Trends and variability of annual precipitation total, annual number of rainy days and two climate change related precipitation indices named Simple Daily Intensity Index (SDII) and Precipitation Concentration Index (PCI) have been investigated in this study. The analysis was based on daily and monthly precipitation data of 35 observatory stations all over Bangladesh for the study period of 1971-2010. Mann Kendall test was performed to detect the trend and Sen’s slope method to determine the magnitude of change. The results indicate statistically significant (95% confidence level) negative trend in 4 stations and significant positive trend in 2 stations for annual precipitation total. Significant positive trend in 9 stations for annual number of rainy days, significant negative trend in 6 stations for SDII and for PCI, and significant negative trend in 6 stations were found all over Bangladesh in this study. The values of PCI indicate strongly irregular precipitation distribution in South Eastern Region (SER) and mostly irregular distribution in other regions. On the other hand values of SDII indicate strong precipitation intensity in SER and mostly moderate intensity in other regions all over the country.
Climate variability and its unfriendly concomitants are of growing concern worldwide. The climate of Bangladesh is undergoing changes and extremes are becoming more unprecedented every year. Floods, cyclones or droughts are often in its colossal mood here, which are aggravated by climate change and its variability being experienced more frequently in Bangladesh than ever before. Change of precipitation trends is in its way because of climate change. According to the intergovernmental panel on climate change (IPCC) report [
In the past three decades, higher Precipitation Concentration Index (PCI > 20) has been recorded in semi-arid and tropical humid environments in the cardamom hill slopes, south western ghats, India, which implies very long dry periods for up to 3 - 5 months [
Daily and monthly precipitation data of 35 BMD (Bangladesh Meteorological Department) stations (
The Simple Daily Intensity Index (SDII) [
Let RRwj be the daily precipitation total for the wet days W (RR ≥ 1 mm) in period j. If W is the number of the wet days within the period j, then:
The Precipitation Concentration Index [
where Pi is the monthly precipitation in month i and Pt is the annual precipitation total.
Mann-Kendall test [
SDII and PCI both are extreme precipitation indices which measure intensity and distribution uniformity of rainfall events respectively. SDII depends on annual rainfall amounts and annual number of rainy days. Therefore, trends of both annual rainfall amounts and annual number of rainy days have effects on trends of SDII. Mann-Kendall trend test values for annual rainfall amounts, annual number of rainy days, SDII and PCI are shown in
(a) Study area with regions: Bangladesh (b) BMD stations
Summary of the results obtained for the trends of the four study parameters using Mann-Kendall test
. Significance of SDII and PCI values [6] [7] [12] [13]
SDII Value | Significance (Intensity) | PCI Value | Significance (Temporal Distribution) |
---|---|---|---|
To 15 inclusive | Low intensity | To 10 inclusive | Uniform distribution |
Over 15 to 20 | Moderate intensity | Over 10 to 15 | Moderate distribution |
Over 20 to 25 | High intensity | Over 15 to 20 | Irregular distribution |
Over 25 to 30 | Strong Intensity | Over 20 | Strongly irregular distribution |
Over 30 | Very strong intensity |
. Trends of annual rainfall total, annual number of rainy days, SDII and PCI for the study period at different BMD stations
Region | Stations | Trends (unit/year) | |||
---|---|---|---|---|---|
Annual Rainfall Total (mm) | Annual Number of Rainy Days | SDII (mm/day) | PCI | ||
NWR | Bogra | −1.9 | 0.01 | −0.026 | −0.064* |
Dinajpur | −3.69 | −0.063 | −0.033 | −0.085* | |
Ishurdi | −9.48 | 0.07 | −0.128* | −0.011 | |
Rajshahi | −9.50* | 0.059 | −0.109* | −0.033 | |
Rangpur | 10.08 | 0.167 | 0.038 | −0.064* | |
Saidpur | 15.15 | 1.25 | −0.149 | −0.134 | |
CR | Dhaka | −1.64 | −0.286 | 0.025 | 0.018 |
Tangail | −1.93 | −0.03 | −0.054 | 0.045 | |
Mymensingh | 1.32 | 0.216 | −0.055 | −0.071* | |
Faridpur | −9 | 0.093 | −0.108 | −0.007 | |
Madaripur | −12.25* | −0.164 | −0.062 | 0.065 | |
Chandpur | −24.41* | −0.467 | −0.098* | −0.092 | |
Comilla | 4.86 | 0.458* | −0.008 | 0.035 | |
Feni | −3 | −0.25 | 0.034 | −0.008 | |
M. Court | −1.03 | 1.019* | −0.165* | −0.041 | |
NER | Srimangal | 9.18 | 0.889* | −0.045 | −0.081* |
Sylhet | −3 | 0.167 | −0.072 | −0.028 | |
SER | Rangamati | 9.28 | 0.500* | −0.007 | −0.046 |
Kutubdia | −8.06 | 0.609* | −0.149* | −0.073 | |
Cox’s Bazar | 8.32 | 0.304 | 0.042 | −0.059 | |
Teknaf | 19.13* | 0.2 | 0.11 | −0.033 | |
Chittagong | 11.62 | −0.008 | 0.106 | −0.033 | |
Ambagan | −6.22 | −0.938 | 0.187 | 0.175 | |
SWR | Hatya | 17.66 | 0.104 | 0.117 | 0.045 |
Chuadanga | 8.87 | −0.071 | 0.086 | 0.034 | |
Jessore | 7.5 | 0.375* | 0.016 | 0.032 | |
Khulna | −3.7 | 0.025 | −0.053 | −0.071 | |
Mongla | −4.74 | −0.29 | 0.077 | 0.108 | |
Satkhira | 4.35 | 0.075 | 0.03 | −0.071* | |
Barisal | −2.76 | 0.068 | −0.025 | 0.044 | |
Bhola | −14.08* | −0.347 | −0.066 | 0.017 | |
Khepupara | 14.18* | 0.583* | −0.018 | 0.016 | |
Potuakhali | 0.56 | 0.5 | −0.066 | 0.019 | |
Sitakunda | −6.93 | 0.500* | −0.17 | −0.007 | |
Sandwip | 3.21 | 0.456* | −0.131* | 0.021 |
The asterisk (*) denotes statistically significant figures at 95% confidence level.
Intensive rainfall can be uniform if it occurs all over the time period, but if it occurs within a short instance of the time period then its distribution can be called irregular. Accordingly, when both SDII and PCI shows similar trend, rainfall will become uniform and less intensive (decreasing trend) or irregular and intensive (increasing trend). On the contrary, when the two trends are opposite, outcomes may be uniform and intensive or irregular and less intensive. Thus, in NWR and NER rainfall tends to be uniform and less intensive (
Six cases of SDII trend change related to the trend of annual rainfall amounts and annual number of rainy days are detected in this study (shown in
Temporal evolution of (a) Annual rainfall amounts (mm) (b) Annual number of rainy days (c) SDII (d) PCI in Faridpur (one of the BMD stations) for the study period of 1971-2010 along with 5- year moving average (black line) and linear trend (dotted line)
easy to understand as Equation (1) explains. Cases IV and V are the opposite of cases I and II.
The values of PCI shows irregular to strongly irregular precipitation distribution in North Western Region (NWR) and mostly strong irregularity in South Eastern Region (SER); also irregular distribution is evident in Central Region (CR), North Eastern Region (NER) and South Western region (SWR) (
. Cases of SDII change (1971-2010)
Cases | Trend of SDII (mm/day) | Trend of Annual Rainfall Amounts (mm) | Trend of Annual Number of Rainy Days |
---|---|---|---|
I | Increase | Decrease | Decrease |
II | Increase | Increase | Increase |
III | Increase | Increase | Decrease |
IV | Decrease | Decrease | Decrease |
V | Decrease | Increase | Increase |
VI | Decrease | Decrease | Increase |
. Average value of SDII and PCI for the study period at different BMD stations and their significance according to values (Table 1)
Region | Stations | SDII (mm/day) | PCI | Comments on SDII Value in Terms of Intensity | Comments on PCI Value in Terms of Temporal Distribution |
---|---|---|---|---|---|
NWR | Bogra | 16.641 | 18.66 | Moderate | Irregular |
Dinajpur | 20.475 | 20.112 | High | Strongly Irregular | |
Ishurdi | 16.277 | 17.925 | Moderate | Irregular | |
Rajshahi | 15.386 | 18.303 | Moderate | Irregular | |
Rangpur | 20.765 | 19.023 | High | Irregular | |
Saidpur | 22.158 | 19.394 | High | Irregular | |
CR | Dhaka | 17.37 | 16.227 | Moderate | Irregular |
Tangail | 16.491 | 16.255 | Moderate | Irregular | |
Mymensingh | 18.718 | 19.345 | Moderate | Irregular | |
Faridpur | 16.787 | 16.858 | Moderate | Irregular | |
Madaripur | 17.471 | 16.684 | Moderate | Irregular | |
Chandpur | 18.865 | 18.694 | Moderate | Irregular | |
Comilla | 18.226 | 16.954 | Moderate | Irregular | |
Feni | 24.735 | 19.63 | High | Irregular | |
M.court | 26.535 | 19.356 | Strong | Irregular | |
NER | Srimangal | 17.568 | 17.04 | Moderate | Irregular |
Sylhet | 25.374 | 16.44 | Strong | Irregular | |
SER | Rangamati | 19.708 | 17.856 | Moderate | Irregular |
Kutubdia | 26.443 | 20.155 | Strong | Strongly Irregular | |
Cox's Bazar | 28.681 | 20.331 | Strong | Strongly Irregular | |
Teknaf | 32.459 | 21.373 | Very strong | Strongly Irregular | |
Chittagong | 24.502 | 19.965 | High | Irregular | |
Ambagan | 25.19 | 19.298 | Strong | Irregular | |
SWR | Hatya | 25.298 | 19.368 | Strong | Irregular |
Chuadanga | 14.81 | 19.191 | Low | Irregular | |
Jessore | 15.522 | 17.71 | Moderate | Irregular | |
Khulna | 16.196 | 17.913 | Moderate | Irregular | |
Mongla | 16.012 | 16.787 | Moderate | Irregular | |
Satkhira | 15.428 | 18.423 | Moderate | Irregular | |
Barisal | 17.548 | 17.206 | Moderate | Irregular | |
Bhola | 19.454 | 17.681 | Moderate | Irregular | |
Khepupara | 22.648 | 18.247 | High | Irregular | |
Potuakhali | 21.898 | 19.7 | High | Irregular | |
Sitakunda | 26.863 | 18.278 | Strong | Irregular | |
Sandwip | 30.909 | 19.611 | Very Strong | Irregular |
NER, mostly strong intensity in SER and low to very strong intensity in SWR (
Spatial distribution of trends for annual rainfall amounts (mm), annual number of rainy days, SDII and PCI over the study period of 1971-2010 is shown in
Spatial distribution of trends of (a) Annual rainfall amounts (mm) (b) Annual number of rainy days (c) SDII and (d) PCI over the study period of 1971-2010 (+ for positive, − for negative, o for negative and significant, ∆ for positive and significant change)
As a whole, this paper shows a guideline to apply climate indices in detecting intensity and temporal distribution of precipitation pattern on regional scale. Our analysis yields some grimacing results for South Eastern Region (SER) and a little part of South Western Region (SWR) (Sitakunda, Sandwip, Hatya) since precipitation is of high intensity and strong irregularity there. Such irregular and highly intensive precipitation can have high erosion-potential. This may reduce the elevation of these regions over mean sea level making it prone to being engulfed by the sea as sea water surface is continually rising due to climate change [
Due to erosion, silt charges in river water increase. Consequently conveyance capacity of river gets low as silts settle down to the river bed. This can cause longer lasting floods. Moreover, due to sudden highly intensive rainfall, flash floods and water clogging may occur in cities due to the lack of drainage facilities. Bangladesh is a country which largely depends on its agriculture and hydroelectricity. So the findings of this study can also be useful for developing various decision-support tools in different hydrologic, ecological and agricultural applications.
Contribution of Bangladesh Meteorological Department (BMD) for providing the necessary meteorological data is acknowledged here.