Impact of the Alamosa gap-filling radar on streamflow in the National Water Model

Joseph A. Grim, Yongxin Zhang, David J. Gochis

Research output: Contribution to journalArticlepeer-review

Abstract

The installation of the Alamosa gap-filling radar in 2019 not only greatly improved surveillance of current precipitation in the mountain-ringed San Luis valley, but also improved estimates of rain and snow accumulation. This is particularly important for hydrological prediction in this headwaters region, as it provides vital information for potential downstream floods and reservoir storage. This study performs three experiments using the community WRF-Hydro modeling system (the core model of the National Water Model) during 2021 to estimate the effect of the new Alamosa gap-filling radar, as integrated into the National Severe Storms Laboratory Multi-radar Multi-sensor quantitative precipitation estimate product on model-predicted streamflow. The first model experiment utilizes the Multi-Radar Multi-Sensor data, including from the new Alamosa radar; the second utilizes a spatially-downscaled version of the NLDAS-2 precipitation field, mapped to a high resolution WRF-Hydro model grid; while the third experiment uses a combination of the two, with MRMS used in areas observed by the Alamosa radar. Emphasis is placed on analyzing the impact of the radar quantitative precipitation estimate on total seasonal runoff in the Conejos River basin and overall runoff throughout the Upper Rio Grande River basin in southern Colorado.

Original languageEnglish
Article number995424
JournalFrontiers in Earth Science
Volume10
DOIs
StatePublished - Jan 9 2023

Keywords

  • National Water Model
  • San Luis valley
  • WRF-Hydro
  • gap-filling radar
  • streamflow

Fingerprint

Dive into the research topics of 'Impact of the Alamosa gap-filling radar on streamflow in the National Water Model'. Together they form a unique fingerprint.

Cite this