Comprehensive applicability evaluation of four precipitation products at multiple spatiotemporal scales in Northwest China

doi:10.1007/s40333-024-0084-1

Journal of Arid Land

2024, Vol. 16

Issue (9): 1232-1254 DOI: 10.1007/s40333-024-0084-1 CSTR: 32276.14.s40333-024-0084-1

CSTR: 32276.14.JAL.02400841

Research article

Comprehensive applicability evaluation of four precipitation products at multiple spatiotemporal scales in Northwest China

WANG Xiangyu¹^,², XU Min²^,³^,^*(

), KANG Shichang²^,³, LI Xuemei⁴, HAN Haidong²^,³, LI Xingdong¹

¹School of Mathematics, Lanzhou Jiaotong University, Lanzhou 730070, China
²Key Laboratory of Cryospheric Science and Frozen Soil Engineering, Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou 730000, China
³University of Chinese Academy of Sciences, Beijing 100049, China
⁴Faculty of Geomatics, Lanzhou Jiaotong University, Lanzhou 730070, China

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Abstract

Precipitation plays a crucial role in the water cycle of Northwest China. Obtaining accurate precipitation data is crucial for regional water resource management, hydrological forecasting, flood control and drought relief. Currently, the applicability of multi-source precipitation products for long time series in Northwest China has not been thoroughly evaluated. In this study, precipitation data from 183 meteorological stations in Northwest China from 1979 to 2020 were selected to assess the regional applicability of four precipitation products (the fifth generation of European Centre for Medium-Range Weather Forecasts (ECMWF) atmospheric reanalysis of the global climate (ERA5), Global Precipitation Climatology Centre (GPCC), Climatic Research Unit gridded Time Series Version 4.07 (CRU TS v4.07, hereafter CRU), and Tropical Rainfall Measuring Mission (TRMM)) based on the following statistical indicators: correlation coefficient, root mean square error (RMSE), relative bias (RB), mean absolute error (MAE), probability of detection (POD), false alarm ratio (FAR), and equitable threat score (ETS). The results showed that precipitation in Northwest China was generally high in the east and low in the west, and exhibited an increasing trend from 1979 to 2020. Compared with the station observations, ERA5 showed a larger spatial distribution difference than the other products. The overall overestimation of multi-year average precipitation was approximately 200.00 mm and the degree of overestimation increased with increasing precipitation intensity. The multi-year average precipitation of GPCC and CRU was relatively close to that of station observations. The trend of annual precipitation of TRMM was overestimated in high-altitude regions and the eastern part of Lanzhou with more precipitation. At the monthly scale, GPCC performed well but underestimated precipitation in the Tarim Basin (RB= -4.11%), while ERA5 and TRMM exhibited poor accuracy in high-altitude regions. ERA5 had a large bias (RB≥120.00%) in winter months and a strong dispersion (RMSE≥35.00 mm) in summer months. TRMM showed a relatively low correlation with station observations in winter months (correlation coefficients≤0.70). The capture performance analysis showed that ERA5, GPCC, and TRMM had lower POD and ETS values and higher FAR values in Northwest China as the precipitation intensity increased. ERA5 showed a high capture performance for small precipitation events and a slower decreasing trend of POD as the precipitation intensity increased. GPCC had the lowest FAR values. TRMM was statistically ineffective for predicting the occurrence of daily precipitation events. The findings provide a reference for data users to select appropriate datasets in Northwest China and for data developers to develop new precipitation products in the future.

Key words： precipitation products the fifth generation of European Centre for Medium-Range Weather Forecasts (ECMWF) atmospheric reanalysis of the global climate (ERA5) Global Precipitation Climatology Centre (GPCC) Climatic Research Unit gridded Time Series (CRU TS) Tropical Rainfall Measuring Mission (TRMM) applicability evaluation Northwest China

Received: 06 March 2024 Published: 30 September 2024

CLC:

32276.14.JAL.02400841

Corresponding Authors: ^*XU Min (E-mail: xumin@lzb.ac.cn)

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	WANG Xiangyu
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	HAN Haidong
	LI Xingdong

Cite this article:

WANG Xiangyu, XU Min, KANG Shichang, LI Xuemei, HAN Haidong, LI Xingdong. Comprehensive applicability evaluation of four precipitation products at multiple spatiotemporal scales in Northwest China. Journal of Arid Land, 2024, 16(9): 1232-1254.

URL:

http://jal.xjegi.com/10.1007/s40333-024-0084-1 OR http://jal.xjegi.com/Y2024/V16/I9/1232

Fig. 1 Overview of Northwest China based on digital elevation model (DEM) and the distribution of 183 meteorological stations. Note that the figure is based on the standard map (GS(2020)4619) of the Map Service System (https://bzdt.ch.mnr.gov.cn/), and the boundary of the standard map has not been modified.

Table 1 Detailed description of data used in this study

Fig. 2 Flow chart of the research method and process in this study. ERA5, the fifth generation of European Centre for Medium-Range Weather Forecasts atmospheric reanalysis of the global climate; GPCC, Global Precipitation Climatology Centre; CRU, Climatic Research Unit gridded Time Series; TRMM, Tropical Rainfall Measuring Mission; RMSE, root mean square error; RB, relative bias; MAE, mean absolute error; POD, probability of detection; FAR, false alarm rate; ETS, equitable threat score.

Statistic indicator	Formula	Range	Optimum value
Correlation coefficient (r)	$r = ∑ i = 1 N (X o i − X o ¯) (X e i − X e ¯) ∑ i = 1 N (X o i − X o ¯) 2 ∑ i = 1 N (X e i − X e ¯) 2$	[-1, 1]	1
Root mean square error (RMSE; mm)	$RMSE = 1 N ∑ i = 1 N (X o i − X e i) 2$	[0, +∞)	0
Relative bias (RB; %)	$RB = ∑ i = 1 N (X e i − X o i) ∑ i = 1 N X o i × 100 %$	(-∞, +∞)	0
Mean absolute error (MAE; mm)	$MAE = 1 N ∑ i = 1 N X o i − X e i$	[0, +∞)	0
Probability of detection (POD)	$POD = H H + M$	[0, 1]	1
False alarm rate (FAR)	$FAR = F H + F$	[0, 1]	0
Equitable threat score (ETS)	$ETS = H − H e H + M + F − H e$ where $ETS = H − H e H + M + F − H e$	[-1/3, 1]	1

Table 2 Statistical indicators for the accuracy assessment of precipitation products

Fig. 4 Temporal variation of observed annual precipitation and estimated annual precipitation of ERA5, GPCC, CRU, and TRMM in Northwest China (a), Altay Mountains (b), Tianshan Mountains (c), Qinghai Plateau (d), Tarim Basin (e), Hexi Corridor (f), eastern part of Lanzhou (g), and Qilian Mountains (h) from 1979 to 2020. Note that the time span for TRMM is 1998-2019.

Fig. 5 Scatter distribution of estimated annual precipitation of ERA5, GPCC, CRU, and TRMM and observed annual precipitation in Northwest China (a), Altay Mountains (b), Tianshan Mountains (c), Qinghai Plateau (d), Tarim Basin (e), Hexi Corridor (f), eastern part of Lanzhou (g), and Qilian Mountains (h). The value for each point is the multi-year average precipitation.

Fig. 6 Taylor diagrams showing correlation coefficient, standard deviation, and RMSE between estimated annual precipitation of each product and observed annual precipitation in Northwest China (a), Altay Mountains (b), Tianshan Mountains (c), Qinghai Plateau (d), Tarim Basin (e), Hexi Corridor (f), eastern part of Lanzhou (g), and Qilian Mountains (h). The radial coordinate gives the magnitude of normalized standard deviation, the green concentric semi-circles are the normalized RMSE values, and the angular coordinate shows the correlation coefficient. Obs, observations.

Fig. 7 Monthly precipitation distribution of multi-source precipitation products (ERA5, GPCC, CRU, and TRMM) and station observations in Northwest China (a), Altay Mountains (b), Tianshan Mountains (c), Qinghai Plateau (d), Tarim Basin (e), Hexi Corridor (f), eastern part of Lanzhou (g), and Qilian Mountains (h) during 1979-2020. Note that the time span for TRMM is 1998-2020.

Fig. 8 Radar plots of correlation coefficient (a), RMSE (b), RB (c), and MAE (d) between estimated monthly precipitation of each product and observed monthly precipitation. The correlation coefficient for each month was tested by the t-test (P<0.05).

Fig. 9 Spatial distribution of correlation coefficient (a-d), RMSE (e-h), RB (i-l), and MAE (m-p) between estimated monthly precipitation of each product and observed monthly precipitation during 1979-2020. The time span for TRMM is 1998-2019. "+" indicates the correlation coefficient passed the significance test (P<0.05). Note that the figures are based on the standard map (GS(2020)4619) of the Map Service System (https:// bzdt.ch.mnr.gov.cn/), and the boundary of the standard map has not been modified.

Fig. 10 Variations of POD (a), FAR (b), and ETS (c) with precipitation threshold for the three products (ERA5, GPCC, and TRMM).

Fig. 11 Spatial distribution of POD (a-c), FAR (d-f), and ETS (g-i) for each precipitation product (ERA5, GPCC, and TRMM) at precipitation threshold of 1.00 mm/d. Note that the figures are based on the standard map (GS(2020)4619) of the Map Service System (https://bzdt.ch.mnr.gov.cn/), and the boundary of the standard map has not been modified.

Fig. 3 Spatial distribution of multi-year average precipitation (a1, b1, c1, d1, and e1) and rate of change in annual precipitation (a2, b2, c2, d2, and e2) of station observations, ERA5, GPCC, CRU, and TRMM in Northwest China from 1979 to 2020. The time span for TRMM is 1998-2019. Note that the figures are based on the standard map (GS(2020)4619) of the Map Service System (https://bzdt.ch.mnr.gov.cn/), and the boundary of the standard map has not been modified.

Fig. 12 Precipitation anomaly analysis of station observations (a), ERA5 (b), GPCC (c), and CRU (d) in Northwest China from 1979 to 2020. The dashed line indicates the year of transition (2023) of the precipitation anomalies.


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