Revision of Frequency Estimates of Extreme Precipitation Based on the Annual Maximum Series in the Jiangsu Province in China

Frequency estimates of extreme precipitation are revised using a regional L-moments method based on the annual maximum series and Chow’s equation at lower return periods for the Jiangsu area in China. First, the study area is divided into five homogeneous regions, and the optimum distribution for each region is determined by an integrative assessment. Second, underestimation of quantiles and the applicability of Chow’s equation are verified. The results show that quantiles are underestimated based on the annual maximum series, and that Chow’s formula is applicable for the study area. Next, two methods are used to correct the underestimation of frequency estimation. A set of rational and reliable frequency estimations is obtained using the regional L-moments method and the two revised methods, which can indirectly provide a robust basis for flood control and water resource management. This study extends previous works by verifying underestimation of the quantiles and the provision of two improved methods for obtaining reliable quantile estimations of extreme precipitation at lower recurrence intervals, especially in solving reliable estimates for a 1-year return period from the integral lower limit of the frequency distribution.

Study of Unbiased Plotting Position Formulae for the Generalized Extreme Value (GEV) Distribution

The determination of appropriate quantile relations between the magnitude of extreme events and the corresponding exceedance probabilities is a prerequisite for optimum design of hydraulic structures. Various plotting position formulae have been proposed for estimating the exceedance probabilities or recurrence in. In this study, eight plotting position formulae recommended for GEV distribution were used for estimating the exceedance probabilities of annual maximum series of River Niger at Baro, Kouroussa and Shintaku hydrological stations. The performance measures of PPCC, RRMSE, PBIAS, MAE and NSE were calculated by applying their individual equations to each pair of observed AMS, arranged in ascending order, and exceedance probabilities calculated using each plotting positions. The result of the study show that Weibull was the best plotting position formula, seconded by Beard and thirdly, In – na and Ngugen. This study underscores the necessity to accurately size water infrastructure. In a recent paper, the author found GEV distribution the best – fit probability distribution model in Nigeria. Thus, the need to develop indepth understanding and accurate estimation of exceedance probabilities and return periods using the GEV distribution. Furthermore, this paper recommends similar studies to be conducted for Pearson Type 3(PR3) and Log Pearson Type 3 (LP3) distributions.

Estimating extreme dry spell risk in Ichkeul Lake Basin (Northern Tunisia): a comparative analysis of annual maxima series with a Gumbel distribution

This paper analyses a 42 year time series of daily precipitation in Ichkeul Lake Basin (northern Tunisia) in order to predict extreme dry-spell risk. Dry events are considered as a sequence of dry days separated by rainfall events from each other. Thus the rainy season is defined as a series of rainfall and subsequent dry events. Rainfall events are defined as the uninterrupted sequence of rainy days, when at last on one day more than a threshold amount of rainfall has been observed. A comparison of observed and estimated maximum dry events (42 year return period) showed that Gumbel distribution fitted to annual maximum series gives better results than the exponential (E) distribution combined with partial duration series (PDS). Indeed, the classical Gumbel approach slightly underestimated the empirical duration of dry events. The AMS–G approach was successfully applied in the study of extreme hydro-climatic variable values. The results reported here could be applied in estimating climatic drought risks in other geographical areas.

Analisis Sistem Drainase Untuk Menanggulangi Banjir pada Kecamatan Jambi Timur

Permasalahan yang terjadi pada sistim drainase Kecamatan Jambi Timur yaitu setiap tahunnya selalu tergenang air, khususnya pada musim penghujan. Pada sejumlah saluran drainase, begitu hujan besar terjadi air meluap keluar dan menggenangi ruas jalan. Faktor yang mempengaruhi daya tampung air tersebut, salah satunya adalah banyak saluran yang sudah menebal endapan lumpurnya.Dalam analisa curah hujan untuk menentukan debit banjir rencana, data curah hujan yang dipergunakan adalah curah hujan maksimum tahunan (Annual Maximum Series). Untuk perhitungan curah hujan rencana, digunakan Metode Distribusi Normal, Distribusi Log Normal, Distribusi Log–Pearson III dan Distribusi Gumbel. Untuk hujanyang terjadi selama 5 menit sampai 2 jam, persamaan intensitas durasi hujan menggunakan Rumus Talbot, Ishiguro, dan Sherman. Luas area daerah tangkapan (Catchment Area) didapat dengan menggunakan Software Global Mapper 12 berdasarkan data Digital Elevation Model SRTM_57_13. Penggunaan Metode Rasional pada daerah pengaliran dengan beberapa sub daerah pengaliran dapat dilakukan dengan pendekatan nilai C gabungan atau C rata–rata. Adapun rumusan perhitungan debit rencana menggunakan Metode Rasional. Nilai debit rencana akan dibandingkan dengan nilai debit kapasitas yang telah dianalisa berdasarkan analisis hidrologi dan hidrolika. Jika nilai Debit Kapasitas (Qsaluran) lebih kecil dari nilai Debit Rencana (Qrencana), maka dilakukan dimensi ulang saluran drainase.

Identification of Flood-Rich and Flood-Poor Periods by Using Annual Maximum Series of Floods in Spain

Currently, there is general concern about the non-stationary behaviour of flood series. Consequently, several studies have been conducted to identify large-scale patterns of change in such flood series. In Spain, a general decreasing trend was found in the period 1959–2009. However, a multi-temporal trend analysis, with varying starting and ending years, showed that trend signs depended on the period considered. Flood oscillations could influence the results, especially when flood-rich and flood-poor periods are located at the beginning or end of the series. In Spain, a flood- rich period in 1950–1970 seemed to lead to the generalised decreasing trend, as it was located at the beginning of the flood series. Nevertheless, the multi-temporal test can only find potential flood- rich and flood-poor periods qualitatively. A methodology has been developed to identify statistically significant flood-rich and flood-poor periods. The expected variability of floods under the stationarity assumption is compared with the variability of floods in observed flood series. The methodology is applied to the longest streamflow series available in Spain. Seven gauging stations located in near-natural catchments, with continuous observations in the period 1942–2014, are selected. Both annual maximum and peak-over-threshold series are considered. Flood-rich and flood-poor periods in terms of flood magnitudes and the annual count of exceedances over a given threshold are identified. A flood-rich period in the beginning of the series and a flood-poor period at its end are identified in most of the selected sites. Accordingly, a flood-rich period placed at the beginning of the series, followed by a flood-poor period, influence the generalised decreasing trend in the flood series previously found in Spain.