Forest Fire in the North-Western Himalaya becomes a pressing issue. Forest fire poses a severe threat to biodiversity, soil fertility, and local livelihoods, especially in the ecologically sensitive Himalayan region and Uttarakhand is one of the major susceptible states. Therefore, this study focuses on comprehensive analysis of forest fire susceptibility in Uttarakhand, India using the Random Forest (RF) machine learning model integrated with geospatial data. By incorporating various topographic, climatic, and anthropogenic factors - ments, and wind speed, this study generates a high-resolution susceptibility map. The analysis reveals that approximately 33.60% of Uttarakhand's regions fall under high forest fire susceptibility, primarily concentrated in the southern and central zones, while only 21.33% regions are deemed insusceptible. Seasonal forest fire trend maps from 2000 to 2020 highlight that the pre-monsoon months (March- May) are the most fire-prone, with pronounced activity also observed in January-February. Validation of the RF model using the receiver operating characteristic (ROC) curve yields an AUC of 85.4%, indicating excellent predictive performance. Additionally, the study explores the role of Chir Pine (Pinus roxburghii) in exacerbating fire risks due to its resinous and highly flammable foliage. These findings underscore the critical need for proactive forest fire management strategies, including early warning systems, community engagement, and sustainable landscape practices. Policymakers, forest managers, and disaster mitigation organizations can use the integrated approach presented here as a useful tool to pinpoint hotspots and distribute resources efficiently to lessen the effects of forest fires in the Himalayas. (c) 2026 COSPAR. Published by Elsevier B.V. All rights are reserved, including those for text and data mining, AI training, and similar

Urbanization-driven heat intensification poses a serious challenge to environmental sustainability and thermal comfort in megacities such as National Capital Territory (NCT) Delhi. The influence of green space structure and configuration on Land Surface Temperature (LST) remains under-explored. This study presents a novel framework that integrates Fragstats-derived green space metrics and explainable artificial intelligence (XAI) to assess the spatio-temporal impact of green space structure and configuration on LST in NCT Delhi. The LST and nine green space metrics were derived using the Landsat-8 (OLI/TIRS) imagery. Three machine and deep learning models i. e., Gradient Boosting Machine (GBM), Distributed Random Forest (DRF), and Deep Learning (DL) were developed to regress and predict LST using H2O AutoML package in Rstudio environment. Among these, GBM produced the highest predictive accuracy (R2 = 0.8859) and was therefore selected for further interpretation using XAI techniques such as SHapley Additive exPlanations (SHAP) and Individual Conditional Expectation (ICE) plots. Results show that fragmented green spaces intensify surface heating, while cohesive and well-connected green space corridors promote cooling through enhanced evapotranspiration and shading. The findings highlight that not only the amount but also the spatial configuration of vegetation determines its cooling efficiency. Areas in East Delhi (Shahdara, Seelampur) and North-West Delhi (Narela, Bawana, Rohini Extension), characterized by high fragmentation, experience higher temperatures, whereas contiguous green space corridors of Central and South Delhi exhibit stronger cooling benefits. These insights provide actionable guidance for urban planners and policymakers to prioritize cohesive green space development in future urban planning for climate adaptation and mitigation.

More than 4.40 million of the Jangalmahal area of Eastern India have been experiencing a prolonged dry condition with a limited capacity for groundwater recharge compared to plain land. Thus, the management of groundwater resource requires accurate information of potential aquifer recharge areas. This study evaluates the groundwater recharge potential zones (GRPZ) using multi-criteria decision making (MCDM) approach, like analytic hierarchy process (AHP), and geo-statistical models like, frequency ratio (FR), weights-of-evidence (WOE), and evidential belief function (EBF) and assess the seasonal groundwater fluctuation level. We used remote sensing techniques to prepare the twelve conditioning layers. The geo-statistical models of FR, WOE, and EBF were used to reclassify, normalise, and analyse the conditioning factors. After developing the groundwater potential index (GPI), the four models are applied to create the GRPZ map. The FR and EBF models revealed comparatively better representation, where 21.94% and 21.25% of highly GRPZ covering the area of 2103 km2 and 2173 km2, respectively. More than half of the total Jangalmahal region (FR = 53% and EBF = 52%) is found as low recharge potential due to its complex lithological structure. A linear regression between model outcomes of GRPZ and Groundwater fluctuation level established the relationship more profoundly. Henceforth, this study proposed some significant strategies to increase the groundwater recharge capacities in the dry region that would be helpful in the sustainable groundwater resource management.

Abstract Irrigation is essential for maintaining the agricultural production and supporting India’s growing population. Land surface models provide an effective approach to estimate irrigation requirements and hydrological fluxes. However, the irrigation modeling tends to be affected by uncertainties related to the spatiotemporal uncertainty in irrigation map, frequency and irrigation factor (related to the target soil water content). To address these uncertainties, the irrigation and hydrological fluxes over India were reconstructed by simulation experiments with the Community Land Model (CLM). Results show that using a season-specific irrigation map improved the transpiration-total evapotranspiration ratio (T/ET) by up to 30% in the pre-monsoon season, implying higher irrigation efficiency. The remote sensing-based evapotranspiration products were used to compare with simulated model results, showing a similar increasing ET-trend in the pre-monsoon season as the irrigation induced CLM. Furthermore, the results show that higher irrigation frequency leads to increased irrigation amounts, evapotranspiration, and surface runoff. These findings demonstrate that incorporating seasonally varying irrigation maps significantly improves irrigation efficiency estimates and reduces overestimation of irrigation amounts and runoff, highlighting the need for dynamic irrigation representation in land surface modeling over regions with pronounced cropping seasonality.

Mountain pastoral livelihoods in the Western Himalaya face compounded pressures from warming, shifting rainfall, and the spread of invasive alien plant species (IAPS), yet integrated evidence linking climate dynamics, vegetation change, and livelihood outcomes remains limited. This study examines how long-term climate trends, IAPS, and shifting livelihood conditions interact along the elevational migration routes of the Bakkarwal pastoralists (BPs) in Jammu and Kashmir, India. We integrate 42 years of gridded daily climate records (1980–2021) with focus group discussions (FGDs) and a structured questionnaire (n = 80) to assess climate variability, perceived vegetation change, and livelihood impacts. The results show that temperatures have risen ( 0.85 °C daytime, 0.70 °C nighttime) significantly in low and high-elevation districts, with stronger post-2000 signals and greater high-elevation sensitivity, while rainfall has redistributed seasonally rather than showing a uniform trend. Field and survey evidence point to the upslope spread of IAPS (Lantana camara, Parthenium hysterophorus, and Ageratum conyzoides) displacing the native fodder and increased overbrowsing pressure on remaining shrubs and trees. Exploratory factor analysis (EFA) of perception data identified latent dimensions of the vegetation and livelihood factors improving the interpretability of the observed dataset. These dimensions reveal how climate-induced vegetation dynamics interact with socio-economic changes such as youth’s preference for education and salaried jobs, policies that impact grazing access, and changes in herding and migration routines which are altering social relationships, reducing the intergenerational knowledge transfer, and gradually reshaping the cultural values that have long supported Bakkarwal pastoralism. The overall findings envisage that developing the resilience in their socio-ecological system will be subject to how they cope with the upcoming climatic risk, IAPS preservation, restoration of critical forage sites, and community-based social policies that support ease and conspicuous livelihood transitions for the BPs.

In recent decades, the effects of climate change and climate variability have attracted significant global attention due to their growing impact on extreme weather and climate events [...]

It is important to identify the suitable zone for urban built-up area development in highly populous countries in the global south. Therefore, this study was conducted in a medium class city in eastern India based on some significant influential factors and their sub-criteria. Different geophysical data, official data, and open street data were used to assess the land suitability for future urban growth by integrating MCDM techniques and different Geo-statistical models namely analytical hierarchy process, frequency ratio, weights of evidence, and evidential belief function. Based on the site suitability assessment outcomes, four different zones were identified such as very high suitability zone, high suitability zone, moderately suitable zone and low suitable zone. The findings of this study revealed that the central part of Midnapore municipality has been observed low suitability while more than 50

Rapid growing population and increasing consumption of food are placing unprecedented demand for agriculture. Today, more than 1.4 billion people in India are becoming a challenge for agricultural land to feed them. The present study outlines how the trend in area, production, and productivity existed in the past and what they will be if continued with similar operations, practices, and climatic conditions. The study assesses the changing trend and pattern of rice crops under the area, production, and productivity over 55 years from 1966 to 2020 in different homogenous meteorological regions (HMRs) of India. In the present study, three statistical trend analysis methods, namely the linear regression model, Mann-Kendall test (M-K test), and Sen’s slope analysis, have been used to analyse the existing trends in the regions. The results show that the area under rice crop has increased in NW, CNE, WC region, and India on a national scale, while it is decreasing in NE and SP regions. The trend line for all the HMRs and India is significant at a 95

India's subtropical monsoon climate, rapid population growth, urban expansion, and industrial development pose long-term challenges to sustainable groundwater management. In this study, GIS-based Fuzzy Analytical Hierarchy Process (Fuzzy-AHP) has been employed to delineate Groundwater Potential (GWP) zones of India at national and state levels. Ten hydro-meteorological and physiographic factors were used and the resulting GWP zonation indicated that high, moderate, and low GWP zones encompass 27.43 %, 42.25 %, and 30.32 % of India's territory, respectively. High GWP zones are concentrated in the Indo-Gangetic plains, coastal regions, and scattered interiors, with states such as Tripura, Bihar, Punjab, Assam, West Bengal, and Uttar Pradesh having a majority of high GWP areas. Hilly states exhibit low GWP, while regions with consolidated formations display extremely low GWP. Moderate GWP is observed in Manipur and Nagaland due to high rainfall and uneven slopes. The reliability of the GWP map was validated with precision, recall, F1-score, F2-score, and accuracy scores of 0.67, 0.75, 0.71, 0.73, and 0.78, respectively. Probabilistic performance metrics of AUC-ROC = 0.86 and AUCPR = 0.85 supported the performance metrics scores. The AUC-SR score indicated strong spatial prioritization capability of the GWP map, as nearly 70 % of validation locations were captured within the top 50 % of the GWP zones of this study. The domain knowledge of experts and the sensitivity analysis highlighted aquifers, drainage density, and groundwater depth as the most influential factors driving India's GWP, whereas LULC contributed with the minimal impact. This study offers a decision-support tool for informed groundwater planning and sustainable water resource management.

Globally, there has been a lot of focus on climate variability, especially variability in annual precipitation and temperatures. Depending on the area, different climate variables have different degrees of variation. Therefore, analyzing the temporal and spatial changes or dynamics of meteorological or climatic variables in light of climate change is crucial to identifying the changes induced by climate and providing workable adaptation solutions. This study examined how climate variability affects tea production in Darjeeling, West Bengal, India. It also looked at trends in temperature and rainfall between 1991 and 2023. In order to identify significant trends in these climatic factors and their relationship to tea productivity, the study used a variety of statistical tests, including the Sen’s Slope Estimator test, the Mann–Kendall’s test, and regression tests. The study revealed a positive growth trend in rainfall (Sen’s slope = 0.25, p = 0.001, R2 = 0.032), maximum temperature (Sen’s slope = 1.02, p = 0.026, R2 = 0.095), and minimum temperature (Sen’s slope = 4.38, p = 0.006, R2 = 0.556). Even with the rise in rainfall, there has been a decline in tea productivity, as seen by the sharp decline in both the tea cultivated area and the production of tea. The results obtained from the regression analysis showed an inverse relationship between temperature anomalies and tea yield (R = −0.45, p = 0.02, R2 = 0.49), indicating that the growing temperatures were not favorable for the production of tea. Rainfall anomalies, on the other hand, positively correlated with tea yield (R = 0.56, p = 0.01, R2 = 0.68), demonstrating that fluctuations in rainfall have the potential to affect production but not enough to offset the detrimental effects of rising temperatures. These results underline how susceptible the tea sector in Darjeeling is to climate change adversities and the necessity of adopting adaptive methods to lessen these negative consequences. The results carry significance not only for regional stakeholders but also for the global tea industry, which encounters comparable obstacles in other areas.

Due to climate change the drop in spring-water discharge poses a serious issue in the Himalayan region, especially in the higher section of Himachal Pradesh. This study used different climatic factors along with long-term rainfall data to understand the decreasing trend in spring-water discharge. It was determined which climate parameter was most closely correlated with spring discharge volumes using a general as well as partial correlation plot. Based on 40 years (1981-2021) of daily average rainfall data, a rainfall-runoff model was utilised to predict and assess trends in spring-water discharge using the MIKE 11 NAM hydrological model. The model's effectiveness was effectively proved by the validation results (NSE = 0.79, R2 = 0.944, RMSE = 0.23, PBIAS = 32%). Model calibration and simulation revealed that both observed and simulated spring-water runoff decreased by almost 29%, within the past 40 years. Consequently, reduced spring-water discharge is made sensitive to the hydrological (groundwater stress, base flow, and stream water flow) and environmental entities (drinking water, evaporation, soil moisture, and evapotranspiration). This study will help researchers and policymakers to think and work on the spring disappearance and water security issues in the Himalayan region.

The study of monsoon variability in India has been the focus of scientific research for many decades due to its impact on agricultural production and the national economy. This study aims to understand the research trend over time and analyses those issues using bibliometric analysis with systematic literature review and meta analysis. A bibliometric and meta analysis was developed to analyses Scopus metadata documents from 1978 to 2022 by using bibliometric R-tool. The meta analysis was conducted at four broad bibliometric indices levels- authors, sources, affiliation and collaboration. The result shows the major area of the research in this field, as well as the institution, authors and journal, are inter-link with each other. Collaboration networks are found between a close group of researchers, institutions and countries, but international collaborations are growing. The reference spectroscopy helps to identify the historical root of this study. This study has created a bridge between the previous research findings and the research gap and how researchers can work on this research gap to achieve sustainable development goals (SDGs) 2, 6 and 13 that are also incorporated through a unified framework. The findings will be helpful for researchers at various stages in the field of monsoon variability by providing an extensive overview and authoritative literature review.

NCT Delhi, the heart of India, remains vulnerable to urban flooding from July to October, when southwest monsoon is active over its area of 1483 km2. To address the paucity of a comprehensive susceptibility map, this study employs urban flood modeling to quantify the spatial sensitivity of NCT Delhi to water logging. Fifteen flood related variables, including elevation, slope, slope aspect, profile curvature, plan curvature, Topographic Wetness Index (TWI), Stream Power Index (SPI), Topographic Roughness Index (TRI), geology, soil type, land use/land cover (LU/LC), Modified Fournier Index (MFI), water level depth, distance from storm drain, and distance from the Yamuna were analyzed. The study applies the data-driven form of ensemble fuzzy weights of evidence-support vector machine (Fuzzy WofE-SVM). The first-level flood susceptibility map was generated using Fuzzy WofE technique. Using this map and secondary data on water logging locations identified by the Delhi Traffic Police, flood inventory datasets were created. To execute the ensemble methodology, SVM was sequentially integrated with the Fuzzy WofE technique. Urban flood susceptibility zonation maps of NCT Delhi were created using four SVM kernel functions: linear (LN), polynomial (PL), radial basis function (RBF), and sigmoid (SIG). The kernel parameters i.e., regularization parameter (C), kernel width (γ) and degree (d) were optimized using python-mediated k-fold cross validation method. Area under the receiver operating characteristic curve (AUROC) analysis validated that all the SVM kernels yield excellent success and prediction rates. However, in terms of success rate, RBF (AUROC = 0.976) outperforms PL (AUROC = 0.968), LN (AUROC = 0.966), and SIG (AUROC = 0.956). With an AUROC of 0.966, RBF again outperforms SIG (AUROC = 0.964), LN (AUROC = 0.955), and PL (AUROC = 0.952) in terms of predictive performance. The novel Coupled Risk (CR) Index has been developed and presented in this study to increase the applicability of flood modeling by translating macro-hazard perspectives into a polished vulnerability scenario of administrative convenience. Spatial analysis of hazard intensity and endangered population using this novel tool would benefit take specific actions for disaster management by prioritizing wards for mitigation strategy planning and implementation.

Thamnocalamus spathiflorus is a shrubby woody bamboo invigorating at the alpine and sub-alpine region of the northwestern Himalayas. The present investigation was conducted to map the potential distribution of Th. spathiflorus in the western Himalayas for current and future climate scenario using Ecological Niche Modelling (ENM). In total, 125 geo-coordinates were collected for the species presence from Himachal Pradesh (HP) and Uttarakhand (UK) states of India and modelled to predict the current distribution using the Maximum Entropy (MaxEnt) model, along with 13 bioclimatic variables selected after multi-collinearity test. Model output was supported with a significant value of the Area Under the “Receiver Operating Characteristics” Curve (AUC = 0.975 ± 0.019), and other confusion matrix-derived accuracy measures. The variables, namely precipitation seasonality (Bio 15), precipitation (Prec), annual temperature range (Bio 7), and altitude (Alt) showed highest level of percentage contribution (72.2%) and permutation importance (60.9%) in predicting the habitat suitability of Th. spathiflorus. The actual (1 km2 buffer zone) and predicted estimates of species cover were ~136 km2 and ~982 km2, respectively. The predicted range was extended from Chamba (HP) in the north to Pithoragarh (UK) in southeast, which further protracted to Nepal. Furthermore, the distribution modelling under future climate change scenarios (RCP 8.5) for year 2050 and 2070 showed an eastern centroidal shift with slight decline of the species area by ~16 km2 and ~46 km2, respectively. This investigation employed the Model for Interdisciplinary Research on Climate (MIROC6)–shared socio-economics pathways (SSP245) for cross-validation purposes. The model was used to determine the habitat suitability and potential distribution of Th. spathiflorus in relation to the current distribution and RCP 8.5 future scenarios for the years 2021–2040 and 2061–2080, respectively. It showed a significant decline in the distribution area of the species between year 2030 and 2070. Overall, this is the pioneer study revealing the eco-distribution prediction modelling of this important high-altitude bamboo species.

Urban heat exposure (UHE) is a widespread micro-climatic event for large metropolitan and rapidly growing cities globally. Therefore, this study mainly focuses on urbanization impacts on the seasonal variation of the UHE in Delhi-National Capital Region (NCR) and its peri-urban environment during 2005-2020 and its reduction strategies based on Landsat dataset and MODIS dataset. To calculate the UHE, this study uses seasonal diurnal and nocturnal land surface temperature (LST), urban heat island (UHI) and urban thermal field variance index (UTFVI) maps for 2005 and 2020. The result shows that winter diurnal and nocturnal LST has been significantly increased by 2.7 degrees C. UTFVI results also shows that the ecological condition in the central to southern part not enough to reduce the heat and increase thermal discomfort. Similarly, UHI intensities have been extended from central to southern part and its surroundings due to rapid urban expansion and population concentration. Finally, we get the UHE maps for 2005 and 2020. Whoever, Delhi has high increasing trend of Urban heat exposure Zone (UHEZ) with maximum areal concentration both seasons. Finally, recommended some area-specific heat mitigation strategies for Delhi-NCR that can be helpful for urban planners and researcher in future land use planning and urban climate studies.

A comprehensive assessment of the distribution, spatial mapping and ecological risk of heavy metals in stream sediments of 17 counties of Ireland based on the Tellus geochemical survey programme in Ireland was carried out. Average trace metal concentrations of chromium (Cr), nickel (Ni), copper (Cu), lead (Pb), zinc (Zn), vanadium (V), cobalt (Co), arsenic (As), and major and minor oxides iron oxide (Fe2O3), manganese oxide (MnO) in the stream sediments were above the background values, but lower than Irish sediment quality guidelines for livestock and Canadian sediment quality guidelines (ISQG); threshold effect level (TEL) and probable effect level (PEL). 69.65% of Ni, 22.13% of Zn, and 96.7% of Pb samples exceeded the low-effect-range (ERL) values, whereas only 21%, 1.80% and 0.69% of the same elements exceeded the median effect range (ERM) values. Hotspots of Zn (>12000 mg kg−1), Pb (>10,000 mg kg−1), Fe2O3 (61.38%), MnO (26%), Cr (>2300 mg kg−1) and Ni (874.2 mg kg−1) were observed at Wicklow, Dublin, Mayo, Galway and Cavan counties. Catchment geochemistry was identified for the presence of heavy metals in the stream sediments. The geo-accumulation index (Igeo) indicated Kildare to be the least contaminated county with Igeo < 0 for Cr, Ni, Cu, Zn, Pb and As for 50% of the samples. The enrichment factor (EF) indicated that some sites located in Mayo and Donegal were highly contaminated. P Inemerow followed the trend, Tipperary > Dublin > Waterford > Monaghan > Donegal > Wexford > Galway > Wicklow > Cavan > Meath > Kilkenny > Leitrim > Mayo > Louth > Sligo > Kildare > Carlow. Based on potential ecological risk index (PERI), 83.1% of the sites posed low ecological risk (PERI <150), 11.9% sites posed moderate risk (150 < RI < 300) and high ecological risk was posed by 0.3% (PERI >1200) of the sites. Lithology, topography and anthropogenic factors influenced the distribution and ecological risks of heavy metals. Non-carcinogenic and carcinogenic risk for adults and children were above the recommended value.

In Odisha, Kalahandi is one of the most exposed and vulnerable districts to malaria incidences due to its poor socioeconomic condition and extreme climate. The study aimed to explore the temporal characteristics of malaria incidences in Kalahandi and to identify its relationship with rainfall for the period from 2011 to 2018. Out of the total blood films examined, 8.84% were found positive in Kalahandi between 2011 and 2018. Plasmodium falciparum is the most dominant species accounted 88.3% of the total cases. Very high mean annual parasitic index (API) >15 is recorded throughout the study years. The highest incidences are recorded in the monsoon season followed by postmonsoon. The correlation value of the annual blood examination rate with P. falciparum, Plasmodium vivax, and API has shown a very high positive correlation. Rainfall shows a (+) correlation with malaria incidences in the cold (0.47) and hot seasons (0.01) and (-) correlation in the monsoon (-0.54) and postmonsoon season (-0.54).

Sea ice fraction (SIF) over the Ross/Amundsen/Bellingshausen Sea (RAB) are investigated using the Modern-Era Retrospective Analysis for Research and Application, Version 2 (MERRA-2), focusing on the differences in time-lagged response to ENSO between the late 20th (1980–2000, L20) and the early 21st century (2001–2021, E21). The findings suggest that the typical Antarctic response to ENSO is influenced by changes in ENSO type/intensity, highlighting the need for caution when investigating the Antarctic teleconnection. Time-lagged regressions onto the mature phase of El Niño reveal that the SIF decrease and SST increase over the RAB is relatively weaker in E21 and most pronounced at 0–4 months lag. Conversely, the SIF in L20 continues to decline and reaches its peak at two-season lag (5–7 months). Tropospheric wind, pressure, and wave activity in response to El Niño in L20 show a zonally oriented high/low-pressure areas with two-season lag, enhancing the poleward flow that plays a key role in sea ice melt in the RAB, while this pattern in E21 is insignificant at the same lag. This study suggests that stronger (weaker) and more eastern (central) Pacific ENSOs on average in L20 (E21) are associated with this decadal change in the SIF response to ENSO.

Forest fires have significant impacts on economies, cultures, and ecologies worldwide. Developing predictive models for forest fire probability is crucial for preventing and managing these fires. Such models contribute to reducing losses and the frequency of forest fires by informing prevention efforts effectively. The objective of this study was to assess and map the forest fire susceptibility (FFS) in the Indian Western Himalayas (IWH) region by employing a GIS-based fuzzy analytic hierarchy process (Fuzzy-AHP) technique, and to evaluate the FFS based on forest type and at district level in the states of Jammu and Kashmir, Himachal Pradesh, and Uttarakhand. Seventeen potential indicators were chosen for the vulnerability assessment of the IWH region to forest fires. These indicators encompassed physiographic factors, meteorological factors, and anthropogenic factors that significantly affect the susceptibility to fire in the region. The significant factors in FFS mapping included FCR, temperature, and distance to settlement. An FFS zone map of the IWH region was generated and classified into five categories of very low, low, medium, high, and very high FFS. The analysis of FFS based on the forest type revealed that tropical moist deciduous forests have a significant vulnerability to forest fire, with 86.85% of its total area having very high FFS. At the district level, FFS was found to be high in sixteen districts and very high in seventeen districts, constituting 25.7% and 22.6% of the area of the IWH region. Particularly, Lahul and Spiti had 63.9% of their total area designated as having very low FSS, making it the district least vulnerable to forest fires, while Udham Singh Nagar had a high vulnerability with approximately 86% of its area classified as having very high FFS. ROC-AUC analysis, which provided an appreciable accuracy of 79.9%, was used to assess the validity of the FFS map produced in the present study. Incorporating the FFS map into sustainable development planning will assist in devising a holistic strategy that harmonizes environmental conservation, community safety, and economic advancement. This approach can empower decision makers and relevant stakeholders to take more proactive and informed actions, promoting resilience and enhancing long-term well-being.