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ARS Home » Plains Area » El Reno, Oklahoma » Grazinglands Research Laboratory » Agroclimate and Natural Resources Research » Research » Publications at this Location » Publication #334516

Research Project: AGRICULTURAL LAND MANAGEMENT TO OPTIMIZE PRODUCTIVITY AND NATURAL RESOURCE CONSERVATION AT FARM AND WATERSHED SCALES

Location: Agroclimate and Natural Resources Research

Title: Impact of length of dataset on streamflow calibration parameters and performance of APEX model

Author
item Nelson, Amanda
item Moriasi, Daniel
item Talebizadeh, Mansour - Orise Fellow
item Steiner, Jean
item Confesor, Rem - Heidelberg University, Ohio
item Gowda, Prasanna
item Starks, Patrick - Pat
item Tadesse, Haile

Submitted to: Journal of the American Water Resources Association
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 6/14/2017
Publication Date: 8/28/2017
Citation: Nelson, A.M., Moriasi, D.N., Talebizadeh, M., Steiner, J.L., Confesor, R., Gowda, P., Starks, P.J., Tadesse, H.K. 2017. Impact of length of dataset on streamflow calibration parameters and performance of APEX model. Journal of the American Water Resources Association. https://doi.org/10.1111/1752-1688.12564.
DOI: https://doi.org/10.1111/1752-1688.12564

Interpretive Summary: Measured data is required to calibrate and validate hydrologic and water quality models to ensure that simulated outputs of impacts of land management, conservation practices and climate change are reasonable. Obtaining continuous long-term measured data is difficult due to financial and human resource constraints. As a result, most models are calibrated and, if possible, validated using limited measured data. However, little research has been done to determine the impact of length of available calibration period data on model parameterization and performance. The main objective of this study was to evaluate the impact of length of available measured data on streamflow calibration parameters and simulation performance of Agricultural Policy/Environmental eXtender (APEX) model at daily, monthly, and annual time steps. Long-term (1984-2015) measured daily streamflow data from Rock Creek watershed, a small agricultural watershed located in northern Ohio, were used for this study. Calibration data were divided into five short (5-year), medium (15-year), and one long (25-year) streamflow calibration data scenarios. The results showed that the length of available calibration data affected the ability of the model to accurately capture temporal variability of simulated streamflow. However, APEX simulated overall average streamflow, water budgets, and crop yields reasonably well irrespective of the length of available measured streamflow data to calibrate the model. Overall, the calibrated streamflow parameter values obtained using the long calibration data scenario yielded the most consistent results for the calibration and validation periods for all three time steps. The results of this study will contribute to the goal of USDA to parameterize and validate APEX to support nation-wide deployment of the nutrient tracking tool.

Technical Abstract: Due to resource constraints, long-term monitoring data for calibration and validation of hydrologic and water quality models are rare. As a result, most models are calibrated and, if possible, validated using limited measured data. However, little research has been done to determine the impact of length of available calibration data on model parameterization and performance. The main objective of this study was to evaluate the impact of length of calibration data (LCD) on parameterization and performance of the Agricultural Policy/Environmental eXtender (APEX) model for predicting daily, monthly, and annual streamflow. Long-term (1984-2015) measured daily streamflow data from Rock Creek watershed, an agricultural watershed in northern Ohio, were used for this study. Data were divided into five Short (5-year), two Medium (15-year), and one Long (25-year) streamflow calibration data scenarios. All LCD scenarios were simulated at three time steps; daily, monthly, and annual. The input parameters that were most sensitive to streamflow were determined and the APEX model was calibrated for all combinations of five LCD scenario and three time steps. The two most sensitive parameters for all scenarios and time steps were the soil evaporation – plant cover factor and the root growth-soil strength parameter, which pertain to evaporation and transpiration processes, respectively. Results showed that LCD affected the ability of the model to accurately capture temporal variability of simulated streamflow. However, simulated overall average streamflow, water budgets, and crop yields reasonably well for all LCD scenarios. Overall, the Long LCD scenario (25-year) yielded the most consistent results for the calibration and validation periods for all three temporal scales.