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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...
Categories: Project;
Types: Map Service,
OGC WFS Layer,
OGC WMS Layer,
OGC WMS Service;
Tags: 2017,
CASC,
Completed,
Drought,
Drought, All tags...
Drought, Fire and Extreme Weather,
Drought, Fire and Extreme Weather,
Fire,
Fire,
Other Water,
Other Water,
Projects by Region,
Rivers, Streams and Lakes,
Rivers, Streams and Lakes,
Southwest,
Southwest CASC,
Water, Coasts and Ice,
Water, Coasts and Ice,
climate change,
drought,
extremes,
forest fire,
management,
snow,
streamflow,
water,
water planning,
water resources,
wildland fire, Fewer tags
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The volume of water reaching reservoirs during the April-July growing season is critical to meeting water demands for agriculture and other human demands. However, our ability to forecast seasonal water supplies is hindered by extreme and changing snowpack. In this research, we investigate how current water supply forecasts will be impacted by a future with less and earlier snowmelt and what can be done to improve those forecasts. Our analysis over 30+ years shows that statistical regression models are generally more skillful than more complex, conceptual models. However, our results suggest that statistical models are less skillful in low snowpack (i.e. snow drought) years than the conceptual models. Results show...
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Accurately modeling the effects of variable forest structure and change on snow distribution and persistence is critical to water resource management. The resolution of many snow models is too coarse to represent heterogeneous canopy structure in forests, and therefore, most models simplify forest effects on snowpack mass and energy budgets. To quantify the loss of snowpack prediction from simplifications of forest canopy-mediated processes, we applied a high-resolution energy balance snowpack model at two forested sites at a fine (1 m2) and coarse (100 m2) spatial resolution. Simulating open and forested areas separately, as is done in many land surface models (LSMs), leads to biases between the coarse and fine-scale...
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Modeling forest change effects on snow is critical to resource management. However, many models either do not appropriately model canopy structure or cannot represent fine‐scale changes in structure following a disturbance. We applied a 1 m2 resolution energy budget snowpack model at a forested site in New Mexico, USA, affected by a wildfire, using input data from lidar to represent prefire and postfire canopy conditions. Both scenarios were forced with 37 years of equivalent meteorology to simulate the effect of fire‐mediated canopy change on snowpack under varying meteorology. Postfire, the simulated snow distribution was substantially altered, and despite an overall increase in snow, 32% of the field area displayed...
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