Precipitation trend analysis using discrete wavelet transform at the Langat River Basin, Selangor, Malaysia

Abstract

The main purpose of this study was to determine the most dominant periodic components that affect the annual and seasonal precipitation trends in each homogenous rainfall region in the Langat River Basin, Malaysia for the period 1982-2011. Performing this research could be essential because in the previous studies on detection of trend in Malaysia, the details of variations of different time scales and the periodic responsible for the observed trends were not investigated. Using discrete wavelet transform (DWT) coupled with Mann-Kendall at the regional scale for the first time particularly in the context of Malaysia is the contribution of this study. In order to form the homogenous rainfall regions, first the total annual and seasonal precipitation in each year was spatialized into 5 km 9 5 km grids using the inverse distance weighting method. The obtained precipitation series for the grids were then grouped applying the Ward's clustering method based on the similarity of precipitation time series. After allocating a cluster number to each grid, the boundary of the regions was formed in ArcGIS software. Following which, in each homogenous region the areal precipitation series were computed by the Thiessen polygon method. The Mann-Kendall (MK) test was used to detect trend and the DWT coupled with the MK test and the sequential MK analysis were then utilized in order to find out the time scale which affected the observed trend in each homogenous region. On annual scale, it was found that D 1 (plus approximation) component in regions Annual Cluster1 (AC1) and AC2 was the periodic mode responsible for trends. On seasonal scale, in regions Northeast monsoon Cluster 1 (NC1), NC3, SC1 and Southwest monsoon Cluster 2 (SC2), D 1 (with approximation), in regions NC4, Inter monsoon 1 Cluster 1 (I1C1), I1C2, Inter monsoon 2 Cluster 1 I2C1 and I2C2, Detail 2 (D 2 ) (plus approximation) and in region NC2, Detail 3 (D 3 ) (with approximation added) component were the most influential periodicity for trends.

Changes of Reference Evapotranspiration and Its Relationship to Dry/Wet Conditions Based on the Aridity Index in the Songnen Grassland, Northeast China

“…Previous studies indicated that the interpolation methods should account for external factors, such as altitude and land use patterns [42]. However, compared with other interpolation methods, the IDW produced slightly better predictions of ET 0 [29,43]. Future studies should consider how different interpolation methods impact the spatial variations of ET 0 at different time scales.…”

“…The change points were detected based on the sequential MK test, which has been largely employed for the detection of change points in different climatic and hydrological data series [28][29][30]. Detailed information related to the sequential MK test can be found in studies published by Palizdan et al [29] and Liang et al [30]. In addition, Sen's slope estimator was used to measure the magnitude of the trend [31].…”

“…This method is a simple deterministic interpolation, and assumes that sample values closer to the prediction location are more representative of the prediction value than sample values farther away [41]. Therefore, the closest value to the prediction location gets the greatest weight and the weight is reduced as a function of distance [29]. The power parameter (α), which controls the importance of the predicted location values, is the main factor affecting the result from the IDW method.…”

“…As a higher α value is selected, more weight is given to the closed location. In this study, α was defined as 2 by following Palizdan et al [29]. All spatial interpolations were conducted using the ArcGIS 10.2 software.…”

Regional scale analysis of trends in rainfall using nonparametric methods and wavelet transforms over a semi‐arid region in India

“…Although, several studies are available focusing on application of DWT in trend analysis (Partal and Küçük, 2006;Adamowski et al, 2009;Nalley et al, 2012;Nalley et al, 2013;Adarsh and Janga Reddy, 2015;Araghi et al, 2015;Adarsh et al, 2016;Joshi et al, 2016;Palizdan et al, 2017), there exists lack of a micro-level study focusing on regional precipitation patterns in India. Considering this gap, the present study is intended to analyse trends and identify dominant periodic component in various rainfall time series over various districts of Chhattisgarh state (a semi-arid region) in India using DWT in conjunction with MK and SQMK test.…”