Abstract
Abstract. Precipitation data with high resolution and high accuracy are significantly important in numerous hydrological applications. To enhance the spatial resolution and accuracy of satellite-based precipitation products, an easy-to-use downscaling-calibration method based on a spatial random forest (SRF-DC) is proposed in this study, where the spatial autocorrelation of precipitation measurements between neighboring locations is considered. SRF-DC consists of two main stages. First, the satellite-based precipitation is downscaled by the SRF with the incorporation of high-resolution variables including latitude, longitude, normalized difference vegetation index (NDVI), digital elevation model (DEM), terrain slope, aspect, relief and land surface temperatures. Then, the downscaled precipitation is calibrated by the SRF with rain gauge observations and the aforementioned high-resolution variables. The monthly Integrated MultisatellitE Retrievals for Global Precipitation Measurement (IMERG) over Sichuan Province, China, from 2015 to 2019 was processed using SRF-DC, and its results were compared with those of classical methods including geographically weighted regression (GWR), artificial neural network (ANN), random forest (RF), kriging interpolation only on gauge measurements, bilinear interpolation-based downscaling and then SRF-based calibration (Bi-SRF), and SRF-based downscaling and then geographical difference analysis (GDA)-based calibration (SRF-GDA). Comparative analyses with respect to root mean square error (RMSE), mean absolute error (MAE) and correlation coefficient (CC) demonstrate that (1) SRF-DC outperforms the classical methods as well as the original IMERG; (2) the monthly based SRF estimation is slightly more accurate than the annually based SRF fraction disaggregation method; (3) SRF-based downscaling and calibration perform better than bilinear downscaling (Bi-SRF) and GDA-based calibration (SRF-GDA); (4) kriging is more accurate than GWR and ANN, whereas its precipitation map loses detailed spatial precipitation patterns; and (5) based on the variable-importance rank of the RF, the precipitation interpolated by kriging on the rain gauge measurements is the most important variable, indicating the significance of incorporating spatial autocorrelation for precipitation estimation.
Highlights
Precipitation is an important variable for promoting our understanding of hydrological cycle and water resource management (Chen et al, 2010; Yue, 2011)
spatial RF (SRF)-DC was compared with two frameworks: (i) the Integrated MultisatellitE Retrievals for Global Precipitation Measurement (IMERG) was first downscaled by the bilinear interpolation and calibrated by the SRF, and (ii) the IMERG was first downscaled by the SRF and calibrated by geographical difference analysis (GDA)
The performance of geographically weighted regression (GWR) is unsatisfactory. This is mainly attributed to the complex relationship between precipitation and predictors, which cannot be properly described by the two models
Summary
Precipitation is an important variable for promoting our understanding of hydrological cycle and water resource management (Chen et al, 2010; Yue, 2011). Previous studies have shown that about 70 %–80 % of hydrological modeling errors are caused by precipitation uncertainties (Gebregiorgis and Hossain, 2013). Precipitation is one of the most difficult meteorological factors to estimate due to its high spatial and temporal heterogeneity (Beck et al, 2019). Point-based rain gauge observations are reliable and accurate, it is difficult to reflect the spatial precipitation pattern because of the sparse and uneven distribution of meteorological stations, especially in remote and mountainous areas (Ullah et al, 2020). C. Chen et al.: Easy-to-use spatial random-forest-based downscaling-calibration method
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