Figure 5 - uploaded by Radhakrishnan Kalidoss
Content may be subject to copyright.
Post-monsoon rainfall trend of India for the year of 1901-2014 and 1985-2014 

Post-monsoon rainfall trend of India for the year of 1901-2014 and 1985-2014 

Source publication
Article
Full-text available
In this work, we aimed to study the trend of the annual and seasonal mean rainfall and temperature of India over the period of 1901–2014. The long-term trend of rainfall and temperature was evaluated by linear regression and Mann–Kendall test. The magnitude of the trend was assessed by Sen’s Slope. In the last 30 years, the annual and summer rainfa...

Contexts in source publication

Context 1
... negative trend was observed in annual rainfall, and a mix of positive (winter and monsoon) and negative (summer and post-monsoon) trends was noticed in seasons; however, it did not show statistical significance at 5%. The magnitude of the trend was evaluated by the Sen's method which showed the decreasing trend annual, winter and monsoon (Figures 1, 2 and 4), and the summer and post-monsoon showed an increasing trend of rainfall in the time series (Figures 3 and 5). The lower bound 5% of the confidence interval demonstrated that the annual and all season rainfall negatively influenced, and but the upper bound 5% found positive. ...
Context 2
... in recent three decades , trend showed that there was a negative trend for the annual and seasonal rainfall (Figures 1 to 5). Annual and summer rainfall had shown a negative trend and exhibit statistically significant at 5% (Table 4). ...

Similar publications

Article
Full-text available
Climatic factor plays a major role in Indian agriculture in that rainfall play a key role. Being rainfall is the important factor for agriculture normally has to rely on secondary data. The study area taken for this analysis is Namakkal district of Tamil Nadu. The extent of the area is extends between 11 0 00' to 11 0 36'10" north Latitudes and 77...

Citations

... Therefore, experts (14 researchers, 6 policy makers and 10 academicians) suggested to apply the Mann-Kendall's test to capture the long-term trend coefficient, which is z statistic, and magnitude of the trend as indicated by the Sen's slope (Jain & Kumar, 2012;Radhakrishnan et al., 2017). The z statistic and Sen's slope of annual precipitation, minimum temperature and maximum temperature of shrimp-producing states were estimated using long-term data. ...
Article
Identification of spatial gradient in the vulnerability of white leg shrimp production to climate change is imperative in the formulation and implementation of suitable adaptive measures. A composite vulnerability index was computed by employing 36 variables pertaining to exposure (11), sensitivity (11) and adaptive capacity (14) dimensions to map the extent of vulnerability in white leg shrimp production across Indian states. Based on its magnitude, the vulnerability index was categorized into three groups, namely low, moderate and high. Results showed that the mean composite vulnerability index was 0.65 and ranged from 0.34 to 0.99 indicating that there was a strong spatial pattern. Among the nine states, Goa (0.99), Kerala (0.84) and Odisha (0.77) were highly vulnerable; Gujarat (0.75), Karnataka (0.57) and West Bengal (0.56) were moderately vulnerable; and Tamil Nadu (0.54), Andhra Pradesh (0.46) and Maharashtra (0.34) were less vulnerable to shrimp production. About one‐fourth of the production and culture area of white leg shrimp were in moderate and highly vulnerable regions. The impact of climate change on shrimp production is diverse but can be reduced by implementing adaptive measures—suitable policies and investment plans—which should be region‐specific.
... Extreme rainfall events are a primary cause of flooding that can bring about devastating impacts on the coastal and lowlying regions (Maharana et al. 2021). The Indian summer monsoon rainfall (ISMR) during June-September accounts for 80% of the total rainfall on which the Indian economy is highly dependent (Gadgil and Gadgil 2006;Kumar et al. 2010;Mondal and Mujumdar 2015;Shukla and Huang 2016;Radhakrishnan et al. 2017). In addition, small contributions also come from the Indian winter monsoon rainfall (IWMR) during November-December (Palazzi et al. 2013;Dimri et al. 2016;Kumar and Dimri 2018). ...
Article
Full-text available
In recent decades, a significant rise in extreme rainfall events has been reported across India, accompanied by large-scale flood/drought-like conditions and catastrophic loss of life. Large-scale climate variability modes like the El Niño-Southern Oscillation (ENSO) and Indian Ocean Dipole (IOD) tend to influence the surface air temperature and rainfall variability over India. In what follows, the complete and independent influences of ENSO using Niño3.4, Niño3, and Niño4 indices, and IOD using Dipole Mode Index (DMI) on seasonal mean and extreme surface air temperature and rainfall over India are examined. A non-stationary generalized extreme value (GEV) distribution is implemented to analyze the seasonal extremes over the period 1979-2019. Niño3.4 induces deficit rainfall over regions such as the Gangetic plains, the entire Deccan plateau, and western India in boreal summer. In autumn, strong positive seasonal mean rainfall responses are evident in peninsular India and eastern parts of Madhya Pradesh (MP). A dipole pattern evident in north India during summer reverses its polarity by autumn. Yet, Niño3 significantly reduces the intensity of rainfall over large parts of MP in summer. Contrarily, Niño4 strengthens the rainfall in similar regions, thereby significantly impacting the rainfall variability over India. Likewise, positive (negative) phases of the IOD lead to wet (dry) conditions over northwestern India in summers and central India in autumn. Overall, a coherent inverse relationship between rainfall and daily maximum temperature is observed. For Niño3.4 independent of IOD (Niño3.4| IOD), a weaker intensity in rainfall is found in northern India compared to the original Niño3.4 response. However, the IOD independent of Niño3.4 (IOD| Niño3.4) rainfall response is weaker in northern India and stronger in central India compared to original IOD responses. Importantly, a composite analysis of rainfall and temperature anomalies during different phase combinations of ENSO and IOD also shows that the IOD mitigates the influence of ENSO in boreal summer and fall whenever such events occur in-phase.
... We used the Mann-Kendall test to detect the temporal trend in the LST datasets [73]. The test has been extensively used in the hydro-climatic time series data and has always proven to be an efficient tool in comparison to other available tools for detecting trends [74][75][76][77][78]. This test has numerous advantages as it analyzes time series that are not required to follow a specific linear or nonlinear trend. ...
Article
Full-text available
This study investigates the diurnal, seasonal, monthly and temporal variation of land surface temperature (LST) and surface urban heat island intensity (SUHII) over the Isfahan metropolitan area, Iran, during 2003–2019 using MODIS data. It also examines the driving factors of SUHII like cropland, built-up areas (BI), the urban–rural difference in enhanced vegetation index (ΔEVI), evapotranspiration (ΔET), and white sky albedo (ΔWSA). The results reveal the presence of urban cool islands during the daytime and urban heat islands at night. The maximum SUHII was observed at 22:30 pm, while the minimum was at 10:30 am. The summer months (June to September) show higher SUHII compared to the winter months (February to May). The daytime SUHII demonstrates a robust positive correlation with cropland and ΔWSA, and a negative correlation with ΔET, ΔEVI, and BI. The nighttime SUHII displays a negative correlation with ΔET and ΔEVI.
... Pingale et al. (2014) reported significantly increasing trends of minimum, maximum and, average temperature in most parts of Rajasthan using MK and SS tests. Radhakrishnan et al. (2017) also showed warming tests in recent years in India using MK, SS, and SLR tests for the period 1985-2014. Meshram et al. (2020) also reported increasing annual and seasonal temperature trends in Chhattisgarh, using MK and SS test for the period of 1901-2000. ...
Article
The study of temperature trends and its impact on agricultural crop is critically important for evaluating the effects of climate change on food security. Spatiotemporal trends of the monthly, seasonal, and annual average temperature of 36 districts of Maharashtra, located in west India, were analyzed using 68 years of gridded temperature data of India Meteorological Department Mann-Kendall, modified Mann-Kendall, and Spearman’s rank correlation tests were used to analyze the trends of temperature, whereas Sen’s slope, Spearman’s Rho, and simple linear regression were used to quantify the magnitude of trends at 1% and 5% levels of significance. Correlation and regression analysis were performed between temperature and detrended yield for sugarcane. Results revealed that only increasing trends of monthly, seasonal, and annual average temperatures were significant in districts of Maharashtra. Significantly rising temperature trends of up to 2.58 °C, 1.78 °C and 1.05°C per 100 year were observed in monthly, seasonal, and annual temperatures, respectively. The magnitude of increasing temperature trends was more in the second half of the year. November and post-monsoon season had the highest increasing magnitude of trend for monthly and seasonal temperatures, respectively. Our analysis reveals increasing trends of average temperature in the region, which has significant negative impacts on Sugarcane (Saccharum officinarum L.). District-wise correlations of yield and monthly temperature anomalies showed 63.9% negative correlations, with significant negative correlations in 7.3% combinations. Overall the state has significant negative correlations of yield and monthly temperature, with Pearson’s correlation coefficient varying from −0.1(September) to −0.32 (July). The study confirms the adverse impacts of climate change on agricultural crop, and the results along with the district-wise temperature trend maps will be helpful for policy planners for agricultural resource management in west India.
... To study the interdependence between rainfall and temperature, the correlation coefficient was found to be essential. Researchers have explored a direct association between temperature and precipitation over the Indian subcontinent (Radhakrishnan et al. 2017). In this study, an attempt is made to illustrate the relationship between precipitation and temperature by examining the correlation between these two climatic elements. ...
Article
Full-text available
This study exclusively focuses on spatial and temporal change of temperature and precipitation before and after COVID-19 lockdown and also examines the extent of their variation and the spatial relationship between them. Our main objective is to analyze the spatiotemporal changes of two climatic variables in Indian subcontinent for the period of 2015–2020. Monthly precipitation and temperature data are collected from NOAA and NASA for January to May month across the four zones (northeast, northwest, central, and peninsular zone) of India. To conduct a zone-wise statistical analysis, we have adopted statistical process control (SPC) methods like exponentially weighted moving average (EWMA) control charts, individual charts (I- Chart) to detect the shift in temperature and precipitation over the study period and Pearson correlation coefficient applied to measure the spatial association between the two variables. The findings revealed that temperature parameter has experienced a lot of positive and negative trends in the span of 6 years and detected a weak to moderate negative correlation in many parts of the country in April 2020 after 2016. This study also identified a weak negative correlation mainly in NE zone in 2020 after 2017. This research provides vital scientific contribution to the effects of monthly temperature and precipitation before and after COVID-19 pandemic lockdown. Graphic abstract
... Shahin et al. (2016) studied on time series analysis, modelling, and forecasting climatic variable rainfall of Rajshahi district in Bangladesh. Radhakrishnan et al. (2017) evaluated the long-term trend of rainfall and temperature in India by using linear regression and Mann-Kendall test. Burney et al. (2017) examined SARIMA (0, 0, 2) (2, 1, 1) 12 is used to forecast the maximum temperature of Karachi city, Pakistan over a period of 12 months. ...
Article
This study attempted to examine the future behaviour of monthly average rainfall and temperature of South Asian countries by using the Seasonal Autoregressive Moving Average model. Mann–Kendall trend test with Sen's Slope Estimator, to find the trending behaviour of all data series. The study has also been attempted to compare the above methods with the help of actual data. The monthly average rainfall and temperature of South Asian countries except Afghanistan and Maldives viz. Bangladesh, Bhutan, India, Nepal, Pakistan, Sri Lanka data from January, 1961 to December, 2016 have been collected from World Bank Group, Climate change knowledge portal. For estimating the trending behaviour, a non-parametric model such as the Mann–Kendall test was used with Sen's slope estimation to determine the magnitude of the trend. Box–Jenkins methodology was also used to develop the model and estimate the forecasting behaviour of rainfall and temperature in South Asian countries. Forecasting is carried out for both monthly rainfall and the average temperature of all the countries using best fitted models based on the data series. The monthly data from January, 1961 to December, 2010 are considered for validation of the model can be regarded as in-sample forecast and the data from January, 2011 to December, 2016 are used as out-sample forecast. The forecasting values with 95% confidence limit from January, 2011 to December, 2021 using best-fitted models for both rainfall and temperature. We conclude that climate change occurs for both rainfall and temperature in South Asian countries from the study period. The selected model can be used for forecasting both rainfall and temperature of respective countries from January, 2011 to December, 2021. As the climatic data analysis is valuable to understand the variation of global climatic change, this study may help for future research work on rainfall and temperature data. Full article- https://rdcu.be/cgXTH
... is concludes that precipitation has been declining with the rapid warming in the last 30 years [6]. is indicates that the growing demands are to be satisfied with limited resources as the rainfall is also decreasing over the period. ...
Article
Full-text available
In tropical countries like India, irrigation is necessary to grow crops in the nonmonsoon period. e conventional methodology for conveying irrigation water from the source to the field is through open canals. However, considering huge losses due to evaporation and percolation, a modern system of irrigation like pipe irrigation network (PIN) is desired. Advancement in technology has led to the progress in the PIN as they are compatible with modern irrigation facilities such as sprinkler and drip irrigation systems. In the present study, the layout of the PIN is designed and optimized in two phases. Initially, the looped network is traced out for the Bakhari distributary of the Kanhan Branch Canal, India. Minimum spanning tree (MST) network is obtained from the looped network using Prim's algorithm to calculate the nodal demands. e layout optimization of the MST is carried out using the Steiner concept to obtain the initial Steiner tree (IST). e steady-state hydraulic analysis and design are carried out for the looped and IST network. e results show that the percentage of length decreasing from the looped network to the MST network is 51.58%. e IST network is the optimized network having the minimum length showing a 12.21% length reduction compared to the MST network. e total reduction in the cost of the Steiner tree is found to be 4.25% compared to the looped network. Steiner concept application to large irrigation networks can reduce the length of the network thereby minimizing the total project cost.
... is concludes that precipitation has been declining with the rapid warming in the last 30 years [6]. is indicates that the growing demands are to be satisfied with limited resources as the rainfall is also decreasing over the period. ...
Research
Full-text available
In tropical countries like India, irrigation is necessary to grow crops in the nonmonsoon period. The conventional methodology for conveying irrigation water from the source to the field is through open canals. However, considering huge losses due to evaporation and percolation, a modern system of irrigation like pipe irrigation network (PIN) is desired. Advancement in technology has led to the progress in the PIN as they are compatible with modern irrigation facilities such as sprinkler and drip irrigation systems. In the present study, the layout of the PIN is designed and optimized in two phases. Initially, the looped network is traced out for the Bakhari distributary of the Kanhan Branch Canal, India. Minimum spanning tree (MST) network is obtained from the looped network using Prim's algorithm to calculate the nodal demands. The layout optimization of the MST is carried out using the Steiner concept to obtain the initial Steiner tree (IST). The steady-state hydraulic analysis and design are carried out for the looped and IST network. The results show that the percentage of length decreasing from the looped network to the MST network is 51.58%. The IST network is the optimized network having the minimum length showing a 12.21% length reduction compared to the MST network. The total reduction in the cost of the Steiner tree is found to be 4.25% compared to the looped network. Steiner concept application to large irrigation networks can reduce the length of the network thereby minimizing the total project cost.
... The Mann-Kendall (MK) test was applied to the LST time series to detect the temporal trend and its significance level (Mann 1945). The MK test has been extensively used over different hydroclimatic time series, proving to be an efficient tool for trend detection (Araghi et al. 2015;Malik et al. 2020;Mohammad and Goswami 2019;Pingale et al. 2014;Radhakrishnan et al. 2017;Zhang et al. 2016). The standardized MK statistics (Z s ) were computed to specify if a trend is significant or not at a specific significance level p. Z s statistics are calculated from the MK coefficient S (in Eq. 1), defined as: ...
Article
The present study intends to understand the variability in land surface temperature and urban heat island over Ahmedabad city, Gujarat, from 2003 to 2018 using MODIS thermal data. The spatio-temporal variability of land surface temperature dynamics is understood by pixel to pixel linear regression analysis along with Mann-Kendall and Sen's slope estimator tests. A Gaussian fitting technique is employed to estimate the surface urban heat island (SUHI) signature with respect to the surrounding rural area. An overall increase in the surface urban heat island magnitude is profoundly visible during the winter season. The spatial extent of SUHI shows a uniform distribution of urban heat inside the city and its accumulation within a high dense central urban area in both summer and winter seasons. A multiple linear regression method is used to predict the SUHI magnitude in the next 16 years, i.e. in 2034, based on enhanced vegetation index, white sky albedo, and evapotranspiration as predicting variables considering two different scenarios presuming the present rate of change of predicting variables as first scenario and a double changing rate as compared to present rate in the second scenario.
... These results are statistically significant at 95% confidence. The change point analysis shows various mutation points depend upon different states, which are possibly influenced by urbanization, industrialization, and climatic factors (Shastri et al. 2015;Kalidoss et al. 2017). A narrow observation is identified through Fig. 3, which represents variations in the annual rainfall series are as follows: significantly increasing in rainfall after 1981 in Jammu and Kashmir, after 1926 in Gujarat, and after 1930in Goa, and decrease in Uttar Pradesh after 1986, in Uttarakhand after 1972, and in Himachal Pradesh after 1967 Here, the change points are detected for annual rainfall data; these change points help to study the significant monotonic trends of monthly, annual, and seasonal rainfall. ...
Article
Full-text available
Change point detection and trend analysis are the adopted techniques of time series analysis. We have applied non-parametric methods on the temporal and spatial-scale data of 115 years between 1901 and 2015 from ten different federal states in the north and the northwestern India to examine the change points as well as to estimate the future scenarios by examining the past trends. The change points were examined by Pettitt’s test, Standard Normal Homogeneity (SNH) test, and Buishand’s test, whereas the trend analyses of monthly, annual, and seasonal rainfall data were carried out using Sen’s slope estimator after assessing their statistical significance by Mann–Kendall (M-K) test. The trend analyses showed non-zero slope values and a few among them were of statistical significance. The results of our statistical experiment concluded that the trends of reduction in winter, pre-monsoon, and post-monsoon rainfalls would have notable effects on the rain-fed agricultural production in the near future, particularly in the areas without proper irrigation facility (e.g., parts of Uttar Pradesh). More extreme events of higher rainfall in some states (e.g., Goa, Maharashtra, and Jammu and Kashmir), however might cause disasters like landslide and flooding.