A two-step merging strategy for incorporating multi-source precipitation products and gauge observations using machine learning classification and regression over China

Abstract

Abstract. Although many multi-source precipitation products (MSPs) with high spatiotemporal resolution have been extensively used in water cycle research, they are still subject to various biases, including false alarm and missed bias. Precipitation merging technology is an effective means to alleviate this uncertainty. However, how to efficiently improve precipitation detection efficiency and precipitation intensity simultaneously is a problem worth exploring. This study presents a two-step merging strategy based on machine learning (ML) algorithms, including gradient boosting decision tree (GBDT), extreme gradient boosting (XGBoost), and random forest (RF). It incorporates six state-of-the-art MSPs (GSMaP, IMERG, PERSIANN-CDR, CMORPH, CHIRPS, and ERA5-Land) and rain gauges to improve the accuracy of precipitation identification and estimation from 2000 to 2017 over China. Multiple environment variables and spatial autocorrelation are combined in the merging process. The strategy first employs classification models to identify wet and dry days and then combines regression models to predict precipitation amounts based on classified wet days. The merged results are compared with traditional methods, including multiple linear regression (MLR), ML regression models, and gauge-based Kriging interpolation. A total of 1680 (70 %) rain gauges are randomly chosen for model training and 692 (30 %) for performance evaluation. The results show that (1) the multi-source merged precipitation products (MSMPs) outperformed all original MSPs in terms of statistical and categorical metrics, which substantially alleviates the temporal and spatial biases. The modified Kling–Gupta efficiency (KGE), critical success index (CSI), and Heidke Skill Score (HSS) of original MSPs are improved by 15 %–85 %, 17 %–155 %, and 21 %–166 %, respectively. (2) The spatial autocorrelation plays a significant role in precipitation merging, which considerably improves the model accuracy. (3) The performance of MSMPs obtained by the proposed method is superior to MLR, Kriging interpolation, and ML regression models. The XGBoost algorithm is recommended more for large-scale data merging owing to its high computational efficiency. (4) The two-step merging strategy performs better when higher-density gauges are used to model training. However, it has strong robustness and can also obtain better performance than original MSPs even when the gauge number is reduced to 10 % (237). This study provides an accurate and reliable method to improve precipitation detection accuracy under complex climatic and topographic conditions. It could be applied to other areas well if rain gauges are available.

A Novel Double Machine Learning Strategy for Producing High‐Precision Multi‐Source Merging Precipitation Estimates Over the Tibetan Plateau

“…Building upon the works of Zhang et al (2021) and Lei et al (2022), we introduce a novel strategy called Double Machine Learning (DML) that merges TML and DL techniques in a two-step process to produce highly accurate precipitation estimates. The proposed strategy aims to simplify the complex task of precipitation estimates by dividing it into two sub-problems: (a) identifying the occurrence of precipitation at specific grid points, and (b) estimating the amount of precipitation if it occurs.…”

Comparison of Tree-Based Ensemble Algorithms for Merging Satellite and Earth-Observed Precipitation Data at the Daily Time Scale

“…These scores were computed with respect to the linear regression algorithm, which models the dependent variable as a linear weighted sum of the predictor variables ( [1], pp. [43][44][45][46][47][48][49][50][51][52][53][54][55]. A squared error scoring function facilitated this algorithm's fitting.…”

“…The importance of this same task can be perceived through the inspection of the major research topics appearing in the hydrological literature (see, e.g., those discussed in [36,37]). Relevant examples of applications and comparisons are available in [38][39][40][41][42][43][44].…”

“…These examples refer to various temporal resolutions and many different geographical regions around the globe (see also the reviews by [45,46]), with the daily temporal resolution and the Unites States (US) being frequent cases. Nonetheless, a relevant comparison of treebased ensemble algorithms for the latter temporal resolution and the latter geographical region is missing from the literature, with the closest investigations at the moment being those available in the work by [43], which focuses on China. In this work, we fill this specific literature gap.…”

Inter-Comparison of Multiple Gridded Precipitation Datasets over Different Climates at Global Scale

“…These datasets exhibit substantial variability with respect to data sources, spatial coverage and resolution, temporal span and resolution, among other factors [8][9][10]. According to data sources, these precipitation datasets fall into four categories: (1) gauge-based, (2) satellite-based, (3) reanalysis and (4) merged [8,[11][12][13].…”