CLIMATE CHANGE EFFECT ON POTENTIAL EVAPOTRANSPIRATION … · Potential evapotranspiration study is crucial for irrigation and water resources development and management in Bangladesh

Proceedings on International Conference on Disaster Risk Management,

Dhaka, Bangladesh, January 12-14, 2019

Page | 319

CLIMATE CHANGE EFFECT ON POTENTIAL EVAPOTRANSPIRATION IN

BANGLADESH

Jannatun Nahar Jerin1and A.R.M. Towfiqul Islam1

ABSTRACT

Potential evapotranspiration (ETo) is considered one of the key factors under changing climate which plays a

pivotal role in irrigation schedule and water resource management. However, how much ETo affected by

climate change has been less understood in Bangladesh. This study aimed to estimate monthly, annual and

decadal ETo using the FAO-56 Penman-Monteith (FAO-56 PM) model based on daily data of 25

meteorological stations across Bangladesh from 1975-2017. To detect ETo trends, the Mann-Kendall (MK)

technique was employed to compare trends with standard. The Sen’s slope estimator was adopted to calculate

the magnitude of trend lines. The results show that approximately 82.67% of the monthly ETo time series had

declined trends, out of which 39.67%, 16.33% and 31% of monthly ETo time series were shown statistically

significant at 0.10, 0.01 and 0.05 levels, respectively. Only 17.33% of the monthly ETo time series had

demonstrated a significant increasing trend. The dominant significant increased trend (p<0.01 level) was

found in February at the Chittagong station, while the dominant significant decreased trend (p<0.01) was

noticed at Mymensing station. Based on an annual and decadal timescale, over 92% and 73% of the stations

had the decreased trend lines; but the ascending trends were observed at Rangamati, Barisal and Bhola

stations, respectively. As a whole, ETo are moving on a decline tendency pattern for almost every station over

Bangladesh; suggesting a paradoxical shifting under background of the global warming.

Keywords: ETo trend, Water use, Bangladesh, Global warming, Penman-Monteith model

Introduction

Potential evapotranspiration study is crucial for irrigation and water resources development and management

in Bangladesh. A better assessment of potential evapotranspiration is important for efficient irrigation

management, crop production, environmental assessment, ecosystem modelers and solar energy system.

According to the fifth assessment report of the Intergovernmental Panel on Climate Change (IPCC), the global

warming trend, which is mainly caused by the increasing amount of greenhouse gas emissions, will increase

continue in future 2100 (IPCC 2014). Bangladesh is already experiencing the effects of climate variability

and climate change (Ali 1996; Mirza 2002; Karim and Mimura 2008; Climate Change Cell 2008,). Different

sectors of natural system such as agriculture, hydrology, and ecosystem have been affected by climate change.

Potential evapotranspiration is expected to increase due to temperature increases. However, the decrease of

observed ETo has been widely detected over recent decades in many areas (Yin et al. 2010;Limjirakan and

Limsakul2012; Darshana et al. 2013). This phenomenon of a discrepancy between expected and observed

trends in evaporation is known as the ‘‘evaporation paradox’ ’Evapotranspiration (ET), one of the primary

elements of the hydrologic cycle that is influenced by climate change. There are a number of climatic

parameters that have been identified to change in ETo. The variation of ETo not only influence by the increased

air temperature, but also other primary climatic factors such as relative humidity, vapor pressure, wind speed

and solar radiation. The changing of ETo at particular station may be influenced by these meteorological

parameters. Although different aspects of ETo in different parts of Bangladesh have been investigated to

some extent; however, no detailed study has yet been conducted on impact of ETo trend in Bangladesh. This

region is known as an agricultural region. Rain-fed cereal production is done most part of the Bangladesh.

Unfortunately, some area has suffered from prolonged droughts during summer season in recent years.

Moreover, over exploitation of groundwater has depleted underground water reservoirs. Drastic shrinkage of

major rivers has created serious problems in its ecosystem and hydrologic balance. In order to meet these

challenges, it seems that a study to detect trends in ETo in Bangladesh is needed.

The objective of this study was to detect monthly, annual and decadal trends of ETo using the FAO-56

Penman-Monteith (FAO-56 PM) model based on daily 25 meteorological stations data in Bangladesh in the

past 4 decades and estimate their magnitudes from 1975-2017 using non-parametric methods, such as MK

and Sen’s estimator.

1 Department of Disaster Management, Begum Rokeya University, Rangpur 5400, Bangladesh

Author:[email protected], *Corresponding author: [email protected]



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Methodology

Study area

Bangladesh Meteorological Department (BMD) has

34 weather stations. In this study, 25 stations were

selected (Fig.1) to calculate ETo.Data were checked

for quality. These data were used to estimate daily

ETo using the FAO-56 PM method.

Figure. 1: Location of the study area and the

meteorological stations.The dataset was provided by

BMD.

Methods

FAO-56 Penman-Monteith (PM) method is considered as a standard approach to calculate ET0, which reads:

𝐸𝑇𝑜 =0.408∆(𝑅𝑛−𝐺)+𝛾

900(𝑇+273)

𝑢2(𝑒𝑠−𝑒𝑎)

∆+𝛾(1+0.34𝑢2)…………………. (1)

where ET0: potential evapotranspiration [mm day−1]; Rn: net radiation at the crop surface[MJm−2

day−1];G:soil heat flux [MJ m−2 day−1]; T: daily mean air temperature at 2 m height [°C]; u2: wind speed

at 2 m height [m s−1]; es: saturation vapor pressure[kPa];ea: actual vapor pressure [kPa]; es −ea: saturation

vapor pressure deficit [kPa]; Δ: slope of the vapor pressure curve [kPa °C−1]; and γ: psychrometric constant

[kPa °C−1].

The detailed method of MK and Sen’s estimator can be found in the literature (Rahman et al. 2016). In order

to investigate the spatial variability of ET0, the inverse distance method (IDW) was used. This method

directly implements the assumption that a value of an attribute at a non-sampled location is a weighted average

of known data points occurring within a local neighborhood surrounding the non-sampled location. That is,

things close to one another are more alike than those that are far away. It is known as an exact interpolator

scheme (Burrough and McDonnell 1998).

Results

Preliminary observations and analysis

Table 1 represents descriptive statistics summary of monthly ET0 data obtained from historical climate of all

the stations along with their locations, data standard errors (SE), Range, standard deviation (SD) and

coefficient of variances.

Monthly ETo trends

The spatial distribution of monthly ETo values in the selected station represented in Figs.2 and 3.During the

study period from 1975 to 2017, the highest value of monthly ETo was 236 mm/month (in May), while the

lowest monthly ETo value was 41.2 mm/month (in December).The lowest value of Z in monthly time series

was observed in January at the Mymensing Station (Z=-5.46). On the other hand, some of the monthly ETo

times series had upward trends in which, the largest one belonged to the Chittagong station in February which

was equal to 3.27 (p<0.01) [Table-2]. This station is located in the south-eastern region of Bangladesh.

Annually ETo trends

By summing the monthly values of ETo at each of the selected stations, the annual ETo values obtained [Fig

4]. The overall average of annual ETo was found as 1374.1 mm. As can be seen from the last column of Table

2, above 92% of the stations exhibited downward trends (Table 2). However, only 8% of the stations showed

upward increasing trends. This increased up to 4 % at the 10% level. The largest and smallest values of Z

were equal to 3.14 (p < 0.01) and – 4.65 (p < 0.10), respectively. These two opposite trends belonged to the



Page | 321

cox’s bazar and Hatia stations, respectively.

Table 1: Detailed the geographic and statistical characteristics of the selected stations across Bangladesh

Note that the values in the Table are statistical characteristics. Lat. Means Latitude, Long means Longitude,

Alt means altitude, mamsl meters above the mean sea level, N means north, E means east.

Figure 2. Spatial distribution of monthly ET0 (mm) in the 1st six months

Station

name

Lat.

(N)

Long.

(E)

Alt.,

m amsl

Mean

(m)

Median

(m) Skew Range

Standard

Deviation

Standard

Error Kurtosis Barisal 22.43 90.22 2.1 108.96 106.00 0.43 122.50 25.64 1.13 -0.54

Bogra 24.51 89.22 17.9 113.46 113.05 0.18 160.60 28.62 1.26 -0.50

Comilla 23.26 91.11 7.5 112.20 113.65 0.19 132.70 25.09 1.10 -0.30

Chandpur 23.14 90.42 4.88 113.55 113.20 0.18 129.10 26.05 1.15 -0.65

Feni 23.02 91.25 6.4 112.87 113.36 0.17 130.90 25.43 1.12 -0.51

Coxs bazaar 21.27 91.58 2.1 122.44 117.60 0.69 106.50 22.39 0.99 -0.38

Chittagong 22.13 91.48 5.5 125.74 121.60 0.52 117.50 22.91 1.01 -0.28

Swandip 22.29 91.26 2.1 124.09 118.80 0.69 98.55 21.87 0.96 -0.40

M.court 22.52 91.06 4.87 121.87 117.34 0.59 102.25 22.14 0.97 -0.39

Khepupara 23.59 90.41 1.83 117.99 113.31 0.63 108.10 21.87 0.96 -0.42

Faridpur 23.36 89.51 8.1 114.23 112.54 0.21 127.73 26.93 1.19 -0.73

Dhaka 23.46 90.23 8.45 114.90 112.55 0.26 129.40 28.05 1.23 -0.74

Hatia 22.27 91.06 2.44 111.89 108.70 0.53 124.20 22.80 1.00 -0.26

Khulna 22.47 89.34 2.1 120.58 114.36 0.59 155.48 33.31 1.47 -0.42

Jessore 23.12 89.2 6.1 124.45 117.80 0.63 167.90 36.21 1.59 -0.33

Satkhira 22.43 89.05 3.96 116.71 111.10 0.54 143.65 30.55 1.34 -0.49

Mymensing 24.44 90.25 18 106.27 106.95 0.17 150.20 26.05 1.15 -0.31

Patuakhali 22.2 90.2 1.5 109.04 105.30 0.50 113.20 23.69 1.04 -0.52

Bhola 22.41 90.39 4.3 109.00 105.88 0.46 116.40 24.41 1.07 -0.56

Rajshahi 24.22 88.42 19.5 115.76 113.90 0.42 183.00 32.30 1.42 -0.23

Ishurdi 24.09 89.02 12.9 114.61 114.00 0.28 171.80 30.18 1.33 -0.42

Rangamati 22.22 92.09 68.89 111.07 109.60 0.25 109.20 24.70 1.09 -0.65

Rangpur 25.44 89.16 32.61 107.72 110.00 0.00 136.20 27.92 1.23 -0.70

Srimongol 24.18 91.44 21.95 104.69 108.00 -0.12 145.00 29.03 1.28 -0.85

Sylhet 24.54 91.53 33.53 108.63 106.00 0.42 119.60 21.24 0.94 -0.18



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Figure 3. Spatial distribution of monthly ET0 (mm) in the 2nd six months

Figure 4. Spatial distribution of Annual ETo (mm) in Bangladesh

Figure 5. Spatial distribution of decadal ETo in Bangladesh



Page | 323

Decadal ETo trend

The spatial distribution of decadal ETo values in the selected station represented in Fig 5. By summing the

10 years values of ETo at each of the selected stations, the decadal ETo values obtained. The overall average

of decadal ETo value was found as 13727.8 mm.The highest value was 15954.1 mm in Jessore station during

1st decade (1975-1984), whereas the lowest was 11936.6 mm located in Mymensing station during 4th decade

(2005-2014)..Results indicated that at the decadal timescale, about 73% of the stations exhibited decrease

trend. Among them, 14 and 19% showed decreasing ETo trends at 5 and 10% significance level respectively.

Meanwhile, only 27% of the decadal ETo time series had displayed an increasing trend (Table-2).

Table 2. The values of MK Z statistics for the ETo in monthly, annual and decadal timescales across

Bangladesh

Note that the values in the Table are Z statistics. Significant trends (at the p<0.10 level) indicated by bold

numbers, significant trends (at the p<0.05 level) indicated by an asterisk on the numbers, and significant

trends (at the p<0.01 level) indicated by italicized numbers

Station Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Annual D1 D2 D3 D4

Barisal -

3.30* -0.06 -1.89 -0.29 -0.33 0.49 1.85 -0.07 -1.10 -0.74 -1.77 -3.77* -1.65* -1.61 -1.43 -0.18 -0.89

Bogra -5.12 -

2.02*

-

3.06* -1.88 -0.47 -0.47 1.85 -1.32 -0.24 -1.59 -2.37* -3.45* -4.05*

-

2.15* -0.18 -1.43 -0.36

Comilla -4.27 -1.49 -

2.22* -0.47 -1.97* 0.49 1.75 -0.33 -0.38 -2.14* -0.86 -3.60* -1.61

-

1.97*

-

2.15* 1.43 -0.72

Chadpur -4.68 -1.11 -1.51 -1.35 -1.25 -0.32 1.87 -1.37 -1.43 -2.73* -1.79 -3.91 -2.28 -

2.15* -0.72 1.07 -1.07

Feni -4.43 -1.38 -1.81 -1.00 -1.44 -0.07 1.64 -0.67 -1.16 -2.59* -1.77 -3.76* -1.80* -

2.15* -1.43 1.25 -0.72

Coxsbazar -1.12 0.85 -1.25 1.25 0.06 1.84 1.19 1.06 -0.63 -0.95 -1.04 -1.65 3.14 -0.54 -0.36 -1.25 -

1.79*

Chittagong 3.12* 3.27* -0.02 -2.00* -5.42 -1.13 -

0.38

-

2.65* -2.41* -4.94 -3.39* -0.97 -2.53 0.36 -1.43 -0.54 -0.72

Swandip -1.76 0.79 -1.51 0.03 -1.11 1.40 0.45 -0.27 -1.89 -1.92 -1.22 -1.65 -1.80 -0.18 -1.25 -0.89 -1.61

M.court -

2.18* -0.17

-

2.41* -1.40 -2.20* 0.21

-

0.18 -1.42 -2.38* -2.43* -1.42 -1.85 -3.10 -0.36 -1.25 -0.72 -1.07

Khepupara -

3.09* -0.90

-

2.83* -1.17 -2.30* 0.61 0.36 -0.86 -1.99* -2.74* -2.39* -2.69* -3.50* -0.36 -1.61 -1.07 -1.61

Faridpur -4.17 -0.90 -1.32 -1.66 -1.02 -0.43 1.66 -

2.38* -1.96 -2.68* -2.21* -3.75* -2.51* -1.25 -0.54 1.07 -1.07

Dhaka -

3.49* -0.27 -0.93 -1.66 -0.68 -0.69 1.37

-

2.56* -2.20* -2.64* -1.93 -2.55* -2.22* -1.25 -0.18 1.25 -1.07

Hatiya -

3.31*

-

2.56*

-

3.34* -2.30* -2.48* -0.90 0.39 -1.06 -2.11* -3.41* -2.77* -3.67* -4.65* -0.72

-

2.15* -0.72 -1.25

Khulna -4.72 -1.92 -

2.30* -1.92 -0.36 0.96 1.85 -0.36 -0.86 -2.96* -3.86* -4.85 -3.39

-

2.50*

-

1.97* 0.01 1.07

Jessore -4.61 -

2.04*

-

2.30* -2.05* -0.69 1.25 1.80 -0.74 -0.97 -3.33* -4.10 -4.77 -3.58

-

2.50*

-

1.97* 0.18 1.43

Sathkhira -4.39 -1.45 -

2.46* -1.98* -0.52 0.64 1.65 -0.42 -1.38 -2.71* -3.59* -5.14 -3.08 -1.79

-

1.79* 0.18 0.72

Mymensing -5.46 -

3.20*

-

2.46* -1.98* -0.52 0.64 1.65 -0.42 -1.38 -2.71* -3.59* -5.14 -4.20 0.01 -1.35 -1.61 -1.07

Patuakhali -

3.61*

-

2.01*

-

2.23* -1.55 -1.55 -1.28

-

0.37

-

2.01* -1.94 -1.50 -1.35 -4.03* -1.40 0.18

-

1.97* 1.07 -0.36

Bhula -

3.88* -1.41

-

2.39* -1.28 -0.95 -0.71 0.26 -1.55 -1.88 -1.79 -1.77 -4.66* -1.78* -1.25

-

1.79* 0.72 -0.54

Rajshahi -

5.00* -1.85

-

2.08* -2.42* -1.79 -0.12 1.49 -1.00 -0.89 -2.91* -3.22* -5.09* -3.67* 1.07 -1.25 0.18 0.72

Ishurdi -

5.07*

-

2.52*

-

2.67* -2.45* -0.93 -0.51 1.62 -1.18 -0.93 -2.49* -3.24* -4.50* -4.04* -0.36 -0.72 0.01 0.54

Rangamati -1.66 1.06 -0.98 -0.98 -1.55 0.45 1.49 -0.87 -0.25 -2.94* -2.73* -3.04* -1.64* -

2.15* -0.18 0.36 -0.72

Rangpur -

4.62*

-

2.16*

-

3.69* -3.18* -1.05 -1.59 1.55 -0.76 -0.08 -1.04 -1.52 -1.32 -3.99 0.72 0.01

-

2.15* -0.72

Srimonggol 0.86 1.75 0.63 -0.50 -1.98* -0.76 1.11 -0.28 0.09 -0.15 0.79 0.51 3.14 1.53 -

1.79* -0.18 0.01

Sylhet -

2.51* 0.86 0.46 0.50 -0.43 -0.45 1.84 -0.65 0.99 -0.96 -1.75 -0.91 -0.31

-

2.33* -1.61 1.43 -0.18



Page | 324

Discussion

ETo trend analysis and its driving factors

all climatic factors don’t influence the ETo change equally. ETo is positively related with temperature, solar

radiation, wind speed and negatively correlated with relative humidity and rainfall.

Impact on ecology and vegetation restorations

The change in ETo trends would affect regional ecology. We found that monthly, annual and decadal ETo

had a decreasing trend which represents a decline in evaporative demand. Decreases in wind speed may be

responsible for weak atmospheric circulation. Increasing

drought would hinder the growth of vegetation and may

even lead to increase soil erosion in this fragile ecosystem

in the study area.

Influence on water resources and agricultural

production

The change in ETo trends would affect regional

agricultural production and food security et al.2018.The

quality of groundwater is repo. Rising ETo trends in the

upcoming years will adversely affect the agricultural

production of the country (Rahman rted to have become

poor in the catchment area located in the study area. It is

mentioned that Chola Lake, which is known as the Bay of

Bengal in the world, would vanish in foreseeable future.

Conclusion

Possible impact of ETo in Bangladesh due to climate change in past four decades are observed in this study.

The study shows that a significant decreasing ETo trend most of the selected station.ET0 times series is

changing from positive to negative in upcoming years, which significantly influenced the change in ETo

trends in future.The findings of the study would contribute in irrigation water management and planning of

the country and also in furthering the climate change study using modelled data in the context of Bangladesh.

In order to conserve available fresh water and save the sustainability of water-related activities in the study

area, efficient practical decisions should immediately be made by decision-makers.

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CLIMATE CHANGE EFFECT ON POTENTIAL EVAPOTRANSPIRATION … · Potential evapotranspiration study is crucial for irrigation and water resources development and management in Bangladesh