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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
Article
  • 28 Downloads

Abstract

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.

Keywords

Water yield Spatial and temporal scales Soil and water assessment tool (SWAT) Boosted regression tree (BRT) Yangtze River Basin 

Notes

Acknowledgments

This study is supported by the Major Science and Technology Program for Water Pollution Control and Treatment (Grant No. 2017ZX07207003-01).

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Copyright information

© 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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