Indian Rainfall Research Articles

Paradigm Shift in Typhoon Forecasting for the Korean Peninsula: A Case Study on the Applicability of the Typhoon-Ready System

Climate change has significantly increased multi-hazard disasters caused by typhoons, exposing the limitations of conventional forecasting systems that often neglect regional socio-economic vulnerabilities. This study develops and validates the Typhoon-Ready System (TRS) as an effective disaster-management framework for the Korean Peninsula. The TRS integrates hazard data with socio-economic and environmental vulnerability factors to produce region-specific risk indices. The analysis of four representative typhoons—Lingling (2019), Rusa (2002), Maemi (2003), and Mitak (2019)—demonstrates TRS’s applicability in identifying high-risk zones and supporting disaster preparedness strategies. The TRS framework incorporates indices, such as the Strong Wind Index (SWI), Heavy Rainfall Index (HRI), Storm Surge Index (SSI), and Air Quality Index (AQI), effectively combining meteorological modeling with vulnerability analysis. Results demonstrate that the TRS outperforms traditional systems by accurately identifying high-risk zones and correlating them with observed damage patterns. For example, the TRS successfully pinpointed high wind risks in Seoul and Incheon during Typhoon Lingling and forecasted severe flooding in Gangneung and Samcheok during Typhoon Rusa. By integrating vulnerability factors, including population density, infrastructure aging, and urbanization levels, the TRS provides a more holistic and accurate risk assessment. This research highlights the necessity of a multi-dimensional forecasting approach for enhancing disaster preparedness and resilience against climate change-induced typhoon impacts.

Trends and Drivers of Greenhouse Gas Emissions in India: A Decadal Analysis (2010-2020)

This paper analyses trends and determinants of greenhouse gas (GHG) emissions in India in 2010-2020, the decade with some of the fastest economic growth rates and the increased awareness of environmental issues. The research pays attention to India, the world's third-largest emitter of GHG, which will be analysed regarding emissions in four sectors: energy, industry, agriculture, and waste management. A time series analysis of data drawn from national and international agencies, such as the World Bank and India's Ministry of Environment, Forestry, and Climate Change, was applied in this study. Statistical analysis and data visualisation were performed using the R software. The key findings show an increase of 42% in the total emissions of GHG over the last decade, with the energy sector dominating at 68% in 2020. Other significant contributors were industry (20%), agriculture (9%), and waste (3%). Growth in population, along with accompanying urbanisation and industrial expansion, is cited as the main factor responsible for emission rise. However, there are also indications of decoupling between growth in the economy and emissions intensity. The study analyzes the greenhouse gas emission trends for major emitters between 2010 and 2020, in India. The dynamic panel data model analyses indicate signs of decoupling of emissions from economic growth in developed economies and the role of renewable energy and carbon pricing. Research findings further indicate that stronger climate policy and investment in clean energy is the necessity to decrease global emissions.The paper identifies policy issues that require strong activity in enforcing efficiency measures, fastening the shift toward krenewable sources of energy, and effective sustainable planning and waste management practices in urbanisation processes. In that respect, examination of policies like the PAT scheme and promotion of electric vehicles has shown where more aggressive implementation may be seen as necessary to ensure India's climate commitments. This elaborate decadal review finds most of its utility within the framework of policy designing for sustainable development and climate change mitigation by policymakers and stakeholders in the Indian scenario.

Classification of weather conditions based on automatic weather station data using a multi-layer perceptron neural network

Weather is one of the important elements that greatly determines human activities, especially those related to economic factors. Therefore, understanding weather conditions using weather parameters as a reference is important for human life, so a method is needed to classify weather according to its category so that the information produced can be used for various needs. Determining weather conditions in an area will not run well without a reliable method that can analyze existing weather parameters. Therefore, in this study, the weather condition classification process was carried out using the multilayer perceptron algorithm, a type of neural network (NN) algorithm. All data analyzed were weather parameter data collected by mini weather stations placed on land. The weather parameters used were temperature, humidity, air pressure, wind speed, dew point, wind chill, daily rainfall, solar radiation, and UV index. This study was conducted in Palu city, Central Sulawesi Province, Indonesia. The classification process carried out by the multilayer perceptron algorithm was carried out on the Altair AI Studio application and produced an accuracy value of 93.87%, recall of 92.33%, and precision of 91.29%.

Cloud computing-based estimation of Peninsular India’s long-term climate change impacts on rainfall, surface temperature, and geospatial indices

Realizing the intricate relationships between drought, vegetation dynamics, and climate change is essential for sustainable resource management. Although temperature and rainfall patterns are the primary determinants of these fluctuations, human activity also plays a significant role. Recent decades have witnessed significant climate change events, particularly in peninsular India. Analyzing these year-by-year variations in rainfall and temperature is essential for informed decision-making. This knowledge can guide the development of innovative adaptation strategies to ensure sustainable livelihoods in the region. This study utilizes the Google Earth Engine platform to analyze yearly climate data and relevant geographical indices from 2003 to 2023 across Peninsular India. The analysis reveals a statistically significant increase in mean annual rainfall (0.262 mm/year) alongside a slight rise in regional land surface temperature (LST) trends (0.102 °C/year). However, yearly average anomaly values for LST also show an upward trend, rising from 2.56 in 2003 to 3.23 in 2023. This suggests a potential shift in rainfall patterns, with potential consequences for water availability. Rising temperatures coupled with altered rainfall patterns can lead to water scarcity, especially in regions reliant on rain-fed agriculture. This has a direct impact on crop yields and overall agricultural productivity. Despite rising temperatures, the analysis using drought indices suggests a decline in average annual drought severity across Peninsular India, with values decreasing from 0.33 in 2003 to 2.70 in 2023. Interestingly, we found a strong positive association between rainfall and vegetation indices, while rainfall and LST exhibited a negative correlation. Interestingly, while rainfall and LST exhibited a negative correlation, a strong positive association was found between rainfall and vegetation indices. These comprehensive findings hold significant potential for informing future climate projections and promoting sustainable development in peninsular India through evidence-based applications.

Agricultural Insurance Premium Determination Model for Risk Mitigation Based on Rainfall Index: Systematic Literature Review

Rainfall is significantly essential in the agricultural sector to increase productivity. However, rainfall instability serves as a potential source of risk, causing crop failure and negatively impacting the welfare of farmers. To mitigate this risk, rainfall index-based agricultural insurance offers financial protection to farmers. There is no information on how to set a reasonable premium in index-based agricultural insurance. Therefore, this research aimed to systematically explore a model for determining a rainfall index-based agricultural insurance premium, focusing on the methods used and their effectiveness in mitigating the risk of harvest failure in the agricultural sector. The Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) method and a bibliometric analysis were used to collect and analyze articles from Scopus, ScienceDirect, and Dimensions databases. The results showed that there were 15 articles on determining a rainfall index-based agricultural insurance premium, where 4 used the Black–Scholes method and 11 applied other main methods. Meanwhile, no articles applied the fractional Black–Scholes method in determining agricultural insurance premiums based on the rainfall index, providing new opportunities for further research. The results contributed to the development of a model for agricultural insurance premium determination that could generate more diverse and flexible premium estimates as a sustainable method to mitigate the risk of harvest failure. This research is expected to serve as a reference for developing rainfall index-based agricultural insurance in the future and contribute to the Government of the Agriculture Department’s policy formulation regarding insurance programs for farmers.

Men in the making of nations: understanding the nexus between nation, belonging, and complicit masculinity

ABSTRACT The ideal of masculine Hinduism which is currently being employed within the Indian national rhetoric to sustain and build a modern Hindu Rashtra is a direct consequence of India’s tryst with British imperialism. Since it is men who, as real actors of nationalist movement defend their homeland and the honour of women, the current Indian right wing politics ensures that men continue to uphold the ideals of Hindu hegemonic masculinity defined by martial prowess, muscular strength, moral fortitude and a readiness to battle groups to strengthen the nation. However, while the hegemonic notions of Hindu masculinity are achieved by a small minority who become the public face of gender and sexual politics, the majority are those who reap benefits from such gendered arrangements by being complicit in the hegemonic project. This paper makes an attempt to study the complex interdependencies between hegemonic and complicit masculinities in nation building processes in India. The aim is to uncover how complicit masculinities are created to sustain Hindutva nationalism within the current Indian climate and whether there are alternative possibilities and codes of behaviour in which privileges of masculinity and power are confronted and exposed.

Impacts of climate variability on the spatio-temporal dynamics of plant formations in the forest-savannah transition zone: the case of the Lamto Scientific Reserve, Central Côte d’Ivoire

Understanding climatic variability’s effects on land and biodiversity is vital for guiding sustainability, conservation, and climate impact predictions in fragile ecosystems like Côte d’Ivoire’s forest-savanna transition zone. This study aims to analyse the impact of climate variability on the spatio-temporal dynamics of land use in the Lamto Scientific Reserve. To do this, a set of monthly climate data covering the period from 1990 to 2022 was used, including indicators such as rainfall, maximum, minimum and average temperatures, drought and standardised rainfall indices. The study also involved the classification of Landsat images dating from 1990, 2002, 2012 and 2022, enabling changes in land use to be observed. The corresponding areas were correlated with the climatic variables using a Spearman correlation test. The results show a transition from savannah to denser tree cover in the reserve. In addition, an increase in rainfall, varying between 900 and 1687 mm, suggests that Lamto could be classified as a humid region. The analysis highlights the complex interactions between climate change, particularly high temperatures, and land-use dynamics. Gallery and semi-deciduous forests show resilience in the face of rising temperatures, favouring their expansion. On the other hand, pre-forest formations, such as open forests and wooded savannahs, are more affected by these temperatures, which hinders their development. Tree savannahs also show a certain resilience, while shrub savannahs and bare land are often associated with ecological degradation processes in response to high temperatures. Finally, although rainfall plays a role, its influence seems minor, suggesting that other environmental or climatic factors, such as watercourses or microclimate, play a more significant role in land use/land cover dynamics.

Relative humidity prediction across the Indian subcontinent using Kumaraswamy distribution based non-Gaussian model.

Relative humidity (RH) significantly influences various aspects of human life, including agriculture, weather prediction, indoor air quality, and energy consumption. Its intricate non-linear behavior poses a significant challenge for accurate estimation. In the context of Indian climate change, precise forecasting of RH is vital for agriculture and weather prediction. This study proposes a new non-Gaussian approach, the Kumaraswamy seasonal autoregressive moving average (KSARMA) model, for RH prediction across India's five climatic zones (hot-dry, warm-humid, moderate, cold, and composite). Analyzing monthly RH data from 1981 to 2021, model selection was based on diagnostic analysis and Akaike's information criterion (AIC), with parameter estimation utilizing conditional maximum likelihood estimators (CMLE). The KSARMA model demonstrated superior predictive accuracy compared to other models (e.g., multilayer perceptron, SARIMA, Holt-Winter, and SARMA) across all climatic zones, except the hot-dry zone, as supported by error metrics including RMSE (0.04-0.12), MAE (0.03-0.1), SMAPE (0.04-0.2), and MASE (0.5-.08).

Evaluating reproduction traits in a crossbreeding program between indigenous and exotic sheep in semi-arid lands

Reproduction traits are important factors determining the efficiency of any sheep production system. This study evaluates the age at first lambing (AFL), lambing interval (LI), litter weight at birth (LBWT), litter weight at weaning (LWWT), birth weight of ewe (EBWT) and weaning weight of ewes (EWWT) in a crossbreeding program between the Red Maasai (RRRR) and Dorper sheep (DDDD) and their crosses, 75% Dorper (DDDR) and 50% Dorper (DDRR) breeds. All the traits significantly (P<0.05) differed across breeds and season of birth of the ewe. LBWT and LWWT were significantly affected by the sex of the lamb, type of birth of the lamb and parity in which the lambs were born in. AFL and LI had very high environment variances. Overall heritability estimates of AFL (0.09±0.04) and LI (0.00±0.01) were not significant from zero while the heritability estimates for EBWT (0.38±0.04), EWWT (0.23±0.03), LBWT (0.19±0.03) and LWWT (0.09±0.02) were significant (P<0.05). The RRRR had the highest genetic gain for all traits while the DDRR had a higher genetic gain among the crosses. LI had negative genetic correlations with LBWT (-0.53±0.08) and LWWT (-0.28±19.59) while AFL had positive genetic correlations with LBWT (0.27±0.46) and LWWT (0.31±0.34). The phenotypic trends for AFL and LWWT showed a negative and positive association, respectively, with the rainfall index over the years. With proper farm management, improved reproduction performance of ewes is possible by indirect selection using LBWT and LWWT for the Red Maasai, Dorper and their crosses within the semi-arid lands.

Settling-on-the-Move: Birsing Char-scapes in the Brahmaputra Valley

Chars are shifting riverine islands. This article focuses on Birsing Char, part of Birsing Jarua Village Panchayat, in the Brahmaputra River near the Indo-Bangladesh border. Generations of families have migrated across this porous border, settling in the Lower Brahmaputra Valley. This migration has intensified the sociocultural othering of Bengali Muslims amid Assam’s identity politics and India’s rising authoritarianism. Through fieldwork and interpretative mapping, the article uncovers forms of alternative knowledge, including local spatial practices and intangible heritage like songs and poetry, threatened by infrastructural development, policies of the Indian government and climate change. It explores how such knowledge can be harnessed and inspire alternative development policy and design in the context of global warming in the Brahmaputra Valley and in Assam’s sociopolitical climate. The case underscores the urgency of recognizing marginalized chars as vital to the region’s water legacy, as they contribute both to local livelihoods and broader ecological systems.

Optimizing the genetic evaluation criteria for the small herd of Saanen x Beetal crossbred dairy goats of Indian sub-tropic

This study focused on the accurate genetic evaluation of a small dairy flock of crossbred dairy goats, specifically developed for high milk production in the Indian sub-tropical climate. The study was conducted on Saanen x Beetal (SxB) crossbred goats, which utilized 12,660 test day records for first parity. Additionally, complete lactation records (N=1283) across multiple parities and for the first parity (N=659) were analyzed separately for genetic evaluation. The main traits examined were 150-day milk yield (150DMY), days in milk (DIM), peak yield (PY), and total milk yield (TMY), with averages of 198.80±2.83 kg, 227.90±4.18 days, 1.17±0.02 kg, and 262.40±6.12 kg, respectively, highlighting the genetic superiority of SB goats over native Indian goats. Across parities, the estimates for 150DMY, DIM, and TMY were 230±6.16 kg, 210±7.0 days, and 277±11.82 kg, respectively. Given the moderate heritability (0.24±0.08) and repeatability (0.29±0.04) estimates of 150DMY, the study recommends using 150DMY as the primary selection criterion for genetic improvement in SxB goats. The single-trait random regression model (RRM) utilizing various orders of orthogonal Legendre polynomials (LEG) and B-spline (BS) functions with heterogeneous residual variances was also employed. The test day milk yield (TDMY) showed a least squares mean of 1.30±0.01 kg, with moderate heritability estimates across test days (0.26±0.08). The optimal model was identified as a quadratic B-spline function with six knots (BS6Q). Positive genetic correlations were observed between consecutive test-day milk yield values, while correlations decreased for more distant test days. The study demonstrated the superiority of the B-spline model in genetically assessing Saanen x Beetal dairy goats, highlighting its benefits in curve fitting, genetic parameter estimation, and higher breeding value prediction accuracy. Looking into the moderate heritability and desirable genetic correlation of 150 DMY with other lactation traits, we recommend using 150 DMY for further selection programs. As the concordance of ranking between different approaches for breeding value prediction was high, we recommend using the random regression test day model (RR-TDM) when data recording across lactation is costly.

Variability of drinking water for pigs and poutry the southern region of Brazil over a twelve – month period

The increase in global food demand has led to intensified production of pigs and poultry. In this context, water quality on farms needs to be monitored to ensure maximum production and animal welfare. This study aimed to evaluate the quality and variability of drinking water for animals over twelve months in properties in the Southern Region of Brazil. Physical, chemical, microbiological parameters, and the average rainfall index of nine water sources were observed. Variations in pH were noted among the evaluated water sources, as well as within the same source over monthly collections. A relationship between average rainfall and elevated iron levels was also demonstrated. Nitrate and nitrite levels exceeded Brazilian legislation at certain sampling points, posing risks to human and animal health. In 67.59% of samplings, total coliforms were present, and 59.26% showed the presence of Escherichia coli. The study highlighted the variability of water sources in the Southern Region of Brazil, reflecting the need for constant monitoring and treatment measures, such as water treatment stations, chlorination systems, and acidification in rural properties.

Temperature and Precipitation Extremes Over Borneo Island: An Integrated Climate Risk Assessment

ABSTRACTGlobal warming has significantly increased the frequency and intensity of extreme events, which have catastrophic consequences for ecosystems and humans. Despite efforts to assess the impact of climate change on the potential risk of Borneo, most research has focused on partial regions, considering short timescales and a limited number of temperature and precipitation extremes indices to quantify the expected climate risks. This study employed a new method of climate risk assessment of Borneo based on the combined changes in various climate parameters. It estimated 23 climate indices at all grid points covering Borneo for three overlapping sub‐periods (1951–1980, 1961–1990, 1991–2020). The modified Mann‐Kendall test was employed to identify grid points exhibiting significant increasing or decreasing trends of each index for each sub‐period. Finally, significant trends of 23 indices were integrated to estimate the potential climate risk indicator (RI) based on the combined changes in various climate parameters for each grid point and sub‐period. Temperature indices showed a clear warming trend across Borneo Island, particularly in the eastern regions, with absolute temperature indices showing an increase of 0.5°C–2.5°C in 1991–2020 compared to the reference period (1951–1980). However, extreme cold temperatures have become less prevalent over the study period. There is a shift from light consecutive rainfall days towards more heavy and short‐duration rainfall events. Therefore, there are indications of intensifying rainfall events over the island's southern half, counterbalanced by drying trends in the northern regions, especially Brunei. The spatial distribution of RI revealed an overall 184% increase in climate risk on the island in recent years (1991–2020) compared to the reference period. The highest rise in RI was in the central east of the island, mostly due to significant increases in rainfall and temperature indices. The findings can inform adaptation initiatives to manage escalating heat and flood risks while guiding additional research to explain further the complex climatic changes occurring in this ecologically and socially vital region.

Spatial Characteristics and Driving Factors of Net Primary Productivity of Vegetation in the Upper and Middle Yellow River Basin

The Net Primary Productivity （NPP） of vegetation plays a crucial role in terrestrial ecosystems, and a detailed investigation into the annual average NPP and its driving factors is of significant importance for promoting regional ecological construction and sustainable development. This research utilized MOD17A3 annual average NPP data from 2000 to 2020 and employed methods such as trend analysis, Hurst index, random forest model, partial dependence model, geographic weighted regression, and partial least squares-structural equation model （PLS-SEM） to analyze the annual variation characteristics of NPP and its relationship with driving factors in the upper and middle reaches of the Yellow River. The results showed： ① During the period from 2000 to 2020, the annual average NPP in the upper and middle reaches of the Yellow River generally exhibited a year-on-year increasing trend, with 79.25% of the region showing a significant improvement in annual average NPP, while 0.25% of the region exhibited a severe degradation trend. ② The dominant influencing factors of the annual average NPP in the upper and middle reaches of the Yellow River included precipitation, NDVI, drought, and relative humidity. The suitable range for increasing annual average NPP was 400 mm to 650 mm for precipitation and 40% to 70% for relative humidity, and NDVI showed a significantly linear positive correlation with annual average NPP. Drought index was negatively correlated with annual average NPP. The annual average NPP tended to stabilize and did not decline further when the drought index exceeded the threshold of 8. ③ Extreme rainfall indirectly affected the variation in annual average NPP in the basin by influencing the vegetation growth condition. The impact degree of extreme rainfall indices on the annual average NPP in the basin was in the following order： R10&gt;R95P&gt;PRCPTOT&gt;SDII&gt;Rx5day&gt;R99P. ④ The next steps in ecological construction in the upper and middle reaches of the Yellow River should focus on the arid and severely arid regions in the northern part of the Yellow River, adapting measures to local conditions and enhancing the regional ecological environment.

Energy Efficiency and Economic Survivance Appraisal of a 375 kWp Rooftop Solar PV System Under Hot and Dry Indian Climate

ABSTRACTIndia's rooftop solar photovoltaic (PV) installations are experiencing rapid growth due to favorable regulations. As climate change becomes a growing concern, researchers are turning their attention to the effects of weather patterns on the performance of rooftop solar panels, and also to optimize their efficiency in a changing environment. Consequently, industry players in the solar sector have been conducting performance validation and feasibility assessments of these plants. A 375 kWp rooftop PV plant is studied as a case example from April 1, 2022 to March 31, 2023, generating 543,666 kWh annually for the grid. The NMBE and MBE were assessed using simulation tools like PVGIS and PV Watts. In addition, a cost–benefit analysis of carbon credits was conducted with and without their inclusion. The energy payback time is calculated at 4.5 years post‐inclusion. Over a 25‐year lifespan, the embodied energy of the PV plant amounts to 2,552,265 kWh. This plant can mitigate CO2 emissions annually by 10,173.57 tons which is equivalent to INR 5,464,925. The current study highlights both environmental and economic benefits by incorporating carbon credits into the project. Further advancement in simulation tools, PV technologies, climate change adaptations are expected which will improve the rooftop system efficiency with shorten pay pabck periods and maximum reductions in CO2 emissions.

Using human observations with instrument-based metrics to understand changing rainfall patterns.

Shifting precipitation regimes are a well-documented and pervasive consequence of climate change. Subsistence-oriented communities worldwide can identify changes in rainfall patterns that most affect their lives. Here we scrutinize the importance of human-based rainfall observations (collated through a literature review spanning from 1994 to 2013) as climate metrics and the relevance of instrument-based precipitation indices to subsistence activities. For comparable time periods (1955-2005), changes observed by humans match well with instrumental records at same locations for well-established indices of rainfall (72% match), drought (76%), and extreme rainfall (81%), demonstrating that we can bring together human and instrumental observations. Many communities (1114 out of 1827) further identify increased variability and unpredictability in the start, end, and continuity of rainy seasons, all of which disrupt the cropping calendar, particularly in the Tropics. These changes in rainfall patterns and predictability are not fully captured by existing indices, and their social-ecological impacts are still understudied.

Политика Индии в области смягчения последствий изменения климата: трудности и перспективы

India is one of the largest emitters of greenhouse gases in the world, largely due to its high dependence on coal. However, since the early 2000s, New Delhi has introduced various tools to help reduce greenhouse gas emissions. In 2022, a new climate plan was adopted that, for the first time, set a goal to achieve carbon neutrality by 2070. To achieve the goal of reducing greenhouse gases in the energy sector, India is using several market-based schemes, which will become the basis of the Indian emissions trading system in the nearest future. Environmentally friendly transport is actively developing. Insights from research in the field of industrial decarbonization, carbon capture, and storage systems and forest protection and afforestation programmes are being implemented. However, the implementation of the identified areas is fraught with difficulties, leading to the implementation of controversial measures on the part of the state which are economically justified but contrary to the climate strategy. This study examines the main directions of Indian climate policy, and prospects and difficulties in their implementation to achieve the goal of carbon neutrality

Assessment of shift in GWPZs in Kashmir Valley of Northwestern Himalayas

Groundwater is a critical resource for the Kashmir Valley, which is increasingly pressured by urbanization and climate change. This study aims to delineate Groundwater Potential Zones (GWPZs) in the Kashmir Valley using the Analytical Hierarchy Process (AHP) and Geographical Information Systems (GIS). The research integrates eight thematic layers, including geology, geomorphology, slope, drainage density, land use/land cover (LULC), lineament density, rainfall, and topographic wetness index (TWI), to assess GWPZs for 1995, 2010, and 2020. Weights for each layer were assigned based on their influence on groundwater dynamics. The generated GWPZs were classified into five classes: very high, high, moderate, low, and very low. Over 25 years, significant changes in GWPZs were observed: very low potential zones decreased by 45.17%, low potential zones by 26.17%, and very high potential zones by 72.95%. Conversely, moderate potential zones increased by 50.87%, and high potential zones saw a slight increase of 9.00%. Results indicated that high and very high GWPZs are predominantly located in the valley's alluvial plains, primarily covered by agricultural and partially horticultural land. Validation of the generated GWPZs using bore well locations, discharge data, and Receiver Operating Characteristic (ROC) curves demonstrated high accuracy, with Area Under the Curve (AUC) values of 0.915, 0.898, and 0.890 for the respective years. These findings underscore the adverse impact of urbanization on groundwater potential zonation and emphasize the need for sustainable water management practices. This study provides valuable insights into the long-term shifts in groundwater potential in response to urban expansion and climate change. It highlights the importance of continuous monitoring and adaptive management to safeguard groundwater resources.

Long-term climate change analysis in northeast and eastern India

Global climate change and landform alteration are correlated with a high impact on rainfall, land surface temperature (LST), vegetation conditions, and soil moisture. This study examines rainfall, temperature, vegetation condition, moisture, and drought index to identify the decadal change from 2002 to 2021 using Landsat and global Standardized Precipitation Evapotranspiration Index-base (SPEIbase) data in Google Earth Engine (GEE). In between 20 years, 50.54 mm of mean rainfall decreased in the study area while 6.31 °C of LST increased in the entire region. The most affected states are Bihar, Jharkhand, and West Bengal. Similarly, in northeast India, Assam, Tripura, and Meghalaya have huge effects on temperature variation. The green space (0.158), and moisture (−0.082) fluctuated mainly in Mizoram, Arunachal Pradesh, Manipur, and Tripura states. The SPEI has increased (−0.232) in Bihar, Manipur, Tripura, Assam, and Meghalaya. The analysis observed that decreased rainfall and high-temperature variation may impact crop production, moisture loss, cyclonic affected in West Bengal coastal areas, floods in the Brahmaputra River basin and Bihar, droughts, and urban heat island-related effects. Some policies like reduction of forest fire (northeast), flood (Assam), urban planning (West Bengal, Bihar, and Assam), industrial pollution, and jhum cultivation decrease can improve the climate change in those states, India.

A machine learning-based approach for flash flood susceptibility mapping considering rainfall extremes in the northeast region of Bangladesh

Flash floods are catastrophic global events, especially in northeast Bangladesh, and assessing flash flood susceptibility is crucial for preparedness and mitigation. Traditional geographic system-based flash flood susceptibility mapping struggles to capture flash floods’ non-linear and complex nature. However, machine learning models have recently emerged as an efficient alternative to address these limitations. This study evaluates four machine learning models—Artificial Neural Network (ANN), Recurrent Neural Network (RNN), Random Forest – Gradient Boosting (RFGB) Hybrid, and Categorical Boosting (CatBoost)—for flash flood susceptibility. It categorizes areas into five susceptibility levels: very low, low, moderate, high, and very high. Covering 24,424.25 km2 across eight districts, the study uses 400 points (200 flood and 200 non-flood) for training and validation, based on field investigation, historical flood information, Sentinel-1 SAR GRD: C-band Synthetic Aperture Radar data using Google Earth Engine, and insights from the local people. The models’ predictive performances are evaluated by incorporating topographical attributes and rainfall indices and using accuracy, precision, recall, F1 score, and Receiver Operating Characteristic (ROC) Area Under the Curve (AUC) score, with 70 % of the data for training and 30 % for testing. The ANN model performs best with rainfall indices, achieving high accuracy (maximum AUC = 0.802). The RFGB hybrid model shows excellent training accuracy (AUC ≥ 0.971) but suffers from overfitting during validation (AUC ≤ 0.674), requiring careful hyperparameter adjustment. The CatBoost model effectively uses both rainfall indices and terrain features, achieving AUC = 0.701 in training and AUC = 0.667 in validation. The ANN model conservatively includes the largest area (2198.3 km2) under ’very high’ susceptibility. This study’s flash flood susceptibility maps, which include rainfall extremes, are more robust than those without, helping local administrative authorities and national flood practitioners prepare for flash floods.