Does Including Soil Moisture Observations Improve Operational Streamflow Forecasts in Snow-Dominated Watersheds?
Location
USU Eccles Conference Center
Event Website
http://water.usu.edu
Start Date
4-6-2016 11:00 AM
End Date
4-6-2016 11:15 AM
Description
Changing climate and growing water demand are increasing the need for robust streamflow forecasts in the Western U.S. Historically, operational streamflow forecasts made by the Natural Resources Conservation Service (NRCS) have relied on precipitation and snow water equivalent (SWE) observations from Snow Telemetry (SNOTEL) sites. In this study, we investigate whether also including SNOTEL soil moisture observations improve April-July streamflow volume forecast accuracy at 0, 1, 2, and 3 month lead times at 12 watersheds in Utah and California. We found significant improvement in 0 and 3-month lead time accuracy in 8 of 12 watersheds and 10 of 12 watersheds for 1 and 2-month lead times. Surprisingly, these improvements were insensitive to metrics derived from soil physical properties. For consistency and simplicity, forecasts were made with volumetric water content (VWC) averaged from October 1 to the forecast date. Including VWC at the 0-month lead time forecasts explained 7.3% more variability and increased the streamflow volume accuracy by 8.4% compared to standard forecasts that already explained 77% of the streamflow variability on average. At 1 to 3-month lead times, the inclusion of soil moisture explained 12.3% to 26.3% more variability than the standard forecast. Our findings indicate that including soil moisture observations into statistical streamflow forecasts can increase water supply reliability in arid regions affected by changing snowpacks.
Does Including Soil Moisture Observations Improve Operational Streamflow Forecasts in Snow-Dominated Watersheds?
USU Eccles Conference Center
Changing climate and growing water demand are increasing the need for robust streamflow forecasts in the Western U.S. Historically, operational streamflow forecasts made by the Natural Resources Conservation Service (NRCS) have relied on precipitation and snow water equivalent (SWE) observations from Snow Telemetry (SNOTEL) sites. In this study, we investigate whether also including SNOTEL soil moisture observations improve April-July streamflow volume forecast accuracy at 0, 1, 2, and 3 month lead times at 12 watersheds in Utah and California. We found significant improvement in 0 and 3-month lead time accuracy in 8 of 12 watersheds and 10 of 12 watersheds for 1 and 2-month lead times. Surprisingly, these improvements were insensitive to metrics derived from soil physical properties. For consistency and simplicity, forecasts were made with volumetric water content (VWC) averaged from October 1 to the forecast date. Including VWC at the 0-month lead time forecasts explained 7.3% more variability and increased the streamflow volume accuracy by 8.4% compared to standard forecasts that already explained 77% of the streamflow variability on average. At 1 to 3-month lead times, the inclusion of soil moisture explained 12.3% to 26.3% more variability than the standard forecast. Our findings indicate that including soil moisture observations into statistical streamflow forecasts can increase water supply reliability in arid regions affected by changing snowpacks.
https://digitalcommons.usu.edu/runoff/2016/2016Abstracts/10
Comments
An oral presentation by Jordan Clayton, who is with the USDA, NRCS