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Water 2014, 6(5), 1118-1133; doi:10.3390/w6051118

Retention and Curve Number Variability in a Small Agricultural Catchment: The Probabilistic Approach

Department of River Engineering, Sedimentation Lab, Warsaw University of Life Sciences—SGGW, Nowoursynowska Street 159, Warsaw 02-787, Poland
Department of Applied Mathematics, University of Agriculture, Balicka Street 253C, Cracow 30-198, Poland
Department of Land and Water Resources Management, Slovak University of Technology, Radlinskeho Street 11, Bratislava 813 68, Slovak Republik
Author to whom correspondence should be addressed.
Received: 31 October 2013 / Revised: 16 April 2014 / Accepted: 21 April 2014 / Published: 29 April 2014
(This article belongs to the Special Issue Flood Estimation and Analysis in a Variable and Changing Environment)
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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 km2 to the gauging station) in central Poland has been assessed using the probabilistic approach: distribution fitting and confidence intervals (CIs). Empirical CNs 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. View Full-Text
Keywords: curve number; retention parameter; theoretical distribution; antecedent runoff conditions curve number; retention parameter; theoretical distribution; antecedent runoff conditions

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Banasik, K.; Rutkowska, A.; Kohnová, S. Retention and Curve Number Variability in a Small Agricultural Catchment: The Probabilistic Approach. Water 2014, 6, 1118-1133.

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