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<br />OOH73 <br /> <br />What makes a GOOD water supply forecast?... a BAD forecast? <br /> <br />Is it as simple as which forecast comes closest to the actual observation? Probably not, as a number of factors <br />necessitate a more sophisticated evaluation of forecast quality be undertaken. Such an evaluation would not be <br />trivial and is beyond the time and space constraints of this note. Nonetheless, with apologies for simplification <br />and omission, some of the factors include: <br /> <br />subsequent climatic conditions - the implicit assumption behind any forecast is that the climatic <br />conditions during the remainder of the snow accumulation and melt season will be "normal". While it may be <br />difficult to adequately define what "normal" is, it is easier to discern conditions that are extreme or "not nonnal". <br />As such, a given forecast at a given time may have been the best forecast possible in light of known conditions, <br />although ultimately turning out to be 20% too low; it just so happened that the ensuing climatic conditions were <br />unusually wet. Just as a good forecast may be made to look bad by abnormal conditions in the future, the reverse <br />situation is also possible. <br /> <br />natural variability of site's streamflow - simply put, some rivers are much more difficult to <br />forecast than others. Historically, such river flows may vary over a wide range and be quite sensitive to changing <br />conditions, particularly in environs where the number of precipitation events are few. On the other hand, some <br />river flows may be relatively constant with the effects of diverse conditions dampened. Oftentimes scale is a good <br />indicator of the variability of flow at a given site. A 20% error on a small stream in Arizona may be more laudable <br />than a 10% error on Lake Powell inflow, <br /> <br />character of the year. by definition, extreme events are rare and forecasting such events becomes <br />more difficult. Because the number of past extreme events is small, less is known about the distribution and <br />variability than in situations with "near-normal" populations. Even if it was possible to remove uncertainty about <br />future climate, there would still be more error associated with forecasting extreme events. <br /> <br />During extreme conditions there is a demand that the forecaster make a more powerful (and potentially more <br />valuable) statement: in effect, that "even normal conditions from here on out wilI not be enough to compensate <br />for current abnormal snowpack and soil states," It is during such events that consideration of information other <br />than just the most probable forecast becomes especially important Probability statements that convey the <br />likelihood' of exceeding a certain level (such as the reasonable maximum and minimum forecasts) help to <br />underscore the uncertainty associated with the forecast <br />So why do it? Although it may not be a simple matter to grade a forecast, it is still useful for users and forecasters <br />alike to review the previous year's forecasts and adjusted observations (provisional as they may be with estimated <br />diversions) so as to act on obvious problems and to gain perspective for the coming forecast season. <br /> <br />- 9- <br />