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

Gong, Haibo ; Jiao, Fusheng ; Cao, Li ; Liu, Huiyu

Water resources management, 2022-07, Vol.36 (9), p.3087-3106 [Periódico revisado por pares]

Dordrecht: Springer Netherlands

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Título:
Long-term Precipitation Estimation Combining Time-Series Retrospective Forecasting and Downscaling-Calibration Procedure
Autor: Gong, Haibo ; Jiao, Fusheng ; Cao, Li ; Liu, Huiyu
Assuntos: Annual precipitation ; Annual rainfall ; Atmospheric Sciences ; Autoregressive models ; Calibration ; Civil Engineering ; Coefficients ; Earth and Environmental Science ; Earth Sciences ; Ecological research ; Environment ; Geotechnical Engineering & Applied Earth Sciences ; Hydrogeology ; Hydrologic data ; Hydrology ; Hydrology/Water Resources ; Meteorological satellites ; Precipitation ; Procedures ; Rainfall ; Resolution ; River basins ; Root-mean-square errors ; Satellite observation ; Satellites ; Spatial discrimination ; Spatial resolution ; Statistical analysis ; TRMM satellite ; Tropical climate ; Weather forecasting
É parte de: Water resources management, 2022-07, Vol.36 (9), p.3087-3106
Descrição: 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 ( R 2 ): 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 ( R 2 : 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.
Editor: Dordrecht: Springer Netherlands
Idioma: Inglês

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

Gong, Haibo ; Jiao, Fusheng ; Cao, Li ; Liu, Huiyu

Dordrecht: Springer Netherlands

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