Preprints
https://doi.org/10.5194/essd-2022-67
https://doi.org/10.5194/essd-2022-67
 
28 Mar 2022
28 Mar 2022
Status: a revised version of this preprint is currently under review for the journal ESSD.

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

Utkarsh Mital1, Dipankar Dwivedi1, James B. Brown2, and Carl I. Steefel1 Utkarsh Mital et al.
  • 1Energy Geosciences Division, Lawrence Berkeley National Laboratory, Berkeley, CA, 94720, USA
  • 2Environmental Genomics and System Biology, Lawrence Berkeley National Laboratory, Berkeley, CA, 94720, USA

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).

Utkarsh Mital et al.

Status: final response (author comments only)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on essd-2022-67', Anonymous Referee #1, 19 Jun 2022
  • RC2: 'Comment on essd-2022-67', Anonymous Referee #2, 05 Aug 2022
  • AC1: 'Comment on essd-2022-67', Utkarsh Mital, 09 Sep 2022

Utkarsh Mital et al.

Data sets

Downscaled precipitation and mean air temperature datasets; East-Taylor subbasin; 2008-2019; daily temporal resolution; 400 m spatial resolution Utkarsh Mital, Dipankar Dwivedi, James B. Brown, Carl I. Steefel https://doi.org/10.15485/1822259

Utkarsh Mital et al.

Viewed

Total article views: 519 (including HTML, PDF, and XML)
HTML PDF XML Total Supplement BibTeX EndNote
399 102 18 519 23 10 8
  • HTML: 399
  • PDF: 102
  • XML: 18
  • Total: 519
  • Supplement: 23
  • BibTeX: 10
  • EndNote: 8
Views and downloads (calculated since 28 Mar 2022)
Cumulative views and downloads (calculated since 28 Mar 2022)

Viewed (geographical distribution)

Total article views: 486 (including HTML, PDF, and XML) Thereof 486 with geography defined and 0 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Latest update: 20 Sep 2022
Download
Short summary
We present a new dataset that estimates small-scale variations in precipitation and temperature in mountainous terrain. The dataset is generated using a new machine learning method that extracts relationships between climate and topography from existing coarse-scale datasets. The generated dataset is shown to capture small-scale variations more reliably than existing datasets, and constitutes a valuable resource to model the water-cycle in the mountains of Colorado, western United States.