Location: Water Management and Conservation ResearchTitle: Comparison of evapotranspiration methods in the DSSAT Cropping System Model: II. Algorithm performance
Submitted to: Computers and Electronics in Agriculture
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 7/29/2020
Publication Date: 8/18/2020
Citation: Thorp, K.R., Marek, G.W., Dejonge, K.C., Evett, S.R. 2020. Comparison of evapotranspiration methods in the DSSAT Cropping System Model: II. Algorithm performance. Computers and Electronics in Agriculture. 177. Article 105679. https://doi.org/10.1016/j.compag.2020.105679.
Interpretive Summary: Evapotranspiration (ET) is a primary pathway for water loss from agricultural fields. Thus, simulation models of agricultural field processes must accurately simulate ET. A number of methods exist for simulating ET, and recent scientific efforts have sought to make comparisons among them. In this study, six ET methods in the DSSAT Cropping System Model were compared using high-quality ET data and other agronomic data for a semi-arid, cotton field site in west Texas. The study identified one ET method that performed statistically better than the other five. The results will guide future model users in their selection of ET methods for use with this model and should lead toward improved simulation results. Furthermore, the results will guide model developers toward improvements to ET methods in the model. Broadly, the study will facilitate improved model implementation to address water management issues in water-limited agricultural environments of the western United States.
Technical Abstract: Accurate calculations of evapotranspiration (ET) are highly important for agroecosystem model simulations, and improvement of ET algorithms is an on-going model development goal. The objective of this study was to evaluate and compare six ET methods in the Decision Support System for Agrotechnology Transfer (DSSAT) Cropping System Model (CSM) using agronomic and weighing lysimetry data from cotton field studies at Bushland, Texas. Three options were tested for estimating potential ET as required by the DSSAT-CSM: 1) a Priestley-Taylor method, 2) a Penman-Monteith combination equation estimate of grass reference ET with a DSSAT-specific single crop coefficient equation, and 3) the ASCE Standardized Reference ET Equation combined with a dual crop coefficient method for non-stressed conditions. The latter two reference ET methods were adapted to provide reasonable estimates for DSSAT-required potential ET. Additionally, two methods for calculation of soil water evaporation were tested, including both the original and updated formulations of Ritchie approaches for DSSAT-CSM. The combinations of the three potential ET and two soil water evaporation approaches led to six possible ET simulation options in the model. A computationally-intensive multiobjective optimization method was used to select among model parameterization options and ensure that modeler bias did not influence ET method comparisons. Among 23 agroecosystem metrics that included lysimeter-based ET, various cotton growth variables, and soil water content in multiple soil layers, the original Ritchie soil water evaporation approach performed statistically equivalent to or better than the more recent Ritchie method (p<=0.05). The default ET method in the model, which involved Priestley-Taylor potential ET with the more recent Ritchie soil water evaporation method, was outperformed by other ET methods for 14 of 23 agroecosystem metrics (p<=0.05). When the original Ritchie soil water evaporation method was combined with potential ET from the ASCE reference ET and dual crop coefficient method, the model performed statistically equivalent to or better than the other five ET options for all but 1 of 23 agroecosystem metrics (p<=0.05). Based on three years of cotton data from the Bushland lysimetry fields, a DSSAT-CSM ET approach based on the standardized ET methodologies described by ASCE and FAO-56 combined with the original Ritchie soil water evaporation method provided holistic improvements to model simulations among multiple agroecosystem metrics.