Water Resources Management

, Volume 33, Issue 10, pp 3449–3468 | Cite as

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

  • Fengyun Sun
  • Alfonso Mejia
  • Yue CheEmail author


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.


Water 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).


  1. Adams KN, Fowler AM (2006) Improving empirical relationships for predicting the effect of vegetation change on annual water yield. J Hydrol 321:90–115CrossRefGoogle Scholar
  2. Arnold JG, Kiniry JR, Srinivasan R, Williams JR, Neitsch SL (2012) Soil and Water Assessment Tool Theoretical Documentation. Accessed June 2019
  3. Berg MD, Marcantonio F, Allison MA, McAlister J, Wilcox BP, Fox WE (2016) Contrasting watershed-scale trends in runoff and sediment yield complicate rangeland water resources planning. Hydrol Earth Syst Sci 20:2295–2307CrossRefGoogle Scholar
  4. Brown AE, Zhang L, McMahon TA, Western AW, Vertessy RA (2005) A review of paired catchment studies for determining changes in water yield resulting from alterations in vegetation. J Hydrol 310:28–61CrossRefGoogle Scholar
  5. Castillo CR, Güneralp İ, Güneralp B (2014) Influence of changes in developed land and precipitation on hydrology of a coastal Texas watershed. Appl Geogr 47:154–167CrossRefGoogle Scholar
  6. Chung YS (2013) Factor complexity of crash occurrence: an empirical demonstration using boosted regression trees. Accid Anal Prev 61:107–118CrossRefGoogle Scholar
  7. De'ath G (2007) Boosted trees for ecological modeling and prediction. Ecology 88:243–251CrossRefGoogle Scholar
  8. de Vente J, Poesen J (2005) Predicting soil erosion and sediment yield at the basin scale: Scale issues and semi-quantitative models. Earth Sci Rev 71:95–125CrossRefGoogle Scholar
  9. Elith J, Leathwick J (2015) Boosted Regression Trees for ecological modeling. Accessed June 2019
  10. Elith J, Leathwick JR, Hastie T (2008) A working guide to boosted regression trees. J Anim Ecol 77:802–813CrossRefGoogle Scholar
  11. Farley KA, Jobbagy EG, Jackson RB (2005) Effects of afforestation on water yield: a global synthesis with implications for policy. Glob Chang Biol 11:1565–1576CrossRefGoogle Scholar
  12. Feng XM, Sun G, Fu BJ, Su CH, Liu Y, Lamparski H (2012) Regional effects of vegetation restoration on water yield across the Loess Plateau, China. Hydrol Earth Syst Sci 16:2617–2628CrossRefGoogle Scholar
  13. Friedman JH, Meulman JJ (2003) Multiple additive regression trees with application in epidemiology. Stat Med 22:1365–1381CrossRefGoogle Scholar
  14. Gharun M, Vervoort RW, Turnbull TL, Adams MA (2014) A test of how coupling of vegetation to the atmosphere and climate spatial variation affects water yield modelling in mountainous catchments. J Hydrol 514:202–213CrossRefGoogle Scholar
  15. Gokbulak F, Sengonul K, Serengil Y, Ozhan S, Yurtseven I, Uygur B, Ozcelik MS (2016) Effect of forest thinning on water yield in a sub-humid mediterranean oak-beech mixed forested watershed. Water Resour Manag 30:5039–5049CrossRefGoogle Scholar
  16. Golden HE, Lane CR, Prues AG, D'Amico E (2016) Boosted regression tree models to explain watershed nutrient concentrations and biological condition. J Am Water Resour Assoc 52:1251–1274CrossRefGoogle Scholar
  17. Hale R, Marshall S, Jeppe K, Pettigrove V (2014) Separating the effects of water physicochemistry and sediment contamination on Chironomus tepperi (Skuse) survival, growth and development: A boosted regression tree approach. Aquat Toxicol 152:66–73CrossRefGoogle Scholar
  18. James G, Witten D, Hastie T, Tibshirani R (2013) An introduction to statistical learning. Accessed June 2019
  19. Kuria FW, Vogel RM (2015) Global storage-reliability-yield relationships for water supply reservoirs. Water Resour Manag 29:1591–1605CrossRefGoogle Scholar
  20. Liu J, Sui C, Deng D, Wang J, Feng B, Liu W, Wu C (2016) Representing conditional preference by boosted regression trees for recommendation. Inf Sci 327:1–20CrossRefGoogle Scholar
  21. Liu Y, Leng X, Deng Z, Wang L, Zhang L, Liu S, An S (2011) Effects of watershed vegetation on tributary water yields during the wet season in the Heishui Valley, China. Water Resour Manag 25:1449–1464CrossRefGoogle Scholar
  22. Lu N, Sun G, Feng XM, Fu BJ (2013) Water yield responses to climate change and variability across the North-South Transect of Eastern China (NSTEC). J Hydrol 481:96–105CrossRefGoogle Scholar
  23. McDonnell J et al (2007) Moving beyond heterogeneity and process complexity: A new vision for watershed hydrology. Water Resour Res 43Google Scholar
  24. Mehta VM, Rosenberg NJ, Mendoza K (2011) Simulated Impacts of Three Decadal Climate Variability Phenomena on Water Yields in the Missouri River Basin1. J Am Water Resour Assoc 47:126–135CrossRefGoogle Scholar
  25. Naghibi SA, Pourghasemi HR, Abbaspour K (2018) A comparison between ten advanced and soft computing models for groundwater qanat potential assessment in Iran using R and GIS. Theor Appl Climatol 131:967–984CrossRefGoogle Scholar
  26. Neitsch SL, Kiniry JG, Williams JR (2009) Soil And Water Assessment Tool: theoretical documentation. Accessed June 2019
  27. Nie W, Yuan Y, Kepner W, Nash MS, Jackson M, Erickson C (2011) Assessing impacts of Landuse and Landcover changes on hydrology for the upper San Pedro watershed. J Hydrol 407:105–114CrossRefGoogle Scholar
  28. Pessacg N, Flaherty S, Brandizi L, Solman S, Pascual M (2015) Getting water right: A case study in water yield modelling based on precipitation data. Sci Total Environ 537:225–234CrossRefGoogle Scholar
  29. Price K (2011) Effects of watershed topography, soils, land use, and climate on baseflow hydrology in humid regions: A review. Prog Phys Geogr 35:465–492CrossRefGoogle Scholar
  30. R Core Team (2014) The R project for statistical computing. Accessed June 2019
  31. Rice JS, Emanuel RE, Vose JM, Nelson SAC (2015) Continental U.S. streamflow trends from 1940 to 2009 and their relationships with watershed spatial characteristics. Water Resour Res 51:6262–6275CrossRefGoogle Scholar
  32. Ridgeway G (2007) Generalized Boosted Models: A guide to the gbm package. Accessed June 2019
  33. Saha D, Alluri P, Gan A (2015) Prioritizing Highway Safety Manual’s crash prediction variables using boosted regression trees. Accid Anal Prev 79:133–144CrossRefGoogle Scholar
  34. Salazar F, Toledo MA, Onate E, Suarez B (2016) Interpretation of dam deformation and leakage with boosted regression trees. Eng Struct 119:230–251CrossRefGoogle Scholar
  35. Salemi LF, Groppo JD, Trevisan R, de Moraes JM, Lima WD, Martinelli LA (2012) Riparian vegetation and water yield: A synthesis. J Hydrol 454:195–202CrossRefGoogle Scholar
  36. Stone MC, Hotchkiss RH, Hubbard CM, Fontaine TA, Mearns LO, Arnold JG (2001) Impacts of climate change on Missouri Rwer Basin water yield. J Am Water Resour Assoc 37:1119–1129CrossRefGoogle Scholar
  37. 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
  38. Sun F, Kuang W, Xiang W, Che Y (2016) Mapping water vulnerability of the Yangtze River Basin: 1994–2013. Environ Manag 58:857–872CrossRefGoogle Scholar
  39. Sun G, Zhou G, Zhang Z, Wei X, McNulty SG, Vose JM (2006) Potential water yield reduction due to forestation across China. J Hydrol 328:548–558CrossRefGoogle Scholar
  40. van Dijk A, Pena-Arancibia JL, Bruijnzeel LA (2012) Land cover and water yield: inference problems when comparing catchments with mixed land cover. Hydrol Earth Syst Sci 16:3461–3473CrossRefGoogle Scholar
  41. Wang G, Yang H, Wang L, Xu Z, Xue B (2014) Using the SWAT model to assess impacts of land use changes on runoff generation in headwaters. Hydrol Process 28:1032–1042CrossRefGoogle Scholar
  42. Wang S, Fu B, He C, Sun G, Gao G (2011) A comparative analysis of forest cover and catchment water yield relationships in northern China. For Ecol Manag 262:1189–1198CrossRefGoogle Scholar
  43. Wang X, Shang S, Yang W, Clary CR, Yang D (2010) Simulation of land use–soil interactive effects on water and sediment yields at watershed scale. Ecol Eng 36:328–344CrossRefGoogle Scholar
  44. 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
  45. Xu J, Yang D, Yi Y, Lei Z, Chen J, Yang W (2008) Spatial and temporal variation of runoff in the Yangtze River basin during the past 40 years. Quat Int 186:32–42CrossRefGoogle Scholar
  46. Yao Y, Cai T, Ju C, He C (2015) Effect of reforestation on annual water yield in a large watershed in northeast China. J For Res 26:697–702CrossRefGoogle Scholar
  47. Zhang Q, Xu C, Zhang Z, Chen Y, Liu C, Lin H (2008) Spatial and temporal variability of precipitation maxima during 1960–2005 in the Yangtze River basin and possible association with large-scale circulation. J Hydrol 353:215–227CrossRefGoogle Scholar
  48. Zhou G et al (2015) Global pattern for the effect of climate and land cover on water yield. Nat Commun 6:5918CrossRefGoogle Scholar

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© Springer Nature B.V. 2019

Authors and Affiliations

  1. 1.School of Ecological and Environmental SciencesEast China Normal UniversityShanghaiChina
  2. 2.Shanghai Key Lab for Urban Ecological Processes and Eco-RestorationEast China Normal UniversityShanghaiChina
  3. 3.Institute of Eco-Chongming (IEC)ShanghaiChina
  4. 4.Department of Civil and Environmental EngineeringThe Pennsylvania State UniversityUniversity ParkUSA

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