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Although the curve number method of the Natural Resources Conservation Service has been used as the foundation of the hydrology algorithms in many nonpoint source water quality models, there are significant problematic issues with the way it has been implemented and interpreted that are not generally recognized. This usage is based on misconceptions about the meaning of the runoff value that the method computes, which is a likely fundamental cause of uncertainty in subsequent erosion and pollutant loading predictions dependent on this value. As a result, there are some major limitations on the conclusions and decisions about the effects of management practices on water quality that can be supported with current nonpoint source water quality models. They also cannot supply the detailed quantitative and spatial information needed to address emerging issues. A key prerequisite for improving model predictions is to improve the hydrologic algorithms contained within them. The use of the curve number method is still appropriate for flood hydrograph engineering applications, but more physically based algorithms that simulate all streamflow generating processes are needed for nonpoint source water quality modeling. Spatially distributed hydrologic modeling has tremendous potential in achieving this goal. (KEY TERMS: nonpoint source pollution; curve number; hydrologic modeling; water quality; agricultural hydrology; geographic information systems.)Garen, David C. and Daniel S. Moore, 2005. Curve Number Hydrology in Water Quality Modeling: Uses, Abuses, and Future Directions.

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An analysis was conducted of almost 5000 operational seasonal streamflow forecast errors across the western United States. These forecasts are for 29 unregulated rivers with diversity in geography and climate. Deterministic evaluations revealed strong correspondence between observations and forecasts issued 1 April. Forecasts issued earlier in the season were more uncertain yet remained skillful. The average change in forecast performance between January and April was primarily linked to the climatological seasonal cycle of precipitation: regions with climatologically wet winters and dry springs (e.g., California) showed much more forecast improvement between January and April than did regions with dry winters and wet springs (e.g., western Great Plains, Colorado Front Range). Other climatological factors played a secondary role; for example, mixed rain-snow basins in the Pacific Northwest did not show as significant an improvement in skill versus lead time as might otherwise be expected. Mixed trends in 1 April forecast skill were noted since the 1980s, with increased skill in California and Nevada, and a decline in skill in the Colorado River basin. Increased variability in streamflow was also noted across most of the western United States, although this did not appear to be the only factor responsible for trends in forecast skill.