Indian Rainfall Research Articles

Relationship between the Rainfall Index and Stock/Agri-commodity Market Indices: Empirical Evidence from India

The rainfall, stock, and agri-commodity indices are the economic indicators for Indian economy. Therefore, research is needed to explore the complex relationship between rainfall and economic indicators and its longterm implications. Only a few studies are available on this relationship, and no study gives complete, conclusive evidence on this relationship. Therefore, the present study focused on determining whether there is any relationship between the rainfall index and stock/agri-commodity indices. This study employed the advanced statistical tools like ADF test, correlation, Granger causality test, and GARCH (1,1) model. The research utilized the monthly average rainfall index and the Sensex index from 1991 to 2020. Similarly, the study employed the monthly average Guarex agri-commodity index for the period from 2018 to 2020. The study found that there is no relationship and causality relationship between the rainfall index and the Sensex index. The GARCH (1,1) model shows that the rainfall index has no impact on the volatility of the Sensex index. There is a moderately negative correlation between the rainfall index and the Guarex index, and rainfall affects the volatility of the Guarex index. This shows that the rainfall index can be a distinct asset class that can be traded through rainfall derivatives. In order to launch rainfall derivatives in India, NCDEX and Skeymet Industries have already approached the SEBI for permission.

Shaping India's climate future: A perspective on harnessing carbon credits from agriculture

Human activities are responsible for emitting greenhouse gases (GHGs) that contribute to global warming and climate change. As the world's second-largest producer of staple food and the third-largest emitter of GHGs, India has been witnessing an increase in demand for food and energy, resulting in increased emissions. Thus, to achieve net carbon neutrality by 2070, India must focus urgently on climate change mitigation. Its agriculture sector has the potential to transition from being a net emitter to a net absorber of GHGs by adopting sustainable farming practices such as zero tillage, laser-assisted precision land leveling, direct seeding of rice, intercropping, biochar application, use of solar energy, and more efficient management of irrigation water, soil nutrients, livestock feed, and manure. To incentivize climate consciousness, a voluntary carbon credit trading system could be utilized in agriculture, supported by a measurement, monitoring, reporting, and verification platform. This system would also bring about social, environmental, and financial co-benefits for its stakeholders. Specifically, the agriculture sector could substantially reduce the country's annual emissions by 84% from 2019 to 2070. But to realize their potential, the carbon markets must overcome the limitations currently set by policy, economic, cultural, and biophysical factors.

Effect of air velocity and relative humidity on passengers’ thermal comfort in naturally ventilated railway coach in hot-dry indian climate

In India, railways still continue to be the most prominent means of transport, especially non-air conditioned railway that carries around 3.5 billion people every year. Thermal comfort in these non-air-conditioned trains is predominantly influenced by windows and fans. The present study orients around the effect of fan air as well as naturally ventilated air on thermal comfort of passengers. The study is part of a field survey followed by an experimental investigation that was carried out in non-air-conditioned railway coaches during summer afternoon, with more than 1500 subjects of different gender and age category. The study significantly contributes in defining an elevated acceptable range of comfort air velocity inside a moving stock. The mean threshold air velocity where passengers responded a neutral PCV (passengers' comfort vote) is found to be 2 m/s which is much higher than conventional standards for enclosed static buildings and premises. The clear dominance of behavioral adaptation has been noticed in expression of thermal comfort by the passengers. This strengthens the idea of adopting some alternative method to deal with thermal discomfort of non–air-conditioned railway passengers. Here a novel term as ‘zone of independence’ (ZoI) has been defined at which the effect of air velocity becomes independent on passengers' comfort vote. This ZoI can significantly be utilized while optimization of thermal comfort in an ICF railway coach.

Evaluation of Weather Yield Index Insurance Exposed to Deluge Risk: The Case of Sugarcane in Thailand

Insurance serves as a mechanism to effectively manage and transfer revenue-related risks. We conducted a study to explore the potential financial advantages of index insurance, which protects agricultural producers, specifically sugarcane, against excessive rainfall. Creation of the index involved utilizing generalized additive regression models, allowing for consideration of non-linear effects and handling complex data by adjusting the complexity of the model through the addition or reduction of terms. Moreover, quantile generalized additive regression was deliberated to evaluate relationships with lower quantiles, such as low-yield events. To quantify the financial benefits for farmers, should they opt for excessive rainfall index insurance, we employed efficiency analysis based on metrics such as conditional tail expectation (CTE), certainty equivalence of revenue (CER), and mean root square loss (MRSL). The results of the regression model demonstrate its accuracy in predicting sugar cane yields, with a split testing R2 of 0.691. MRSL should be taken into consideration initially, as it is a farmer’s revenue assessment that distinguishes between those with and those without insurance. As a result, the GAM model indicates the least fluctuation in farmer income at the 90th percentile. Additionally, our study suggests that this type of insurance could apply to sugarcane farmers and other crop producers in regions where extreme rainfall threatens the financial sustainability of agricultural production.

Hedging rainfall risk: An illustrative analysis of rainfall index-based futures contracts

Various studies have attempted to explore rainfall index (RI)-based derivatives as a tool to hedge rainfall risk. But no study delved into the issue to discuss the potential deployment of those tools for hedging rainfall risk on a model based on RI-based futures (RIBFs). An illustrative analysis of RIBFs contracts as hedging instruments has been undertaken in this study. The study is fundamentally based on empirical data. A brief account of the rainfall risk profile is presented to indicate the risk diversification opportunity. Besides, the results of an analysis of the correlation between the RI and the stock market index are projected to establish the RI as a unique asset class. The monsoon rainfall data for 50 years, i.e., from 1971 to 2020 have been considered. The Sensex and Nifty stock market indices data covered a period of 30 years, from 1991 to 2020. The study used correlation, the GARCH (1,1) model, and ratio analysis to test the hypotheses. A hypothetical insurance company representing major stakeholders in need of rainfall risk hedging has been taken as an example for evaluating the utility of RIBFs contracts as hedging tools. The results reveal that the party buying RIBFs contracts can significantly reduce the loss of revenue due to adverse monsoon outcomes.

Effect of climate change to characteristic of extreme rainfall over Batanghari watershed

Climate change will cause extreme rainfall, both wet and dry. Extreme rainfall will cause catastrophic floods and droughts. Therefore, it is important to analyse extreme rainfall and flood events in the future. Disasters and environmental issues in the Batanghari watershed include flooding, drought, forest and land fires, sedimentation, and water quality degradation. Therefore, the Batanghari watershed is selected as a study area in this research. Research on climate change in the Batanghari watershed is crucial to understanding the trend of extreme conditions now and in the future. Characteristics of extreme rainfall will be analysed using the climate index that was developed by the Expert Team on Climate Change Detection and Indices (ETCCDI). Results show that climate change in the Batanghari watershed is indicated by increasing 5 of 6 extreme rainfall indices: R95p, R99p, Rx1day, Rx5day, and SDII. An increase in extreme rainfall is correlated with an increase in flood events in the Batanghari watershed in similar periods. The extreme rainfall index that most influences flooding in the Batanghari watershed is PRCPTOT and R99p. Flooding is expected to occur more frequently in the future than it does now. The Batanghari Hilir Sub-watershed is more vulnerable to extreme flooding in the future than other sub-watershed.

Calculation of Rice Farming Insurance Premium Price in Magelang City Based on Rainfall Index with Black-Scholes Method

Indonesia is a country with two seasons, the rainy season and the dry season. Unstable rainfall can affect rice production and may cause crop failure. The amount of rice production in Indonesia, one of which is in Magelang City, is quite large, so the losses that may be experienced are quite significant. Therefore, a way to reduce the impact of losses experienced by farmers is needed, one of which is through the rice farming insurance program. The purpose of this study is to determine the premium price of rice farming insurance based on rainfall index based on the exit value and trigger value in each growing season. Insurance using the rainfall index can provide protection to farmers due to too little rainfall or too much rainfall. Too much rainfall can cause damage to rice plants resulting in crop failure. The premium calculation method uses the Black-Scholes principle, while the exit value and trigger value are determined by the Historical Burn Analysis method. The result of this study is to obtain various trigger values and exit values as well as premiums that must be paid by farmers in each normal, high, and low (dry) rainfall condition. This value determines the premium price obtained for normal rainfall which is IDR 735,739.66 to IDR 871,698.64, for high rainfall the premium price obtained is IDR 1,404,184.75 to IDR 1,643,307.75, and for low rainfall (dry season) it is IDR 5,541,806.10 to IDR 6,689,629.88.

Historical Evidences of Indian Climate Change and Their Current Relevance

Historical Evidences of Indian Climate Change and Their Current Relevance

Spatiotemporal assessment of rainfall variability and its characteristics over Rapti River Basin, India

Abstract The determination of rainfall characteristics of a basin is obligatory for mitigation planning and adaptation. This study presents the spatiotemporal analysis of gridded precipitation to understand the variability and seasonality over the Rapti Basin. This basin is affected by frequent flooding; hence it becomes necessary to understand the variability of rainfall. To carry out this analysis, daily precipitation data is extracted for 22 grids for 30 years (1990–2020) of high resolution. Extreme rainfall indices are computed as per the threshold decided by the Indian Meteorological Department (IMD). To determine the trend of the rainfall, Mann Kendall (MK) test, Modified MK test and Innovative Trend Analysis was used and magnitude of trend was determined using slope. Trends of daily rainfall, annual, and seasonal rainfall were determined. Annual rainfall has shown significant decreasing trend over the basin. Monsoon season has a noticeable tendency, but other seasons have only seen small shifts. From all the rainfall indices, PRCPTOT and SDII showed a decreasing trend with Sen Slope of −2.56 and 3.00 respectively. While examining the variance in precipitation across the basin, this study will be helpful.

GEOSPATIAL TECHNIQUES FOR MAPPING AND ANALYSIS OF CLIMATIC VARIABLES IN GOMBE STATE, NORTH-EAST NIGERIA.

This study was carried out to apply satellite- based climatic data in geospatial environment to downscale climatic data into smaller political units and to generate and analyse thematic maps of climatic variables in Gombe State. Spatial climatic data on rainfall, minimum and maximum temperature were obtained from DivaGIS climatic data, while water vapour, solar radiation and wind speed were derived from the WorldClim climatic data. Monthly climatic records from 1981-2021 in twenty-four points evenly spread across Gombe State on rainfall, minimum/maximum temperature and relative humidity were obtained from NASA Power Project to generate the trends and seasonal patterns of temperature and rainfall. Daily climatic records of rainfall were also acquired from globalweather climatic data between 1979 and 2013 and processed to derive the values of rainfall indices using Nieuwolt formula. The monthly climatic records were processed using the kriging module of ArcGIS to transform the point climatic data to spatial data. The result revealed that rainfall in Gombe State occurs mainly between May and September with the peak in August, while the trends in rainfall was decreasing at the southern and northern parts of the state but increasing at the central region. Rainfall onset occurs in the 1st or 2nd week of May in the south, but late (1st week of June) in the north. Rainfall cessation at the central and northern parts of the state occurs in the 3rd or 4th weeks of September, while the southern part experiences late cessation between the 1st and 3rd week of October. The LRS ranges from 95-111 and 160-175 days in the northern and southern parts respectively. High relative humidity was recorded between May and October with the peak in August. Spatial patterns of solar radiation and wind speed were also generated and analyzed. Awareness of the use of satellite based climatic data and geospatial techniques for downscaling climatic data were recommended in order to ease the problems of lack or inadequate climatic data for spatial mapping. Causal factors for the spatial patterns of climatic variables in Gombe state was suggested for further studies.

Space-time analysis of the relationship between landslides occurrence, rainfall variability and ENSO in the Tropical Andean Mountain region in Colombia

According to studies by the Intergovernmental Panel on Climate Change (IPCC), tropical mountainous areas are experiencing increasingly unfavorable climatic conditions regarding geohazards due to a heightened occurrence of intense rainfall events. These climatic shifts contribute to heightened geological risks, notably an elevated frequency of landslides, exacerbating the challenges faced by these regions. There is an urgent need to understand and measure how rainfall variability affects geo-hydrological hazards, which remain difficult to determine and predict. The complex and non-linear space-time relationships and dynamics of rainfall, El Niño-Southern Oscillation (ENSO), and landslides in the Tropical Andean Mountain region in Colombia require an adequate analysis and understanding of their link in terms of its spatial and temporal component at different scales. By evaluation in annual, seasonal, and monthly scales, additional insights on the relationships using a wavelet spectral analysis and a space-time permutation scan statistics method using SaTScan™ are provided. In this study, a space-time and frequency analysis of landslides using a 42-year (1981–2022) rainfall and Multivariate ENSO Index v2.0 time series is presented. According to the results, landslides closely aligned with rainfall patterns, exhibiting a bimodal annual cycle. The ENSO added complexity, with La Niña years leading to more frequent landslides throughout the year and El Niño years showing concentrated occurrences in specific months. This study highlights the influence of rainfall patterns and antecedent rainfall on landslide occurrence, the impact of ENSO phases on rainfall and landslides, and the increasing trend of landslides in Colombia.

Experimental comparison of solar drying based on evacuated tube collector with desiccant drying for dehydrating potato and ginger

AbstractIn this article, a comparative study of different drying techniques, namely, open sun drying (OSD), desiccant drying, solar drying (SD), and solar drying with desiccant (SDWD) was done for drying potato and ginger. The thermal efficiency and air temperature differences of the solar dryer based on evacuated tube collectors were also examined. The average efficiency of the solar dryer was 29.01%. Average drying rates of OSD, desiccant drying, SD, and SDWD bed are 0.435, 0.435, 0.59, and 0.635 g/min. The moisture content of the potato was reduced from 79.6% to 32.9% by OSD, 32.6% by desiccant drying, 9.6% by SD, and 6.9% by SDWD. The moisture content of the ginger was reduced from its initial value of 82.3%–25.3% by OSD, 25% by desiccant drying, 10.1% by SD, and 3.2% by SDWD. SD assisted with a desiccant bed proved to be the best method for drying vegetables. Drying with desiccants is not feasible for drying in winter and spring seasons of the Indian climate.

Investigation of Degradation in 20-Year-Field-Exposed PV Modules From India by Cross-Characterization Using Electroluminescence Imaging and Thermography

The reliability of photovoltaic (PV) modules is essential for ensuring a smooth operation over the anticipated timespan while operating outside. Examining the degradation in field-exposed photovoltaic (FEPV) modules will help to identify the possible degradation modes that can affect PV module performance and functioning. In this paper, to identify the major modes of degradation in Indian subtropical climate conditions, 20-year-old FEPV modules have been investigated by cross-characterization using dark lock-in thermography (DLIT) and electroluminescence (EL) imaging. Cross-characterization using EL and DLIT images has been helpful in investigation of various modes of degradations in FEPV modules in the presence of multiple degradations. Encapsulant and busbar ribbon interface degradations have been identified as the two main modes of degradation observed in Indian sub-tropical climate conditions. Minor degradations include finger interruptions and cell cracks. A major effect of degradation has been observed on the fill factor and short circuit current, which have decreased by up to 30% and 40%, respectively. The results presented in this paper can be used to understand degradation occurring in a sub-tropical climate and for the non-destructive analysis of degradations in the FEPV modules.

Bias Correction of IMERG Data in the Mountainous Areas of Sumatra Based on A Single Gauge Observation

The performance of surface precipitation data from satellite precipitation products (SPPs) in mountainous areas has greater error and bias than in plain areas. In this study, linear scaling (LS), local intensity (LOCI), power transformation (PT), and cumulative distribution function (CDF) methods are used to correct the bias of Integrated Multi-Satellite Retrievals for Global Precipitation Measurement (IMERG) data in the mountainous region of Sumatra based on long-term and high-resolution optical rain gauge (ORG) observations. The ORG is installed at Equatorial Atmospheric Observatory (EAO) in Kototabang, West Sumatra, Indonesia (100.32 °E, 0.20 °S, 865 m above sea level (ASL) with an observation period from 2002 to 2016. The impact of the bias correction method is tested based on accuracy and capability detection tests. The bias correction method is more effective at the daily resolution than the hourly resolution of the IMERG data in the mountainous region of Sumatra. The LS method exhibited the best improvement in accuracy with reduced root-mean-square error (RMSE) and relative bias (RB), although there was no significant increase in coefficient correlation (CC) values. However, the accuracy improvement was not observed in the bias correction for hourly data. The lack of improvement in the accuracy of the hourly IMERG data is due to the high local variability of rainfall in the mountainous area of Sumatra. The high data variability causes large differences in the mean and variance of the IMERG calibration and evaluation data periods. On the other hand, the LOCI, PT, and CDF methods were successfully improved the rain detection capability of IMERG, as indicated by the better critical succession index (CSI) values compared to the original hourly and daily IMERG data. It increased the CSI value by reducing false alarms for rain with intensity below 2 mm/h. Furthermore, the CDF method can improve the analysis of extreme rainfall in the mountainous region of Sumatra by improving the estimation of the extreme rainfall index. Therefore, these methods can be applied to improve the accuracy and detectability of IMERG data in the mountainous region of Sumatra. However, the scale factor and transfer function constructed in this study need to be further evaluated on other rain gauge observation data in Sumatra’s mountainous region to improve performance. HIGHLIGHTS The LS method shows the best improvement in the accuracy of IMERG data in the mountainous area of Sumatera compared to the LOCI, PT, and CDF methods, as indicated by the largest decrease in RMSE and RB values CSI values prove that LOCI, PT, and CDF methods successfully improve the detection capability of IMERG hourly and daily data in the mountainous region of Sumatra The CDF method shows the best quality in improving extreme rainfall observations in the mountainous region of Sumatra GRAPHICAL ABSTRACT

Modelling monthly rainfall of India through transformer-based deep learning architecture

Modelling monthly rainfall of India through transformer-based deep learning architecture

Remote influence on regional scale intraseasonal rainfall variability over Vietnam

AbstractRemote influences on intraseasonal anomalous rainfall over regions that encompass North and South Vietnam are explored using a 38‐year (1979–2016) global dataset over the extended summer (May–October). Wet and Dry composites in filtered daily data with lags of up to 2 weeks are assembled for various rainfall indices over the two subregions, including atmospheric reanalysis products and in‐situ rainfall data. On the regional scale, the moisture flux convergence correlates well with reanalysed rainfall. The large‐scale dynamics associated with these composites are described. Rainfall composites of opposing signs show asymmetrical large‐scale precursors and different pathways of influence. Wet and Dry anomalies in North Vietnam are seen to originate from Europe and propagate at high latitudes. The exact nature of the precursors is sensitive to the definition of the composite index. There is also a pathway of influence along the Asian jet, which impacts South Vietnam, especially for Wet composites which often coincide with Dry conditions in the North. South Vietnam is also influenced by tropical divergent precursors, which are again asymmetric between Wet and Dry composites.

Spatiotemporal Variability of Extreme Rainfall Indices over Nicaragua between 2001 and 2016 and Their Relationship with Teleconnection Patterns

Abstract We investigated nine indices of spatiotemporal extreme precipitation events over Nicaragua during 2001–16, from GPCC, Climate Hazards Group Infrared Precipitation with Station data (CHIRPS), and IMERG, and their correlation with teleconnection patterns. The main objectives were to evaluate the variability of extreme precipitation events, to know the performance of IMERG and CHIRPS in the characterization of these extreme events, using GPCC and four rain gauges as references, and finally to determine the teleconnection patterns that have the highest correlation with these indices. The spatial coverage of the area with the highest number of consecutive days with daily precipitation less than 1 mm corresponds to the Pacific region, with annual mean values of up to 120 continuous days. Some extreme precipitation event indices (RR, RX1day, and RX5day) show a decreasing trend, suggesting that the study area has been experiencing a reduction of extreme precipitation indices in terms of intensities and duration throughout the study period. In addition, it was observed that CHIRPS shows a better fit when dealing with precipitation events that do not exceed certain thresholds and IMERG improves when describing intense precipitation event patterns. We found that the tropical Pacific SST EOF (EOFPAC), Niño-3.4, Pacific warm pool region (PACWARM), and SOI have a greater influence on extreme precipitation events, these results suggest that they are being controlled by ENSO episodes, providing a better understanding of the climate configuration, as a prediction and forecasting potential, useful for agriculture, land use, and risk management.

Variability of time duration analysis for rainfall water using precipitation indexes in Hai town

Abstract Insufficient rainfall has an impact on a variety of natural resources. This work aims to determine the variability of rainfall and drought in Hai town depending on the standardized rainfall index (SRI), rainfall concentration index (RCI), index of wetness (IW), and coefficient of variation (CV). Rainfall series were taken from the Meteorological Station Directorate of Hai Town, Iraq for a period of 30 years (1989–2018). The results indicated that the years 1996 and 2014 had high SRI and were under extremely wet conditions (IW = 195.93 and 165.93, respectively). However, the lowest SRI value was in 2004, with a wetness index of 35.15, whereas the RCI was strongly irregular in rainfall distribution. Also, the CV was highly variable that ranged between 113.78 and 244.01. Mathematical models were created and confirmed for predicting the wetness index using data-fitting software. Model 1 generated best outcomes (R2 = 99.99%, relative error (RE) = 0.221, root mean square error (RMSE = 0.253) and standard error of estimates (SEE = 0.28). The results demonstrated that rain indicators have significant differences and alteration throughout the study period. Hence, the best model for estimating wetness and droughts in Hai town is recommended.

Climatic Variability and Degradation of the Banks of Mayo-Dorbo (South-West Chad)

This article deals with climate variability and degradation of the banks of Mayo-Dorbo, a town located in the southwest of Chad. Degradation is one of the causes creating a socio-economic imbalance for farmers. The objective of this study is to assess the impact of climate variability and bank degradation in the Mayo Dorbo watershed. To carry out this study, the methodology is based on observations, field measurements, and questionnaires. The results show that the 1970s began with negative indices reflecting drought but from the 1990s, the rainfall situation improved with positive rainfall indices. According to field measurements in the dry season and in the rainy season, the degradation process is slow at the start of the rainy season with a decline of 44.5 cm, it reaches its optimum during the floods of the months (July and August) with a retreat on average by 120.5cm where precipitation is significant. The summary of measurements for the 2022 watershed season shows an average retreat of 130. 29cm along the banks. This decline is caused by the abundance of rains 79% soil to 13% and the relief to 8%.

Analysis of The Occurrence of Extreme Events in The City of Rio de Janeiro/RJ

Objective: To investigate the trends of extreme rainfall and temperature indices in the city of Rio de Janeiro, as measures to assess climate change. Theoretical benchmark: Extreme climatic events have been observed more frequently than has become a concern all over the world, so the investigation of trends in extreme precipitation and temperature indices makes it necessary to enter how this dynamic in climatic conditions affects the city of Rio de Janeiro. Method: The methodology for assessing climate change will be used from the indicators defined by the Expert Team on Climate Change Detection, Monitoring and Indices (ETCCDMI) panel, but of the 27 indicators proposed by the panel, 12 indicators were used that were more adapted to the reality of the city of Rio de Janeiro, for the simulation of climate data, data from the stations of the National Institute of Meteorology (INMET) and for the simulation of the indicators in the RClimdex software. Daily data of 53 years of 19 (six) stations obtained from the historical series of the period between 1970 and 2023 were analyzed, for the precipitation indices: PRCPTOT, R10, R20, R95p and R99p, and for the temperature indices: TX90p, TN90p, TMAXmean, TMINmean and DTR. Results and conclusion: The results indicate a trend of decreasing rainfall of low rainfall and a significant increase of extreme rainfall, the data also show a decrease of thermal amplitude, with the increase of minimum temperatures and a moderate decrease of maximum temperatures. Implications of the research: The study in question presents internationally validated indicators that demonstrate how extreme events are intensifying in the city of Rio de Janeiro. Originality/value: The data demonstrated in this research are of extreme importance as a source of research for future works that address this theme, besides serving as a subsidy to decision makers to propose adaptive measures to the territory, in the face of the intensification of extreme events in the city of Rio de Janeiro.