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Developing Tools for Improved Water Supply Forecasting in the Rio Grande Headwaters

Evaluation of runoff forecasting errors from SNOTEL-based empirical models
Principal Investigator
David Clow

Dates

Start Date
2016-08-03
End Date
2020-09-30
Release Date
2016

Summary

The Rio Grande River is a critical source of freshwater for 13 million people in Colorado, Texas, New Mexico, and Mexico. More than half of the Rio Grande’s streamflow originates as snowmelt in Colorado’s mountains, meaning that changes in the amount of snowmelt can impact the water supply for communities along the entire river. Snowmelt runoff is therefore an important component of water supply outlooks for the region, which are used by a variety of stakeholders to anticipate water availability in the springtime. It is critical that these water supply outlooks be as accurate as possible. Errors can cost states millions of dollars due to mis-allocation of water and lost agricultural productivity. There is a perception that runoff [...]

Child Items (3)

Contacts

Principal Investigator :
David Clow
Co-Investigator :
Colin Penn, Graham Sexstone
Funding Agency :
South Central CSC
CMS Group :
Climate Adaptation Science Centers (CASC) Program

Attached Files

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RioGrandeRiver_AlanCressler2.jpg
“Rio Grande River - Credit: Alan Cressler”
thumbnail 213.14 KB image/jpeg

Purpose

Snow is a critical water resource in the western United States (U.S.). Seasonal snowpacks serve as large natural reservoirs in the Rocky Mountain west that store water through the winter and release it during spring and summer months when demand is greatest; melting snowpacks often supply water to meet demands hundreds of miles away from the source (Mote 2006). In the Rio Grande Basin (RGB), snowmelt from seasonal snowpacks in the headwater area supplies 50 – 75% of annual basin streamflow (Rango 2006); therefore, water supply forecasts provided prior to the snowmelt season are of upmost importance for water management decision making. Errors in these statistically-based water supply forecasts are ubiquitous, and forecasting skill has declined over time in certain areas of the country such as Colorado and New Mexico (Pagano et al. 2004). Process changes related to changing climate, land cover, and/or dust deposition, or operational changes in data collection and forecasting may be responsible for this worsening of forecast skill (Pagano et al. 2004). Understanding the conditions that have led to large errors in existing forecasts is an important step towards making science-based recommendations for improving future water supply forecasts in the RGB. This study proposes to evaluate the performance of various operational products for forecasting spring and summer streamflow in the Rio Grande Headwaters basin at the Rio Grande Near Del Norte gage from 2000 through 2015. The products that will be evaluated include the NRCS water supply forecasts, NWS River Forecast Center forecasts, and outputs from the NOHRSC Snow Data Assimilation System (SNODAS). In this study, we will quantify errors in each product in recent years, and will investigate conditions and processes associated with large forecasting errors. Additionally, in collaboration with the USGS National Water Census Program and the NRCS Colorado Snow Survey Program, we propose to test coupling of a physically-based snowpack evolution model (SnowModel) with a physically-based hydrologic model (Precipitation Runoff Modeling System, or PRMS). We will compare streamflow runoff estimates derived from this physically-based modeling approach to those from existing, empirical operational forecasting techniques for the 2016 and 2017 water years. This research will provide recommendations on (1) what currently operational forecasting method provides the best performance over a range of conditions in the Rio Grande Headwaters, and (2) how operational forecasts in the RGB could be improved using new technologies and methods.

Project Extension

parts
typeTechnical Summary
valueSnow is a critical water resource in the western United States (U.S.). Seasonal snowpacks serve as large natural reservoirs in the Rocky Mountain west that store water through the winter and release it during spring and summer months when demand is greatest; melting snowpacks often supply water to meet demands hundreds of miles away from the source (Mote 2006). In the Rio Grande Basin (RGB), snowmelt from seasonal snowpacks in the headwater area supplies 50 – 75% of annual basin streamflow (Rango 2006); therefore, water supply forecasts provided prior to the snowmelt season are of upmost importance for water management decision making. Errors in these statistically-based water supply forecasts are ubiquitous, and forecasting skill has declined over time in certain areas of the country such as Colorado and New Mexico (Pagano et al. 2004). Process changes related to changing climate, land cover, and/or dust deposition, or operational changes in data collection and forecasting may be responsible for this worsening of forecast skill (Pagano et al. 2004). Understanding the conditions that have led to large errors in existing forecasts is an important step towards making science-based recommendations for improving future water supply forecasts in the RGB. This study proposes to evaluate the performance of various operational products for forecasting spring and summer streamflow in the Rio Grande Headwaters basin at the Rio Grande Near Del Norte gage from 2000 through 2015. The products that will be evaluated include the NRCS water supply forecasts, NWS River Forecast Center forecasts, and outputs from the NOHRSC Snow Data Assimilation System (SNODAS). In this study, we will quantify errors in each product in recent years, and will investigate conditions and processes associated with large forecasting errors. Additionally, in collaboration with the USGS National Water Census Program and the NRCS Colorado Snow Survey Program, we propose to test coupling of a physically-based snowpack evolution model (SnowModel) with a physically-based hydrologic model (Precipitation Runoff Modeling System, or PRMS). We will compare streamflow runoff estimates derived from this physically-based modeling approach to those from existing, empirical operational forecasting techniques for the 2016 and 2017 water years. This research will provide recommendations on (1) what currently operational forecasting method provides the best performance over a range of conditions in the Rio Grande Headwaters, and (2) how operational forecasts in the RGB could be improved using new technologies and methods.
projectStatusIn Progress

Additional Information

Identifiers

Type Scheme Key
RegistrationUUID NCCWSC c5fa287d-20ee-49c0-8cfb-1bc4f2a84385
StampID NCCWSC SC16-PD0712

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