Post-processing R tool for SWAT efficiently studying climate change impacts on hydrology, water quality, and crop growth. (October 2022)

• Record Type: Journal Article
• Authors: Ding, Beibei; Liu, Haipeng; Li, Yingxuan; Zhang, Xueliang; Feng, Puyu; Liu, De Li; Marek, Gary W.; Ale, Srinivasulu; Brauer, David K.; Srinivasan, Raghavan; Chen, Yong
• Is Part Of: Environmental modelling & software. Volume 156(2022)
• Publication Date: 2022-10
• Subjects: SWAT-MAD -- CMIP6 -- Hydrologic cycle -- Total nitrogen load -- Cotton -- Semi-arid region; Environmental monitoring -- Computer programs -- Periodicals; Ecology -- Computer simulation -- Periodicals; Digital computer simulation -- Periodicals; Computer software -- Periodicals; Environmental Monitoring -- Periodicals; Computer Simulation -- Periodicals; Environnement -- Surveillance -- Logiciels -- Périodiques; Écologie -- Simulation, Méthodes de -- Périodiques; Simulation par ordinateur -- Périodiques; Logiciels -- Périodiques; Computer software; Digital computer simulation; Ecology -- Computer simulation; Environmental monitoring -- Computer programs; Periodicals; Electronic journals; 363.70015118
• DOI: 10.1016/j.envsoft.2022.105492
• Languages: English
• ISSNs: 1364-8152

Abstract

Abstract: Soil and Water Assessment Tool (SWAT) is widely used for watershed-scale assessment of climate change impacts, but post-processing of model outputs is a tedious job. An R tool was developed in this study for batch processing of SWAT output results. A case study was then performed in the Double Mountain Fork Brazos watershed in the Texas Panhandle using an improved SWAT model with the R tool to evaluate the simulated future changes in water balance components, total nitrogen (TN) load, and crop growth over the watershed. The results showed that the average annual future surface runoff increased by 8.9–17.9 mm and 11.5–22.6 mm in the irrigated and dryland cotton areas, respectively. Similarly, future TN load in irrigated and dryland cotton areas increased by approximately 0.4–0.9 kg ha −1 and 1.9–2.4 kg ha −1 . The yields of irrigated and dryland cotton increased by 91.1%–122.1% and 47.5%–84.0% under the future climate scenarios, respectively. Graphical abstract: Image 1 Highlights: An algorithm for rapidly post-processing SWAT output.hru data was developed. SWAT-MAD model was driven by bias-corrected CMIP6 data for impact assessments. Future CO2, temperatures, radiation, runoff, and N loss were projected to increase. Decreases in irrigation and actual ET were found for 2071–2100 SSP5-8.5 scenario. Both irrigated and dryland cotton yields were projected to increase in the future.