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In India, Agriculture plays a pivotal role in the Indian economy, contributing approximately 17% to the total GDP and employing over 60% of the population. Not only in India, agriculture sector is one of the most important sectors in all over the world. In the past few years many machine learning algorithms are being used to recommend the best crop for farmers under the different conditions. It is important to recommend the best crop to the farmers not only for the better growth but also for the decrease the risk of farmers. Tn this paper we are doing the review of different machine learning algorithms like Logistic Regression, Naïve Bayes, Support Vector Machine, Extra Trees, Decision Tree, Randam Forest and many more. And then we find out the best algorithms based on their accuracy rate and used that algorithm in our project to recommend the crop. It also requires the dataset to train our system. So, we take the dataset of values like N, P, K, temperature, humidity, ph, and rainfall. [ABSTRACT FROM AUTHOR]

Holistically, after the green revolution, India has been a global agricultural powerhouse of various crops and in present most of the farmers adopt the traditional irrigation methods but challenging task is in utilizing minimum water resource for more productivity per crop in diversified Indian environmental conditions. Water is a precious & scarce resource in many areas of the country due to traditional agricultural irrigation & industrial use. Hence, to limit water loss & wastage in farming it is important for maximum water application efficiency. Indeed, Systematic Irrigation System (SIS) for farming is need of hour and hence, attempt is made for development of SIS using computational device, and NodeMCU & Sensor technology are illustrated in this article. The SIS prototype has developed deploying ESP8266 Wi-Fi SoC development board and various sensors such as humidity, temperature, soil moisture, PIR & rain sensor . Graphic User Interface is developed in Blynk platform. This prototype has been tested in the laboratory successfully for application efficiency and has found good performance as expected. [ABSTRACT FROM AUTHOR]

The world has achieved massive economic progress in the past few decades, along with population explosion and rapid urbanization/industrialization, the effects of which are being realized across the world in the form of global climate change with huge environmental impact. Though climate change is a natural phenomenon, the release of several greenhouse gases (GHGs) into the atmosphere through human activities has made a greater contribution to global warming. The present paper is a review of Indian climate features/trends and their consequences across the country. Recent countrywide trends in some selected climatic parameters like rainfall and temperature have been depicted. India is an agrarian country and is exposed to various hydro-ecological vulnerabilities, particularly successively occurring, extreme, climatic hazards like drought, flood and cyclone. Based on some indices, possible vulnerable areas due to the effect of climate change have also been depicted. Although the analyses are based on national level secondary data on selected parameters, some region-specific micro-level studies may better reveal the significance of these vulnerabilities. The present paper an attempt to map the climate variability across the nation. [ABSTRACT FROM AUTHOR]

The article offers some strategies for mitigating weather risks pose to the agricultural sector in India in 2012. It warns about the impact of climate change on agriculture. It highlights a decrease in agricultural productivity due to lower rainfall and higher temperatures. The article suggests the use of technology to counter the impact of climate change.

On 4 October 2023, a glacier lake outburst flood (GLOF) occurred at South Lhonak Lake in the northwest of Sikkim, India, posing a severe threat to downstream lives and property. Given the serious consequences of GLOFs, understanding their triggering factors is urgent. This paper conducts a comprehensive analysis of optical imagery and InSAR deformation results to study changes in the surrounding surface of the glacial lake before and after the GLOF event. To expedite the processing of massive InSAR data, an InSAR processing system based on the SBAS-InSAR data processing flow and the AI Earth cloud platform was developed. Sentinel-1 SAR images spanning from January 2021 to March 2024 were used to calculate surface deformation velocity. The evolution of the lake area and surface variations in the landslide area were observed using optical images. The results reveal a significant deformation area within the moraine encircling the lake before the GLOF, aligning with the area where the landslide ultimately occurred. Further research suggests a certain correlation between InSAR deformation results and multiple factors, such as rainfall, lake area, and slope. We speculate that heavy rainfall triggering landslides in the moraine may have contributed to breaching the moraine dam and causing the GLOF. Although the landslide region is relatively stable overall, the presence of a crack in the toparea of landslide raises concerns about potential secondary landslides. Our study may improve GLOF risk assessment and management, thereby mitigating or preventing their hazards. [ABSTRACT FROM AUTHOR]

Under global warming and climate change (CC), the vulnerable piece of land is India's Eastern-Himalayan-Region (EHR), where millions' livelihood activities are at risk. Quantifying the degree of CC-induced vulnerability over the highly climate-sensitive mountain zone is challenging due to data inadequacy. This paper attempted to assess the CC perception of kiwifruit growers under EHR and the impact of CC on its cultivation. Perceived and scientific data were used for accurate decision-support information. The climatic variables of two locations were analyzed for trend using Mann–Kendall's test. Results suggested that the rainfall trend was found non-significant for both locations, but there was a negative trend during winter for Pasighat and during all seasons for Itanagar. Maximum temperature exhibited an increasing trend for annual, and minimum temperature showed an increasing trend across seasons for the Pasighat region. For the Itanagar region, the maximum temperature was found to have a significantly increasing trend in all seasons, while the minimum temperature showed an increasing trend during winter and annual seasons. Morning relative humidity showed an increasing trend for annual data for Pasighat, while annual evening relative humidity was significantly increased for Itanagar. The study shows that the indigenous farming communities were following shifting cultivation and diversifying their traditional agriculture to kiwifruit cultivation as an adaptation strategy. The community had a clear perception of CC trends and an inadequate perception of the impact on agriculture, therefore, engaging indigenous communities and other stakeholders to postulate a comprehensive adaptation and mitigation strategy for CC. [ABSTRACT FROM AUTHOR]

Purpose: The goal of this paper is to compare the GDP growth rate with the change in rainfall to demonstrate the opportunity to introduce rainfall index-based futures. The purpose of this paper is to suggest a new kind of versatile tool to overcome the rainfall risk. This study shows the application of rainfall index-based futures contracts for agricultural rainfall risk. Finally, this article examines the feasibility and effectiveness of rainfall indexbased futures in the new era of the weather risk market as a rainfall risk management tool. Design/Methodology/Approach: The study used statistical tools like correlation and regression for the period from 1961 to 2020. The market for rainfall risk is comprised of traditional insurance, index insurance, and reinsurance. But these are all failures to manage the rainfall risk effectively. Findings: This study shows the application of rainfall index-based futures contracts for agricultural rainfall risk. Finally, this article examines the feasibility and effectiveness of rainfall index-based futures in the new era of the weather risk market as a rainfall risk management tool. Practical implications: Rainfall derivatives are currently needed for the creation of a fullfledged rainfall risk market and to complete the incomplete weather risk market. Originality value: The present study design the rainfall index based futures contract to hedge the rainfall risk in India. [ABSTRACT FROM AUTHOR]

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Indian southwest monsoon rainfall has a huge socioeconomic influence, and any changes in monsoon rainfall patterns will have major consequences for agriculture, water supplies, and other connected sectors of economy. The paper explores the charactering and forecasting of the southwest monsoon rainfall (June to September) patterns over the Central region of India for the period (1901–2021: 121 years) using linear state space modeling. The study period is split into two periods, i.e., earlier period (EP: 1901–1950) and the recent period (RP: 1951–2021). The linear state space model (LSSM) with significant latent components such as trend, seasonal, periodic and random variations or noise is selected for modeling the southwest monsoon rainfall (SWMR) based on Bayesian information criteria (BIC) and statistical fit for the two periods. The result indicates that an increase in linear time trend during the EP and the RP has a decreasing local linear time trend in SWMR, and it is interesting to notice that the EP had no noteworthy periodic variations, whereas the RP exhibits significant periodic variations in SWMR. Further, the selected LSSM forecasts SWMR patterns for the period 2022–2028, and it has been observed that the Central Indian region is expected to receive substantial rainfall in the years 2026, 2027 and 2028. [ABSTRACT FROM AUTHOR]

Better understanding on the phases of Indian summer monsoon i.e., onset, progress and withdrawal helps in the decision making and proper management. In this paper, date of onset of monsoon (DOM) and date of withdrawal of monsoon (DWM) is estimated using the long-term daily rainfall data observed by the India Meteorological Department (IMD) and cumulative rainfall anomaly concept, as the onset/withdrawal represents a signature of rainfall transition. The results are promising with a better match between the estimated DOM and IMD announced DOM over Kerala. The length of monsoon season variability is also analysed. The concept of onset at grid point (OGP) in India is also evaluated and progress of monsoon is monitored by considering the latitudinal variation, persistent & strength of rainfall in the continental India. The zonal classification of the spread of monsoon in terms of the intensity and time (duration) is presented. Zone wise pentad analysis of onset clearly signifies the progress of monsoon and its dependency on various physical factors like SST, OLR, TTG, ITCZ, Energy budget and wind circulation. The impact of ENSO on the OGP is brought out, which clearly shows that DOM, DWM and progress is almost influenced by the largescale processes. The intensity–duration–frequency analysis quantified the actual ISM length, seasonal rainfall and rainy days at all locations in India. This study will help modellers in better understanding of onset and progress of monsoon dynamics in India and can be integrated with NWP for the accurate and advance prediction of DOM and the progress. [ABSTRACT FROM AUTHOR]

Short-duration rainfall data are basic inputs to many rainfall-runoff models for generating the flood hydrographs. But these data are scarce in comparison to daily data, which is abundantly available, but cannot be directly used unless disaggregated to a shorter duration. This paper adopts a simple scaling approach for disaggregation of daily design precipitation into the desired duration and the time distribution of precipitation based on 3p Beta distribution herein referred to as the scaled 3p-Beta approach. The rainfall quantiles and their temporal distribution, when used as an input to the rainfall-runoff model, generate the design flood hydrograph at the ungauged location. The Upper Baitarani Basin under Mahanadi Sub-zone (3d) of India has been selected as the test catchment. The efficacy of the proposed scaled 3p-Beta approach, and also the rational approach coupled with CWC-2p Gamma UH was verified in the test catchment for design flood computation. The findings reveal that the methodology ensures encouraging results directly derivable from the abundantly available daily rainfall data and can be applied in any ungauged catchment in a region. [ABSTRACT FROM AUTHOR]

The present research study aims to analyze flood frequency to relate flood magnitudes with corresponding return periods. These estimates are crucial for the development of flood-preventive hydrological infrastructures and flood plain zoning, etc. Basin-wide high intensity of rainfall and high discharge from the upper riparian region during monsoon months creates flooding in the middle and lower reaches of the Ghagra River basin. Therefore, the flood estimates for three gauging sites situated on the mainstream of the Ghagra River viz., Elginbridge, Ayodhya, and Turtipar, have been comprehended using the Gumbel distribution (Extreme Value Type I) method and the Log-Pearson Type III distribution method. Flood estimates are calculated for 1.01, 1.05, 1.11, 1.25, 2, 5, 10, 20, 50, 100, 200, and 500 years return periods considering flood time series of 50 years to reduce estimation uncertainty. The findings of flood frequency analysis (FFA) revealed that the probability of occurrence of the flood is more than 80% at all three sites because Ghagra River can carry around 7800 to 9000 m3/s of water discharge without posing a high risk of levee break. The upper and lower limits of discharge carrying capacity depend on the river's desiltation process. Hence, the flood occurs almost every year in the basin; its only variation is its severity. The rainfall-runoff relationship is estimated by integrating a simple linear model and for rainfall trend analysis Mann-Kendall is applied. Linear regression analysis-based rainfall-runoff relationship outcomes revealed a significant relationship with a positive correlation coefficient i.e., 0.2722 for Elginbridge, 0.39624 for Ayodhya, and 0.4844 for Turtipar gauging site in the monsoon season but other factors like a high amount of water discharge from dams in upper riparian regions, etc. are also responsible for flooding in the middle and lower reaches. The Mann-Kendall trend analysis shows a decrease in annual average rainfall and rainfall in the monsoon season. Discharge variability indicates the direct relationship between flood fury and changes in climatic patterns in recent decades. This paper identifies that future research is needed to better inform the policy planners who strive to design sustainable infrastructure. [ABSTRACT FROM AUTHOR]

The present study explored district-wise monthly rainfall of two climatic regimes (1960–1990 and 1991–2020) of the West Bengal state of India, which receives an annual rainfall of about 1500 mm with huge seasonal rainfall variability. A simple time series analysis does not show any noticeable trend. However, decomposing rainfall parameters into several indicators may explore the change. From this perspective, the present paper intends to explore the nature, degree, and direction of rainfall alteration. The range of variability approach (RVA) considered 29 indicators was adopted for unveiling it. RVA explored that in the 1991–2017 phase, the rainfall failure rate above the upper threshold (75th percentile) is 33% which is 10% greater than in 1961–1990. Seasonal variation in failure rate was detected, but overall larger area of the state recorded failure rate (FR) above the threshold. This result leads to increasing wetness conditions. Very low to moderate (< 0.4) degree of impact of rainfall alteration (DIRA) was found in the recent period. The areal occupation of moderate DIRA was considerable in pre-monsoon, post-monsoon, and winter seasons. Time series rainfall data-based flow duration curves (FDCs) indicated the dominance of the eco-surplus rainfall state over greater parts of the state (72–84%) in all the seasons except winter (49%). Such surplus rainfall may be beneficial to the ecological and agricultural perspective of the state. [ABSTRACT FROM AUTHOR]

Since antiquity, Puri, known for excellence in the field of art and architecture, has been one of the Char Dhams (holy places) of the Hindu Pilgrimage of India. India is rich in temples and historical settlements from the ancient Kashi to Char Dhams. The abode of Shree Jagannath, Puri, is one of the cardinal centres of pilgrimage for Hinduism. Puri, the cultural capital of Odisha, connects thousands of pilgrims to spirituality through its art, culture, heritage and Jagannath traditions. In the eastern state of India, Odisha is an uncharted gem of cultures and traditions with literature and architecture, picturesque beaches and wilderness, with Puri as its focal point. Puri has a distinctive blend of ethnicity, culture and tradition with an eclectic flair. The city accounts for about half of the tourist inflow to the state while tripling it during the Rath Yatra — the annual chariot festival. Situated on the Bay of Bengal, Puri, a high disaster risk zone, often encounters natural catastrophes such as heavy rainfall, floods, thunderstorms and severe cyclonic storms. Despite its importance and numerous governmental schemes, cultural heritage is always vulnerable to damage and destruction. This makes the documentation and sensitive planning approach to the conservation of the pilgrim city of the utmost importance. Based on secondary data and extensive surveys, this paper construes the multifaceted challenges of Puri with emphasis on its sociocultural identities, critically exploring the city in its contemporary form with the urban conservation initiatives undertaken by authorities. It summarises that modification in the planning approach is mandated to achieve its optimum potential to be revered as a world-class heritage city. [ABSTRACT FROM AUTHOR]

Potential changes in hydro-meteorological events have been causing mass damage to the economy and lives. Among several other factors, the progression of climate change over a long time is expected to cause non-stationarity in annual maximum rainfall. Understanding the characteristics of annual maximum rainfall series is crucial for coastal cities as they are highly vulnerable due to the greatly varying weather patterns. In this paper, we propose stationary and non-stationary methods to model the effect of non-stationarity on the differing duration of annual maximum rainfall and demonstrate the impacts on nine coastal cities spread across the Arabian Sea and Bay of Bengal stretch of India. The Bayesian inference parameter estimation technique was used. It was found that while stationary models often fit well for longer-duration rainfall, non-stationary models often best fit the short duration. [ABSTRACT FROM AUTHOR]

The rainfall over the Indian region, governed majorly by the monsoonal flow, is a point of research in the perspective of climate change. In this paper, we compute the change points in the rainfall series at every grid of the India Meteorological Department (IMD) daily gridded rainfall data for a period of 120 years (1901 to 2020). The map shows clearly demarcated regions indicating different zones, where the rainfall statistics have altered at different periods. It is observed that in a major part of central India, the shift in rainfall intensity is mainly associated with the time frame 1955–1965; in the Indo-Gangetic plain, the changes are found to be more recent (1990), while the latest changes (post 2000) are observed particularly for North Eastern region and some parts along the East Indian coast. The changeover years are significant at a 95% confidence level for most part of the Indian landmass. The causes may be surmised due to moisture transport from the Arabian Sea (Central India), the presence of aerosol (Gangetic Plain), and the possible revival of monsoon due to land–ocean gradient (Eastern coast and North East India). This is the first-ever study which provides a comprehensive daily rainfall change point map over India using 120 years of gridded station data. [ABSTRACT FROM AUTHOR]

This paper shows that, first, the effects of monsoon rainfall shocks on agricultural yield in India are highly asymmetric: yield falls strongly after droughts, whereas excessive rainfall has only little effects. Second, our key novel finding is that the short‐lived yield loss after a widespread drought elicits a persistent decline (increase) in wages (food prices), which lasts for up to five years. Third, affiliation to the same National Sample Survey region (and thus to the same state) seems to be a key determinant of internal migration, whereas distance appears more relevant for food arbitrage trading. [ABSTRACT FROM AUTHOR]

In this paper, we discuss an approach that predicts the quantitative value of rainfall. The proposed algorithm uses a combination of data mining and neuro-fuzzy inference system for prediction. The model is demonstrated on north interior Karnataka (a state in India) rainfall data as a case study. This model is applicable to any geographical area provided apt predictors are included. For north interior Karnataka rainfall prediction predictors are derived from local and global climate conditions. The local condition variables are derived from the mean sea level pressure, temperature, and wind speed in south India. The global variables affecting the north interior Karnataka rainfall include, Darwin sea level pressure, the ENSO indices and southern oscillation. The data mining technique, association rule mining, is used to study the correlation among the predictors; clustering is used for predictor selection as well as membership function creation for fuzzyfication. Neuro-fuzzy inference system is further used for fine tuning the "If-then" rules and crisp value prediction of the rainfall. The prediction accuracy is observed to be good considering Tropical Meteorological Department data. [ABSTRACT FROM AUTHOR]

The mountainous areas are vulnerable to climate change and may have many socio-economic and environmental implications. The changing pattern of meteorological variables has deleterious effects on natural resources and livelihood. This paper makes an attempt to analyse trend and forecast metrological variables in Nainital district of India. Monthly, seasonal, and annual trends in rainfall and temperature were examined by Modified Mann–Kendall during 1989–2019. The magnitude of trend in temperature and rainfall was determined using Sen's slope estimator. Ensemble machine learning model was utilized for forecasting the variables for the next 16 years (2020–2035). The effectiveness of the model was examined through statistical performance assessors. The results revealed a significant increasing trend in the rainfall (at the rate of 9.42 mm/year) during 1989–2019. Increasing trend in the mean, minimum, and maximum temperatures on an annual basis was observed in the district. A remarkable increase in the rainfall and temperature was forecasted during various seasons. The findings of the study may help the stakeholders in devising suitable adaptation measures to climate variability. The bagging approach has shown its effectiveness in forecasting meteorological variables. The other geographical regions may find the methodology effective for analyzing climate variability and lessening its impact. [ABSTRACT FROM AUTHOR]

Climate change is a major concern, as it profoundly affects many facets of our lives. It has brought about several issues, including declining water supply, reduced agricultural yields, increased drought occurrences, and increased heat waves. Amidst these challenges, the influence of short-term drought events on plant growth and irrigation schedule emerges as a critical concern. However, despite these evident consequences, a nuanced understanding of the intricate relationship between the severity and duration of short-period drought/deficit events still needs to be explored. This paper analyses fortnightly water deficit periods over different regions of India, which would be more relevant to re-scheduling the irrigation events than monthly or longer duration in preventing crops from reaching the permanent wilting point. Hence, this work considers analyzing 15-day Standardized Precipitation Index (SPI) trends and drought characteristics using conventional methods and Innovative Trend Analysis (ITA) techniques. The analysis uses gridded rainfall data from the India Meteorological Department (IMD), which has a spatial resolution of 0.250-E and 0.250-N. The data spans the period from 1970 to 2021. The ITA and Mann Kendall (MK) displayed nearly identical areas of increasing and decreasing trends, but ITA effectively identified significant trends. While MK and Modified Mann Kendall (MMK) could only indicate significant trends for 12.94%, 9.57%, and 9.9% of grid points for SPI, drought severity, and duration, respectively, ITA was able to identify significant trends at 44.31%, 10.9%, and 10.1% on an annual scale. The ITA method effectively identified the significant trends and magnitudes of fortnightly SPI and drought characteristics. The Tropical monsoon (Am), Tropical savannah (Aw), Arid desert hot (BWh), Arid steppe hot (BSh), and Temperate dry winter warm summer (Cwb) climatic zones have shown a significant increase in annual drought severity. Similarly, a significant increase in monsoon drought severity is observed across several states, including Gujarat and Mizoram, impacting diverse geographic extents. The present study can help policymakers and water resource managers decide on water allocation, irrigation, and crop management practices. [ABSTRACT FROM AUTHOR]

The east coast of India is highly prone to devastating winds, torrential rainfall, and storm surges caused by tropical cyclones. The storm surge is affected by ocean basin characteristics involving the width and slope of the continental shelf. The bed roughness plays a major role in surge formation. The east coast of India is characterized by a broader shelf in the north and a narrow shelf in the south. This paper uses a hybrid Finite Volume Method–Finite Element Method based Shallow water equation (SWE) solver to predict the storm surges during different cyclone events, and the roughness parameter Manning's n is used in bed friction calculations. The bottom friction coefficient parameterization involving bed roughness is used to calibrate the resistance to flow in the numerical model. The calibration exercises are carried out with different values of n for each surge simulations for different cyclones to predict the surface elevation. Different statistical parameters against the measured values are used to analyze the impact of varying n values on predicted surge levels, and the most suitable n value is carefully chosen. The relationship between n and the bed slope is established as an expression, to replace the formulations involving Manning's n, thereby minimizing the usual computational efforts. The performance of the novel bed friction formulation involving the physical parameter in bed slope is demonstrated through statistical evaluations. [ABSTRACT FROM AUTHOR]

In recent years, semi-arid regions of India, especially Marathwada, have been continuously under the grip of drought. Increasing water scarcity and depleting ground water levels have accentuated the agrarian crisis with an increased number of farmers committing suicide in this region. To understand this issue, the present paper deals with the roots of the drought severities concerning the summer monsoon rainfall and changing crop types over the districts of the Marathwada region, India, from 1996 to 2018. In this study, drought severities were quantified using station-based rainfall, groundwater level, and crop data (10 most cultivated crops) collected from various national agencies. The increasing rainfall trends over the Latur, Beed, and Aurangabad districts depict positive signs for agriculture. In contrast, other districts were under decreasing rainfall trends, but these declining rates were not statistically significant. The alarming fall of ground water level from 4 to 5 m during the considered period was noticed over most of the region, which is a cause for concern. The significant changes in agricultural practices from low-water-requirement crops such millet (bajra), sorghum (jowar), and wheat to high-water-requirement crops such as sugarcane and cotton were observed over Beed, Latur, Osmanabad, and Parbhani. An increase in the yield of cash crops demands an augmented water supply, which is becoming responsible for the rapidly declining ground water level. Therefore, this study claims that more than rainfall vagaries, the severe impact of droughts is a reflection of changing agricultural practices and poor management of water resources. [ABSTRACT FROM AUTHOR]

Floods are a recurrent phenomenon in many parts of the world. In India, they occur almost every year in some parts of the country and in recent years several areas have been experiencing severe floods, which were almost unheard of a few decades ago. Advances in data collection and in methods of rainfall and hydrologic forecasting and improved flood management by governmental organizations notwithstanding flood risk in each flood season year after year is quite high and consequent loss of life and property continues unabated. This paper revisits flood management in the country and discusses strategies for reducing flood risk and the resulting damage. It also reflects on the role climate change, besides anthropogenic changes, may be playing in the havoc being wrought each year by floods. [ABSTRACT FROM AUTHOR]

Summary: The importance of monsoon rainfall cannot be ignored as it affects round‐the‐year activities ranging from agricultural to industrial. Due to the nonlinear and dynamic nature of monsoon rainfall, its accurate forecast becomes a challenging task. In this paper, we propose a novel deep and wide neural network model to predict summer monsoon rainfall using historical time‐series data of 118 years. We have considered Rajasthan as our area of study, which is the largest state of India with varying atmospheric regions. Two different types of datasets are used: gridded rainfall dataset from the Indian Meteorological Department (IMD) and station rainfall data from Water Resources Department (WRD). Results obtained on both the datasets demonstrate that a single model works well in efficiently predicting summer monsoon rainfall in different geographical regions of Rajasthan, ranging from plains and plateaus to deserts and hills. We compare the results with various advanced deep learning algorithms such as 1‐dimensional convolutional neural network, long short term memory, and multilayer perceptron. The comparison results show that the proposed method clearly outperforms these algorithms in terms of its forecasting ability, which is evaluated using root mean square error and mean absolute error. [ABSTRACT FROM AUTHOR]

A dense network of rain gauges and considerably large variability of the southwest monsoon precipitation across the country make India a suitable test-bed to evaluate any satellite-based precipitation product. In this paper, three real-time infrared-only precipitation products derived from the INSAT-3D satellite namely, INSAT Multispectral Rainfall (IMR), Corrected IMR (IMC) and Hydro-Estimator (HEM) and three rain gauge-adjusted Global Precipitation Measurement (GPM)-based multi-satellite precipitation products namely, Integrated Multi-satellitE Retrievals for GPM (IMERG), Global Satellite Mapping of Precipitation (GSMaP) and an Indian merged satellite-gauge product (INMSG) have been evaluated over India at a daily timescale for the southwest monsoon seasons of 2020 and 2021. An evaluation against rain gauge-based gridded reference dataset shows noticeable reduction of bias in IMC product over IMR, primarily over the orographic regions. However, INSAT-3D infrared-only precipitation retrieval algorithms have limitations in shallow and convective precipitation estimation. Among rain gauge-adjusted multi-satellite products, INMSG is shown to be the best product in the monsoon precipitation estimation over India due to use of rather larger number of rain gauges than IMERG and GSMaP products. All satellite-derived precipitation products, i.e. infrared-only and gauge-adjusted multi-satellite products underestimate heavy monsoon precipitation by 50–70%. The bias decomposition analysis indicates that a simple statistical bias correction would considerably improve the performance of the INSAT-3D precipitation products over the central India, but the same might not work over the west coast due to rather larger contributions of both positive and negative hit bias components. Although rain gauge-adjusted multi-satellite precipitation products show very small or negligible total biases in the monsoon precipitation estimation, positive and negative hit bias components are considerable over the west coast and central India. Furthermore, rain gauge-adjusted multi-satellite precipitation products underestimate very heavy to extremely heavy precipitation with larger magnitudes than the INSAT-3D derived precipitation products over the central India. Among the rain gauge-adjusted multi-satellite precipitation products, INMSG has smaller bias and error than IMERG and GSMaP products for very heavy to extremely heavy monsoon precipitation over the west coast and central India. Preliminary results of this study would be useful for end users in choosing a better precipitation product for real-time and research applications as well as for algorithm developers in further improving these products. [ABSTRACT FROM AUTHOR]

Rainfall forecasting is critical for the economy, but it has proven difficult due to the uncertainties, complexities, and interdependencies that exist in climatic systems. An efficient rainfall forecasting model will be beneficial in implementing suitable measures against natural disasters such as floods and landslides. In this paper, a novel hybrid model of empirical mode decomposition (EMD) and random forest (RF) was developed to enhance the accuracy of annual rainfall prediction. The EMD technique was utilized to decompose the rainfall signal into six intrinsic mode functions (IMFs) to extract underlying patterns, while the RF algorithm was employed to make predictions based on the IMFs. The hybrid RF–IMF model was trained and tested using a dataset of annual rainfall in Kerala from 1871 to 2020, and its performance was compared to traditional models such as RF regression and the autoregressive moving average (ARMA) model. Mean absolute error (MAE), mean absolute percentage error (MAPE), mean squared error (MSE), root mean squared error (RMSE), and coefficient of determination or R-squared (R2) were used to compare the performances of these three models. Model evaluation metrics show that the RF–IMF model outperformed both the RF model and ARMA model. [ABSTRACT FROM AUTHOR]

Agriculture is the primary profession in India which relies on several climatic factors such as rainfall, temperature, humidity, etc., for the successful growth of crops. Weather and drought forecasting may help to take preventive measures in an unusual situation due to crop failure. Most of the existing work attempts to forecast the weather or analyse the reason for the occurrence of drought and its effects in the past. This paper adopts the machine learning model called the long short-term memory (LSTM) neural network to forecast the long-term rainfall and standardised precipitation index for drought estimation. The predicted annual rainfall from LSTM is taken as an input to forecast the drought conditions of India for the upcoming years, which is a unique approach and objective, and based on this work appropriate decisions can be made for future actions. The past 117 years of rainfall and drought conditions are compared with the recent 50 years by analysing the data in several ways in order to predict the future scenario. The forecasted results are compared with actual observations to demonstrate the effectiveness of the LSTM model to produce adequate results. The error and network loss of the model is 0.059 and 0.0036, which is minimal, and the forecasted rainfall level is almost equal to actual level specifically accuracy is 99.46% for the previous year, 2021. It was found that there is a rainfall decline of 0.04% every year. Apart from the prediction for the country, a clear picture of the region regarding drought forecasts is presented in this work. The real-time drought level is mild and moderate for most of the regions in the country which matches with the drought level determined using forecasted rainfall. [ABSTRACT FROM AUTHOR]

Quantification of rainfall intermittency via. interevent time distribution, series of continuous wet spells (burst size) and variability in interevent times between rainfall events is essential for planning and management of water resources and hydrologic extremes. However, their structure, quantification and association with long-term climatology are less explored. In this paper, a complex system-based measure – burstiness – is used to quantify the variability of interevent times across six meteorologically homogenous zones of India. It is observed that burstiness is related to the burst size as well as long-term rainfall climatology. The existence of unimodal and bimodal structure in burstiness distribution reveals the uniqueness and differences in the rainfall patterns. The differences in sensitivity of rainfall to burstiness highlight the role of interplay between climate-landscape and reveals the importance of the intermittent structure of rainfall. The study provides an approach to model intermittency by preserving the temporal structure of the interevent time distribution. [ABSTRACT FROM AUTHOR]

In the context of climate change and increasing occurrences of extreme events, it is essential to understand farmers' responses to weather shocks and adaptations. This paper uses a panel dataset of 311 selected Indian districts ranging from 1966 to 2009 to investigate how application of chemical fertilizers varies in response to rainfall shocks. Two rainfall shock measures are constructed based on deviation in rainfall from the normal, a categorical measure of rainfall shock; and another, a continuous index of negative rainfall deviation. Based on a panel fixed effect regression, the study finds no apparent reduction in the level of fertilizer use in negative rainfall shock years. However, with a one-year lagged rainfall shock, a reduction in fertilizer application rate is observed for the continuous drought index. Further, exposure to higher intensity droughts in the previous year leads to an increase in the amount of fertilizer application in the current year. [ABSTRACT FROM AUTHOR]

Purpose: Since finance is an efficacious instrument for economic development, social inclusion and women empowerment, the present paper examines the determinants of accessing institutional and non-institutional finance across male- and female-headed households in rural India. Design/methodology/approach: Multinomial logistic regression is applied for categorizing households' accessing finance in four categories, namely Only Institutional Finance (IF), Only Non-institutional Finance (NIF), Both Sources of Finance (BF) and Neither Source of Finance (N). Both household and state-level determinants have been analysed. Household data set is sourced from the Situation Assessment Survey (NSSO, 70th round) and state-level data sets from Basic Road Statistics 2016, Agricultural Statistics at a Glance 2016, Rainfall Statistics of India 2014, database on Indian Economy RBI and Census 2011. Econometric regressions have been evaluated for female-headed households (FHHs), male-headed households (MHHs) and overall pooled households (HHs). Findings: Four important findings emerge. First, FHHs have a lower probability of accessing IF and a higher probability of accessing NIF vis-a-vis MHHs. Second, in general, education levels, monthly household consumption expenditure, land size holding, irrigated area and penetration of scheduled commercial banks favourably influence FHHs accessing IF. Third, FHHs belonging to socially disadvantaged castes have a lower probability of accessing IF. Fourth, a substantial proportion of FHHs accesses neither IF nor NIF relative to MHHs. Practical implications: The paper thoroughly addresses the issue of accessing finance by FHHs and MHHs, which will further assist policymakers in formulating holistic financial policies for rural India. Social implications: The paper recommends increasing women's access to financial services as an effective tool for reducing poverty and lowering income inequality in rural India. Originality/value: This article contributes to the scant empirical literature on finance and gender. [ABSTRACT FROM AUTHOR]

The Indian economy and food grain production are intricately tied to the spatial distribution and the amount of rainfall during the summer monsoon period (June to September). Numerous studies have delved into modeling methods for forecasting Indian summer monsoon rainfall. This paper utilizes monthly rainfall data spanning 72 years (1950–2021) to analyze, model, and predict the monsoon over Northwest India using Dynamic Linear Modeling (DLM). In view of this, the well-accepted dynamic linear model with significant components is selected from the parsimonious models of DLM based on Bayesian Information Criteria (BIC), significant tests, and statistical fit. The dynamic linear model comprises a significant linear time trend component, a deterministic trigonometric seasonal component, a deterministic cycle component, and a stochastic irregular component has been selected. The validation of the selected model passed through the normal and correlation diagnostics of model residuals. The analysis reveals a subtle decreasing trend in monsoon rainfall, showcasing consistent behaviour in deterministic seasonality, and highlighting the deterministic nature of cyclical component with a period of approximately 2.5 years, alongside irregular variations. Utilizing this model, future monsoon rainfall over Northwest India has been forecasted for the years 2022–2027, providing a 95% confidence interval. [ABSTRACT FROM AUTHOR]

Vast stretches of India comes under meteorological drought this year or the other. A huge population base in rural India are rendered highly vulnerable to this drought because of their primary dependency on agriculture and in turn they may respond through temporary migration out of the drought affected rural areas in search of alternative livelihoods. This study aims to investigate the association between drought and temporary migration in rural India by fitting binary logistic regression models on a cross-sectional dataset involving both National Sample Survey Organization (NSSO) 64th round data and India Meteorological Department (IMD) rainfall data. The paper also examines whether this association varies across the different socio-economic groups. Out of the total temporary migrants generated in rural India in the study period, 99.46% migrated internally and 67.12% were rural to urban migrants. The study finds that there is a positive association between drought instances and probability of a household to have at least one temporary migrant member in rural India (OR 1.64 with p<0.001) while controlling all other covariates. The study also concludes that the probability of temporary migration on account of drought is more severe among the socio-economically marginalised sections of the rural population compared to their better-off counterparts. [ABSTRACT FROM AUTHOR]

Hydroelectricity is one of the most important sources of renewable power, making it an integral part of emissionless power generation plan for many developing economies, including India. But hydropower firms based on rain-fed rivers in Indian plains and plateaus are highly monsoon-dependent for their generation. The primary objective of this study is to explore the intensity of the risk of fluctuating monsoon rain and securitization of that risk through financial instruments for Indian hydroelectricity sector. The paper analyzes the applicability and efficiency of a designed monsoon derivative in mitigating the monsoon risk of a representative hydroelectricity firm, namely, Damodar Valley Corporation (DVC). The study suggests the use of a weather option contract based on the accumulated monsoon rainfall of the river basin in mitigating the risk. The parameters of such an instrument are estimated and a pricing mechanism is suggested through fitting parametric distribution to the rain data and using a Gaussian copula to capture the interdependencies of rainfall in various locations of the catchment area of the river basin during the monsoon season. The study reveals that the firm can successfully hedge the rainfall risk through financial derivatives and attain a superior position vis-à-vis risk-return. [ABSTRACT FROM AUTHOR]

Weather is a key source of income risk, especially in emerging market economies. This paper uses a randomized controlled trial involving Indian farmers to study how an innovative rainfall insurance product affects production decisions. We find that insurance provision induces farmers to invest more in higher-return but rainfall-sensitive cash crops, particularly among educated farmers. This shift in behavior occurs ex ante, when realized monsoon rainfall is still uncertain. Our results suggest that financial innovation can mitigate the real effects of uninsured production risk. [ABSTRACT FROM AUTHOR]

The rainfall prediction is one of the major problems in meteorological department since it is related to the economy and human life. In addition, the heavy rainfall is the major problem of natural disasters like drought and flood. The accuracy of the rainfall forecasting has been spotted for countries like India and economy is greatly dependent by agriculture. This paper introduces a new rainfall prediction model consists of Feature extraction and Prediction. The features are extracted by modified Empirical Mean Curve Decomposition namely Empirical Mean Median Curve Decomposition, and then subjected to statistical analysis. The results of the statistical features are processed by the classification process. Neural network (NN) is used for the rainfall prediction. To improve the accuracy of proposed work, an optimal weight is and predicted result are multiplied, then, the weight in NN is optimally selected by a Rider Exploitation based Whale Optimization Algorithm. Finally, the performance of the proposed work is compared and proved over the state‐of‐the‐art models in terms of prediction rate. [ABSTRACT FROM AUTHOR]

This paper delineates the groundwater potential zones (GWPZ) within the Tadri River Basin in the Western Ghats of India, using analytical hierarchical process (AHP) and geoinformatics-based techniques. Eight discrete parameters (slope, rainfall, drainage density, lineament density, lithological units, geomorphological units, soil types and land use, and land cover classes) were selected and weighted according to their influence on groundwater availability and combined in a hierarchical manner to obtain the resulting GWPZ map. This was classified into five classes (very good, good, moderate, low and very low) depending upon the relative groundwater availability within each. Excellent GWPZ were present in the lower part of the Tadri Basin and along the Western Ghats' foothills beside the coast while the pediment-pediplain complex had less viability. The GWPZ map was validated using the area's average groundwater depth and well density, via the receiver operating characteristic (ROC) curve and the area under curve (AUC), which elicited an accuracy of 79% and 82.1%, respectively. Sensitivity analysis revealed that the basin's lithology, land use/land cover, drainage density and lineament density had the most impact in GWPZ delineation. [ABSTRACT FROM AUTHOR]

Mine waste dump material has no economic value to the industry. Therefore, the mine waste is dumped, forming slopes. Mine waste dump slopes obtain 30% to 50% of the mining area. To reduce the land occupancy of these slopes, they are created with high altitudes. Hence, they are susceptible to failure. Slope stability analysis is a major aspect of geotechnical engineering. Slope stability analyses are mostly done with assumptions on the geometry. This is avoided in this paper with the usage of UAVs. The 3D model is created from UAV imagery of a coal mine in Raniganj coalfield, India. The model is fine-tuned with the DGPS survey. Geotechnical data were collected and tested in the laboratory for various numerical analyses. An active mine waste dump slope is analyzed for slope failure. Earthquakes and rainfall cannot be controlled, and their effects on the stability of the mine waste dump slope were examined. The study extracted various factor of safety (FOS) analyses on static, seismic, and rainfall conditions. The seismic condition simulates a condition of the slope to be failed with low (0.948) FOS. However, rainfall condition predicts the slope to be more stable. The deformation pattern and magnitude of the slope failure are also discussed. [ABSTRACT FROM AUTHOR]

The impacts of anthropogenic activities on the earth atmosphere and its systems have been engaging attention planners, governments and politicians worldwide. The countries across the world are engaged in working out the impacts and associated variability's to the projected climate change. In the twentieth century many recent studies show changes in rainfall patterns of India as well as changes in weather conditions climate changes, decrease in rainfall duration and rainfall amount and increase in rainfall intensity paper describe Climate Change Impact Studies on Rain Madhya Pradesh, India. The rainfall data collected for a period of 1901 to 2019from the District Land Record Department of Chhatarpur and Indian Meteorological Department, New Delhi (IMD). The results historical rainfall records of Chhatarpur district have been analysis using statistical and Mann Results are also discussed for the future climate change scenario in rainfall variability of thearea. [ABSTRACT FROM AUTHOR]

A large fraction of the world's poor rely on rain-fed agriculture, which makes them vulnerable to changes in rainfall patterns. In this paper, we examine whether spatial correlation in rainfall results in these households also being vulnerable to an adverse spatial-spillover effect. In particular, we use household-level panel data from India along with high-resolution meteorological data to show how rural household consumption varies with own-region rainfall as well as rainfall in neighboring areas. We find that while greater own rainfall has a positive effect on rural household consumption, greater rainfall in neighboring regions has an adverse spatial-spillover effect. Our results suggest that when this spillover effect is taken into account, the positive impact of own-region rainfall on household consumption falls by 38 percent. [ABSTRACT FROM AUTHOR]

This paper analyzes the dynamic nature of rainfall insurance purchasing decisions. Customers of the Indian microfinance institution BASIX who receive an insurance payout are 9 to 22 percentage points more likely to purchase insurance the following year. This effect cannot be satisfactorily explained by trust, learning, or direct effects of weather, leading to the conclusion that it is driven by behavioral effects. Overall, low repurchasing rates even after payouts suggest that the studied rainfall index insurance products are likely to continue struggling to achieve significant sales at market prices. [ABSTRACT FROM AUTHOR]

Rainfall-induced landslides on steep slopes are potential hazards to life and property. The state of Kerala in India is witnessing unprecedented death and damage since last 3 years due to landslides triggered by heavy rainfall. In this paper, we present a cause-impact analysis of landslides that occurred due to the monsoon rainfall in the years 2018, 2019 and 2020. We have created an event-based landslide inventory post-rainfall season for the entire state and then selected six major landslides for detailed analysis. Event-based landslide inventory maps are prepared from Earth observation satellite data such as Resourcesat-2, 2A, Cartosat-2S, WorldView-2, Pleiades-1A and 1B, SPOT-6 and 7 and Sentinel-2 acquired before and after the rainfall events. Then, rainfall data were analysed to find out the role of daily and antecedent rainfalls on landslide trigger. Finally, we have carried out numerical debris flow modeling to characterise landslides and assess the impact. Our results show that landslides are triggered by antecedent rainfall of variable durations and are mostly debris flow type that has followed the channel path. The runout length and lateral spreads are controlled by variable frictional parameters of the slope material. Furthermore, reactivation at the crown region is also noticed in few cases that need to be monitored. [ABSTRACT FROM AUTHOR]

Accurate prediction of rainfall can save farmers from crop damage as erratic rains in India have caused agricultural losses. Due to continuous climate change, rainfall and weather prediction has become essential. Rain causes damage to people and property. So, rainfall forecasting is crucial for crop as our country’s economy is still heavily dependent on agriculture. Using machine learning (ML) and deep learning (DL) models, we can train these models for the rainfall dataset and make predictions. In this paper, we estimate rainfall over the Karnataka region using a stacking ensemble model on a rainfall dataset collected between 1901 and 2015. [ABSTRACT FROM AUTHOR]

Interactions over South Asia between tropical depressions (TDs) and extratropical storms known as western disturbances (WDs) are known to cause extreme precipitation events, including those responsible for the 2013 floods over northern India. In this study, existing databases of WD and TD tracks are used to identify potential WD–TD interactions from 1979 to 2015; these are filtered according to proximity and intensity, leaving 59 cases that form the basis of this paper. Synoptic charts, vorticity budgets, and moisture trajectory analyses are employed to identify and elucidate common interaction types among these cases. Two broad families of interaction emerge. First, a dynamical coupling of the WD and TD, whereby either the upper- and lower-level vortices superpose (a vortex merger), or the TD is intensified as it passes into the entrance region of a jet streak associated with the WD (a jet-streak excitation). Second, a moisture exchange between the WD and TD, whereby either anomalous moisture is advected from the TD to the WD, resulting in anomalous precipitation near the WD (a TD-to-WD moisture exchange), or anomalous moisture is advected from the WD to the TD (a WD-to-TD moisture exchange). Interactions are most common in the post-monsoon period as the subtropical jet, which brings WDs to the subcontinent, returns south; there is a smaller peak in May and June, driven by monsoon onset vortices. Precipitation is heaviest in dynamically coupled interactions, particularly jet-streak excitations. Criteria for automated identification of interaction types are proposed, and schematics for each type are presented to highlight key mechanisms. [ABSTRACT FROM AUTHOR]

This paper presents the lightning activity, aerosol optical depth (AOD) and climatic parameters (Bowen ratio, relative humidity, rainfall, maximum surface temperature and maximum updraft speed) over wet (Northeast India—NEI) and dry (Northwest India—NWI) land surfaces in a comparative analysis. The analyses are performed on flash counts and weather data of 17 years (1998–2014) retrieved from the Tropical Rainfall Measuring Mission (TRMM) lightning imaging sensors (LIS) and the Moderate Resolution Imaging Spectroradiometer (MODIS), respectively. The first higher peak for flash counts during pre-monsoon (April–May) and second lower peak during September over wet NEI and dry NWI regions indicate the development of strong electrified storms during pre-monsoon and weakly electrified clouds during the withdrawal phase of the southwest summer monsoon. The monthly means of flash counts, Bowen ratio, maximum surface temperature, AOD and maximum updraft speed are higher by 27, 81, 16, 50 and 16%, respectively, for NWI than those of NEI. The relative humidity and rainfall are higher by 12% and 73% for NEI than those of NWI, respectively. The Pearson's correlation coefficients of lightning activity with climatic parameters and AOD demonstrate the distinctive orographic lifting, moisture content and vertical wind shear in the upper troposphere in the regional climatic zone of dry NWI in comparison to that of wet NEI. [ABSTRACT FROM AUTHOR]

Prediction of long-term rainfall patterns is a highly challenging task in the hydrological field due to random nature of rainfall events. The contribution of monthly rainfall is important in agriculture and hydrological tasks. This paper proposes two data-driven models, namely biogeography-based extreme learning machine (BBO-ELM) and deep neural network (DNN), to predict one, two, and three month-ahead rainfall over India (All-India and six other homogeneous regions). Three other data-driven models called ELM, genetic algorithm (GA)-based ELM, and particle swarm optimization (PSO)-based ELM are used to compare the performance of the proposed models. Firstly, partial autocorrelation function (PACF) is applied in all datasets to select the optimal number of lags for input to the models. Secondly, the wavelet-based data pre-processing technique is applied in selected optimal lags and feed to the proposed models for achieving higher prediction performance. To investigate the performance of proposed models, a non-parametric statistical test, Anderson–Darling' Normality test, is performed in all India dataset. The wavelet-based proposed hybrid models show better prediction capability compared to optimal lag-based proposed models. This study shows the successful application of time-series data using proposed techniques (optimal lags-based BBO-ELM and wavelet-based DNN) in the hydrological field which may be used for risk mitigation from dreadful natural events. [ABSTRACT FROM AUTHOR]