Does Including Soil Moisture Observations Improve Operational Streamflow Forecasts in Snow-Dominated Watersheds?

Changing climate and growing water demand are increasing the need for robust streamflow forecasts. Historically, operational streamflow forecasts made by the Natural Resources Conservation Service have relied on precipitation and snow water equivalent observations from Snow Telemetry (SNOTEL) sites. 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 statistically 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 soil moisture metrics derived from soil physical properties. Forecasts were made with volumetric water content (VWC) averaged from October 1 to the forecast date. By including VWC at the 0-month lead time the forecasts explained 7.3% more variability and increased the streamflow volume accuracy by 8.4% on average compared to standard forecasts that already explained an average 77% of the variability. At 1 to 3-month lead times, the inclusion of soil moisture explained 12.3-26.3% more variability than the standard forecast on average. Our findings indicate including soil moisture observations increased statistical streamflow forecast accuracy and thus, could potentially improve water supply reliability in regions affected by changing snowpacks.