Search

The article provides a long-term trend analysis of the Kesinga catchment daily gridded rainfall at a (0.25° × 0.25°) high spatial resolution from 1901 to 2020 (120 years). The trend in seasonal and annual rainfall was detected using rank-based nonparametric statistical tests, namely Spearman's rho and Mann-Kendall test, which are used for detecting monotonic trends in time series data at the 5% significant level, smoothing curve, Sen's slope test, and a plot of innovative trend analysis. The results showed that statistically significant trends had a pattern with both positive (increasing) and negative (decreasing) trends, with positive and negative trends evident in the winter and negative trends shown in the monsoon, PREMON, and annual seasons. The middle of the study area revealed the highest negative trend, and the lower Kesinga catchment showed the lowest negative annual rainfall trend. In the entire Kesinga catchment, the seasonal data and annual rainfall both showed statistically significant and non-significant patterns. Consistently, the MK and SR tests were both conducted at the validated significance level. In various contexts, the statistically significant massive trend that has occurred was negative (70%). If the current pattern continues in the future, there will be a scarcity of water and more strain on the control of water resources at the given grids in corresponding temporal scales. [ABSTRACT FROM AUTHOR]

Aridity is a climatic phenomenon characterized by shortage of water availability in a given time and space resulting in low moisture and reduced carrying capacity of ecosystems. It is represented by a numerical indicator known as Aridity Index (AI), a function of rainfall and temperature. Aridification is a slow and steady effect of climate change and assessing its spread and change is vital in context of global climatic variations. Aridity is predominantly significant for agrarian countries like India, where a slight rise in drylands area can have a significant impact on the economy and community sustenance. AI is an inclusive indicator of climatic conditions in most arid and semi-arid regions. It helps in identifying and interpreting large scale trend in temperature and precipitation; and thus, classifying region into different climatic classes. The present study assessed long-term AI based on precipitation and temperature data obtained from the India Meteorological Department at the resolution of 1 × 1 degree for years 1969–2017. AI is estimated as a ratio of mean precipitation to mean potential evapotranspiration, calculated using Thornthwaite method. The results highlight the trend of aridity over pan-India with Innovative Trend Analysis and Mann–Kendall test. The study concludes that there is a relatively slow, however steadily progressive drier conditions being established in most of the regions. A shift from 'Semi-arid' towards 'Arid' class appeared in central mainland. The north-eastern Himalaya showed decrease in humid conditions ('Humid' to 'Sub-humid'). The study implies that there is a rising aridity trend over the years due to changing climatic conditions. The shifts in aridity can have serious implications on agriculture, long-term water resource utilization and land use management plans. Our results have scope for future landscape management studies in drylands and better adaptation methods in arid regions. [ABSTRACT FROM AUTHOR]

Climate change is a pressing issue that is affecting the lives and livelihoods of millions of people across the world. This study investigates the trend analysis and spatial-temporal variations of temperature and precipitation on a monthly, seasonal, and annual basis in Rajasthan state, India, over the past 40 years (1981-2020). The trend analysis of temperature and precipitation were analysed using the Mann-Kendall test at the confidence level of 95%. The magnitude (slope) was determined by using Theil-Sen's slope test. The results of the analysis revealed significant positive and negative trends of temperature and precipitation observed on a monthly, seasonal, and annual basis in all the 33 districts of Rajasthan state. The summer season experienced the maximum average temperature, while the winter season had the minimum. The study also found that the northern and western parts of Rajasthan experience "Mawat" rain during the winter due to cyclones happening in the Mediterranean Sea during that season. The annual average temperature and precipitation were observed to be maximum in the southern part and minimum in the northern and western parts of the state. The findings of this study provide valuable information for the future management of water resources and the likely impact of activities on the hydrologic cycle and natural resources in Rajasthan state. [ABSTRACT FROM AUTHOR]

Climate variability has significant implications for human health, necessitating a comprehensive understanding of the relationship between the two. The present study examines the trend analysis of climatic variables, and their possible impacts on health and identifies specific challenges faced by the people of Himachal Pradesh, India. The Mann-Kendall test, a non-parametric test, was used to examine temperature and rainfall trends. A survey was conducted to gauge public awareness regarding climate change and its health risks in the region. The study analysed weather patterns over 10 years, revealing fluctuating temperatures with the hottest years being 2012, 2015-17. Rainfall exhibited irregular patterns with less rainfall in 2012, 2014, 2017 and 2019. Trend analysis showed significant temperature trends in Dharamshala and Kangra. Weather-related incidents and fatalities peaked during 2012-14, while diseases, such as acute respiratory infections (ARI), asthma, cardiovascular diseases, diabetes, hypertension, and vector and water-borne diseases saw an increase over the decade. The survey results showed that the people of this region suffered mostly from respiratory problems, water-borne diseases and mental health issues. This study contributes to the existing knowledge by establishing a possible relationship between climate variability and the health of the people. Furthermore, it also provides a health database, facilitating the formulation of targeted interventions to address health-related challenges arising from the observed changes in weather patterns. [ABSTRACT FROM AUTHOR]

Analysis of rainfall distribution and its changing pattern plays a vital role in managing water resources in a region. This work examined the spatio-temporal variability and trend of rainfall on yearly and season-wise scales during 1971–2013 in the Wainganga river basin situated in middle India. The Mann–Kendall (MK) test was implemented for identifying the temporal variation in rainfall trends. The magnitude of this changing trend was estimated by applying Sen's slope (SS) method. A paired sample t-test was also employed to appraise the statistical significance of changes in rainfall data. The results of MK and SS tests show both upward and downward trends. The results show positive and negative trends in the south-eastern and northwestern parts of the study area for annual rainfall. The monsoon rainfall also shows very close proximity with annual rainfall data trends. The t-test states that the observed changes in precipitation are statistically significant. After trend and pattern analysis, state-space models (SSMs) were used to predict rainfall for future scenarios. Four SSMs (single, double, and triple exponential, autoregressive integrated moving average (ARIMA)) were employed for rainfall prediction. The analysis shows that the ARIMA is best for rainfall prediction with architecture of (0,0,0) (2,1,0) and can be used in Wainganga River basin. [ABSTRACT FROM AUTHOR]

The current study focuses on the altering historical rainfall data analysis and its variability in Kolkata (Kolkata Municipal Corporation), a metropolitan city in India. The research area experiences detrimental urban floods (pluvial floods) at near-annual regularity during the monsoon, and during the pre-monsoon seasons it commonly experiences water shortage problems. Analysing trends and temporal variability of rainfall over 120 years from 1901 to 2020 is the main objective of this study. The original Mann-Kendall (M-K) test has been applied to the rainfall dataset in conjunction with Sen's Slope Estimator using Python 3.10, after the Durbin-Watson (DW) statistic initially suggested that there is no serial correlation effect. The M-K test, with a Kendall's tau of 0.17058 (significant at a 5% level), shows an upward trend in annual rainfall between 1901 and 2020. The Sen's slope, which measures the rate of change annually, has a value of 2.48152. Regression analysis and other dispersion measures are also used in this study to investigate the monthly rainfall trend and its variability. The phase-wise (30-year) analysis of annual rainfall variability reveals a considerable variation over 120 years. While fitting the linear regression line month by month over the entire period, mostly negative trends were found in the pre-monsoon and positive trends in the monsoon and post-monsoon seasons. The findings of this analysis could be useful to urban planners for water supply and management in the study area. The primary concern of planners for effectively managing rainwater and the accompanying issues should be the growing variability of annual precipitation. [ABSTRACT FROM AUTHOR]

Since historical times, the climate of the earth has been changing. In terms of diversity and population, India is a vast country. Any significant change in its climatic conditions can have an impact on the country's entire economic and cultural structure. Temperature is the most important variable when focussing on climate change study. This study aims to recognise the changing trend of temperature in India. For this purpose, temperature data from 1901 to 2020 has been analysed. To find out the variability in temperature, various statistical tools were applied. The Mann-Kendall method has been used along with Sen's slope estimator for determining the trend and slope magnitude. ARIMA modelling is applied to the data series to forecast the temperature trend. The study has shown that variability in temperature is increasing in recent years and the temperature trend is increasing in all the months, especially in the cold months of November, December and January. The average annual temperature of India in the year has increased significantly in the past 120 years. The trend analysis of temperature is tested significantly statistically at a 95% confidence level. [ABSTRACT FROM AUTHOR]

Climate change has a substantial influence on a range of meteorological factors and the global hydrological cycle. This paper presents an analysis of the annual rainfall (P), temperature (including maximum temperature: T max , minimum temperature: T min , and average temperature: T avg), stream flow (Q) data from 6 gauging sites, and groundwater level (GL) data from 7 groundwater stations in and around the Barak River Basin in India from 1986 to 2020. To identify patterns in the time series, the simple linear regression (SLR) approach, the Mann-Kendall (MK) test, the Hamid Mann-Kendall (HMK) test and the Spearman's Rho (SRO) tests have been employed. Pettit test has been used to identify the change points in the time series. The results indicated an increase in the T max and the T min across the study area, while no clear patterns were found in the T avg time series. Except for the 'Hailakhandi' station, the annual total rainfall series showed an increasing trend. T max and T min showed a directional change point between 1999 and 2005, and between 2001 and 2007, respectively, according to change point analysis. Except for two stations, overall declining trends in the annual stream flow were found. Moreover, a total of five out of seven groundwater stations indicated a rise in groundwater levels. The findings presented here offer valuable insights into the current condition of the Barak basin and emphasize the importance of implementing sustainable water management practices. • Significant impact of climate change on hydro-meteorological parameters in the study area. • A noteworthy increase in both maximum and minimum temperatures. • Overall declining trends in annual stream flow across most stations in the basin. • Groundwater stations demonstrate rising groundwater levels. [ABSTRACT FROM AUTHOR]

Rainfall is a key hydro meteorological variable. Climate change is disrupting the hydrological cycle and altering the usual cycle of rainfall, which frequently results in long-lasting storms with significant rainfall. A first step in hydrologic design of project is to determine the design storm or rainfall events to be used. For deriving design storm, researchers concluded that instead of using generalized readily available curves or maps, it is better to estimate design storm based on site specific historical rainfall data. The objective of the study is to analyze the rainfall data in the koyna watershed area in order to evaluate the design storm, which will be further used as an input data for HEC-HMS event based hydrological modelling of flood peak attenuation of design storm flow at koyna dam during extreme rainfall event. In this study, 40 years (1982–2021) of rainfall data from 8 rain gauge stations in Koyna Dam Catchment area is used initially for performing trend analysis through statistical and graphical techniques and then for Isopluvial analysis. The Sen's slope test and the Mann-Kendall test are the statistical techniques employed, and Innovative Trend Analysis is the graphical technique used. IDF approach is used for deriving design storm, and using Gumbel's frequency distribution method Isohyetal maps, IDF tables and curves are prepared for 2,10,25,50,75 and 100 year return periods and 6,12,24,48 and 96 h durations. Results obtained from statistical and graphical trend analysis of annual rainfall series are consistent. No statistically significant trend in annual rainfall series is observed, however there is rising and falling trend was observed in annual as well as monthly rainfall series. From the results of design storm study, the design storm hyetograph of 10 years return period and 96 h duration is selected, which gives the rainfall intensity of 10.88 mm/h for the koyna catchment. There are various dams nearby koyna catchment, The Isohyet maps, IDF curves and table output available from this study can be more reliably used during planning and design of hydraulic structure for other areas near by koyna catchment. • Through rainfall analysis design storm,evaluated and further used as an input data for HEC-HMS hydrological modelling. • Annual and monthly Trend analysis of 40 years rainfall from 8 rain gauge stations is studied using statistical tests, and graphical technique. • Using IDF method with Gumbel's frequency distribution, Isohyetal maps, IDF curves and tables were prepared. • No statistically significant trend was found and results from graphical and Statistical Trend Analysis methods are consistent. • The results obtained are site specific, so more reliable, hence can be used with greater confidence for deriving design storm. [ABSTRACT FROM AUTHOR]

Climate change is viewed as the main obstacle to agricultural development in developing countries. The high dependence on agriculture and allied sectors makes many countries vulnerable to the climate change phenomenon. There is a gap in macro and micro-level understanding of climate change. Thoughtful farmers' perceptions and impacts of climate change on farming are fundamental for developing various mitigation and adaptation strategies. Therefore, the main aim of the present study was to understand the pattern of climate variability, farmers' perceptions about climate change, and farmers' adaptation strategies based on their socio-cultural background in the villages of Goa, on the west coast of India. The results reveal that about 62% of the sampled farmers have experienced climate change in terms of meteorological indicators such as increased average temperature, decreased total rainfall, delayed onset of monsoon, and an increase in the length of the summer season. The temperature trend analysis (0.009 °C/year) validated farmers' perceptions, while the perception of rainfall differed (−1.49 mm/year). Farmers are convinced that climate change has affected their farming (declining crop and livestock productivity, water depletion, and other related farm operations). They strive to adapt to climate change through crop diversification, an integrated crop-livestock system, contingency crop planning, and the adaptation of new crops and varieties. This study could be helpful for policymakers to establish a climate-resilient agriculture system by ensuring timely availability of farm inputs, accurate weather forecasting, and encouraging insurance products for crop and livestock enterprises, which will help farmers cope with the changing climate to enhance their income and economic wellbeing. Further, adaption of integrated farming, agroforestry, and indigenous technical knowledge is imperative to combat the ill effects of climate change. [ABSTRACT FROM AUTHOR]

Rainfall is time concentric in nature. The spatial and temporal distribution of rainfall is changing over the Earth with recent anthropogenic warming. The study explores various characteristics of annual and seasonal concentration of rainfall across India using the precipitation concentration index (PCI) and its trends during the period of 1986–2015, based on a high‐resolution gauge‐based rainfall data (0.25 × 0.25°), obtained from the India Meteorological Department (IMD). An intercomparison is made with 11 other gridded rainfall datasets to infer whether these datasets can reasonably reproduce the spatiotemporal distribution of PCI and its trends in various homogenous rainfall regions of India or not. These datasets are categorized into gauge‐based (APHRODITE, GPCC, CPC), satellite‐derived (CHIRPS, PERSIANN‐CDR), and reanalysis (JRA‐55, MERRA‐2, NCEP‐2, PGF, ERA‐Interim, ERA‐5). On annual scale, about 8.52, 24.1, and 67.38% of area of India are under moderate, irregular, and strongly irregular rainfall distribution, respectively. Spatial variation of PCI in India is influenced by geographical factors such as latitude, longitude, and elevation. Significant increasing and decreasing trends in annual PCI have been observed in the northeast, eastern, and western coasts of India, respectively. Gridded data intercomparison suggests that the gauge‐based APHRODITE, GPCC, and satellite‐derived CHIRPS datasets better perform in capturing the temporal and spatial variation of PCI across India when compared to the IMD gridded dataset, whereas the ERA‐5 performs better among the reanalysis datasets. However, the rainfall datasets exhibited marked differences with the IMD while estimating the annual and seasonal trend and its magnitude across various regions of India. The JRA‐55 overestimated areas of positive trend and its magnitude on annual and seasonal scale. These findings have practical implications for hydroclimatic studies. [ABSTRACT FROM AUTHOR]

Climate variability analysis is essential for predicting the behavior of various extreme weather events and making communities resilient. Notwithstanding the profound concerns, climate variability assessment faces numerous challenges due to inadequate and sometimes unavailability of data at spatiotemporal scales. This study makes an attempt to analyse climate variability in the Bhagirathi Sub-basin of India. Six meteorological variables were analysed from fourteen weather stations located in the Sub-basin during 1968–2017. Modified Mann–Kendall test was employed to ascertain the trends in meteorological variables. One-way ANOVA was used to assess the relationship between and within the variables. A total of 432 households were selected for reaffirming climate variability and impact on landscape. Significant trends were detected in highest maximum, mean maximum (Mmax) and mean minimum (Mmin) temperatures, relative humidity (Rh), rainfall and vapour pressure (Vp) at annual and seasonal scales. Stations located in eastern and deltaic Sub-basins registered varying trends in these meteorological variables due to anthropogenic activities-induced land use changes. ANOVA revealed a robust relation among rainfall, Vp, Mmin and Mmax. Perceptions of the sampled households revealed that climate variability has considerably affected flood intensity, vegetation, soil, water resources and agricultural pattern. We find modified Mann–Kendall method effective in analysing climate variability in the Sub-basin. Thus, this method can be utilized for effective analysis of climate variability at spatial scales in geographical regions. [ABSTRACT FROM AUTHOR]

Rainfall is the key climatic variable that governs the regional hydrologic cycle and availability of water resources. Rainfall trend analysis in a localized watershed can improve many aspects of water resource management not only to the catchment itself but also to some of the related other catchments. The trend analysis of monthly rainfall data over Cherrapunji of Meghalaya in India for the period 1872-2007 has been carried out in this work. While the magnitude of the trend in the time series has been determined using Sen's estimator, the significance of the trend in monthly rainfall series has been tested using Mann-Kendall test. During the time span 1872-2007, an increasing trend has been found in the monthly rainfall for the months July, October and November, and a decreasing trend has been found in the monthly rainfall for the months February to June, August and September. On the other hand, it was found that none of Mann-Kendall Z values was significant at 5% level of significance. Therefore, from Mann-Kendall Z test, it can be concluded that there is no trend in any month in monthly rainfall for the station Cherrapunji. For the better assessment of the temporal variation in monthly rainfall trend, whole period was divided into two halves, 1872-1939 and 1940-2007. Then, trend magnitude through Sen's estimator and Mann-Kendall Z for test of significance were determined for these two time periods separately. The analysis of trends of monthly rainfall in these two halves showed large variability in the magnitude and direction of the trend in various months from one half to another. Accurate prediction of trends in monthly rainfall is an important aspect of climate research and we believe that present study could provide a scope to correlate between current rainfall trend and climate change scenario of the study area. [ABSTRACT FROM AUTHOR]

Global warming is a biggest issue around the world. In this research paper, the temporal and spatial trend analysis of seasonal and annual rainfall on Bhogavo River watersheds in Sabarmati lower basin of Gujarat state of India has been analysed using the data of 11 rain gauge stations installed in Bhogavo watershed. Linear regression, Mann–Kendall Test, Sen's slope test and innovative trend analysis methods are used to carry out monthly and annual rainfall trend analysis. In addition to the rainfall analysis, a number of rainy days change in magnitude as a percentage of mean rainfall have also been analysed using linear regression and Sen's slope method, respectively. The IDW method is used to develop a spatial distribution of annual and seasonal rainfall trend over the study area. From the results, it is concluded that annual rainfall shown increasing (positive) trend at nine stations out of 11 stations. The highest value of change in magnitude of trend as a percentage of mean monthly rainfall has been obtained in the month of July, attributing increasing trend at Sayla station and lowest value magnitude of trend as a percentage of mean rainfall in the monthly rainfall has been obtained in the month of August, attributing decreasing trend at Bavla station. [ABSTRACT FROM AUTHOR]

The current study analyzes the changes in the climate pattern of Bharathapuzha river basin in Kerala, India, which experiences a significant imbalance in water availability since 1990. The analysis of the climatic variables shows that there are three climatic zones existing in the basin. The trend in the historic climatic variables is estimated using the non-parametric Mann-Kendall test and the Sen slope analysis. A discrete wavelet analysis is also performed to separate only the long period component of the signal, and its trend is also studied. A significant decreasing trend is observed in the rainfall. However, a monotonous long-term increasing trend is detected for monthly average temperatures and monthly minimum temperatures. The sequential Mann-Kendall test result shows a significant change in the climatic pattern after 1980. The rainfall effectiveness of the basin is analyzed using indices. A drought analysis is also conducted using Standardized Precipitation Index (SPI) in different time scales since climatic zones 2 and 3 in the catchment are drought-prone areas in recent years. A detailed analysis on the existence of climate change in this region is conducted for the first time. These results can be further used for planning and development of climate change adaptation policies for the basin. [ABSTRACT FROM AUTHOR]

The present study analyzed precipitation and temperature trends of 45 years (1971–2015) over two protected areas (Kanha Tiger Reserve representing Sal forest and Satpura Tiger Reserve representing Teak forest) in Central India. The specific trends (decadal, annual, and seasonal) are calculated from various available data sources (IMD, REMO, and Station) which are at different horizontal resolutions. The non-parametric Mann–Kendall test statistic and Sen's estimate methods are used to detect the presence and quantum of a trend. The results indicate significant decrease in post-monsoon and winter precipitation and an increasing significant temperature trend (Tmean, Tmax, Tmin) at seasonal, annual, and decadal scale, with a maximum increase during post-monsoon. Also, the last decade (2001–2010) was warmer than the previous three decades. The presence of break points and structural changes in climatic variables are analyzed using OLS-CUSUM and MOSUM tests available in "strucchange" package in R. A structural change in precipitation was observed only in REMO dataset over Satpura Tiger Reserve, whereas significant structural change in temperature was observed in REMO dataset over both the regions. These findings are useful for assessing precipitation and temperature trends in protected areas. In particular, it can aid in understanding the impact of climate change on forest/species. On comparative analysis among different data sources at varying resolutions, the study recommends the use of finer resolution datasets for regional/micro-scale studies for formulating and planning effective adaptation strategies. [ABSTRACT FROM AUTHOR]

The rapid change in Earth's surface temperature is affecting the patterns of weather and climate. Such changes have a large impact, especially on agriculture and water resources throughout the world, and therefore, the projection of surface temperature in the near future is an urgent need. The current study is aimed to evaluate model performance in simulating the surface temperature (T), surface maximum temperature (Tmax) and surface minimum temperature (Tmin) over the land points of India under historical experiment (1971–2005) of the Coupled Model Intercomparison Project phase 5 (CMIP5). Eyeball verification of spatial distribution, bias, skill score and Taylor diagram is used to evaluate the model's performance during 1971–2005. The relatively better performing models are used for future projection (2021–2055) of temperature under representative concentrations pathways (RCPs) 4.5 and 8.5, and the Student's t test at 99% and 95% confidence levels are applied for a limit of certainty. Under RCPs 4.5 and 8.5, an increase of 0.5–0.6 °C in T at a 99% confidence level is noticed over the regions of North Central (NC), North West (NW) and West-Central (WC) India, while an increase of 0.2–0.5 °C is found over other regions of India. The Mann–Kendall (MK) test, at a 95% confidence level, suggests the maximum warming trend of Tmax in March/April and Tmin in December/February for the period of 1971–2005, and a similar trend in Tmax (March/April) and Tmin (December/February) may be possible in the near future (2021–2055). [ABSTRACT FROM AUTHOR]

Abstract This study proposed a novel groundwater-quality stability index (GQSI), which considers probabilistic estimate of reliability and resilience based on multi-year dataset. The developed index is validated and optimized adopting optimum index factor approach. The vulnerabilities of different groundwater quality parameters are also computed to provide an insight about the deviations of their concentrations from the safe drinking water limits. The application of the developed stability index is demonstrated through a case study in quaternary alluvial and quartzite aquifer system of India. In addition, trends in the groundwater quality parameters are identified by using variance-corrected Mann-Kendall test, and trends are quantified by using Sen's slope estimation test. Box-whisker plots revealed that EC and TDS mostly exceed their maximum permissible limits prescribed for drinking water in the southern and southwest hard-rock formations. Whereas, most parameters do not cross their maximum desirable limits in the central and northern alluvial formations. Increasing trends of potassium and bicarbonate, and decreasing trends of carbonate, calcium, sulfate, and fluoride are found prominent. The GQSI values indicated high stability of groundwater quality under older alluvium geology and low stability under the gneiss and mica-schist. Results of the GQSI are found in agreement with that of groundwater-quality index (GQI) at 84% sites, which proved adequacy of the developed GQSI. Also, three classes ('low'/'poor', 'moderate', and 'high'/'good') of both the GQSI and GQI showed a good coherence at 83, 78, and 87% sites. However, GQSI is more advantageous than GQI due to former's statistical framework, consistency and comparability over different areas. Three optimum index factors, i.e., TDS, pH and nitrate, are found to have the maximum impact on overall groundwater quality with their largest variations. Results of the optimum groundwater-quality stability index (OGQSI) and GQSI closely matched with each other, and a significant linear relationship (R2 = 0.70) existed between them. Therefore, OGQSI is a cost-effective approach for adequate monitoring and satisfactory evaluation of the groundwater quality in low-income nations. Highlights • A novel groundwater-quality stability index (GQSI) is proposed and validated. • The GQSI integrates probabilistic indicators (i.e., reliability and resilience). • The cost-effective GQSI has advantages of statistics, consistency and comparability. • Application of the developed stability index is demonstrated through a case study. • Groundwater quality trends are identified and quantified. [ABSTRACT FROM AUTHOR]

This study examines the effect of autocorrelation on step and monotonic trends in seasonal and annual rainfall. Initially, for step change, modified-Pettitt test is applied in two ways. First, using the corrected and unbiased trend-free-pre-whitening (TFPWcu) approach. Second, using a new approach in which time series is modelled by intervention analysis for modified Pettitt test. Subsequently, for monotonic trends, Mann-Kendall (MK) and six approaches of modified Mann-Kendall (MMK) test are applied to NCDC data for period 1901-2012 and its sub-periods. Approaches of MMK include pre-whitening (PW), trend-free-pre-whitening (TFPW), TFPWcu, two Variance Correction Approaches (VCAs) based on empirical formula (VCA:CF1) and Monte-Carlo-Simulations (VCA:CF2) and long term persistence (MK-LTP). A single change point is identified in 1970 for annual and monsoon rainfall from original and modified-Pettitt's test using TFPWcu, while time series modelling approach has not exhibited any change point. Process shift in rainfall series is also studied using CUSUM and multiple change points are identified using Segment-Neighbourhood method. Outcomes of MMK show that TFPWcu is able to efficiently limit the effect of autocorrelation and may be preferred over PW and TFPW. The VCA:CF2 is not dependent on whole autocorrelation structure and corrects variance of all data series using lag-1 autocorrelation and may be preferred over VCA:CF1. MK-LTP considers long term persistence and it has exhibited presence of weaker trends than exhibited by other approaches. VCA:CF2 and MK-LTP are used to study trends of rainfall in Dehradun. [ABSTRACT FROM AUTHOR]

In this study, seasonal trends as well as dominant and significant periods of variability of drought variables were analyzed for 30 rainfall subdivisions in India over 141 years (1871–2012). Standardized precipitation index (SPI) was used as a meteorological drought indicator, and various drought variables (monsoon SPI, non-monsoon SPI, yearly SPI, annual drought duration, annual drought severity and annual drought peak) were analyzed. Discrete wavelet transform was used in conjunction with the Mann-Kendall test to analyze trends and dominant periodicities associated with the drought variables. Furthermore, continuous wavelet transform (CWT) based global wavelet spectrum was used to analyze significant periods of variability associated with the drought variables. From the trend analysis, we observed that over the second half of the 20th century, drought occurrences increased significantly in subdivisions of Northeast and Central India. In both short-term (2–8 years) and decadal (16–32 years) periodicities, the drought variables were found to influence the trend. However, CWT analysis indicated that the dominant periodic components were not significant for most of the geographical subdivisions. Although inter-annual and inter-decadal periodic components play an important role, they may not completely explain the variability associated with the drought variables across the country. [ABSTRACT FROM AUTHOR]

The present study is mainly concerned with detecting the trend of run-off over the mainland of India, during a time period of 35 years, from 1971-2005 (May-October). Rainfall, soil texture, land cover types, slope, etc., were processed and run-off modelling was done using the Natural Resources Conservation Service (NRCS) model with modifications and cell size of 5×5 km. The slope and antecedent moisture corrections were incorporated in the existing model. Trend analysis of estimated run-off was done by taking into account different analysis windows such as cell, medium and major river basins, meteorological sub-divisions and elevation zones across India. It was estimated that out of the average 1012.5 mm of rainfall over India (considering the study period of 35 years), 33.8% got converted to surface run-off. An exponential model was developed between the rainfall and the run-off that predicted the run-off with an R of 0.97 and RMSE of 8.31 mm. The run-off trend analysed using the Mann-Kendall test revealed that a significant pattern exists in 22 medium, two major river basins and three meteorological sub-divisions, while there was no evidence of a statistically significant trend in the elevation zones. Among the medium river basins, the highest positive rate of change in the run-off was observed in the Kameng basin (13.6 mm/yr), while the highest negative trend was observed in the Tista upstream basin (−21.4 mm/yr). Changes in run-off provide valuable information for understanding the region's sensitivity to climatic variability. [ABSTRACT FROM AUTHOR]

During the pre-monsoon season in the Eastern and North-eastern Indian regions, thunderstorms are the primary source of precipitation. The intensity and frequency of these thunderstorms are changing over time in these areas. The knowledge of atmospheric instability associated with these pre-monsoon thunderstorms can be aptly collected using thermodynamic indices. Thermodynamic indices integrate the comprehending convective atmosphere's geographical and temporal interpretation and are crucial in thunderstorm nowcasting. The present work investigated the spatial variation of thermodynamic indices for 1987–2016 over two tropical north-eastern states of India (Assam and Tripura) using the ERA-5 reanalysis data with 0.25° × 0.25° (31 km) resolution. The analysis incorporated information on thunderstorm days (TD) and non-thunderstorm days (NTD) provided by the India Meteorological Department (IMD) for differentiating the spatial variations of thermodynamic indices. The thermodynamic indices used in this work are categorised as conditional, potential, and convective instabilities. The results reveal that indices associated with convective and potential instabilities perform better for differentiating thunderstorms. We have incorporated the Man-Kendall trend analysis to investigate the trends in the spatial variation of thermodynamic indices. The results indicate that the region is showing higher instability for thunderstorm occurrences on TD, and there is higher moisture availability for the area during the whole pre-monsoon season. The inhibition is higher over NTD days, limiting the convective activity occurrence on certain days. The severity of thunderstorms as analysed with the help of thermodynamic indices are not changing over the region. • Thermodynamic indices maps have been generated over Tripura and Assam, India, for pre-monsoon. • The difference has been observed in thermodynamical variations during thunderstorms for 1987–2016. • The conditional/convective instability indices differentiate thunderstorms and clear days. • Climate change impact is visible as the threshold values are changing over the years for the study. [ABSTRACT FROM AUTHOR]

Summary The Mahanadi river basin, a major river basin in eastern India, is expected to be the worst affected river basins in India in terms of the increase in intensity of floods due to climate change. The last decade has already seen five high floods occurring in the Mahanadi basin. Are these recent high floods an indication that Mahanadi floods are already getting worse? If so, is it due to an increase in extreme rainfalls over the basin? To answer these, we analyse the trends of peak discharge at the mouth of the delta at Naraj gauging site (the flow which is responsible for floods in the delta of Mahanadi basin) and peak releases from Hirakud dam in Mahanadi basin. Subsequently, grid based analysis of extreme rainfall is carried out for the entire Mahanadi basin to analyse the spatially varied extreme rainfall trends. Also, a region based (upper and middle regions of Mahanadi basin) analysis of extreme rainfall is carried out using daily mean areal rainfall. The trends of extreme rainfalls are analysed in relation to the trends of peak floods. The entire analysis pertains to post-Hirakud dam construction period. The analysis reveals that the recent incidences of high floods in Mahanadi basin is due to an increase in extreme rainfall in the middle reaches of the basin. [ABSTRACT FROM AUTHOR]

In this study, reference evapotranspiration (ET) has been estimated using Penman-Monteith (PM) method on monthly time step. The monthly values have been subsequently used to estimate annual and seasonal ET values. The trend analysis has been carried out for monthly, annual and seasonal ET values for three meteorological stations namely Allahabad, Rewa and Satna located in the Tons River Basin in Central India. Further, the trend of weather variables that affect ET have been examined using the Mann-Kendall test after removing the effect of significant lag-1 serial correlation from the time series using trend free-pre-whitening (TFPW) method. The magnitude of trends has been calculated using Sen's slope estimator. Almost all the months show the significant decreasing trend in ET values at a significance level of 1, 5 and 10 %. The significant decreasing trends were also found in annual and seasonal ET values during the period of analysis. The magnitude of decrease in annual ET varied from −1.75 to −8.98 mm/year. On the seasonal scale, stronger decreasing trends were identified in ET in pre monsoon and monsoon season as compare to that of winter and post monsoon season. The significant decreasing trends were found in monthly, annual and seasonal wind speed. However, significant increase was found in annual air temperature (maximum, minimum, mean and dew point temperature) and relative humidity. Using the sensitivity analysis, maximum temperature and net solar radiation was found to be the most dominant variables which influence the rate of annual ET over all the stations. [ABSTRACT FROM AUTHOR]

Abstract: In the present study, an investigation has been made to study the spatial and temporal variability in the maximum, the minimum and the mean air temperatures at Madhya Pradesh (MP), in central India on monthly, annual and seasonal time scale from 1901 to 2002. Further, impact of urbanization and cloud cover on air temperature has been studied. The annual mean, maximum and minimum temperatures are increased by 0.60, 0.60 and 0.62°C over the past 102 years, respectively. Seasonally, the warming is more pronounced during winter than summer. The temperature decreased during the less urbanized period (from 1901 to 1951) and increased during the more urbanized period (1961 to 2001). It is also found that the minimum temperature increased at higher rate (0.42°C) followed by the mean (0.36°C) and the maximum (0.32°C) temperature during the more urbanized period. Furthermore, cloud cover is significantly negatively related with air temperature in monsoon season and as a whole of the year. [Copyright &y& Elsevier]

The study of precipitation trends is critically important for a country like India whose food security and economy are dependent on the timely availability of water. In this work, monthly, seasonal and annual trends of rainfall have been studied using monthly data series of 135 years (1871-2005) for 30 sub-divisions (sub-regions) in India. Half of the sub-divisions showed an increasing trend in annual rainfall, but for only three (Haryana, Punjab and Coastal Karnataka), this trend was statistically significant. Similarly, only one sub-division (Chattisgarh) indicated a significant decreasing trend out of the 15 sub-divisions showing decreasing trend in annual rainfall. In India, the monsoon months of June to September account for more than 80% of the annual rainfall. During June and July, the number of sub-divisions showing increasing rainfall is almost equal to those showing decreasing rainfall. In August, the number of sub-divisions showing an increasing trend exceeds those showing a decreasing trend, whereas in September, the situation is the opposite. The majority of sub-divisions showed very little change in rainfall in non-monsoon months. The five main regions of India showed no significant trend in annual, seasonal and monthly rainfall in most of the months. For the whole of India, no significant trend was detected for annual, seasonal, or monthly rainfall. Annual and monsoon rainfall decreased, while pre-monsoon, post-monsoon and winter rainfall increased at the national scale. Rainfall in June, July and September decreased, whereas in August it increased, at the national scale. Citation Kumar, V., Jain, S. K. & Singh, Y. (2010) Analysis of long-term rainfall trends in India. Hydrol. Sci. J. 55(4), 484-496. [ABSTRACT FROM AUTHOR]

Summary: Investigating the response of groundwater levels to the extreme weather events provides essential information for sustainable planning and management of water resources. The aim of this study was to identify and quantify the groundwater level trend of the state Orissa (India) to understand the forcing mechanism of droughts in conjunction with the anthropogenic pressure using the non-parametric Mann–Kendall statistical procedure. The pre- and post-monsoon groundwater level records of 1002 monitoring stations during the period 1994–2003 were analyzed. The results show that the drawdowns due to deficient rainfall during dry years, high temperatures, and anthropogenic pressure have not been recovered through the recharge in wet years. However, this study does not determine whether drought, high temperatures or anthropogenic effects have had largest influence on the groundwater levels decline. The cases of significant water table declines are higher in number than those expected to occur by chance. In the pre-monsoon season, 59% of the monitoring stations experienced groundwater declines as against 51% in the post-monsoon season for the study area as a whole. This could be interpreted that the fluctuation is not a part of noise, but that a signal is being identified. Further, the trend result showed wide spatial and seasonal differences. Irrespective of seasons, the consolidated rock formation that covers 80% of the geographical area experienced significant water table decline. However, the semi-consolidated and unconsolidated formations experienced water table decline in the pre-monsoon (summer) season only. The vulnerable sites where the groundwater-level declined significantly were identified so that recharge measures could be taken up. [Copyright &y& Elsevier]

A comprehensive study on the Indian summer monsoonal rainfall (ISMR) is performed in the light of decadal changes in the continuous rainfall events and the number of rainy days using 68 years (1951–2018) of gridded rain gauge data. Non-parametric Mann–Kendall's test is applied on total rainfall amount, the number of rainy days, number of continuous rainfall events, and rainfall magnitude to find trends over different climatic zones of India for the two periods, 1951–1984 and 1985–2018. Our results found a decreasing trend for more than 4-days of continuous rainfall events during the recent 34 years (1985–2018) compared to 1951–1984. The rate of increase/decrease in extreme/continuous rainfall events does not follow a similar trend in number of continuous rainfall events and magnitude. Moreover, the rainfall is shifted towards a lesser number of continuous rainfall days with higher magnitudes during 1985–2018. During the crop's sow season (i.e., the first 45 days from the onset date of Indian monsoon), the total number of rainy days decreased by a half day during the last 34 years. Over the Central and North East regions of India, the number of rainfall days decreased by ~0.1 days/yr and ~0.3 days/yr, respectively, during 1985–2018. Overall, the decreasing trends in continuous rainfall days may escalate water scarcity and lead to lower soil moisture over rain-fed irrigated land. Additionally, an upsurge in heavy rainfall episodes will lead to an unexpected floods. On a daily scale, rainfall correlates with soil moisture and evaporation up to 0.87 over various land cover and land use regions of India. Continuous light-moderate rainfall seems to be a controlling factor for replenishing soil moisture in upper levels. A change in rainfall characteristics may force the monsoon-fed rice cultivation period to adopt changing rainfall patterns. [ABSTRACT FROM AUTHOR]

The pre-monsoon aerosol concentration plays a significant role in modifying precipitation amount over the Indian sub-continent. A large variety of aerosol from different sources produces a complex radiative and climate response through the interaction with the hydrometeorological parameters. In this study, we analyzed the space-time dynamics of aerosol optical depth (AOD) in relation to the meteorological and surface parameters over Indian sub-continent during pre-monsoon season from 2007 to 2016. The level-3 daily aerosol products from Moderate Resolution Imaging Spectroradiometer (MODIS) and Multi-angle Imaging Spectroradiometer (MISR) were used. The IMD gridded rainfall and temperature, ECMWF derived RH and wind velocity, and GLADS derived soil moisture data were also used at daily time scale. For the space-time model, the Mann-Kendall trend test and a pixel-based multiple linear regression were used, while, wavelet transformation was used on these daily observations for analyzing periodicities of AOD. The time-series average shows moderate to high AOD (0.4–0.8), including a consistent positive anomaly in the Indo-Gangetic basin (IGP) in north India. A significant inter-annual variation is also observed both in MODIS and MISR datasets. The trend statistics shows an increasing trend of aerosol concentration in the eastern and southern India. The linear regression shows a variable response of AOD with changing magnitude of meteorological factors. However, a substantial spatial coverage of significantly decreased AOD is observed with increasing soil moisture content (β < −0.04). The wavelet analysis manifests the abundances of 32–128 days of cycle with a periodic interjection of 8–32 days of cycle suggesting occurrence of fine and coarse mode aerosols events, respectively. The coherency of time series AOD and other covariates shows varying leading and lagging dynamics in these two principal periodicities. The findings, however, evidenced a notable difference in the space-time patterns of AOD in MODIS and MISR datasets. The analyzed AOD cycles are coincided with the Madden-Julian-Oscillation (MJO) that recurs every 30–60 days interval. The findings also support the theoretical proposition of elevated heat pump theory (EHP) driven by fine mode aerosols for occurring pre-monsoon and monsoon precipitation over the Indian sub-continent. The analyzed periodicity of AOD can provide useful insights in studying the short/long term variability of precipitation over polluted environments during the pre-monsoon season. Image 1 • Mann-Kendell test reveals increasing trend of AOD in the eastern provinces • Significant negative relationship of AOD is obtained with soil moisture • Presence of 32–128 days cycle in the time series is evident of fine mode aerosols • Differences are observed for wavelet and regression results between MODIS and MISR • The longer periodicity of AOD support the theoretical proposition of EHP over India [ABSTRACT FROM AUTHOR]

• A normalized entropy measure, Standardized Variability Index, is proposed. • Trend analysis shows one-third of the country experienced a significant increase. • More extreme rainfall events in the near future are anticipated. Understanding the spatiotemporal variability of rainfall is vital for water resources planning and management, flood and drought mitigation, and erosion control, among others. Despite the progress in this direction, through proposal of many different approaches and their applications to rainfall data at various regions around the world, our knowledge of the spatiotemporal variability of rainfall remains limited. Studies in this direction have largely focused on the amount of rainfall and its spatial patterns, and investigations of spatiotemporal variability at multiscale are limited. In this study, we introduce a novel measure, Standardized Variability Index (SVI), based on the concept of entropy to investigate the spatiotemporal variability of gridded rainfall in the Indian subcontinent at different timescales. The results show distinct spatial patterns in the inter-annual rainfall variability at all timescales. Also, the intra-annual variability of rainfall amount, as well as rainy days, is found to increase from east to west of India. The Mann-Kendall trend test at different timescales reveals significant increase in rainfall variability. In addition, coupling the mean annual rainfall with SVI enables a relative assessment of the water resources availability. [ABSTRACT FROM AUTHOR]