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.

A Novel Framework for Urban Flood Damage Assessment

The study of non-stationary effects of hydrological time series and land-use changes in urban areas is essential to predict future floods and their probable damage. In the current study, a novel method was proposed for analyzing their simultaneous impact. For this purpose, rainfall frequency and land-use changes analyses were conducted for two different long-term periods, and the results were compared. Then, hydrologic modeling of the catchment was carried out using the HEC-HMS model, and obtained hydrographs were fed to the HEC-RAS2D model for estimating flood inundation areas. Using the economic information of assets and their damage functions, flood damages related to these two periods were evaluated through the HEC-FIA model. The results indicated that in the low return periods (e.g., 2-year flood), the damage in the second period was increased with respect to the first one but increased for the return periods of 5 to 100 years. Furthermore, surface runoff showed a 4.65% increase due to land-use change and a 12% increase due to rainfall non-stationarity. Moreover, flood damage showed a 136% increase on average, and among the two studied factors, the non-stationarity of rainfalls is considerably more effective on flood intensification.

Extreme rainfall events over Odisha state, India

Extreme rainfall events are a significant cause of loss of life and livelihoods in Odisha. Objectives of the present study are to determine the trend of the extreme rainfall events during 1991-2014 and to compare the events between two periods before and after 1991. Block level daily rainfall data were used in identifying the extreme rainfall events, while district level aggregation was used in analysing the trend in three categories, viz., heavy, very heavy and extremely heavy rainfall as per criteria given by India Meteorological Department (IMD). The state as a whole received one extremely heavy, nine very heavy, and forty heavy rainfall events in a year. When percentage of occurrence of each category out of the total extreme events over different districts was considered, maximum % of extremely heavy rainfall occurred in Kalahandi (5.8%), very heavy rainfall in Bolangir (23.8%) and heavy rainfall in Keonjhargarh (85.4%). Trend analysis showed that number of extreme rainfall events increased in a few districts, namely, Bolangir, Nuapada, Keonjhargarh, Koraput, Malkangiri, and Nawarangapur and did not change in other districts. In Puri district, extremely heavy rainfall frequency decreased. New all-time record high one-day rainfall events were observed in twenty districts during 1992 to 2014, surpassing the earlier records, which could be attributed to climate change induced by global warming. Interior south Odisha was found as the hot spot for extreme rainfalls.

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.

Differential Signal of Change Among Multiple Components of West African Rainfall

Rainfall components likely differ in the magnitude and direction of their long-term changes for any given location, and some rainfall components may carry a greater regional signal of change than rainfall totals. This study evaluates the magnitude of change of multiple rainfall components relative to other components, and the greatest regions of change across all rainfall components in West Africa. Hourly rainfall data from the ERA5 reanalysis dataset was used to derive twelve rainfall components, which were evaluated for long-term means, interannual variability, and long-term changes. For rainfall totals and rainfall intensity, the central Sahel is witnessing increasing trends while the western Sahel is experiencing significant decreasing trends. In general, decreasing trends predominate in the study domain, especially in the northwestern Congo Basin, where annual rainfall is decreasing by 120mm per decade. Importantly, rainfall frequency accounts for 49% of all significant grid-point trends for the whole domain. In contrast, rainfall totals account for 26% of all combined significant trends across the domain, while rainfall intensity (12.6%), rainy season length (9.5%), and seasonality (3.3%) account for the remaining signals of change. Most of the changes among the rainfall components are in the Tropical Wet and Dry regions (59% of all significant trends); the Saharan and Equatorial regions account for the least changes. This study finds evidence that rainfall frequency is changing more across the regions compared to rainfall totals and should be explored as rainfall inputs in climate models to potentially improve regional predictions of future rainfall.

Rainfall Determines Shallow Soil Seepage in a Piedmont Summer Pasture of Alpine Meadow on the Northeastern Qinghai—Tibetan Plateau

Soil seepage is an important component used for quantifying hydrological processes that remains unclear in high-altitude alpine meadows. Shallow soil seepage was continuously measured using an auto-logged micro-lysimeter (diameter = 30 cm, depth = 30 cm) from July 2018 to June 2019 in a piedmont summer pasture of alpine meadow on the Northeastern Qinghai–Tibetan Plateau. The results showed that all the shallow soil seepage events occurred during the non-frozen period from April to September and that the cumulative amount was 106.8 mm, representing about 1/5 of the annual precipitation. The maximum and minimum monthly soil seepage were 30.7 mm in September and 1.0 mm in April, respectively. The boosted regression trees (BRT) model’s area under the curve averaged 0.92 and revealed that the daily half-hour rainfall frequency, volumetric soil water content, and air temperature played significant roles in the daily soil seepage probability, with the cumulative relative contribution of 68%. The stepwise linear regression analysis showed that the rainfall amount accounted for 59% of the variation in the daily amount of soil seepage. The monthly soil seepage was found to be significantly correlated with the monthly rainfall frequency (r = 0.86, p = 0.005). Our results highlighted that rainfall, including its amount and frequency, was the key determinant of the probability and amount of shallow soil seepage in the piedmont summer pasture of alpine meadows. These findings will be helpful for improving predictions of the water budgets of piedmont alpine meadows.

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.