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Comparing the Hydrological Responses of Conceptual and Process-Based Models with Varying Rain Gauge Density and Distribution

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Listed:
  • Zhaokai Yin

    (State Key Laboratory of Hydraulic Engineering Simulation and Safety, Tianjin University, Tianjin 300072, China)

  • Weihong Liao

    (State Key Laboratory of Simulation and Regulation of Water Cycle in River Basin, China Institute of Water Resources and Hydropower Research, Beijing 100038, China)

  • Xiaohui Lei

    (State Key Laboratory of Simulation and Regulation of Water Cycle in River Basin, China Institute of Water Resources and Hydropower Research, Beijing 100038, China
    Science and Technology Innovation Center for Smart Water, Northeastern University, Shenyang 110819, China)

  • Hao Wang

    (State Key Laboratory of Simulation and Regulation of Water Cycle in River Basin, China Institute of Water Resources and Hydropower Research, Beijing 100038, China
    Science and Technology Innovation Center for Smart Water, Northeastern University, Shenyang 110819, China)

  • Ruojia Wang

    (Department of Information Management, Peking University, Beijing 100871, China
    Institute of Ocean Research, Peking University, Beijing 100871, China)

Abstract

Precipitation provides the most crucial input for hydrological modeling. However, rain gauge networks, the most common precipitation measurement mechanisms, are sometimes sparse and inadequately distributed in practice, resulting in an imperfect representation of rainfall spatial variability. The objective of this study is to analyze the sensitivity of different model structures to the different density and distribution of rain gauges and evaluate their reliability and robustness. Based on a rain gauge network of 20 gauges in the Jinjiang River Basin, south-eastern China, this study compared the performance of two conceptual models (the hydrologic model (HYMOD) and Xinanjiang) and one process-based distributed model (the water and energy transfer between soil, plants and atmosphere model (WetSpa)) with different rain gauge distributions. The results show that the average accuracy for the three models is generally stable as the number of rain gauges decreases but is sensitive to changes in the network distribution. HYMOD has the highest calibration uncertainty, followed by Xinanjiang and WetSpa. Differing model responses are consistent with changes in network distribution, while calibration uncertainties are more related to model structures.

Suggested Citation

  • Zhaokai Yin & Weihong Liao & Xiaohui Lei & Hao Wang & Ruojia Wang, 2018. "Comparing the Hydrological Responses of Conceptual and Process-Based Models with Varying Rain Gauge Density and Distribution," Sustainability, MDPI, vol. 10(9), pages 1-22, September.
    • Handle: RePEc:gam:jsusta:v:10:y:2018:i:9:p:3209-:d:168448
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    References listed on IDEAS

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    1. Jiazheng Lu & Jun Guo & Li Yang & Xunjian Xu, 2017. "Research of reservoir watershed fine zoning and flood forecasting method," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 89(3), pages 1291-1306, December.
    2. Patrick E. Brown & Peter J. Diggle & Martin E. Lord & Peter C. Young, 2001. "Space–time calibration of radar rainfall data," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 50(2), pages 221-241.
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    Cited by:

    1. Yan Liu & Ting Zhang & Aiqing Kang & Jianzhu Li & Xiaohui Lei, 2021. "Research on Runoff Simulations Using Deep-Learning Methods," Sustainability, MDPI, vol. 13(3), pages 1-20, January.
    2. de Oliveira Simoyama, Felipe & Croope, Silvana & de Salles Neto, Luiz Leduino & Santos, Leonardo Bacelar Lima, 2023. "Optimization of rain gauge networks—A systematic literature review," Socio-Economic Planning Sciences, Elsevier, vol. 86(C).

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