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considering the environmental aspects, rainfall is one of the key parameter. It
is one of the most unpredictable natural parameters that have temporal and
spatial variations. Even though the new methods and technologies are available
to forecast the weather, people are struggling with most of the cases when
forecasting the rainfall.

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Lanka is an island located within the tropics between 5° 55′ to 9° 50′ North
latitude and 79° 42′ to 81° 53′ East longitude. The mean annual rainfall varies
from under 900mm in the driest parts (south-eastern and north-western) to over
5000mm in the wettest parts (western slopes of the central highlands). (Department Of Meteorology, n.d.) So it can be seen that even though the country is spread over a small
area, there is a large variation of the annual rainfall in different part of
the country. This is because rainfall climate of the country is mainly governed
by the seasonally varying monsoon system. Therefore the climate of the island
could be characterized as tropical monsoon climate. According to the monsoon
system, there are two inter monsoon rainfall seasons (first inter monsoon &
second inter monsoon) and two principal rainfall seasons (south-west monsoon
& north-east monsoon).

to these monsoon climate, the precipitation around the country have spatial and
temporal variations.

Kurunegala is a major city which is located in North Western Province.
The main cultivations of the city are coconut, rubber and paddy. Those
cultivations helps for the development of the economy of the country. When
talking about the rainfall climate of the city, there are two rainfall peaks
occur during the year. One in October – November and other in April – May.
Since the area gets a huge rainfall during these peaks, the day to day life of
the people as well as the above mentioned cultivations are massively depend on
the rain. So the prediction of the rainfall should be done with greater
accuracy. If not the socio economic profile of the region will be affected
hugely. So new technically advanced methods should be used to analyse rainfall
data to identify time trends in rainfall patterns to predict the design
rainfall events with greater accuracy. It will help the society to better
preparation and to minimize the extreme rainfall situations.


2.1 Climate change

Climate change is a prominent issue at this moment in the world. The
reason why this issue becomes so important is because of the adverse impacts
that have been affecting the world since the beginning of the 21st century. For
an example; between 1998 and 2001, due to floods in European rivers, there were
700 deaths, half a million trips and losses of 25 billion economic losses
(Thielen, Bartholmes, Ramos, and de Roo, 2009). From the research, he has found that the main reason for climate change is global
warming. The emission of greenhouse gases that has increased with industrial
development is the main source of greenhouse effect and is the main reason for
the increase in temperature. The other main thing that is related to this topic
is the hydrological cycle with extreme rainfall and draft, melting of glaziers,
etc. With the current extreme events that are happening, we can easily identify
that climate change has already been affected.

In 1999 Trenberth discovered that the ultraviolet rays produced by the
greenhouse effect can increase the evaporation of water and this helps to
increase the capacity of the atmosphere to contain more water vapor on hot
days. This is one of the main reasons for the intensification of the
hydrological cycle and increases the frequency of extreme rainfall events
(Trenberth, Climate and climatic extremes, 1999). From a research conducted in 2007 it was discovered that the surface
temperature increased by 0.7 C in the last century with significant warming in
several regions and another thing that found that the rate of increase in
temperature in terrestrial areas was higher than in the oceans
(Trenberth, et al., 2007)


2.2 Rainfall patterns

Rainfall is one of the most unpredictable
climatic variables. Although it varies unpredictably if we analyse rainfall
data over a period of time, it shows some patterns. These patterns may vary
according to the area in which we obtained the data and the methods we use to analyse
(Douka, 2017). Then, rainfall patterns can be spatially identified. For
example, in Sri Lanka we can identify three main parts on the island according
to the rainfall. These are wet, intermediate and dry areas.

1: Climatic zones in Sri Lanka

Not only spatial patterns but also the
temporal patterns of rainfall can also be identified. These temporal variations
in rainfall occur according to how the winds are behaving in the region,
location and the geographical properties of the country. For Sri Lanka the rainfall
is governed by the monsoon system and according to that system Sri Lanka gets
rainfall under four monsoons in four time periods every year. So because of
these patterns it has been easier to predict rainfall events.

But with the climate change these
patterns has started to show slight deviations from their usual paths and
because of that the design rain events is being deviating from the actual
rainfall events. This make the world consider the time trends in rainfall
patterns to predict or design rain events more accurately. For analysing
rainfall data to identify time trends specialized methods should be used such
as Mann-Kendall test, linear regression line method etc. For the analysis,
continuous series of data should be collected from the gauging stations at
least up to 30 years and data should be sorted according to our objective. As
an example if we want to identify trends in annual maximums we have to sort the
data and take the annual maximum rainfall values.


2.3 Data representation

In general, representation of precipitation
data is done using tables and graphs. Data can be present according to various
gauging station to check trends individually.

2: Annual and monthly rainfall (mm)

3: Average annual precipitation

Other than that, various types of graphs
can be used to represent variation of rainfall properties such as intensities,
depth etc and trends with the time.

4:  Average trend in different months


 2.4 Analysing methods

Various parametric and nonparametric
methods have been used to identify the trends in time series of rainfall. Linear
regression line analysis and Mann Kendall test are the methods used mostly to
analyse the hydro meteorological time series among them.


2.4.1 Mann-Kendall test

This test is widely used in different fields of researches for the detection of trend
in time series because of its simplicity and its ability to deal with
missing values and values below a detection limit. Mann-Kendall test equations
are as follows (Ampitiyawatta & Guo, 2010).




Zmk  Mann-Kendall statistics,
n is the length of the data set, Xj and Xi are sequential
data values, m is the number of tied groups (tied group is a set of sample data
with the same value), and t is the number of data points in the mth group.
A positive value of Zmk indicate an increasing trend and negative
value of Zmk indicate a decreasing trend.


2.4.1 Linear regression line analysis

parameters are used to calculate the trend of the rainfall pattern.


  Is the slope of the trend line.

value means a increasing trend & negative value means a decreasing trend.

2.5 Results

Significant results on time trends in
rainfall patterns have been obtained up to now by various researches all around
the world in their respective study areas. (Ampitiyawatta & Guo,
A research was done in the Kalu Ganga basin to identify time trends in that
basin using eight gauging stations and identified following trends in those
gauging stations

Table 1: Mann-Kendall statistic values in         gauging stations

They have also obtained the temporal
distribution of monthly rainfall trends from January to December. The results imply
that the Kalu Ganga basin shows a decreasing rainfall except January.

Table 2: Magnitude of positive, negative
and average trends for different months


3. Conclusion

By considering above mentioned
information, we can conclude that past researches have been able to find the
time trends in rainfall patterns in respective study areas. In Sri Lanka also
considerable number of researches have been conducted for limited areas towards
the same course. But in Sri Lanka those results were not taken to predict the
impact of the time trends on the designing of the rain events. Since the
research is focussed on Kurunegala rainfall pattern and still there are no
proper identified time trends in the area with the ongoing climate changes, the
same procedures will be able used for the data analysis. And for the prediction
of the impact of these trends on design rain events, previously used methods
and the new methods which are found through the previous researches will have
to be checked and then a proper method will be concluded later.



The authors is grateful to the research
supervisor Dr.T.M.N.Wijerathna for the guidance. And to the Department of Meteorology
for the rainfall data and the colleagues for the support.

Post Author: admin


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