Submitted as: model evaluation paper
09 May 2023
Submitted as: model evaluation paper |  | 09 May 2023
Status: this preprint is currently under review for the journal GMD.

Evaluating Three Decades of Precipitation in the Upper Colorado River Basin from a High-Resolution Regional Climate Model

William Rudisill, Alejandro Flores, and Rosemary Carroll

Abstract. Convection permitting Regional Climate Models (RCM) have recently become tractable for applications at multi-decadal timescales. These types of models have tremendous utility for water resource studies, but better characterization of precipitation biases is needed, particularly for water-resource critical mountain regions where precipitation is highly variable in space, observations are sparse, and societal water need is great. This study examines 34 years (1987–2020) of RCM precipitation from the Weather Research and Forecasting model (WRF; V.3.8.1) using Climate Forecast System Reanalysis (CFSR; CFSv2) initial and lateral boundary conditions a 1 × 1 km innermost grid spacing. The RCM is centered over the Upper Colorado River Basin with a focus on the high-elevation, 750 km2 East River Watershed (ERW) where a variety of high-impact scientific activities are currently ongoing. Precipitation is compared against point observations (NRCS SNOTEL), gridded climate datasets (Newman, Livneh, and PRISM), and Bayesian reconstructions of watershed-mean precipitation conditioned on streamflow and high-resolution snow remote sensing products. We find that the cool-season precipitation percent error between WRF and 23 SNOTEL gauges has a low overall bias (x̂  = .25 %, s = 13.63 %), and that WRF has a higher percent error during the warm season (x̂  = 10.37 %, s = 12.79 %). Warm season bias manifests as a high number of low-precipitation days, though the low resolution or SNOTEL gauges limits some of the conclusions that can be drawn. Regional comparisons between WRF precipitation accumulation and three different gridded datasets show differences on the order of +/−20 %, and particularly at the highest elevations and in keeping with findings from other studies. We find that WRF agrees slightly better with the Bayesian reconstruction of precipitation in the ERW compared to the gridded precipitation datasets, particularly when changing SNOTEL densities are taken into account. The conclusions are that the RCM reasonably captures orographic precipitation in this region, and demonstrates that leveraging additional hydrologic information (streamflow, snow remote sensing data) improves the ability to characterize biases in RCM precipitation fields. Error characteristics reported in this study are essential for leveraging RCM model outputs for studies of past and future climates and water resource applications. The methods developed in this study can be applied to other watersheds and model configurations. Hourly, 1 × 1 kilometer precipitation and other meteorological outputs from this dataset are publicly available and suitable for a wide variety of applications.

William Rudisill et al.

Status: open (until 11 Jul 2023)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse

William Rudisill et al.

William Rudisill et al.


Total article views: 153 (including HTML, PDF, and XML)
HTML PDF XML Total Supplement BibTeX EndNote
121 29 3 153 10 0 0
  • HTML: 121
  • PDF: 29
  • XML: 3
  • Total: 153
  • Supplement: 10
  • BibTeX: 0
  • EndNote: 0
Views and downloads (calculated since 09 May 2023)
Cumulative views and downloads (calculated since 09 May 2023)

Viewed (geographical distribution)

Total article views: 144 (including HTML, PDF, and XML) Thereof 144 with geography defined and 0 with unknown origin.
Country # Views %
  • 1
Latest update: 05 Jun 2023
Short summary
It's important to know how well atmospheric models do in the mountains, but there aren't very many weather stations. We evaluate rain and snow from a model from 1987–2020 in the Upper Colorado river basin against the data that's available. The model works pretty well but, there are still some uncertainties in remote locations. We then use snow maps collected by aircraft, streamflow measurements, and some advanced statistics to help identify how well the model works in ways we couldn't before.