Sensitivity of meteorological-forcing resolution on hydrologic variables

Abstract

<jats:p>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. </jats:p>

A review of hydrologic signatures and their applications

“…In ecology, flow signatures are used to group watersheds into scale‐independent classes according to their dynamics, before developing within‐class relationships between flow alteration and ecological responses (Kennard, Pusey, et al, 2010; Poff & Ward, 1989). However, signatures can sometimes be sensitive to scale, such as modeled future changes in signatures that depend on climate model scale (Maina, Siirila‐Woodburn, & Vahmani, 2020; Mendoza et al, 2016).…”

The Effects of Spatial and Temporal Resolution of Gridded Meteorological Forcing on Watershed Hydrological Responses

“…For example, the SWE distribution under 400 m and 800 m PRISM resembles more closely with ASO SWE than results under much coarser spatial resolution. Maina et al (2020) also found the SWE distribution to be sensitive to the spatial resolution of meteorological forcing, with the finer resolution being able to accurately reproduce SWE spatial distribution as well as total SWE volume. In addition, a higher spatial resolution of forcing would preserve the spatial heterogeneity of distributed variables better and may provide better estimates of variables at point locations (e.g., SWE at the NRCS's Snowpack Telemetry (SNOTEL) stations and groundwater table at wells).…”

“…It has the flexibility of generating much finer spatial and temporal resolution output while providing all available meteorological forcing. Maina et al (2020) used the nested-domain configuration of WRF to dynamically generate meteorological forcing variables at various spatial resolutions (from 0.5 km to 13.5 km) for use with ParFlow-CLM. Although forcing generated by WRF provides a viable option for meteorological input in the hydrologic model, it requires more effort to simulate the forcing using WRF than directly using the publicly available gridded forcing (e.g., Daymet).…”

Projecting the impacts of end of century climate extremes on the hydrology in California

“…Simulated key parameters controlling the snow dynamics such as peak snow and timing of snow ablation were also in agreement with remotely sensed data for both dry and wet years. More details about model calibration and validation can be found in previous publications (Maina et al, 2020a, Maina et al, 2020bMaina and SiirilaWoodburn, 2020c). The model has also been successfully used in recent investigations of post-wildfire and climate extremes hydrologic conditions and to assess the role of meteorological forcing scale on simulated watershed dynamics (Maina et al, 2020a, b;Maina and SiirilaWoodburn, 2020c).…”

“…We perform these couplings on spatial and temporal scales relevant for atmosphere-toland, and land-to-subsurface interactions, an important consideration, given the recent work showing the importance of meteorological forcing resolution in representing the hydrologic cycle (Kampenhout et al, 2019;Maina et al, 2020b;Rhoades et al, 2016;Rhoades, Ullrich, Zarzycki, et al, 2018c;Wu et al, 2017). Climate conditions for EoC (2070-2100) and a 30-year historical period are simulated to identify the median, wettest, and driest water year (WY) in each.…”