Sensitivity Analysis and Parameter Estimation for the APEX Model on Runoff, Sediments and Phosphorus
Author : Yi Jiang
Publisher :
ISBN 13 :
Total Pages : 174 pages
Book Rating : 4.:/5 (968 download)
Book Synopsis Sensitivity Analysis and Parameter Estimation for the APEX Model on Runoff, Sediments and Phosphorus by : Yi Jiang
Download or read book Sensitivity Analysis and Parameter Estimation for the APEX Model on Runoff, Sediments and Phosphorus written by Yi Jiang and published by . This book was released on 2016 with total page 174 pages. Available in PDF, EPUB and Kindle. Book excerpt: Sensitivity analysis is essential for the hydrologic models to help gain insight into model’s behavior, and assess the model structure and conceptualization. Parameter estimation in the distributed hydrologic models is difficult due to the high-dimensional parameter spaces. Sensitivity analysis identified the influential and non-influential parameters in the modeling process, thus it will benefit the calibration process. This study identified, applied and evaluated two sensitivity analysis methods for the APEX model. The screening methods, the Morris method, and LH-OAT method, were implemented in the experimental site in North Carolina for modeling runoff, sediment loss, TP and DP losses. At the beginning of the application, the run number evaluation was conducted for the Morris method. The result suggested that 2760 runs were sufficient for 45 input parameters to get reliable sensitivity result. Sensitivity result for the five management scenarios in the study site indicated that the Morris method and LH-OAT method provided similar results on the sensitivity of the input parameters, except the difference on the importance of PARM2, PARM8, PARM12, PARM15, PARM20, PARM49, PARM76, PARM81, PARM84, and PARM85. The results for the five management scenarios indicated the very influential parameters were consistent in most cases, such as PARM23, PARM34, and PARM84. The “sensitive” parameters had good overlaps between different scenarios. In addition, little variation was observed in the importance of the sensitive parameters in the different scenarios, such as PARM26. The optimization process with the most influential parameters from sensitivity analysis showed great improvement on the APEX modeling performance in all scenarios by the objective functions, PI1, NSE, and GLUE.