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The application of partial least-squares regression (PLSR) in seasonal streamflow forecasting was investigated using snow water equivalent, precipitation, temperature from automatic Snow Telemetry sites, and previous flow conditions as input variables. The forecast performance of PLSR models was compared to principal components regression (PCR) models as well as to the Natural Resources Conservation Service (NRCS) official forecasts in three Rio Grande watersheds including the Rio Grande Headwater Basin, Conejos River Basin in Colorado, and Rio Grande Basin above Elephant Butte Reservoir, New Mexico. The results indicated that using a correlation-weighted precipitation index is a relatively effective method in both improving forecast accuracy and developing relatively parsimonious regression models. In comparison of PLSR and PCR, similar forecast accuracies were obtained for both methods in jackknife cross validation and the test period (2003–2007) although PLSR has higher calibration coefficient of determination (R2) and can reach its minimal prediction error with a smaller number of components than PCR. The comparison with NRCS official forecasts showed that the application of PLSR in seasonal streamflow forecasting is promising. This approach could be combined into NRCS’s operational forecasting environment for possible forecast improvement. [ABSTRACT FROM AUTHOR]

Official seasonal water supply outlooks for the western United States are typically produced once per month from January through June. The Natural Resources Conservation Service has developed a new outlook product that allows the automated production and delivery of this type of forecast year-round and with a daily update frequency. Daily snow water equivalent and water year-to-date precipitation data from multiple SNOTEL stations are combined using a statistical forecasting technique (“ Z-Score Regression”) to predict seasonal streamflow volume. The skill of these forecasts vs. lead-time is comparable to the official published outlooks. The new product matches the intra-monthly trends in the official forecasts until the target period is partly in the past, when the official forecasts begin to use information about observed streamflows to date. Geographically, the patterns of skill also match the official outlooks, with highest skill in Idaho and southern Colorado and lowest skill in the Colorado Front Range, eastern New Mexico, and eastern Montana. The direct and frequent delivery of objective guidance to users is a significant new development in the operational hydrologic seasonal forecasting community. [ABSTRACT FROM AUTHOR]

Discusses the factors affecting weather forecast use in Arizona water management. Significance of the case study on the 1997–1998 El Niño; Development of operational water management decision processes; Findings emphasized by the study.

The article provides information on innovative operational seasonal water supply forecasting technologies in the United States. The history of water supply forecasting technologies is explored in the article. The Natural Resources Conservation Service (NRCS) and its methods are tackled. The development of the viper technology from the NCRS is discussed. It is stated that the water supply forecasting technology has been affected by climate variability and water issues which make it difficult to forecast seasonal water supplies.