Precipitation concentration is an important component of climate, and an unbalanced distribution of precipitation can yield excess or scarcity of water resources, which in turn can influence plant growth, flood risk, and water resource use. The precipitation concentration index (PCI) is a well-known indicator for the measurement of temporal precipitation in a short or long area. The purpose of this study was to analyze precipitation concentration rates in different regions of Bangladesh using the precipitation concentration index (PCI) and the inverse distance weighting method. In this study, the rainfall data from 30 meteorological observatory stations across Bangladesh were collected for the period 1980 to 2011. We defined periods of varying lengths (i.e., annual, supraseasonal, seasonal, and three- and two-month rainfall concentrations) and compared their PCI values. The results showed that precipitation concentrations were mostly irregular when rainfall was concentrated within two to four months of the year. Higher PCI values were mainly identified in the eastern region and have strong seasonal influences, whereas lower PCI values were mostly observed in the northern region. The analyses of periodic variation and precipitation in Bangladesh generally follow through the SW–NE direction due to the summer monsoon, while during the winter monsoon, they follow the N–S direction where JAS and JFM showed higher and lower PCI values. We observed variations in PCI among different regions using the Kruskal–Wallis test of the mean PCI on a decadal scale (1980–1989, 1990–1999, and 2000–2011). The result showed that significant changes in the precipitation occurred during the period of 1980–2011. At a two-month scale, significant changes were identified during transition periods where PCI values were lower from 2000 to 2011 than those in the earlier decades.
Climate change due to global worming is a major concerning issue in the world. Precipitation is changing on global and regional scales by the influence of warming [
The IPCC–2007 report found a precipitation increase from 1900 to 2005 north of latitude of 30° [
Although many studies in Bangladesh have considered general rainfall and temperature characteristics, few have studied the intensity and distribution of rainfall in this region [
This study analyzed the spatial and temporal precipitation variability using the PCI calculated from dense precipitation datasets collected by the Bangladesh Meteorological Department (BMD). A geographic information system (GIS) was used for the development of a regional variation map to which we applied the inverse distance weighted method. The PCI was calculated for annual, supraseasonal, seasonal, and three- and two-month data over a period of 30 years (1980–2011). This enabled decadal comparisons (1980–1989, 1990–1999, and 2000–2011) between four regions, covering the whole study area.
Bangladesh is a low-lying river-dominated country consisting primarily of flat plains. It occupies an area of 147,570 km2 and geographically extends from 20°34′ N to 26°38′ N and from 88°01′ E to 92°41′ E. Most of the population lives in rural areas and directly or indirectly depends on agricultural activities. A sub-tropical monsoon climate is strongly active, and the country is sometimes affected by tropical cyclones [
Map of the study area.
In this study, precipitation concentration changes at different temporal intervals were compared for the four regions of Bangladesh. Data were collected from the BMD from 1980 to 2011 and used as the input data for PCI analysis. Dataset quality is an important indicator for obtaining good results and was carefully controlled before the data were released. Generally, all stations were found to be homogeneous; therefore, the datasets from each station were included in this analysis. First, we performed a comparative correlation between the PCI values for the two- and three-month temporal intervals to understand specific variations. Then, we defined uniform, moderate, irregular, and highly irregular precipitation distributions and seasonal variations for specific regions. For the spatial analysis of precipitation, we interpolated data from all weather stations onto a regular grid. Finally, we analyzed the annual, supraseasonal, seasonal, and three- and two-month PCI values using the Kruskal–Wallis test to identify statistical significance among the decades. The major objective of this research was to analyze rainfall trend and irregular distribution over the study area at various temporal intervals.
The geographic information system (GIS) tools have been used for the PCI mapping. Under the geostatistical analysis, the inverse distance weighted (IDW) method was considered in the study to interpolate the PCI data of the country. In interpolation, the power was considered as 2, the searching neighborhood was standard, the neighborhoods were at least 10 and neighbors to include was 15, major semiaxis was 1.52, minor semiaxis was 1.52, and the angle was 0. The inverse distance weighting (IDW) is an established deterministic method for the precipitation concentration index mapping and one of the most frequently used deterministic models in spatial interpolation [
PCI is an indicator of rainfall concentration and rainfall erosivity [
We also calculated the three- and two-month PCI intervals (Equations (
As defined by Oliver [
To investigate the temporal changes in PCI, the PCI values were calculated for ten decadal subperiods: 1980–1989, 1990–1999, and 2000–2011. The statistical significance of the difference between decadal periods at each grid point was assessed using a trend test with different levels of probability categorized as follows: exceptionally likely (
The annual PCI values were moderate with some irregularities (Figure
Mean PCI values during 1980–2011. BD, Bangladesh; NNWR, north-northwest region; CR, central region; ESER, east-southeast region; SSWR, south-southwest region.
(a) Annual; (b, c) supraseasonal (b = JJ, c = JD); (d–f) seasonal (d = NDJF, e = MAMJ, f = JASO); (g–j) three months (g = JFM, h = AMJ, i = JAS, j = OND).
The PCI on a supraseasonal scale demonstrated a complex precipitation distribution across the study area. During the JJ period, the PCI continued to show moderate-to-high irregularities. The north–west and south–southeast areas had higher PCI values (>20). The central (excluding Dhaka) and west areas also showed comparatively higher values, while low but irregularly distributed values were observed in the central to north–east regions (Figure
Three distinct seasons are recognized in Bangladesh: the premonsoon or summer season (MAMJ), the monsoon/rainy season (JASO), and the postmonsoon or winter season (NDJF) [
The three-month PCI values were highest in the north and south–east and lowest in the central and north–east regions, depending on the annual weather conditions. The JFM period showed irregular PCI values for all regions, with higher values in the south–east than those in the north–west (Figure
The results from two-month data analysis showed relatively moderate-to-uniform precipitation distributions and no strong irregularities for any region (Figure
Mean two-month PCI during (a) JF; (b) FM; (c) MA; (d) AM; (e) MJ; (f) JJ; (g) JA; (h) AS; (i) SO; (j) ON; (k) ND; (l) DJ.
The decadal analysis showed that the annual PCI values varied over time and exhibited an irregular distribution. Significant changes in the PCI values between 1980 and 1989 and between 1990 and 1999 were found for the extreme northwestern and southeastern regions. The west-central and northeastern regions had similar values, but from 1990 to 1999, the central region changed significantly. From 2000 to 2011, the PCI changed significantly with the northwestern and southeastern regions displaying irregularities, while the central and northeastern regions showed irregular distributions (Figures
When analyzed on a six months basis, the PCI values of the JJ period for the decades 1980–1989, 1990–1999, and 2000–2011 showed significant irregularities. High precipitation concentrations occurred in the north-northwest and south-southeast regions, but the precipitation distribution was irregular from the central to northeast regions (Figures
The PCI calculated on a seasonal scale varied across the study area, from values of less than 10 to more than 20. During winter (NDJF), strong irregularities occurred in all decades (1980–1989, 1990–1999, and 2000–2011) while, irregular distributions occurred in the south–west and north–east regions during 1980–1989 and 1990–1999 (Figures
Decadal analysis of annual PCI for (a) 1980–1989; (b) 1990–1990; (c) = 2000–2011. Six-month PCI of JJ for (d) 1980–1989; (e) 1990–1990; (f) 2000–2011. Six-month PCI of JD for (g) 980–1989; (h) 1990–1990; (i) 2000–2011.
The PCI values calculated on a three month scale showed complex distributions. JFM had moderate and irregular distributions during 1980–1989 and 1990–1999 but irregular and strongly irregular values during 2000–2011 (Figures
Decadal analysis of seasonal PCI of NDJF for (a) 1980–1989, (b) 1990–1990, and (c) 2000–2011; MAMJ for (d) 1980–1989, (e) 1990–1990, and (f) 2000–2011; JASO for (g) 1980–1989, (h) 1990–1990, and (i) 2000–2011.
We observed differences in the mean annual precipitation among the four regions. The decadal analysis revealed a significant differentiation during the decades of 1980–1989 and 1990–1999, while 2000–2011 showed no significant differences (Table
The regional differences in the mean PCI using the Kruskal–Wallis test during 1980–1989, 1990–1999, and 2000–2011 in Bangladesh.
Timescalea |
| |||
---|---|---|---|---|
Mean | 1980–1989 | 1990–1999 | 2000–2011 | |
PCI annual | 0.036 |
0.031 |
0.021 |
0.097 |
Supraseasonal (JJ) | 0.061 | 0.063 | 0.004 |
0.219 |
Supraseasonal (JD) | 0.063 | 0.092 | 0.089 | 0.018 |
NDJF | 0.333 | 0.227 | 0.02 |
0.105 |
MAMJ | 0.015 |
0.012 |
0.003 |
0.091 |
JASO | 0.237 | 0.216 | 0.159 | 0.034 |
JFM | 0.011 |
0.024 |
0.068 | 0.014 |
AMJ | 0.014 |
0.011 |
0.005 |
0.026 |
JAS | 0.455 | 0.204 | 0.033 |
0.19 |
OND | 0.003 |
0.005 |
0.068 |
0.001 |
JF | 0.011 | 0.227 | 0.107 | 0.028 |
FM | 0.125 | 0.332 | 0.773 | 0.05 |
MA | 0.026 |
0.018 |
0.028 |
0.028 |
AM | 0.025 |
0.003 |
0.024 |
0.018 |
MJ | 0.051 |
0.067 |
0.005 |
0.192 |
JJ | 0.508 | 0.032 |
0.642 | 0.371 |
JA | 0.394 | 0.424 | 0.023 |
0.235 |
AS | 0.368 | 0.361 | 0.199 | 0.114 |
SO | 0.027 |
0.344 | 0.026 |
0.052 |
ON | 0.005 |
0.009 |
0.062 |
0.001 |
ND | 0.362 | 0.611 | 0.607 | 0.477 |
DJ | 0.135 | 0.497 | 0.39 | 0.39 |
aLetters denote the months of the year (e.g., JJ is January–June and JD is July–December).
Decadal analysis of the three-month PCI for JFM during (a) 1980–1989, (b) 1990–1990, and (c) 2000–2011; AMJ during (d) 1980–1989, (e) 1990–1990, and (f) 2000–2011; JAS during (g) 1980–1989, (h) 1990–1990, and (i) 2000–2011; OND during (j) 1980–1989, (k) 1990–1990, and (l) 2000–2011.
Decadal analysis of the two-month PCI (a, b, c) JF; (d, e, f) FM; (g, h, i) MA; (j, k, l) AM; (m, n, o) MJ; (p, q, r) JJ; (s, t, u) JA; (v, w, x) AS; (y, z, A) SO; (B, C, D) ON; (E, F, G) ND; (H, I, J) DJ for the representative decades 1980–1989, 1990–1999, and 2000–2011 over Bangladesh.
Differentiation was observed for MAMJ in all decades except 2000–2011. Differentiation was observed for NDJF during 1990–1999 and for JASO during 2000–2011. The three month PCI showed differences in mean precipitation concentrations for most decades, although for the monsoon period (JAS), we observed no differentiations in mean precipitation concentration across the country. For most decades, the three month PCI values showed variations in precipitation concentration, with the exception of JFM and JAS during 1990–1999 and 1980–1989. The two month PCI values showed that precipitation variation mostly occurred during seasonal transition periods. Precipitation concentration changes were lower from 2000 to 2011 than in the earlier decades. Most climate research has suggested that annual rainfall will increase and winter rainfall will decrease in South Asia owing to global climate change [
Various studies have indicated due to climate change rainfall pattern in Bangladesh is most likely to change which would have adverse impacts on lives and livelihoods of millions of people [
Two-month PCI changes in different periods from long-term mean PCI (1980–2011) (series 1 = 1980–1989, series 2 = 1990–1999, and series 3 = 2000–2011).
Annual to seasonal PCI changes in different periods from long-term mean PCI (1980–2011) (series 1 = 1980–1989, series 2 = 1990–1999, and series 3 = 2000–2011).
PCI trend over the period of 1980–2011.
Station | Annual | 6 month_JJ | 6 month_JD | NDJF | MAMJ | JASO | JFM | AMJ | JAS | OND | JF | FM | MA | AM | MJ | JJ | JA | As | SO | ON | ND | DJ |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Bogra | 0.03 | 0.01 | −0.01 | 0.16 | 0.00 | 0.02 | 0.05 | 0.00 | 0.01 | 0.09 | 0.10 | 0.06 | 0.01 | −0.04 | 0.01 | 0.00 | 0.01 | 0.00 | 0.00 | 0.00 | 0.06 | 0.10 |
Dinajpur | −0.05 | −0.01 | −0.02 | 0.21 | −0.02 | −0.02 | 0.10 | −0.02 | 0.00 | 0.10 | 0.01 | 0.03 | 0.01 | −0.06 | −0.01 | −0.05 | 0.00 | 0.01 | 0.00 | 0.01 | −0.03 | 0.04 |
Ishurdi | 0.06 | 0.02 | 0.02 | 0.25 | 0.00 | 0.01 | 0.12 | 0.01 | 0.00 | 0.08 | 0.05 | 0.06 | 0.00 | −0.05 | 0.02 | 0.00 | −0.01 | 0.00 | 0.03 | 0.06 | 0.03 | 0.08 |
Rajshahi | 0.04 | 0.05 | 0.04 | 0.40 | 0.01 | 0.01 | 0.16 | 0.00 | 0.01 | 0.11 | 0.13 | 0.07 | 0.03 | 0.00 | −0.03 | 0.00 | 0.01 | −0.03 | 0.02 | 0.05 | 0.06 | 0.09 |
Rangpur | −0.05 | −0.07 | −0.04 | 0.16 | −0.06 | −0.04 | 0.11 | −0.05 | 0.01 | 0.04 | 0.02 | 0.09 | −0.02 | −0.05 | −0.02 | −0.03 | 0.01 | 0.03 | −0.07 | 0.01 | 0.03 | 0.04 |
Barisal | 0.06 | 0.11 | −0.01 | 0.05 | 0.06 | −0.02 | 0.12 | 0.06 | 0.00 | 0.13 | 0.08 | 0.03 | −0.04 | 0.00 | 0.02 | 0.01 | 0.00 | −0.01 | 0.01 | 0.03 | 0.04 | 0.12 |
Bhola | 0.07 | 0.10 | −0.01 | 0.20 | 0.04 | −0.02 | 0.10 | 0.02 | 0.01 | 0.13 | 0.02 | 0.08 | −0.06 | 0.05 | −0.02 | 0.02 | 0.02 | −0.01 | −0.04 | 0.06 | 0.08 | 0.00 |
Hatiya | 0.03 | 0.13 | −0.03 | 0.02 | 0.09 | −0.03 | 0.09 | 0.04 | −0.01 | 0.19 | 0.02 | 0.01 | −0.03 | 0.07 | −0.02 | 0.00 | 0.01 | −0.01 | −0.05 | 0.08 | 0.09 | 0.15 |
Jessore | 0.07 | 0.06 | 0.01 | 0.06 | 0.01 | −0.01 | 0.05 | 0.00 | 0.00 | −0.02 | −0.03 | 0.03 | −0.03 | −0.01 | −0.02 | 0.01 | 0.01 | −0.04 | 0.02 | 0.00 | −0.02 | 0.08 |
Kepupara | 0.01 | 0.04 | −0.06 | 0.10 | 0.03 | −0.05 | 0.14 | 0.01 | −0.02 | 0.16 | −0.04 | 0.21 | 0.09 | 0.05 | −0.02 | 0.03 | 0.00 | −0.03 | −0.05 | 0.04 | 0.12 | 0.12 |
Khulna | 0.05 | 0.10 | −0.03 | 0.21 | 0.05 | −0.04 | 0.12 | 0.02 | −0.03 | 0.12 | 0.05 | 0.07 | −0.07 | 0.03 | −0.02 | 0.00 | −0.01 | −0.04 | 0.04 | 0.03 | 0.07 | 0.10 |
M. Court | −0.03 | 0.16 | −0.10 | 0.07 | 0.09 | −0.07 | 0.08 | 0.06 | −0.01 | 0.11 | 0.00 | 0.02 | 0.07 | 0.03 | 0.00 | −0.01 | −0.01 | −0.03 | −0.07 | 0.02 | 0.15 | 0.07 |
Ppatuakhali | 0.09 | 0.10 | −0.01 | 0.23 | 0.08 | −0.02 | 0.20 | 0.05 | 0.01 | 0.15 | 0.02 | 0.14 | −0.02 | 0.04 | −0.01 | 0.04 | 0.02 | −0.01 | −0.07 | 0.06 | 0.08 | 0.08 |
Sandwip | 0.05 | 0.10 | −0.04 | 0.04 | 0.06 | −0.03 | 0.24 | 0.04 | −0.02 | 0.11 | 0.04 | 0.11 | −0.02 | 0.11 | −0.05 | −0.01 | −0.01 | 0.00 | −0.04 | 0.04 | 0.00 | 0.04 |
Satkhira | 0.04 | 0.13 | −0.01 | 0.06 | 0.05 | −0.02 | 0.24 | 0.03 | −0.01 | 0.04 | 0.06 | 0.10 | −0.01 | 0.03 | 0.01 | 0.00 | 0.01 | 0.00 | −0.03 | 0.02 | 0.12 | 0.09 |
Chandpur | 0.03 | 0.10 | −0.03 | 0.21 | 0.07 | 0.01 | 0.06 | 0.05 | 0.01 | 0.06 | 0.04 | 0.03 | −0.02 | 0.05 | 0.01 | 0.04 | 0.01 | −0.01 | 0.00 | 0.00 | 0.04 | 0.08 |
Comilla | 0.10 | 0.13 | 0.01 | 0.06 | 0.08 | 0.00 | 0.07 | 0.05 | 0.02 | 0.09 | 0.04 | 0.00 | −0.02 | 0.05 | 0.02 | 0.03 | 0.01 | 0.00 | −0.07 | 0.08 | 0.10 | 0.09 |
Dhaka | 0.10 | 0.12 | 0.04 | 0.04 | 0.08 | 0.02 | 0.12 | 0.06 | 0.02 | 0.12 | −0.08 | 0.04 | 0.01 | 0.03 | 0.02 | 0.00 | −0.03 | 0.02 | −0.01 | 0.06 | 0.13 | 0.14 |
Faridpur | 0.14 | 0.22 | 0.08 | 0.01 | 0.16 | 0.03 | 0.15 | 0.12 | 0.03 | 0.13 | −0.03 | 0.12 | 0.12 | 0.03 | 0.06 | 0.01 | 0.01 | 0.00 | 0.00 | 0.06 | 0.00 | 0.01 |
Madaripur | −0.03 | 0.16 | −0.10 | 0.07 | 0.09 | −0.07 | 0.08 | 0.06 | −0.01 | 0.11 | 0.00 | 0.02 | 0.07 | 0.03 | 0.00 | −0.01 | −0.01 | −0.03 | −0.07 | 0.02 | 0.15 | 0.07 |
Mmymensingh | 0.02 | 0.01 | 0.03 | 0.08 | −0.01 | 0.01 | 0.06 | 0.00 | 0.00 | 0.06 | 0.02 | −0.01 | 0.02 | −0.03 | 0.00 | −0.02 | −0.02 | 0.00 | 0.02 | 0.02 | 0.02 | 0.12 |
Tangail | 0.09 | 0.10 | 0.06 | 0.30 | 0.06 | 0.03 | 0.13 | 0.05 | 0.02 | 0.16 | 0.02 | 0.11 | 0.01 | 0.03 | 0.00 | 0.01 | −0.01 | 0.01 | 0.00 | 0.06 | 0.04 | 0.15 |
Chittagong | 0.01 | 0.09 | −0.13 | 0.06 | 0.05 | −0.09 | 0.10 | 0.02 | −0.06 | −0.01 | 0.02 | 0.08 | 0.05 | 0.04 | −0.03 | 0.03 | −0.03 | 0.00 | 0.00 | 0.00 | 0.00 | 0.03 |
cox's Cox’s bBBazar | −0.03 | −0.12 | −0.04 | 0.02 | −0.09 | −0.05 | 0.09 | −0.09 | −0.02 | 0.11 | −0.04 | 0.11 | 0.00 | 0.06 | −0.09 | 0.01 | −0.01 | −0.03 | −0.05 | 0.06 | 0.03 | 0.11 |
Khutubdia | −0.03 | −0.01 | −0.13 | 0.09 | −0.01 | −0.11 | 0.09 | −0.02 | −0.07 | 0.25 | 0.02 | 0.08 | −0.02 | 0.10 | −0.06 | 0.02 | −0.04 | −0.05 | −0.04 | 0.14 | 0.04 | 0.11 |
Rangamati | 0.13 | 0.30 | −0.01 | 0.03 | 0.18 | −0.02 | 0.09 | 0.11 | −0.01 | 0.08 | −0.07 | 0.13 | 0.04 | 0.06 | 0.03 | 0.02 | 0.00 | −0.01 | −0.01 | 0.08 | 0.03 | 0.00 |
Sithakundu | 0.06 | 0.09 | −0.01 | −0.01 | 0.04 | −0.02 | 0.12 | 0.00 | −0.03 | 0.13 | 0.09 | 0.08 | 0.01 | 0.02 | −0.04 | 0.01 | −0.03 | 0.00 | −0.03 | 0.07 | 0.00 | 0.02 |
Srimongal | 0.01 | 0.04 | 0.00 | 0.22 | 0.03 | −0.01 | 0.10 | 0.02 | 0.01 | 0.12 | 0.02 | 0.05 | −0.05 | 0.04 | −0.02 | 0.00 | 0.00 | 0.01 | −0.05 | 0.04 | 0.10 | 0.11 |
Sylhet | 0.03 | 0.04 | 0.01 | 0.28 | 0.02 | 0.01 | 0.12 | 0.02 | 0.00 | 0.11 | 0.05 | 0.06 | 0.00 | 0.01 | −0.01 | 0.00 | 0.00 | 0.00 | −0.05 | 0.00 | −0.05 | 0.18 |
Teknaf | 0.09 | 0.09 | 0.06 | 0.27 | 0.05 | 0.03 | 0.14 | 0.05 | 0.02 | 0.15 | 0.01 | 0.10 | 0.01 | 0.03 | 0.00 | 0.01 | −0.01 | 0.01 | 0.00 | 0.05 | 0.04 | 0.15 |
Three-month PCI changes in different periods from long-term mean PCI (1980–2011) (series 1= 1980–1989, series 2 = 1990–1999, and series 3 = 2000–2011).
Rainfall variability in space and time is one of the most relevant characteristics of the climate of Bangladesh where hydrological disaster is a common phenomenon [
Bangladesh experiences a monsoon climate and has a flood-plain topography, making it prone to hydrological disasters. In this study, we considered the rainfall distribution over different spatial and temporal scales from 1989 to 2011. The results showed that precipitation is the most uniform during the monsoon season. For all regions, the annual PCI values were moderate and irregular, and the six-month (JJ and JD) PCI values were irregular to moderate. For the seasonal and three-month analyses, the values were generally moderately irregular. Finally for shorter two-month intervals (MJ, JJ, JA, and AS), most PCI values showed a uniform precipitation distribution. In general, the northwestern and southeastern areas of Bangladesh showed the highest PCI values, reflecting an irregular precipitation distribution. The results showed that the rainfall in Bangladesh is moderately seasonal, with an intensity increasing from the premonsoon to the monsoon season, driven by weak tropical depressions from the Bay of Bengal. Most premonsoon rainfall occurs in the central and western regions, where agricultural production is extensive. The decadal analysis (1980–1989, 1990–1999, and 2000–2011) showed that the annual PCI values have varied over time and exhibited irregular distributions. The north–west and south–east regions had strong irregularities during 2000–2011, while the shorter JJ and JD six-month periods showed significant and moderate irregularities, respectively. The seasonal analysis showed that the JASO period exhibited a uniform subtropical monsoon climate; therefore, the three-month JAS period also showed a uniform distribution. The two-month PCI analyses indicated that precipitation variation was mostly found in seasonal transition periods. For short time periods, precipitation was strongly linked to season. Short-term variations also increased during the decade 2000–2011, during which the OND period saw the most irregular precipitation distribution.
The data used to support the findings of this study are available from the corresponding author upon request.
The authors declare that there are no conflicts of interest regarding the publication of this paper.
Md. Anarul Haque Mondol and Al-Mamun designed the research idea, analyzed the data, and wrote the manuscript; Mehedi Iqbal contributed for critical evaluation of the manuscript. Dong-Ho Jang revised the manuscript critically and provided important intellectual content.
This work was supported by the Ministry of Education of the Republic of Korea and the National Research Foundation of Korea (NRF-2016S1A3A2924243). The authors are grateful to Bangladesh Meteorological Department (BMD) for providing the rainfall data.