Snow is a critical water resource for much of the U.S. and failure to account for changes in climate could deleteriously impact military assets. It is also a critical precipitation input, and changes in the amount, timing, and departure of snow impact the hydrological cycle and can result in faster snowmelt rates that may lead to an increase in spring flooding events. Understanding the nature of the effects of a changing snowpack at appropriate temporal and spatial scales remains a formidable challenge. Knowledge of snow on the ground is limited, especially at middle-to-lower elevations and in landscapes where snow lingers. With a heterogeneous snowpack, sparsely distributed snow gauges makes it difficult to collect sufficient information about the spatial distribution of snow, particularly in windy environments. The absence or presence of snow can be estimated by remote sensing, but estimates of water equivalent in the snowpack are too coarse or unreliable. Therefore, to estimate anticipated effects of climate change in snow-dominated watersheds, a modeling approach represents an attractive method and also the only possible solution to projecting future snow distribution realistically.

The objectives of this study were (1) to investigate the timing of and intensity of snow accumulation, snowmelt, and runoff for historical and future climate scenarios at regional and watershed scales and (2) to produce historical and future intensity-duration-frequency (IDF) curves for the study sites. This study produced historical and future snow trends through modeling at three military sites (in Washington, Colorado, and North Dakota) and the Western U.S.

Technical Approach

The approach centered on observed data synthesis and modeling of snow processes. The project team focused the snow accumulation and ablation characterization at a regional scale of the Western U.S., Joint Base Lewis McChord Yakima Training Center, Washington; Fort Carson, Colorado; and Grand Forks Air Force Base, North Dakota. This project relied heavily on field data collection to validate model outputs and weather information. SnowModel (Liston and Elder 2006) was the choice of model for snow simulations. To more accurately characterize snow distribution patterns and the amount of snow on the ground, the project team carried out spatial snow depth and snow water equivalent measurements. The project team performed historical snow simulations, including 36 years (1 September 1979 to 1 September 2015) for the three local sites and the Western U.S. From the snow-modeling simulations, the project team produced spatially explicit historical and future snow depth, duration, and snowmelt events. For the hydrological modeling effort, the project team used the HydroFlow and variable infiltration capacity (Liang et al. 1994; Liang, Wood, and Lettenmaier 1996) models. Modeled and observed streamflow in selected watersheds at each study sites were compared. For selected rivers, seasonal trend analysis of discharge extremes were performed. The project team calculated flood frequency curves and estimated the probability of occurrence of future annual maximum daily rainfall depths. Additionally, the project team generated IDF to find rainfall intensities at several return levels.


Generally, the results showed a decreasing trend in historical and future snow duration, rain-on-snow events, and snowmelt runoff. This decreasing trend in snowpack could reduce water resources. A statistically significant increase in maximum streamflow for most rivers at the Washington and North Dakota sites occurred for several months of the year. In Colorado, only a few months indicated such an increase. Future IDF curves for Colorado and North Dakota indicated a slight increase in rainfall intensity whereas the Washington site had about a twofold increase.


This study advances the understanding of a spatial snow and runoff climatology of past and future projections at a regional scale and at selected military installations. Not only are the presented streamflow analysis and precipitation-intensity estimates for the study sites important for infrastructure planning and risk assessments, but the methods are also transferable to a myriad of locations where snowmelt and its subsequent runoff present infrastructure challenges. Additionally, this work provides a new understanding of IDF curves in a changing climate. Overall, the work presented helps DoD define impacts from a changing climate, providing the information necessary to develop mitigation or adaptation strategies and lowering operational costs.


Liang, Xu, Dennis P. Lettenmaier, Eric F. Wood, and Stephen J. Burges. 1994. “A Simple Hydrologically Based Model of Land Surface Water and Energy Fluxes for General Circulation Models.” Journal of Geophysical Research: Atmospheres 99 (D7): 14415–28. https://doi.org/10.1029/94JD00483.

Liang, Xu, Eric F. Wood, and Dennis P. Lettenmaier. 1996. “Surface Soil Moisture Parameterization of the VIC-2L Model: Evaluation and Modification.” Global and Planetary Change 13 (1–4): 195–206. https://doi.org/10.1016/0921-8181(95)00046-1.

Liston, Glen E., and Kelly Elder. 2006. “A Distributed Snow-Evolution Modeling System (SnowModel).” Journal of Hydrometeorology 7:1259–76. https://doi.org/10.1175/JHM548.1.