Hydrological drought index calculation using Principle of Maximum Entropy
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Abstract:
Selecting the appropriate probability distribution function (PDF) for hydrological variables is of significant importance to calculating standardized drought indices. In this study , we used the Principle of Maximum Entropy (POME) method to model the PDFs of aggregated monthly streamflow on varying time scales for 30 sub-basins of the Hanjiang River Basin. The first three original moments of the cumulative monthly streamflow data were chosen as the constraint functions for maximizing the entropy by the Lagrange Multiplier. The Streamflow Drought Index (SDI) was computed based on monthly streamflow records derived from several theoretical probability distributions such as POME, Normal, Gamma, Weibull, and Pearson Typeó . Results showed that the POME-based PDFs could make the best use of the information from observed records while avoiding mistakenly introducing redundant information. They showed satisfying applicability . We found that the PDFs of cumulative monthly streamflow would trend towards normalization as the time scale increased. The proposed method can be a practical tool for calculating hydrological drought indices, analyzing drought characteristics, and performing drought frequency analysis.
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