Downscaling the GPM-based satellite precipitation retrievals using gradient boosting decision tree approach over Mainland China

Shen, Zhehui; Yong, Bin

doi:10.1016/j.jhydrol.2021.126803

Cited by 41 publications

(18 citation statements)

References 42 publications

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“…Although normalized differential vegetation index (NDVI) is often used as a critical auxiliary variable to predict precipitation, it is susceptible to soil type and human activities. NDVI is more suitable for monthly or annual applications due to its temporal resolution (Ghorbanpour et al, 2021;Shen and Yong, 2021;Tan et al, 2021). Inversely, the response of air temperature and soil moisture to daily precipitation is better than NDVI, especially in the desert and bare land (Bhuiyan et al, 2018).…”

Section: Environment Variablesmentioning

confidence: 99%

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

Lei

Zhao

2022

Hydrol. Earth Syst. Sci.

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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.

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Section: Environment Variablesmentioning

confidence: 99%

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

Lei

Zhao

2022

Hydrol. Earth Syst. Sci.

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“…Also, the more instantaneous LST‐precipitation relationship compared to NDVI‐precipitation made it a potential variable for spatial downscaling at finer time scales than annual (Jing et al ., 2016). Shen and Yong (2021) downscaled a PP over China using DEM, NDVI, LST, and geographical coordinates by establishing a climate‐specific gradient boosting decision trees algorithm. Their results showed that the relative importance of each variable varies significantly across different climatic regions, emphasizing on the utilization of non‐stationary models.…”

Section: Introductionmentioning

confidence: 99%

A locally weighted linear ridge regression framework for spatial interpolation of monthly precipitation over an orographically complex area

Zandi

Nasseri

Zahraie

2023

Intl Journal of Climatology

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This article aimed to investigate the potential of using three large‐scale precipitation products (PPs), including TRMM, ERA5‐Land, and MSWEP, and two land surface characteristics, including NDVI and Land Surface Temperature (LST), to improve the accuracy of an elevation‐based spatial non‐stationary, Locally Weighted Linear Regression (LWLR) method. A two‐step approach consisting of downscaling and merging was proposed and assessed across an orographically complex region in Iran to construct monthly precipitation maps for the 2001–2015 period. In the first step, three large‐scale PPs were downscaled to 1 km spatial resolution via the Area To Point Kriging (ATPK) method to address the spatial resolution discrepancy between the coarse pixels of PPs and pointwise gauge data. In the second step, five regression models with different combinations of predictors were developed and compared with the benchmark precipitation‐elevation regression model. The L2 regularization method was adopted to overcome the potential multicollinearity issue. The proposed framework could successfully diagnose the multicollinearity issues and dynamically estimate the regularization parameter in space and time. We utilized the holdout method to validate and compare the developed models, in which 50% of gauges were used for fitting the regression functions, and the remaining gauges were assigned to the validation dataset. The validation results showed that the overall monthly accuracy of the benchmark univariate elevation‐based model was improved where the MAE metric was decreased by 16.5%, and CC and KGE were increased by 3.4 and 5.8%, respectively. Also, the rBias was enhanced from −5.72 to 1.24%. Mean monthly precipitation maps of the 2001–2015 period generated by the model with the best combination of predictors and the benchmark model displayed the same spatial pattern consistent with the topography. Further analysis under different validation scenarios revealed that the contribution of PPs and auxiliary variables is the greatest when the training gauge network is sparse. Also, the developed multivariate model has a higher extrapolation ability and is more robust than the benchmark model.

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“…The land surface characteristics impact the spatial pattern of precipitation with spatially heterogeneous relationships in China Ma, Zhou, et al (2017). Additionally, local topography (Jia et al, 2011), pre‐storm meteorological conditions (Fang et al, 2013), and cloud properties (Ma, Xu, He, et al, 2020) also have been considered in the precipitation downscaling process, and the selection of predictors shows a very diverse effect on precipitation predictands in different climate zones (Shen & Yong, 2021).…”

Section: Introductionmentioning

confidence: 99%

The evaluation and downscaling‐calibration of IMERG precipitation products at sub‐daily scales over a metropolitan region

Zhuang

Zhou

Liu

et al. 2023

J Flood Risk Management

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The Global Precipitation Measurement (GPM) Integrated Multi‐satellitE Retrievals for GPM products (i.e., IMERG) provide new‐generation satellite precipitation measurements. For urban contexts, however, the issues of its bias and insufficient resolutions still exist. This study aims to develop high‐precision and high‐resolution (e.g., 0.01°/1 h) data for a metropolitan region based on IMERG and gauge precipitations. The original IMERG product is evaluated using hourly in situ precipitations from 47 gauges. A spatial downscaling‐calibration (DC) technique is then developed to enhance the IMERG using the Normalized Difference Vegetation Index. The results show the limited capability of IMERG to capture sub‐daily precipitation and high‐intensity precipitation. The proposed DC method significantly improves IMERG performance, with correlation coefficient (CC) increasing from 0.07 to 0.75, and probability of detection improving from 0.34 to 0.90 at the hourly scale. In terms of spatial rainfall distribution, 86% of mean absolute error and 80% of RMSE are improved with CC increasing from 0.07 to 0.91 on average. Additionally, the calibrated downscaled product provides finer information in local areas, capturing three times more spatial variabilities of urban precipitation against the original IMERG input data. The results highlight the necessity of improving urban observations for flood risk management at fine spatiotemporal resolutions.

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Downscaling the GPM-based satellite precipitation retrievals using gradient boosting decision tree approach over Mainland China

Cited by 41 publications

References 42 publications

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

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

A locally weighted linear ridge regression framework for spatial interpolation of monthly precipitation over an orographically complex area

The evaluation and downscaling‐calibration of IMERG precipitation products at sub‐daily scales over a metropolitan region

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