One of the most important tools in water management is the accurate forecast of long-term and short-term extreme values for flood and drought conditions. Traditional methods of trend detection are not suited for hydrologic systems while traditional methods of predicting extreme frequencies may be highly inaccurate in lakes. Traditional frequency estimates assume independence from trend or initial stage. However, due to autocorrelation of lake levels, initial stage can greatly influence the severity of an event. This research utilizes the generalized extreme value (GEV) distribution with time and starting stage covariates to more accurately identify trend direction and magnitude and provide improved predictions of flood and drought stages. Traditional methods of predicting flood or drought stages significantly overpredict or underpredict stages depending on the initial stage. Prediction differences can exceed one meter, a substantial amount in regions with flat topography; these differences could result in significant alterations in evacuation plans or other management decisions such as how much lake water to release in preparation for an approaching hurricane, appropriate lake levels to maintain, minimum structure floor elevations and more accurate forecasting of future water supply or impacts to tourism. The methods utilized in this research can be applied globally.
Use of generalized extreme value covariates to improve estimation of trends and return frequencies for lake levels
Shayne Paynter, Mahmood Nachabe; Use of generalized extreme value covariates to improve estimation of trends and return frequencies for lake levels. Journal of Hydroinformatics 1 January 2011; 13 (1): 13–24. doi: https://doi.org/10.2166/hydro.2010.077
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