Easy-to-use spatial Random Forest-based downscaling-calibration method for producing high resolution and accurate precipitation data

Abstract

Abstract. High resolution and accurate precipitation data is significantly important for numerous hydrological applications. To enhance the spatial resolution and accuracy of satellite-based precipitation products, an easy-to-use downscaling-calibration method based on spatial Random Forest (SRF) is proposed in this paper, where the spatial autocorrelation between precipitation measurements is taken into account. The proposed method consists of two main stages. Firstly, the satellite-based precipitation was downscaled by SRF with the incorporation of some high-resolution covariates including latitude, longitude, DEM, NDVI, terrain slope, aspect, relief, and land surface temperatures. Then, the downscaled precipitation was calibrated by SRF with rain gauge observations and the aforementioned high-resolution variables. The monthly Integrated MultisatellitE Retrievals for Global Precipitation Measurement (IMERG) located in Sichuan province, China from 2015 to 2019 was processed using our method and its results were compared with those of some 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). Results show that: (1) the proposed method outperforms the other methods as well as the original IMERG; (2) the monthly-based SRF estimation is slightly more accurate than the annual-based SRF fraction disaggregation method; (3) SRF-based downscaling and calibration preforms better than bilinear downscaling (Bi-SRF) and GDA-based calibration (SRF-GDA); (4) kriging seems more accurate than GWR and ANN in terms of quantitative accuracy measures, whereas its precipitation map cannot capture the detailed spatial precipitation patterns; and (5) among the predictors for calibration, the precipitation interpolated by kriging on the gauge measurements is the most important variable, indicating the significance for the inclusion of spatial autocorrelation information in gauge measurements.

Comparison and Evaluation of Machine-Learning-Based Spatial Downscaling Approaches on Satellite-Derived Precipitation Data

“…Precipitation is an important component in global water cycle and energy balance (Chen et al, 2021). The amount and distribution of precipitation have significant impact on the water resource management, climate research, and environmental monitoring (Karbalaye Ghorbanpour et al, 2021).…”

“…Yan et al (2021) constructed a downscaling-merging scheme based on RF and cokriging to acquire high-resolution precipitation data, and greatly improved its accuracy and spatial details. Chen et al (2021) introduced the spatial autocorrelation to the RF model and proposed a spatial random forest (SRF) for downscaling. They found that the SRF outperformed other conventional algorithms and illustrated the importance of incorporating spatial autocorrelation to ML approaches.…”

Multi-Source Precipitation Data Merging for High-Resolution Daily Rainfall in Complex Terrain

“…The western region mainly consists of plateaus and mountains, with elevations averaging around Remote Sens. 2023, 15, 4345 3 of 19 4000 m. In contrast, the eastern part is dominated by plains, basins, and hills, with elevations below 1500 m [17]. The dramatic variation in elevation leads to noticeable differences in the spatiotemporal distribution of precipitation in Sichuan province.…”

“…Sichuan province is characterized by complex terrain and significant variations in elevation, with a general trend of higher elevation in the west and lower elevation in the east. The western region mainly consists of plateaus and mountains, with elevations averaging around 4000 m. In contrast, the eastern part is dominated by plains, basins, and hills, with elevations below 1500 m [17]. The dramatic variation in elevation leads to noticeable differences in the spatiotemporal distribution of precipitation in Sichuan province.…”