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

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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.

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The data and materials that support the findings of this study are available from the corresponding author upon reasonable request.

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Acknowledgements

The authors thank the National Natural Science Foundation of China for support. Related data are acquired from the National Aeronautics and Space Administration (NASA) the European Centre for Medium-Range Weather Forecasts (ECWMF). The authors are also thankful to the Key Laboratory of Virtual Geographic Environment (Nanjing Normal University) for providing the lab facility and other support.

Funding

This work was funded by the National Natural Science Foundation of China (No. 41971382, 31470519) and the Priority Academic Program Development of Jiangsu Higher Education Institutions (164320H116).

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Authors and Affiliations

  1. State Key Laboratory Cultivation Base of Geographical Environment Evolution (Jiangsu Province), Nanjing, 210023, China

    Haibo Gong, Fusheng Jiao, Li Cao & Huiyu Liu

  2. Key Laboratory of Virtual Geographic Environment (Nanjing Normal University), Ministry of Education, Nanjing, 210023, China

    Haibo Gong, Fusheng Jiao, Li Cao & Huiyu Liu

  3. Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing, 210023, China

    Haibo Gong, Fusheng Jiao, Li Cao & Huiyu Liu

  4. College of Geography Science, Nanjing Normal University, Nanjing, 210023, China

    Haibo Gong, Fusheng Jiao, Li Cao & Huiyu Liu

  5. Jiangsu Key Laboratory of Environmental Change and Ecological Construction, Nanjing Normal University, Nanjing, 210023, China

    Haibo Gong, Fusheng Jiao, Li Cao & Huiyu Liu

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  1. Haibo Gong
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Contributions

All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by Haibo Gong, Fusheng Jiao, Li Cao and Huiyu Liu. The first draft of the manuscript was written by Haibo Gong and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

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Correspondence to Huiyu Liu.

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Highlights

• A new framework to obtain long-term precipitation (1982-2018) with 1km spatial resolution over the YRB.

• Time-series retrospective forecasting could supplement TRMM precipitation during 1982-1997.

• Downscaling results with 10CV-GDA are superior to that with GDA.

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Gong, H., Jiao, F., Cao, L. et al. Long-term Precipitation Estimation Combining Time-Series Retrospective Forecasting and Downscaling-Calibration Procedure. Water Resour Manage 36, 3087–3106 (2022). https://doi.org/10.1007/s11269-022-03190-5

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  • DOI: https://doi.org/10.1007/s11269-022-03190-5

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