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

Abstract

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.

Mitigating the Impacts of Climate Nonstationarity on Seasonal Streamflow Predictability in the U.S. Southwest

“…Specifically, individual PCs are used in a linear regression and the significance of the regression coefficients is determined via a t test; only PCs are retained that result in significant regression coefficients and that show a physically plausible relationship with streamflow (i.e., positive coefficients, indicating that high precipitation and SWE typically leads to high streamflow and vice versa). In our case, one PC is retained for all streamflow gages, consistent with Harpold et al (), who also duplicated the NRCS's WSF. For each forecast issue date, forecasts are cross‐validated by training the model on 29 of the 30 years and forecast the remaining (out‐of‐sample) year, loop through all 30 years to evaluate performance.…”

Bioclimatic and Soil Moisture Monitoring Across Elevation in a Mountain Watershed: Opportunities for Research and Resource Management

“…Specifically, the difference in the timing of the spring melt event across elevations and across years may be directly observed in the data. Additionally, the correspondence between events such as soil saturation and snowmelt indicates a potential to combine iRON data with data sets from outside the network to contribute to hydrologic models and generate improved forecasts of events such as the timing of snowmelt, runoff, and streamflow dynamics (Harpold et al, ; Mahanama et al, ). Partnerships are currently being developed with researchers working on water models to explore the possibility of using the Roaring Fork Watershed as a case study for applying observational soil moisture data to improve the representation of soil moisture in hydrologic models in mountainous terrain.…”

A Multi‐Scale Soil Moisture Monitoring Strategy for California: Design and Validation

“…; Harpold et al. ). This information will greatly improve water resource management and reservoir operations; however, the ability to manage for anomalous soil moisture conditions requires new water‐management strategies supported by real‐time soil moisture monitoring.…”

“…Expected increases in climatic variability (IPCC 2014) and more frequent episodes of water scarcity make strategic management of water resources increasingly important. As climate change results in novel hydrologic conditions, real-time soil moisture monitoring is now considered an integral component of operational water resource management at local to regional scales (Schaefer et al 2007;Illston et al 2008;Dobriyal et al 2012;Trenberth and Asrar 2014;Harpold et al 2017).…”

“…Incorporating soil moisture conditions into runoff forecast models will enable more accurate predictions of the timing and volume of runoff (Crow and Ryu 2009;Tayfur et al 2014;Harpold et al 2017). This information will greatly improve water resource management and reservoir operations; however, the ability to manage for anomalous soil moisture conditions requires new water-management strategies supported by real-time soil moisture monitoring.…”