Downscaled hyper-resolution (400 m) gridded datasets of daily precipitation and temperature (2008–2019) for East Taylor subbasin (western United States)

Abstract

Abstract. High resolution gridded datasets of meteorological variables are needed in order to resolve fine-scale hydrological gradients in complex mountainous terrain. Across the United States, the highest available spatial resolution of gridded datasets of daily meteorological records is approximately 800 m. This work presents gridded datasets of daily precipitation and mean temperature for the East-Taylor subbasin (in western United States) covering a 12-year period (2008–2019) at a high spatial resolution (400 m). The datasets are generated using a downscaling framework that uses data-driven models to learn relationships between climate variables and topography. We observe that downscaled datasets of precipitation and mean temperature exhibit smoother spatial gradients compared to their coarser counterparts. Additionally, we also observe that when downscaled datasets are reaggregated to the original resolution (800 m), the mean residual error is almost zero, ensuring spatial consistency with the original data. Furthermore, the downscaled datasets are observed to be linearly related to elevation, which is consistent with the methodology underlying the original 800 m product. Finally, we validate the spatial patterns exhibited by downscaled datasets via an example use case that models lidar-derived estimates of snowpack. The presented dataset constitutes a valuable resource to resolve fine-sale hydrological gradients in the mountainous terrain of the East-Taylor subbasin, which is an important study area in the context of water security for southwestern United States and Mexico. The dataset is publicly available at https://doi.org/10.15485/1822259 (Mital et al., 2021).

The effects of spatial and temporal resolution of gridded meteorological forcing on watershed hydrological responses

“…To study the effect of spatial resolution of meteorological forcing, precipitation and temperature from 800 m PRISM and 1 km Daymet have been downscaled (upscaled) into finer (coarser) spatial resolutions. The downscaling of 800 m PRISM or 1 km Daymet into 400 m used a data-driven downscaling approach (Mital et al, 2022). Specifically, random forests (Breiman, 2001) were used to extract the relationships between precipitation (or average temperature) and topography.…”

Using Hydrological Models and Remote Sensing to Characterize Snow Pack Behaviour in High Mountain Environments

“…The topographic complexity of montane terrain increases the spatial variability in snow mass and snow extent. This is influenced by a myriad of factors including elevation, slope, aspect, topography, orographic effects, vegetation coverage, wind redistribution, aerodynamic roughness atmospheric circulation [6,7]. Given the importance and the dynamic nature of mountain snowpacks, accurately estimating the SWE stored within these regions is an important scientific and societal [8,9].…”