Learning From Recent Snow Droughts to Improve Forecasting of Water Availability for People and Forests Active
In the dry southwestern United States, snowmelt plays a crucial role as a water source for people, vegetation, and wildlife. However, snow droughts significantly lower snow accumulations, disrupting these critical water supplies for local communities and ecosystems. Despite its large influence on land- and water-resource management, snow drought has only recently been properly defined and its historical distribution and effects on key natural resources are essentially unknown. To remedy this serious knowledge gap, project researchers are examining the causes, effects, and forecastability of snow drought to provide needed scientific information and guidance to planners and decision makers.
The central goals of this proposal are to better quantify the impact of snow droughts on municipal and ecosystem water supplies and improve the scientific information accessible to a wide range of resource managers. The project consists of three primary objectives:
1) Document the types, frequencies, and proximate causes of historical snow drought using snow and climate observations,
2) Assess streamflow forecasting abilities following snow drought using the operational regression-based forecasts used by water management agencies, and
3) Identify areas where streamflow forecast skill is improved by incorporating snow drought information.
Preliminary analyses indicate that several different types of snow droughts occur in the Southwest, arising from a variety of different factors. “Dry snow drought” is caused by a lack of winter precipitation needed to accumulate snow. “Warm snow drought” can be caused by early snowmelt or by precipitation falling as rain rather than snow. The project team is also using SNOTEL data in models to predict the occurrence of these different types of snow droughts across the Southwest. Their next steps will be to determine how these different types of snow drought affect streamflow forecasting and develop strategies to improve these forecasts.
- Source: USGS Sciencebase (id: 5937201ae4b0f6c2d0d89a85)
In the dry southwestern United States, snowmelt plays a crucial role as a water source for people, vegetation, and wildlife. However, snow droughts significantly lower snow accumulations, disrupting these critical water supplies for local communities and ecosystems. Despite its large influence on land- and water-resource management, snow drought has only recently been properly defined and its historical distribution and effects on key natural resources are essentially unknown. To remedy this serious knowledge gap, project researchers are examining the causes, effects, and forecastability of snow drought to provide needed scientific information and guidance to planners and decision makers.
The central goals of this proposal are to better quantify the impact of snow droughts on municipal and ecosystem water supplies and improve the scientific information accessible to a wide range of resource managers. The project consists of three primary objectives:
1) Document the types, frequencies, and proximate causes of historical snow drought using snow and climate observations,
2) Assess streamflow forecasting abilities following snow drought using the operational regression-based forecasts used by water management agencies, and
3) Identify areas where streamflow forecast skill is improved by incorporating snow drought information.
Preliminary analyses indicate that several different types of snow droughts occur in the Southwest, arising from a variety of different factors. “Dry snow drought” is caused by a lack of winter precipitation needed to accumulate snow. “Warm snow drought” can be caused by early snowmelt or by precipitation falling as rain rather than snow. The project team is also using SNOTEL data in models to predict the occurrence of these different types of snow droughts across the Southwest. Their next steps will be to determine how these different types of snow drought affect streamflow forecasting and develop strategies to improve these forecasts.
- Source: USGS Sciencebase (id: 5937201ae4b0f6c2d0d89a85)