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Erschienen in: Water Resources Management 10/2019

23.07.2019

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

verfasst von: Fengyun Sun, Alfonso Mejia, Yue Che

Erschienen in: Water Resources Management | Ausgabe 10/2019

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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.

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Metadaten
Titel
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
verfasst von
Fengyun Sun
Alfonso Mejia
Yue Che
Publikationsdatum
23.07.2019
Verlag
Springer Netherlands
Erschienen in
Water Resources Management / Ausgabe 10/2019
Print ISSN: 0920-4741
Elektronische ISSN: 1573-1650
DOI
https://doi.org/10.1007/s11269-019-02310-y

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