Regional analysis of maximum rainfall using L-moment and LH-moment: A comparative case study for the northeast India

Rainfall data of the northeast region of India has been considered for selecting best fit model for rainfall frequency analysis. The methods of L-moment has been employed for estimation of parameters five probability distributions, namely Generalized extreme value (GEV), Generalized Logistic(GLO), Pearson type 3 (PE3), 3 parameter Log normal (LN3) and Generalized Pareto (GPA) distributions. The methods of LH-moment of four orders (L1 L2, L3 & L4-moments) have also been used for estimating the parameters of three probability distributions namely Generalized extreme value (GEV), Generalized Logistic (GLO) and Generalized Pareto (GPA) distributions. PE3 distribution has been selected as the best fitting distribution using L-moment, GPA distribution using L1-moment and GLO distribution using L2, L3 & L4-moments. Relative root mean square error (RRMSE) and RBIAS are employed to compare between the results found from L-moment and LH-moment analysis. It is found that GPA distribution designated by L1-moment method is the most suitable and the best fitting distribution for rainfall frequency analysis of the northeast India. Also the L1-moment method is significantly more efficient than L-moment and other orders of LH-moment for rainfall frequency analysis of the northeast India.

Heavy rainfall frequency analysis in the Benin section of the Niger and Volta Rivers basins: is the Gumbel's distribution a one-size-fits-all model?

West African populations are increasingly exposed to heavy rainfall events which cause devastating floods. For the design of rainwater drainage facilities (to protect populations), practitioners systematically use the Gumbel distribution regardless of rainfall statistical behaviour. The objective of this study is twofold. The first is to update existing knowledge on heavy rainfall frequency analysis in West Africa to check whether the systematic preference for Gumbel's distribution is not misleading, and subsequently to quantify biases induced by the use of the Gumbel distribution on stations fitting other distributions. Annual maximum daily rainfall of 12 stations located in the Benin sections of the Niger and Volta Rivers' basins covering a period of 96 years (1921–2016) were used. Five statistical distributions (Gumbel, GEV, Lognormal, Pearson type III, and Log-Pearson type III) were used for the frequency analysis and the most appropriate distribution was selected based on the Akaike (AIC) and Bayesian (BIC) criteria. The study shows that the Gumbel's distribution best represents the data of 2/3 of the stations studied, while the remaining 1/3 of the stations fit better GEV, Lognormal, and Pearson type III distributions. The systematic application of Gumbel's distribution for the frequency analysis of extreme rainfall is therefore misleading. For stations whose data best fit the other distributions, annual daily rainfall maxima were estimated both using these distributions and the Gumbel's distribution for different return periods. Depending on the return period, results demonstrate that the use of the Gumbel distribution instead of these distributions leads to an overestimation (of up to +6.1 %) and an underestimation (of up to −45.9 %) of the annual daily rainfall maxima and therefore to an uncertain design of flood protection facilities. For better validity, the findings presented here should be tested on larger datasets.

How much water can bioretention retain, and where does it go?

Bioretention is a type of green stormwater infrastructure for the urban environment that mimics a natural hydrologic system by reducing peak flows and runoff volumes and encouraging infiltration and evapotranspiration. This study examines the complete water balance of a bioretention system located in Vaughan, Ontario, Canada, between 2018 and 2019. The water balance was further broken down by event size, where the event size was determined by rainfall frequency analysis. Recharge was the largest component of the water balance overall (86 % of inflow), as well as by event size. Evapotranspiration was the next largest water balance component (7 % of inflow overall), and was a significant component of inflow (21 %) when considering only small events (50 % probability of recurrence). Evapotranspiration is a slow but consistent process, averaging 2.3 mm/day overall and 2.9 mm/day during the growing season. Climate change is likely to bring more wet days and higher temperatures, which will impact the bioretention water balance by increasing evapotranspiration and inflow. Design standards for retention targets should be updated based on the most recent rainfall frequency analyses to adjust for changing climate conditions.

Regional Rainfall Frequency Analysis on Seven Sites of Punjab, Pakistan using L-moments

The hydro-meteorological variables of extreme rainfall are not easy to explain due to unexpected changes in climate and varied usage of water with growing population. Regional rainfall frequency analysis is the one such method that is useful for the requirement of more accurate estimates of rainfall yearly or desineally for the regions having lack of fresh water resources. The series of Annual Maximum Monthly Rainfall Totals (AMMRT) has been used for the seven sites of northern Punjab, Pakistan using L-moments. The results of different test, the run test, lag-1 correlation and Mann-Whitney U test illustrate that the data series of the seven sites of northern Punjab were found random and independently and identically distributed and have no serial correlation. Heterogeneity measure exposed that the region is homogeneous and discordancy measure gives the evidence that no site is discordant among the seven. The result of goodness of fit test including L-moment Ratio diagrams, ZDIST statistic and Mean Absolute Deviation Index exposed the Pearson Type III (PE3), Generalized Normal (GNO) and Generalized Extreme Value(GEV) are best suitable of the regional distribution for the quantiles estimation. The quantiles estimates obtained for different return periods. A linear regression model was developed with good fit between the at site characteristics and the mean of the AMMRT of the sites. The estimates of the study may be used for the estimation of the rainfall quantiles of the seven sites for different return periods. The estimates will be useful to design future preventive measures for the harmful impact of hydro meteorological events at these sites in Punjab Pakistan.