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Long-term Precipitation Estimation Combining Time-Series Retrospective Forecasting and Downscaling-Calibration Procedure

Author

Listed:
  • Haibo Gong

    (State Key Laboratory Cultivation Base of Geographical Environment Evolution (Jiangsu Province)
    Key Laboratory of Virtual Geographic Environment (Nanjing Normal University), Ministry of Education
    Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application
    Nanjing Normal University)

  • Fusheng Jiao

    (State Key Laboratory Cultivation Base of Geographical Environment Evolution (Jiangsu Province)
    Key Laboratory of Virtual Geographic Environment (Nanjing Normal University), Ministry of Education
    Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application
    Nanjing Normal University)

  • Li Cao

    (State Key Laboratory Cultivation Base of Geographical Environment Evolution (Jiangsu Province)
    Key Laboratory of Virtual Geographic Environment (Nanjing Normal University), Ministry of Education
    Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application
    Nanjing Normal University)

  • Huiyu Liu

    (State Key Laboratory Cultivation Base of Geographical Environment Evolution (Jiangsu Province)
    Key Laboratory of Virtual Geographic Environment (Nanjing Normal University), Ministry of Education
    Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application
    Nanjing Normal University)

Abstract

Long-term precipitation datasets with high spatial resolution are crucial for hydrological, meteorological, and ecological research. However, due to the coarse spatial resolution and relatively short observation period of satellite-derived precipitation, obtaining long-term precipitation data with high spatial resolution remains to be a challenging task. In this study, autoregressive integrated moving average models (ARIMA and ARIMAX) are applied to retrospectively forecast month and annual Tropical Rainfall Measuring Mission (TRMM) precipitation during 1982–1997 at a 0.25°-pixel scale. The downscaling-calibration procedure including geographically weighted regression (GWR) and ten-fold cross-validation geographical difference analysis (10CV-GDA) is employed to obtain long-term precipitation (1982–2018) at high spatial resolution over the Yangtze River Basin (YRB). The precision of supplementary TRMM precipitation generated by ARIMAX is significantly higher than those generated by ARIMA, and supplementary TRMM precipitation is considered suitable for the subsequent downscaling-calibration procedure. The 10CV-GDA calibration significantly improved the precision of the downscaled results in comparison to the results obtained without or with geographical difference analysis (GDA) calibration (coefficients of determination (R2): 0.86, mean absolute error (MAE):106.29 mm, and root mean square error (RMSE): 147.29 mm). The annual downscaled results are disaggregated into 1 km monthly precipitation, and the overall performances of precipitation estimation are excellent (R2: 0.88, MAE: 18.11 mm, RMSE: 31.87 mm). This study provides a novel framework for long-term precipitation estimates at high spatial resolution and has great potential for enhancing the utilizability of satellite-derived precipitation.

Suggested Citation

  • Haibo Gong & Fusheng Jiao & Li Cao & Huiyu Liu, 2022. "Long-term Precipitation Estimation Combining Time-Series Retrospective Forecasting and Downscaling-Calibration Procedure," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(9), pages 3087-3106, July.
    • Handle: RePEc:spr:waterr:v:36:y:2022:i:9:d:10.1007_s11269-022-03190-5
    DOI: 10.1007/s11269-022-03190-5
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    References listed on IDEAS

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    1. Yongtao Wang & Jian Liu & Rong Li & Xinyu Suo & EnHui Lu, 2022. "Medium and Long-term Precipitation Prediction Using Wavelet Decomposition-prediction-reconstruction Model," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(3), pages 971-987, February.
    2. Radhikesh Kumar & Maheshwari Prasad Singh & Bishwajit Roy & Afzal Hussain Shahid, 2021. "A Comparative Assessment of Metaheuristic Optimized Extreme Learning Machine and Deep Neural Network in Multi-Step-Ahead Long-term Rainfall Prediction for All-Indian Regions," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 35(6), pages 1927-1960, April.
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