TY - JOUR
T1 - The hydrologic model as a source of nutrient loading uncertainty in a future climate
AU - Kujawa, Haley
AU - Kalcic, Margaret
AU - Martin, Jay
AU - Aloysius, Noel
AU - Apostel, Anna
AU - Kast, Jeffrey
AU - Murumkar, Asmita
AU - Evenson, Grey
AU - Becker, Richard
AU - Boles, Chelsie
AU - Confesor, Remegio
AU - Dagnew, Awoke
AU - Guo, Tian
AU - Logsdon Muenich, Rebecca
AU - Redder, Todd
AU - Scavia, Donald
AU - Wang, Yu Chen
N1 - Publisher Copyright:
© 2018 Elsevier B.V.
PY - 2020/7/1
Y1 - 2020/7/1
N2 - Hydrologic models are applied increasingly with climate projections to provide insights into future hydrologic conditions. However, both hydrologic models and climate models can produce a wide range of predictions based on model inputs, assumptions, and structure. To characterize a range of future predictions, it is common to use multiple climate models to drive hydrologic models, yet it is less common to also use a suite of hydrologic models. It is also common for hydrologic models to report riverine discharge and assume that nutrient loading will follow similar patterns, but this may not be the case. In this study, we characterized uncertainty from both climate models and hydrologic models in predicting riverine discharge and nutrient loading. Six climate models drawn from the Coupled Model Intercomparison Project Phase 5 ensemble were used to drive five independently developed and calibrated Soil and Water Assessment Tool models to assess hydrology and nutrient loadings for mid-century (2046–2065) in the Maumee River Watershed,the largest watershedsdraining to the Laurentian Great Lakes. Under those conditions, there was no clear agreement on the direction of change in future nutrient loadings or discharge. Analysis of variance demonstrated that variation among climate models was the dominant source of uncertainty in predicting future total discharge, tile discharge (i.e. subsurface drainage), evapotranspiration, and total nitrogen loading, while hydrologic models were the main source of uncertainty in predicted surface runoff and phosphorus loadings. This innovative study quantifies the importance of hydrologic model in the prediction of riverine nutrient loadings under a future climate.
AB - Hydrologic models are applied increasingly with climate projections to provide insights into future hydrologic conditions. However, both hydrologic models and climate models can produce a wide range of predictions based on model inputs, assumptions, and structure. To characterize a range of future predictions, it is common to use multiple climate models to drive hydrologic models, yet it is less common to also use a suite of hydrologic models. It is also common for hydrologic models to report riverine discharge and assume that nutrient loading will follow similar patterns, but this may not be the case. In this study, we characterized uncertainty from both climate models and hydrologic models in predicting riverine discharge and nutrient loading. Six climate models drawn from the Coupled Model Intercomparison Project Phase 5 ensemble were used to drive five independently developed and calibrated Soil and Water Assessment Tool models to assess hydrology and nutrient loadings for mid-century (2046–2065) in the Maumee River Watershed,the largest watershedsdraining to the Laurentian Great Lakes. Under those conditions, there was no clear agreement on the direction of change in future nutrient loadings or discharge. Analysis of variance demonstrated that variation among climate models was the dominant source of uncertainty in predicting future total discharge, tile discharge (i.e. subsurface drainage), evapotranspiration, and total nitrogen loading, while hydrologic models were the main source of uncertainty in predicted surface runoff and phosphorus loadings. This innovative study quantifies the importance of hydrologic model in the prediction of riverine nutrient loadings under a future climate.
KW - Climate change
KW - Hydrology
KW - Nutrients
KW - Soil and Water Assessment Tool (SWAT)
KW - Uncertainty
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U2 - 10.1016/j.scitotenv.2020.138004
DO - 10.1016/j.scitotenv.2020.138004
M3 - Article
C2 - 32408425
AN - SCOPUS:85083113330
SN - 0048-9697
VL - 724
JO - Science of the Total Environment
JF - Science of the Total Environment
M1 - 138004
ER -