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The runoff curve number (also called a curve number or simply CN) is an empirical parameter used in hydrology for predicting direct runoff or infiltration from rainfall excess. [1] The curve number method was developed by the USDA Natural Resources Conservation Service , which was formerly called the Soil Conservation Service or SCS — the ...
This approach is adopted from the NRCS (SCS) curve number method for estimating runoff. It assumes that the total infiltration capacity of a soil can be found from the soil's tabulated curve number. During a rain event this capacity is depleted as a function of cumulative rainfall and remaining capacity.
Among these are the Green and Ampt (1911) [9] method, Parlange et al. (1982). [10] Beyond these methods, there are a host of empirical methods such as SCS method, Horton's method, etc., that are little more than curve fitting exercises.
A number of methods can be used to calculate time of concentration, including the Kirpich (1940) [2] and NRCS (1997) [3] methods. Time of concentration is useful in predicting flow rates that would result from hypothetical storms, which are based on statistically derived return periods through IDF curves .
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There are many ways to classify research designs. Nonetheless, the list below offers a number of useful distinctions between possible research designs. A research design is an arrangement of conditions or collection. [5] Descriptive (e.g., case-study, naturalistic observation, survey) Correlational (e.g., case-control study, observational study)
Blocking evolved over the years, leading to the formalization of randomized block designs and Latin square designs. [1] Today, blocking still plays a pivotal role in experimental design, and in recent years, advancements in statistical software and computational capabilities have allowed researchers to explore more intricate blocking designs.
k = number of factors (= 1 for these designs) L = number of levels; n = number of replications; and the total sample size (number of runs) is N = k × L × n. Balance dictates that the number of replications be the same at each level of the factor (this will maximize the sensitivity of subsequent statistical t- (or F-) tests).