A regional frequency analysis of precipitation extremes in Mainland China with fuzzy c‐means and L‐moments approaches

Wang, Zhaoli; Zeng, Zhaoyang; Lai, Chengguang; Lin, Wenjun; Wu, Xushu; Chen, Xiaohong

doi:10.1002/joc.5013

Cited by 43 publications

(31 citation statements)

References 49 publications

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“…This suggests that satellite-based estimates combined with gauge information would be effective to identify extreme precipitation. Similar results were found by [19] and [37], who showed that post-real-time products agreed well with the gauge-based observations and presented satisfactory performance for China. In addition, [63] compared the performance of TRMM, IMERG-F, and GSMaP-Gauge in Brazil and demonstrated that IMERG-F and GSMaP-Gauge presented better performance in comparison with near-real-time products.…”

Section: Discussionsupporting

confidence: 87%

Assessment of the Extreme Precipitation by Satellite Estimates over South America

et al. 2020

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In developing countries, accurate rainfall estimation with adequate spatial distribution is limited due to sparse rain gauge networks. One way to solve this problem is the use of satellite-based precipitation products. These satellite products have significant spatial coverage of rainfall estimates and it is of fundamental importance to investigate their performance across space–time scales and the factors that affect their uncertainties. In the open literature, some studies have already analyzed the ability of satellite-based rain estimation products to estimate average rainfall values. These investigations have found very close agreement between the estimates and observed data. However, further evaluation of the satellite precipitation products is necessary to improve their reliability to estimate extreme values. In this scenario, the main goal of this work is to evaluate the ability of satellite-based precipitation products to capture the characteristics of extreme precipitation over the tropical region of South America. The products evaluated in this investigation were 3B42 RT v7.0, 3B42 RT v7.0 uncalibrated, CMORPH V1.0 RAW, CMORPH V1.0 CRT, GSMAP-NRT-no gauge v6.0, GSMAP-NRT- gauge v6.0, CHIRP V2.0, CHIRPS V2.0, PERSIANN CDR v1 r1, CoSch and TAPEER v1.5 from Frequent Rainfall Observations on GridS (FROGS) database. Some products considered in this investigation are adjusted with rain gauge values and others only with satellite information. In this study, these two sets of products were considered. In addition, gauge-based daily precipitation data, provided by Brazil’s National Institute for Space Research, were used as reference in the analyses. In order to compare gauge-based daily precipitation and satellite-based data for extreme values, statistical techniques were used to evaluate the performance the selected satellite products over the tropical region of South America. According to the results, the threshold for rain to be considered an extreme event in South America presented high variability, ranging from 20 to 150 mm/day, depending on the region and the percentile threshold chosen for analysis. In addition, the results showed that the ability of the satellite estimates to retrieve rainfall extremes depends on the geographical location and large-scale rainfall regimes.

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Section: Discussionsupporting

confidence: 87%

Assessment of the Extreme Precipitation by Satellite Estimates over South America

et al. 2020

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“…The data quality was previously checked by Zhao, Zhu [22] using cross-validation and error analysis. The data have been widely used in climate analysis, numerical model verification, and hydrological studies [23,24].…”

Section: Study Area and Datamentioning

confidence: 99%

Multi-Space Seasonal Precipitation Prediction Model Applied to the Source Region of the Yangtze River, China

Berndtsson

et al. 2019

Water

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This paper developed a multi-space prediction model for seasonal precipitation using a high-resolution grid dataset (0.5 • × 0.5 • ) together with climate indices. The model is based on principal component analyses (PCA) and artificial neural networks (ANN). Trend analyses show that mean annual and seasonal precipitation in the area is increasing depending on spatial location. For this reason, a multi-space model is especially suited for prediction purposes. The PCA-ANN model was examined using a 64-grid mesh over the source region of the Yangtze River (SRYR) and was compared to a traditional multiple regression model with a three-fold cross-validation method. Seasonal precipitation anomalies were converted using PCA into principal components. Hierarchical lag relationships between principal components and each potential predictor were identified by Spearman rank correlation analyses. The performance was compared to observed precipitation and evaluated using mean absolute error, root mean squared error, and correlation coefficient. The proposed PCA-ANN model provides accurate seasonal precipitation prediction that is better than traditional regression techniques. The prediction results displayed good agreement with observations for all seasons with correlation coefficients in excess of 0.6 for all spatial locations.

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“…Regular grid points were created by CMA based on observed precipitation data (six stations inside SRYR and more than 15 stations outside) and a Digital Elevation Model from GTOPO30 (Hutchinson, 1998). The dataset has been widely used in climate analysis, numerical model verification, and hydrological studies (Shi et al, 2017;Wang et al, 2017). The quality of the dataset has been checked by using cross validation and error analysis by Zhao et al (2014).…”

Section: Datamentioning

confidence: 99%

Integrated large‐scale circulation impact on rainy season precipitation in the source region of the Yangtze River

Berndtsson

et al. 2019

Intl Journal of Climatology

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Monthly precipitation data at regular grids of 0.5° × 0.5° derived from observations during June–August 1961–2016 were used to reveal characteristics of large‐scale circulations associated with rainy season precipitation over the source region of the Yangtze River (SRYR). The integrated impact of major influencing circulation patterns was examined by principal component analysis and composites. Results showed that the first rainy season precipitation mode associates with the Southern Oscillation Index (SOI) and the Pacific Decadal Oscillation (PDO), explaining 64% of spatial and temporal rainy season precipitation variance in the region. Composites of precipitation pattern under different phases of SOI and PDO revealed that the effect of PDO on precipitation varies with the SOI phase. When in phase with the SOI, PDO‐induced precipitation anomalies are magnified. When out‐of‐phase, anomalies weaken or even disappear. Composites of moisture flux patterns show that large‐scale atmospheric circulation affects the strength of westerlies that transport moisture to the study area and formation of convergence. In coming decades, the PDO is likely to continue in a negative phase with La Niña (positive SOI) events, implying more precipitation during the rainy season. Consequently, this knowledge can be used to improve decision making regarding water supply and flood risk management in the SRYR.

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A regional frequency analysis of precipitation extremes in Mainland China with fuzzy c‐means and L‐moments approaches

Cited by 43 publications

References 49 publications

Assessment of the Extreme Precipitation by Satellite Estimates over South America

Assessment of the Extreme Precipitation by Satellite Estimates over South America

Multi-Space Seasonal Precipitation Prediction Model Applied to the Source Region of the Yangtze River, China

Integrated large‐scale circulation impact on rainy season precipitation in the source region of the Yangtze River

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