L-Moment-Based Regional Frequency Analysis of Annual Extreme Precipitation and its Uncertainty Analysis

Liang, Ye; Guo, Yiping; Hua, Hong Xing

doi:10.1007/s11269-017-1715-5

Cited by 17 publications

(8 citation statements)

References 36 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…The list of stations included in the study are Silchar (1), Dholai (2), Goalpara (3), Guwahati (4), North Lakimpur (5), Choudhoughat (6), Batadighat (7), Kampur (8), Sibsagar (9), Beki Rd. Bridge (10), Dibrugarh (11), Jorhat (12), Neamatighat (13), Kherunighat (14), Bokajan (15), Gossaigaon (16), Kokrajhar (17), Tezpur (18), Mellabazar (19) Aie NH.Xing (20), Dharamtul (21), Golaghat (22), Dhollabazar (23), Margherita (24), Gharmura (25), Shillong (26), Cherrapunjee (27), Mawsynram (28), Kohima (29), Imphal (30), Aizwal (31), Agartala (32), and Kailashahar (33).…”

Section: Study Area and Rainfall Datamentioning

confidence: 99%

“…With uncertainty and suitability involved in the selection of an appropriate probability distribution for any regional frequency analysis, it is crucial to have a performance comparison of fitted distributions against at-site analysis for the region. Numerous works on uncertainty analysis in regional frequency analysis of rainfall [10][11][12][13] have been published. Notable recent studies on the comparison of uncertainty analysis of rainfall from regional and at-site analysis can be found in the studies of [14][15][16], but very scarce studies are available for north east India.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Uncertainty Analysis of Regional Rainfall Frequency Estimates in Northeast India

et al. 2021

View full text Add to dashboard Cite

Estimation of rainfall quantile is an important step in regional frequency analysis for planning and design of any water resources project. Related evaluations of accuracy and uncertainty help to further assist in enhancing the reliability of design estimates. In this study, therefore, we investigate the accuracy and uncertainty of regional frequency analysis of extreme rainfall computed from genetic algorithm-based clustering. Uncertainty assessment is explored with prediction of quantiles with a new spatial Information Transfer Index (ITI) and Monte Carlo simulation framework. And, accuracy assessment is done with the comparison of regional growth curves to at-site analysis for each homogenous region. Further, uncertainty assessment with the ITI method is compared with Maximum Likelihood estimation (MLE) optimized by a genetic algorithm (GA) to check the suitability of the method. Results obtained suggest the ITI-based uncertainty assessment for regional estimates outperformed those of at-site estimates. The MLE-GA method based on at-site estimates was found to be better than at-site estimates based on L-moments, suggesting the former as a better alternative to compare with regional frequency estimates. Moreover, minimal bias and least deviation of the regional growth curve were obtained in the rainfall regions. The confidence intervals of regional estimates were seen to be well within the bounds of normality assumptions. Doi: 10.28991/cej-2021-03091762 Full Text: PDF

show abstract

Section: Study Area and Rainfall Datamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Uncertainty Analysis of Regional Rainfall Frequency Estimates in Northeast India

et al. 2021

View full text Add to dashboard Cite

show abstract

“…Selain itu, Lmoment juga memiliki parameter yang lebih stabil pula terhadap seri data yang lebih pendek (Central Water Commision, 2010). Metode ini telah banyak diaplikasikan di berbagai wilayah/negara, seperti Pakistan (Khan, et al, 2017), Iran (Malekinezhad & Zare-Garizi, 2014), Basin Taihu di China (Liang, et al, 2017), Eropa (Salinas, et al, 2014) dan wilayah lainnya. Bahkan, penelitian menggunakan metode Lmoment dengan menambahkan faktor perubahan iklim pun telah diaplikasikan di Qatar (Al Mamoon, et al, 2014).…”

Section: Analisis Frekuensi Wilayah Dengan Metode Lmomentunclassified

Nilai Faktor Pertumbuhan untuk Estimasi Hujan Rencana di Pulau Jawa

Adiyani

2019

JSDA

View full text Add to dashboard Cite

Pola iklim baru akibat perubahan iklim yang diperburuk dengan adanya degradasi Daerah Aliran Sungai (DAS), telah meningkatkan frekuensi dan intensitas bencana banjir di Indonesia. Hal ini berdampak pada kondisi hidrologi, pertanian dan sosial-ekonomi yang semakin memburuk sehingga diperlukan suatu perhitungan banjir rencana. Perhitungan debit banjir rencana dilakukan berdasarkan besaran hujan rencana melalui analisis frekuensi. Estimasi parameter hasil analisis frekuensi selanjutnya digunakan untuk menghitung faktor pertumbuhan, yaitu faktor yang jika dikalikan dengan median seri data hujan harian maksimum tahunan dapat menghasilkan besaran hujan T-tahun. Tujuan dari studi ini adalah menghitung faktor pertumbuhan untuk estimasi hujan rencana pada beberapa periode ulang. Data hujan harian maksimum tahunan pada 2.611

show abstract

“…The regional frequency analysis method was provided to be suitable, accurate, and robust for the study of extreme precipitation spatiotemporal characteristics (Hosking et al ., 1985), and the regional frequency analysis method based on L‐moments has been extensively applied in many areas (Alvarez et al ., 2016; Liang et al ., 2017). However, many studies using precipitation data from meteorological stations for regional frequency analysis (Anli et al ., 2009; Chen et al ., 2014b) may result in two problems: one is sparsely distributed stations causing an approximate division with imprecise boundaries (Yang et al ., 2010); the other is that the method cannot be applied in an area without station coverage.…”

Section: Introductionmentioning

confidence: 99%

Spatiotemporal characteristics and estimates of extreme precipitation in the Yangtze River Basin using GLDAS data

Chen

Zeng

Shen

et al. 2020

Intl Journal of Climatology

View full text Add to dashboard Cite

The Yangtze River Basin has periodically been subject to torrential rains and floods. It is of great significance to characterize the extreme precipitation patterns to learn about frequent flood characteristics in the Yangtze River Basin. Commonly, spatiotemporal characteristics of extreme precipitation was studied by regional frequency analysis method with site data. Spatial sparse site data may cause imprecise divisions of homogeneous regions. In this paper, the spatiotemporal characteristics of extreme precipitation was studied by regional frequency analysis with corrected satellite-based grid precipitation data (Global Land Data Assimilation System, GLDAS) rather than site data. The results show that: (1) The corrected GLDAS daily precipitation data had greatly improved its ability to capture extreme precipitation events in Yangtze River Basin, as the data average accuracy increased from 0.215 before correction to 0.849 after correction. It is feasible to use satellite-based grid precipitation data to replace the site data for the regional frequency analysis of extreme precipitation. (2) The Yangtze River Basin was categorized into seven homogeneous regions for the annual maximum 1-day (RX1DAY) index with an automatic subjective adjustment method. (3) The regional growth curves and quantiles of the Yangtze River Basin were drawn for the return period for 2-100 years. (4) Spatial patterns of extreme daily precipitation series with a return period of 100 years indicated that the precipitation amount increases gradually from the upper to the lower Yangtze River Basin, from the "arid zone" to the "wet zone" and then to the "special wet zone", and the 100-year return level of RX1DAY varied from 30.3 to 301.8 mm. There were three main precipitation centres, the Sichuan Basin, Dongting Lake Basin, and a great triangle area covering the Poyang Lake Basin and the south foot of Dabie Mountain.

show abstract

L-Moment-Based Regional Frequency Analysis of Annual Extreme Precipitation and its Uncertainty Analysis

Cited by 17 publications

References 36 publications

Uncertainty Analysis of Regional Rainfall Frequency Estimates in Northeast India

Uncertainty Analysis of Regional Rainfall Frequency Estimates in Northeast India

Nilai Faktor Pertumbuhan untuk Estimasi Hujan Rencana di Pulau Jawa

Spatiotemporal characteristics and estimates of extreme precipitation in the Yangtze River Basin using GLDAS data

Contact Info

Product

Resources

About