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MULTIYEAR DROUGHT SIMULATION 1
Author(s) -
Wijayaratne Lankeswara H.,
Golub Eugene
Publication year - 1991
Publication title -
jawra journal of the american water resources association
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.957
H-Index - 105
eISSN - 1752-1688
pISSN - 1093-474X
DOI - 10.1111/j.1752-1688.1991.tb01438.x
Subject(s) - unavailability , watershed , series (stratigraphy) , flow (mathematics) , environmental science , truncation (statistics) , duration (music) , hydrology (agriculture) , magnitude (astronomy) , return period , statistics , water resources , mathematics , climatology , computer science , geography , geology , ecology , art , paleontology , physics , geometry , literature , geotechnical engineering , astronomy , machine learning , archaeology , biology , flood myth
Previous studies on multiyear droughts have often been limited to the analysis of historic annual flow series. A major disadvantage in this approach can be described as the unavailability of long historic flow records needed to obtain a significant number of drought events for the analysis. To overcome this difficulty, the present study proposes to use synthetically generated annual flow series. A methodology is presented to model annual flows based on an analysis of the harmonic and stochastic properties of the observed flows. Once the model is determined, it can be utilized to generate a flow series of desired length so as to include many hydrologic cycles within the process. The key parameter for a successful drought study is the truncation level used to distinguish low flows from high flows. In this paper, a concept of selecting the truncation level is also presented. The drought simulation procedure is illustrated by a case study of the Pequest watershed in New Jersey. For the above watershed, multiyear droughts were derived from both historic and generated flow series. Three important drought parameters, namely, the duration, severity, and magnitude, were determined for each drought event, and their probability distributions were studied. It was found that gamma and log normal probaility functions produce the best fit for the duration and severity, respectively. The derived probability curves from generated flows can be reliably used to predict the longest drought duration and the largest drought severity within a given return period.