SWAT Literature Database for Peer-Reviewed Journal Articles

Title:Winter wheat phenology simulations improve when adding responses to water stress 
Authors:McMaster, G.S., D.A. Edmunds, R. Marquez, S. Haley, G. Buchleiter, P. Byrne, T.R. Green, R. Erskine, N. Lighthart, H. Kipka, F. Fox, L. Wagner, J. Tatarko, M. Moragues and J. Ascough II 
Journal:Agronomy Journal 
Volume (Issue):111 
Article ID: 
URL (non-DOI journals): 
Broad Application Category:crop/plant/tree growth or production 
Primary Application Category:crop growth/yield and/or parameters 
Secondary Application Category:model comparison 
Watershed Description:Two research sites located in the semiarid region of northeastern Colorado, U.S. 
Calibration Summary: 
Validation Summary: 
General Comments: 
Abstract:Phenology is critical in simulating crop production and hydrology and must be sufficiently robust to respond to varying environments, soils, and management practices. Phenological algorithms typically focus on the air temperature response function and rarely quantify the phenological responses to varying water deficits, particularly for versions of the Environmental Policy Integrated Climate model (EPIC)-based plant growth component used in many agroecosystem models. Three EPICbased plant growth components (Soil Water Assessment Tool [SWAT], Wind Erosion Prediction System [WEPS], and the Unified Plant Growth Model [UPGM]) have been incorporated into the spatially distributed Agricultural Ecosystems Services model [AgES], and only the UPGM includes a phenological response to varying water deficits. These three plant components were used to evaluate the phenological responses of winter wheat (Triticum aestivum L.) to varying water deficits and whether having a water stress factor in UPGM improves the simulation of phenology. A 3-yr irrigation study and a 4-yr study across a rainfed landscape were used in the evaluation. Only the UPGM simulated all five of the developmental stagesmeasured. The UPGM was the only component that simulated a phenological response to variable water deficits, resulting in better prediction of phenology. For example, the RMSE (days) and relative error (RE, days) decreased and index of agreement (d) increased in predicting maturity from SWAT (RMSE = 18.4; RE = 9.2; d = 0.34) to WEPS (RMSE = 6.2; RE = 1.0, d = 0.63) to the UPGM (RMSE = 6.1; RE = 0.1; d = 0.70). Incorporating phenological responses to varying water deficits improves the accuracy and robustness of predicting phenology, which is particularly important in spatially distributed agroecosystem models.