Annual Temperature Trends Research Articles

Trends of climate variability and its association with farmers’ perceptions of climate change in Gassera district, southeastern Ethiopia

Climate change and variability have threatened rain-fed subsistence agriculture by affecting the livelihoods of rural communities in Ethiopia. The Gassera district is among the high-potential crop production areas and is severely affected by recurrent drought resulting from climate change. This study examined trends in climatic variability, farmers’ perceptions, and the determinants of climate change in the Gassera district, southeastern Ethiopia. A cross-sectional survey design was employed to select agroecologies and collect data from 444 farm households via multistage random sampling. Linear regression, the chi-square test, and rainfall anomalies were used for the data analysis. The results revealed significant (Ρ = 0.00) increasing trends in annual temperature, a decrease in annual rainfall of 2.92 mm/year, and severe rainfall variability for the Belg season. Most of the interviewed farmers reported increases in temperature, erratic rainfall, and drought frequency of 96.24%, 86.47%, and 89.73%, respectively. The study concludes that climate change variability is described by long-term average increases in temperature, erratic rainfall, and the occurrence of drought. This research also concluded that farmers’ perceptions matched well with records of increases in temperature, erratic rainfall, and drought frequency. The results also revealed gender, education, and agroecological settings were the determinant factors related to farmers’ perceptions of climate variability.

Analyzing Land Use/Land Cover Dynamics in Mountain Tourism Areas: A Case Study of the Core and Buffer Zones of Sagarmatha and Khaptad National Parks, Nepal

Monitoring land use/land cover (LULC) dynamics facilitates effective management and mitigation measures by providing timely and accurate information on the landscape. This study investigates LULC dynamics in Sagarmatha National Park (SNP), one of the most popular destinations for mountain tourism, and Khaptad National Park (KNP), which are emerging destinations, though popular among domestic tourists. A random forest classification algorithm was employed to generate LULC dynamics using Landsat data. High-resolution Planet Scope images and Google Earth images were used for accuracy assessment. Archived tourist and climatic data were analyzed to explore the impacts on LULC change. Cellular automata–artificial neural network (CA-ANN)-based LULC predictions were employed to predict future LULC. LULC dynamics of SNP revealed an increase in bare land, grassland, shrubland, glacial lakes, agriculture, and water bodies; however, snow/glacier and forest cover experienced substantial decreases of 140.25 km2 and 15.36 km2, respectively, from 1989 to 2021. In KNP, LULC dynamics showed an increasing trend in grassland, agriculture, water bodies, and bare land; however, forest and shrubland experienced a decrease of 18.63 km2 and 10.48 km2. The forest loss (19.33 km2) in the buffer zone of KNP was greater compared to the buffer zone of SNP (13.45 km2). The increment in built-up area was 0.80 km2 in SNP and 1.11 km2 in KNP, indicating escalating tourist activities and population growth. For SNP, the mean annual precipitation and temperature data from 1994 to 2023 showed decreasing and increasing patterns, respectively. However, the mean annual precipitation and temperature trends in KNP demonstrated an increasing pattern. Under the business-as-usual scenario, the estimated forest loss will be 1.61 km2 in SNP by 2032 and 23.8 km2 in KNP by 2030. A significant decline in snow/glaciers is projected for the core zone of SNP, with a loss of 22.84 km2 expected by 2032. This study provides a baseline information on LULC changes in SNP and KNP. Further, it showcases the necessity of diversified national park policies as per the requirement.

A hybrid ecological evaluation of the fisheries in changing climate: case study from a peri-urban tropical wetland of Kolkata, Eastern India.

The degradation of peri-urban wetlands has been a significant consequence of urban development and climate change. The present study discovered the decadal changes in land cover and climate impact on Raja Wetland, revealing significant alterations from 2011 to 2021. The analysis indicates substantial reductions in agricultural land (36.36%), fallow land (30.90%), water spread areas (10.14%) and surrounding wetlands (18.06%). Conversely, settlements, terrestrial vegetation and aquatic macrophytes increased by 19.77%, 3.39% and 1.16%, respectively. The primary driver of wetland shrinkage was urban expansion leading to a decrease in wetland area from 43.39ha in 2011 to 38.99ha in 2021. Climate data from 1991 to 2020 show a decreasing trend in annual rainfall (τ = - 0.274, p = 0.035) and an increasing trend in annual temperature (τ = 0.339, p = 0.009), with significant warming particularly during the monsoon months. The wetland's physicochemical attributes fluctuate seasonally, with eutrophic conditions prevailing (TSI range: 61.41-80.36). Notably, fish diversity is impacted by the dominance of the invasive species Oreochromis niloticus, which constitutes 89.31% of the catch. The established planktonic indicator genera of organic pollution were found to be abundant throughout the study period. These, combined with urban pollution and eutrophication, have led to a reduction in native fish species and overall aquatic health. The study highlights the urgent need for conservation measures to address the ecological imbalance and restore wetland resilience amidst ongoing climate and anthropogenic pressures. The communication also proposes various recommendations for the recovery and sustainable future use of wetland fisheries in the context of ongoing changes.

Impact of climate change on vegetation growth trends in a citrus farmland in the south-eastern Sicily

Abstract. The environmental repercussions of climate change triggered substantial alterations in existing ecosystem balances leading to a gradual reduction in biodiversity and the increasing degradation of soil and the built environment. This has a direct effect on agricultural production in particular, leading to increased crop water requirements, the spread of new pests and pathogens that are difficult to manage, and, thus, a general reduction in productivity. Through the analysis of multispectral data remotely sensed with latest generation of optical sensors (RGB, thermal, NIR and multi-spectral cameras), it is possible to recognize the health status and growth stages of crops. In fact, in relation to the amount of radiation reflected in the different bands it is possible to calculate spectral indices, or vegetation indices. The main vegetative indices are NDVI, SAVI, MSAVI2, GNDVI and NDRE, which are calculated by differentially combining the ultraviolet spectrum, the visible range, and the near and mid-infrared band.In this paper the outcomes of a multi-year monitoring of a citrus grove field near Rosolini (Syracuse), in south-eastern Sicily-Italy, are presented. This monitoring activity was conducted to highlight areas of productive greenery under stress, on which to act as a priority with specific agronomic interventions. In parallel with the direct monitoring phases of the citrus grove, the trend of average monthly and annual temperature and precipitation values over the past 24 years was also studied to more specifically contextualize the possible presence of significant variations in climatic trends and relate them to the results returned by the calculation of the indices.

CLIMATE CHANGE TRENDS IN THE RED RIVER DELTA

The Red River Delta (RRD) is one of the regions most significantly impacted by climate change (CC) in Vietnam. This study utilizes correlation assessment methods and regression equations to evaluate the trends of certain meteorological factors and extreme weather events during the period 1961 - 2020 in the RRD. Overall, climate factors across the region exhibit quite clear trends of change. The highest trends in annual average temperature and maximum annual average temperature are observed in the northwestern part of the RRD, especially in major cities such as Ha Noi (the increase in mean annual temperature ranges from 0.0317 - 0.0336 ºC/year). The lowest trends in these temperatures are found in coastal, island, and mountainous areas (the increase in mean annual temperature ranges from 0.0133 - 0.0224 ºC/year). The annual rainfall reports a decreasing trend at 17 out of 23 meteorological stations in the RRD, varying from -11 to 9 mm/year. Extreme weather phenomena report little significant change, with maximum daily rainfall records a decreasing trend, varying from -3.119 to 1.395 mm/year; the number of days with rainfall exceeding 50 mm also reports a decrease, varying from -0.058 to 0.017 days/year. The results of this study will serve as a basis for developing action plans to respond to CC in the RRD.

Precipitation in Kathmandu Valley: A Spatial-temporal Study

Over the past decade, advancements in atmospheric research have significantly improved the analysis of precipitation monitoring, understanding climate trends and variability, and identifying climatic anomalies associated with dry and wet periods. A key objective of this research is to enhance the understanding of precipitation patterns in the Kathmandu Valley, particularly in relation to the placement of gauges for long-range predictions and subsequent forecasting challenges. This study focuses on the evolving patterns of precipitation across the three districts within the Kathmandu Valley, the most developed and densely populated area in Nepal, encircled by four mountain ranges. We employ interpolation methods in ArcGIS Pro to identify, estimate, and forecast precipitation trends, and use the Mann-Kendall test to reveal statistically significant trends and provide insights into broader climatic processes. The research analyzes trends in both seasonal and annual precipitation and temperature across four time periods—pre-monsoon, monsoon, post-monsoon, and winter—using the Mann-Kendall test. The analysis reveals a decreasing trend in monthly rainfall from 2003 to 2017 at the majority of stations (14 stations out of 19), with areas along the valley slopes receiving significantly more precipitation compared to the central valley. The results of this study contribute to a deeper understanding of climate change in the Kathmandu Valley.

Climatic and non-climatic factors driving the livelihood vulnerability of smallholder farmers in Ahafo Ano North District, Ghana

Smallholder farmers in Ahafo Ano North District, Ghana, face multiple climatic and non-climatic issues. This study assessed the factors contributing to the livelihood vulnerability of smallholder farmers in this district by household surveys with 200 respondents and focus group discussions (FGDs) with 10 respondents. The Mann–Kendall trend test was used to assess mean annual rainfall and temperature trends from 2002 to 2022. The relative importance index (RII) value was used to rank the climatic and non-climatic factors perceived by respondents. The socioeconomic characteristics affecting smallholder farmers’ perceptions of climatic and non-climatic factors were evaluated by the binary logistic regression model. Results showed that mean annual rainfall decreased (P>0.05) but mean annual temperature significantly increased (P<0.05) from 2002 to 2022 in the district. The key climatic factors perceived by smallholder farmers were extreme heat or increasing temperature (RII=0.498), erratic rainfall (RII=0.485), and increased windstorms (RII=0.475). The critical non-climatic factors were high cost of farm inputs (RII=0.485), high cost of healthcare (RII=0.435), and poor condition of roads to farms (RII=0.415). Smallholder farmers’ perceptions of climatic and non-climatic factors were significantly affected by their socioeconomic characteristics (P<0.05). This study concluded that these factors negatively impact the livelihoods and well-being of smallholder farmers and socioeconomic characteristics influence their perceptions of these factors. Therefore, to enhance the resilience of smallholder farmers to climate change, it is necessary to adopt a comprehensive and context-specific approach that accounts for climatic and non-climatic factors.

Trends of maximum annual sea surface temperature in the Eastern China Seas

The increasing ocean warming due to climate change significantly threatens regional marine ecosystems by raising the frequency and severity of extreme temperature events. This study examines patterns and trends of maximum annual sea surface temperature (Tmax) in the Eastern China Seas from 1985 to 2022. The results show a significant warming trend in Tmax, exceeding the global average, with notable differences between southern and northern regions. The northern Tmax warming rate is faster, with occurrence times significantly advancing, while the southern Tmax warming rate is slower, with occurrence times significantly delayed. The southern Tmax and its timing are closely correlated with the annual maximum air temperature and its timing. In the north, Tmax timing is influenced by latent heat flux (QLH); a significant increase in August QLH inhibits the continued rise of SST, causing Tmax to advance. The study also highlights a significant increase in marine heatwaves at Tmax timing, with higher Tmax indicating a higher occurrence probability. By elucidating these Tmax trends and dynamics, our study enhances understanding of regional climate impacts, supporting targeted conservation efforts and adaptive ecosystem management strategies in the Eastern China Seas.

Trend analysis of air temperature in a megacity between two continents: the synoptic weather station in İstanbul Atatürk Airport

İstanbul is the largest city located in the Mediterranean Basin and has a medium to high risk of climate change and future climate risks. Changes in temperature and other weather variables have had significant impacts on İstanbul. In this context, there is a need for studies on the issues of climate monitoring and climate change vulnerability to reduce the adverse impacts. The aim of this study is to investigate the temperature trends of synoptic weather station in İstanbul Atatürk Airport between 1973 and 2023 to have a general idea about how the temperature has changed over the last half-century and to establish statistically whether a trend is significant or not. The values of minimum (Tmin), maximum (Tmax) and mean (Tmean) temperature related parameters were estimated. Annual, monthly and seasonal temperature trends are also analyzed. The findings of this study indicate a significant (p < 0.001) rise in the mean air temperature (Tmean) of İstanbul over the past 51 years (1973–2023), with an annual warming trend of 0.06 °C. The strongest increasing trend in seasonal mean air temperatures has been observed in the summer season, with an increase of 0.08 °C per year. The trend analysis also shows a statistically non-significant increase in yearly average minimum temperature (Tmin) between 1973 and 2023, with a rate of 0.04 °C per year. However, the annual maximum temperature (Tmax) has shown no changes.

Pollen-based seasonal temperature reconstruction in Northeast China over the past 10,000 years, and its implications for understanding the Holocene Temperature Conundrum

The Holocene Temperature Conundrum refers to the mismatch between proxy-based temperature records and those based on climate model simulations. A possible reason for this mismatch is a putative proxy-based bias in reconstructed summer temperatures, and therefore, regional reconstructions of seasonal temperature are crucial for resolving the conundrum. In this paper, we reconstruct vegetation and climate changes over the last ∼10,000 years BP based on a high-resolution pollen record from Gushantun peatland, Changbai Mountains, Northeast China. Multiple quantitative reconstruction approaches were used and weighted averaging partial least squares regression (WAPLS) was found to be the most appropriate method for reconstructing Holocene temperature and precipitation. The reconstructed climate record shows that the Holocene Climate Optimum occurred between 8 ka and 6 ka and exhibited a cold month mean temperature that was 3 °C warmer than modern temperatures. Climate gradually cooled during late Holocene with a minimum cold month temperature of −19.6 °C. Four prominent cold events occurred around 8.7 ka BP, 7.8 ka BP, 5.7 ka BP, and 2.5 ka BP with an amplitude variation up to 3 °C. The synthesized seasonal temperature time series and a comparison with other proxies show that the decreasing trend in mean annual temperature is not a seasonal bias caused by summer temperature change. This study provides evidence of a Holocene seasonal temperature change at a regional scale and insights for further understanding of the Holocene Temperature Conundrum.

Assessment of Soil Temperature and Its Change Trends in the Permafrost Regions of the Northern Hemisphere

In this paper, we used data from 42 soil temperature observation sites in permafrost regions throughout the Northern Hemisphere to analyze the characteristics and variability in soil temperature. The observation data were used to evaluate soil temperature simulations at different depths from 10 CMIP6 models in the permafrost region of the Northern Hemisphere. The results showed that the annual average soil temperature in the permafrost regions in the Northern Hemisphere gradually decreased with increasing latitude, and the soil temperature gradually decreased with depth. The average soil temperatures at different depths were mainly concentrated around 0 °C. The 10 CMIP6 models performed well in simulating soil temperature, but most models tended to underestimate temperatures compared to the measured values. Overall, the CESM2 model yielded the best simulation results, whereas the CNRM-CM6-1 model performed the worst. The change trends in annual average soil temperature across the 42 sites ranged from −0.17 °C/10a to 0.41 °C/10a from 1900 to 2014, the closer to the Arctic, the faster the soil warming rate. The rate of soil temperature change also varied at different depths between 1900–2014 and 1980–2014. The rate of soil temperature change from 1980 to 2014 was approximately three times greater than that from 1900 to 2014.

Investigation of Temperature, Precipitation, Evapotranspiration, and New Thornthwaite Climate Classification in Thailand

This study aims (1) to study the trend and characteristics of average annual air temperature (Tann), annual precipitation (Prann), and annual evapotranspiration (PETann) in Thailand over the present period (1987–2021) and (2) to extract the climate pattern in form of a map using the New Thornthwaite Climate Classification method in Thailand considering the present period. The data were prepared by the Thai Meteorological Department. Data variability, the mean of the data calculation in time series, the homogeneity test of data, and abrupt changes were examined. The trends of each variable were calculated using the Mann–Kendal and Sen’s slope test. The results indicated that the high Tann found in Bangkok gradually decreased in the next area. Tann data were heterogeneous with the abrupt change period, and increasing trends were found. Prann values were high in the west side of the southern area and the bottom area of the eastern area; in addition, low rainfall was found in the inner area of the land. Prann data were homogenous with no abrupt change period and slight changes in trends. PETann and %CV spatial distribution were determined for the same pattern of Tann. PETann data were heterogeneous with abrupt change periods and rising trends. The torrid thermal index determined based on the New Thornthwaite Climate Classification results indicated an overall torrid-type climate. A semi-arid climate pattern was found in the small area of the middle of Thailand, and then it shifted toward a moist-type pattern in the next area with an in precipitation. The most climate variability was found to be extreme with the power of temperature changes.

Variability and Changes in Climate in Northern Uganda

Variability and changes in climate are generally expected to occur. However, there remain gaps on dynamics of expected regional variations in climatic changes. This study assessed historic and projected climatic conditions up to the year 2033. The study hypothesized that temperature rather than rainfall significantly increased for the period 1980-2010 and rainfall rather than temperature is likely to decrease significantly by 2033 for Gulu District in northern Uganda. To determine historic climatic trends, rainfall and temperature data were obtained from Uganda National Meteorological Authority (UNMA) while for future climate, the PRECIS (Providing Regional Climates for Impact Studies) modelled data based on projected conditions at a 50 km spatial resolution was used. These data sets were subjected to trend analysis and the differences in means were detected at a 95% confidence level. Contrary to the evidences from other empirical studies, results generally indicated decreasing rainfall for the period 1980-2010. However, the decrease was not significant (P &gt; 0.05) while both historic mean annual maximum and minimum temperature trends showed a statistically significant increase (P&lt;0.05). Projections for 2033 reveal a significant decrease in rainfall (P &lt; 0.05) while both maximum and minimum temperature will remain quasi uniform

Spatiotemporal characteristics and variability in the thermal state of permafrost on the Qinghai–Tibet Plateau

AbstractPermafrost degradation on the Qinghai–Tibet Plateau (QTP) has significant impacts on climate, hydrology, and engineering and environmental systems. To understand the temporal and spatial characteristics of permafrost on the QTP, we quantified the variation in active layer thickness (ALT), permafrost thermal state, and future permafrost change under different scenarios using observational data, reanalysis data, and the numerical permafrost model. Generally, ALT ranged from 0.5 to 6.0 m with an average of 2.39 m, and mean annual ground temperature (at a depth of zero annual amplitude for ground temperature) mainly ranged between 0 and −3°C with an average of −0.85°C. The soil temperatures in different layers based on the ERA5‐Land data revealed even stronger increasing trends, for example, 0.245, 0.245, 0.244, and 0.238°C/decade at depths of 0–7, 7–28, 28–100, and 100–289 cm from 1980 to 2021, compared to those during the period from 1960 to 2021, which were 0.153, 0.156, 0.155, and 0.149°C/decade, respectively. The average warming trends in annual mean soil temperature were 0.153 and 0.243°C/decade from 1960 to 2021 and 1980 to 2021, respectively. The average rate of thickening of the ALT among the 10 active layer observation sites was 2.84 cm/year. There was a significant warming trend in ground temperature above ~15 m with warming of 0.063 to 0.120, 0.026 to 0.182, 0.101 to 0.314, and 0.189 to 0.303°C/decade at the QTB01, QTB06, QTB08, and XDTGT sites, respectively, and yearly minimum ground temperatures exhibited stronger warming trends than maximum ground temperatures. In addition, the simulation revealed significant increases in ground temperature at the Xidatan (XDT) and Tanggula (TGL) sites under both historical and future Representative Concentration Pathway (RCP) scenarios, but the increases in ground temperature were significantly greater at TGL than XDT. These findings provide important information for understanding the variability in permafrost degradation processes and improving simulations of permafrost change under climate change on the QTP.

Conjunto de modelos para análise estatística de tendências de longo prazo de dados de precipitação e temperatura observados em grade na Bacia do Rio São Francisco, Brasil

ABSTRACT For effective management practices and decision-making, the uncertainties associated with regional climate models (RCMs) and their scenarios need to be assessed in the context of climate change. This study analyzes long-term trends in precipitation and temperature data sets (maximum and minimum values) from the NASA, Earth Exchange Global Daily Downscaled Prediction (NEX-GDDP), under the São Francisco River Basin Representative Concentration Path (RCP) 4.5 and 8.5, using the REA (Reliable Ensemble Average) method. In each grid, the built multi-model was bias-corrected using the CMhyd software for annual, dry, wet, and pre-season periods – for historical (1961-2005) and future (2006-2100) periods. The multi-model and four different methods, namely: The Mann-Kendall, Mann-Kendall pre-brightening test, bias-corrected pre-brightening, and Spearman correlation, were used to detect trends in precipitation, and minimum and maximum temperature. In the analysis of precipitation and temperature metrics, the results for the NRMSD showed that, in general, the CSIRO model presented more satisfactory results in all physiographic regions. Person's correlation coefficient showed a better adjustment of precipitation for the MIROC5, EC.EARTH and NORESMI1 models, in areas of sub-medium and upper São Francisco. For the minimum temperature, the CSIRO and NORESMI1 models showed the best fit, in general. At maximum temperature, the EC.EARTH and CSIRO models showed more satisfactory results. With regard to trend analysis, the results indicated an increasing trend in mean annual temperature and precipitation across the basin. When analyzed by subregion, the results show an increasing trend in monthly average minimum and maximum temperatures in the middle and lower SFRB, while average monthly rainfall increases during the rainy season and preseason in Upper São Francisco. The results of this research can be used by government entities, such as Civil Defense, to subsidize decision-making that requires actions/measures to relocate people/communities to less risky locations to minimize risk or vulnerability situations for the population living nearby to the river.

Are hotspots and frequencies of heat waves changing over time? Exploring causes of heat waves in a tropical country.

Heat waves significantly impact people's lives and livelihoods and are becoming very alarming and recognized as hot topics worldwide, including in Bangladesh. However, much less is understood regarding recent hotspots, the frequency of heat waves over time, and their underlying causes in Bangladesh. The objective of the study is to explore the current scenario and frequency of heat waves and their possible causes across Bangladesh. The Mann-Kendall and Sen's slope techniques were used to determine seasonal and annual temperature trend patterns of heat wave frequencies. Daily maximum temperature datasets collected from the Bangladesh Meteorological Department (BMD) during 1991-2021 are applied. The frequency of days with Tmax≥ 36°C as the threshold was used to compute different types of heat waves based on the BMD's operational definition. The results show that the mild heat wave (MHW) days followed the subsequent hotspot order: Rajshahi (103) > Chuadanga (79), Ishurdi (60), and Jessore (58), respectively. The frequency of days with Tmax≥36°C was persistence for many days in 2014, especially in the western part of Bangladesh compared to other parts. Similarly, the heat waves condition shown its deadliest event by increasing more days in 2021. The highest increasing trend was identified at the Patuakhali site, with a rate of 0.516 days/year, while the highest decreasing trend was noticed at the Chuadanga site, with a rate of -0.588 days/year. The frequency of days (Tmax≥36°C) is an increasing trend in the south-western part of Bangladesh. The synoptic condition in and around Bangladesh demonstrates that the entrance of heat waves in Bangladesh is due to the advection of higher temperatures from the south/southwest of the Bay of Bengal. The outcomes will guide the national appraisal of heatwave effects, shedding light on the primary causes of definite heatwave phenomena, which are crucial for developing practical adaptation tools.

Analysis of annual rainfall and temperature trend by using the Mann-Kendall Test of Kohima Station, Nagaland, India

Analysis of annual rainfall and temperature trend by using the Mann-Kendall Test of Kohima Station, Nagaland, India

Reconstruction of temperature and monsoon precipitation in southwestern China since the last deglaciation

Monsoon precipitation and temperature are the crucial components of the Indian monsoon climate, yet their spatial-temporal relationship remains unclear. In this study, we reconstruct the changes in monsoon precipitation and mean annual temperature simultaneously over the past 16,000 years using multiple proxies, including isoprenoid glycerol dialkyl glycerol tetraethers (isoGDGTs) and n-alkanes, from an alpine lake in southwestern China. On the one hand, the reconstructed monsoon precipitation shows a long-term increasing trend during the last deglaciation and a gradual decrease during the Holocene, with the highest-humidity period occurring in the early Holocene. Our result displays good consistency with the changes in regional mean annual precipitation, summer precipitation, and monsoon intensity during the last deglaciation and the Holocene. On the other hand, the reconstructed mean annual temperature inferred from the n-alkane proxy shows a long-term warm trend since the last deglaciation. Our result is consistent with the regional reconstruction of both mean annual and summer temperatures during the last deglaciation, while evident seasonal differences were observed for Holocene records with a cooling trend of summer temperature and a warm trend of mean annual temperature. Comprehensive comparison reveals that monsoon precipitation in the Indian monsoon region is mainly related to changes in the Intertropical Convergence Zone (ITCZ) and land-sea thermal contrast controlled by boreal summer insolation. In addition, the increase in temperature during deglaciation is primarily attributed to the escalation of greenhouse gases (GHGs), while the divergent trend of seasonal temperature during the Holocene is mainly attributed to the changes in local seasonal insolation. Our research emphasizes that monsoon precipitation is a summer signature in the Indian monsoon region, which is consistent with the changes in summer temperature, while contrasting with the changes in mean annual temperature. Therefore, it is necessary to consider the seasonal difference in temperature changes when investigating the hydrothermal combination in Indian monsoon regions.

Contemporary Climate Change in Montenegro

Contemporary Climate Change in Montenegro

Ecological assessment of a subtropical floodplain wetland of the Ganga basin in the context of changing climate using GIS tools

Abstract Climate change associated with anthropogenic stressors are considered the main threat to the tropical wetlands, resulting in reduced water connection followed by a decline in ecological functions. This article presents a systemic approach to assess the present ecological status of a tropical floodplain wetland concerning the fisheries and associated ecosystem services. The analysis of historic climatic data indicated significant change (increasing average annual air temperature, R2 = 0.098; decreasing total annual rainfall, R2 = 0.042). In addition, a significant reduction in the wetland area was also observed. Altogether, 45 fish species were reported in the studied wetland, of which 2 are listed as endangered and 7 are near threatened. The present study demonstrated the variation of the important fisheries-related environment and nutrient parameters of the wetland through the geographic information system (GIS)-based spatial distribution map for the reader's digest. It has been found that the provisioning ecosystem services are higher in number (n=9) followed by supporting (n=6), regulating (n=4), and cultural (n=2) ecosystem services. Finally, we have discussed some important case-specific sustainable climate-smart adaptation and mitigation approaches to strengthen the resilience and adaptive capacity of fishers.