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<br />storm-related random variability and to estimate its contribution to the <br />sampling variance of raingage networks. The larger the local random varia- <br />bility is, the denser the gage! network needs to be to achieve a desired level <br />of accuracy in estimating total rain volume. According to the framework of <br />this paper, we can consider that the local variabil ity is caused by perturba- <br />tions of a scale much smaller than that of a raincell and that they are <br />randomly superimposed on the storm and rai ncell i sohyets. For the purposes <br />of this paper, we assume that these small-scale perturbations do not represent <br />meaningful components of the total rainfall of raincells, and basing the <br />analysis on raincells is valid. Since there is only limited, indirect <br />evidence to support this assumption, the importance of local random variabil ity <br />is still an open question which must be left to future additional work to <br />resolve. <br /> <br />3. Procedure <br /> <br />Area-depth and i sohyet-area formul ae rel ate the average preci pit,at ion <br />depth inside an isohyet, or the value of the isohyet itself, to the area <br />within (or distance to) the isohyet. They are usually obtained I~raphically <br />from isohyetal maps. A number of area-depth formulae have been proposed <br />(e.g., Court, 1961; Huff, 196Ba; Fogel and Duckstein, 1969) which are <br />based on various powers of storm area, its logarithm, or its exponential, <br />with each implicitly having a unique precipitation gradient. The isohyet- <br />distance formula used in this work to model raincells is different from <br />previously proposed formulae 'in that it is not based on any part"icular <br />set of data. The approach in this paper is more general in that more degrees <br /> <br />5 <br />