Identifying meaningful covariates that can improve the interpolation of monsoon rainfall in a low‐lying tropical region. (5th August 2021)

• Record Type: Journal Article
• Authors: Das, Samiran; Wahiduzzaman, Md
• Is Part Of: International journal of climatology. Volume 42:Number 3(2022)
• Page Start: 1500
• Page End: 1515
• Publication Date: 2021-08-05
• Subjects: Bangladesh -- kriging -- monsoon rainfall -- remote sensing -- spatial interpolation; Climatology -- Periodicals; Climat -- Périodiques; Climatologie -- Périodiques; 551.605
• DOI: 10.1002/joc.7316
• Languages: English
• ISSNs: 0899-8418

Abstract

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. Abstract : Potential covariates including the remotely sensed information are incorporated in the kriging system for the assessment of interpolation of monsoon rainfall in Bangladesh Remotely sensed annual average monsoon rainfall is identified as the most significant covariate with the kriging with external drift (KED) method The selected KED model is found superior to the standard interpolation techniques