The border-line Rivers are #3 and #10 for which the confidence li

The border-line Rivers are #3 and #10 for which the confidence limits are ±0.29, ±0.27 and therefore their respective sample estimates of 0.28 and 0.26 for ρ1 are found to be well contained within the confidence limits. So for the purpose of hydrologic drought analysis, the annual SHI sequences of rivers considered in this paper Selleck CYC202 are regarded to be independent normal sequences. For each river, the values of statistics μ, σ or cv and γ of monthly flow series were computed ( Table 2) and necessary plots were prepared

in terms of the product moments and L-moments. The scatter of points (γ against cv) in the product moment ratio diagram ( Fig. 2A) is a good indicator of the probability distribution of monthly flows to be Gamma rather than Lognormal Anti-infection Compound Library solubility dmso pdf. To affirm the hypothesis of the Gamma distribution, the L-moments were computed for the Gamma pdf and the plot of L-skewness (τ−3) versus L-kurtosis (τ−4) ( Vogel and Fennessey, 1993) was drawn. The L-moment plot (L-kurtosis versus

L-skewness) exhibits a good correspondence between the observed and the Gamma distributed points ( Fig. 2B) thus affirming the hypothesis that the Gamma pdf is a reasonable descriptor of the monthly flow series for rivers under consideration. It is to be noted that 12 sets of cv and γ values were averaged-out (designated as cvav and γav (where, γav represents the average value of 12 values of cross correlations between adjoining months. That is, the cross correlation between January–February, February–March, and so

on (as summarized in Table 2) for plotting purposes and they also proved to be a better estimator of the drought duration, E(LT) and magnitude, E(MT). Once the underlying probability distribution of monthly flows was chosen, the next step was to identify the dependence structure in the SHI sequences using lag-1 autocorrelation (ρ1). The computed values Sitaxentan of ρ1 were found to be significant ( Table 2), which alludes to that monthly SHI sequences possess dependence structure. Furthermore, the autocorrelation function of the SHI sequences ( Box and Jenkins, 1976) was found to mimic the process of an autoregressive order one (AR-1). The diagnostic checks based on the Portmanteau statistics (computed from first 25 values of autocorrelations of the residuals in the SHI sequences after fitting AR-1 model) further affirmed the Markovian dependence. In succinct terms, the monthly SHI sequences possess the first order dependence implying that a drought length model must contain terms to account for such dependence. Based on the foregoing analysis, the extreme number theorem and the Markov chain-1 models can be considered as potential models to capture the first order dependence structure in monthly SHI sequences. For identification of the pdf of weekly flow series, the same procedure used for monthly flows was adopted.

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