Impacts identified using Linear Regression Line, Sen’s slope

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Last updated: July 19, 2019

 Impactsof time trends in colombo rainfall pattern on design rain events A.K.D.

Y. AbeywickramaUniversity of Moratuwa, Sri LankaEmail : [email protected]

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T.M.N.

WijayaratnaUniversity of Moratuwa, Sri Lanka Abstract: Seasonal varying rainfall can have a great socio-economic impact on anagricultural dependent tropical country like Sri Lanka. More accurate rainfallprediction can lead for a better performance in water resource management, floodmitigation, agricultural management etc. Slowly varying components in theclimate system as sea surface temperature.

The trends of the precipitationwere identified using Linear Regression Line, Sen’s slope estimator andMann-Kendall methods. It was shown that there is an increasing trend in theaverage intensities and peak intensities. Furthermore southwest monsoon shows adecreasing trend both average intensity and peak intensity while 2ndinter monsoon shows an increasing trend. Keywords:  timetrends; Colombo rainfall; pattern analysis, non-parametric methods; designrain           events1.

   IntroductionSri Lanka is an islandwhich is extends between 5°55? to 9°51?N and 79°42? to 81°53?E and ricecultivation plays a major role in country’s economy as in the case of mostcountries in South and Southeast Asia. Irrigated agriculture contributes to 22%of Sri Lankan exports while 75% of exports are powered from the national gridelectricity which 65% is generated via hydropower. So as country which its economy is heavily depends on the rainfall and theavailability of water resources, studies in rainfall pattern analysis is reallyimportant for Sri Lanka. Also these pattern analysis is far moreimportant to manage, to plan and to predict the rainfall related adverseeffects such as high floods due to excessive rainfall and more accuraterainfall forecasting will come in handy in crop cultivation managing. Design ofhydraulic structures such as dams, spillways, culverts, sluice gates etc. alsodepend on the time trends of rainfall pattern.

Even though the country has aconsiderably high average rainfall, due to its seasonal and spatial variabilitythere is some shortage of water for agricultural, hydropower and domestic usefrom time to time. Since this is a tropical country, air temperature doesn’tvaries much throughout a year, except in the upcountry area. Therefore the mainvisible climate change is related to annual rainfall and due to Sri Lanka’slocation it is identified that this rainfall is governed by the seasonalvarying monsoon system in Indian Ocean. The mean annual rainfall can be high as4500-5000mm in high lands and some areas such as southeast and northwest in thecountry it can be low as 800-1200mm. The four monsoon seasons were identifiedas;1st Intermonsoon – from March to AprilSouthwest monsoon – fromMay to September – YALA agricultural season2nd Intermonsoon – from October to NovemberNortheast monsoon –from December to February – MAHA agricultural season However over the pastfew decades due to the extreme environmental pollution, excessive populationgrowth, rise of greenhouse gasses, development projects related to irrigationand agricultural projects the balance of the nature was pushed off a cliff andthe regular rainfall pattern of Sri Lanka was affected in a bad manner. Overthe past few years Sri Lanka was suffered from several severe flood situationswhich damaged several hydraulic structures as well as immeasurable damages forthe public properties. Therefore many believed that the annual precipitationwas increased.

However those disastrous heavy rainfalls were normally followedby heavy droughts which lasts for months. So, actually what happened was eventhough the number of rainfall events were reduced, the intensity of theoccurred rainfalls were massive and this couldn’t tolerate by the hydraulicstructures which was designed without considering the time trends in rainfallpattern.2.   Time Trends in Rainfall PatternAs the capital of thecountry Colombo has a far more importance than the other cities since it is theeconomical centre of the country. Colombo metropolitan area has a population of5.

6 million people in average and over 1 million people come to the city in aday for various purposes. According to the data from the Department ofMeteorology, Colombo approximately gets an average rainfall of 2400mm per yearand 200mm per month. Furthermore May is the wettest month with an average of382mm of rain and the driest month is January with a rainfall of 62mm. Also thehottest and the coldest months are April (29°C) and January (27°C)respectively. In recent past Colombo was frequently flooded even for a 2-4 hourrainfall and it caused a lot of property loss and disabled the whole economy inSri Lanka. So, a proper knowledge in rainfall pattern in Colombo will far morehelpful in flood mitigation process in the city.

Seasurface temperature (SST) in Indian Ocean is directly influenced to the precipitationin Sri Lanka. High sea surface temperatures in western Indian Ocean, IndianOcean Dipole will cause large convergence in lower troposphere which will eventuallycause enhanced rainfall in Sri Lanka. Most studies conducted for the long-termvariations of rainfall in country was influenced by monthly tools. So, an analysiswhich based on daily rainfall data and observe how seasonal rainfall totalsreflect the frequency of daily totals can be more effective in rainfall patternanalysis since it allows to determine the number of times totals exceed a giventhreshold in a given period of time. Also evaporation is lead to the cool downof Sea Surface Temperatures, which cause to the reduction of convection of thefollowing year.Thereare three major characteristics for rainfall as amount, frequency and intensitywhich depend on both time and spatial factors. The annual rainfall received fora particular region may depend on several factors such as depletion of theozone layer, global warming, rate of consumption of fossil fuel, deforestationetc.

For the case study which was done by Karunathilaka and Dabare, 2017, toidentify the changes in rainfall pattern in Sri Lanka, they studied monthlyrainfall data up to 1998 which was recorded at 15 gauging stations. In thisstudy non parametric statistic methods such as Mann-Kendall method followed bySen’s slope estimator, regression line method had been used to identify thetime trends in rainfall pattern. To simplify the study the data set had beendivided into two set according to the time period, i.e. as short-range study (from36 to 50 years) and long-range study (from 98 to 130 years). a.

    Mann-Kendall MethodMann (1945) presented a non-parametrictest for the randomness against time which is widely used to identify thetrends in meteorological and hydrological time series. This is a non-parametricmethod which has been widely used in rainfall trend detection and producedpromising results over the years. The methodology of this test is to determinewhether the value of a random variable is generally increasing or decreasing instatistical terms over a certain period of time. The advantage of this test isthere is no need for the assumption of normality of the random variable andthis test will only determine the direction and not the magnitude of thetrends. However the detectable trends by the Mann-Kendal method may not benecessarily linear but this test is less affected by the outliers due to it isbased on sign of differences and not on the values of the random variables.  b.

    Sen’s Slope MethodSen’sslope method is a non-parametric linear slope estimator. The main advantages ofthis method is that it is not affected by gross data error, missing data oroutliers as in linear regression method. This method is mainly used todetermine the magnitude of the trend line and the method of approach is basedon using the slope medians as an estimation of the overall slope to compute theslopes for all the pairs of ordinal time points.  Procedurefor calculating the slope as a change in measurement per change in time.                                  (1)  The variance for Sen’s slope is determined by following equation.              The ranks of the lower (M1) and upper (M2)confidence limits are given by equation (2) and (3).

             …….(2)            ……. (3)c.    Student’s t-testThis is the parametric test of non-parametriclinear regression method and it considers the linear regression of the randomvariable Y on time X. The regression coefficient ? (the Pearson correlation coefficient) is thecomputed regression line slope coefficient computed from the data.

This givesby the following equation. ………..

(4) 2.4 Trend AnalysisAccording tothe results from the case study in Sri Lanka, to monitor rainfall trends to predictthe adverse impacts. In that mountainous region in Sri Lanka was used. The mainreason for selecting this area is that most of the hydro power generated thisregion and rainfall is the main factor for these. Also, most of the tea estatelocated at there and changes in rainfall is directly affected to theircultivation.

For the analysis, 30 years daily rainfall data in 62 rain gaugeswere used. In here, universal multifactoral analysis was used & trendanalysis was done using fitting a linear regression line for each station.  The results showed that decreasing trend inrainfall. Apart from this, highest decrease in rainfall showed in 1stinter monsoon. 2nd inter monsoon also showed the decrease inrainfall and both south west monsoon & north east monsoon showed mixing ofincreasing & a decreasing trend in rainfall. Similar to the rainfall, rainydays also decreased. So, this reducing cause to increase the rainfall intensityin that area. Also, according to the analysis, they found that duration ofrainfall become shorter.

Due to this, recharge of ground water was decreased& surface water flow was increased. This lead to drying up the smallstreams and canals in that area. So, they were suggested that furtherinvestigation should be carried out for analysis this situation in that area.

Toidentify the time series changing points, a preliminary graphical inspection ishighly helpful and meaningful. The annual rainfall time series, averaged overthe whole dataset and also the corresponding interpolated regression line is needed.Department ofMeteorology predicts rainfall conditions for a period in the daily weatherforecast highlighting the possibility of isolated heavy rainfalls. However itis not easy to predict quantitative weather forecast by using only the conventionalmethods. Because of the importance of the Quantitative Precipitation Estimation(QPE), to estimate the possible rainfall amount.The mainfactor of using 11 indices are can get more accurate and reliable results in agiven area by using more indices. So, according to the test results, increasingtrends are identified & significant in the all indices.

Also, slopesoccurred through the test results were shown statistically insignificant inmost of the cases. Further results show that there is an irregular pattern inthe distribution of both positive and negative slopes.According tothe (I.C. Mercy, 2015) was done the Trend analysis of rainfall in Enugu state,Nigeria. For that, monthly rainfall data were used from 2000 to 2013. Alsomonthly mean precipitation and mean rainy days were used.

This Enugu statearea, the livelihood of the most people are agricultural works. So, most of thepeople are farmers. So, identification of changes in rainfall is very importantfor their occupations. During therainy seasons they have got very heavy rainfall and other time periods, thisarea is mostly very hot and dusty. Rainfall patterns were shown so muchirregularity in this area. This was really affected the livelihood of thefarmers. So, finding these irregularity patterns were done under this study.

Under thestudy, rainfall trend was calculated using the sequential plot using timeseries analysis. This can be presented as follows (where Y is the value of thevariable under consideration at time t).                         Y=f(t)Yt= a + btYt= ?o + ?1t + ?t?t= ?0 + ?0t2.5Average Intensity and Peak IntensityAverage intensity andpeak intensity were calculated for each rain event and graph were plotted forthe relevant time period.

However rather than compare the rainfall events in time series it isbetter to compare the rainfall events according to the seasons. Because someseasons affect much higher than the others. In that case comparison of allevents in one time series does not represent accurate behavior of theprecipitation. Althoughconvectional type rainfall and tropical depression (especially in second intermonsoon) initiating at the Bay of Bengal and heavy rainfall spells occur withinshort time periods are more frequent in this season (B.A.

Malmgren et al.,2003).Also, second inter-monsoon and south-west monsoon was affected much higher thanother monsoon for the Colombo. (Department of Meteorology).

3.   ConclusionIt was shown thatthere is an increase in the high intensity rain events, especially after 2010,all the storm events were identified as high intense rain events. The resultswere shown that there was an increasing trend in both average intensity as wellas in peak intensity. However peak intensity was shown higher increasing trendthan average intensity.

According to the monsoon seasons, south-west monsoonwas shown to increase in the high intensity rain events while 2ndinter monsoon was shown irregularity in the occurrence of high intensity or lowintense rain events. North-east monsoon was starting to affect after 2010 &both storm events were identified as high intense rain events. AcknowledgementIt was a great opportunityfor me to appreciate the guidance of the Dr T.M.N. Wijarathna, the supervisorfor my undergraduate research project.References                            

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