Notes: : The El Nino/Southern Oscillation (ENSO) phenomena alter global weather patterns with consequences for fresh water supply. ENSO events impact regions and their natural resource sectors around the globe. For example, in 1997 and 1998, a strong El Nino brought warm ocean temperatures, flooding, and record snowfall to the west coast of the United States. Research on ENSO events has improved long range climate predictions, affording the potential to reduce the damage and economic cost of these weather patterns. Here, using the Hydrologic Unit Model for the United States (HUMUS), we simulate the impacts of four types of ENSO states (Neutral, El Niño, La Niña, and strong El Niño) on water resources in the conterminous United States. The simulations show that La Niña conditions increase water yield across much of the country. We find that water yield increases during El Niño years across the south while declining in much of the rest of the country. However, under strong El Niño conditions, regional water yields are much higher than Neutral, especially along the West Coast. Strong El Niño is not simply an amplification of El Niño; it leads to strikingly different patterns of water resource response.

Notes: Abstract The economic value of long-range weather prediction is measured by the increase in social welfare arising from the use of the prediction in economic decisionmaking. This paper describes a study of the economic value of ENSO prediction to U.S. agriculture. The interdisciplinary study involved the analysis of data and models from meteorology, plant science, and economics under a framework based on Bayesian decision analysis. The estimated annual value of perfect ENSO prediction to U.S. agriculture is $323 million.

Notes: Abstract Historical daily thermal and precipitation data from selected stations across the United States are composited into climate scenarios for the three phases of ENSO: Warm Events (El Niño), Cold Events (El Viejo or La Niña), and Neutral. Using these scenarios, yields of 7 field crops were simulated using the EPIC biophysical model during the one-year period coincident with maximum SST anomalies in the equatorial Pacific. The response of simulated agricultural productivity to the ENSO-related climate-variability parameters, is presented. A sensitivity calculation confirms the relevance of precipitation totals/medians and suggests ENSO-related yields are sensitive to changes in statistical properties characterizing precipitation distribution and occurrence. Results are spatially dependent, with the southwest and northern plains regions indicating the highest sensitivity to the inclusion of additional precipitation characteristics. The southeast yields are not as sensitive. The yield deviations (expressed as normalized differences to neutral yields) associated with the two extreme ENSO phases (Warm Events and Cold Events) are spatially and crop dependent with ranges up to ±120%. The largest yield deviations are in the south, southwest, and northern plains. Overall, Cold Events demonstrate larger impacts in the southern regions and Warm Events have a larger impact in the north. Additionally, the notion that climate anomalies associated with Cold and Warm Events and subsequent impacts on yields should be of opposite sign (i.e., linear) is not valid in many regions. For the eastern half of the U.S., modeled yield deviations under Warm Event conditions are nearly all less than neutral. Conversely, in the western half, results are more mixed. Under Cold Event conditions, yields in the east are enhanced in the south, but worsened in the north; while in the western half, yields have decreased in general. The results highlight the critical role of climate and production-related data on station or county levels in quantifying the impact of ENSO climate anomalies on yields. Both the diverse nature of the ENSO-related yield deviations as well as their sensitivity to monthly frequency distribution and occurrence characteristics imply that ENSO-related seasonal precipitation forecasts might be beneficial for agricultural application only if details were provided regarding not only totals, but also predicted changes in temporal and spatial variability of a more comprehensive suite of characteristics.