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Reconstruction of hydrometeorological time series and its uncertainties for the Kaidu River Basin using multiple data sources

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Abstract

This paper tried to reconstruct the time series (TS) of monthly average temperature (MAT), monthly accumulated precipitation (MAP), and monthly accumulated runoff (MAR) during 1901–1960 in the Kaidu River Basin using the Delta method and the three-layered feed forward neural network with backpropagation algorithm (TLBP-FFNN) model. Uncertainties in the reconstruction of hydrometeorological parameters were also discussed. Available monthly observed hydrometeorological data covering the period 1961–2000 from the Kaidu River Basin, the monthly observed meteorological data from three stations in Central Asia, monthly grid climatic data from the Climatic Research Unit (CRU), and Coupled Model Intercomparison Project Phase 3 (CMIP3) dataset covering the period 1901–2000 were used for the reconstruction. It was found that the Delta method performed very well for calibrated and verified MAT in the Kaidu River Basin based on the monthly observed meteorological data from Central Asia, the monthly grid climatic data from CRU, and the CMIP3 dataset from 1961 to 2000. Although calibration and verification of MAP did not perform as well as MAT, MAP at Bayinbuluke station, an alpine meteorological station, showed a satisfactory result based on the data from CRU and CMIP3, indicating that the Delta method can be applied to reconstruct MAT in the Kaidu River Basin on the basis of the selected three data sources and MAP in the mountain area based on CRU and CMIP3. MAR at Dashankou station, a hydrological gauge station on the verge of the Tianshan Mountains, from 1961 to 2000 was well calibrated and verified using the TLBP-FFNN model with structure (8,1,1) by taking MAT and MAP of four meteorological stations from observation; CRU and CMIP3 data, respectively, as inputs; and the model was expanded to reconstruct TS during 1901–1960. While the characteristics of annual periodicity were depicted well by the TS of MAT, MAP, and MAR reconstructed over the target stations during the period 1901–1960, different high frequency signals were captured also. The annual average temperature (AAT) show a significant increasing trend during the 20th century, but annual accumulated precipitation (AAP) and annual accumulated runoff (AAR) do not. Although some uncertainties exist in the hydrometeorological reconstruction, this work should provide a viable reference for studying long-term change of climate and water resources as well as risk assessment of flood and drought in the Kaidu River Basin, a region of fast economic development.

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Acknowledgments

We acknowledge the modeling groups for providing their data for analysis, the PCMDI and the WCRP's Coupled Model Intercomparison Project for collecting and archiving the model output and organizing the model data analysis activity. The data has been collected, analyzed, and are provided by the National Climate Center. This work was financially supported by the State Key Development Program for Basic Research of China (973 program, Grant No. 2010CB951002), the Natural Sciences Foundation of China (Grant No. 40871027), and the opened subject of Xinjiang Key Laboratory of Water Cycle and Utilization in Arid Zone (Grant No. XJYS0907-2011-04).

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Authors and Affiliations

  1. State Key Laboratory of Desert and Oasis Ecology, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, 818 South Beijing Road, Urumqi, Xinjiang, 830011, China

    Xuemei Li, Lanhai Li, Xixi Wang & Fengqing Jiang

  2. Graduate School, Chinese Academy of Sciences, Beijing, 100049, China

    Xuemei Li

  3. Department of Civil and Environmental Engineering, Frank Batten College of Engineering and Technology, Old Dominion University, Norfolk, VA, 23529-0241, USA

    Xixi Wang

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  1. Xuemei Li
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  2. Lanhai Li
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Correspondence to Lanhai Li.

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Li, X., Li, L., Wang, X. et al. Reconstruction of hydrometeorological time series and its uncertainties for the Kaidu River Basin using multiple data sources. Theor Appl Climatol 113, 45–62 (2013). https://doi.org/10.1007/s00704-012-0771-2

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