986, HydroGeoSphere of Panday and Huyakorn, 2004; and ParFlow-CLM – Community Land Model – of Maxwell and Miller, 2005). Their implementation can be plagued by several sources of uncertainty. Maina et al.: Sensitivity of meteorological-forcing resolution on hydrologic variables
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