Decreasing trends in annual rainfalls over Indonesia: A threat for the national water resource?

Transcription

1 Decreasing trends in annual rainfalls over Indonesia: A threat for the national water resource? Edvin Aldrian 1 Agency for the Assessment and Application of Technology, Abstract This study diagnoses and identifies climatic climate trends on the annual accumulated rainfall all over Indonesia. For that purposes, we investigated trends from 63 rainfall stations whose data varies from the earliest of 1950 and the latest 1974 until 1997 using the Mann Kendall trend test and the linear regression of Sen s estimate. On average the annual data spans at about 43.1 years. Except for stations in Lesser Sunda Islands and an eastern coast of Java, all stations show decreasing trend of annual rainfall accumulation during last decades. The decrease varies among stations in different regions and Kalimantan is the most susceptible area for such a trend with the largest potential water lost yearly at about million m 3 on average. It was found out that the largest decrease of trend occurs in Bengkulu with mm/year from 1968 to 1997 alone. Introduction Global climate change and its impacts have attracted many scientists and researchers. Focuses are given to the availability of the almost all fresh water supply needed by human being, the rainfall. For the regional scale such as the maritime continent Indonesia, a study on climate variability in relation to the climatic trend or water availability has not been performed. In their report, IPCC (2001) predicted a small change (below 5%) of rainfall over the Southeast region as resulted from inter-model consistency. What happen in reality with this climate model prediction has until now still not been studied. Has the climate change in reality reduced or increased rainfall over this region during past century or decades? Such a question is very important nowadays, since during past decades, pressure on demographic and population tension as well as agriculture and land cover change has put some areas of the world in water deficits. In this region, Suppiah et al. (1998) and later Haylock and Nicholls (2000) has studied and reported an increasing trend of the Australia. Furthermore, such a study does not exist for other countries in the region or the Southeast Asia. The closest effort was made by Manton et al. (2001) on trends of daily rainfall and temperature extreme of 1 Peneliti Madya at UPTHB BPP Teknologi, Jl. MH Thamrin no 8, Jakarta aldrian@lycos.com

2 Southeast Asia and Australia. They used 6 rainfall stations over Indonesia (Pangkal Pinang, Jakarta, Balikpapan, Manado, Ambon and Palu) and concluded that there were no significant trends in any of the extreme rainfall indices in Indonesia, and homogenous temperature data were not available. The goal of this study is to diagnose climatic changes (in the time scale of more than 30 years) of the annual Indonesian rainfall. Focuses of the study is the total accumulated annual rainfall, thus disregard the monthly and seasonal variability, and the identification of the climatic trend detected from rainfall data all over Indonesia. In doing so the study is divided into chapters of: Data and methods, which would introduce data source as well as the methods used for deriving the results. Then, we presents the results from those method and discuss possible implications and some small interpretation of the spatial and temporal differences and how the relation of this study to the question of water availability. Finally, the last chapter concludes the whole study with some remarks. Data and Methods 2.1 Data Data from 63 rainfall stations all over Indonesia from the Badan Meteorologi dan Geofisika (Indonesian Meteorology and Geophysics Bureau) are used in this study. Formerly similar data were used by Kirono and Tapper (1999) and Kirono et al. (1999), Haylock and McBride (2001), Hendon (2003). Data length varies according to availability from only 19 years in Agats to maximum of 48 years from 1950 to On average, data span reaches 43.1 years. Figure 1. Locations of 63 rain gauge stations (dark triangles) all over Indonesia.

3 The data have passed several homogeneity tests and were subjected to a statistical test of Buishand (1982) to check for artificial jumps, outliers, and trends in the monthly series. This method examines the cumulative deviation of the series from the mean, tested against critical values derived from randomly generated samples. The test assumes a normally distributed series, a condition that was examined using a Kolmogorov Smirnov test on the monthly and seasonal series. Stations failing the Buishand test were not included in the final set. The monthly data were accumulated into annual data and feed into the two trend detection methods described below. The two methods developed and described by Salmi et al. (2002). First the presence of a monotonic increasing or decreasing trend is tested with the nonparametric Mann-Kendall test and secondly the slope of a linear trend is estimated with the nonparametric Sen s method (Gilbert 1987). These methods are here used in their basic forms; the Mann-Kendall test is suitable for cases where the trend may be assumed to be monotonic and thus no seasonal or other cycle is present in the data. In our case, we are dealing only with annual accumulated rainfall data, disregard of the monthly and seasonal variation. The Sen s method uses a linear model to estimate the slope of the trend and the variance of the residuals should be constant in time. These methods offer many advantages that have made them useful in analyzing climate data. Missing values are allowed and the data need not conform to any particular distribution. Besides, the Sen s method is not greatly affected by single data errors or outliers. 2.2 Mann-Kendall test The Mann-Kendall test is applicable in cases when the data values xi of a time series can be assumed to obey the model x = f ( ) + ε (1) i t i i where f(t i ) is a continuous monotonic increasing or decreasing function of time and the residuals ε i can be assumed to be from the same distribution with zero mean. It is therefore assumed that the variance of the distribution is constant in time. We want to test the null hypothesis of no trend, H o, i.e. the observations x i are randomly ordered in time, against the alternative hypothesis, H 1, where there is an increasing or decreasing monotonic trend. Our computation exploits the normal approximation (Zstatistics).

4 The Mann-Kendall test statistic S is calculated using the formula), n 1 n S = sgn( x j x k ) (2) k = 1 j= k + 1 where x j and x k are the annual values in years j and k, j > k, respectively, and 1 if x j xk > 0 sgn( x j xk ) = 0 if x j xk = 0 (3) 1 if x j xk < 0 If n is 9 or less, the absolute value of S is compared directly to the theoretical distribution of S derived by Mann and Kendall (Gilbert, 1987). The two-tailed test is used for four different significance levels α: 0.1, 0.05, 0.01 and At certain probability level H o is rejected in favor of H 1 if the absolute value of S equals or exceeds a specified value S α/2, where S α/2 is the smallest S which has the probability less than α/2 to appear in case of no trend. A positive (negative) value of S indicates an upward (downward) trend. The significance level means that there is a 0.1% probability that the values x i are from a random distribution and with that probability we make a mistake when rejecting H o of no trend. Thus the significance level means that the existence of a monotonic trend is very probable. Respectively the significance level 0.1 means that there is a 10% probability that we make a mistake when rejecting H o. The variance of S is computed by the following equation which takes into account that ties may be present: q 1 VAR( S) = n( n 1)(2n + 5) t p ( t p 1)(2t p + 5) (4) 18 p= 1 Here q is the number of tied groups and t p is the number of data values in the p th group. The values of S and VAR(S) are used to compute the test statistic Z as follows S 1 if S > 0 VAR( S) Z = 0 if S = 0 (5) S + 1 if S < 0 VAR( S) The presence of a statistically significant trend is evaluated using the Z value. A positive (negative) value of Z indicates an upward (downward) trend. The statistic Z has a normal distribution. To test for either an upward or downward monotone trend (a two-tailed test) at α level of significance, H 0 is rejected if the absolute value of Z is greater than Z 1-α/2, where Z 1-α/2

5 is obtained from the standard normal cumulative distribution tables. In our computation the tested significance levels α are 0.001, 0.01, 0.05 and 0.1, which is represented in our results, respectively, by ***, **, *, + (see Table 1). 2.3 The linear regression Sen s method To estimate the true slope of an existing trend (change per year) the Sen's non parametric method is used. The Sen s method can be used in cases where the trend can be assumed to be linear. This means that f(t) in equation (1) is equal to f ( t) = Qt + B (6) where Q is the slope and B is a constant. To get the slope estimate Q in equation (6) we first calculate the slopes of all data value pairs x j xk Qi = (7) j k where j>k. If there are n values x j in the time series we get as many as N = n(n-1)/2 slope estimates Q i. The Sen s estimator of slope is the median of these N values of Q i. The N values of Q i are ranked from the smallest to the largest and the Sen s estimator is or Q = Q[ ( N +1) / 2], if N is odd (8) ( Q + Q ) if N is even 1 Q = [ N / 2] [( N + 2) / 2], (9) 2 A 100(1-α)% two-sided confidence interval about the slope estimate is obtained by the non parametric technique based on the normal distribution. The method is valid for n as small as 10 unless there are many ties. To obtain an estimate of B in equation (6) the n values of differences x i Q ti are calculated. The median of these values gives an estimate of B (Sirois 1998). The estimates for the constant B of lines of the 99% and 95% confidence intervals are calculated by a similar procedure. Results and discussion The result of the application of the Mann Kendall trend test and the Sen s slope estimation is given in Table 1. In that table we have the information of each station and the result of both tests. The performance of Mann-Kendall trend test is given in two parameters the Z values and the significant level. The Z parameter represents the actual trend in decreasing or

6 increasing type. Important parameter is the significant level represent by some note marks. Beside the result of the Mann Kendall trend test, we have as well the linear regression estimation using the Sen s slope estimation. This estimation method gives two parameters the Q and B factors. The Q represents the slope gradient or the actual annual decrease in mm/year, which is the more important parameter between two. In general, as shown in Fig. 2, Z and Q have a linear relationship between each other with several exceptions on large Q values. Table 1. Results from Mann Kendall test and Sen s slope estimation. Period Mann-Kendall trend Sen's slope estimate location No Station name from to n Test Z Signific Q B longitude latitude 1 Banda Aceh , , ,7 95,43 5,52 2 Meulaboh ,473 *** -26, ,5 96,10 4,20 3 Medan ,462-1, ,7 98,48 3,57 4 Sibolga ,847 ** -24, ,1 98,92 1,57 5 Padang ,449-14, ,0 100,35-0,88 6 Pekanbaru , , ,8 101,43 0,47 7 Tanjung Pinang , , ,6 104,50 0,90 8 Jambi ,988 * -11, ,6 103,70-1,60 9 Bengkulu ,229 *** -71, ,7 102,33-3,88 10 Palembang ,120-6, ,7 104,70-2,90 11 Rejosari ,229-7, ,3 105,11-5,15 12 Pangkal Pinang ,347 *** -25, ,7 106,13-2,17 13 Tanjung Pandan ,498 * -17, ,7 107,80-2,80 14 Pontianak ,173 ** -14, ,9 109,40-0,10 15 Ketapang ,204 *** -29, ,0 110,00-1,90 16 Pangkalan Bun ,815 ** -18, ,7 111,70-2,70 17 Nangahpinoh ,889 ** -20, ,2 111,70-0,40 18 Muaratewe ,852 *** -30, ,5 114,80-0,40 19 Banjarmasin ,394 * -15, ,0 114,80-3,40 20 Balikpapan ,302 * -12, ,2 116,90-1,30 21 Tarakan ,409 * -18, ,0 117,57 3,33 22 Palu ,468-3,58 783,3 119,88-0,90 23 Gorontalo ,392-1, ,4 123,10 0,50 24 Manado ,262-9, ,1 124,90 1,50 25 Luwuk ,358-4, ,9 122,70-0,90 26 Poso ,769-17, ,8 120,80-1,40 27 Majene ,767-4, ,4 118,97-3,52 28 Kendari , , ,9 122,40-4,10 29 Bau Bau ,609-13, ,0 122,62-5,47 30 Makassar ,200-9, ,7 119,60-5,10 31 Jakarta ,124 0, ,9 106,82-6,17 32 Bandung ,806 ** -14, ,5 107,60-6,90 33 Jatiwangi ,746 ** -16, ,3 108,27-6,75 34 Tegal ,293 * -10, ,0 109,15-6,85

7 35 Semarang ,844-6, ,0 110,40-7,00 36 Cilacap ,384 * -31, ,2 109,02-7,73 37 Jogjakarta ,421 * -14, ,0 110,26-7,47 38 Madiun ,624-3, ,2 111,52-7,62 39 Banyuwangi ,924 3, ,1 114,40-8,20 40 Kalianget ,641 ** -9, ,5 113,97-7,05 41 Bawean ,564 * -26, ,1 112,63-5,85 42 Denpasar ,880-5, ,0 115,10-8,45 43 Ampenan ,862 5, ,7 116,07-8,53 44 Sumbawa ,059 6, ,7 117,42-8,43 45 Waingapu ,133 0,29 823,2 120,30-9,70 46 Kupang ,178 0, ,7 123,70-10,20 47 Dilli , ,21 978,2 125,60-8,60 48 Saumlaki ,681 ** -14, ,2 131,30-7,98 49 Tual ,311-9, ,1 132,80-5,70 50 Geser , , ,8 113,00-7,00 51 Ambon ,244-13, ,3 128,10-3,70 52 Sanana ,074-1, ,2 126,00-2,30 53 Ternate ,501-27, ,0 127,40 0,80 54 Sorong ,826 *** -36, ,5 131,12-0,93 55 Manokwari ,361-9, ,3 134,05-0,88 56 Biak ,225-7, ,9 136,12-1,18 57 Sarmi ,779 ** -29, ,6 138,75-1,85 58 Sentani ,293 * -7, ,2 140,72-2,37 59 Wamena , , ,6 138,92-4,08 60 Nabire ,746 ** -57, ,5 135,50-3,33 61 Kaimana ,823-8, ,0 133,75-3,67 62 Merauke ,212-5, ,3 140,38-8,47 63 Agats ,225-57, ,2 138,10-5,50 Q 20,0 10,0 0,0-10,0-20,0-30,0-40,0-50,0-60,0-70,0-80,0 y = 7,7888x - 0,4999 R 2 = 0,4651-6,0-5,0-4,0-3,0-2,0-1,0 0,0 1,0 2,0 Z Q Linear (Q) Figure 2. Relationship between the Mann Kendall test Z parameter and the Sen s slope estimator Q. In Table 1, we notice almost unanimous decreasing trends all over the place as shown by the Z and Q parameters except for some stations in the most eastern coast of Java and Lesser

8 Sunda (Nusa Tenggara) Islands. Also, 33 out of 63 (52%) stations have significant level α below 0.1 and all of those significant stations lie on the side of the negative trend. While stations with increasing trend do not reach adequate significant level at all and have a very small annual increase trend or unnoticeable in comparison to their negative trend counterparts. Thus the majority of stations data have absolute or significant decreasing trend. From stations with negative trends, we have the minimum Q values over Kendari and Medan (-1.27 mm/year). However, as is also described in Fig. 2, the minimum value for Q does not in accordance to the Z value where the minimum Z value occurs in Sanana (-0.074). The maximum Q value occur in Bengkulu ( mm/year), which is not in accordance to the maximum Z values (occur in Sorong, i.e ). Furthermore, all Q values below -20 shall have significant level except in Ternate and Agats, which probability due to very high fluctuation in interannual data. Length of data seems do not contribute to the quality of significant level as well, as the high significant level trend sometimes coincides, or mostly with a long data record. In order to analyze the results better, we sub divided the whole area into regions of large islands in Indonesia and two archipelagoes; the Molucca and the Lesser Sunda islands. Thus we have stations over Sumatera from rows 1 to 13, Kalimantan from rows 14 to 21, Sulawesi from rows 22 to 30, Java from rows 31 to 41, Lesser Sunda from rows 42 to 47, Molucca from rows 48 to 53 and Papua from rows 54 to 63. After this division, we notice that large decrease occurs over Sumatera, Kalimantan and Papua, while the lowest decrease occurs over the Molucca archipelago and some parts of Sulawesi. All stations in Kalimantan, most of stations in Java and Sumatra reach the adequate significant level. Thus the decreasing trend is more prominent over Kalimantan. Regions of North Molucca and eastern Sulawesi do not have adequate decreasing trend, indicating a weak trend. According to Aldrian and Susanto (2003), these areas are classified into the anti monsoonal region with a very unique climate, which is different from the rest of the country. The climate over this area is mainly driven by local oceanic phenomenon over the Molucca sea. Moreover, we also see that there is no significant level below 0.1 over the Molucca and most east Sulawesi areas, while such a case does not occur over other areas.

9 All increasing trend occurs in similar region of Lesser Sunda islands and none has the significant level. With exception of Denpasar in Bali, all stations over this region show an unanimous agreement of increasing trend of annual rainfall. This study is limited only to annual data and disregards the monthly and seasonal variations. Further study is needed to understand the phenomena as why this region is an exception for receiving more rainfall than others. A very simple argument in relating to the climate change and global warming phenomena is the increase of tropical cyclones occurrences over North Australia, which largely impacts on accumulated rainfall especially between January and March. In order to prove such a hypothesis, we need to conduct a thorough investigation into monthly data and relates them to the historical cyclone records. mm/year y = x Bengkulu Linear (Bengkulu) mm/year y = x Ketapang Linear (Ketapang) Figure 3. Two examples of significant decreasing annual rainfall trend in Bengkulu of Sumatra and.ketapang of Kalimantan.

10 How serious and dangerous the decrease level that we are studying here? One way to get a slight impression on the problem, we can use the B factor from Table 1 to understand to position of the variability. The B factor represents where the linear regression line crossing the absica or the x-axis 0. For example the maximum decrease level like Bengkulu has the highest B values around 5450 mm/year. Hence the interception to the null level for this station shall take a very long time and probably longer than that of Dilli with B values of mm/year. Thus, such a high decrease level in Bengkulu, although very significant, will have not a serious impact as dangerous as other places. Table 2. Potential annual water lost due to decreasing trend in annual rainfall according to region defined in text. Island group Area Z Q (mm/year) annual water lost from Q x area (million m 3 ) (km 2 )* ave max min ave max min ave min max Sumatra ,24-0,46-4,35-18,00-1,27-71, Kalimantan ** ,00-2,30-4,20-19,89-12,41-30, Sulawesi ,19-0,39-1,87-8,52-1,99-17, Java ,66 0,92-2,81-11,70 3,38-31, Papua *** ,03-0,82-4,83-22,59-5,06-57, *) Source: Wikipedia, the Free encyclopedia ( **) Total Kalimantan km 2 Sarawak km 2 Sabah km 2 Brunei 5765 km 2 ***) Total Papua km 2 - Papua New Guenea km² Another method to calculate the danger of water lost due to the declining climatic trend in the annual rainfall is by calculating the annual water lost from Q data multiplied by the island area as shown in Table 2. From that table the largest potential lost occurs in Kalimantan with an annual average of million m 3 water. Notice that all stations in this island record significant trend levels, thus the result presented here is a very conclusive or trustworthy. Among the five largest islands, Java has the lowest potential lost, which in this case has some contribution of the increasing trend of the Banyuwangi station (minimum lost). Although results over Java and Sulawesi are comparatively similar, however the land area over Java is much smaller, thus the per-km-square decrease over Java is relatively larger than that over Sulawesi. Calculation for the other two regions, the Molucca and Lesser Sunda Islands could not be performed due to lack of geographic resource data. However, this simple and rough calculation could be used as reference of further studies.

11 Conclusions We have diagnosed and identified climatic trend of annual rainfall among rainfall stations in Indonesia. On average the data has span at about 43.1 year in each station for overall 63 stations. The method of analyses used in this study were the Mann Kendall trend test and the linear regression estimation of Spen s estimation. The quality of the Mann Kendall trend test were judge by the significant level as the calculation is given per station. We found out that almost unanimous agreement on decreasing annual rainfall trend of the whole rainfall station investigated except for some stations in eastern Java and the Lesser Sunda Islands. We argued that the increase trend over that area is probably due to increasing trend of strong cyclones between January and March over north Australia. Thus further study on the monthly climate trend and the study of cyclone activity over the area are needed. We found out that Kalimantan is the most susceptible on water lost due to the decreasing trend of annual rainfall during the last century. All stations data over this island recorded a very high significant level and in the potential water lost calculation, this island ranks first among the other five large islands. Java and Sulawesi have the least severe problem with water lost due to declining trend in climate, but in km 2, Sulawesi should be the least susceptible large island in Indonesia. Acknowledgements The author is very grateful to Dr. CDG Kirono for sharing the data and to BMG as the main source of rainfall data in Indonesia. References Aldrian, E., R. D. Susanto, Identification of three dominant rainfall regions within Indonesia and their relationship to sea surface temperature, International Journal of Climatology, 23, Buishand, T.A., 1982: Some methods for testing the homogeneity of rainfall records. J. Hydrol., 58, Gilbert, R.O., Statistical methods for environmental pollution monitoring. Van Nostrand Reinhold, New York. Haylock M, J.L. McBride Spatial coherence and predictability of Indonesian wet season rainfall. Journal of Climate 14: Haylock M, N. Nicholls, Trends in extreme rainfall indices for an updated high quality data set for Australia, Int J Climatol 20:

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