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Snow-water equivalent (SWE) data measured at several hundred montane sites in the western United States are used to examine the historic effects of El Nino and La Nina events on seasonal snowpack evolution in the major subbasins in the Columbia and Colorado River systems. Results are used to predict annual runoff. In the Columbia River Basin, there is a general tendency for decreased SWE during El Nino years and increased SWE in La Nina years. However, the SWE anomalies for El Nino years are much less pronounced. This occurs in part because midlatitude circulation anomalies in El Nino years are located 35° east of those in La Nina years. This eastward shift is most evident in midwinter, at which time, SWE anomalies associated with El Nino are actually positive in coastal regions of the Columbia River Basin. In the Colorado River Basin, mean anomalies in SWE and annual runoff during El Nino years depict a transition between drier-than-average conditions in the north, and wetter-than-average conditions in the southwest. Associations during La Nina years are generally opposite those in El Nino years. SWE anomalies tend to be more pronounced in spring in the Lower Colorado River Basin. Our predictions of runoff reveal modest skill for scenarios using only historic El Nino and La Nina information. Predictions based on the water stored in the seasonal snowpack are, in almost all cases, much higher than those based on El Nino-Southern Oscillation (ENSO) information alone. However, combining observed midwinter snow conditions with information on seasonal snowpack evolution associated with ENSO improves predictions for basins in which ENSO signals exhibit strong seasonality.

Correlation and correlation-based measures (e.g., the coefficient of determination) have been widely used to evaluate the “goodness-of-fit” of hydrologic and hydroclimatic models. These measures are oversensitive to extreme values (outliers) and are insensitive to additive and proportional differences between model predictions and observations. Because of these limitations, correlation-based measures can indicate that a model is a good predictor, even when it is not. In this paper, useful alternative goodness-of-fit or relative error measures (including the coefficient of efficiency and the index of agreement) that overcome many of the limitations of correlation-based measures are discussed. Modifications to these statistics to aid in interpretation are presented. It is concluded that correlation and correlation-based measures should not be used to assess the goodness-of-fit of a hydrologic or hydroclimatic model and that additional evaluation measures (such as summary statistics and absolute error measures) should supplement model evaluation tools.

Single flow direction (sfd) and multiple flow direction (mfd) algorithms were used to compute the spatial and statistical distributions of the topographic index used in the watershed model TOPMODEL. An sfd algorithm assumes that subsurface flow occurs only in the steepest downslope direction from any given point; an mfd algorithm assumes that subsurface flow occurs in all downslope directions from any given point. The topographic index in TOPMODEL is In (a/tan/3), where In is the Napierian logarithm, a is the upslope area per unit contour length, and tan/3 is the slope gradient. The In (a/tan /3) distributions were computed from digital elevation model (DEM) data for locations with diverse topography in Arizona, Colorado, Louisiana, Nebraska, North Carolina, Oregon, Pennsylvania, Tennessee, Vermont, and Virginia. The means of the In (a/tan/3) distributions were higher when the mfd algorithm was used for computation compared to when the sfd algorithm was used. The variances and skews of the distributions were lower for the mfd algorithm compared to the sfd algorithm. The differences between the mfd and sfd algorithms in the mean, variance, and skew of the In (a/tan/3) distribution were almost identical for the various DEMs and were not affected by DEM resolution or watershed size. TOPMODEL model efficiency and simulated flow paths were affected only slightly when the In (a/tan/3) distribution was computed with the sfd algorithm instead of the mfd algorithm. Any difference in the model efficiency and simulated flow paths between the sfd and mfd algorithms essentially disappeared when the model was calibrated by adjusting subsurface hydraulic parameters.

[1] A water-balance model is used to simulate time series of water-year runoff for 4 km × 4 km grid cells for the conterminous United States during the 1900–2008 period. Model outputs are used to examine the separate effects of precipitation and temperature on runoff variability. Overall, water-year runoff has increased in the conterminous United States and precipitation has accounted for almost all of the variability in water-year runoff during the past century. In contrast, temperature effects on runoff have been small for most locations in the United States even during periods when temperatures for most of the United States increased significantly.

[1] Models suggest that average temperatures in the central Rocky Mountains will increase by >3°C over the next century, while precipitation may remain within late Holocene boundaries. This study investigates the potential hydrologic effects of such warming when combined with the full range of precipitation variability experienced over the past millennium. Using the upper Yellowstone drainage as a test case, a water balance model is constructed to estimate river discharge from precipitation and temperature inputs (r = 0.85 versus observed). The model then was run using tree ring precipitation estimates for 1177–1910 and 1911–1995 A.D. combined with (1) average observed temperatures 1896–1995; (2) reconstructed Northern Hemisphere temperatures since 1177; or (3) Intergovernmental Panel on Climate Change temperature projections for 2025, 2050, and 2100. Discharge estimates generated by driving the model with tree ring precipitation for 1911–1995 and observed temperatures served as a baseline for comparisons with other climate runoff scenarios. All combinations of the various temperature scenarios and pre-1911 precipitation resulted in mean discharge below the gauge period baseline. Projected temperatures for 2050 and 2100 produced the lowest mean discharge at 85% and 76% of baseline, respectively. Combining observed temperatures with the paleoprecipitation scenarios created numerous multidecadal periods with discharge

A method of precipitation simulation that incorporates climatological information has been developed. A Markovian-based model is used to generate temporal sequences of six daily weather types: high pressure; coastal return; maritime tropical return; frontal maritime tropical return; cold frontal overrunning; and warm frontal overrunning. Precipitation values are assigned to individual days by using observed statistical relations between weather types and precipitation characteristics. When this method was applied to an area in the Delaware River basin, the statistics describing average precipitation, extreme precipitation, and drought conditions for simulated precipitation closely matched those of the observed data. Potential applications of this weather type precipitation model include climatic change research and modeling of temperature and evapotranspiration.