SPATIO-TEMPORAL ANALYSIS OF CHANGES IN RAINFALL PATTERN OVER GUJARAT STATE, INDIA

SPATIO-TEMPORAL ANALYSIS OF CHANGES IN RAINFALL PATTERN OVER GUJARAT STATE, INDIA Nirdesh Shah 1, Priyank J. Sharma 2, V. D. Loliyana 3, P. V. Timbadiya 4 and P. L. Patel 5 1 UG Student, Department of Civil Engineering, Institute of Infrastructure Technology Research and Management, Ahmedabad 380026; Email : nirdeshshah13@gmail.com 2 DST INSPIRE Fellow, Department of Civil Engineering, Sardar Vallabhbhai National Institute of Technology Surat, Surat 395007; Email : pjs230688@gmail.com 3 Research Scholar, Department of Civil Engineering, Sardar Vallabhbhai National Institute of Technology Surat, Surat 395007; Email : viraj_nishi@yahoo.com 4 Assistant Professor, Department of Civil Engineering, Sardar Vallabhbhai National Institute of Technology Surat, Surat 395007; Email : pvtimbadiyal@ced.svnit.ac.in 5 Professor, Department of Civil Engineering, Sardar Vallabhbhai National Institute of Technology Surat, Surat 395007; Email : plpatel@ced.svnit.ac.in ABSTRACT In present study, the changes in rainfall pattern on seasonal and annual basis across the Gujarat state have been analyzed. Gujarat state lies on the west coast of India having a geographical area of 1,96,000 km 2 and a fairly long coast line of 1600 km. The geographical extent of Gujarat state exhibits wide variability from sea coast to mountain ranges and from dense forests to barren deserts. Thus, study of rainfall pattern and its variability on spatial and temporal scales is of key importance in resolving water scarcity issues in this geographically diverse region. The monthly rainfall data set of 102 years (from 1901-2002) available for 20 districts have been analyzed for detecting the presence of trends in the time series. The presence of monotonic trends in seasonal (summer, monsoon, winter) and annual rainfall time series were checked by applying non-parametric Mann-Kendall and Sen s slope estimator tests. The results showed decreasing trends for annual, summer and monsoon rainfall; while the winter rainfall showed increasing trend across the state. Moreover, the urban centres like Ahmedabad, Surat and Vadodara showed decrease in annual rainfall, with Rajkot reporting an increasing trend. The present analysis would be useful for implementing water conservation measures in the region to avoid water scarcity and related drought problems in near future given the decrease in amount of total rainfall over the century. Keywords: Rainfall Pattern, Non-parametric test, trend and variability, Gujarat state 1. INTRODUCTION Water is a primary need for humans and animals for their survival. Rainfall is the major source of water utilized by us for different activities. Rainfall is a key component of the hydrological cycle which brings back the transported water from the atmosphere to the surface of the Earth. For a country like India, whose economy is largely dependent on agriculture, the understanding of rainfall variability in space and time domain is very essential. The agricultural activities in India are mostly rain fed, supplemented by artificial supply through canals and extraction of ground water. However, irregular and untimely distribution of rainfall in the country may adversely affect the crop production due to resulting concurrent flood and drought situations in different parts of the country. Many studies have been carried out to investigate the spatial and temporal variability and trends in rainfall, country-wide (Goswami et al., 2006; Rajeevan et al., 2008; Dash et al., 2009; Jain and Kumar, 2012; Mondal et al., 2015), basin-wide (Kothiyari et al., 1997; Singh et al., 2008; Deka et al., 2013; Taxak et al., 2014; Thomas et al., 2015), state/region-wide (Kumar et al., 2005; Krishnakumar et al., 2009; Duhan and Pandey, 2013; Goyal, 2014; Kundu et al., 2015; Meshram et al., 2016; Machiwal et al., 2016), city-wide WRH-61-1

(Kharol et al., 2013; Sonar, 2014). The study of trends in hydroclimatic variables on different spatial scales, viz., country-wide, basin-wide, state/region-wide or city-wide, and temporal scales, viz., monthly, seasonal, annual or decadal, may have different implications and interpretations, and thereby, adequate policy formulation can be undertaken. Singh et al. (2008) studied changes in rainfall and relative humidity over the river basins of Northwest and Central India considering the data from 43 stations. In their analysis, they considered Deesa, Ahmedabad and Surat stations, representing different river basins of Gujarat state. Their study indicated a decrease in annual rainy days for most of the river basins in Gujarat state. Duhan and Pandey (2013) investigated long-term trends in precipitation series across different stations in Madhya Pradesh state, India over a period of 102 years (1901 2002). They reported year 1978 as the most probable change point of annual precipitation across the state. Moreover, annual precipitation was found to decrease by 2.59% over the entire state in 102 years. Goyal (2014) carried out statistical trend tests on seasonal and annual rainfall series for 21 stations of Assam state, India over a period of 102 years (1901 2002). The study reported no significant trend in annual rainfall in the past century, however, the monsoon rainfall exhibited stronger correlation with annual rainfall, followed by summer and winter rainfall. Chandniha et al. (2016) examined the variability in monthly precipitation series of 111 years (1901 2011) of 18 stations in Jharkhand state, India. The homogeneity tests indicated 1949 as the change point, and found increasing trend prior to 1949, while reversal in trend was observed post 1949, in the precipitation across the state. The present study aims to examine the spatial and temporal variability in annual rainfall during period of 102 years (1901 2002) across the Gujarat state, India. 2. MATERIALS AND METHODS 2.1 Study Area and Data Source Gujarat state is located on the West coast of India, spread across a geographical area of 1,96,000 km 2, between longitudes 68 01 to 74 34 N and latitudes 24 41 to 20 54 E. Gujarat is the seventh largest state in India in terms of total area. Physiographically, Gujarat state is divided into five divisions, viz., (i) Plains, (ii) Hilly areas, (iii) Highlands, (iv) Deserts and (v) Coastal areas. Gujarat state has the longest coastline of about 1600 km. The maximum area of Gujarat consists of plains. There are 33 districts in Gujarat state at present, however, for the period of study till year 2002, 25 districts have been considered (see Figure 1). Further, Gujarat state is geographically divided into five regions: (i) North Gujarat (Banaskantha, Patan, Sabarkantha and Mehsana districts), (ii) Central Gujarat (Ahmedabad, Kheda, Panchmahal, Dahod, Gandhinagar, Anand and Vadodara districts), (iv) South Gujarat (Bharuch, Narmada, Surat, Navsari, The Dangs and Valsad districts), and (iv) Saurashtra (Surendranagar, Rajkot, Jamnagar, Porbandar, Junagadh, Amreli and Bhavnagar districts), and (v) Kuchchh (Kuchchh district including Rann of Kuchchh). Due to geographical variations of Gujarat, the climate characteristics of the state also varies. The regions of Central and North Gujarat experience more cold during winters and more heat during summers, while the coastal region experiences a moderate climate. The climate of desert region of Kuchchh is temperate. The rainfall depends on the local winds which change their directions according to the seasons. The winds blowing over the Arabian Sea towards the end of summer season brings monsoon rainfall to the state. Gujarat state has total 185 river basins, out of which Saurashtra region has 71 river basins, Kuchchh region has 97 river basins, while South and Central Gujarat region has only 17 river basins but accounts for most of the surface water reserves of the state. The major rivers of Gujarat draining into Arabian Sea are Narmada, Tapi, Sabarmati, Mahi, Bhadar, Dhadhar, WRH-61-2

Shetrunji and Ambica. The population of Gujarat state according to the 2011 census data is 60,383,628, with a population density of 308 km 2, whereas, the per capita availability per annum is less than 1200 m 3, thus falling under water-stressed category. The water availability varies widely with South and Central Gujarat has 1932 m 3 /person/year while North Gujarat has only 342 m 3 /person/year. The monthly data of rainfall was downloaded for 20 districts of Gujarat state from India Water Portal (www.indiawaterportal.org). However, the data for other five districts, namely, Jamnagar, Junagadh, Porbandar, Navsari and Valsad were not available due to high gaps in the recorded data. Therefore, further analysis have been carried out for 20 districts on annual and seasonal (summer, monsoon, winter) basis. The summer months were considered as February, March, April and May (FMAM); monsoon months as June, July, August and September (JJAS); and winter months as October, November, December and January (ONDJ) in the present analysis. Figure 1: Geographical map of Gujarat state for year 2002 The land use pattern of Gujarat state is shown in Figure 2. It can be seen that major land use is net sown area (agriculture), followed by land not suitable for cultivation (desert) and forests. The major crops produced in the state are tobacco, cotton, groundnuts, rice, wheat, jowar, bajra, maize, tur and gram. The regions of Central and South Gujarat are the most fertile plains owing to alluvial soil deposits as well as better water availability, due to construction of major water resources projects such as Sardar Sarovar, Ukai, Kadana and Dharoi in the region, while the desert regions of Kuchchh are not cultivable. It is seen that half of the total land area of state is crop area, which emphasizes that Gujarat has an agricultural economy. Thus, rainfall plays a very crucial role in social, economic and cultural prosperity of the state. The study of rainfall trends and its variability becomes very important in context of changing climatic conditions, increasing population as well as changing land use pattern over the period of time for sustainability of ecosystem. WRH-61-3

Forests 52.21% 9.83% 19.89% 0.02% 0.06% 4.50% 10.48% 3.01% Not available for cultivation Permanent pastures and other grazing lands Land under miscellaneous tree crops & groves Culturable wasteland Fallow lands other than current fallows Current fallows Net area sown Figure 2: Land use pattern of Gujarat state (Source: India State of Forest Report, 2011) 2.2 Methodology The monthly data has been aggregated to prepare seasonal and annual rainfall series for different districts. The statistical parameters like mean, median, standard deviation, coefficient of variation, maximum, minimum, skewness were computed for these series. Further, the rainfall series were checked for presence of serial correlation (at 5% significance level), and if the series were found to be correlated, pre-whitening was carried out to remove correlation effects. The nature of trend in time series were determined by linear regression method. Further, statistical non-parametric tests, viz., Mann-Kendall (MK) and Sen s slope estimator test were applied to the rainfall series. The Mann-Kendall Z-statistic for time series xt (t = 1, 2,, n) was computed using Eqn. (1), as per the procedure described by (Araghinejad, 2014). The p-value (probability value) for a two-tailed test is computed using Z-statistic, which signifies whether the trends are statistically significant or not, generally at 5% significance level, see Eqn. (3). Sen s slope β-value is another non-parametric estimate for quantifying monotonic trend in hydrologic time series (Hirsch et al., 1982) as per Eqn. (5). The step by step procedure of the methodology adopted in present study is described in Figure 3. The Mann-Kendall (MK) Z-statistic can be computed as: S 1 Var(S) if S > 0 Z = 0 if S = 0 (1) S + 1 Var(S) if S < 0 where, n 1 n S = sgn(x j x k ) k=1 j=k+1 WRH-61-4 (2) The p-value (probability value) is the lowest level (of significance) at which the observed value of the test statistic is significant (Walpole et al., 2016), which is given by: p value = 2 B (3)

Preparation of seasonal (winter, summer and monsoon) and annual rainfall series from monthly data for different districts Preliminary statistical analysis of rainfall data (mean, standard deviation, coefficient of variation, skewness) Yes Check data for presence of serial correlation Pre-whitening the data series No Compute Mann-Kendall (MK) Z- statistic to detect the presence of trend in the time series Compute p-value and check whether trends are statistically significant at 5% significance level Calculate Sen s slope β-value to detect nature of trend in time series Represent the spatial variation in nature of temporal rainfall trends across the Gujarat state Figure 3: Step wise procedure for spatio-temporal trend analysis adopted in present study where, B = 1 2 [1 + 0.196854 Z + 0.115194 Z 2 + 0.000344 Z 3 + 0.019527 Z 4 ] 4 (4) The Sen s slope β-value is given by: 3. RESULTS AND DISCUSSIONS β = Median ( x j x i ) i < j (5) (j i) 3.1 Statistical Characteristics of Annual and Seasonal Rainfall The basic statistical attributes such as mean, standard deviation (SD), coefficient of variation WRH-61-5

(CV) of seasonal and annual rainfall series for the period of 102 years (1901-2002) of Gujarat state were analyzed, see Table 1. It was observed that annual mean rainfall varies greatly from 341.2 mm in Kuchchh district to 1575.0 mm in The Dangs district of Gujarat. It can be seen that most of the rainfall in the state, i.e., around 94.6% is received during the monsoon period only, followed by 4.1% in winter and 1.3% in summer. Thus monsoon rainfall is the main source of water for irrigation, drinking, industrial and domestic purposes. The coefficient of variation (CV), a statistical measure of the dispersion of data points in a data series around the mean, has been computed for all stations in order to investigate spatial pattern of seasonal and annual rainfall variability over Gujarat state. The CV of monsoon rainfall is highest for Kuchchh district (47.1%), which is mainly desert land, and receives very scanty rainfall comparatively. Further, the CV of monsoon rainfall is low for Dang, Dahod, Surat, Vadodara, Panchmahal and Narmada districts, i.e., 24.1%, 27.9%, 28.0%, 28.4%, 29.0% and 29.8% respectively, located in Central and South Gujarat which have considerable forest cover. It is vital to comprehend the variation of rainfall in order to evaluate a detailed assessment of additional water requirements. The variability in rainfall patterns analyzed using CV for 1901-2002 for the Gujarat state designates that inter-annual variability of post-monsoon rainfall is greater than that of annual rainfall, see Table 1. The districts with higher inter-annual variability in rainfall are more vulnerable to hydrological extremes, i.e. floods and droughts (Chandniha et al., 2016). 3.2 Serial Correlation The seasonal and annual rainfall series was checked for presence of serial correation in time series. It is known that the existence of serial correlation in a time series will affect the ability of the test to assess the site significance of a trend (Yue et al., 2003). The lag-one serial correlation was assessed for the prevalence of any existing trends in the time series to prior application of Mann-Kendall test. The winter rainfall series for Banaskantha, Gandhinagar, Kuchchh, Mahesana and Patan districts reported presence of correlation at 5% significance level, which were pre-whitened prior to application of MK test. The summer, monsoon and annual rainfall series did not exhibit serial correlation effects. 3.3 Temporal Rainfall Trend Analysis The statistical non-parametric tests, viz., Mann-Kendall and Sen s slope estimator tests were applied to detect the trends in seasonal (winter, summer and monsoon) and annual rainfall series for Gujarat state using. For period 1901-2002, the MK Z-statistic and Sen s slope β- values district wise for seasonal and annual rainfall series are depicted in Tables 2. A positive Z-statistic value indicates an upward trend in rainfall, while a negative Z-statistic value indicates a downward trend in the time series. The trend tests suggested that 17 districts showed downward trend, while remaining three districts (Amreli, Rajkot and The Dangs) showed upward trend in monsoon rainfall. However, the winter rainfall exhibited increasing trends for all districts except Surendranagar district. The increase in winter rainfall could attribute to the increase in untimely rainfall known as Mavthu in Gujarat. The winter rainfall in the mid or late stage of Rabi season (October March) could damage the standing crops ready for harvesting. Further, the summer rainfall shows decreasing trend at 14 districts, with Bhavnagar district showing statistically significant trend, while remaining six districts (Kuchchh, Vadodara, Bharuch, Narmada, Surat and The Dangs) showing increasing trends. The summer rainfall is occasionally accompanied by strong gusty winds, which result due to development of low pressure area in the Arabian Sea, which could also damage the Mango and other crops. WRH-61-6

Station Mean (mm) Table 1: Preliminary statistics of seasonal and annual rainfall for Gujarat state Winter Summer Monsoon Annual SD CV Mean SD CV Mean SD CV Mean SD (mm) (%) (mm) (mm) (%) (mm) (mm) (%) (mm) (mm) Ahmedabad 27.9 35.4 126.9 8.9 11.9 133.7 563.9 189.0 33.5 600.7 194.9 32.4 Amreli 30.8 44.6 145.1 11.1 16.7 150.7 620.4 246.1 39.7 662.2 256.1 38.7 Anand 29.8 37.1 124.5 9.4 13.3 141.7 647.9 204.1 31.5 687.1 208.6 30.4 Banaskantha 16.6 20.6 124.5 10.2 13.9 136.1 493.0 213.6 43.3 519.7 215.6 41.5 Bharuch 41.8 52.3 125.1 8.6 14.5 168.3 975.4 300.1 30.8 1025.8 306.1 29.8 Bhavnagar 36.0 45.5 126.4 10.8 14.7 135.5 786.5 264.8 33.7 833.3 273.1 32.8 Dahod 33.1 36.5 110.1 9.7 9.2 94.9 909.9 253.5 27.9 952.7 253.6 26.6 Gandhinagar 23.4 28.3 120.9 12.5 17.0 136.2 641.2 226.8 35.4 677.1 231.0 34.1 Kuchchh 18.2 23.7 129.8 6.0 7.4 123.5 317.0 149.3 47.1 341.2 154.0 45.1 Kheda 23.9 28.1 117.9 9.7 12.3 126.9 716.7 226.7 31.6 750.3 228.5 30.5 Mahesana 24.0 28.4 118.4 12.2 15.9 129.7 596.4 216.7 36.3 632.6 221.8 35.1 Narmada 49.7 60.6 122.0 6.7 12.4 183.8 1406.1 418.7 29.8 1462.5 423.0 28.9 Panchmahal 31.2 36.8 118.2 9.4 10.1 107.8 880.7 255.6 29.0 921.2 257.1 27.9 Patan 22.6 30.5 135.0 8.2 11.7 143.0 502.6 214.1 42.6 533.4 219.5 41.2 Rajkot 36.2 51.9 143.4 8.3 12.1 145.4 614.7 227.5 37.0 659.2 242.7 36.8 Sabarkantha 22.3 24.2 108.2 15.0 18.7 125.0 775.5 262.6 33.9 812.8 263.1 32.4 Surat 45.5 51.0 112.2 6.5 11.6 177.4 1075.7 301.1 28.0 1127.7 306.1 27.1 Surendranagar 32.8 45.7 139.2 8.5 11.8 138.0 561.7 200.4 35.7 603.0 210.6 34.9 The Dangs 62.4 54.9 88.0 10.1 13.2 130.3 1502.6 361.8 24.1 1575.0 366.9 23.3 Vadodara 37.7 43.6 115.4 8.1 11.1 136.2 926.8 263.5 28.4 972.7 269.2 27.7 SD Standard Deviation, CV Coefficient of Variation CV (%) WRH-61-7

Station Table 2: Mann-Kendall and Sen s slope statistics of seasonal and annual rainfall for Gujarat state Winter Summer Monsoon Annual Sen s Sen s Sen s MK Z- MK Z- MK Z- MK Z- p-value slope p-value slope p-value slope p-value statistic statistic statistic statistic (β) (β) (β) Ahmedabad 1.541 0.123 0.063-1.799 0.072-0.032-0.740 0.459-0.544-0.526 0.598-0.369 Amreli 1.538 0.124 0.056-1.935 0.053-0.031 0.179 0.858 0.146 0.168 0.867 0.124 Anand 1.466 0.143 0.066-0.642 0.521-0.009-0.925 0.355-0.695-0.711 0.477-0.546 Banaskantha 1.664 0.096 0.062-0.622 0.534-0.010-0.706 0.480-0.530-0.509 0.610-0.391 Bharuch 1.585 0.113 0.091 0.813 0.417 0.008-0.636 0.524-0.635-0.422 0.673-0.457 Bhavnagar 1.425 0.154 0.080-2.586 0.010-0.072-0.127 0.899-0.106 0.000 1.000-0.002 Dahod 1.113 0.266 0.063-0.917 0.360-0.021-1.018 0.309-1.008-1.053 0.293-1.015 Gandhinagar 0.989 0.323 0.058-1.471 0.141-0.042-0.463 0.643-0.376-0.335 0.737-0.228 Kuchchh 1.124 0.262 0.048 0.159 0.874 0.002-0.422 0.673-0.203-0.046 0.963-0.031 Kheda 1.264 0.207 0.049-1.744 0.081-0.033-1.133 0.258-0.881-1.070 0.285-0.842 Mahesana 0.963 0.336 0.077-1.507 0.132-0.040-0.740 0.459-0.593-0.463 0.643-0.373 Narmada 1.648 0.099 0.128 1.304 0.192 0.012-0.416 0.677-0.609-0.387 0.698-0.625 Panchmahal 1.414 0.157 0.065-1.018 0.309-0.022-1.347 0.178-1.348-1.307 0.192-1.299 Patan 1.793 0.073 0.092-0.766 0.444-0.008-0.885 0.377-0.698-0.590 0.555-0.413 Rajkot 1.689 0.091 0.069-1.285 0.199-0.017 0.185 0.854 0.171 0.457 0.647 0.465 Sabarkantha 1.319 0.188 0.048-1.116 0.265-0.032-1.295 0.195-1.098-1.261 0.208-1.031 Surat 1.313 0.190 0.887 1.564 0.118 0.014-0.185 0.854-0.211-0.168 0.867-0.183 Surendranagar -1.408 0.159-0.015-0.590 0.555-0.448-0.121 0.904-0.119-0.121 0.904-0.119 The Dangs 0.960 0.338 0.134 1.698 0.089 0.037 0.613 0.540 0.862 0.648 0.517 1.021 Vadodara 1.637 0.101 0.095 0.150 0.881 0.002-1.018 0.309-0.910-0.925 0.355-0.789 Bold values indicate trends are statistically significant at 5% significance level. Sen s slope (β) WRH-61-8

3.4 Spatial Variability in Rainfall Trends across Gujarat state The spatial variation in the temporal seasonal and annual rainfall trends for Gujarat state are shown in Figures 3 6. It is seen that the winter rainfall exhibits uniform nature in trends across the entire state, barring Surendranagar district. Further, the summer rainfall tends to decrease in most parts of Gujarat, except Kuchchh and some regions of Central and South Gujarat. The monsoon rainfall on other hand is decreasing all across the state with Amreli, Rajkot and The Dangs showing slight increase over the period of 102 years. The annual rainfall reflects the same picture as that of monsoon rainfall, except Bhavnagar district showing no trend over the period of 102 years. The implications of decrease in monsoon rainfall could be significant given the fact that Gujarat already faces water scarcity per capita. The agricultural activities shall also be affected largely with reduction in crop production and state s economy. The Central and South Gujarat regions which receive maximum rainfall compared to other regions of Gujarat are the water storage centers of the state. The major irrigation projects such as Sardar Sarovar, Mahi and Ukai-Kakrapar project provide drinking and irrigation water to most parts of the state. The Sardar Sarovar canal supplies water to the far Saurashtra region as well as North Gujarat and Kuchchh region. The decrease in annual rainfall in these regions will upscale the water demands from these projects and thereby may create regional rifts amongst the citizens of the state. The reduction in forest cover should also be checked, whereas water conservation measures and efficient irrigation techniques should be enforced to save each drop of water. Figure 3: Spatial-temporal variation of trends in winter rainfall over Gujarat WRH-61-9

Figure 4: Spatial-temporal variation of trends in summer rainfall over Gujarat Figure 5: Spatial-temporal variation of trends in monsoon rainfall over Gujarat WRH-61-10

Figure 6: Spatial-temporal variation of trends in annual rainfall over Gujarat 4. CONCLUSIONS The key conclusions derived from the present study are listed as under: (a) The analysis of rainfall records show that there is wide variability in spatial distribution of rainfall across the state, with Kuchchh district receiving annual average rainfall of 341.2 mm, whereas The Dangs district receiving 1575.0 mm rainfall. Moreover, there are wide temporal variations also, viz., the summer and winter rainfalls exhibit very high variability compared to monsoon rainfall across the state. (b) The trends in seasonal and annual rainfall series were analyzed for 20 districts in Gujarat state, India. The results of Mann-Kendall and Sen s slope estimator tests were in agreement for all the cases. The winter rainfall showed overall increasing trend, while the summer and monsoon rainfall showed overall decreasing trend. (c) The annual rainfall has also shown decreasing trend for 16 districts, with three districts (Rajkot, Amreli and The Dangs) showing increasing trend and no trend was found for Bavnagar district. Moreover the urban centres Ahmedabad, Surat and Vadodara showed decrease in annual rainfall, while an increase was reported for Rajkot. (d) The present investigation of long-term rainfall trends shall be useful for agriculture and water resources managers, and shall be of vital importance in policy formation, for efficient and sustainable management of the water resources of Gujarat state. ACKNOWLEDGEMENT The authors are thankful to Centre of Excellence (CoE) on Water Resources and Flood Management, TEQIP- II, Ministry of Human Resources Development (MHRD), Government of India, for providing necessary infrastructural and financial support for conducting the study reported in the paper. The second author wishes to acknowledge the financial support received from Department of Science and Technology (DST), Ministry of Science and Technology, Government of India vide their letter no. DST/INSPIRE Fellowship/2015/IF150634 dated January 11, 2016. WRH-61-11

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