This study investigates the groundwater quality in the Faridpur district of central Bangladesh based on preselected 60 sample points. Water evaluation indices and a number of statistical approaches such as multivariate statistics and geostatistics are applied to characterize water quality, which is a major factor for controlling the groundwater quality in term of drinking purposes. The study reveal that EC, TDS, Ca2+, total As and Fe values of groundwater samples exceeded Bangladesh and international standards. Ground water quality index (GWQI) exhibited that about 47% of the samples were belonging to good quality water for drinking purposes. The heavy metal pollution index (HPI), degree of contamination (Cd), heavy metal evaluation index (HEI) reveal that most of the samples belong to low level of pollution. However, Cd provide better alternative than other indices. Principle component analysis (PCA) suggests that groundwater quality is mainly related to geogenic (rock–water interaction) and anthropogenic source (agrogenic and domestic sewage) in the study area. Subsequently, the findings of cluster analysis (CA) and correlation matrix (CM) are also consistent with the PCA results. The spatial distributions of groundwater quality parameters are determined by geostatistical modeling. The exponential semivariagram model is validated as the best fitted models for most of the indices values. It is expected that outcomes of the study will provide insights for decision makers taking proper measures for groundwater quality management in central Bangladesh

Reliable estimation of design extreme rainfall at an ungauged site is regarded to be an important task in engineering hydrology. This study compares two approaches of extreme rainfall estimation at ungauged locations: region‐of‐influence (ROI) approach of regional estimation and interpolation‐based at‐site estimation in a low‐lying country where the density of rainfall measurements is relatively low. Both approaches incorporate generalized extreme value (GEV) based index‐flood estimation procedure in which the growth factor is used as the means of comparison. The geographical proximity based ROI scheme is assessed for its suitability in ungauged cases whereas popular interpolation techniques—inverse distance weighting (IDW) and kriging—are examined to find an appropriate model for the same purpose. The estimation of index is required in the index‐flood method to get a complete frequency curve at ungauged locations. This study also compares several interpolation approaches in this regard. Annual maximum daily rainfall data at 34 stations located in Bangladesh have been used to assess the performance. The successful evaluation of homogeneity test and the unbounded characteristics of frequency model prove the appropriateness of the ROI scheme in ungauged conditions. The ordinary kriging (OK) is found to be superior to the IDW method in terms of cross‐validation error measures. The estimates of index rainfall obtained by OK with or without anisotropy produce very similar results, although a slight improvement is achieved when an anisotropic semi‐variogram in east direction is used. Regarding comparison between OK and ROI, both methods show a similar performance, indicating that both can be used for ungauged estimation. The overall results suggest that the spatial information about rainfall is an important factor in terms of formation of governing character of extreme rainfall in a low‐lying region like Bangladesh.

Flood frequency analysis is a necessary and important part of flood risk assessment and management studies. Regional flood frequency methods, in which flood data from groups of catchments are pooled together in order to enhance the precision of flood estimates at project locations, is an accepted part of such studies. This enhancement of precision is based on the assumption that catchments so pooled together are homogeneous in their flood producing properties. If homogeneity is assured then a homogeneous pooling group of sites lead to a reduction in the error of quantile estimates, relative to estimators based on single at-site data series alone. Homogeneous pooling groups are selected by using a previously nominated rule and this paper examines how effective one such rule is in selecting homogeneous groups. In this paper a study, based on annual maximum series obtained from 85 Irish gauging stations, examines how successful a common method of identifying pooling group membership is in selecting groups that actually are homogeneous. Each station has its own unique pooling group selected by use of a Euclidean distance measure in catchment descriptor space, commonly denoted d_ij and with a minimum of 500 station years of data in the pooling group. It was found that d_ij could be effectively defined in terms of catchment area, mean rainfall and baseflow index. The study then investigated how effective this selected method is in selecting groups of catchments that are actually homogenous as indicated by their L-Cv values. The sampling distribution of L-CV (t₂) in each pooling group and the 95% confidence limits about the pooled estimate of t₂ are obtained by simulation. The t₂ values of the selected group members are compared with these confidence limits both graphically and numerically. Of the 85 stations, only 1 station's pooling group members have all their t₂ values within the confidence limits, while 7, 33 and 44 of them have 1, 2 or 3 or more, t₂ values outside the confidence limits. The outcomes are also compared with the heterogeneity measures H1 and H2. The H1 values show an upward trend with the ranges of t₂ values in the pooling group whereas the H2 values do not show any such dependency. A selection of 27 pooling groups, found to be heterogeneous, were further examined with the help of box-plots of catchment descriptor values and one particular case is considered in detail. Overall the results show that even with a carefully considered selection procedure, it is not certain that perfectly homogeneous pooling groups are identified.