Fusing enhanced radar precipitation, in-situ hydrometeorological measurements and airborne LIDAR snowpack estimates in a hyper-resolution hydrologic model to improve seasonal water supply forecasts

Abstract:

Scarcity of spatially- and temporally-continuous observations of precipitation and snowpack conditions in
remote mountain watersheds results in fundamental limitations in water supply forecasting. These limitations
in observational capabilities can result in strong biases in total snowmelt-driven runoff amount, the elevational distribution of runoff, river basin tributary contributions to total basin runoff and, equally important for water management, the timing of runoff. The Upper Rio Grande River basin in Colorado and New Mexico is one basin where observational deficiencies are hypothesized to have significant adverse impacts on estimates of snowpack melt-out rates and on water supply forecasts. We present findings from a coordinated observational-modeling study within Upper Rio Grande River basin whose aim was to quanitfy the impact enhanced precipitation, meteorological and snowpack measurements on the simulation and prediction of snowmelt driven streamflow. The Rio Grande SNOwpack and streamFLOW (RIO-SNO-FLOW) Prediction Project conducted enhanced observing activities during the 2014-2015 water year. Measurements from a gap-filling, polarimetric radar (NOXP) and in-situ meteorological and snowpack measurement stations were assimilated into the WRF-Hydro modeling framework to provide continuous analyses of snowpack and streamflow conditions. Airborne lidar estimates of snowpack conditions from the NASA Airborne Snow Observatory during mid-April and mid-May were used as additional independent validations against the various model simulations and forecasts of snowpack conditions during the melt-out season. Uncalibrated WRF-Hydro model performance from simulations and forecasts driven by enhanced observational analyses were compared against results driven by currently operational data inputs. Precipitation estimates from the NOXP research radar validate significantly better against independent in situ observations of precipitation and snow-pack increases. Correcting the operational NLDAS2 forcing data with the experimental observations led to significant improvements in the seasonal accumulation and ablation of mountain snowpack and ultimately led to marked improvement in model simulated streamflow as compared with streamflow observations.