Monitoring snow-cover depletion by coupling satellite imagery with a distributed snowmelt model

Many watersheds in Canada and in the northern United States see most of their precipitation in the form of snow. Many of these watersheds are the sites of important hydropower development projects. During snowmelt, watershed managers require information on snowpack depletion in order to optimize power production while minimizing flooding risk. In many cases, management techniques are based on simple correlations extracted from data collected in previous years or on inappropriate tools for data interpretation. Both of these factors can affect the reliability of forecasts and result in production losses or increased risk in downstream areas. This paper presents an approach to improve snowmelt forecasts. A simple distributed snowmelt model based on the degree-days approach is used to predict snowmelt based on weather forecasts. To improve forecasts, a feedback algorithm is presented that allows for real-time model adjustment using the integration of NOAA-AVHRR remote sensing data. A case study is presented based on a central Quebec watershed for the 1999 snowmelt season. This watershed is mostly under the management of Alcan Inc., which uses hydropower for the production of aluminum in its Jonquiere, Que., Canada plant. A real-time simulation was carried out that resulted in a significant improvement of the timing of the flood peak forecast. With only one satellite image, the forecasting error of the flood peak was decreased by 5 days (from 7 to 2) and reduced to less than I day with the use of a second image, acquired 2 days later. For this watershed, each one-day improvement in the timing of the peak flood forecast is worth tens of thousands of dollars in hydropower.