Imperial Journal of Interdisciplinary Research (IJIR) Vol ... · ecosystem, located in the...

Imperial Journal of Interdisciplinary Research (IJIR)

Vol-2, Issue-12, 2016

ISSN: 2454-1362, http://www.onlinejournal.in

Imperial Journal of Interdisciplinary Research (IJIR) Page 2174

Wetland Water Level Analysis and Prediction:

A Case Study on Hakaluki Haor

Imranur Rahman1, Luthfur Rahman

2 & Badhan Talukder

3

Abstract: Wetlands are considered as the world’s most productive ecosystems as they provide a wide

range of economic, societal and ecological

benefits. Hakaluki haor, which is the country's

largest inland freshwater wetland ecosystem owing

to its ecological significance. The water-level

regime of this wetland is regarded to be an

important factor for its ecosystem functioning that

affects differently. This paper mainly focused on

studying the past trend and employing the Time

series analysis in order to forecast which leads to

assess the ecological impact. Juri River, having

connection with Hakaluki haor is brought into

consideration to collect data as a secondary

source. The variation of the water level is found

stable during the time period of January to March

where the highest disparity noticed at the time

period of April to October. The study suggests that

more hydrometric stations should be incorporated

in order to secure an upgrade assessment of the

water level analysis.

1. Introduction

Wetlands are characterized by the areas under

water wholly or partially. It is considered as the

world’s most productive ecosystems as they protect

and improve water quality, provide fish and

wildlife habitats, store floodwaters and maintain

surface water flow during dry periods. Wetlands

are playing an enormous role in production of

ecosystems on the Earth [14], and it provides so

many services for human welfare [31]. Wetlands

hold a great importance in both ecology and inland

freshwater fisheries by supporting a wide range of

invertebrate fauna, providing feeding grounds for

young and growing fish and provide refugia against

predators [3,12]. According to Mitsch and

Gosselink, the wetlands are interfaces between

terrestrial and aquatic ecosystems [20] where the

floodplain is a broad term used to refer to one or

more wetland types [28].

The wetland of Bangladesh is significant in the

world and is home to the number of fish, plants,

birds and other fauna. In Bangladesh around four

million hectares of land are flooded each year

during the monsoon (rainy) season, and over a

large portion of the nation is submerged in an

unusual flood year [2]. It gives the natural

surroundings to more than 260 fish species [27]

and a huge number of moving flying birds [6], and

sustenance for a huge number of family units in

bucolic Bangladesh, especially poor people.

Upwards of 80% of provincial families capture fish

for sustenance or to sell [20,32,13] and around 60%

of creature protein utilization originates from fish

[4].

Haors are floodplain lake and marsh frameworks,

generally portrayed as "bowl-molded depression

among the common levees of a river, that are

overflowing each year by rainstorm surges from

April/May until October" [23]. Hakaluki haor is the

country's largest inland freshwater wetland

ecosystem, located in the Fenchuganj and

Golapganj upazila, sub-district of Sylhet district,

and also Baralekha, Juri and Kulaura upazila in the

Moulvibazar district. There are more than 238

small, medium and large interconnecting beels,

some of which are perennial and others seasonal.

The region is surrounded nearly 4,400 ha by beels

during dry season. Due to precipitation, almost

every part of the haor remains under water, which

exists up to half of the year. Rivers namely Juri,

sonai, Damini, Fanai and Kuiachara are the water

sources of this haor where Juri and Sonai have

originated from India. A range of wildlife and

aquatic resources once supported by the Hakaluki

haor. In any case, lately this has turned into a quick

debased terrain and confronting expanded weights

and dangers from various sources, including over

utilization of its resources by nearby individuals

[8].

The water stages in Hakaluki haor fluctuate

typically as a result of the difference between the

inflow and the outflow. The intensity of

precipitation, the morphological attributes of the

haor and its watershed as well as the temperature

and wind speed (which indicated the water

losses) is the elements immensely affect the

deviation of these magnitudes.

Periodically, at the end of the rainy season wetland

fluctuate by its upper limit where at the end of the

dry season it appears its lower limit. The wetlands

and shallow lakes are the focal points for

evaluating the WLF by a greater part of researchers

[18]. For the compelling survival and well-being of

many species, these natural fluctuations are an

intrinsic feature of haor ecosystems that demand

their biological clock to those variations and

http://www.onlinejournal.in/



Vol-2, Issue-12, 2016


Imperial Journal of Interdisciplinary Research (IJIR) 175

requiring a variety of services for the ecological

community [14,35]. Despite that, extreme WLF has

negative impacts on the human and the ecosystems

[7].

A relatively broad study reveals that excessive as

well as diminishing WLF not only lessens the

ecosystem functioning, but eventually brings the

clean water into the turbid water (Coops et al.

2003, Beklioglu et al. 2007). The alternation of

water level can also be seen as the part of

environmental change and may lead to soil erosion

[30].The extent of fluctuation as well as the

temporal order of the minimum and maximum

water levels and the alternation rate of water level

impacted natural topography of the water level

regime [34]. The wetland formation, activity and

biodiversity are more prone to more critical

impacts by excessive WLF than the ongoing

climate change [1,16].The ecological sustainability

of wetland and rivers are threatened by the

changing flow regimes [19,22,29,36]. A wide range

of spatial and temporal scales of the watershed

ecology is impacted by the flowing water across

the landscape [17,24,25,29,33].

Given this background, the primary goal of this

paper is to recapitulate the current knowledge and

analyse and forecast the water level fluctuation of

Hakaluki haor.

2. Study Area Situated in the north-east of Bangladesh

latitudinally between 24o35’N and 24o45’N and

Longitudinally between 92o00E and 92

o08E,

Hakaluki Haor is a shallow basin settled between

the Patharia and Madhab slopes in the East and the

Bhatera mountains toward the West. Officially,

Hakaluki Haor falls under the administration of two

regions (Moulvibazar and Sylhet), five Upazilas

(Kulaura, Barlekha, Fenchugonj, Paschim Juri, and

Golapgonj), and eleven Unions (Bhatera,

Baramchal, Bhakshimail, Jaifarnagar, Barni,

Talimpur, Sujanagar, Paschim Juri, Gilachhara,

Uttar Bade Pasha, and Sarifganj). Hakaluki Haor is

comprised of more than 238 little, medium and big

interconnecting beels some of which are permanent

and others cyclic (CNRS report).

Figure 1. Hakaluki Haor

3. Methodology

To evaluate the water level oscillation of Hakaluki

hour, the regular and periodical water level data of

the Juri River collected from Bangladesh Water

Development Board (BWDB). The changeable Juri

river leads to impact the water level of Hakaluki

haor, as it is directly being connected and fed by

the Juri river. Raising or lowering river stages

contributes the high water level, which assists to

detect the water level of the Hakaluki haor.The

collected data were processed to make them an

error free and demonstrated in the form of

illustrations and analysed to evaluate the pattern of

deviation by time.




Vol-2, Issue-12, 2016



Time series method is employed to predict the

maximum and minimum water level. This process

involves seeing a pattern in the historical data and

then infer the pattern into the hereafter. The

forecast is based exclusively on past values of the

variable and/or on past forecast errors.Time series

can be disintegrated into three parts, namely Trend

(Tt), Seasonal (St) and Irregular component (It).

Additive model and the Multiplicative model are

the two models of decomposition of time series.

Here, Multiplicative model is used for prediction.

Yt = St x Tt x It

Where,

Yt - the data at period t,

St- the seasonal component at period t,

Tt- the trend-cycle component at period t,

It- the irregular component at period t.

Steps of Forecasting by Multiplicative Time Series

Model:

1. At first, separate the trend –cycle

components from seasonal-irregular

components. Then, calculate the M-period

centred moving averages CMAt.

CMA(M) t= Tt× Ct

2.Separation of seasonal components (St) from

irregular (error) (It) components.

St x It = Yt / CMA(M)t

Thus, leaving only St value by removing

irregularity.

3.Calculate Yt (SA), the seasonally adjusted series

(deseasonalize) which has only seasonal

component removed:

Yt (SA ) = Yt / St = Tt x It

4.Using a simple linear regression analysis by

plotting deseasonalize data as the Y variable

against the X as a time code variable (t). After,

achieving the co-efficient data, then, data trend

component for forecast is obtained by the following

formula:

Tt= Intercept+ Slope* Time code (For

each row)

5.Finally, the forecast is obtained by the following

formula:

Forecast= Seasonal Component (St)

*Trend Component (Tt)

4. Results and Discussions

4.1. Data Analysis

Data of pre-existing sources have been applied in

this case study recorded by the Bangladesh

Agricultural Development Corporation. The data

are evaluated between the year of 2007 and 2015

days. After the average water level fluctuation

(maximum and minimum) of every month is

demonstrated graphically on a yearly basis. It is

manifest that the highest water level lies in the

month as of May to October where the lowest

water level occurred within the month in the

middle of November and December.




Vol-2, Issue-12, 2016



Figures 2-10. Monthly Maximum and Minimum Water Level from 2007 to 2015

The monthly mean water level information in the

period of time between 2007 to 2015 is shown

below. Both the maximum and minimum average

water level show an almost similar trend. The




Vol-2, Issue-12, 2016



three main phases are easily identified in the

illustration. The initial stage what is from January

to March is the most stable period with steady

water level variation. The greatest water level

disparity is found amid April to October, which is

in the medial phase. The ultimate stage, which,

commencing from November shows the

declining course of water level and lasts up to

December. In short, August is overwhelmingly the

peak (11.80) period for water level and November

(7.64) is the month with the mean water level. The

highest and lowest water level was correspondingly

11.80 m and 7.64 m. There were a few surprising

changes took place in the period beginning April to

November due to year to year diversity.

Figure 11. Comparisons of Monthly Water Level from 2007 to 2015

4.2. Data Prediction

The prediction for minimum water level is

exhibited below. In a multiple linear regression

model, the part of the divergence in the dependent

variable accounted by the explanatory variables is

measured by the adjusted coefficient of

determination (AR2). Here, the adjusted R

2 is less

than the coefficient of determination (R2) which

considered to be a good fit to assess. As the value

of “Significance F” is lower (0.0180) than 0.05, then the forecast is statistically significant and

hence multiple regression model is broadly

satisfactory. The model is useful and true to

forecast as the “P-value” for coefficient is also below 0.05.

Figure 12. Illustration of Predicted Minimum Water Level of 2016




Vol-2, Issue-12, 2016



ANOVA

df SS MS F Significance F

Regression 1 2.090130641 2.090131 5.764278 0.01809725

Residual 106 38.43566288 0.362601

Total 107 40.52579352

Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Lower 95.0% Upper 95.0%

Intercept 8.495044059 0.116695834 72.79646 2.48E-92 8.263683225 8.72640489 8.263683225 8.72640489

t 0.004462316 0.001858608 2.400891 0.018097 0.000777444 0.00814719 0.000777444 0.00814719

Table 1. ANOVA Table Result of Predicted Minimum Water Level

5. Conclusion

The present analysis unveils that surface water

level in Hakaluki hoar area changes throughout the

year as regards to time. The highest oscillation

resides in the month starting April to October and

then starts descending from Novemver. January to

March proves the most unwavering periods

concerning the water level. Further probe is

required to contemplate the tendencies of water

level by evaluating the river stage data as of other

rivers. Statistics for more hydrometric stations

within the watershed should be incorporated to

secure an upgrade assessment of the water level.

The linkage between climate and hydrologic trends

should be ascertained by additional works.

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