Annual Average Maximum Temperature Research Articles

Quantifying Climate Change Variability for the Better Management of Water Resources: The Case of Kobo Valley, Danakil Basin, Ethiopia

Alterations in the hydrological cycle due to climate change are one of the key threats to the future accessibility of natural resources. This study used 12 GCM climate models from CMIP6 to evaluate future climate change scenarios by applying model performance measures and trend analysis in Kobo Valley, Ethiopia. The models were ranked based on their ability to analyze the historical datasets. The result of this study showed that the outputs of the FIO-ESM-2-0 CIMP6 model had a good overall ranking for both precipitation and temperature. After bias correction of the model-based projections with the observed data, the average annual precipitation in the average scenario (SSP2-4.5) decreased by 4.4% and 13% in 2054 and 2084, respectively. Similarly, in the worst-case scenario (SSP5-8.5), by the end of 2054 and 2084, decreases of 4% and 12.8%, respectively, were predicted. The average annual maximum temperature under the SSP2-4.5 scenario increased by 1.5 °C in 2054 and by 2.1 °C in 2084. The average annual maximum temperature under the worst-case (SSP5-8.5) scenario increased by 1.7 °C in 2054 and by 3.2 °C in 2084. In the middle scenario (SSP4.5), the average annual minimum temperature increased by 2.2 °C in 2054 and by 3 °C in 2084. The average annual minimum temperature under the worst-case (SSP5-8.5) scenario increased by 2.6 °C in 2054 and by 4.3 °C in 2084. The seasonal variability in precipitation in the studied valley will decrease in the winter and increase in the summer. A decrease in precipitation combined with an increase in temperature will strengthen the risk of drought events in the future.

Detected and projected temperature changes in the area of Mediterranean Montenegro

AbstractThe goal of this research is to present the results of a detailed analysis of detected and projected temperature changes in Montenegro. A total of 10 temperature parameters from 18 meteorological stations were used. Initially, an analysis of temperature changes during the instrumental period (1961–2020) was conducted, followed by the results of high‐resolution (12.5 km) bias‐corrected projections from the regional ALADIN, REMO and CCLM4 models for the period 2021–2100, according to the RCP4.5 and RCP8.5 scenarios (representative concentration pathways). The simple difference method and trend method were employed for research purposes. Compared with the period 1961–1990, the highest temperature increase during 1991–2020 is observed in summer (TSu), while the rates of increase were slower in spring, winter and autumn (TSp, TW and TA). The average annual maximum temperature (TYx) registered a higher increase compared with the average minimum (TYn). Frosty days (FD) decreased, while the number of summer and tropical days (SU and TD) increased. The projection results indicate that the period 2021–2050 is expected to be warmer, the period 2051–2080 even warmer, and the period 2081–2100 is expected to be the warmest. In the last analysed period (2081–2100), according to the RCP8.5 scenario, the average TSu is expected to be higher by 3.7–4.6°C (CCLM4), 4.1–4.7°C (ALADIN) and 4.7–5.8°C (REMO), with an anticipated increase in TW ranging between 3.3–4.5°C (ALADIN), 3.6–5.4°C (CCLM4) and 3.4–5.6°C (REMO). In the period 2021–2100, according to the RCP4.5 scenario, the expected average trend values for TYx (TYn) are expected to be (°C/decade) 0.24 (0.26), 0.16 (0.12) and 0.20 (0.23) for the ALADIN, REMO and CCLM4 models, respectively, and according to the RCP8.5 scenario, 0.47 (0.52), 0.63 (0.57) and 0.51 (0.53), respectively. These findings have implications for planning mitigation and adaptation measures to address climate change, particularly in strategic economic sectors such as tourism, agriculture and water management in Montenegro.

Modelling spatiotemporal tendencies of climate types by Markov chain approach : A case study in Sanliurfa province in the south-eastern of Turkey

Identification of spatiotemporal tendencies of climate types may help water managers mitigate the negative impacts of droughts on water-demanding sectors. The primary objective of this study was to figure out the spatiotemporal tendencies of climatetypes in Sanliurfa province by using Erinc’s aridity index (EDI). To that end, long-term (1965-2018) annual precipitation and average annual maximum temperature series of meteorological stations were obtained and utilized to calculate the EDI series on a yearly basis. The EDI series of each station was divided into three periods, non-overlapping and successive, i.e., P1 (1965-1981), P2 (1982-1999) and P3 (2000-2018). Outliers were detected, andremoved from the EDI series; missing data were completed by regression analysis. The Markov transition probability matrix of the climate classes for the three periods was estimated for each station. Maps of the initial probability vectors and steady-state probabilities for the three periods of each climate class were generated by the inverse distance-weighted technique. Hypsometric curves for each climate class, as well as period, were developed and areal coverage of occurrence probabilities (OP) was determined. Results indicated that, as time progressed, the areal extent of severe-arid and arid climatic classes continued consistently to spread from the south to the north. Areas of semi-arid climate type showed a slight tendency towards the arid-climate type. Construction of large dams in the region could not prevent the shifts in the climate in favour of developing arid zones. The humid climate class is likely to vanish away in the future. Research led us to conclude that the expansion of the aridzone from south to northhas been alarming in terms of the adequacy of water resources. It is strongly recommended that spatiotemporal climate change studies should be periodically conducted in tandem with forest management practices for the region.

Explanatory models for tick-borne disease incidence (Astrakhan rickettsial fever and Crimean-Congo hemorrhagic fever)

Introduction. The study focuses on methods providing mathematical substantiation of discrepancies between actual incidence rates of Astrakhan rickettsial fever (ARF) and Crimean-Congo hemorrhagic fever (CCHF) and predicted rates due to the indirect impact of weather conditions during the current epidemic season.
 The purpose of the study was to develop explanatory models for ARF and CCHF incidence using satellite monitoring (remote sensing) data and to present the results of their practical evaluation in the Stavropol Territory and Astrakhan Region.
 Materials and methods. The materials included climate data provided by the Space Research Institute of the Russian Academy of Sciences as well as epidemiological data on CCHF and ARF incidence from 2005 to 2021. The explanatory models incorporated the Bayes theorem and Wald sequential analysis. All the calculations were completed using the Microsoft Excel 2010-based program developed by the authors.
 Results. It has been found that the greatest indirect effect on development of the CCHF epidemiological situation is produced by the normalized difference vegetation index and relative air humidity in June-July in the Stavropol Territory and by the maximum, minimum and average air temperature in October as well as the minimum air temperature in July in the Astrakhan Region. ARF incidence rates depend on the indirect effect of the annual average and average annual maximum temperature, maximum temperature and the normalized difference vegetation index in April-July. The match between explanatory model-based results and prediction model-based results ranged within 46.2-100%.
 Discussion. In addition to projecting incidence rates, which could be reached with the observed values of climatic factors in the current year, the explanatory models can be used for indirect verification of prediction models and for identification of factors causing differences in results.
 Conclusion. The practical evaluation of explanatory models confirms the prospects and benefits of the study that should be continued, involving other regions highly endemic for tick-borne infections.

Camel livestock in the Algerian Sahara under the context of climate change: Milk properties and livestock production practices

Camel livestock is an ancestral activity in Algeria; however, climate change has forced camel herders to modify their breeding practices to make them more sustainable. This study summarized livestock production practices, milk qualities, and the potential of camel livestock to preserve production ability under global warming. To collect data related to livestock farming practices, 10 camel herders were interviewed using a formal questionnaire. Then, 15 milk samples (9 samples of raw milk and 6 samples that had undergone heat treatment) were collected in the region of Oued Souf in southeastern Algeria to carry out the physicochemical and bacteriological analysis. From 1990 to 2021, results showed severe drought accompanied by a significant increase in the annual average maximum temperature with a temporal slope of 0.04 °C year−1 and a significant decline in annual precipitation with a temporal slope of −0.07 mm year−1. A socio-demographic survey revealed a low educational level for camel herders. They owned small herd of camels (6.84 ± 8.66 camels) in the transhumant and extensive system or > 150 heads in the nomadic and extensive system. The average daily milk production in the nomadic system was very low (<3 L/day); it was less important compared to that in the transhumant system (4–5 L/day), with an acceptable physicochemical quality but poor bacteriological quality. Given the susceptibility of the research area, we recorded that camel livestock and travel mobility were used as adaptation strategies to the effects of climate change. On the one hand, camel breed conservation programs can enhance biodiversity and a sustainable ecosystem. On the other side, a genetic improvement program that might boost productivity and profitability might be advantageous. Smallholders may benefit from this by receiving a fair and secure income and good working conditions, which could contribute significantly to social equity and local economies.

Differences in fruit yields and essential oil contents and composition among natural provenances of Litsea cubeba in China and their relationships with main habitat factors

Litsea cubeba (Lour.) Pers. is an important industrial tree in southern China and Southeast Asia. We surveyed and sampled wild L. cubeba in 32 provenances spread across nine provinces of China. We used nested variance analysis, multiple comparisons analysis, hierarchical clustering analysis, Pearson’s correlation analysis, path analysis with multiple stepwise regression, detrended correspondence analysis, redundancy analysis, and variance partitioning analysis to evaluate the phenotypic diversity of fresh fruit yield (FFY) and essential oil (EO) characteristics of L. cubeba, and to explore the relationship between the main habitat factors and phenotypic diversity. We detected wide phenotypic diversity in FFY, EO content (EOC), citral yield (CitrY), and the proportions of the seven main components of EOs (citral, D-limonene, 3,7-dimethyl-3,6-octadienal, 4-methyl-3-pentenal, linalool, citronellal, and 1-(cyclopanecarbonyl)piperidin-4-one). The variation was mainly derived from among provenances (P ≤ 0.01) rather than within provenances, and the variation in FFY and CitrY was greater than that of EOC and the seven dominant components among provenances. We also detected several region-specific components of EOs in fresh fruit, including α-terpineol, geraniol, eucalyptol, and 7-methyl-3-methyleneoct-6-enal. Most of the provenances with higher FFY were distributed in southwestern China (lower longitude and latitude, and higher elevation), while most of those with higher EOC were distributed in southeastern China (higher longitude and latitude, and lower elevation). The three provenances with excellent FFY were YN-XC, GZ-ZY, and SC-KJ; the two with higher EOC were SC-KJ and HN-DX; and CitrP was higher in GZ-ZF than in other provenances. Longitude, total potassium content (TotalKC), and bulk density (BD) were the most important factors that influenced FFY diversity, while latitude, elevation, annual average relative humidity (AARH), and organic carbon content (OCC) most strongly influenced EOC diversity, and annual average temperature and annual average maximum temperature (AAMaxT) influenced CitrP diversity. The six main habitat factors together explaining the largest proportions of phenotypic diversity were longitude, AARH, AAMaxT, elevation, latitude, and soil total phosphorus content (TotalPC). Higher soil TotalKC, BD, and TotalPC were suitable conditions for cultivation of L. cubeba to achieve higher FFY, while lower AARH and OCC were favorable for accumulation of EOs, and only lower temperature was more likely conducive to citral synthesis. The wide diversity of FFY and EO characteristics should be taken into account when selecting germplasms for protection and utilization, and the identification of the most important environmental factors affecting phenotypic diversity provides a theoretical basis for the optimization of L. cubeba cultivation.

Distribution characteristics of soil microbial communities and their responses to environmental factors in the sea buckthorn forest in the water-wind erosion crisscross region.

Soil microorganisms are an important part of forest ecosystems, and their community structure and ecological adaptations are important for explaining soil material cycles in the fragile ecological areas. We used high-throughput sequencing technology to examine the species composition and diversity of soil bacterial and fungal communities in sea buckthorn forests at five sites in the water-wind erosion crisscross in northern Shaanxi (about 400 km long). The results are described as follows: (1) The soil bacterial community of the sea buckthorn forest in the study region was mainly dominated by Actinobacteria, Proteobacteria, and Acidobacteria, and the fungi community was mainly dominated by Ascomycota. (2) The coefficient of variation of alpha diversity of microbial communities was higher in the 0-10 cm soil layer than in the 10-20 cm soil layer. (3) Soil electrical conductivity (36.1%), available phosphorous (AP) (21.0%), available potassium (16.2%), total nitrogen (12.7%), and the meteorological factors average annual maximum temperature (33.3%) and average annual temperature (27.1%) were identified as the main drivers of structural changes in the bacterial community. Available potassium (39.4%), soil organic carbon (21.4%), available nitrogen (AN) (13.8%), and the meteorological factors average annual maximum wind speed (38.0%) and average annual temperature (26.8%) were identified as the main drivers of structural changes in the fungal community. The explanation rate of soil factors on changes in bacterial and fungal communities was 26.6 and 12.0%, respectively, whereas that of meteorological factors on changes in bacterial and fungal communities was 1.22 and 1.17%, respectively. The combined explanation rate of environmental factors (soil and meteorological factors) on bacterial and fungal communities was 72.2 and 86.6%, respectively. The results of the study offer valuable insights into the diversity of soil microbial communities in the water-wind erosion crisscross region and the mechanisms underlying their interaction with environmental factors.

ANALYSIS OF RAINFALL, HUMIDITY AND TEMPERATURE DURING THE YEAR JANUARY 1990 TO DECEMBER 2020 IN SOLAPUR DISTRICT, MAHARASHTRA STATE, INDIA

Climate change has become a global issue that is taking a toll on the agricultural sector. This study has focused on climate change in the Solapur area of Maharashtra State, India. It is necessary to investigate the local meteorological characteristics using various statistical techniques to verify or regulate such a situation. To check the trend, meteorological data from the Solapur district, including rainfall, humidity, and temperature data, were analyzed by using the Man-Kendall test, and regression analysis was used to check the positive or negative trend. The result from an analysis of trends reveals that there is an increasing trend in the annual average maximum temperature and the decreasing trend in annual average humidity. There is no significant trend in the average yearly rainfall pattern.

Understanding Future Climate in the Upper Awash Basin (UASB) with Selected Climate Model Outputs under CMIP6

Climate change makes the climate system of a given region unpredictable and increases the risk of water-related problems. GCMs (global climate models) help in understanding future climate conditions over a given region. In this study, 12 GCMs from the CMIP6 (coupled model intercomparison project six) were evaluated and ranked based on their abilities to describe the historical observed series. The ensemble mean of bias-adjusted best five models of average annual precipitation showed an increment with an uncertainty range of (2.0–11.9) and change in the mean of 6.4% for SSP2-4.5 and (6.1–16.1) 10.6% for SSP5-8.5 in 2040–2069 relative to the historical period. Similarly, for 2070–2099, increments of (2.2–15.0) 7.9% and (11.8–29.4) 19.7% were predicted for the two scenarios, respectively. The average annual maximum temperature series showed increments of (1.3–2.0) 1.6 °C for SSP2-4.5 and (1.7–2.3) 2.0 °C for SSP5-8.5 in 2040–2069. At the same time, increments of (1.7–2.3) 2.0 °C and (2.8–3.2) 3.0 °C were predicted for 2070–2099. Furthermore, it was predicted that the average annual minimum temperature series will have increments of (1.6–2.3) 2.0 °C and (2.2–2.9) 2.5 °C for 2040–2069 and (2.1–2.7) 2.4 °C and (3.7–4.2) 4.0 °C for 2070–2099 for the two scenarios, respectively. An increase in precipitation with increased land degradation in the sub-basin results in a higher risk of flood events in the future. Improved soil and water conservation practices may minimize the adverse impacts of future climate change on the loss of agricultural productivity.

Evaluating the Impact of Climate Change on the Stream Flow in Soan River Basin (Pakistan)

The global hydrological cycle is susceptible to climate change (CC), particularly in underdeveloped countries like Pakistan that lack appropriate management of precious freshwater resources. The study aims to evaluate CC impact on stream flow in the Soan River Basin (SRB). The study explores two general circulation models (GCMs), which involve Access 1.0 and CNRM-CM5 using three metrological stations (Rawalpindi, Islamabad, and Murree) data under two emission scenarios of representative concentration pathways (RCPs), such as RCP-4.5 and RCP-8.5. The CNRM-CM5 was selected as an appropriate model due to the higher coefficient of determination (R2) value for future the prediction of early century (2021–2045), mid-century (2046–2070), and late century (2071–2095) with baseline period of 1991–2017. After that, the soil and water assessment tool (SWAT) was utilized to simulate the stream flow of watersheds at the SRB for selected time periods. For both calibration and validation periods, the SWAT model’s performance was estimated based on the coefficient of determination (R2), percent bias (PBIAS), and Nash Sutcliffe Efficiency (NSE). The results showed that the average annual precipitation for Rawalpindi, Islamabad, and Murree will be decrease by 43.86 mm, 60.85 mm, and 86.86 mm, respectively, while average annual maximum temperature will be increased by 3.73 °C, 4.12 °C, and 1.33 °C, respectively, and average annual minimum temperature will be increased by 3.59 °C, 3.89 °C, and 2.33 °C, respectively, in early to late century under RCP-4.5 and RCP-8.5. Consequently, the average annual stream flow will be decreased in the future. According to the results, we found that it is possible to assess how CC will affect small water regions in the RCPs using small scale climate projections.

Temperature Trends at Madhira, Khammam District of Telangana State

Climate change is one of the most important global environmental challenges at global, national and regional level. The resultant global warming due to increase in temperature by 1.5°C in the near-term (2021-2040) can cause unavoidable increase in multiple climate hazards and present unknown challenges facing humanity with implications for food production, natural ecosystems, freshwater supply, health, etc. In this context, an analysis was carried out to identify trends in temperature over time series at Madhira, Khammam district in Telangana. In this trend analysis study, the annual average maximum temperature value of skewness was asymmetric and left skewed. The annual mean and maximum temperatures were significant with long-term increasing trend. In the pre monsoon season, maximum and mean temperature showed significant increase in trends in by all methods i.e., M-K (Mann – Kendall), Spearman's Rho and Linear regression tests. But minimum temperature showed non-significant increasing trend. Here interestingly, monsoon season showed non-significance increase in temperature trends in all three mentioned tests. The mean of monthly maximum temperature increased at a faster rate than the average and minimum temperature. The linear regression equation indicated positive slope and R2 was 27.0% of variability for mean annual temperature.

Temporal Variability of Temperature, Precipitation and Drought Indices in Hyper-Arid Region of Northwest China for the Past 60 Years

The temporal variability and abrupt change analysis of temperature and precipitation in Turpan was investigated and examined based on a monthly data set of temperature, precipitation and drought indices (1960–2019) from three meteorological stations over the study area. The Yamamoto method, Mann–Kendall test, Standardized Precipitation Index (SPI), Standardized Precipitation Evaporation Index (SPEI), and Reconnaissance Drought Index (RDI) were applied to reveal temperature, precipitation and drought indices trends in their annual volumes. The conclusions were as follows: (1) in the past 60 years, the annual average temperature in the Turpan region has increased at a rate of 0.33 °C·(10a)−1. Whereas the temperature has accelerated since the 1980s, the annual average minimum temperature has increased more than the annual average maximum temperature, and the temperature difference between winter and summer has increased since the 1990s. (2) The inter-annual, inter-decadal and normal value changes in precipitation in the Turpan region and its three meteorological stations indicated a decreasing trend during 1960–2019. Though the normal value of summer and autumn precipitation decreased and increased as a whole, the normal values of spring, summer, winter and annual precipitation in the Turpan region displayed downward trends. (3) Abrupt changes in temperature were observed in the mid-1990s, and abrupt changes in precipitation were not obvious. (4) The SPI and RDI responded quickly to precipitation and temperature, and the overall characteristics of dry and wet trend changes were consistent. When the SPEI considered the combined effect of temperature and precipitation, the SPI and SPEI are better correlated, and the SPI and RDI are better correlated than the SPEI and RDI. On the whole, the occurrence of drought has obvious regional and seasonal characteristics. These conclusions not only provide scientific data for sustainable development in Turpan but also offer scientific information to further understand the trends and periodicity of climate change and drought conditions in hyper-arid regions around the world.

Long-term trends in elephant mortality and their causes in Kenya

High mortality poses a serious threat to sustainable conservation of the African elephant (Loxodonta africana). Using detected carcass data collected by the Kenya Wildlife Service (KWS) during 1992-2017, we analyze temporal and spatial variation in elephant mortality in Kenya. We investigate the major mortality causes and means used to kill elephants, carcass category, tusk recovery status, variation in mortality with elephant age and sex classes, differences between inside and outside protected areas (PAs), the Proportion of Illegally Killed Elephants (PIKE) and the overall mortality rate (MR — the number of dead/number of live elephants in a given year). In total 9,182 elephant deaths were recorded during the 26 years. Elephant mortality increased over time and was attributed primarily to natural (33.1%) and human-related causes, particularly ivory poaching (31.5%) and human-elephant conflicts (19.9%). Elephant mortality varied across Kenya’s 47 counties in correspondence with variation in elephant population size and was the highest in the leading elephant range counties of Taita Taveta, Laikipia, Samburu and Meru. Mortality was higher for males and adults and outside the protected areas. Most elephant carcasses had tusks (75.1%) but a few did not (12.5%). Yearly PIKE values peaked in 2012, the year with the highest poaching levels in Kenya during 1992-2017. MR increased throughout 1992-2017. Temporal variation in elephant mortality probability was significantly influenced by human and livestock population densities, average annual maximum temperature and total annual rainfall and the strength of these influences varied across the seven leading elephant range counties of Kenya. Natural processes are increasingly contributing to elephant mortality likely due to climate change and the associated food and water stress, exacerbated by contracting range. Enhancing anti-poaching and strategies for mitigating climate change impacts and human-elephant conflict and reducing range contraction while sustaining habitat connectivity can help reduce mortality and promote elephant conservation. Strengthening enforcement of international wildlife laws can further reduce illegal trade in tusks and killing of elephants.

Changes in Vegetation Health of the Tropical Rainforest of Assam due to Climate Change and Anthropogenic Intervention

Jeypore Reserved Forest, the remnant patch of the tropical rainforest of Assam, harbours rich diversity of flora and fauna and is facing disturbance due to anthropogenic interventions such as stone and sand collection, coalmining as well as encroachment for tea plantations in the peripheral areas of the forest. Using geospatial technology, an attempt was made in this study to analyse the changes in vegetation health of the tropical rainforest of Assam due to climate change and anthropogenic intervention over the period 19862020. A high positive correlation was observed between the vegetation health and average post-monsoon rainfall of the tropical rainforest of Assam, while the average annual maximum temperature and average winter rainfall were found to be highly negatively correlated with vegetation health of the area. Although there was no significant impact of climate change on the vegetation health of the tropical rainforest of Assam during the study period; however, anthropogenic changes in land cover/land-use may alter the climatic parameters of the region in the long-run, which in turn may affect the vegetation health of this pristine forest.

Impact of Climate Change on Water Resources and Crop Production in Western Nepal: Implications and Adaptation Strategies

Irrigation-led farming system intensification and efficient use of ground and surface water resources are currently being championed as a crucial ingredient for achieving food security and reducing poverty in Nepal. The potential scope and sustainability of irrigation interventions under current and future climates however remains poorly understood. Potential adaptation options in Western Nepal were analyzed using bias-corrected Regional Climate Model (RCM) data and the Soil and Water Assessment Tool (SWAT) model. The RCM climate change scenario suggested that average annual rainfall will increase by about 4% with occurrence of increased number and intensity of rainfall events in the winter. RCM outputs also suggested that average annual maximum temperature could decrease by 1.4 °C, and average annual minimum temperature may increase by 0.3 °C from 2021 to 2050. Similarly, average monthly streamflow volume could increase by about 65% from March–April, although it could decrease by about 10% in June. Our results highlight the tight hydrological coupling of surface and groundwater. Farmers making use of surface water for irrigation in upstream subbasins may inadvertently cause a decrease in average water availability in downstream subbasins at approximately 14%, which may result in increased need to abstract groundwater to compensate for deficits. Well-designed irrigated crop rotations that fully utilize both surface and groundwater conversely may increase groundwater levels by an average of 45 mm from 2022 to 2050, suggesting that in particular subbasins the cultivation of two crops a year may not cause long-term groundwater depletion. Modeled crop yield for the winter and spring seasons were however lower under future climate change scenarios, even with sufficient irrigation application. Lower yields were associated with shortened growing periods and high temperature stress. Irrigation intensification appears to be feasible if both surface and groundwater resources are appropriately targeted and rationally used. Conjunctive irrigation planning is required for equitable and year-round irrigation supply as neither the streamflow nor groundwater can provide full and year-round irrigation for intensified cropping systems without causing the degradation of natural resources.

Climatic Trends of Variable Temperate Environment: A Complete Time Series Analysis during 1980–2020

The western Himalayan region is susceptible to minor climate changes because of its fragile ecology, which might threaten the valley’s prestigious ecosystems and socio-economic components. The Himalayas’s local climate and weather are vulnerable to and interlinked with world-scale climatic changes since the region’s hydrology is predominantly dominated by snow and glaciers. The Himalayas, notably the Jammu and Kashmir region in the western Himalayas, has clearly shown distinct and robust evidence of climate change. This study used observed data to examine the climatic variability and trends of change in precipitation and temperature for the Kashmir valley between 1980 and 2020. Gulmarg, Pahalgam, Kokernag, Qazigund, Kupwara, and Srinagar (Shalimar) meteorological stations in the Kashmir valley were studied in detail for long- and short-term as well as localized fluctuations in temperature and precipitation. The annual temperature and precipitation fluctuations were calculated using Sen’s slope approach, and the sloping trend was determined using linear regression. The research showed statistically insignificant growing trends in maximum and minimum temperatures throughout the Kashmir valley. The average annual temperature in the Kashmir valley increased by 1.55 °C during the last 41 years (from 1980 to 2020), with a higher rise in maximum and minimum temperature by 2.00 and 1.10 °C, respectively. However, precipitation showed a non-significant decreasing trend concerning time series analysis over 1980 to 2020 in Kashmir valley. Results of annual average maximum temperature at all the stations revealed that Pahalgam (2.2 °C), Kokernag (1.8 °C), and Kupwara (1.8 °C) displayed a steep upsurge and statistically significant trends; however, annual average minimum temperature followed an increasing trend from 1980 to 2020 at all the stations except Shalimar. However, non-significant declining trends in precipitation were recorded at all the locations in Kashmir valley. This changing pattern of temperature and precipitation could have significant environmental consequences, affecting the western Himalayan region’s food security and ecological sustainability.

Impacts of climate and land use change on groundwater recharge under shared socioeconomic pathways: A case of Siem Reap, Cambodia

The rapid pace of urbanization blended with climate change has significantly altered surface and groundwater flows. In the context of tourism-driven economic potential areas, these drivers have greater effects, including threatening groundwater availability. This study assessed the combined impacts of climate and land use changes on the groundwater recharge (GWR) in Siem Reap, Cambodia utilizing Phase Six of the Coupled Model Intercomparison Project (CMIP6) global climate models (GCMs), DynaCLUE land-use model, and Soil Water Assessment Tool (SWAT). Three climate models CanESM5, EC_Earth3, and MIROC6, out of seven, best captured the observed data after performance evaluation through the entropy method, were bias-corrected linearly for two shared socioeconomic pathways (SSPs) - SSP2-4.5 and SSP5-8.5. The results indicate a general increase in precipitation under both SSPs, while the average annual maximum temperature is likely to increase by 0.024 °C/year and 0.049 °C/year under SSP2-4.5 and SSP5-8.5, respectively. A similar trend but relatively higher increase is expected for the minimum temperature. Furthermore, the historical land use change showed the expansion of urban settlement by 373% between 2004 and 2019 at the expense of forest and shrubland. Future land use projections from the DynaCLUE model show that the urban settlements in the study area are likely to expand, from their 2019 condition, by 55% in 2030, 209% in 2060, and 369% in 2090 under SSP2 and at double of these rates under SSP5 scenario. The GWR is expected to rise by 39–53% during the wet season and decrease by 13–29% during the dry season under both scenarios. Meanwhile, under constant land use, the GWR is likely to increase more compared to other scenarios, highlighting the importance of land use planning to policymakers and planners. Additionally, the study shall also be important to practitioners and researchers in understanding, planning, and evaluating the performance of multiple climate models in groundwater assessment.

Spatio-Temporal Analysis of Climatic Variables in the Munneru River Basin, India, Using NEX-GDDP Data and the REA Approach

For effective management practices and decision-making, the uncertainty associated with Regional Climate Models (RCMs) and their scenarios need to be assessed in the context of climate change. The present study analyzes the various uncertainties in the precipitation and temperature datasets of NASA Earth Exchange Global Daily Downscaled Projections (NEX-GDDP) under Representative Concentrative Pathways (RCPs) 4.5 and 8.5 over the Munneru river basin, in India, using the Reliable Ensemble Averaging (REA) method. From the available 21 RCMs, the top five ranked are ensembled and bias-corrected at each grid using the non-parametric quantile mapping method for the precipitation and temperature datasets. The spatio-temporal variations in precipitation and temperature data for the future periods, i.e., 2021–2039 (near future), 2040–2069 (mid future) and 2070–2099 (far future) are analyzed. For the period 2021–2099, annual average precipitation increases by 233 mm and 287 mm, respectively, the in RCP 4.5 and RCP 8.5 scenarios when compared to the observed period (1951–2005). In both the RCP 4.5 and RCP 8.5 scenarios, the annual average maximum temperature rises by 1.8 °C and 1.9 °C, respectively. Similarly, the annual average minimum temperature rises by 1.8 °C and 2.5 °C for the RCP 4.5 and RCP 8.5 scenarios, respectively. The spatio-temporal climatic variations for future periods obtained from high-resolution climate model data aid in the preparation of water resource planning and management options in the study basin under the changing climate. The methodology developed in this study can be applied to any other basin to analyze the climatic variables suitable for climate change impact studies that require a finer scale, but the biases present in the historical simulations can be attributed to uncertainties in the estimation of climatic variable projections. The findings of the study indicate that NEX-GDDP datasets are in good agreement with IMD datasets on monthly scales but not on daily scales over the observed period, implying that these data should be scrutinized more closely on daily scales, especially when utilized in impact studies.

Geographic information system-assisted site quality assessment for hazelnut cultivation using multi-criteria decision analysis in the Black Sea region, Turkey.

Developing land suitability models for strategically critical agricultural products to expand sustainable agricultural policies and sensitive agriculture management has become a significant trend. This study aims to improve a unique land suitability model for hazelnut cultivation by applying the criteria set (7 main criteria, 35 sub-criteria) including qualitative and quantitative reasons, integrated fuzzy analytic hierarchy process, inverse distance weighting, multi-criteria decision analysis, geographic information system, and weighted linear combination approaches. The model developed in the present study was applied and tested in Ünye District of Ordu Province, where hazelnut production in the Eastern Black Sea region of Turkey is an important economic activity. While 71.17% of the study area is classified as very highly suitable, highly suitable, and moderately suitable, 28.83% of the study area has marginally suitable and unsuitable properties for hazelnut cultivation. Generally, it was determined that the coastal parts of the study area were the most suitable areas for hazelnut growing. The hazelnut land suitability model's two main criteria impacting the final score values are climatic and topographic conditions, respectively. Heavy metal pollution and physical, chemical, and fertility conditions related to soil properties followed these, respectively. The first ten sub-criteria with the highest weight value were determined as elevation, annual average temperature, annual average precipitation, aspect, annual average relative humidity, nickel (pollution), slope, annual average maximum temperature, lead (pollution), and soil depth, respectively. Existing hazelnut cultivation areas were used to test the model. Of the existing cultivation areas, 75.59% coincided with the very highly suitable, highly suitable, and moderately suitable classes presented in this study, while 17.15% were in marginally suitable and 7.26% in unsuitable classes. The study results reveal that the hazelnut land suitability model developed is suitable in mild climate conditions. Using this model as a general transition model will be beneficial to test it in areas containing similar climatic conditions and various soil properties. This study will create a rational background in ensuring the sustainable food production system and security, agricultural land use planning, strategic planning and management of the hazelnut plant, increasing agricultural productivity and income, and the ecosystem.

三江源国家公园植被净初级生产力变化趋势及影响因素

PDF HTML阅读 XML下载 导出引用 引用提醒 三江源国家公园植被净初级生产力变化趋势及影响因素 DOI: 10.5846/stxb202106131577 作者: 作者单位: 作者简介: 通讯作者: 中图分类号: 基金项目: 第二次青藏高原综合科学考察研究项目（2019QZKK0302）；中国科学院青海省人民政府三江源国家公园联合研究专项专项（LHZX-2020-07） Changes and influencing factors of vegetation net primary productivity in the Sanjiangyuan National Park Author: Affiliation: Fund Project: 摘要 | 图/表 | 访问统计 | 参考文献 | 相似文献 | 引证文献 | 资源附件 | 文章评论 摘要:净初级生产力（NPP）是评估全球气候变化和人类活动下生态系统状况、过程和机制的重要指标之一。研究以中国首批国家公园之一的三江源国家公园为对象，利用GLOPEM-CEVSA耦合模型，以1981-2018年空间插值的气象数据和基于遥感反演的FPAR数据为输入，分别估算仅气候驱动的潜在NPP （NPPCL）和气候遥感共同驱动的现实NPP （NPPRS），以二者之差厘定人类活动影响的NPP （NPPHA），进而探究全球气候变化下人类活动的影响。结果表明：（1）三江源地区NPPRS多年均值为309.70 g C m-2 a-1，占NPPCL的61.65%。其中，黄河源、长江源和澜沧江源园区NPPRS分别为249.88 g C m-2 a-1、140.18 g C m-2 a-1和330.55 g C m-2 a-1。（2）全区NPPRS以2.00 g C m-2 a-1速率显著增加，高于NPPCL（1.74 g C m-2 a-1），其中黄河源、长江源和澜沧江源园区NPPRS增长速率分别占各自NPPCL增长速率的89.13%、90.23%和77.43%，澜沧江源园区整体受人类活动影响最大。（3）气候影响方面，年降水、年平均日最高气温和年平均日最低气温共同可解释全区NPPCL和NPPRS年际变化的51%和73%，可分别解释黄河源、长江源和澜沧江源园区的48%和58%、52%和69%、42%和50%，其中气温对NPP年际变化趋势影响更大。（4）人类活动在大部分区域呈负影响，存在西北向东南负面影响增强的空间分布特征，但2000年前后人类活动对生产力变化趋势呈负影响的面积从79.12%降低到56.34%，NPP变化量从-71.41 Tg C降低到-38.72 Tg C，作为主导因子的变化范围从18.73%增加至38.76%，三江源地区生态保护与恢复等措施促进了植被生产力增加，但需进一步实施生态保护与恢复措施，这是一项长期而艰巨的任务。 Abstract:The Net Primary Productivity (NPP) is an important indicator quantifying ecosystem status, process and undying mechanisms due to global climate change and human activities. The potential NPP (NPPCL) and actual NPP (NPPRS) were estimated through GLOPEM-CEVSA model driven by the only interpolated climate data, and both climate and remote sensing data, respectively, for the near 40 years from 1981 to 2018. The human activities dominated NPP (NPPHA) was defined as the difference between NPPRS and NPPCL. The method was applied in the Sanjiangyuan National Park, one of the first national parks in China, to explore the impacts of human activity under global climate change. The results showed that:(1) the average NPPRS was 309.70 g C m-2 a-1, accounting for 61.65% of the NPPCL for the whole region. For the three sub-parks, the NPPRS were 249.88 g C m-2 a-1, 140.18 g C m-2 a-1 and 330.55 g C m-2 a-1 in the Yellow River headwater, Yangtze River headwater and Lancang River headwater, respectively. (2) The NPPRS was increasing significantly by a speed of 2.00 g C m-2 a-1, which was faster than the NPPCL (1.74 g C m-2 a-1). The speed ratio of NPPRS to NPPCL were 89.13%, 90.23% and 77.43% for the three sub-parks by the above oder, respectively, which meant the Lancang Park was most impacted by human activities. (3) In the terms of climate impact, temperature had a stronger impact on the trend of NPP. Specifically, annual total precipitation, average annual maximum temperature and minimum temperature explained 51% and 73% of the interannual variation of NPPCL and NPPRS in the whole region, which can explain 48% and 58%, 52% and 69%, 42% and 50% of the interannual variability in the NPP timeseries in the Yellow River, Yangtze River and Lancang River sub-park, respectively. (4) Human activities had a negative influence over the most areas and became stronger to the southeast from the northwest. And the area showing a negative influence from human activities on the productivity changes decreased from 79.12% before 2000 to 56.34% after 2000. The total NPP over the whore negative area decreased from -71.41 Tg C to -38.72 Tg C, and the human activity as the dominant factor increased from 18.73% to 38.76% for the same two periods. It illustrated that the ecological protection and restoration of the Three-River Headwaters Region have contributed to an increasing vegetation productivity, but the protection and restoration measures should be further implemented. 参考文献 相似文献 引证文献