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Last modified
7/28/2009 2:27:39 PM
Creation date
10/1/2006 2:12:02 PM
Metadata
Fields
Template:
Weather Modification
Applicant
Steven M. Hunter
Sponsor Name
California Energy Commission
Project Name
Optimizing Cloud Seeding for Water and Energy in California
Title
Optimizing Cloud Seeding for Water and Energy in California
Prepared For
California Energy Commission
Prepared By
Steven M. Hunter
Date
3/31/2006
State
CA
Country
United States
Weather Modification - Doc Type
Report
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<br />evaluations that \vere conducted previously into perspective, and to assess the applicability of <br />this statistical approach for this purpose. Second. an cvaluation of all the seeding programs was <br />conducted using ratio statistics (Gabriel, 1999), a statistical approach that is inherently more <br />prccise and more robust than the historical regression approach. Gabriel reported that ratio <br />statistics produces results that approximate those from re-randomintion for sample sizes of 100 or <br />more: however, experience by this author has shown that comparable results arc obtained for even <br />smaller samplc sizes. Ratio statistics were emphasized since it is computationally easier to apply <br />than re-randomil.ation and it providcs additional, useful infonnation about confidence intervals. <br />Thc main emphasis in the presentation ofthc results is on confidcncc intcrvals because they <br />infer a range within which thc true elTect lies whereas null hypothesis significance tests infer only <br />whether there is any effect at all (Gabriel, 2002; Nicholls, 2001), It should be noted, ho\vcver, that <br />statements of signilicance are implicit to contidencc intcrval statements. Saying, for example, that <br />therc is 90% contidence that the truc effect of seeding lics bctwecn a Single Ratio (SR) of SR901.O <br />and SR'l()1H is tantamount to saying that the confidence interval result is sih'1liticant at a 2-sided <br />levcl of significancc of 0,10 (l-sidt.x1 level of significance of 0,05), 'olUS, a 90% contidence <br />interval that includes an SR value of 1.0 indicates that the experimcntal rcsult is not signiticant at <br />a 2-sided Icvcl ofsigniticance of 0,10 (I-sided Icvel of significance of 0,05), <br />This is an exploratory study and, as such. it involvcs consideration of a number of <br />hypothcses/analyses. A fter applying the BonfelToni method (see, e,g" Gabriel, 2000) to partition <br />the usual significancc Icvel of 0.05 among aUthc specified tcsts, it is unlikely that any test could <br />satisfy the resuhing level of signiticance, On the other hand. with a largc number of tests, a fe\v <br />might yield significant results purely by chance. Thercfore, it is emphasized that the results of <br />the evaluations in this study arc used only as measurcs of the strenglh of lhe suggcsted sceding <br />elTect. Strictly speaking, the suggested cll'ects that are indicated must bc confimled through new. a <br />priori cxpcriments specitically designed to establish their validity. Ncvertheless, a suggcsted ctTect <br />of sceding might bc of considerable valuc to a watcr manager if, for example, it was known with <br />80% conlidencc that the rcsult of the seeding operation was positive. <br /> <br />4. Entlualion of Kings Ri\'er by the historical regression method <br />A rcgrcssion equation that predicts thc stTeamllow at the targct station, KGF, as a <br />function of the streamflow at the control station, MDP, was derived for thc 33.water year <br />historical pcriod (1922-1954) prior to the start of opcrational secding. The predictor equation for <br />thc water year. October-to-Septembcr, is <br />KGF = 3.832176 * ;VUW -160272.2 Acre-Feet (AF) <br />The correlation coetlicient is 0.936 (see Table 2), This is comparable to the corrclation <br />coetlicient of 0.947 betwcen thesc 2 stations obtaincd by lIendcrson (1966) for thc 25-year base <br />period of 1926-1950, When llcndcrson (1966) added a control station from the Kcrn River in his <br />evaluation of the tirst 10 years of the Kings River seeding program. hc obtaincd a multiple <br />regression cquation having a correlation coellicicnt of 0.989 with a smaller standard elTor of the <br />estimate than the regression cquation based on MDP alone. When IIcndcrson (2003b) cvaluatcd <br />the Kings River seeding program ovcr the first 47 years of the program, he could not usc the <br />Kcrn Rivcr control station because it had bccome a targct of operational seeding that bcgan in <br />1978, so he used Cottonwood Creck as a second control station. <br />Assuming that the relationship bctween the target and control station is valid during the <br />operational period. thc rcgression equation for thc watcr year Octobcr-to-Septcmber was used to <br />predict the streamtlow at thc target station that would have occurred in the absence of seeding for <br /> <br />36 <br />
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