Forecasting Snowmelt: Making Probabilistic SWE Modeling More Accessible

In snowmelt driven watersheds, spring water supply forecasts hinge on one key variable: snow water equivalent (SWE). Most forecasters today rely on familiar visual tools — plotting the current year's SWE trajectory against the backdrop of all historical traces. These charts tell us where we stand relative to the past, but not necessarily where we might go.

In this article, I introduce a more accessible approach to snowmelt forecasting that produces an ensemble of possible futures.

Using a pair of stochastic weather generation tools for daily minimum/maximum temperature and precipitation totals, I built a model that forecasts random daily weather from today forward, then runs those values through a SNOW-17 snowmelt model to calculate future SWE. This produces a full range of plausible snowpack and melt outcomes, based on the statistical patterns observed in historical data at the Parley’s Summit SNOTEL site in Utah.

The model is published in the GoldSim Model Library and can be run using the free GoldSim Player. Anyone can download the model, run the parameter generator using your data, and generate a custom probabilistic forecast for snow accumulation and melt to assist with water supply forecasts.

This work builds on a strong foundation of peer-reviewed science. Stochastic weather generators and ensemble snowmelt forecasting have been explored in academic research for decades — including the use of K-nearest-neighbor weather generators and climate-conditioned simulations (e.g., Hobson, 2005; Yates et al., 2003). But these tools have often remained trapped in complicated toolkits, obscure scripts, or proprietary modeling frameworks. My goal is to make them easier to use and more widely available.

Why Go Beyond Historical Traces?

Most current snowpack forecast plots show the current year's SWE overlaid on a shaded band of historical data. This approach, derived from the Ensemble Streamflow Prediction (ESP) method, is simple and intuitive. Below are a couple of examples of this type of visualization that you might find online.

Image sources: NWCC Snow Water Equivalent Plot

Another example, showing statistical categories relative to the water year to date, measured.

But this method of visualizing snowpack has limitations:

• It only includes the number of traces equal to the number of historical years. That means your forecast spread is inherently narrow and may underrepresent extremes.
• It assumes the future weather will match some year from the past. In a changing climate, this is a questionable assumption.

Stochastic modeling breaks free from those constraints by generating hundreds (or thousands) of synthetic weather sequences that match the statistical patterns of historical weather, but are not identical to any one year. It’s a more flexible, forward-looking approach that enables you to explore plausible futures, not just past analogs.

How It Works

This model begins with today's measured SWE and then simulates daily temperature and precipitation forward in time using calibrated stochastic weather generators like TempGen and PrecipGen:

• PrecipGen uses a Markov chain-gamma process to simulate wet/dry days and daily rainfall amounts.
• TempGen simulates daily minimum and maximum temperatures, preserving the observed seasonal cycles and variability from historical data.

These synthetic weather sequences then drive a SNOW-17 snowmelt model implementation built in GoldSim. Each realization represents one possible future, and the results can be aggregated to show percentiles, average outcomes, or even outliers.

The result is a transparent, easy-to-run, and statistically grounded forecast of future SWE, customizable for your location and forecasting needs. This allows for risk-based decision-making, grounded in site-specific climate data and updated in real time.

Instead of viewing the chart with shades for each percentile range, you can view specific probabilities, like Min (<1%), Max(>99%), and Mean. The blue line is the measured SWE up until 03/01/2025 (when this study was done).

Model Testing and Validation

You can also use this model to test the performance by comparing statistical summaries to those of the historical record. Below are screen captures of plots made for the model using data from the Parley’s Summit SNOTEL site.

Notice the temperature and precipitation outputs are similar to the same statistics of the 21 years of historical records, with the exception of the maximum of historical SWE years. That maximum historical SWE line corresponds to water year 2014, which I have been unable to reproduce with this initial modeling work. Nonetheless, the model tracks quite closely with real SWE accumulation since March 1.

How is our model performing relative to observed SWE since March 1? As of March 20, the observed SWE was just above 16 inches — falling close to the >99% forecasted value from 20 days earlier, based on our stochastic simulation. That gives us confidence that the model is capturing the upper bound of melt potential quite well.

It looks like we are currently on track to be near the maximum observed snow accumulation rate, measuring just above 16 inches as seen above. Looking at this point on my March first forecast, that corresponds to a value just below the maximum probable (>99% chance) SWE that could occur from the deterministic state we found ourselves in 20 days ago, then proceeded with near record-breaking precip and temperatures allowing for added SWE this late in the season.

Try It Yourself — Free

All of this runs in the GoldSim Player, which is free to download and doesn’t require a license. Once you download the Parley’s Summit SWE Forecast model from the GoldSim Model Library, just open the file and hit Run. The model outputs a full set of future SWE traces — and you can change the number of realizations, simulation duration, or even connect it to your own runoff or reservoir models.

This isn’t just a tool for researchers. It’s designed for:

• Water managers who need to explore a range of snowmelt scenarios.
• Engineers modeling reservoir operations based on snowpack uncertainty.
• Educators teaching the principles of probabilistic forecasting.

Building Tools for a Changing Climate

Stochastic modeling of weather variables like precipitation and temperature offers a powerful way to understand uncertainty and variability in future water supply conditions. By generating a wide range of plausible weather scenarios—not just repeating historical years—we can better anticipate how a watershed might respond under different conditions.

This approach provides a more realistic representation of risk than deterministic or analog-based methods, especially in a changing climate where historical norms may no longer apply. When used to drive hydrologic models like SNOW-17 (or others like HBV, widely used in Europe), stochastic weather simulations can help identify system vulnerabilities, inform reservoir operations, and support more robust flood and drought preparedness.

Making these tools available through the GoldSim Player means agencies, consultants, and educators can now incorporate probabilistic thinking into everyday water decisions — without requiring complex software or technical expertise.

Download the Parley's Summit SWE Forecast application model (Goldsim or Player version) from here: Snow Accumulation and Ablation Model - Snow-17 – GoldSim Help Center

References

Hobson, A. (2005). Use of a Stochastic Weather Generator in a Watershed Model for Streamflow Simulation. Master’s Thesis, University of Colorado, Boulder. Available online

Rajagopalan, B., & Lall, U. (1999). A K-nearest-neighbor simulator for daily precipitation and other weather variables. Water Resources Research, 35(10), 3089–3101. https://doi.org/10.1029/1999WR900028