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<br />potcntial increases from vcry \\"ct winter seasons may be truncatcd however due to seeding <br />suspcnsion criteria bcing invoked (c.g., high pcrccnt ofnonnal snowpack. potential flood <br />producing storms. etc.). One vcry significant advantage of the Colorado River system is the <br />presence of impoundments that offer significant storage capacity (i.c. Flaming Gorge. Lake <br />Powcll and Lake ~tcad). Excess runoff produced through cloud seeding during "',:et years can <br />almost always yield valuable earryovcr storagc in these reservoirs. <br /> <br />As a consequence. routine application of weather modification technology year after ycar <br />can hclp stabilize and bolster the watcr supplics in both surface and underground storagc. <br />Commitment to conduct a program cach winter provides stability and acceptance by funding <br />agcncies and the general public. Programs can be designcd so that they can be temporarily <br />suspended or tcmlinated during a given winter season should snowpack accumulatc to the point <br />where additional \....ater may not be beneficial. <br /> <br />Other reasons to conduct programs in an ongoing fashion. rathcr than only during drier- <br />than-normal wintcrs. arc that I) it is vcry difficult to predict a wet or dry season in advance, 2) a <br />scason could start out wet but turn dry. resulting in missed seeding opportunities in the wet <br />period. 3} dricr seasons, by dcfinition. \vill have fewer seeding opportunities. which mcans the <br />total \....atcr increase due to sceding ......ill be less. and 4) seeding in nonnal and abo....e-nonnal water <br />years will provide additional water supplies (surfaec and undcrground carryover) for use in dry <br />periods. <br /> <br />13.0 :\Ierhods of F.nllu:lting the F.Uecti':eness of Operational C10uLl Seeding <br />Programs <br /> <br />The task of determining the effects of cloud sceding has received considerablc attention <br />ovcr thc years. Evaluating the results of a cloud seeding program for a particular season is rather <br />dillieuh. Thc primary rcason for the difficulty stems from the largc natural variability in the <br />amounts of precipitation that occur in a givcn area and between onc area and another during a <br />givcn scason. Sincc cloud seeding is normally feasible only whcn existing clouds arc nearly (or <br />already arc) producing precipitation. it is hard to tell if. and how mllch. the precipitation was <br />actually increased by seeding. The ability to dctcct a seeding effect becomcs a function of the <br />magnitude of the seeding increase and the number of seeded cvents. comparcd \\'ith the natural <br />variability in the precipitation rceord. Larger seeding elfects can be detected more easily and <br />with a smaller number of sce-ded cases than are required to detect small increases. Therc arc threc <br />basic methods of potentially detecting the effects of cloud seeding: I) statistical approaches. 2) <br />physical approaches. and J) modeling approaches. <br /> <br />13.1 Statistical Approaches <br /> <br />Historically. the most significant seeding results have been observed in wintertime <br />seeding programs in mountainous areas. However. the apparent dilTerences due to seeding arc <br />relatively small relative to natural precipitation variability. being on the order ofa 5-20 percent <br />scasonal increase. In part. this accounts for thc significant number of cases required to cstablish <br />thcse results (oftcn five years or more). <br />