A method to study the impact of climate change on variability of river flow: an example from the Guadalupe River in Texas (original) (raw)

Abstract

This work introduced a method to study river flow variability in response to climate change by using remote sensing precipitation data, downscaled climate model outputs with bias corrections, and a land surface model. A meteorological forcing dataset representing future climate was constructed via the delta change method in which the modeled change was added to the present-day conditions. The delta change was conducted at a fine spatial and temporal scale to contain the signals of weather events, which exhibit substantial responses to climate change. An empirical transformation technique was further applied to the constructed forcing to ensure a realistic range. The meteorological forcing was then used to drive the land surface model to simulate the future river flow. The results show that preserving fine-scale processes in response to climate change is a necessity to assess climatic impacts on the variability of river flow events.

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Acknowledgements

This work was supported by NASA IDS Grants (NNX07AL79G and NNX11AE42G). Xiaoyan Jiang provided downscaled WRF output. We thank insightful discussions with David Gochis at The National Center for Atmospheric Research (NCAR). We are grateful for the constructive comments from the reviewers.

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Authors and Affiliations

  1. Institute of Oceanology, Chinese Academy of Sciences, 7 Nanhai Road, Qingdao, 266071, PR China
    Yongsheng Xu
  2. Department of Geological Sciences, The John A. and Katherine G. Jackson School of Geosciences, The University of Texas at Austin, Austin, TX, 78712-0254, USA
    Yongsheng Xu & Zong-Liang Yang

Authors

  1. Yongsheng Xu
  2. Zong-Liang Yang

Corresponding author

Correspondence toYongsheng Xu.

Appendix

Appendix

1.1 Physically-based parsimonious multivariate-regression algorithm

Physically-based parsimonious multivariate-regression algorithm is an statistical algorithm used to downscale low-resolution spatial precipitation fields (Guan et al. [2009](/article/10.1007/s10584-011-0366-4#ref-CR17 "Guan H, Wilson JL, Xie H (2009) A cluster-optimizing regression-based approach for precipitation spatial downscaling in mountainous terrain. J Hydrol 375:578–588. doi: 10.1016/j.jhydrol.2009.07.007

            ")). This algorithm auto-searches precipitation spatial structures (rain-pixel clusters), and orographic effects on precipitation distribution without prior knowledge of atmospheric setting. It is composed of three components: rain-pixel clustering, multivariate regression, and random cascade. The first step is clustering, which separate the rain pixels into different clusters by rain rates and their spatial connections, because the storm structure and the associated physical processes are believed to be more similar within one raining pixel cluster than between clusters. The second step is to examine alternative cluster structures, and find the one having the best agreement between the regression estimates and the original NEXRAD pixel values. For all identified clusters, ASOADeK regression \[Guan et al. [2009](/article/10.1007/s10584-011-0366-4#ref-CR17 "Guan H, Wilson JL, Xie H (2009) A cluster-optimizing regression-based approach for precipitation spatial downscaling in mountainous terrain. J Hydrol 375:578–588. doi:
                10.1016/j.jhydrol.2009.07.007
                
              
            "), [2005](/article/10.1007/s10584-011-0366-4#ref-CR16 "Guan H, Wilson JL, Makhnin O (2005) Geostatistical mapping of mountain precipitation incorporating autosearched effects of terrain and climatic characteristics. J Hydrometeorol 6:1018–1031")\] is applied,

P=bo+b1X+b2Y+b3XY+b4X2+b5Y2+b6Z+b7cosalpha+b8sinalphaP = {b_o} + {b_1}X + {b_2}Y + {b_3}XY + {b_4}{X^2} + {b_5}{Y^2} + {b_6}Z + {b_7}\cos \alpha + {b_8}\sin \alphaP=bo+b_1X+b_2Y+b_3XY+b_4X2+b_5Y2+b_6Z+b_7cosalpha+b_8sinalpha

where P is precipitation rate, X is the longitudinal geographic coordinate, Y is the latitudinal coordinate, Z is the elevation, α is the terrain aspect, and the b i are fitted coefficients. After regression, the sum of precipitation for the small pixels (calculated from the regression function) is compared to the original large pixel value, and their correlation coefficient is calculated for each cluster and assigned to each large pixel and small pixels within the cluster. Guan et al. ([2009](/article/10.1007/s10584-011-0366-4#ref-CR17 "Guan H, Wilson JL, Xie H (2009) A cluster-optimizing regression-based approach for precipitation spatial downscaling in mountainous terrain. J Hydrol 375:578–588. doi: 10.1016/j.jhydrol.2009.07.007

            ")) demonstrated the good performance of the algorithm for downscaling NEXRAD precipitation at both daily and hourly temporal resolutions for the northern New Mexico mountainous terrain and the central Texas Hill Country.

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Xu, Y., Yang, ZL. A method to study the impact of climate change on variability of river flow: an example from the Guadalupe River in Texas.Climatic Change 113, 965–979 (2012). https://doi.org/10.1007/s10584-011-0366-4

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