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Dramatic Fluctuations of Devils Lake North DakotaUtah Water Research Laboratory Utah State University Logan UT 84322-8200The recent 1992-date record rise in the level of the Devils Lake North Dakota has led to a number of questions as to the nature of regional and global climate variability and the utility of existing methods for forecasting lake levels and assessing the associated flood risk A purpose of the work presented here was to explore the connection of the Devils Lake volumetric fluctuations to interannual and longer regional and global climate fluctuations and to test the performance of recently proposed time series forecasting methods Wiche and Vecchia 1995 and Osborne 1998 provide background information on the lake and prior forecasting and climate analysis Key trends in hydroclimatic variables in the Devils Lake region are first identified and discussed in the context of large-scale hydroclimate variations Hypotheses as to operative climatic mechanisms that have led to the recent rise in the lake level are developed from this analysis Two types of long-range lake forecasts are then considered A forecast of lake levels for the near future 1-5 years or an inter-annual period is developed for assessing the potential of continued flooding and associated needs for disaster relief Second since closed basin lakes typically exhibit long memory procedures to estimate conditional probabilities of lake levels for extended horizons eg over a 30-year flood control project or inter-decadal periods given current conditions were explored Nonlinear time series analysis methods using the historical volumes of Devils Lake and selected climate indices as predictors were used to develop the inter-annual forecasts as conditional means of expected future volumes A variety of time series modeling approaches were explored for the inter-decadal forecasts Results are presented here for a linear Bayesian autoregressive time series model that incorporates model and parameter uncertainty We conclude that direct applications of existing time series analysis methods are not well suited for the development of long-range probabilistic forecasts of Devils
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