The objective of this study was the development of models by
which air temperature and precipitation data can be generated for
spring periods for use in mathematical runoff models, to predict the
range and probability of snowmelt floods.
In the upper midwestern United States, spring floods due to a
combination of snowmelt and rainfall inflict large amounts of damage.
To predict snowmelt floods, information is needed concerning the water
equivalent of snow on the ground, soil conditions and hydrometeorological
data. The water equivalent of snow and soil conditions can usually
be obtained at the time of the forecast. However, it is currently not
possible to predict temperature, precipitation and other meteorological
factors over a three or four-week interval critical to the spring floods.
Simulation of such data by stochastic models should provide a basis for
determination of flood probabilities and the range of possible flood
magnitudes for current conditions.
Hydrologic Laboratory National Weather Service; National Oceanic and Atmospheric Administration
Kim, Kwonshik; Bowers, C. Edward.
Stochostic Analysis of Spring Meteorological Data in the Upper Midwest.
St. Anthony Falls Hydraulic Laboratory.
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