Analyses of streamflow, snow mass temperature, and precipitation in snowmelt-dominated river basins in the western United States indicate an advance in the timing of peak spring season flows over the past 50 years. Warm temperature spells in spring have occurred much earlier in recent years, which explains in part the trend in the timing of the spring peak flow. In addition, a decrease in snow water equivalent and a general increase in winter precipitation are evident for many stations in the western United States. It appears that in recent decades more of the precipitation is coming as rain rather than snow. The trends are strongest at lower elevations and in the Pacific Northwest region, where winter temperatures are closer to the melting point; it appears that in this region in particular, modest shifts in temperature are capable of forcing large shifts in basin hydrologic response. It is speculated that these trends could be potentially a manifestation of the general global warming trend in recent decades and also due to enhanced ENSO activity. The observed trends in hydroclimatology over the western United States can have significant impacts on water resources planning and management.

extends hydrologie ensemble services from 6-hour to year-ahead forecasts and includes additional weather and climate infoi'mation as well as improved quantification of major uncertainties.

[1] We propose a multimodel ensemble forecast framework for streamflow forecasts at multiple locations that incorporates large-scale climate information. It has four broad steps: (1) Principal component analysis is performed on the spatial streamflows to identify the dominant modes of variability. (2) Potential predictors of the dominant streamflow modes are identified from several large-scale climate features and snow water equivalent information. (3) Objective criterion is used to select a suite of candidate nonlinear regression models each with different predictors. (4) Ensemble forecasts of the dominant streamflow modes are generated from the candidate models and are combined objectively to produce a multimodel ensemble, which are then back transformed to produce spatially coherent streamflow forecasts at all the locations. The utility of the framework is demonstrated in the skillful forecast of spring seasonal streamflows at six locations in the Gunnison River Basin at several lead times. The generated ensemble streamflow forecast provides valuable and useful information for optimal management and planning of water resources in the basin.

Abstract. We present a nonparametric approach based on local polynomial regression for ensemble forecast of time series. The state space is first reconstructed by embedding the univariate time series of the response variable in a space of dimension (D) with a delay time (τ ). To obtain a forecast from a given time point t, three steps are involved: (i) the current state of the system is mapped on to the state space, known as the feature vector, (ii) a small number (K = α * n, α=fraction (0,1] of the data, n=data length) of neighbors (and their future evolution) to the feature vector are identified in the state space, and (iii) a polynomial of order p is fitted to the identified neighbors, which is then used for prediction.

No abstract

No abstract

No abstract

Categorical forecasts of streamflow are important for effective water resources management. Typically, these are obtained by generating ensemble forecasts of streamflow and counting the proportion of ensembles in the desired category. Here we develop a simple and direct method to produce categorical streamflow forecasts at multiple sites. The method involves predicting the probability of the leading mode (or principal component) of the basin streamflows above a given threshold and subsequently translating the predicted probabilities to all the sites in the basin. The categorical probabilistic forecasts are obtained via logistic regression using a set of large‐scale climate predictors. Application to categorical forecasts of the spring (April–June) streamflows at six locations in the Gunnison River Basin exhibited significant long‐lead forecast skill.