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Development of a New Quantile-Based Method for the Assessment of Regional Water Resources in a Highly-Regulated River Basin

Author

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  • Salam A. Abbas

    (Swansea University Bay Campus)

  • Yunqing Xuan

    (Swansea University Bay Campus)

Abstract

In this paper, we present a study of assessing regional water resources in a highly regulated river basin, the Dee river basin in the UK. The aims of this study include: 1) to address the issue of hydrological simulations for regulated river catchments; 2) to develop a new method revealing the trends of water resources for different scenarios (e.g. dry and wet) and 3) to facilitate water resources assessment under both climate change impacts and regulations. We use the SWAT model to model the hydrological process of the river basin with carefully designed configurations to isolate the impact from the water use regulations and practice. The spatially-distributed model simulations are then analysed with the quantile regression method to reveal the spatial and temporal patterns of regional water resources. The results show that this approach excels in presenting distributed, spatially focused trend information for extremely dry and wet scenarios, which can well address the needs of practitioners and decision-makers in dealing with long-term planning and climate change impact. The representation of the management practice in the modelling process helps identify the impact from both climate change and necessary regulatory practices, and as such lays a foundation for further study on how various management practices can mitigate the impact from other sources such as those from climate change. The novelty of the study lies in three aspects: 1) it devises a new way of isolating and representing management practice in the hydrological modelling process for regulated river basins; 2) it integrates the QR technique to study spatial-temporal trends of catchment water yield in a distributed fashion, for wet and dry scenarios instead of the mean; 3) the combination of the methods are able to reveal the impacts from various sources as well as their interactions with catchment water resources.

Suggested Citation

  • Salam A. Abbas & Yunqing Xuan, 2019. "Development of a New Quantile-Based Method for the Assessment of Regional Water Resources in a Highly-Regulated River Basin," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 33(9), pages 3187-3210, July.
    • Handle: RePEc:spr:waterr:v:33:y:2019:i:9:d:10.1007_s11269-019-02290-z
    DOI: 10.1007/s11269-019-02290-z
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    References listed on IDEAS

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    1. Jay Breidt, 2005. "Nonlinear Time Series: Nonparametric and Parametric Methods. Jianqing Fan and Qiwei Yao," Journal of the American Statistical Association, American Statistical Association, vol. 100, pages 348-349, March.
    2. Koenker, Roger W & Bassett, Gilbert, Jr, 1978. "Regression Quantiles," Econometrica, Econometric Society, vol. 46(1), pages 33-50, January.
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    Cited by:

    1. Salam A. Abbas & Yunqing Xuan & Xiaomeng Song, 2019. "Quantile Regression Based Methods for Investigating Rainfall Trends Associated with Flooding and Drought Conditions," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 33(12), pages 4249-4264, September.
    2. Naveed Ahmed & Genxu Wang & Martijn J. Booij & Sun Xiangyang & Fiaz Hussain & Ghulam Nabi, 2022. "Separation of the Impact of Landuse/Landcover Change and Climate Change on Runoff in the Upstream Area of the Yangtze River, China," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(1), pages 181-201, January.
    3. Renata Graf & Viktor Vyshnevskyi, 2022. "Forecasting Monthly River Flows in Ukraine under Different Climatic Conditions," Resources, MDPI, vol. 11(12), pages 1-24, November.

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