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Article

Reliability of Satellite Data in Capturing Spatiotemporal Changes of Precipitation Extremes in the Middle Reaches of the Yellow River Basin

by
Qianxi Yang
1,2,
Qiuyu Xie
1 and
Ximeng Xu
1,2,*
1
Key Laboratory of Water Cycle and Related Land Surface Processes, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
2
University of Chinese Academy of Sciences, Beijing 100049, China
*
Author to whom correspondence should be addressed.
Remote Sens. 2025, 17(19), 3308; https://doi.org/10.3390/rs17193308
Submission received: 24 August 2025 / Revised: 22 September 2025 / Accepted: 23 September 2025 / Published: 26 September 2025
Browse Figures
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Abstract

Highlights

What are the main findings?
  • GPM reliably reproduces the frequency, intensity, and cumulative amounts of extreme precipitation in the Middle Reaches of the Yellow River Basin (MRYRB).
  • Frequency, intensity, and cumulative amounts of extreme precipitation increased from 2001 to 2022, with the He-Long reach experiencing an accelerating shift toward short-duration, high-intensity precipitation.
What is the implication of the main finding?
  • The study demonstrates that GPM reliably provides a continuous, full-coverage extreme precipitation observation in the MRYRB.
  • The findings deliver actionable insights for extreme precipitation prevention and disaster risk reduction in the MRYRB, and inform policy under a changing climate.

Abstract

Extreme precipitation in the Middle Reaches of the Yellow River Basin (MRYRB) has increased significantly and unevenly, heightening the urgency for rapid and accurate monitoring of such extremes. Satellite precipitation data have proved effective in capturing precipitation extremes but have not been validated in the MRYRB. Thus, station-interpolated data were used to validate the reliability of satellite data (GPM IMERG) in characterizing spatiotemporal changes in nine extreme precipitation indices across the entire MRYRB and its ten sub-basins from 2001 to 2022. The results show that all frequency, intensity, and cumulative amount indices exhibit significantly increasing trends. Spatially, extreme precipitation exhibits a clear southeast–northwest gradient. The higher values occur in the southeastern sub-basins. Characterized by high-intensity, short-duration precipitation, the central sub-basins exhibit the lower values of extreme precipitation indices, yet have experienced the most rapid upward trends in those indices. The comparative analysis demonstrates that GPM reliably reproduces indices such as the number of days and amounts with precipitation above a threshold (R10, R20, R95p), maximum precipitation over five days (RX5day), and total precipitation (PRCPTOT) (with regression slopes close to 1, coefficient of determination R2 and Nash-Sutcliffe efficiency (NSE) greater than 0.7, and residual sum of squares ratio (RSR) less than 0.6, with negligible relative bias), particularly in the southern sub-basins. However, it tends to underestimate continuous wet days (CWD) and total precipitation when precipitation is over the 99th percentile (R99p). These findings advance current understanding of GPM applicability at watershed scales and offer actionable insight for water-sediment prediction under the world’s changing climate.

1. Introduction

In a warming world, the frequency and intensity of precipitation extremes have intensified in recent decades [1,2], leading to severe droughts and floods [3,4,5]. Regional extreme precipitation is highly sensitive to global warming, and as time progresses, it may become increasingly extreme [6]. Extreme precipitation characteristics can be effectively represented and analyzed using extreme precipitation indices [7,8]. Understanding shifts in precipitation extremes at regional scales is crucial for forecasting their frequency and severity, thereby mitigating the impacts of climate change.
The Middle Reaches of the Yellow River Basin (MRYRB) represent the primary heavy-rainfall corridor of the Yellow River Basin and are a major source of flooding in its lower reaches [9]. Located in the Loess Plateau of China, the MRYRB is characterized by a rugged topography and loose soil. In addition, the region experiences concentrated and intense rainfall during the summer, which makes it prone to soil erosion. The complex terrain also leads to significant differences in precipitation patterns, runoff characteristics, and water-sediment transport between different sub-basins [9,10,11]. In recent decades, intensified climate change and human activities have posed significant challenges to the fragile ecosystem of the MRYRB [10,11]. Studies have shown that climate change has contributed to a 51.03% reduction in mean annual streamflow in the region [12]. Additionally, the frequency of extreme precipitation events has significantly increased in the MRYRB, further complicating the water-sediment relationship of the Yellow River [13]. Analyzing extreme precipitation characteristics in this region under climate change is crucial for understanding its ecological environment and ensuring the safety of the Yellow River Basin [14]. Most studies have examined the spatiotemporal evolution of extreme precipitation in the MRYRB using meteorological station data [13,14,15]. However, a major challenge is obtaining continuous and spatially comprehensive station data. Many stations suffer from data gaps or incomplete records, limiting the accuracy and representativeness of results. Moreover, the uneven distribution of stations and difficulties in timely access further hinder data collection. These limitations restrict our understanding of extreme precipitation changes in the MRYRB under climate change.
The rapid development of remote sensing, providing continuous and high-precision real-time precipitation datasets, has enabled long-term spatiotemporal estimation of extreme precipitation [16]. However, the reliability of satellite-based estimates in specific regions, such as the MRYRB, remains insufficiently evaluated, particularly given the unique climate and geographical features in this region, as mentioned above. For the global scale, Wang et al. [17] conducted an analysis of extreme precipitation using multiple satellite precipitation datasets, including the Integrated Multi-satellitE Retrievals for the Global Precipitation Measurement (GPM) Mission (IMERG), GSMaP, PERSIANN-CCS, T3B42, and CMORPH. Their study demonstrates that despite certain limitations, satellite data remains a crucial tool for estimating extreme precipitation. For the regional scale, Fang et al. [18] compared Tropical Rainfall Measuring Mission (TRMM) and GPM data and found that GPM better captures the spatial patterns of extreme precipitation in China. Yu et al. [19] compared different precipitation products from the GPM satellite and discovered that the IMERG Final Run better captures the variations in extreme precipitation in mainland China. Moreover, Liu et al. [20] examined extreme precipitation changes in the Yangtze River Basin from 2014 to 2017 using GPM data and found that GPM produced accurate estimations of extreme precipitation events with short-term to medium-term recurrence intervals, showing good capability at the watershed scale. Despite these valuable contributions, there is still a gap in understanding the performance and reliability of using the satellite precipitation products to capture the extreme precipitation in the MRYRB.
Therefore, the purposes of the study are (1) to demonstrate the applicability and robustness of GPM data in quantifying extreme precipitation by using station-interpolated data as validation in the MRYRB and (2) to analyze the spatiotemporal variations in extreme precipitation indices in the MRYRB, especially in different sub-basins of the MRYRB.

2. Materials and Methods

2.1. Study Area

The Yellow River basin is located in an arid and semi-arid region, where the total water resources are limited and unevenly distributed. It has long been known for its severe water scarcity and sedimentation issues, with a highly unbalanced water-sediment relationship. The MRYRB refers to the section from the Toudaoguai to Huayuankou hydrological stations (Figure 1), covering an area of 362,000 km2. Along this transect, elevation rises sharply from roughly 30 m in the east to over 3800 m in the west, and this dramatic relief strongly shapes local climate patterns. The MRYRB accounts for nearly 44% of the total runoff in the Yellow River and encompasses the most severely eroded part of the Loess Plateau [21,22]. This region is characterized by water erosion and diverse hazards, serving as a primary indicator of ecological vulnerability [23]. The MRYRB exhibits a sensitive response to extreme hydrological events, with frequent flood and drought disasters and a significant increase in disaster intensity, posing immense challenges to the ecological environment and sustainable development of the basin [24,25]. In this study, the MRYRB is divided into ten sub-basins, each characterized by distinct geographical features (Figure 1). The region from Hekou to Longmen (including the left bank of the mainstream from Hekou to Longmen, HLL, and the right bank of the mainstream from Hekou to Longmen, HLR) generally features high elevations on both sides with a lower central area, and includes the Coarse Sandy Hilly Catchments [26]. In contrast, the Beiluo River (BLR), Jing River (JR), and Wei River (including the upstream of Wei River, WRU, and downstream of Wei River, WRD) sub-basins generally show a terrain that is higher in the west and lower in the east and are located in a semi-dry and semi-humid monsoon climate zone [27]. In the southern part of the MRYRB, the Qin River (QR), mainstream from Longmen to Huayuankou (LH), and Yiluo River (YLR) sub-basins are relatively flat and fall under a semi-humid climate.

2.2. Precipitation Data

Satellite-based daily precipitation data of GPM IMERG V07B Final Run (https://gpm.nasa.gov/data/directory, accessed on 11 November 2024) over the MRYRB between January 2001 and December 2022 were used in this study. IMERG is a unified algorithm that was developed by Huffman et al. [28] in the National Aeronautics and Space Administration to estimate the precipitation based on GPM observations. IMERG offers 30 min precipitation with a spatial resolution of 0.1° × 0.1° [29], which has been proven to be efficient in precipitation estimations of various scales [30,31]. Until the fifth version, IMERG includes only the GPM era. Later, in version 6, the IMERG was applied to TRMM (Tropical Rainfall Measuring Mission) observations to extend back the starting date from March 2014 to June 2000 [32]. Compared with earlier versions, GPM IMERG V07B incorporates enhancements in its algorithms, data calibration, and other aspects. These improvements are expected to enhance the estimation of extreme precipitation events [33]. IMERG includes three types of products, i.e., early, late, and final runs, with a time latency of 4 h, 12 h, and 3.5 months, respectively. Currently, the final run is adjusted using the Global Precipitation Climatology Centre dataset on a monthly scale [28].
The CN05.1 product (hereinafter CN05) from the China Meteorological Data Service Center is a gridded 0.25° × 0.25° dataset based on a dense network of 2416 in situ meteorological stations [34]. The daily precipitation data from CN05 is proven to have greater advantages compared to datasets like the 0.5° gauge-based daily precipitation datasets over East Asia (EA05) and the Asian Precipitation-Highly Resolved Observational Data Integration Towards Evaluation of Water Resources (APHRODITE), especially in its ability to capture precipitation in mountainous regions and extreme precipitation events [34]. The dataset is extensively applied in climate analysis and model evaluation across China [35,36,37]. The daily CN05 data from January 2001 to December 2022 were used to validate the accuracy and applicability of satellite data in the MRYRB. Then, we resampled the 0.1° GPM results to match the 0.25° CN05 dataset, facilitating subsequent validation.

2.3. Extreme Precipitation Indices

The nine widely used extreme precipitation indices were selected to assess changes in characteristics of extreme precipitation in the MRYRB (Table 1). These indices are defined by the Expert Team on Climate Change Detection and Indices (ETCCDI) (https://etccdi.pacificclimate.org/list_27_indices.shtml, accessed on 9 November 2024). They are grouped into six functional classes based on the physical quantities represented and the statistical thresholds applied [38]. To clearly describe the characteristics of extreme precipitation, this study further classifies these indices into four categories: a spell index (CWD), frequency indices (R10, R20), intensity indices (RX1day, RX5day, SDII), and cumulative amount indices (R95p, R99p, PRCPTOT). This taxonomy furnishes a comprehensive framework for analyzing the frequency, intensity, and magnitude contribution of extreme precipitation.

2.4. Trend Analysis

In this study, the trends of nine extreme precipitation indices in the MRYRB from 2001 to 2022 were analyzed. The temporal change rates of nine indices were quantified by the slopes of linear fits (hereafter referred to as SlopeCN05 and SlopeGPM). Spatial trends were estimated using the Theil–Sen slope estimator, and their statistical significance was assessed with the Mann–Kendall test at the 95% confidence level. The Theil-Sen slope estimator is a non-parametric method commonly used to estimate long-term trends in time series data [39]. The slope calculated using Theil–Sen estimation is a robust measure of trend magnitude, widely applied to identify trend slopes in hydrological time series. The Mann–Kendall trend test is also a non-parametric method [40]. This method has the advantage of not requiring the data to follow a normal distribution and is resistant to the effects of missing values and outliers, making it suitable for assessing the significance of trends in hydrological time series.

3. Results

3.1. Spatiotemporal Variations in Extreme Precipitation Indices in the Middle Reaches of the Yellow River Basin

Figure 2 contrasts the variation in nine ETCCDI extreme precipitation indices across the MRYRB from 2001 to 2022, calculated from the gridded observation product CN05 and the GPM satellite datasets. Except for CWD (Figure 2a), both datasets exhibit significant upward trends (p < 0.05) in indices that characterize event frequency (R10, R20), intensity (RX1day, RX5day, and SDII) and cumulative amounts (R95p, R99p, and PRCPTOT) (Figure 2b–i). GPM captures the annual trends of precipitation extremes. However, its trend magnitudes are consistently larger than those from CN05, averaging about twice as high, especially for heavy-rainfall amount indices (Figure 2d–g). The best correlations occur for R20 and SDII (Corr = 0.44 and 0.45, respectively) (p < 0.05). Notably, CWD shows no significant change in either record, and the implied trend directions even diverge, highlighting the uncertainty associated with the spell index.
Extreme precipitation exhibits a pronounced spatial gradient, decreasing from the southeast toward the northwest, with higher values concentrated in sub-basins including WRD, YLR, and QR (Figure 3 and Figure 4). This characteristic of extreme precipitation contrasts with the general topography of the MRYRB, which is higher in the northwest and lower in the southeast. However, higher values of extreme precipitation are mainly concentrated in the lower elevation areas. Interestingly, intensity indices like RX1day, RX5day, and SDII show concentrated higher values in the central part of the MRYRB (e.g., HLL and HLR sub-basins), indicating a high intensity of short-duration storms and persistent heavy rainfall events in this lower elevation region. The spatial distributions and magnitudes of R10, R20, R95P, RX1day, RX5day, PRCPTOT, and SDII are consistent between the two datasets (Figure 3 and Figure 4).
Figure 5 presents the spatial distribution of differences in extreme precipitation indices between GPM and CN05 datasets across the MRYRB. Overall, pronounced spatial heterogeneity exists across the 9 indices. For the frequency indices (R10 and R20) (Figure 5b,c), GPM generally reports slightly higher values than CN05 in the southern sub-basins, with widespread positive differences over 50%. Conversely, negative biases dominate the northwestern regions. In contrast, cumulative amount (R95p and R99p) and intensity indices (RX1day, RX5day) (Figure 5d−g) show negative deviations in the central basin, indicating that GPM underestimates extreme rainfall intensities compared to CN05, particularly in areas around the mainstream of the Yellow River. For PRCPTOT (Figure 5h), GPM significantly overestimates total precipitation in the southern region, with differences exceeding 50 mm in some areas. Notably, SDII (Figure 5i) exhibits consistent overestimation by GPM across most sub-basins, especially in the southeastern part, suggesting higher daily precipitation intensities derived from satellite data.

3.2. Spatial Distributions of Trends in Extreme Precipitation Indices

Figure 6 and Figure 7 illustrate the spatial trends of nine extreme precipitation indices across the MRYRB during 2001–2022, as derived from CN05 station-interpolated data and GPM satellite observations, with significance assessed by the Mann–Kendall test. Both datasets consistently show that, except for CWD, all frequency (R10, R20), intensity (RX1day, RX5day, SDII), and cumulative amount (R95p, R99p, PRCPTOT) indices exhibit significant positive trends (p < 0.05) over the past two decades, particularly in the central sub-basins (HLR and HLL). The larger annual increases in the central MRYRB occur in R20 (>0.03 days yr−1 by CN05; >0.15 days yr−1 by GPM) (Figure 6c and Figure 7c) and in R99p (>0.6 mm yr−1 by CN05; >2 days yr−1 by GPM) (Figure 6e and Figure 7e), while RX1day and RX5day also show robust upward trends. Concurrent rises in PRCPTOT and SDII further confirm an overall intensification of extreme precipitation. In contrast, CWD displays a non-significant downward tendency in the central MRYRB (Figure 6a and Figure 7a). Although GPM generally yields higher trend magnitudes than CN05, the spatial distribution patterns are remarkably consistent; the paucity of ground stations in the southwestern mountains likely leads CN05 to underestimate localized increases in extreme precipitation.

3.3. Sub-Basin Variations in Extreme Precipitation Indices

Nine extreme precipitation indices across ten sub-basins in the MRYRB during 2001–2022 were comparatively analyzed (Figure 8). The results reveal distinct spatial patterns in extreme precipitation characteristics across the study area. The extreme precipitation indices all demonstrate a pronounced spatial pattern of “high in the south and east, low in the north and west” across the MRYRB (Figure 3, Figure 4 and Figure 8). Southern and eastern sub-basins (e.g., WRD, YLR, and QR) exhibited consistently higher values across multiple indices, such as R10, R95p, and PRCPTOT (Figure 1 and Figure 8). In contrast, central sub-basins (e.g., HLR and HLL) showed significantly lower values for all extreme precipitation indices.
Comparative analysis between CN05 and GPM datasets revealed the capability of GPM to estimate extreme precipitation in some sub-basins. At the basin-wide scale, GPM showed good agreement with CN05 for specific extreme precipitation indices. The discrepancies were minimal, particularly for RX5day and PRCPTOT (Figure 8g,h). At the sub-basin scale, GPM demonstrated superior performance in estimating extreme precipitation values in the southern sub-basins, including WRU, JR, and QR, where the differences from CN05 data were statistically insignificant (p > 0.05) (Figure 8). However, notable limitations were identified in the estimation capabilities of GPM, particularly for CWD indices, where performance was considerably weaker (Figure 8a).

4. Discussion

4.1. Applicability of Satellite Data to Capture the Extreme Precipitation Indices

Satellite-based estimations of extreme precipitation have been widely applied across a range of spatial and temporal scales [18,41]. The results of this study show that GPM and CN05 agree well in most extreme precipitation indices. In particular, the number of moderate to heavy rainfall days (R10, R20), R95p, RX5day, and PRCPTOT all exhibit regression slopes near unity, high coefficients of determination (R2  >  0.7), Nash–Sutcliffe efficiencies (NSE  >  0.7), Root Mean Square Error–observations Standard Deviation Ratio (RSR <0.6), and negligible relative biases (rBias ≈ 0) (Figure 9b–d,g,h). Moreover, the close agreement between GPM and ground-based observations for R10, R20, and R95p confirms the high detection skill of satellite-based precipitation products for threshold-exceedance events [42,43]. By contrast, the CWD and the R99p are less faithfully reproduced (Figure 9a,e), with CWD in particular showing a significant negative correlation (Corr = −0.43, p < 0.05) (Figure 2a). These discrepancies likely arise from differences in retrieval algorithms and the influence of complex terrain (Figure 1, Figure 3a and Figure 4a) [31,44], and the underestimations of GPM results to CWD reflect limitations in its ability to capture persistent light-rain episodes [33,45].
The result of this study confirms that GPM data are generally reliable for most extreme precipitation characteristics, while also identifying continuous rainfall duration and the most extreme intensities as aspects that still need further refinement [33]. These results demonstrate the feasibility of using GPM as a complementary or alternative source to ground-based observations for regional hydrometeorological assessments. This enhances our capacity to monitor and model hydrological and related processes in a timely manner under a changing climate.

4.2. Spatial and Temporal Changes in Extreme Precipitation Indices in the MRYRB

A growing body of evidence indicates that extreme precipitation is intensifying across the MRYRB. Using data from a dense network of meteorological stations, He et al. [46] showed that accelerated warming and moistening since 1991 have produced pronounced inter-annual increases in indices such as RX5day, SDII, and R95p. Extending the record back to 1960, Ren et al. [47] analyzed daily observations from 37 stations in the HLL sub-basin and found upward trends in R10, R20, PRCPTOT, SDII, R95p, and R99p. Consistent with these studies, the result in this study for 2001–2022 reveals significant increases (p < 0.05) in the frequency (R10, R20), intensity (RX1day, RX5day, SDII), and cumulative amount (R95p, R99p, PRCPTOT) indices throughout the MRYRB (Figure 2). Spatially, the magnitude of these indices declines steadily from the southeast to the northwest, with the central sub-basins emerging as hotspots for both high-intensity and short-duration precipitation (Figure 3, Figure 4, Figure 6 and Figure 7). This pattern corroborates the elevated extreme precipitation risks reported by Zhang et al. [14] for the southeastern Yellow River Basin and the intensifying extremes documented by Zhao et al. [15] in the semi-arid zones. Same as the spatial distribution of extreme precipitation (Figure 3d and Figure 4d), further support comes from Dou et al. [48], who found that the higher R95p values cluster in the central MRYRB (e.g., the HLR and HLL sub-basins), and from Feng et al. [49], who projected a similar increase under future climate scenarios. Collectively, these findings suggest that the MRYRB responds sensitively to climate warming, exhibiting substantial amplification of multiple extreme precipitation indices; however, the complex topography of the MRYRB generates pronounced sub-basin-scale variations in the magnitude and distribution of these changes. In the context of increasing extreme precipitation, new challenges may arise for water-sediment management in the MRYRB region. For instance, heavy rainfall and prolonged storms can increase surface runoff, leading to significant fluctuations in water volume and flow velocity [13,23], which in turn affect the water-sediment transport process. This is particularly evident in sub-basins such as HLL and HLR, where the soil is loose, the topography is more rugged, and extreme precipitation events are more frequent. While heavy rainfall increases runoff, it also exacerbates soil erosion, resulting in the transport of large amounts of sediment, which affects sedimentation and overall water-sediment dynamics.

4.3. Limitations and Future Perspective

This study is based on a relatively short time span (2001–2022). While this period is valuable for detecting recent trends in extreme precipitation, it may not adequately capture long-term climate variability or differentiate between natural fluctuations and anthropogenic influences [20]. Extending the time series would enable more robust detection of persistent trends and climate cycles. Furthermore, the use of a pixel-by-pixel approach to analyze extreme precipitation indices (e.g., RX1day, R95p) is effective in identifying broad spatiotemporal patterns [50,51], but it fails to account for the continuity and evolution of precipitation systems. This method may overlook critical dynamics such as storm development and movement [52,53].
This study provides a critical entry point for understanding the evolution of water–sediment relationships in the MRYRB, a region increasingly affected by extreme hydrological events due to climate change and human activities [24,25,54]. The complex interplay between vegetation, sediment, and hydrological processes has made the water–sediment relationship more unpredictable. By capturing spatial and temporal patterns of extreme precipitation from 2001 to 2022 using satellite data, the findings of this study confirm the valuable role that satellite data can play in future research on the emerging water–sediment relationships in the MRYRB [15]. These insights can support future efforts to develop more adaptive, integrated watershed management strategies and improve predictive models of water–sediment interactions under changing climate conditions.

5. Conclusions

This study comprehensively analyzes the spatiotemporal of nine extreme precipitation indices across ten sub-basins in the MRYRB from 2001 to 2022, using daily scale GPM satellite precipitation data and station-interpolated CN05 data. The main results are as follows:
(1) All frequency (R10, R20), intensity (RX1day, RX5day, and SDII) and cumulative amount (R95p, R99p and PRCPTOT) indices exhibit statistically significant increasing trends (p < 0.05) from 2001 to 2022.
(2) Spatially, extreme precipitation decreases from the southeastern to northwestern sub-basins. The higher values of extreme precipitation indices are observed in the QR (Qin River), YLR (Yiluo River), and WRD (downstream of the Wei River) basins. Characterized by high-intensity, short-duration precipitation, the central sub-basins (Hekou to Longmen basins) exhibit the lower values of extreme precipitation indices. However, the Hekou to Longmen basins have experienced the most rapid upward trends in those indices over the study period.
(3) GPM successfully reproduces R10, R20, R95p, RX5day, and PRCPTOT with regression slopes close to 1, R2, and NSE values exceeding 0.7, RSR values below 0.6, and negligible relative bias (rBias), particularly in the southern sub-basins (Wei, Jing, and Qin River). However, it tends to underestimate CWD and R99p, suggesting that estimates of light rainfall and very extreme precipitation need improvement.

Author Contributions

Conceptualization, X.X.; methodology, Q.Y.; formal analysis, Q.Y. and Q.X.; visualization, Q.Y.; writing—original draft preparation, Q.Y.; writing—review and editing, Q.X., X.X.; supervision, X.X.; project administration and funding acquisition, X.X. All authors have read and agreed to the published version of the manuscript.

Funding

This study was financially supported by the National Natural Science Foundation of China (No. U2243226, 42277339).

Data Availability Statement

The raw data supporting the conclusions of this article will be made available by the authors on request.

Acknowledgments

We would like to thank the editors and anonymous reviewers for their constructive comments and suggestions.

Conflicts of Interest

The authors declare no conflicts of interest.

References

  1. Pendergrass, A.G.; Knutti, R.; Lehner, F.; Deser, C.; Sanderson, B.M. Precipitation variability increases in a warmer climate. Sci. Rep. 2017, 7, 17966. [Google Scholar] [CrossRef]
  2. IPCC. Climate Change 2023: Synthesis Report. In Sixth Assessment Report of the Intergovernmental Panel on Climate Change; Core Writing Team, Lee, H., Romero, J., Eds.; IPCC: Geneva, Switzerland, 2023; pp. 35–115. [Google Scholar]
  3. Orlowsky, B.; Seneviratne, S.I. Global changes in extreme events: Regional and seasonal dimension. Clim. Change 2012, 110, 669–696. [Google Scholar] [CrossRef]
  4. Ivancic, T.J.; Shaw, S.B. Examining why trends in very heavy precipitation should not be mistaken for trends in very high river discharge. Clim. Change 2015, 133, 681–693. [Google Scholar] [CrossRef]
  5. Wasko, C.; Nathan, R. Influence of changes in rainfall and soil moisture on trends in flooding. J. Hydrol. 2019, 575, 432–441. [Google Scholar] [CrossRef]
  6. Qin, P.; Xie, Z.; Zou, J.; Liu, S.; Chen, S. Future precipitation extremes in China under climate change and their physical quantification based on a regional climate model and CMIP5 model simulations. Adv. Atmos. Sci. 2021, 38, 460–479. [Google Scholar] [CrossRef]
  7. Myhre, G.; Alterskjær, K.; Stjern, C.W.; Hodnebrog, Ø.; Marelle, L.; Samset, B.H.; Sillmann, J.; Schaller, N.; Fischer, E.; Schulz, M.; et al. Frequency of extreme precipitation increases extensively with event rareness under global warming. Sci. Rep. 2019, 9, 16063. [Google Scholar] [CrossRef] [PubMed]
  8. Li, S.; Chen, Y.; Wei, W.; Fang, G.; Duan, W. The increase in extreme precipitation and its proportion over global land. J. Hydrol. 2024, 628, 130456. [Google Scholar] [CrossRef]
  9. Li, H.; Shi, C.; Sun, P.; Zhang, Y.; Collins, A.L. Attribution of runoff changes in the main tributaries of the middle Yellow River, China, based on the Budyko model with a time-varying parameter. Catena 2021, 206, 105557. [Google Scholar] [CrossRef]
  10. Yan, Z.; Wang, T.; Ma, T.; Yang, D. Water-carbon-sediment synergies and trade-offs: Multi-faceted impacts of large-scale ecological restoration in the Middle Yellow River Basin. J. Hydrol. 2024, 634, 131099. [Google Scholar] [CrossRef]
  11. Huang, Z.; Tang, Z.; Tian, J.; Zhang, X.; Ma, N.; Bai, X.; Zhang, Y. Climate change dominates recent increase in streamflow in the Yellow River Basin. J. Hydrol. 2025, 661, 133737. [Google Scholar] [CrossRef]
  12. Hu, Z.; Wang, L.; Wang, Z.; Hong, Y.; Zheng, H. Quantitative assessment of climate and human impacts on surface water resources in a typical semi-arid watershed in the middle reaches of the Yellow River from 1985 to 2006. Int. J. Climatol. 2015, 35, 97–113. [Google Scholar] [CrossRef]
  13. Zhao, Y.; Cao, W.; Hu, C.; Wang, Y.; Wang, Z.; Zhang, X.; Zhu, B.; Cheng, C.; Yin, X.; Liu, B.; et al. Analysis of changes in characteristics of flood and sediment yield in typical basins of the Yellow River under extreme rainfall events. Catena 2019, 177, 31–40. [Google Scholar] [CrossRef]
  14. Zhang, Y.; Xia, J.; She, D. Spatiotemporal variation and statistical characteristic of extreme precipitation in the middle reaches of the Yellow River Basin during 1960–2013. Theor. Appl. Climatol. 2019, 135, 391–408. [Google Scholar] [CrossRef]
  15. Zhao, Y.; Xu, X.; Huang, W.; Wang, Y.; Xu, Y.; Chen, H.; Kang, Z. Trends in observed mean and extreme precipitation within the Yellow River Basin, China. Theor. Appl. Climatol. 2019, 136, 1387–1396. [Google Scholar] [CrossRef]
  16. Yang, L.; Shi, Z.; Liu, R.; Xing, M. Evaluating the performance of global precipitation products for precipitation and extreme precipitation in arid and semiarid China. Int. J. Appl. Earth Obs. Geoinf. 2024, 130, 103888. [Google Scholar] [CrossRef]
  17. Wang, T.; Li, Z.; Ma, Z.; Gao, Z.; Tang, G. Diverging identifications of extreme precipitation events from satellite observations and reanalysis products: A global perspective based on an object-tracking method. Remote Sens. Environ. 2023, 288, 113490. [Google Scholar] [CrossRef]
  18. Fang, J.; Yang, W.; Luan, Y.; Du, J.; Lin, A.; Zhao, L. Evaluation of the TRMM 3B42 and GPM IMERG products for extreme precipitation analysis over China. Atmos. Res. 2019, 223, 24–38. [Google Scholar] [CrossRef]
  19. Yu, L.; Leng, G.; Python, A. A comprehensive validation for GPM IMERG precipitation products to detect extremes and drought over mainland China. Weather Clim. Extrem. 2022, 36, 100458. [Google Scholar] [CrossRef]
  20. Liu, J.; Xia, J.; She, D.; Li, L.; Wang, Q.; Zou, L. Evaluation of six satellite-based precipitation products and their ability for capturing characteristics of extreme precipitation events over a climate transition area in China. Remote Sens. 2019, 11, 1477. [Google Scholar] [CrossRef]
  21. Zhao, G.; Tian, P.; Mu, X.; Jiao, J.; Wang, F.; Gao, P. Quantifying the impact of climate variability and human activities on streamflow in the middle reaches of the Yellow River basin, China. J. Hydrol. 2014, 519, 387–398. [Google Scholar] [CrossRef]
  22. Zhang, W.; Liang, W.; Tian, L.; Zhao, X. Climatic and different human influences on annual and seasonal streamflow with considering the soil water storage change in the middle reaches of the Yellow River basin, China. J. Hydrol. 2023, 619, 129298. [Google Scholar] [CrossRef]
  23. Zheng, H.; Miao, C.; Wu, J.; Lei, X.; Liao, W.; Li, H. Temporal and spatial variations in water discharge and sediment load on the Loess Plateau, China: A high-density study. Sci. Total Environ. 2019, 666, 875–886. [Google Scholar] [CrossRef]
  24. Xu, J.; Jiang, X.; Sun, H.; Xu, H.; Zhong, X.; Liu, B.; Li, L. Driving forces of nature and human activities on water and sediment changes in the middle reaches of the Yellow River in the past 100 years. J. Soils Sediments 2021, 21, 2450–2464. [Google Scholar] [CrossRef]
  25. Yin, S.; Gao, G.; Ran, L.; Li, D.; Lu, X.; Fu, B. Extreme streamflow and sediment load changes in the Yellow River Basin: Impacts of climate change and human activities. J. Hydrol. 2023, 619, 129372. [Google Scholar] [CrossRef]
  26. Gao, P.; Deng, J.; Chai, X.; Mu, X.; Zhao, G.; Shao, H.; Sun, W. Dynamic sediment discharge in the Hekou–Longmen region of Yellow River and soil and water conservation implications. Sci. Total Environ. 2017, 578, 56–66. [Google Scholar] [CrossRef] [PubMed]
  27. Liu, S.; Huang, S.; Huang, Q.; Xie, Y.; Leng, G.; Luan, J.; Song, X.; Wei, X.; Li, X. Identification of the non-stationarity of extreme precipitation events and correlations with large-scale ocean-atmospheric circulation patterns: A case study in the Wei River Basin, China. J. Hydrol. 2017, 548, 184–195. [Google Scholar] [CrossRef]
  28. Huffman, G.J.; Bolvin, D.T.; Nelkin, E.J.; Tan, J. Integrated Multi-satellite Retrievals for GPM (IMERG) technical documentation. NASA GSFC Code 2015, 612, 2019. [Google Scholar]
  29. Skofronick-Jackson, G.; Petersen, W.A.; Berg, W.; Kidd, C.; Stocker, E.F.; Kirschbaum, D.B.; Kakar, R.; Braun, S.A.; Huffman, G.J.; Iguchi, T.; et al. The Global Precipitation Measurement (GPM) mission for science and society. Bull. Am. Meteorol. Soc. 2017, 98, 1679–1695. [Google Scholar] [CrossRef]
  30. Hosseini-Moghari, S.M.; Tang, Q. Can IMERG data capture the scaling of precipitation extremes with temperature at different time scales? Geophys. Res. Lett. 2022, 49, e2021GL096392. [Google Scholar] [CrossRef]
  31. Yang, Q.; Xu, X.; Tang, Q.; Jia, G. The spatiotemporal variations of global rainfall erosivity and erosive rainfall event based on half-hourly satellite rainfall data. Catena 2025, 252, 108831. [Google Scholar] [CrossRef]
  32. Hosseini-Moghari, S.M.; Tang, Q. Validation of GPM IMERG V05 and V06 precipitation products over Iran. J. Hydrometeorol. 2020, 21, 1011–1037. [Google Scholar] [CrossRef]
  33. Xiong, J.; Tang, G.; Yang, Y. Continental evaluation of GPM IMERG V07B precipitation on a sub-daily scale. Remote Sens. Environ. 2025, 321, 114690. [Google Scholar] [CrossRef]
  34. Wu, J.; Gao, X. A gridded daily observation dataset over China region and comparison with the other datasets. Chin. J. Geophys. 2013, 56, 1102–1111. [Google Scholar]
  35. Huang, D.; Gao, S. Impact of different reanalysis data on WRF dynamical downscaling over China. Atmos. Res. 2018, 200, 25–35. [Google Scholar] [CrossRef]
  36. Jiang, M.; Weng, B.; Chen, J.; Huang, T.; Ye, F.; You, L. Transformer-enhanced spatiotemporal neural network for post-processing of precipitation forecasts. J. Hydrol. 2024, 630, 130720. [Google Scholar] [CrossRef]
  37. Ning, Z.; Wu, N.; Zhang, J.; Ruan, Y.; Tang, Z.; Sun, J.; Shi, J.; Liu, C.; Wang, G. Wetter trend in source region of Yangtze River by runoff simulating based on Grid-RCCC-WBM. J. Hydrol. 2024, 631, 130702. [Google Scholar] [CrossRef]
  38. Zhao, J.; Gan, T.Y.; Zhang, G.; Zhang, S. Projected changes of precipitation extremes in North America using CMIP6 multi-climate model ensembles. J. Hydrol. 2023, 621, 129598. [Google Scholar] [CrossRef]
  39. Fernandes, R.; Leblanc, S.G. Parametric (modified least squares) and non-parametric (Theil–Sen) linear regressions for predicting biophysical parameters in the presence of measurement errors. Remote Sens. Environ. 2005, 95, 303–316. [Google Scholar] [CrossRef]
  40. Hamed, K.H. Trend detection in hydrologic data: The Mann-Kendall trend test under the scaling hypothesis. J. Hydrol. 2008, 349, 350–363. [Google Scholar] [CrossRef]
  41. Su, J.; Lü, H.; Wang, J.; Sadeghi, A.M.; Zhu, Y. Evaluating the applicability of four latest satellite–gauge combined precipitation estimates for extreme precipitation and streamflow predictions over the upper Yellow River basins in China. Remote Sens. 2017, 9, 1176. [Google Scholar] [CrossRef]
  42. Aksu, H.; Taflan, G.Y.; Yaldiz, S.G.; Akgül, M.A. Evaluation of IMERG for GPM satellite-based precipitation products for extreme precipitation indices over Turkiye. Atmos. Res. 2023, 291, 106826. [Google Scholar] [CrossRef]
  43. Keikhosravi-Kiany, M.S.; Balling, R.C. Evaluation of GPM IMERG early, late, and final run in representing extreme rainfall indices in Southwestern Iran. Remote Sens. 2024, 16, 2779. [Google Scholar] [CrossRef]
  44. Vergni, L.; Parisi, A.; Todisco, F. Evaluation of the GPM IMERG half-hourly final precipitation product in the quantification of rainfall erosivity in central Italy. In Proceedings of the 2022 IEEE Workshop on Metrology for Agriculture and Forestry (MetroAgriFor), Perugia, Italy, 3–5 November 2022; pp. 282–286. [Google Scholar]
  45. Ramadhan, R.; Marzuki, M.; Yusnaini, H.; Muharsyah, R.; Suryanto, W.; Sholihun, S.; Vonnisa, M.; Battaglia, A.; Hashiguchi, H. Capability of GPM IMERG products for extreme precipitation analysis over the Indonesian Maritime Continent. Remote Sens. 2022, 14, 412. [Google Scholar] [CrossRef]
  46. He, S.; Chen, C.; Li, Z.; Feng, A.; Yan, J.; Wu, S. Characteristics of extreme precipitation and its sensitivity to regional climate change in the upper and middle reaches of the Yellow River Basin. Resour. Sci. 2024, 46, 524–537. [Google Scholar]
  47. Ren, Z.; Ma, X.; Wang, K.; Li, Z. Effects of extreme precipitation on runoff and sediment yield in the middle reaches of the Yellow River. Atmosphere 2023, 14, 1415. [Google Scholar] [CrossRef]
  48. Dou, J.; Xia, R.; Xia, X.; Jiang, X.; Zhang, K.; Chen, Y.; Li, L.; Yan, C. Extreme rainfall drives the shift in dominant water quality “sink” from forest land to grassland in the Yellow River Basin. J. Hydrol. 2025, 658, 133218. [Google Scholar] [CrossRef]
  49. Feng, A.; Zhang, Q.; Song, J.; Wang, G.; Wu, W. Spatiotemporal characteristics of extreme precipitation in the Yellow River Basin based on CMIP6. J. Beijing Norm. Univ. (Nat. Sci.) 2024, 60, 270–284. [Google Scholar]
  50. Wang, Y.; Zhou, L. Observed trends in extreme precipitation events in China during 1961–2001 and the associated changes in large-scale circulation. Geophys. Res. Lett. 2005, 32, L09707. [Google Scholar]
  51. Torsah, G.A.; Osei, M.A.; Aryee, J.N.A.; Oti, J.A.A.; Amekudzi, L.K. Triggers of inland heavy rainfall inducing convective storms in West Africa: Case study of June 2021. Weather Clim. Extrem. 2025, 47, 100740. [Google Scholar] [CrossRef]
  52. Keil, C.; Craig, G.C. A displacement and amplitude score employing an optical flow technique. Weather Forecast. 2009, 24, 1297–1308. [Google Scholar] [CrossRef]
  53. Li, R.; Wang, K.; Qi, D. Event-based evaluation of the GPM multisatellite merged precipitation product from 2014 to 2018 over China: Methods and results. J. Geophys. Res. Atmos. 2021, 126, e2020JD033692. [Google Scholar] [CrossRef]
  54. Sánchez-García, C.; Francos, M. Human-environmental interaction with extreme hydrological events and climate change scenarios as background. Geogr. Sustain. 2022, 3, 232–236. [Google Scholar] [CrossRef]
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Figure 1. Location and sub-basins of the Middle Reaches of the Yellow River Basin (MRYRB). The names of sub-basins are as follows: (1) HLL: Left bank of mainstream from Hekou to Longmen; (2) HLR: Right bank of mainstream from Hekou to Longmen; (3) FR: Fen River; (4) QR: Qin River; (5) BLR: Beiluo River; (6) LH: mainstream from Longmen to Huayuankou; (7) JR: Jing River; (8) WRU: Upstream of Wei River; (9) WRD: Downstream of Wei River; (10) YLR: Yiluo River.
Figure 1. Location and sub-basins of the Middle Reaches of the Yellow River Basin (MRYRB). The names of sub-basins are as follows: (1) HLL: Left bank of mainstream from Hekou to Longmen; (2) HLR: Right bank of mainstream from Hekou to Longmen; (3) FR: Fen River; (4) QR: Qin River; (5) BLR: Beiluo River; (6) LH: mainstream from Longmen to Huayuankou; (7) JR: Jing River; (8) WRU: Upstream of Wei River; (9) WRD: Downstream of Wei River; (10) YLR: Yiluo River.
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Figure 2. Temporal variations in nine extreme precipitation indices in the MRYRB derived from CN05 and GPM (**, and *** indicate statistically significant at p <0.05, and 0.01, respectively). (a–i) show the temporal variations in CWD, R10, R20, R95p, R99p, RX1day, RX5day, PRCPTOT, and SDII, respectively. The Corr means the correlation coefficient.
Figure 2. Temporal variations in nine extreme precipitation indices in the MRYRB derived from CN05 and GPM (**, and *** indicate statistically significant at p <0.05, and 0.01, respectively). (a–i) show the temporal variations in CWD, R10, R20, R95p, R99p, RX1day, RX5day, PRCPTOT, and SDII, respectively. The Corr means the correlation coefficient.
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Figure 3. Spatial variations in nine extreme precipitation indices derived from CN05 in the MRYRB. (a–i) show the spatial variations in CWD, R10, R20, R95p, R99p, RX1day, RX5day, PRCPTOT, and SDII, respectively.
Figure 3. Spatial variations in nine extreme precipitation indices derived from CN05 in the MRYRB. (a–i) show the spatial variations in CWD, R10, R20, R95p, R99p, RX1day, RX5day, PRCPTOT, and SDII, respectively.
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Figure 4. Spatial variations in nine extreme precipitation indices derived from GPM in the MRYRB. (a–i) show the spatial variations in CWD, R10, R20, R95p, R99p, RX1day, RX5day, PRCPTOT, and SDII, respectively.
Figure 4. Spatial variations in nine extreme precipitation indices derived from GPM in the MRYRB. (a–i) show the spatial variations in CWD, R10, R20, R95p, R99p, RX1day, RX5day, PRCPTOT, and SDII, respectively.
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Figure 5. Spatial distribution of differences between GPM and CN05 for nine extreme precipitation indices (GPM minus CN05). (a–i) show the differences in CWD, R10, R20, R95p, R99p, RX1day, RX5day, PRCPTOT, and SDII, respectively. Bar charts illustrate the percentage distribution across different value ranges.
Figure 5. Spatial distribution of differences between GPM and CN05 for nine extreme precipitation indices (GPM minus CN05). (a–i) show the differences in CWD, R10, R20, R95p, R99p, RX1day, RX5day, PRCPTOT, and SDII, respectively. Bar charts illustrate the percentage distribution across different value ranges.
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Figure 6. Spatial distributions of trends in extreme precipitation indices derived from CN05 data in the MRYRB. (a–i) show the distributions of CWD, R10, R20, R95p, R99p, RX1day, RX5day, PRCPTOT, and SDII, respectively. Black dots indicate significance at the 0.05 level.
Figure 6. Spatial distributions of trends in extreme precipitation indices derived from CN05 data in the MRYRB. (a–i) show the distributions of CWD, R10, R20, R95p, R99p, RX1day, RX5day, PRCPTOT, and SDII, respectively. Black dots indicate significance at the 0.05 level.
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Figure 7. Spatial distributions of trends in extreme precipitation indices derived from GPM data in the MRYRB. (a–i) show the distributions of CWD, R10, R20, R95p, R99p, RX1day, RX5day, PRCPTOT, and SDII, respectively. Black dots indicate significance at the 0.05 level.
Figure 7. Spatial distributions of trends in extreme precipitation indices derived from GPM data in the MRYRB. (a–i) show the distributions of CWD, R10, R20, R95p, R99p, RX1day, RX5day, PRCPTOT, and SDII, respectively. Black dots indicate significance at the 0.05 level.
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Figure 8. Extreme precipitation indices in different sub-basins of the MRYRB. (a–i) show CWD, R10, R20, R95p, R99p, RX1day, RX5day, PRCPTOT, and SDII, respectively (*, **, and *** indicate statistically significant differences between groups at p <0.1, 0.05, and 0.01, respectively). The scale next to the torus represents the imaginary circle.
Figure 8. Extreme precipitation indices in different sub-basins of the MRYRB. (a–i) show CWD, R10, R20, R95p, R99p, RX1day, RX5day, PRCPTOT, and SDII, respectively (*, **, and *** indicate statistically significant differences between groups at p <0.1, 0.05, and 0.01, respectively). The scale next to the torus represents the imaginary circle.
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Figure 9. Validation of extreme precipitation indices between GPM and CN05 in the MRYRB. (a–i) show the validation result of CWD, R10, R20, R95p, R99p, RX1day, RX5day, PRCPTOT, and SDII, respectively. The black solid line represents the fitting line between the extreme precipitation indices derived from GPM and those derived from CN05, while the black dashed line represents the 1:1 line. Color shading represents the probability density of point distribution: red indicates higher concentration, and black indicates lower concentration.
Figure 9. Validation of extreme precipitation indices between GPM and CN05 in the MRYRB. (a–i) show the validation result of CWD, R10, R20, R95p, R99p, RX1day, RX5day, PRCPTOT, and SDII, respectively. The black solid line represents the fitting line between the extreme precipitation indices derived from GPM and those derived from CN05, while the black dashed line represents the 1:1 line. Color shading represents the probability density of point distribution: red indicates higher concentration, and black indicates lower concentration.
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Table 1. Definitions of nine extreme precipitation indices from the ETCCDI.
Table 1. Definitions of nine extreme precipitation indices from the ETCCDI.
No.DefinitionIndex NameUnit
1Maximum number of consecutive wet days when precipitation ≥1 mmCWDdays
2Annual number of days when daily precipitation ≥10 mm R10days
3Annual number of days when daily precipitation ≥20 mmR20days
4Annual total precipitation from days ≥1 mmPRCPTOTmm
5Annual total precipitation when precipitation >95th percentile R95pmm
6Annual total precipitation when precipitation >99th percentile R99pmm
7Annual maximum 1-day precipitation RX1daymm
8Annual maximum consecutive 5-day precipitationRX5daymm
9The ratio of annual total precipitation to the number of wet days (≥1 mm) SDIImm/day
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Yang, Q.; Xie, Q.; Xu, X. Reliability of Satellite Data in Capturing Spatiotemporal Changes of Precipitation Extremes in the Middle Reaches of the Yellow River Basin. Remote Sens. 2025, 17, 3308. https://doi.org/10.3390/rs17193308

AMA Style

Yang Q, Xie Q, Xu X. Reliability of Satellite Data in Capturing Spatiotemporal Changes of Precipitation Extremes in the Middle Reaches of the Yellow River Basin. Remote Sensing. 2025; 17(19):3308. https://doi.org/10.3390/rs17193308

Chicago/Turabian Style

Yang, Qianxi, Qiuyu Xie, and Ximeng Xu. 2025. "Reliability of Satellite Data in Capturing Spatiotemporal Changes of Precipitation Extremes in the Middle Reaches of the Yellow River Basin" Remote Sensing 17, no. 19: 3308. https://doi.org/10.3390/rs17193308

APA Style

Yang, Q., Xie, Q., & Xu, X. (2025). Reliability of Satellite Data in Capturing Spatiotemporal Changes of Precipitation Extremes in the Middle Reaches of the Yellow River Basin. Remote Sensing, 17(19), 3308. https://doi.org/10.3390/rs17193308

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