Disentangling the Contributions of Climate and Basin Characteristics to Water Yield Across Spatial and Temporal Scales in the Yangtze River Basin: A Combined Hydrological Model and Boosted Regression Approach
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The dependence and contribution of explanatory variables or predictors to water yield need to be closely analyzed and accurately quantified to better understand water balances as well as for effective water resources management. It is generally challenging, however, to disentangle the contribution of individual climate variables from that of basin characteristics to the integrated water yield response. Here we propose a method to concurrently quantify and analyze the effects of climate and basin predictors on water yields. This method employs the Soil and Water Assessment Tool (SWAT) to simulate water yield. Simulated results are then analyzed and compared using Boosted Regression Trees (BRTs) at multiple spatial and temporal scales. Results indicate that in the Yangtze River Basin (YRB) on average, precipitation is of paramount importance, followed by land cover, while slope has the lowest contribution. The average relative contributions of soil moisture, maximum and minimum temperatures are different among temporal scales. More stable and reliable results are derived at the daily scale compared to the yearly and monthly scale. Our results make evident that generalizations about water yield response made in the absence of a comprehensive and accurate description of site- and scale-specific contributions can lead to misleading assessments. This proposed approach can be useful for informing and supporting more effective water resources management goals.
KeywordsWater yield Spatial and temporal scales Soil and water assessment tool (SWAT) Boosted regression tree (BRT) Yangtze River Basin
This study is supported by the Major Science and Technology Program for Water Pollution Control and Treatment (Grant No. 2017ZX07207003-01).
- Arnold JG, Kiniry JR, Srinivasan R, Williams JR, Neitsch SL (2012) Soil and Water Assessment Tool Theoretical Documentation. https://swat.tamu.edu/media/99192/swat2009-theory.pdf. Accessed June 2019
- Elith J, Leathwick J (2015) Boosted Regression Trees for ecological modeling. https://cran.r-project.org/web/packages/dismo/vignettes/brt.pdf. Accessed June 2019
- James G, Witten D, Hastie T, Tibshirani R (2013) An introduction to statistical learning. https://link.springer.com/content/pdf/10.1007/978-1-4614-7138-7.pdf. Accessed June 2019
- McDonnell J et al (2007) Moving beyond heterogeneity and process complexity: A new vision for watershed hydrology. Water Resour Res 43Google Scholar
- Neitsch SL, Kiniry JG, Williams JR (2009) Soil And Water Assessment Tool: theoretical documentation. https://swat.tamu.edu/media/99192/swat2009-theory.pdf. Accessed June 2019
- R Core Team (2014) The R project for statistical computing. https://www.r-project.org. Accessed June 2019
- Ridgeway G (2007) Generalized Boosted Models: A guide to the gbm package. http://www.saedsayad.com/docs/gbm2.pdf. Accessed June 2019
- Stone MC, Hotchkiss RH, Mearns LO (2003) Water yield responses to high and low spatial resolution climate change scenarios in the Missouri River Basin. Geophys Res Lett 30Google Scholar
- Wu F, Zhan J, Chen J, He C, Zhang Q (2015) Water yield variation due to forestry change in the head-water area of Heihe River basin, Northwest China. Adv Meteorol 2015:1–8Google Scholar