NonFloodAdj: Non-flood adjustment
In UKFE: UK Flood Estimation
NonFloodAdj R Documentation
Non-flood adjustment
Description
Adjusts the linear coefficient of variation (Lcv) and the linear skewness (LSkew) to account for non-flood years
Usage
NonFloodAdj(x)
Arguments
x
The annual maximum sample. Numeric vector
Details
The method is the "permeable adjustment method" detailed in chapter 19, volume three of the Flood Estimation Handbook, 1999. The method makes no difference for sites where there are no annual maximums (AM) in the sample that are < median(AM)/2. Once applied the results can be used with the LRatioChange function to update the associated member of a pooling group. There is also the NonFloodAdjPool() function which can be used for multiple sites in a pooling group. The non flood adjustment procedure makes the assumption that annual maxima below QMED/2 are not from the same distribution and will result in a biased estimate. In turn it assumes that the AMAX are from a stationary process. The process adds uncertainty to the usual fitting process for three main reasons. Firstly, the definition of non-flood year (QMED/2). Secondly, the reduced sample size. Thirdly, the calculation process is based, in part, on the proportion of non-flood years to flood years. This proportion has uncertainty as a function of the sample size and the proportion because the standard error of a proportion (p) = sqrt((p * (1 - p)) / n).
Value
A list is returned. The first element of the list is a dataframe with one row and two columns - the adjusted Lcv in the first column and Lskew in the second. The second element of the list is another dataframe with one row and three columns. Number of non-flood years in the first column, sample size in the second and the percent of non-flood year in the third.
Author(s)
Anthony Hammond
Examples
# Get an annual maximum sample with a BFIHOST above 0.65 and with some
# annual maxima lower than half the median of the AMAX series, then apply the function
NonFloodAdj(GetAM(44013)[, 2])
UKFE documentation built on Nov. 5, 2025, 6:25 p.m.
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| NonFloodAdj | R Documentation |
Non-flood adjustment
Description
Adjusts the linear coefficient of variation (Lcv) and the linear skewness (LSkew) to account for non-flood years
Usage
NonFloodAdj(x)
Arguments
x |
The annual maximum sample. Numeric vector |
Details
The method is the "permeable adjustment method" detailed in chapter 19, volume three of the Flood Estimation Handbook, 1999. The method makes no difference for sites where there are no annual maximums (AM) in the sample that are < median(AM)/2. Once applied the results can be used with the LRatioChange function to update the associated member of a pooling group. There is also the NonFloodAdjPool() function which can be used for multiple sites in a pooling group. The non flood adjustment procedure makes the assumption that annual maxima below QMED/2 are not from the same distribution and will result in a biased estimate. In turn it assumes that the AMAX are from a stationary process. The process adds uncertainty to the usual fitting process for three main reasons. Firstly, the definition of non-flood year (QMED/2). Secondly, the reduced sample size. Thirdly, the calculation process is based, in part, on the proportion of non-flood years to flood years. This proportion has uncertainty as a function of the sample size and the proportion because the standard error of a proportion (p) = sqrt((p * (1 - p)) / n).
Value
A list is returned. The first element of the list is a dataframe with one row and two columns - the adjusted Lcv in the first column and Lskew in the second. The second element of the list is another dataframe with one row and three columns. Number of non-flood years in the first column, sample size in the second and the percent of non-flood year in the third.
Author(s)
Anthony Hammond
Examples
# Get an annual maximum sample with a BFIHOST above 0.65 and with some
# annual maxima lower than half the median of the AMAX series, then apply the function
NonFloodAdj(GetAM(44013)[, 2])
R Package Documentation
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