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The main aim of the article was application of some statistical tests for investigation of weak stationarity of hydrologic time series. The tests were applied to mean monthly flow and maximum annual flow on three rivers: two Polish and one American river. Firstly, the modified Mann–Kendall test for autocorrelated data was used to detect trend. After detrending we used “unit root tests” based on the DF test and “stationarity tests” based on the KPSS test. The tests were investigated and compared in some aspects: analysis of residuals, application to seasonal series, AIC and Schwarz values.

Abstract:The variability of the curve number (CN ) and the retention parameter (S) of the Soil Conservation Service (SCS)-CN method in a small agricultural, lowland watershed (23.4 km 2 to the gauging station) in central Poland has been assessed using the probabilistic approach: distribution fitting and confidence intervals (CIs). Empirical CN s and Ss were computed directly from recorded rainfall depths and direct runoff volumes. Two measures of the goodness of fit were used as selection criteria in the identification of the parent distribution function. The measures specified the generalized extreme value (GEV), normal and general logistic (GLO) distributions for 100-CN and GLO, lognormal and GEV distributions for S. The characteristics estimated from theoretical distribution (median, quantiles) were compared to the tabulated CN and to the antecedent runoff conditions of Hawkins and Hjelmfelt. The distribution fitting for the whole sample revealed a good agreement between the tabulated CN and the median and between the antecedent runoff conditions (ARCs) of Hawkins and Hjelmfelt, which certified a good calibration of the model. However, the division of the CN sample due to heavy and moderate rainfall depths revealed a serious inconsistency between the parameters mentioned. This analysis proves that the application of the SCS-CN method should rely on deep insight into the probabilistic properties of CN and S.

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How well do climate models reproduce variability in observed rainfall? A case study of the Lake Victoria basin considering CMIP3, CMIP5 and CORDEX simulations STOCHASTIC ENVIRONMENTAL RESEARCH AND RISK ASSESSMENT, 33(3), 687-707.

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