Identifying meaningful covariates that can improve the interpolation of monsoon rainfall in a low‐lying tropical region

Abstract

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.

Assessing intensity-duration-frequency equations and spatialization techniques across the Grande River Basin in the state of Bahia, Brazil

“…in which i z denotes the known value of variable z at a site i, N denotes the number of neighbor stations, i d is the Euclidean distance from site i to ungauged site, and p is a control parameter. Following the recommendations of Pellicone et al (2018) and Das and Wahiduzzaman (2022), we have adopted a value of 2 for parameter p, which entails a larger weight to nearby sites in the interpolation procedure.…”

“…For the spatialization of the quantiles, both the IDW method and ordinary kriging were considered, as suggested by Das and Wahiduzzaman (2022), for comparison. The ordinary kriging, which is based on a continuous Gaussian field with covariance function materialized by semivariograms, is the most common kriging method (Carvalho & Vieira, 2001;Libertino et al, 2018).…”

“…The ordinary kriging, which is based on a continuous Gaussian field with covariance function materialized by semivariograms, is the most common kriging method (Carvalho & Vieira, 2001;Libertino et al, 2018). For modeling purposes, the empirical semivariance, which is a discrete function used to measure the variability between pairs of points and is formally defined in Equation 6, and a theoretical semi-variograms must be computed (Das & Wahiduzzaman, 2022;Pellicone et al, 2018).…”

Novel hybrid models to enhance the efficiency of groundwater potentiality model

“…Recently, researchers have found that just the assimilation of several parameters does not provide highly accurate and robust GWP maps; therefore, to achieve the accurate GWP maps, researchers have to use different methods, which can assimilate mathematically all parameters having different data patterns and direction (Hembram et al 2019;Das et al 2021). Therefore, researchers have been paid higher interest in developing such accurate GWP modeling methods (Nguyen et al 2020).…”

“…Therefore, we classified all methods as per their operational background, such as (1) statistical approaches for zoning groundwater potential, which have a long history of use (Mallick et al 2021c;Pham et al 2021). Statistical procedures that are now in use include frequency ratio (Abd Manap et al 2014;Guru et al 2017), logistic regression (Rizeei et al 2019), weight of evidence (Rane and Jayaraj 2021;Das et al 2021), certainty factor (Razandi et al 2015), and evidential belief function (Tahmassebipoor et al 2016). However, they have several disadvantages, including a lack of precision (Chen et al 2020).…”