5.2. Comparison of Trends
The characteristics of rainfall data were analyzed before trend analysis to reveal the presence of autocorrelations and multi-decadal variability in time series. The autocorrelation function (AFC) was used in this study to find a significant correlation for various time lags, while the presence of decadal and multi-decadal variability in the time series of climate indices were assessed through wavelet decomposition of time series data [50
]. The AFC plot of annual rainfall data at two locations is shown in Figure 6
. The vertical lines in the plot that exceed the blue confidence band (horizontal lines) indicate significant correlation. The figure clearly shows positive autocorrelation up to 7-lag years in the time series.
Different levels of decompositions reveal the presence of different cycles in the time series. The obtained results for annual rainfall at two stations are shown in Figure 7
. The fourth-level decomposition of data revealed the presence of a cycle of nearly 20 years in both stations. The x-axis of the graph shows the number of years and the y-axis shows the decomposed precipitation anomaly. Similar results were obtained at other stations. The results indicate the presence of short- and long-term autocorrelations in the annual rainfall of Bangladesh. The presence of such multi-decadal variations in annual rainfall time series can significantly affect the trend in rainfall if it is not taken into consideration during trend analysis. Therefore, the mMK test along with the MK test was also used in the present study.
Changes in annual and seasonal rainfall in Bangladesh were assessed using six gridded and observed rainfall data for the period 1979–2010. The monthly rainfall data were converted to annual and seasonal total rainfall to assess the trends. The obtained results were used to prepare maps to show the spatial pattern of the change (Sen’s slope) in the annual and seasonal rainfall at 0.5° × 0.5° grid. For the comparison of slopes in observed and gridded data, observed data were gridded to the resolution of gridded data (0.5° × 0.5°) and the areal average rainfall for each grid box was computed. The grid-to-grid comparison of slopes was conducted by comparing the Sen’s slope estimated for the areal average of observed rainfall at each grid box with the Sen’s slope estimated for gridded data.
The spatial distribution of the changes in annual rainfall in Bangladesh obtained using different gridded data and observed data is shown in Figure 8
. The colour gradients of the maps in Figure 8
represent the Sen’s slopes and the signs (positive or negative) represent the significance of trends at a 95% level of confidence at the grid/station location. The black signs represent significance in trend estimated by MK test while the white signs represent the significance of trend estimated by MK and mMK. Table 3
represents the percentage of areal coverage where different gridded data products showed a significant change in rainfall at a 95% level of confidence.
The spatial distribution of Sen’s slope in annual rainfall (Figure 8
) showed negative values (0 to −183 mm/decade) in most parts of Bangladesh. A significant decrease in annual rainfall was observed at two stations (Chandpur and Faridpur) by both MK and mMK tests and a significant increase at two stations (Teknaf and Khepupara) by only MK test at a 95% level of confidence. None of the gridded rainfall data showed exactly the same spatial distribution of Sen’s slope obtained using observed data. However, GPCC showed a significant positive trend in annual rainfall in the southwest corner of the country (216 mm/decade) and negative trends in the south-central region (−183 mm/decade) where positive and negative trends were detected using observed data. The CPC showed a significant increase in annual rainfall in most of the country, while CRU and PGF showed no change at any grid point over Bangladesh.
The spatial distributions of the trends in pre-monsoon rainfall are shown in Figure 9
. Overall, the Sen’s slope estimated negative changes (−31 to −121 mm/decade) in pre-monsoon rainfall in the centre region and positive changes (93 mm/decade) in the southeast of Bangladesh. Trend analysis results showed that the Sen’s slopes estimated using observed data were significant only at a few grid points in the central and south-central regions (negative) and mountainous southeast corner (positive). A very similar result was obtained using GPCC, which showed negative trends in pre-monsoon rainfall in the central region and positive trends in the southeast corner. However, GPCC showed negative trends at more grid points in the central region compared to that obtained using observed data. On the contrary, CRU showed a negative trend for both MK and mMK tests in the southeast corner. CPC showed no change in the central region, however showed increases in pre-monsoon rainfall in the northeast and southeast.
The spatial distributions of trends in monsoon rainfall are presented in Figure 10
. The Sen’s slope estimated for monsoon rainfall using observational data showed negative slopes (0 to −81 mm/decade) in most parts of Bangladesh, except at a few locations in the southeast, northeast, and northwest. Trend analysis results obtained using MK and mMK tests revealed that the slopes were not significant at any of the stations. Among the six gridded data, only GPCC showed no significant trend in monsoon rainfall at any grid point, while the others showed increasing/decreasing trends in different parts. CPC showed an increase in rainfall in most parts of the country, APHRODITE showed a significant increase in the southeast, CRU showed a decrease in the northeast, PGF showed a decrease in the north, and UDel showed a decrease at three grid points in the central region. The results revealed highly contradictory results in the trends of monsoon rainfall, which shares a major portion of annual total rainfall in the country.
The spatial distributions of the Sen’s slopes estimated for post-monsoon rainfall are presented in Figure 11
. The spatial distribution of slopes obtained using GPCC and APHRODITE was found to be consistent with that obtained using observed data. A significant increasing trend in post-monsoon rainfall was found only at two stations located in the southern coastal region using the mMK test. Only GPCC was found to replicate the spatial distribution of the observed trend in post-monsoon rainfall. GPCC also showed a significant increasing trend at two grid points near to those observed stations but only one using the mMK test and another using the MK test. A large variation in trends was found for other data products. The CPC showed an increase in post-monsoon rainfall over the whole country except in the southeast. The increase was found to be significant for most of the grid points by the MK test and in the central and southern areas for the mMK test. APHRODITE also showed a significant increase in the southern coastal region and the north of Bangladesh. CRU showed no significant change at any grid point, while UDel showed a decrease at a few grid points in the central and southern regions, where APHRODITE and PGF showed an increase. The results clearly indicate large variability in the spatial pattern of post-monsoon rainfall trends obtained using different gridded data products.
Compared to other seasons, better consistency in rainfall changes among the gridded data was observed for winter (Figure 12
). Nevertheless, a large variation was noticed in the patterns of significant trends. CRU showed a significant decrease in winter rainfall mostly in the southwest, UDel in the south-central region, and PGF in the north, while APHRODITE showed almost no changes and CPC showed an increase in the whole central and southern regions. Station data showed a decrease in winter rainfall only at two stations, one located in the north and the other in the southwest, which was found to match better with APHRODITE and GPCC. GPCC showed an increase in winter rainfall at two grid points, one in the north and the other in the southwest, while APHRODITE showed a decrease in rainfall at two grid points in the southwest.
The JSI was calculated for Sen’s slope maps prepared for each gridded data against the observed data at 0.5° resolution (Figure 6
, Figure 7
, Figure 8
, Figure 9
and Figure 10
). The results (Table 4
) showed more similarly of the GPCC map with the observed map for annual and all seasonal rainfall except pre-monsoon. The JSI was found to be 22%, 21%, 80%, and 22% for annual, monsoon, post-monsoon, and winter rainfall for GPCC. APHRODITE was found to be more similar for pre-monsoon rainfall. However, it was found to score zero for annual rainfall.
The results of the three statistical indices used to assess the spatial similarity are presents in Table 5
. The statistical indices were calculated between the slopes estimated from interpolated observed data and gridded data at all the grid points to assess the spatial similarity in slopes. The Pbias of the Sen’s slopes estimated for GPCC at different grid points was found to be very near to zero for annual and all seasonal rainfall expect post-monsoon, for which UDel was found to outperform the GPCC. For the monsoon, all the products were found to overestimate the rate of change, while in winter all of them were found to underestimate the change. Also, GPCC obtained the highest score of md for all the seasons except post-monsoon. The md for post-monsoon was highest for APHRODITE (0.4) while it was second-highest (0.39) for GPCC. In the case of SS, GPCC was found to be best at replicating the PDF of annual and all seasonal rainfall trends. Overall, the results revealed the ability of GPCC to generate the most accurate rainfall slopes and trends in Bangladesh.
Though GPCC was found to be most suitable in estimating rainfall changes and trends in Bangladesh; the statistical scores of GPCC for winter were very low (Pbias −552.6%, md = 0.05, SS = −0.26). The winter rainfall in Bangladesh accounts for only 3% of the total annual rainfall. In some years, it is 0 at some stations. Therefore, a small deviation in winter rainfall between observed and GPCC caused a large variation in bias and other statistics. The mean winter rainfall in Bangladesh is 27 mm, whereas the GPCC estimated the mean winter rainfall as 4.89 mm; thus, the Pbias is −552.6%.
shows the POD score for different gridded data for annual and seasonal rainfall trends. POD was estimated based only on the ability of gridded data to detect the sign of significant trend (positive and negative) and no trend in observed data. Changes in other grid points where no station data was available were not taken into consideration during the computation of POD. Overall, PODs of all products were found to be above 0.5, except for CPC, in detecting trends in annual and post-monsoon (0.44 and 0.35, respectively) rainfall using the MK test. GPCC showed the highest POD, which indicates its capability of accurate detection of trends in annual and seasonal rainfall expect for pre-monsoon. The POD of GPCC in detecting monsoon and post-monsoon rainfall trends using the MK test was 1, while annual, pre-monsoon, and winter rainfall were 0.88, 0.82, and 0.94, respectively. The POD of APHRODITE was the highest in detecting trends in pre-monsoon rainfall using both the MK and mMK tests. However, PGF can be considered as the second best in terms of POD, and the CPC as the worst in detecting trends using the MK test and the UDel using mMK.