Climate Variability Change Research Articles

Changes in climate, vegetation cover and vegetation composition affect runoff generation in the Gulf of Guinea Basin

AbstractAlthough considerable effort has been deployed to understand the impact of climate variability and vegetation change on runoff in major basins across Africa, such studies are scarce in the Gulf of Guinea Basin (GGB). This study combines the Budyko framework and elasticity concept along with geospatial data to fill this research gap in 44 nested sub‐basins in the GGB. Annual rainfall from 1982 to 2021 show significant decreasing and increasing trends in the northern and southern parts of the GGB, respectively. Annual potential evapotranspiration (PET) also shows significant increasing trends with higher magnitudes observed in the northern parts of the GGB. Changing trends in climate variables corroborates with shift to arid and wetter conditions in the north and south, respectively. From 2000 to 2020 vegetation cover estimated using enhanced vegetation index (EVI) shows significant increasing trends in all sub‐basins including those experiencing a decline in annual rainfall. Vegetation composition measured using vegetation continuous fields (VCFs) from 2000 to 2020 show an increase in tree canopy cover (TC), a decline in short vegetation cover and marginal changes in bare ground cover (BG). Elasticity coefficients show that a 10% increase in annual rainfall and PET may lead to a 33% increase and 24% decline in runoff, respectively. On the other hand, a 10% increase in EVI may lead to a 4% decline in runoff while a 10% increase in TC, SV and BG may reduce runoff by 4% and increase runoff by 3% and 2%, respectively. Even though changes are marginal, decomposing vegetation into different parameters using EVI and VCFs may lead to different hydrological effects on runoff which is one of the novelties of this study that may be used for implementing nature‐based solutions. The study also demonstrates that freely available geospatial data together with analytical methods are a promising approach for understanding the impact of climate variability and vegetation change on hydrology in data‐scarce regions.

Validation of global precipitation time series products against tree ring records and remotely sensed vegetation greenness.

Global interpolated climate products are widely used in ecological research to investigate biosphere-climate interactions and to track ecological response to climate variability and climate change. In turn, biological data could also be used for an independent validation of one aspect of climate data quality. All else being equal, more variance explained in biological data identifies the better climate data product. Here, we compare seven global precipitation time series products, including gauge-based datasets (CRU-TS, UDEL-TS, GPCC), re-analysis products (ERA5, CHELSA), a satellite-based dataset (PERSIANN) and a multi-source product that draws on gauge, re-analysis, and satellite data (MSWEP). We focus on precipitation variables, because they are more difficult to interpolate than temperature, and show larger divergence among gridded data products. Our validation is based on 20 years of remotely sensed vegetation greenness (MODIS-EVI) and 120 years of tree ring records from the International Tree Ring Data Bank (ITRDB). The results for the 20-year EVI based validation shows that all gauge and re-analysis data products performed similarly, but were outperformed by the multi-source MSWEP product, especially in regions with low weather station coverage, such as Africa. For analyzing long 120-year time-series, UDEL-TS showed superior performance prior to the 1940s, with especially large margins for northern Asia and the Himalayas region. For other regions, CRU-TS and GPCC could be recommended. We provide maps that can guide the best regional choice of climate product for research involving time series of biological response to historic climate variability and climate change.

Climate change impacts on coffee production in Indonesia: A review

Indonesia is ranked in the top 5 coffee production countries in the world with the largest production being Arabica and Robusta coffee beans. Approximately ninety percent of coffee produced in Indonesia is being exported and as the trend in local consumption increases, the demand for coffee is escalating. However, future coffee production is challenged by climate change. Background: This paper outlines the impacts of climate change variables including environmental stressors on coffee production quality and quantity of Arabica and Robusta species, discusses the influences on suitable areas for coffee cultivation and the coffee pests related to coffee productivity, as well as suggests mitigation and adaptation approaches in Indonesia. Methods: The literature review approach was utilized to complete this paper which involves collecting, analyzing, and correlating the findings of prior studies. The paper only considered literature with the following terms in the title; climate change impact, coffee production, mitigation, adaptation, and Indonesia. Finding: Increasing temperature degrades coffee production both in Arabica and Robusta coffee beans. The overall suitable area for cultivating coffee is shifting towards higher altitudes However, Indonesia is projected to lose more than half of the suitable area for cultivating Arabica by the year 2050. Climate change improves coffee pests' thermal tolerance and enables them to grow in warm temperatures. Conclusion: Common mitigation approaches include shade plantation and soil moisturization control which both improve coffee productivity. Another strategy is the cultivation calendar modified the coffee life cycle with climate prediction.

Extreme weather events and Spatio-temporal characterization of climate change variables in Bangladesh during 1975–2019

Bangladesh is susceptible to climate change, thus a detailed study, including the analyses of trends, sub-trends, extreme events and indices was conducted to obtain a complete picture of the climate change pattern in Bangladesh utilizing daily rainfall, maximum, minimum and average temperature data of 26 stations from 1975 to 2019 using R 4.0.2 software. For the trend analysis Mann Kendal (MK), modified Mann Kendall (mMK), Innovative Trend Analysis (ITA) and Sen’s slope methods were used. Sub-trend analysis was conducted using ITA. Standard Anomaly Index (SAI) has been used to identify the frequency and severity of extreme events. ClimPACT2 software was used to check the homogeneity and calculate the extremes of temperature and rainfall data. Our analysis showed that during the last four decades, climate variables changed their patterns and trend heterogeneously over Bangladesh. Most stations showed decremental rainfall trend when central part of the country showed a substantial decrease. The northern and central parts of the country showed significant growth of trend for annual average temperature. The temperature in the monsoon season increased, whereas those in dry season decreased. The rainfall and maximum temperature were inversely related during monsoon whereas during dry season both of them decrease. The pre-monsoon and post-monsoonal rainfall also showed decreasing trends, indicating prevailing drought conditions especially in northern and central parts of the country. The SAI analysis showed alternating drought and wet years in almost all the stations. In the past 20 years, the country’s western region experienced more drought years than before whereas the coastal region experienced more wet years. The analysis of climate extreme indices suggests that, Consecutive Dry Days (CDD), Hot Days (TX90P) and Hot Nights (TN90P) show significant increasing trend throughout the country. The agricultural productivity, water resource management and food security are anticipated to benefit from this study.

A Relative Sea Surface Temperature Index for Classifying ENSO Events in a Changing Climate

Abstract El Niño–Southern Oscillation (ENSO) is often characterized through the use of sea surface temperature (SST) departures from their climatological values, as in the Niño-3.4 index. However, this approach is problematic in a changing climate when the climatology itself is varying. To address this issue, van Oldenborgh et al. proposed a relative Niño-3.4 SST index, which subtracts the tropical mean SST anomaly from the Niño-3.4 index and multiplies by a scaling factor. We extend their work by providing a simplified calculation procedure for the scaling factor, and confirm that the relative index demonstrates reduced sensitivity to climate change and multidecadal variability. In particular, we show in three observational SST datasets that the relative index provides a more consistent classification of historical El Niño and La Niña oceanic conditions that is more robust across climatological periods compared to the nonrelative index. Forecast skill of the relative Niño-3.4 index in the North American Multimodel Ensemble (NMME) and ACCESS-S2 is slightly reduced for targets during the first half of the year because subtracting the tropical mean removes a source of additional skill. For targets in the second half of the year, the relative and nonrelative indices are equally skillful. Observed ENSO teleconnections in 200-hPa geopotential height and precipitation during key seasons are sharper and explain more variability over Australia and the contiguous United States when computed with the relative index. Overall, the relative Niño-3.4 index provides a more robust option for real-time monitoring and forecasting ENSO in a changing climate. Significance Statement The goal of this study is to further explore a relative sea surface temperature index for monitoring and prediction of El Niño–Southern Oscillation. Sea surface temperature indices are typically computed as a difference from a 30-yr climatological average, and El Niño and La Niña events occur when values exceed a certain threshold. This method is suitable when the climate is stationary. However, because of climate change and other lower-frequency variations, historical El Niño and La Niña events are reclassified depending on which climatological period is selected. A relative index is investigated to ameliorate this problem.

Intensifying human-driven heatwaves characteristics and heat related mortality over Africa

Heatwaves in Africa are expected to increase in frequency, number, magnitude, and duration. This is significant because the health burden is only expected to worsen as heatwaves intensify. Inadequate knowledge of the climate’s impact on health in developing nations such as Africa makes safeguarding the health of vulnerable groups at risk challenging. In this study, we quantify possible roles of human activity in heatwave intensification during the historical period, and project the future risk of heat-related mortality in Africa under two Representative Concentration Pathways (RCP26) and (RCP60). Heatwaves are measured using the Excess Heat Factor (EHF); the daily minimum (Tn ) and maximum (Tx ) are used to compute the EHF index; by averaging Tx and Tn . Two heat factors, significance and acclimatization are combined in the EHF to quantify the total excess heat. Our results confirm the intensification of heatwaves across Africa in recent years is due anthropogenic activity (increase in greenhouse gas concentration and changes in land use). The Return event highlights the potential future escalation of heatwave conditions brought on by climate change and socioeconomic variables. RCP26 projects a substantial rise in heat-related mortality, with an increase from about 9000 mortality per year in the historical period to approximately 23 000 mortality per year at the end of the 21st century. Similarly, RCP60 showed an even more significant increase, with heat-related mortality increasing to about 43 000 annually. This study highlights the potentially growing risk of intensifying heatwaves in Africa under different emission scenarios. It projects a significant increase in heatwave magnitude, number, duration, frequency, and heat-related mortality. Africa’s low adaptive capacity will amplify the impact, emphasizing the need for emissions reduction and effective adaptation measures.

Effects of Climate Change on Horticulture Sector Productivity: A Review

This comprehensive analysis provides a thorough examination of the significant influence of climate change variability on the horticultural yield. The focus is specifically on recent trends and changing patterns within various major sectors of horticulture, namely Pomology, Olericulture, Floriculture, Post-Harvest, and Landscaping. The impact of rising temperatures altered precipitation patterns, and increased occurrences of extreme weather events is profoundly transforming the horticultural landscape. By delving into in-depth analyses of recent studies, this article sheds light on noteworthy changes in flowering and fruiting patterns in Pomology, as well as alterations in phenological stages in Olericulture. Furthermore, it highlights the innovative approaches in controlled-environment agriculture that have been adopted in Floriculture. The effects of climate change on post-harvest processes and the adoption of novel technologies are also explored within the context of post-harvest. Additionally, the integration of horticulture and landscape architecture is emphasized as a crucial strategy in adapting Landscaping practices to the variability brought about by climate change. The review is supported by various facts that illustrate global temperature anomalies, fruiting periods, phonological shifts, impacts on flower quality, adoption rates of post-harvest technologies, and the integration of horticulture and landscape architecture. The main objective of the review is to offer a brief understanding on the impact of climate change on horticultural productivity. Ultimately, the review highlights the urgent need to implement adaptive strategies and sustainable practices across diverse sectors of horticulture to ensure resilience in the face of ongoing climate change. This study suggests that vertical farming, climate resilient landscaping, Genomic Wide Association Studies, and Smart Breeding of crops as well as Controlled Environmental Agricultural methods might be suitable to encounter climate change.

Evaluation of climate change impact on plants and hydrology

Climate change (CC) is the menace of the hour impacting every facet of human existence. Regional CC and its impact studies are crucial in that they contribute to global change. The current study aims to investigate the prevalence of CC in Charsadda, Pakistan and its impact on vegetation and hydrology of the region to understand microclimate variability contribution to global CC. Utilizing local climate data for 20 years (2001–2020), Modified Mann-Kendall and Sen’s Slope statistics were employed to determine monthly and seasonal trends in climate variables. Significant changing climate variables were regressed on Moderate resolution Imaging Spectroradiometer (MODIS) satellite dataset viz. normalized difference vegetation index (NDVI). Due to the prominent climate factor impacting vegetation, NDVI was further correlated to MODIS land surface temperature (LST). Floods being the conspicuous climate calamity were mapped for 2005 and 2010 using satellites Landsat 5 and 7 dataset viz. normalized difference water index (NDWI) with flood risk assessment by watershed delineation. The findings revealed significant (p < 0.05) variability in climate variables (average monthly and summer maximum temperature, and average monthly and summer precipitation) that are driving CC and impacting vegetation and hydrology in the region. Temperature and solar radiation affect NDVI adversely while precipitation and relative humidity has positive impact on vegetation. NDVI varied greatly spatiotemporally, often increasing but worsening in some areas (Shabqadar, Abazai, Palai and Charsadda city with NDVI = 0.1–0.3) of the study region as a result of extreme weather events. Temporally, NDVI improved with an overall positive trend with a stage (2007–2016) of noticeable zigzag fluctuation. Spatial grids with higher LST (>40°C) were either devoid of or with sparse NDVI (<0.3) presenting global warming as peril to vegetation. NDWI maps (2005, 2010 floods) indicate that after floods wreaked havoc on the region altering the vegetation pattern revealing heavy irregular precipitation as the next to temperature in jeopardizing vegetation of the region. Lower elevation regions along the Swat and Kabul Rivers with a greater risk of flooding were identified by watershed delineation. The study suggests that local governments and stakeholders implement CC mitigation strategies and plans for vegetation restoration, flood alerts with post-flood management for regional sustainable development.

Performance Evaluation of High-resolution Reanalysis Datasets Over North-central British Columbia

abstract Version 2.1 of Environment and Climate Change Canada’s Canadian Surface Reanalysis (CaSR), based on the Regional Deterministic Reforecast System (RDRS), was implemented in 2022 to provide temporally complete meteorological data over 1980–2018 covering Canada at 10 km spatial resolution. Similarly, the fifth generation of the European Centre for Medium-Range Weather Forecasts (ECMWF) Re-Analysis-Land (ERA5-Land) dataset at ∼9 km spatial resolution became available. To assess their performance in complex topography, this paper undertakes spatiotemporal inter-comparisons between the RDRS and ERA5-Land reanalysis products with station-based data across British Columbia’s Skeena and Nechako watersheds for 1980–2018. Results reveal persistent cold biases, ranging from −6.2°C to −1.6°C basin-wide, in reanalysis mean annual air temperatures relative to observations, but biases vary in both space and time. Conversely, reanalysis total annual precipitation shows wet biases, ranging from 25% to 59% basin-wide. Analyses generally show wetting trends for observations and ERA5-Land while RDRS exhibits drying trends. Reanalysis datasets achieve better agreement overall with observations over the Nechako Watershed, likely due to its denser network of meteorological stations and less complex terrain than the Skeena Watershed. Despite some deficiencies, the RDRS and ERA5-Land reanalyses remain particularly useful products to assess regional climate variability and climatic change given their generally skilful representation of spatial patterns and temporal trends in meteorological variables across the Nechako and Skeena watersheds.

Waterborne Diseases That Are Sensitive to Climate Variability and Climate Change.

Waterborne Diseases That Are Sensitive to Climate Variability and Climate Change.

Assessing the influence of climate variability and land cover change on water resources in the Wami river catchment, Tanzania

Assessing the influence of climate variability and land cover change on water resources in the Wami river catchment, Tanzania

Long term (1901−2021) trends and prediction of climatic variability in selected agro‐ecological zones of Himachal Pradesh using coupled statistical and machine learning approaches

This study analyses the trends of changing climatic elements in the hydrological regime of the Indian Himalayan Region with specific focus on Agro‐ecological zone II & III of Himachal Pradesh for the period of 1901−2021. The upper, middle, and lower catchment areas of Sutlej River Basin were studied to reveal regional trends in climatic parameters. The Mann‐Kendall test and Sen Slope analysis were used to estimate annual and seasonal trends and their magnitude. The results showed a significant decreasing trend in the lower catchment area, with a break point year estimated to be 1953 for the entire basin. Two blocks of analysis, 1901−1953 and 1954−2021, showed significant variations. Annual rainfall data revealed a statistically significant decreasing trend at different stations. The lower catchment area received a significant increase in rainfall compared to higher altitude stations. In terms of seasonal variation, the pre‐monsoon season showed a significant decrease, while the entire basin recorded a significant increase in average monthly temperature. The study concludes by generating future time series predictions using an ANN model for the period of 2022−2050. Overall, the study's findings indicate a significant change in climatic variables with signs of increasing monthly temperatures and decreasing annual rainfall.

Cash crop farmers’ perceptions of, and adaptation to climate change: A preliminary study of Black Bush Polder, Guyana

Farmers Black Bush Polder, Guyana, have been experiencing changes in weather variability and climate parameters as well as the impacts of those changes on their livelihoods. Understanding farmers’ perception of climate change and their adaptation strategies is crucial to assisting with efforts for developing suitable intervention strategies to combat the effects of climate variability and climate change. In this non-experimental research engaging fifty (50) randomly selected farmers the aim was to: (1) examine how cash crop farmers in Black Bush Polder perceived the effects of climate change; (2) identify adaptation strategies used by farmers in combating the effects of climate change; (3) identify the factors that influence the adaptation strategies chosen by farmers; (4) identify perceived barriers in the implementation of adaptation measures; and (5) make recommendations in light of the difficulties encountered by farmers. A self-administered structured questionnaire survey was used to collect primary data and the responses to the items using a modified Likert scale. Descriptive statistics, and Pearson Correlation Coefficient were used to analyze the data. The results showed that there was variation in both temperature and rainfall in the Black Bush Polder Area. Overall, farmers had a general perception that these variations had an impact on their farming activities as well as on their choice of adaptation measures. Key recommendations made were: planting improved, or more resistant varieties of crops, changing planting times as dictated by rainfall, collection of water in furrows near plants, utilizing drip irrigation systems and techniques for preparing crop beds, and using of shade houses and greenhouses to lessen the effects of climate change.

Modeling the Effects of Climate Change and Socio-Ecomonic Variables on Agricultural Production

Modeling the Effects of Climate Change and Socio-Ecomonic Variables on Agricultural Production

Impact of climate variability and environmental policies on vegetation dynamics in the semi-arid Tigray

Anthropogenic and climate-related phenomena are among the main factors responsible for variations in vegetation structure and composition worldwide. However, studies that integrate the effects of human activities and climate variability in fragile tropical ecosystems, including the semi-arid Tigray region, are lacking. The objective of this study was to examine the effects of climate variability and environmental policy changes on the spatial distribution and pattern of vegetation cover in the semi-arid Tigray region of Ethiopia over the past four decades. We used satellite-based vegetation index (normalized difference vegetation index) and monthly rainfall data to analyze the relationship between vegetation cover and climatic variability. Residual analysis was also used to further disentangle the effects of climatic variability and environmental policy on vegetation cover. The regression analysis (r2 = 0.19) showed an insignificant causal relationship between vegetation dynamics and precipitation over the 41-years study period. This study also highlighted negative impact of the global rise in temperature on vegetation cover due to water stress caused by evapotranspiration. On the other hand, the residual analysis results (r = − 0.55, z-stat = − 11.58, p < 0.01) indicated a strong relationship between vegetation change and environmental policies implemented within the specified study period. Overall, the study revealed that environmental policies had a greater impact than climate variables on vegetation. Policymakers should, therefore, prioritize implementing effective environmental policies to restore degraded ecosystems and mitigate the effects of climate change.

Assessment of Climate Trend, Meteorological Drought and Farmers' Perception to Climate Change and Variability in Hadero Tunto District, Southern Ethiopia

Currently, climate change and variability are a hot issue, and more attention needs to be paid to their impact on Ethiopia. Farmers' perception plays a significant role in successfully implementing of adaptation strategies to reduce climate change impacts. The main objective of this study was to assess climate trends, meteorological drought and farmers' perception of climate change variability in Hadero Tunto district, Kembata Tembaro Zone, Southern Ethiopia. To determine historical climate trends, 30 years of rainfall and temperature data were obtained from the Hawassa branch of the Ethiopian National Meteorological Service, and trend analysis was performed. The multistage sampling technique was used to select 150 farm households to be surveyed. The modified Mann–Kendall and Sens' slope estimator trend tests were applied to detect the statistical significance of the trend as well as the magnitude in the time series data. The standardized precipitation index was computed for short and long-term scales to characterize the moisture content in the study area. Results of the standardized precipitation index revealed that the total number of drought events was higher in spring than summer. Still, the most extreme drought was recorded in summer. The years 1987 and 2015 experienced extreme drought in summer with indices values of -2.89 and -2.03, respectively, while, -2.27 was the spring season of 2003. In the long-term scale, -1.7 was recorded as the severe value. The historical average annual maximum and minimum temperatures both showed a significant upward trend. Historical annual rainfall shows a negligible downward trend from 1987 to 2016. This trend of rising temperatures and changing rainfall threatens smallholders´ agriculture, who are already limited by access to basic equipment and land use challenges. The descriptive result revealed that most people perceived long-term variability in the rainfall amount and distribution pattern and an increasing trend and variability of temperature. Based on these results, the study recommends that agricultural extension services be enhanced to sensitize the farmers about climate change, thus improving their perception. Further, studies at a larger scale to illustrate the associations between farmers' perceptions of climate change with meteorological data to figure out the risk of climate change are also critical

Effects of changes in climatic means, variability, and agro-technologies on future wheat and maize yields at 10 sites across the globe

To address the rising global food demand in a changing climate, yield gaps (YG), the difference between potential yields under irrigated (YP) or rainfed conditions (YWL) and actual farmers’ yields (Ya), must be significantly narrowed whilst raising potential yields. Here, we examined the likely impacts of climate change (including changes in climatic variability) and improvements in agricultural technologies on crop yields and yield gaps. Eight rigorously tested crop simulation models were calibrated for wheat and maize and run at 10 different sites around the world. Simulations were performed to estimate YP, YWL and yields achievable under three locally defined technology packages: TP0 represents average farmer's practice, while TP1 and TP2 are increasingly advanced technologies. Simulations were run for the baseline (1981–2010) and twelve future climate scenarios for 2050, representing changes in the means of climate variables and in the variability of daily temperature and dry/wet spell durations. Our basic hypotheses were that (H1) mean climate changes combined with increased weather variability lead to more negative yield impacts than mean climate changes alone, and (H2) advanced technologies would serve as effective adaptations under future climatic conditions. We found that crop responses were dependent on site characteristics, climate scenarios and adopted technologies. Our findings did not support H1. As for H2, the improved technology packages increased wheat and maize yields at all sites, but yield gap reduction varied substantially among sites. Future studies should consider a broader range of climate scenarios and methods for analysing potential shifts in climate variability. Moreover, it is recommended to co-create and evaluate climate zone-specific climate-smart crop production technologies in interaction with a wide range of local stakeholders.

Compound flood impacts from Hurricane Sandy on New York City in climate-driven storylines

Abstract. High impact events like Hurricane Sandy (2012) significantly affect society and decision-making around weather/climate adaptation. Our understanding of the potential effects of such events is limited to their rare historical occurrences. Climate change might alter these events to an extent that current adaptation responses become insufficient. Furthermore, internal climate variability in the current climate might also lead to slightly different events with possible larger societal impacts. Therefore, exploring high impact events under different conditions becomes important for (future) impact assessment. In this study, we create storylines of Sandy to assess compound coastal flooding on critical infrastructure in New York City under different scenarios, including climate change effects (on the storm and through sea level rise) and internal variability (variations in the storm's intensity and location). We find that 1 m of sea level rise increases average flood volumes by 4.2 times, while maximised precipitation scenarios (internal variability) lead to a 2.5-fold increase in flood volumes. The maximised precipitation scenarios impact inland critical infrastructure assets with low water levels, while sea level rise impacts fewer coastal assets though with high water levels. The diversity in hazards and impacts demonstrates the importance of building a set of relevant scenarios, including those representing the effects of climate change and internal variability. The integration of a modelling framework connecting meteorological conditions to local hazards and impacts provides relevant and accessible information that can directly be integrated into high impact event assessments.

Deep Learning Forecasting: An LSTM Neural Architecture based Approach to Rainfall and Flood Impact Predictions in Bihar

The increasing prominence of natural disasters and the variability in climate change have underscored the significance of research in contemporary and prospective studies. This investigation zeroes in on floods, a ubiquitous natural calamity that plagues regions globally. Bihar stands out as a state exceptionally vulnerable to persistent and intense flooding, with a staggering 69.70 lakh hectares—equivalent to nearly 74 percent of its geographical expanse—enduring its detrimental consequences. The repercussions of such inundations are widespread, permeating multiple facets of life and erecting impediments to both the welfare and the developmental trajectory of local communities. Vital sectors, including infrastructure, agriculture, and livestock, bear the brunt of the damage, catalyzing the dislocation of innumerable households. In an agile response to these adversities, the state's administrative machinery has mobilized disaster response brigades, ensuring timely aid and succor to the distressed populace. To adeptly predict impending floods, this study harnesses data from 1991 to 2022, integrating pivotal metrics such as the ’Total Population affected (in lacs)’, ’Mean Annual Rainfall’, and ’Crop damage (in INR lakhs)’. By adopting an LSTM (Long Short-Term Memory) model, the research elucidates its findings via illustrative line plots, furnishing an accessible rendition of the flood trajectory predictions over the forthcoming five-year span. These predictive insights are instrumental for disaster management agencies, enhancing their strategic planning and readied responses to imminent flood events in Bihar. Capitalizing on these forecasts enables authorities to architect proactive interventions, ensuring reduced flood repercussions, bolstered community resilience, and an elevated state of disaster preparedness in the region.

The Impact of Climate Change on Economic Growth in South Asian Countries: The Role of Financial Development, Trade Openness and Renewable Energy Consumption

This study examines the complex correlation between climate change and economic growth in South Asian nations, focusing specifically on the significant influences of financial development, trade openness, and renewable energy usage. This research endeavors to address the existing information gaps pertaining to the impact of climate change on economic development, with a specific focus on the susceptibility of South Asian nations. This study employs quantitative research approach. Economic indicators, climate data, and relevant financial and trade metrics are scrutinized to discern patterns and relationships. The methodology incorporates a multi-faceted approach to capture the complexity of the interactions between climate change variables and economic parameters. The study’s results provide significant insights into the intricate relationship among climate change, financial growth, trade openness, and renewable energy use in the South Asian region. The research elucidates how climate-induced disruptions impact economic sectors crucial to the region’s development. Moreover, it dissects the specific mechanisms through which financial structures, trade policies, and renewable energy initiatives influence economic resilience in the effect of climate challenges. Based on the findings, the study offers a set of targeted recommendations for policymakers, emphasizing the need for integrated approaches to climate change alleviation and adjustment. These recommendations encompass strategies for enhancing financial mechanisms, optimizing trade policies, and promoting sustainable practices in renewable energy consumption. By aligning economic policies with environmental sustainability goals, the recommendations aim to guide South Asian countries toward resilient and sustainable development pathways amidst the evolving landscape of climate change challenges.