Patterns Of Climate Change Research Articles

Ocean-scale patterns of environment and climate changes driving global marine phytoplankton biomass dynamics

Effects of marine environment and climate changes on phytoplankton dynamics in global oceans have received increasing attention but remain a mystery. This study used a comprehensive approach combining correlation and information flow to explore relationships among phytoplankton biomass, marine environment, and climate forcing based on global observations over the past multi-decadal period. Correlation and causality between phytoplankton biomass and environmental factors exhibit spatial asymmetry—regions where environmental factors directly drive biomass variations were concentrated in oceanic currents and subtropical circulations. Temperature, light, and mixed layer depth show pronounced influences on global phytoplankton interdecadal variations. Climate forcing over interdecadal timescales directly affects phytoplankton biomass in the equatorial Pacific, South Pacific, and Indian Oceans, with more uncertain biomass variability in the equatorial Pacific due to multiple climate events. Our findings revealed that environment and climate changes directly affect phytoplankton interdecadal variability only in specific regions at the oceanic scale.

An Urban Lake Drainage Catena: Influences of Terrain, Soils, and Precipitation

Urban lake drainage systems are heavily impacted by terrain, soil characteristics, and precipitation, which influence water infiltration and groundwater movement. This study focused on the drainage catena around Lake Nokomis in Minneapolis, Minnesota, where local residents have experienced wet basements and yards. The primary goal was to identify the factors contributing to these water-related problems, particularly soil permeability and how it responds to precipitation. By conducting soil borings, using pressure transducers, and measuring saturated hydraulic conductivity (Kfs), the study compared upland and lowland areas. Findings indicated that upland soils, primarily composed of sandy fill, had much higher infiltration rates, with Kfs values ranging from 72.4 cm/hr to 149 cm/hr. In contrast, lowland areas characterized by lacustrine and organic soils exhibited significantly lower Kfs values, ranging from 1 cm/hr to 14.8 cm/hr. Between 2022 and 2024, wet and dry seasons occurred, yet recorded more than 127.5 cm of rain and snow water equivalent, further contributing to groundwater rise and surface water presence in low-lying regions. The study concluded that increased precipitation, coupled with specific hydrogeologic conditions, was the main factor causing elevated groundwater levels and surface saturation in these areas. To address these challenges, Minnesota's water management authorities are encouraged to implement strategies that consider the increasing magnitude and intensity of precipitation events due to climate change. Incorporating hydrogeologic assessments into urban planning is recommended to better manage water infiltration, reduce flood risks, and strengthen the resilience of drainage systems to changing climate patterns.

Long-Term Spatiotemporal Trends in Precipitation, Temperature, and Evapotranspiration Across Arid Asia and Africa

This study examines trends in precipitation (PRE), maximum temperature (TMAX), minimum temperature (TMIN), and potential evapotranspiration (PET) using the Modified Mann-Kendall test and Sen’s slope estimator between 1901 and 2022 in the arid lands of Central Asia, West Asia and North Africa. The results reveal complex spatial and temporal climate change patterns across the study area. Annual PRE shows a slight negative trend (Z = −0.881, p = 0.378), with significant decreases from 1951–2000 (Z = −3.329, p = 0.001). The temperatures exhibit strong warming trends (TMIN: Z = 9.591, p < 0.001; TMAX: Z = 8.405, p < 0.001). PET increased significantly (Z = 6.041, p < 0.001), with acceleration in recent decades. Spatially, precipitation decreased by 10% in maximum annual values, while PET increased by 10–15% in many areas. Temperature increases of 2–3 °C were observed, with TMAX rising from 36–39 °C to 39–42 °C in some MENA regions. Seasonal analysis shows winter precipitation decreasing significantly in recent years (Z = −1.974, p = 0.048), while summer PET shows the strongest increasing trend (Z = 5.647, p < 0.001). Spatial analysis revealed clear latitudinal gradients in temperature and PET, with higher values in southern regions. PRE patterns were more complex, with coastal and mountainous areas receiving more precipitation. The combination of rising temperatures, increasing PET, and variable PRE trends suggest an overall intensification of aridity in many parts of the region. This analysis provides crucial insights into the climate variability of these water-scarce areas, emphasizing the need for targeted adaptation strategies in water resource management, agriculture, and ecosystem conservation.

Exploring a Data‐Driven Approach to Identify Regions of Change Associated With Future Climate Scenarios

AbstractA key consideration for evaluating climate projections is uncertainty in future radiative forcing scenarios. Although it is straightforward to monitor greenhouse gas concentrations and compare observations with specified climate scenarios, it remains less obvious how to detect and attribute regional pattern changes with plausible future mitigation scenarios. Here we introduce a machine learning approach for linking patterns of climate change with radiative forcing scenarios and use a feature attribution method to understand how these linkages are made. We train a neural network using output from the SPEAR Large Ensemble to classify whether temperature or precipitation maps are most likely to originate from one of several potential radiative forcing scenarios. Despite substantial atmospheric internal variability, the neural network learns to identify “fingerprint” patterns, including significant localized regions of change, that associate specific patterns of climate change with radiative forcing scenarios in each year of the simulations. We illustrate this using output from additional ensembles with sharp reductions in future greenhouse gases and highlight specific regions (in this example, the subpolar North Atlantic and Central Africa) that are critical for associating the new simulations with changes in radiative forcing scenarios. Overall, this framework suggests that explainable machine learning could provide one strategy for detecting a regional climate response to future mitigation efforts.

Will They Continue to Endure: How Conflict and “Climate-Induced” Migration is Affecting the Lake Chad Region

The effects of climate change are undoubtedly global. Reports show that West Africa is in a precarious position, regarding some of the worst consequences of climate change, occasioning extreme events, such as violent conflict, insecurity, and loss of livelihoods in the Lake Chad region. The impact on human lives is full-blown as ‘climate-induced’ migration results in drought, desertification, and the intensifying water stress has on human migration in the region. Notably, the deterioration of individual and local living conditions has compelled inhabitants of the region in Nigeria to migrate to neighboring states as Internally Displaced Persons (IDPs) or ‘environmental refugees” in neighboring countries. However, existing studies focused on the impact and patterns of migration in West Africa thereby creating a “knowledge gap” on how ‘climate-induced’ migration is affecting the Lake Chad region. This paper employs secondary data collection, analyzing dynamic trends and patterns of climate change and challenges management. Findings revealed that while the activities of terrorists and insurgent groups have strongly impacted migration within the Lake Chad region, the continuous “dryness” and the ever-changing climate conditions, such as rising temperature, desertification, flooding, and drought have dramatically worsened and contributed to the mass decision to leave the region.

Trends in Vegetation Seasonality in the Iberian Peninsula: Spatiotemporal Analysis Using AVHRR-NDVI Data (1982–2023)

Vegetation seasonality is a critical indicator of ecological responses to global climate change, especially in the Iberian Peninsula, where the intersection of human activity and climate variability amplifies these effects. Understanding these changes is vital for adopting ecogeographical sustainability and developing effective climate adaptation strategies. This study examines trends in vegetation seasonality in the Iberian Peninsula from 1982 to 2023, based on weekly AVHRR NDVI data (2184 images). By integrating Seasonal Trend Analysis (STA) with Robust Trend Analysis (RTA)—including the Theil–Sen (TS) slope estimator, the Contextual Mann–Kendall (CMK) test (α = 0.05), and false discovery rate (FDR) control—we identified significant phenological shifts and widespread vegetation greening. The results reveal a regional response to global patterns of climate change, with 94.2% of the study area exhibiting significant trends, particularly in the Mediterranean ecoregion, where earlier growing seasons are becoming increasingly common. These shifts highlight the urgent need for sustainable land and resource management in the face of accelerating global change. Our findings provide critical insights into the ecological dynamics of the Iberian Peninsula, offering a robust foundation for formulating policies that promote environmental sustainability and enhance resilience to climate change.

Linking land use and precipitation changes to water quality changes in Lake Victoria using earth observation data

Due to the continued increase in land use changes and changing climatic patterns in the Lake Victoria basin, understanding the impacts of these changes on the water quality of Lake Victoria is imperative for safeguarding the integrity of the freshwater ecosystem. Thus, we analyzed spatial and temporal patterns of land cover, precipitation, and water quality changes in the Lake Victoria basin between 2000 and 2022 using global satellite products. Focusing on chlorophyll-a (Chl-a) and turbidity (TUR) in Lake Victoria, we used statistical metrics (correlation coefficient, trend analysis, change budget, and intensity analysis) to understand the relationship between land use and precipitation changes in the basin with changes in Chl-a and TUR at two major pollution hotspots on the lake, i.e., Winam Gulf and Inner Murchison Bay (IMB). Results show that the Chl-a and TUR concentrations in the Winam gulf increase with increases in precipitation. Through increases in precipitation, the erosion risks are increased and transport of nutrients from land to the lake system, promoting algal growth and turbidity. In the IMB, Chl-a and TUR concentrations decrease with an increase in precipitation, possibly due to dilution, but peak during moderate rainfall. Interestingly, changes in land use and land cover (LULC) at 5-year intervals showed no substantial correlation with water quality changes at selected hotspots even though a broader LULC change analysis over the past two decades indicated a notable 300% increase in built-up areas across the Lake Victoria basin. These findings underscore the dominant influence of precipitation changes over LULC changes on the water quality of Lake Victoria for the selected hotspot areas.

THE IMPACT OF SEED SIZE ON INITIAL DROUGHT STRESS RESILIENCE AND YIELD IN WHEAT CULTIVATION

Wheat yield is affected severely by drought in this era of changing climate patterns, including high temperatures and altered precipitation patterns. Drought is among the most challenging environmental stressors, limiting wheat cultivars' growth, productivity, and performance. The current study was conducted during the rabi season 2022 at the Research Area, Department of Agronomy, University of the Punjab, Lahore, Pakistan. Therefore, the present study evaluated the potential of diverse seed sizes to advance wheat crop growth, development, and yield when subjected to different drought levels. The study comprised two experiments. The first was a lab experiment that included different drought levels (DL), DL0: 0.0 bar, DL1: -2 bar, DL2: -4 bar, and DL3: -6 bar (drought levels were induced by solutions of PEG-6000 at different concentrations) and three wheat seed size classes, i.e., bold grain (>38 g), medium grain (<33 g), and small grain (<25 g). In the field experiment, drought stress levels were DL0 (regular irrigation), DL1 (first irrigation at 30 days), DL2 (first irrigation at 45 days), and DL3 (first irrigation at 60 days). Seed sizes included W1 (bold >38 g), W2 (medium <33 g), and W3 (small <25 g). Drought severity increased with DL1 to DL3. The outcomes of the field experiment revealed that varying levels of drought stress and seed size classes significantly affected parameters such as emergence time, growth traits, biomass allocation, tiller count, plant height, and grain and biomass outcomes. Bold seeds contributed to higher biomass and grain yield, while severe drought decreased yields. Notably, the Harvest Index was affected, indicating bold seeds allocate more biomass to grains. In conclusion, proper seed size selection, favouring bold seeds, can enhance resilience to drought, benefiting wheat cultivation in water-scarce regions.

Cocoa Farmers’ Perceptions of Drought and Adaptive Strategies in the Ghana–Togo Transboundary Cocoa Belt

This study investigated the perception of drought by cocoa farmers and explored the effectiveness of adaptive strategies (ASs) used in smallholding farms in the transboundary region between Ghana and Togo. Drought significantly threatens cocoa production in this region, affecting farmers’ livelihoods and cocoa supply chains. This study used a multistage sampling approach, which involved surveys with questionnaires administered to 330 cocoa farmers throughout the study area, along with on-site observations. Statistical analysis included binary logistic and Poisson regression models to explore the relationship between farmer socioeconomic characteristics and adaptation practices. The findings revealed that cocoa farmers in the region have a nuanced understanding of drought, attributed to changing climatic patterns and unsustainable land management practices such as deforestation. To mitigate its impacts, farmers employ a variety of ASs, including investment in farm management, soil management, and intercropping with crop diversification. Furthermore, socioeconomic factors, including age, formal education, household size, land tenure right, adaptation cost assessment, and an underestimation of self-efficacy, were shown to affect the choice in the AS. Among the ASs adopted, only farm management practices (weeding, pruning, fertilizer application, etc.) significantly improved yield. This study contributes to understanding drought as a critical issue for cocoa farmers and the adaptation practices used by smallholder cocoa farmers. Given that among the strategies adopted, only farm management practices, also known as good agricultural practices (GAPs), significantly improves yield, this study recommends well-designed and innovative packages of sustainable farm management based on farm and owner characteristics. These include irrigation schemes, timely soil fertilizer monitoring and supply, and the provision of drought-resistant varieties along with technical itineraries. Additional interventions require drought emergency responses, with other factors such as education and financial support mechanisms expected to improve farmers’ timely decision-making to adapt and improve cocoa production resilience to drought episodes in international transboundary regions with complex governance structures.

Shifts in geographic vulnerability of US corn crops under different climate change scenarios: corn flea beetle (Chaetocnema pulicaria) and Stewart’s Wilt (Pantoea stewartii) bacterium

Abstract Changing climate patterns will likely affect insect pressure on many agricultural crops. Mild winters may decrease the number of insects that experience reduced fecundity or that are killed during hard freezes. This may result in larger populations in subsequent years and allow for range expansion. Direct effects from pests are compounded by indirect effects, such as crop damage resulting from insect-vectored diseases. Corn flea beetle (Chaetocnema pulicaria) infestations have both direct and indirect effects on crops. This beetle is a pest on all types of corn in the United States, including sweet corn and grain corn (sometimes referred to as dent corn). It is responsible for damage to plant foliage and also serves as the primary overwintering vector for Pantoea stewartii bacterium, which causes Stewart’s Wilt, a disease that can severely impact the health and productivity of corn. Evidence suggests that warmer winters will contribute to a geographic range expansion for the corn flea beetle. Here we show the projected northward expansion of economically damaging crop losses caused by Stewart’s Wilt: (A) from 1980 to 2011, (B) projected by mid-century, and (C) projected by end-century. Our work suggests that climate change and associated increasing winter temperatures in the United States will lead to a dramatic increase in the probability of severe damage from corn flea beetle across the United States, including the Corn Belt. Predicted increases in pest and disease pressure will have negative ramifications for corn production and are likely to exacerbate issues associated with specific management tactics, such as pesticide application.

Short-term associations of diarrhoeal diseases in children with temperature and precipitation in seven low- and middle-income countries from Sub-Saharan Africa and South Asia in the Global Enteric Multicenter Study.

Diarrhoeal diseases cause a heavy burden in developing countries. Although studies have described the seasonality of diarrhoeal diseases, the association of weather variables with diarrhoeal diseases has not been well characterized in resource-limited settings where the burden remains high. We examined short-term associations between ambient temperature, precipitation and hospital visits due to diarrhoea among children in seven low- and middle-income countries. Hospital visits due to diarrhoeal diseases under 5 years old were collected from seven sites in The Gambia, Mali, Mozambique, Kenya, India, Bangladesh, and Pakistan via the Global Enteric Multicenter Study from December 2007 to March 2011. Daily weather data during the same period were downloaded from the ERA5-Land. We fitted time-series regression models to examine the relationships of daily diarrhoea cases with daily ambient temperature and precipitation. Then, we used meta-analytic tools to examine the heterogeneity between the site-specific estimates. The cumulative relative risk (RR) of diarrhoea for temperature exposure (95th percentile vs. 1st percentile) ranged from 0.24 to 8.07, with Mozambique and Bangladesh showing positive associations, while Mali and Pakistan showed negative associations. The RR for precipitation (95th percentile vs. 1st percentile) ranged from 0.77 to 1.55, with Mali and India showing positive associations, while the only negative association was observed in Pakistan. Meta-analysis showed substantial heterogeneity in the association between temperature-diarrhoea and precipitation-diarrhoea across sites, with I2 of 84.2% and 67.5%, respectively. Child diarrhoea and weather factors have diverse and complex associations across South Asia and Sub-Saharan Africa. Diarrhoeal surveillance system settings should be conceptualized based on the observed pattern of climate change in these locations.

Inter- and intra-annual carbon isotope fluctuations in Pinus sylvestris L. tree rings whole wood and cellulose in north-eastern Lithuania

In temperate regions trees typically exhibit growth sensitivity to climatic conditions during the growth season. Annual tree ring growth increments correlate with a variety of environmental factors. As an index of water use efficiency, δ13C is a preferred proxy to allow accurate interpretation of environmental factors critical for the tree growth, including changes in climate patterns, variation in the ambient temperature and precipitation. We assumed that isotopic differences within individual tree rings are likely to produce seasonal isotope fluctuations in the chronology that might be interpreted as response to environmental impacts. To verify the assumption, we measured δ13C in annual tree rings of Pinus Sylvestris L. split into 13 intra-annual segments each and checked δ13C correlations with temperature, precipitation, the number of sunshine hours and relative air humidity. For the investigation, we chose a site in north-eastern Lithuania, Zarasai, located in boreal latitude and remote from industrial pollution sources. The methodology of the research was based on high coherence of δ13C chronologies measured in the whole wood and α-cellulose extracted by means of two different methods. The experiment produced strong δ13C correlations with hydrometeorological parameters, especially in the earlywood formed in June

Cosmogenic surface exposure (10Be) dating of raised beaches in Marguerite bay, Antarctic Peninsula: Implications for relative sea-level history

Understanding the dynamics of ice mass loss in polar regions is crucial for deciphering climate change and Glacio Isostatic Adjustment patterns. This study focuses on Marguerite Bay, located in the south-central Antarctic Peninsula. We dated raised beaches to investigate relative sea-level changes using the cosmogenic surface exposure (10Be) method. Previous studies have provided valuable insights into the region's glacial history, but limitations in dating techniques and age estimates necessitate further investigation. By analysing raised shingle beaches in Gaul Cove of Horseshoe Island and the southern coast of Calmette Bay, this research aims to contribute relative sea-level change history for these areas. In Horseshoe Island's Gaul Cove, raised beaches clustered on prominent steps reveal a 15 m relative sea-level change over the last 3.31 ka. Differently, Calmette Bay exhibits a 36 m relative sea-level fall over the last 7.29 ka. These findings indicate significant and differential glacial-isostatic adjustments in both regions during the middle and late Holocene. Additionally, our data reveal accelerated sea-level fall periods corresponding to Holocene deglaciation and glacial advance events, indicating the shorelines' relative sea-level change sensitivity to climate change.

Assessing the population structure and genetic diversity of wheat germplasm with the iPBS-retrotransposons marker system

Context Wheat (Triticum aestivum L.) is an important crop that provides food to millions of people all over the world. Currently, wheat production is limited due to various biotic and abiotic stresses resulting from uneven patterns of climate change. Therefore, it is very important to develop climate-resilient wheat cultivars. Crop genetic diversity allows the scientific community to identify genetic variations that can be utilised in the development of improved cultivars. Aims This study planned to characterise the wheat germplasm with the iPBS-retrotransposons marker system. Methods A total of 30 iPBS-retrotransposons markers were screened and among these, the 12 most polymorphic markers were selected for further analysis. Key results Molecular characterisation yielded a total of 170 bands, of which 143 were polymorphic. A substantial level of genetic diversity was observed (mean effective number of alleles: 1.37, Shannon’s information index: 0.23, gene diversity: 0.35). Maximum genetic distance was observed in G9 and G60 genotypes. Analysis of molecular variance revealed that most genetic variation (95%) occurred within the populations. The model-based structure algorithm divided the studied germplasm into three populations based on their collection regions. Similarly, the neighbour-joining analysis also divided 70 tested wheat genotypes into three populations, whereas principal coordinate analysis divided the evaluated germplasm into four populations. Conclusions This study confirms the iPBS-retrotransposons as an ideal marker for the genetic diversity assessment studies for any crop, especially for wheat. Implications The results presented here will be helpful for the scientific community in the marker-assisted breeding of wheat.

Long-term stability in protected-areas? A vision from American/New World amphibians

Protected areas (PA) have proven to be one of the best ways to conserve biodiversity against environmental changes. Amphibians are considered the most threatened group, with habitat loss due to deforestation identified as their major threat. Here, we assessed for each PA of the American continent: 1) amphibian’s occurrence (Global Biodiversity Information Facility (GBIF) vs. International Union for Conservation of Nature (IUCN) data); 2) temperature velocity and estimated the climate residence time, and using the latest models of the land future use; 3) we estimated the changes of natural vs. modified cover in three future scenarios. Amphibian occurrence showed differences between databases, while GBIF data shows that 52 % of the amphibian species occurring in the continent are in PA, based on IUCN data, 85 % are protected. Results from climate change show a low pace of climate velocity during the last century that is maintained in the green scenario (SSP126). However, change in temperature increases in rate in the rest of the scenarios, with scenario SSP58 showing the highest velocity of temperature change. Future estimates of residence times in PA show that lower levels as emission scenarios tend to be higher. These results are worrisome since climate lag, specifically temperature increase over the PA will probably affect amphibian communities as shown in previous studies. Changes in climate patterns have a direct—mostly negative—impact on amphibians’ ability to disperse and reproduce. The results of land use change were unexpected, since the categories showed minimal changes. However, the data on urbanization changes do not seem to be reflecting the trends of other databases, which may be causing artifacts in the comparisons in the future models of land use. Further research will be necessary to evaluate the extent of similarities and differences in future projections of land use including urbanization and human population between different databases.

Assessing heatwave resilience in municipalities around Lake Balaton: A comparative analysis

Changing climate patterns represent a major challenge for Hungarian municipalities, particularly with regard to the increasing severity and frequency of heatwaves. As a result of the COVID-19 lockdowns, thousands of people moved to communities around Lake Balaton; therefore, cities and villages should place more emphasis on their long-term sustainability and climate resilience. This article addresses the literature gap in assessing the heatwave resilience of Hungarian settlements, focusing on the municipalities of the Lake Balaton Resort Area. Our main objective was to uncover spatial and temporal patterns in the 180 settlements involved in the analysis by using an indicator-based comparative method. The set of indicators included nine sensitivity and six adaptive capacity measures referring to the base years 2015 and 2022. Our results show heterogeneous spatial patterns across the analysed categories; however, several regional clusters can be identified: 1) in general, settlements from the northern part of the study area had above-average adaptive capacity, while the southern and south-western municipalities had significantly lower values, 2) only one micro-regional cluster can be defined in terms of sensitivity values in the northern part of the study area; 3) below average resilience values were found in the south-western and southern areas; 4) finally, neither sensitivity nor adaptive capacity nor overall resilience scores had changed significantly over time at the regional level. The applied methodology can easily be adopted in other Hungarian or even Central and Eastern European cities; consequently, new results can contribute to a better understanding of inter- and intra-regional patterns of heatwave resilience at the local level.

Coping with climate change: A livelihood vulnerability and adaptation analysis in Gaibandha, Rangpur, Bangladesh

AbstractThis study aimed to assess the impact of climate change using the Livelihood Vulnerability Index (LVI) and the adaptation strategies of communities, as measured by the Adaptation Strategy Index (ASI), among 120 households across six villages in Fulchari and Shaghata Upazilas of Gaibandha district in northern Bangladesh. The findings highlight changing climatic patterns and show that while respondents are generally aware of the impacts of climate change, there are notable knowledge gaps. Galna Adarshapara was identified as the most vulnerable village according to the LVI, indicating its high susceptibility in areas such as socio‐demographic profile, food security, health, natural hazards, and climate sensitivity. Factors contributing to this vulnerability include geographical isolation, limited education, underdeveloped infrastructure, inadequate health facilities, and a lack of alternative income sources during extreme events. However, coping mechanisms such as irrigation, crop diversification, and the use of double‐platform tube wells are commonly employed to address these climatic impacts. To enhance resilience, it is crucial to implement policy initiatives and institutional arrangements that support local communities in improving their living conditions and adapting to climate change challenges.

A climate change signal in the tropical Pacific emerges from decadal variability.

The eastern tropical Pacific has defied the global warming trend. There has been a debate about whether this observed trend is forced or natural (i.e., the Interdecadal Pacific Oscillation; IPO) and this study shows that there are two patterns, one that oscillates along with the IPO, and one that is emerging since the mid-1950s, herein called the Pacific Climate Change (PCC) pattern. Here we show these have distinctive and distinguishable atmosphere-ocean signatures. While the IPO features a meridionally broad wedge-shaped SST pattern, the PCC pattern is marked by a narrow equatorial cooling band. These different SST patterns are related to distinct wind-driven ocean dynamical processes. We further show that the recent trends during the satellite era are a combination of IPO and PCC. Our findings set a path to distinguish climate change signals from internal variability through the underlying dynamics of each.

Enhancing sweet cherry resilience to spring frost and rain-induced cracking with pre-harvest melatonin treatments

Sweet cherry producers annually confront climatic challenges such as spring frost and fruit cracking. This vulnerability arises primarily from spring frost during bloom or cracking at critical maturity stages during persistent rainfall. With changing climate patterns, innovative strategies are essential to mitigate these adversities. Foliar applications of melatonin (MT) at 0, 0.01, 0.05 and 0.1 mM were tested on the ‘Prime Giant’ and ‘Sweetheart’ cultivars over four different production cycles (2020–2023) to evaluate the effect on frost resilience on flower buds and fruit cracking reduction. MT-treated flower buds showed reduced malondialdehyde content and increased fruit set in most seasons, reducing their vulnerability to extreme weather events. In addition, MT consistently decreased sweet cherry cracking incidence across all studied seasons, indicating a strong effect between the fruit ripening stage and susceptibility to fruit cracking, which was cultivar dependent. Quality parameters at harvest, including fruit firmness, and colour at harvest, were either delayed or unaffected in MT-treated fruits compared with controls. However, other ripening parameters were stimulated by pre-harvest MT applications in several growing cycles, such as total soluble solids, which slightly reduced total acidity in MT-treated fruits. In summary, pre-harvest MT treatments can be a promising strategy for climate change adaptation and stress mitigation, potentially increasing sweet cherry production under extreme weather conditions.

A Multi-criterian Approach to Identify Groundwater Potential Zones in the Subarnarekha River Basin Using Integrated Analytical Hierarchy Process and Geospatial Technology

The intense extraction of groundwater and changing climate patterns have strained global groundwater supplies. As the demand for drinkable water rises worldwide, assessing groundwater availability and aquifer output becomes increasingly important. Geographic information systems (GIS) are being used more frequently for groundwater exploration due to their speed and provision of preliminary data. This study focuses on mapping groundwater availability in a minor tropical river basin in India, using a combination of GIS and the analytical hierarchy process (AHP). Ten thematic layers were generated and analyzed, including Geology, Geomorphology, Land use and land cover (LULC), Lineament density, Drainage density, Rainfall, Soil type, Slope, Topographic Wetness Index ( TWI), and curvature to delineate groundwater potential zones. AHP was employed to assign weights to each category based on their water retention properties. The resulting map was validated against existing groundwater data, demonstrating an accuracy of approximately 85%. The groundwater potential zones were classified as high, moderate, and, low. The moderate potential zone covered 59% of the basin, while the low and high potential zones constituted 29% and 11% respectively. High and low potential regions were limited to small sections of the basin. In conclusion, this research successfully mapped the groundwater availability in the Rarhu River watershed using GIS and AHP. The findings highlight the distribution of different groundwater potential zones, providing valuable information for sustainable water resource management in the area.