Browsing Hydrology and Water Resources in Arizona and the Southwest, Volume 02 (1972) by Subjects
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Significance of Antecedent Soil Moisture to a Semiarid Watershed Rainfall-Runoff RelationNumerous reports from the southwest claim that soil moisture prior to rainfall-runoff event has no influence on the resulting flow volumes and peak rates. Runoff occurs from many storms that would not be expected to produce runoff, and an explanation lies in the occurrence of antecedent rains. This hypothesis is tested by dividing runoff events into 2 subsets--one with no rain within the preceding 120 hours, and the other with some rain within the preceding 24 hours--and to test the null hypothesis. The hypothesis was tested with rainfall and runoff data from a 40-acre agricultural research service watershed west of Albuquerque, New Mexico, using the Wilcoxon's rank sum test. Various levels of statistical significance are discussed, and shown graphically, to conclude conclusively that antecedent rainfall influences runoff from a semiarid watershed.
A Solution to Small Sample Bias in Flood EstimationIn order to design culverts and bridges, it is necessary to compute an estimate of the design flood. Regionalization of flows by regression analysis is currently the method advocated by the U.S. Geological Survey to provide an estimate of the culvert and bridge design floods. In the regression analysis a set of simultaneous equations is solved for the regression coefficients which will be used to compute a design flood prediction for a construction site. The dependent variables in the set of simultaneous equations are the historical estimates of the design flood computed from the historical records of gaged sites in a region. If a log normal distribution of the annual peak flows is assumed, then the historical estimate of the design flood for site i may be computed by the normal as log Q(d,i) = x(i) + k(d)s(i). However because of the relatively small samples of peak flows commonly used in this problem, this paper shows that the historical estimate should be computed by to log Q(d,i) = X(i) + t(d,n-1) √((n+1)/n) s(i) where t(d,n-1) is obtained from tables of the Student's t. This t-estimate when used as input to the regression analysis provides a more realistic prediction in light of the small sample size, than the estimate yielded by the normal.