Storm event analysis of four forested catchments on the Atlantic coastal plain using a modified SCS-CN rainfall-runoff model
In this study, we calibrated and tested the Soil Conservation Service Curve Number (SCS-CN) based Modified Sahu-Mishra-Eldo (MSME) model for predicting storm event direct runoff (Qtot) and its soil saturation coefficient α as a threshold antecedent moisture condition for partitioning into overland surface and shallow subsurface runoff components. The model calibration was performed using 36 storm events from 2008 to 2015 on a 160-ha low-gradient forested watershed (WS80) on poorly drained soil. The model was further validated without calibration using data from 2011 to 2015 on two sites [115 ha (Conifer) and 210 ha (Eccles Church)] and from 2008 to 2011 on a third site, the 100-ha Upper Debidue Creek (UDC), all similar forested watersheds on the Atlantic Coastal Plain, USA. The calibrated MSME model was able to accurately predict the estimated Qtot_pred for the WS80 watershed, with calculated Nash-Sutcliffe efficiency coefficient (NSE), RMSE-standard deviation ratio (RSR), and percent bias (PBIAS) of 0.80, 0.44, and 16.7%, respectively. By applying the same calibrated α value of 0.639 from the WS80 to two other similar poorly drained watersheds, the MSME model satisfactorily predicted the estimated Qtot_pred for both the Eccles Church (NSE = 0.64; RSR = 0.57; PBIAS = 28.9%) and Conifer (NSE = 0.60; RSR = 0.58; PBIAS = 21.3%) watersheds, respectively. The MSME model, however, yielded unsatisfactory results (NSE = -0.13, RSR = 2.06, PBIAS = 616.3%) on the UDC watershed with coarse-textured soils, indicating the possible association of the α coefficient with soil subsurface texture. Based on the analysis of event rainfall and pre-event water table elevation, and linking them with the calibrated α coefficient that describes the proportion of saturated depth in a soil profile, it was found that rainfall was the main determining factor for overland runoff generation. These results demonstrate the MSME model’s potential to predict direct runoff in poorly drained forested watersheds, which serve as a reference for urbanizing coastal landscapes in a changing climate.