Spatial variability of precipitation in the San Dimas Experimental Forest and its effect on simulated streamflow.

The effect of altitude on individual storm precipitation in some of the San Dimas experimental watersheds is investigated. It is found that there is a well-defined increase of storm precipitation with altitude for storms greater than one inch. This increase is a linear function of storm depth. Using...

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Bibliographic Details
Main Author: Phanartzis, Christos Apostolou,1936-
Other Authors: Kisiel, Chester C.
Language:en
Published: The University of Arizona. 1972
Subjects:
Online Access:http://hdl.handle.net/10150/191559
http://arizona.openrepository.com/arizona/handle/10150/191559
Description
Summary:The effect of altitude on individual storm precipitation in some of the San Dimas experimental watersheds is investigated. It is found that there is a well-defined increase of storm precipitation with altitude for storms greater than one inch. This increase is a linear function of storm depth. Using 41 storms of different magnitudes, a precipitation-altitude relationship is derived for a small area in the San Dimas Experimental Forest. The regionalization of this relationship and its transferability are tested by analyzing differences (errors) between computed and observed storm precipitation values in each case. In testing the regionalization of the precipitation-altitude relationship by computing mean areal storm precipitation over a larger area the standard error of estimate is around 11 percent. In transfering the same relationship the results are not as good and give a standard error of 16 percent. For individual points, however, the error is much higher. A rainfall-runoff model is used as a tool for evaluating the effect of precipitation errors, on simulated streamflow, in a watershed of 4.5 square miles. For annual flows, errors range between 3.4 and 12. 8 percent while errors in simulated monthly flows are as high as 22 percent. It is also evident that there is a strong dependence of the error magnitude on the state (wet, dry, etc.) of the preceding year or months, whichever is applicable. An error propagation is observed as a result of consistently over-estimating the precipitation input to the model. This evaluation is more of a qualitative nature and the values of error given should he viewed in this sense.