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Abstract Agricultural nutrient runoff has been a major contributor to hypoxia in many downstream coastal ecosystems. Although programs have been designed to reduce nutrient loading in individual coastal waters, cross watershed interdependencies of nutrient runoff have not been quantified due to a lack of suitable modeling tools. Cross-watershed pollution leakage can occur when nutrient runoff moves from more to less regulated regions. We illustrate the use of an integrated assessment model IAM that combines economic and process-based biophysical tools to quantify Nitrogen loading leakage across three major US watersheds. We also assess losses in consumer and producer surplus from decreased commodity supply and higher prices when nutrient delivery to select coastal ecosystems is restricted. Reducing agricultural N loading in the Gulf of Mexico by 45% (a) increases loading in the Chesapeake Bay and Western Lake Erie by 4.2% and 5.5%, respectively, and (b) results in annual surplus losses of $7.1 and $6.95 billion with and without restrictions on leakage to the Chesapeake Bay and Lake Erie, respectively. 

In this study, we evaluate the implications of a bias correction method on a combination of Global/Regional Climate Models (GCM and RCM) for simulating precipitation and, subsequently, streamflow, surface runoff, and water yield in the Soil and Water Assessment Tool (SWAT). The study area is the Des Moines River Basin, U.S.A. The climate projections are two RCMs driven by two GCMs for historical simulations (1981–2005) and future projections (2030–2050). Bias correction improves historical precipitation for annual volumes, seasonality, spatial distribution, and mean error. Simulated monthly historical streamflow was compared across 26 monitoring stations with mostly satisfactory results for percent bias (Pbias). There were no changes in annual trends for future scenarios except for raw WRF models. Seasonal variability remained the same; however, most models predicted an increase in monthly precipitation from January to March and a reduction for June and July. Meanwhile, the bias-corrected models showed changes in prediction signals. In some cases, raw models projected an increase in surface runoff and water yield, but the bias-corrected models projected a reduction in these variables. This suggests the bias correction may be larger than the climate-change signal and indicates the procedure is not a small correction but a major factor.