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The assessment of spatio‐temporal distribution of rainfall over the entire monsoon season is critical for the water resources management in a tropical low‐lying region like Bangladesh. The primary objective is to find suitable interpolation methods for each monsoon month as well as the accumulated rainfall for the entire season in Bangladesh. As the unique position of Bangladesh and monsoon system both contribute the variation in rainfall, the identification of associated suitable covariates can offer reliable estimates when applied with the appropriate interpolation model. Several potential covariates are identified diligently including the remotely sensed annul average monsoon rainfall (RAAMR), distance to Bay‐of‐Bengal and distance to Hindu‐Kush Himalayan Region, among others. This study mainly considers the multivariate approach: kriging with external drift (KED) which is able to take external information to positive impact. The method is then compared with the more traditional univariate, ordinary kriging (OK) and the inverse distance weighting (IDW) method using the cross‐validation technique. Daily rainfall data from 1970 to 2016 at 34 stations were used for the assessment. Result shows that the KED model is identified suitable for the considered variants of June, August, September and annual average monsoon rainfall whereas the OK is appropriate for July. Overall, the KED is found superior model with the incorporation of RAAMR data. The successful inclusion of remotely sensed data in the kriging system paves a new dimension in the climatological research for countries where climate‐measuring resources are limited. [ABSTRACT FROM AUTHOR]

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. Maps of growth factors (X10 and X100) derived by inverse distance weighting (IDW) and ordinary kriging (OK) approaches. The observed estimate is indicated by circle relative to the maximum value. [ABSTRACT FROM AUTHOR]

• Ungauged estimation is carried out based on spatial interpolation of model parameters. • Spatial models that can incorporate covariates are assessed for the interpolation. • Potential covariates unique to the study area are derived and evaluated. • Information about monsoon rainfall and spatial data are found significant. • The selected model offers a promising result compared to a traditional method. Bangladesh, a tropical low-lying region, is stranded between the Bay of Bengal in the south and the Himalayas in the north. The unique characteristics allow receiving huge rainfall, in particular, in the monsoon season. The physical phenomena such as the Bay of Bengal, the Himalayas and the monsoon season dominate the extreme rainfall pattern in Bangladesh. However, how to include that information to good effect in the estimation of extreme rainfall at ungauged locations is yet to be investigated. The objective of this work is to examine the information that needs to be included in the estimation of extreme rainfall in such regions. The approach that permits parameters of an extreme value distribution to vary spatially is used to determine the probability model. The methodology, subsequently, authorizes the frequency analysis to be performed at ungauged conditions. The geostatistical technique in the form of kriging with external drift (KED) which has the ability to take secondary information is used to produce spatial maps of the parameters. This study primarily assessed KED interpolation scheme and its ability to accurately predict the model parameters. The scheme was compared against the more widely used ordinary kriging (OK) model. The cross- validation was used to find the robust scheme for each parameter. Annual maximum daily rainfall data from 34 measuring stations were used for the assessment. The KED with the covariate annual mean monsoon rainfall (AMMR) appears to be the best model for location parameter. However, for the scale and shape parameter the KED models were unable to score past the OK model. The availability of parameter maps allows the estimation of extreme rainfall at locations where rainfall records were absent and offers a much better elucidation than the traditional at-site quantile based interpolation. Overall, the inclusion of monsoon rainfall and spatial information are deemed necessary for the estimation of extreme rainfall at ungauged locations in a low-lying monsoon region. [ABSTRACT FROM AUTHOR]