Bias correction of climate model outputs influences watershed model nutrient load predictions

Waterbodies around the world experience problems associated with elevated phosphorus (P) and nitrogen (N) loads. While vital for ecosystem functioning, when present in excess amounts these nutrients can impair water quality create symptoms of eutrophication, including harmful algal blooms. Under a changing climate, nutrient loads are likely to change. climate models serve as inputs watershed models, often do not adequately represent distribution observed data, generating uncertainties that be addressed some degree bias correction. However, impacts correction on well understood. This study compares 4 univariate 3 multivariate methods, which correct precipitation temperature variables from historical (1980–1999) mid-century future (2046–2065) time periods. These served calibrated Soil Water Assessment Tool (SWAT) model Lake Erie's Maumee River watershed. We compared performance SWAT outputs driven were bias-corrected (BC) (no-BC) dissolved reactive P, total N. Results based graphical comparisons goodness fit metrics showed choice BC method both direction change magnitude hydrological processes. Delta performed best, it should used caution since considers variable relationships basis predictions, may hold true under climate. Quantile Mapping (QDM) Multivariate Bias Correction N-dimensional probability density function transform (MBCn) methods also work non-stationary scenarios. Furthermore, results suggest February–July cumulative load basin is decrease runoff snowfall decrease, evapotranspiration increases warming temperatures.