Downscaled hyper-resolution (400 m) gridded datasets of daily precipitation and temperature (2008–2019) for the 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 the 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 (while preserving the spatial variability) when compared to their coarser counterparts. Additionally, we also observe that when downscaled datasets are upscaled to the original resolution (800 m), the mean residual error is almost zero, ensuring no bias when compared 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-scale hydrological gradients in the mountainous terrain of the East–Taylor subbasin, which is an important study area in the context of water security for the southwestern United States and Mexico. The dataset is publicly available at https://doi.org/10.15485/1822259 (Mital et al., 2021).

Mapping 30-m Resolution Bioclimatic Variables During 1991–2020 Climate Normals for Hubei Province, the Yangtze River Middle Reaches

“…This study aligns with previous research by using climate variables from the same source [27], [76], but the quality of precipitation is comparatively lower. The comparison of precipitation from our sourced data and ERA5-Land dataset (Fig.…”

“…Terrain and spatial variables were usually used in climate downscaling [27], [46], [47]. It has been observed the inclusion of detailed terrain variables such as slope and aspect makes limited contribution to accuracy of climate downscaling if they were not considered in original dataset [21].…”

“…ML has been widely used in downscaling tasks of air temperature [27], [48] and precipitation [49]. Given the extensive modeling and predicting demands for this long time-series study, the choice of models should consider the balance between accuracy and efficiency, with the benefits of shorter runtimes, ease of operation and fewer artifacts besides high accuracy.…”

“…Among the downscaling methods, statistical downscaling has been proved effective to obtain higher resolution climate data [26], [27], [28]. This method involves using regression algorithms to generate high-resolution regional climate data by establishing a statistical correlation between coarser global or national climate products and high-resolution auxiliary data.…”

Exploring the sensitivity to precipitation, blowing snow, and horizontal resolution of the spatial distribution of simulated snow cover

“…However, the quantification of the precipitation is still impacted by important uncertainties (Clark et al, 2011;Ménard et al, 2019;Lundquist et al, 2019). Downscaling tools can be used to better represent the local meteorology from NWP models (Sen Gupta and Tarboton, 2016;Marsh et al, 2023;Mital et al, 2022). Eventually, the representation of post-depositional processes, notably blowing snow transport, benefits from the development of dedicated high-resolution models coupled to or forced by atmospheric models (e.g., Lehning et al, 2006;Liston et al, 2007;Marsh et al, 2020;Sharma et al, 2021;Baron et al, 2023;Quéno et al, 2023).…”