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The effect of the October 1983 floods in southeastern Arizona, on a previously established generalized envelope for floods expected once in 100 years (Q100),is studied. The design envelope is found to produce more conservative estimates of Q100than individual data sets find. The design envelope for Q100is revised to correct for some longer periods of record now available, and to be consistent with floods on a wider range of drainage area than previously considered. Additional design envelopes for floods expected once in 2 years (Q2)and once in 10 years (Q10)are prepared, and the three envelopes are used to provide conservative estimates of flood frequencies on ungaged watersheds in southeastern Arizona with drainage areas between 0.01km2and 10,000km2.A procedure is presented for developing regional flood frequency estimates that could be used in geographically and climatically homogeneous areas.

Variations in surface storage capacity over a watershed produce partial areas of saturation overland flow, which vary from storm to storm. A method is described by which the proportions of a watershed that contributes overland flow in different storms and at different times during the same storm can be determined by analysis of rainfall and runoff records. The method is demonstrated on two catchments in Queensland, Australia, with different hydrological characteristics. One watershed is 16.8 ha in area with only two to three events per year of overland flow no baseflow averaging 35 mm per annum runoff; the other is 7 km in area with substantial baseflow and an average of 920 mm per annum runoff. The calculated amounts and proportions of surface storage capacity are incorporated into water balance models of watershed runoff to show the accuracy of runoff estimation.

A method is presented by which the proportions of a watershed that contribute surface runoff in different storms and at different times during the same storm can be determined by analysis of rainfall and runoff records. The method is a modification of a method described in an earlier paper, which eliminates the need for subjective selection of particular runoff events for analysis. The method is illustrated using data from a 16.8ha watershed in Queensland, Australia, and the estimates of runoff from the different source areas are compared with actual records of runoff from the whole watershed. Runoff occurred from the entire watershed area on only three occasions in the 15yr study period, about 10% of runoff events. In about twothirds of runoff events, runoff came only from the 15% of the watershed that has the smallest surface storage capacity.

The paper describes a procedure for direct, optimal fitting of the logBoughton frequency distribution to either complete sets or subsets of annual flood data. The analytical basis and derivation of the procedure are given. Annual floods from 65 yr of record on the 5,755‐km2Santa Cruz River at Tucson, Arizona, are used to show the fitting of the distribution to a complete data set, and annual floods from 24 yr of record on the 149‐km2Walnut Gulch watershed, in southeastern Arizona, are used to show the fitting of the distribution to a subset of data. Substantial differences between the logBoughton and logPearson type 3 distributions occur in fitting to the Walnut Gulch data, due mainly to the large negative skew coefficient (-2.83)of this data set. The computer program which fits the logBoughton distribution plots the data points on a probability paper which is automatically scaled to linearize the fitted distribution.

The paper describes a procedure for direct, optimal fitting of the log‐Boughton frequency distribution to either complete sets or subsets of annual flood data. The analytical basis and derivation of the procedure are given. Annual floods from 65 yr of record on the 5,755‐km2 Santa Cruz River at Tucson, Arizona, are used to show the fitting of the distribution to a complete data set, and annual floods from 24 yr of record on the 149‐km2 Walnut Gulch watershed, in southeastern Arizona, are used to show the fitting of the distribution to a subset of data. Substantial differences between the log‐Boughton and log‐Pearson type 3 distributions occur in fitting to the Walnut Gulch data, due mainly to the large negative skew coefficient (−2.83) of this data set. The computer program which fits the log‐Boughton distribution plots the data points on a probability paper which is automatically scaled to linearize the fitted distribution.