|Talebizadeh, Mansour - Orise Fellow|
|Starks, Patrick - Pat|
Submitted to: Journal of Soil and Water Conservation
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 3/31/2018
Publication Date: 11/7/2018
Citation: Nelson, A.M., Moriasi, D.N., Talebizadeh, M., Steiner, J.L., Gowda, P.H., Starks, P.J., Tadesse, H.K. 2018. Use of soft data for multicriteria calibration and validation of agricultural policy environmental eXtender: impact on model simulations. Journal of Soil and Water Conservation. 73(6):623-636. https://doi:10.2489/jswc.73.6.623.
DOI: https://doi.org/10.2489/jswc.73.6.623 Interpretive Summary: The Agricultural Policy/Environmental eXtender (APEX) was developed to quantify the impacts of climate and land use change, land management, and conservation practices on water resources for projects like the national Conservation Effects Assessment Project (CEAP) analysis and many of the USDA Agricultural Research Service CEAP watershed assessments. Few studies have determined the impact of soft data (information on processes within a budget that may not be directly measured) on APEX model outputs when used to constrain calibration. In this study, we sought to determine the impact of soft data on APEX model streamflow, total nitrogen (TN), and total phosphorus (TP) simulations and the impact of tile drainage on the same components. Long-term measured water quantity and quality data from Rock Creek watershed, northern Ohio, were used. The number of models that met the criteria decreased from 3325 to 2, as soft data were added. In general, when soft data were used to constrain model calibration, the statistical model performance decreased for streamflow, TN, and TP. However, when soft data was used the model processes were better represented. For example, the simulated tile drainage error ranged from -80% to 6% for the case in which soft data were not used, compared with -15% to -13% when soft data were used to constrain model calibration. This led to major differences in simulated effects of tile drainage on streamflow, TN, and TP. For example, when soft data were used, the simulated impact of tile drainage was 30% higher, 36% higher, and 123% lower for streamflow, TP, and TN, respectively, compared to when soft data were not used. Overall, the results show the importance of utilizing soft data to obtain realistic simulations of various management practices.
Technical Abstract: It is widely known that the use of soft data and multiple model performance criteria in model calibration and validation is critical to ensuring the model capture major hydrologic and water quality processes. The Agricultural Policy/Environmental eXtender (APEX) is a hydrologic and water quality model developed for evaluating the effect of agricultural production management practices on the environment. However, there are few studies on the impact of soft data on APEX model outputs. This study sought to determine the impact of soft data on APEX model streamflow, total nitrogen (TN), and total phosphorus (TP) simulations when used to constrain calibration and the impact of tile drainage on the same components. Long-term measured water quality and quantity data from Rock Creek watershed, northern Ohio, were used. The number of models that meet the criteria decreased from 3325 to 2, as the number of calibration criteria increased. In general, when soft data were used to constrain the model, the NSE values decreased for all components. For example, NSE for flow decreased from 0.79 for scenario A to 0.56 for scenario B when the soft data were used, and NSE for TP decreased from 0.34 for scenario C to 0.30 for scenario D. However, the scenarios that used soft data with lower NSE values represented the processes better. For all calibration/validation scenarios, streamflow, TN, and TP values were drastically reduced when the model was run without tile drainage. However, there was hardly any tile drainage impact on ET, surface runoff, or crop yields for all scenarios, indicating that tile drainage, water table, and crop growth routine improvements may be needed. Overall, the results show the importance of utilizing soft data to obtain realistic simulations of various management practices.