Recent Changes in Average Recurrence Interval Precipitation Extremes in the Middle-Atlantic United States

Increases in the frequency of extreme rainfall occurrence have emerged as one of the more consistent climate trends in recent decades, particularly in the eastern United States. Such changes challenge the veracity of the conventional assumption of stationarity that has been applied in the published extreme rainfall analyses that are the foundation for engineering design assessments and resiliency planning. Using partial duration series with varying record lengths, temporal changes in daily and hourly rainfall extremes corresponding to average annual recurrence probabilities ranging from 50% (i.e. the 2-year storm) to 1% (i.e. the 100-year storm) are evaluated. From 2000 through 2019, extreme rainfall amounts across a range of durations and recurrence probabilities have increased at 75% of the long-term precipitation observation stations in the Middle-Atlantic region. At about a quarter of the stations, increases in extreme rainfall have exceeded 5% from 2000 through 2019, with some stations experiencing increases in excess of 10% for both daily and hourly durations. At over 40% of the stations the rainfall extremes based on the 1950-1999 partial duration series show a significant (p >0.90) change in the 100-yr ARI relative to the 1950-2019 period. Collectively the results indicate that given recent trends in extreme rainfall, routine updates of extreme rainfall analyses are warranted on 20-year intervals.

Technical Note: Flood frequency study using partial duration series coupled with entropy principle

Quality discharge measurements and frequency analysis are two major prerequisites for defining a design flood. Flood frequency analysis (FFA) utilizes a comprehensive understanding of the probabilistic behavior of extreme events but has certain limitations regarding the sampling method and choice of distribution models. Entropy as a modern-day tool has found several applications in FFA, mainly in the derivation of probability distributions and their parameter estimation as per the principle of maximum entropy (POME) theory. The present study explores a new dimension to this area of research, where POME theory is applied in the partial duration series (PDS) modeling of FFA to locate the optimum threshold and the respective distribution models. The proposed methodology is applied to the Waimakariri River at the Old Highway Bridge site in New Zealand, as it has one of the best quality discharge data. The catchment also has a history of significant flood events in the last few decades. The degree of fitness of models to the exceedances is compared with the standardized statistical approach followed in literature. Also, the threshold estimated from this study is matched with some previous findings. Various return period quantiles are calculated, and their predictive ability is tested by bootstrap sampling. An overall analysis of results shows that entropy can be also be used as an effective tool for threshold identification in PDS modeling of flood frequency studies.

A Comparative Flood Frequency Analysis of High-Flow between Annual Maximum and Partial Duration Series at Sungai Langat Basin

Flood frequency analysis should consider small and frequent floods. Despite the complexities in partial duration series implementation, it can give a better flood estimation in a way that it does not exclude any significant high flow events, even if it is not the highest event of the year. This study employs the streamflow data recorded at Kajang station, Sungai Langat, Malaysia over a 36-year period spanning from 1978 to 2013. The paper attempts to conduct flood frequency analysis using two approaches, annual maximum and partial duration series. The optimal threshold value is selected to be 48.7 m3/s, where the dispersion index stabilizes at around 1, DI = 1 . The results have shown that generalized extreme value (GEV) distribution describes the annual maximum data while the lognormal (LN3) and generalized Pareto (GPA) distribution is chosen as the best fit distribution at Kajang station for a partial duration series. There is a slight difference between estimated streamflow magnitude when using GPA and LN3 for selected return periods, while a considerable difference was observed when using annual maximum at a higher return period. As a conclusion, PDS gives more relevant magnitude estimation rather than AMS. Flood frequency plays an important role in understanding the nature and magnitude of high flow, which in turn can assist relevant agencies in the design of hydrological structures and reduce flood impacts.

Design Runoff Estimation Using an Efficient Rainfall Analysis Model for Low Data Available Catchment

The challenge of various methods of analysis of rainfall intensity for design runoff estimation is in the availability of data for the given area. This work aims to evaluate the use of the Intensity Duration and Frequency (IDF) method in design runoff estimation using a rainfall analysis model for low data catchment areas such as Nigeria. The source of the data for the study is the Nigerian Meteorological Agency, and it is for Lokoja, a city in the North Central Zone of the country. Analysis of the data compared to the annual and partial duration series for rainfall intensities at various return periods. Also analyzed was the daily rainfall data to determine the rainfall intensity for the different duration and return periods for the study area. From the analysis, partial duration series analysis offered better design estimates than the annual series. Also noted is the fact that the intensity of rainfall increases with a return period of a given duration. The work also indicates that frequency curves can be used to extrapolate rainfall intensity of a given duration for a longer period than years of record. The paper, therefore, presents a design intensity equation useful for the estimation runoff necessary for sustainable infrastructures such as in water utilization and controls.

Two Ways to Quantify Korean Drought Frequency: Partial Duration Series and Bivariate Exponential Distribution, and Application to Climate Change

Studies using drought index to examine return levels of drought can be classified into two approaches: univariate frequency analysis using annual series extracted from drought index time series and multivariate frequency analysis that simultaneously reflects various characteristics of drought. In the case of drought analysis, it is important to properly consider the duration, so, in this study, univariate frequency analysis is performed using the partial duration series. In addition, a bivariate frequency analysis is performed using a relatively simple bivariate exponential distribution to give a more realistic return level to major drought events in the past while reflecting the correlation between drought severities and durations. The drought severity–duration–frequency curves using each of the two frequency analyses are derived, and these curves are used to examine how the drought phenomenon currently in progress is evolving. From this, the advantages and disadvantages of the two approaches, as well as the points to be aware of in application, are discussed. Finally, using the two approaches to the proposed drought frequency analysis, the behavior of Korea’s future extreme droughts is investigated under the conditions of various future climate change scenarios.