Abstract
The present study comprises the seasonal & annual trends of different climatic variables for Mulde location of Maharashtra state. The trend analysis for different climatic variables such as maximum temperature (TMax), minimum temperature (TMin), rainfall (RF), morning relative humidity (RH I), afternoon relative humidity (RH II), evaporation (EVP), wind speed (WS) and sunshine hours (BSS) was carried out for 25 years from 1991-2015. The trend of TMax was tested and indicated that the trend was significantly increasing except southwest monsoon while in case of TMin trend was non significantly increasing during different periods. In case of rainfall trend was increasing during annual, winter and summer but statistically non-significant at 95 % level of significance and during southwest monsoon and northeast monsoon decreasing trend but statistically non-significant at 95 % level of significance. The trend analysis for RH I indicated that during annual, summer and northeast season the trend was significantly increasing at 95 % level of significance and while RH II, linear regression analysis indicated for all periods the trend was significantly increasing. In case of evaporation, analysis indicated that trends of all periods increasing and statistically significant at 95 % level of significance except summer and northeast monsoon while for BSS, indicated that trend was decreasing and statistically significant at 95 % level of significance except southwest monsoon. In case of WS, analysis showed that trend was significantly decreasing for all periods at 95 % level of significance.
Keywords: Linear regression test, M-K test, Sen’s slope estimator test, Mulde.
Received: 6 April 2020 / Revised: 12 May 2020 / Accepted: 16 June 2020/ Published: 27 July 2020
Contribution/ Originality
The present work on trend analysis using non-parametric test such as MK test was carried out for Mulde Maharashtra. The work was based upon the historical data about 25 years. Very few attempts were made earlier regarding the trend analysis of climatic parameters using MK test. The study will be very useful for water resouces planing, crop planning and for precision agriculture.
1. INTRODUCTION
Trend analysis found great significance for anlysing the repeated extreme weather events during last decade. Due to castophoic changes and climate change the adverse effect and change in the natural phenomenon was occurred during the recent time. The trend analysis tool found very supervisor to study the trends, change and detection of pattern of these changes. The changes such as floods and drought, extreme heat weaves etc. along with trends in various hydrometeorological variables. The study of these events is very essential for future planning, optimization of resources and adaptation strategies for the sustainable utilization of natural resources (Huang, Pang, & Edmunds, 2013; Kampata, Parida, & Moalafhi, 2008; Some'e, Ezani, & Tabari, 2012).Without studying the trends, adoption of farming system to an area might be unsuccessful with the future climatic conditions. Many reserachers work on the trend analysis according to Longobardi and Villani (2009) that trend appeared predominantly negative, both at annual and seasonalscale, except for the summer period in southern Italy and shown high significance of study in the resources planning. Karaburun, Demirci, and Kara (2012) reported that there is a significantly increasing trend in temperature during the spring, summer andautumn; positive trends in the annual mean and meanmaximum temperature with 90 % and 99 %significance level in the Marmara region. Khushu, Singh, Sharma, Sharma, and Kaushal (2012) studied the temperature of five selected locations Jammu region and concluded that warming trend in the region. The study also found that the rate of increase in temperature had adverse effect on most the crops in the region in terms of productivity and ultimately the total production of the state. Based on these review it also observed that the very little work has been done for south Konkan region of Maharashtra specially for Mulde weather station. By viewing the importance of the study an attempt was made for anlysizing the different climatic parameters of Mulde station.
2. MATERIAL AND METHODS
The Konkan region is distinguished from the rest of Maharashtra State by virtue of its disitinct agroclimatic conditions, soil types, topography, its location between the Sahyadri ranges and the Arabian sea. Geographically Mulde located between 16.02 o North Latitude, 73.42o East Longitude and 18 m Altitude from mean sea level. The data of maximum temperature (TMax), minimum temperature (TMin), rainfall (RF), morning relative humidity (RH I), afternoon relative humidity (RH II), evaporation (EVP), wind speed (WS) and sunshine hours (BSS) were collected from Agricultural Research Station, Mulde, Dist. Sindhudurg (M.S.), Dr. Balasaheb sawant konkan Krishi Vidyapeeth, Dapoli. Meteorological data for a period of 25 years (1991-2015) were used in the study.
2.1. Trend Analysis
A trend refers to an association or correlation between concentration and time or spatial location, but can also refer to any population characteristic changing in some predictable mannerwith another variable. Trend takes various forms,such as increasing, decreasing, or periodic (cyclic).
2.2. Linear Regression
Linear regression consists of finding the best fitting straight line through the points. The best fitting line is called a regression line. The formula for a regression line is
y = mx + C, Where, y = Predicted score, m = Slope of the line, x= regression coefficient, C = Intercept.
2.3. Mann-Kendall test (MK Test)
To identify the trends of different climatic variebles the non parametric, MK Test was used. The test was tested with tau coefficient (τ) and sen slope estimator (s). Kendall (τ) is a ratio between actual rating score of correlation, to the maximum possible score. To obtain the rating score for a time series, the dataset is sorted in ascending order according to time and formulated as:
2.4. Sen Slope Estimator Test
Sen’s statistic is the median slope of each point-pairslope in a dataset (Sen, 1968). To perform the complete Sen’s test, several rules and conditionsshould be satisfied; the time series should be equallyspaced, i.e. the interval between data points shouldbe equal. However, Sen’s method considers missing data. The data should be sorted ascending accordingto time, and then apply the following formula to calculate Sen’s slope estimator (Q) as the median of Sen’s matrix members:
Where, Q = Sen’s slope estimator, x = datavalue i and j = Counters, n = Number of data valuesin the series.
3. RESULTS AND DISCUSSION
3.1. Trends of Weather Parameters at Mulde Location
The trend analysis of different climatic variables such as maximum temperature (TMax), minimum temperature (TMin), rainfall (RF), morning relative humidity (RH I), afternoon relative humidity (RH II), evaporation (EVP), wind speed (WS) and sunshine hours (BSS) was carried out for 25 years data period the analysed mean annual data for aforesaid parameters are presented in Table 1.
3.2. Maximum Temperature (TMax)
The trend of maximum temperature was tested using linear regression method and it indicated that, it ranged from 0.01oC to 0.07oC for different periods.
The trend were made confirmed by MK test analysis and revealed that the trend of maximum temperature for different periods was significantly increasing except southwest monsoon. The conformity of trend for maximum temperature was tested at 95% level of significance and indicated that the trend was increasing at Annual (z = 3.28), Winter (z = 3.28), Summer (z = 3.57) and Northeast monsoon (z = 2.75). The magnitude of trend also estimated using sen’s slope estimator and found that, it ranged from 0.01oC/year for south-west monsoon to 0.07oC/year for summer season and 0.04oC annually. The magnitude of maximum temperature was depicting in Table 1. The trends of annual maximum temperature using linear regression method and MK test are shown in Figure 1.
Table-1. Mann Kendall trend analysis and Sens’s slope estimator of climatic variables for Mulde Maharashtra.
| Parameters | Period | Z-statistics | Trend | Trend at 95 % significance level | Sen’s slope | Confidence limits for slope at 95 % significance level | Linear regression slope |
| Tmax | Annual | 3.28** | Increasing | Yes | 0.046 | Lower limit=0.025; Upper limit=0.065 | 0.0401 |
| Winter | 3.28** | Increasing | Yes | 0.057 | Lower limit= 0.030; Upper limit= 0.090 | 0.0536 | |
| Summer | 3.57*** | Increasing | Yes | 0.069 | Lower limit= 0.040; Upper limit= 0.100 | 0.0695 | |
| Southwest monsoon | 0.75 | Increasing | No | 0.014 | Lower limit= -0.020; Upper limit= 0.044 | 0.0128 | |
| Northeast monsoon | 2.75** | Increasing | Yes | 0.054 | Lower limit= 0.014; Upper limit= 0.089 | 0.0495 | |
| Tmin | Annual | 0.00 | Increasing | No | 0.000 | Lower limit= -0.040; Upper limit= 0.060 | 0.0037 |
| Winter | 0.07 | Increasing | No | 0.000 | Lower limit= -0.075; Upper limit= 0.049 | 0.0048 | |
| Summer | -0.30 | Decreasing | No | -0.008 | Lower limit= -0.062; Upper limit= 0.042 | -0.0122 | |
| Southwest monsoon | -0.94 | Decreasing | No | -0.013 | Lower limit= -0.038; Upper limit= 0.020 | -0.0148 | |
| Northeast monsoon | 0.80 | Increasing | No | 0.023 | Lower limit= -0.040; Upper limit= 0.100 | 0.0334 | |
| Rainfall (mm) | Annual | 0.44 | Increasing | No | 9.456 | Lower limit= -28.284; Upper limit= 45.375 | 4.4928 |
| Winter | 0.44 | Increasing | No | 0.000 | Lower limit= 0.000; Upper limit= 0.000 | 0.054 | |
| Summer | 2.17* | Increasing | Yes | 1.756 | Lower limit= -1.407; Upper limit= 6.111 | 2.3609 | |
| Southwest monsoon | -0.02 | Decreasing | No | -1.700 | Lower limit= -31.703; Upper limit= 33.576 | 0.8113 | |
| Northeast monsoon | -0.26 | Decreasing | No | -1.629 | Lower limit= -8.732; Upper limit= 4.641 | 1.2666 | |
| RH-I (%) | Annual | 2.08* | Increasing | Yes | 0.164 | Lower limit= 0.001; Upper limit= 0.253 | 0.1745 |
| Winter | 1.80+ | Increasing | No | 0.253 | Lower limit= -0.018; Upper limit= 0.400 | 0.2674 | |
| Summer | 2.17* | Increasing | Yes | 0.196 | Lower limit= 0.027; Upper limit= 0.363 | 0.2047 | |
| Southwest monsoon | 1.03 | Increasing | No | 0.051 | Lower limit= -0.047; Upper limit= 0.134 | 0.0715 | |
| Northeast monsoon | 2.64** | Increasing | Yes | 0.179 | Lower limit= 0.061; Upper limit= 0.284 | 0.2112 | |
| RH-II (%) | Annual | 3.69*** | Increasing | Yes | 0.457 | Lower limit= 0.251; Upper limit= 0.621 | 0.4595 |
| Winter | 3.53*** | Increasing | Yes | 0.679 | Lower limit= 0.390; Upper limit= 1.037 | 0.6617 | |
| Summer | 2.03* | Increasing | Yes | 0.285 | Lower limit= 0.012; Upper limit= 0.530 | 0.3025 | |
| Southwest monsoon | 4.46*** | Increasing | Yes | 0.417 | Lower limit= 0.269; Upper limit= 0.586 | 0.4016 | |
| Northeast monsoon | 2.50* | Increasing | Yes | 0.493 | Lower limit= 0.106; Upper limit= 0.833 | 0.4592 | |
| Evaporation (mm) | Annual | -2.27* | Decreasing | Yes | -13.697 | Lower limit= -20.607; Upper limit= -2.162 | -9.1902 |
| Winter | -2.34* | Decreasing | Yes | -2.536 | Lower limit= -5.054; Upper limit= -0.459 | -1.9142 | |
| Summer | -1.0 | Decreasing | No | -3.505 | Lower limit= -5.961; Upper limit= 1.705 | -1.6887 | |
| Southwest monsoon | -2.87** | Decreasing | Yes | -5.687 | Lower limit= -9.226; Upper limit= -2.484 | -3.2823 | |
| Northeast monsoon | -1.87+ | Decreasing | No | -2.347 | Lower limit= -4.921; Upper limit= 0.199 | -2.305 | |
| Bright sunshine hours (hrs) | Annual | -2.25* | Decreasing | Yes | -0.030 | Lower limit= -0.060; Upper limit= -0.001 | -0.0271 |
| Winter | -3.66*** | Decreasing | Yes | -0.050 | Lower limit= -0.069; Upper limit= -0.025 | -0.0463 | |
| Summer | -2.81** | Decreasing | Yes | -0.050 | Lower limit= -0.093; Upper limit= -0.019 | -0.0552 | |
| Southwest monsoon | -1.69+ | Decreasing | No | -0.040 | Lower limit= -0.075; Upper limit= 0.000 | -0.0236 | |
| Northeast monsoon | -3.15** | Decreasing | Yes | -0.042 | Lower limit= -0.067; Upper limit= -0.019 | -0.0436 | |
| Wind speed (Kmph) | Annual | -4.15*** | Decreasing | Yes | -0.118 | Lower limit= -0.166; Upper limit= -0.073 | -0.1138 |
| Winter | -3.93*** | Decreasing | Yes | -0.100 | Lower limit= -0.133; Upper limit= -0.060 | -0.092 | |
| Summer | -4.49*** | Decreasing | Yes | -0.120 | Lower limit= -0.165; Upper limit= -0.080 | -0.1172 | |
| Southwest monsoon | -3.53*** | Decreasing | Yes | -0.144 | Lower limit= -0.192; Upper limit= -0.080 | -0.1335 | |
| Northeast monsoon | -3.30*** | Decreasing | Yes | -0.095 | Lower limit= -0.147; Upper limit= -0.040 | -0.0932 | |
Note :Winter: Jan-Feb, Summer: Mar-May, Southwest monsoon: Jun-Sep, Northeast monsoon: Oct-Nov.
Figure-1. Annual trend analysis of maximum temperature (oC) at Mulde.
3.3. Minimum Temperature (TMin)
The linear regression analysis showed increasing trend for minimum temperature except summer and south west monsoon season. To confirmed the trend MK test was carried out with 95 % level of significance and indicated that the trend was non significantly increasing during different periods.The annual trend of minimum temperature using linear regression method given in Table 1 and shown in Figure 2.
Figure-2. Annual trend analysis of minimum temperature (oC) at Mulde
3.4. Rainfall (RF)
The trend analysis for rainfall using linear regression analysis indicated that the trend was increasing for all periods except southeast and northwest monsoon season. For confirmation of trend, MK test was carried out and found that during annual, winter and summer trend was increasing but statistically non-significant at 95 % level of significance and during southwest mansoon and northeast monsoon decreasing trend but statistically non-significant at 95 % level of significance. The magnitude of trend was also estimated using sen slope and found within that rainfall increasing at the rate of 9.46 mm per year. The trends are shown in Figure 3 and details are given in Table 1.
Figure-3. Annual trend analysis of rainfall (mm) at Mulde.
3.5. Morning Relative Humidity (RH-I)
The trend analysis for morning relative humidity using linear regression analysis indicated that the trend was increasing for all periods. For confirmation of trend, Mann-Kendall test was carried out and found that during annual, summer and northeast season the trend was significantly increasing at 95 % level of significance. The magnitude of trend was also estimated using sen slope and found within limit at the rate of 0.05 % to 0.25 % per year for different periods. The trends are shown in Figure 4 and details are given in Table 1.
Figure-4. Annual trend analysis of morning relative humidity (RH-I) at Mulde
3.6. Afternoon Relative Humidity (RH-II)
The linear regression trend analysis was carried out for afternoon relative humidity and found that the trend was increasing for all periods. For confirmation of trend, MK test was carried out and found that for all periods the trend was increasing and statistically significant at 95 % level of significance. The magnitude of trend was also estimated using sen slope and found within limit at the rate of 0.28 % to 0.68 % per year for different periods. The details of statistical parameters are shown in Table 1 and Figure 5.
Figure-5. Annual trend analysis of afternoon relative humidity (RH-II) at Mulde
3.7. Evaporation (EVP)
The trend analysis for evaporation using linear regression analysis indicated that the trend was decreasing for all periods. For confirmation of trend, MK test was carried out and found that trends of all periods increasing and statistically significant at 95 % level of significance except summer and northeast monsoon. The magnitude of trend was also estimated using sen slope and found that evaporation decreasing annually by 13.69 mm. The trends are shown in Figure 6 and details are given in Table 1.
3.8. Bright Sunshine Hours (BSS)
The trend analysis for bright sunshine hours using linear regression analysis indicated that the trend was decreasing for all periods. For confirmation of trend, MK test was carried out and found that trend was decreasing and statistically significant at 95 % level of significance except southwest monsoon. The mangnitude of the bright sunshine hours showing that bright sunshine hours decreasing at the rate of 0.03 hours per year. The trends are shown in Figure 7 and details are given in Table 1.
Figure-6. Annual trend analysis of evaporation (mm) at Mulde
Figure-7. Annual trend analysis of bright sunshine hours (hr) at Mulde
3.9. Wind Speed (WS)
The linear regression trend analysis was carried out for wind speed for different period. The analysis showed that trend was decreasing for all periods. For confirmation of trend, MK test was carried out and found that during different periods trend was significantly decreasing at 95 % level of significance. The magnitude of wind speed was decreasing at the rate of 0.12 kmph per year. The detailed z values and sen slope estimator are presents in Table 1 and corresponding trend is shown in Figure 8.
Figure-8. Annual trend analysis of wind speed (kmph) at Mulde
4. CONCLUSION
The trend of maximum temperature was tested and indicated that the trend was significantly increasing except southwest monsoon while in case of minimum temperature trend was non significantly increasing during different periods. In case of rainfall trend was increasing during annual, winter and summer but statistically non-significant at 95 % level of significance and during southwest monsoon and northeast monsoon decreasing trend but statistically non-significant at 95 % level of significance. The trend analysis for morning relative humidity indicated that during annual, summer and northeast season the trend was significantly increasing at 95 % level of significance and while afternoon relative humidity, linear regression analysis indicated for all periods the trend was increasing and statistically significant at 95 % level of significance. The trend analysis using linear regression analysis for evaporation indicated that trends of all periods increasing and statistically significant at 95 % level of significance except summer and northeast monsoon while for bright sunshine hours indicated that trend was decreasing and statistically significant at 95 % level of significance except southwest monsoon. In case of wind speed, analysis showed that trend was significantly decreasing for all periods at 95 % level of significance.
Funding: This study received no specific financial support. |
Competing Interests: The authors declare that they have no competing interests. |
Acknowledgement: All authors contributed equally to the conception and design of the study. |
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