Term Papers on Dramatic Fluctuations Of Devils Lake, ND

Term Paper Title	Dramatic Fluctuations Of Devils Lake, ND
# of Words	10453
# of Pages (250 words per page double spaced)	41.81

Dramatic Fluctuations Of Devil's Lake, ND

Dramatic Fluctuations of Devils Lake, North Dakota:
Climate Connections and Forecasts

Connely K. Baldwin and Upmanu Lall
Utah Water Research Laboratory, Utah State University, Logan, UT 84322-8200
Introduction

The 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 (e.g., 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 Lake. The recent trends of Devils Lake are consistent with large-scale changes seen elsewhere. However, whether these changes are part of the natural long-term variability of climate or represent a changed climate due to human influence remains inconclusive. Consequently, while we are able to relate the recent Devils Lake trend to causative hydroclimatic factors, we are unable to confidently predict the long-term future levels of the lake. Only qualitative remarks are offered to characterize the uncertainty associated with using the past as a guide to the future of Devils Lake.

Trends in Local, Regional and Hemispheric Hydroclimatic Variables

Like most closed basin lakes, the Devils Lake exhibits dramatic fluctuations (Figure 1) over decadal and longer periods, that derive from climatic fluctuations. There is a limited literature (e.g., Lall and Mann, 1995, Mann et al, 1995, Pusc, 1993, Wiche et al., 1986) diagnosing the climatic causes of such fluctuations. The contributing drainage area of such lakes varies with climate state. The chain lakes above Devils Lake and other depressions store water during dry periods, but contribute runoff during protracted wet periods. This change in drainage area may be a key explanation for the dramatic changes in the Lake’s volume, subsequent to moderate changes in the climate signal. Notable increases occur in 1950’s, 1970, 1980’s, and the late 1990’s. These periods are also important at the regional streamflow stations.

It is useful to first look at the annual cycle of monthly changes in the Devils Lake volume (Figure 2), to motivate the search for trends in monthly precipitation, temperature, Sea Level Pressure (SLP), and cloudiness. The influence of ice c...

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