Mean Annual Maximum Temperature Research Articles

Introduction to the special issue of The Natural History of Two Peoples Bay Nature Reserve, Western Australia

Context This paper introduces the special issue of Pacific Conservation Biology devoted to the natural history of Two Peoples Bay Nature Reserve on the south coast of Western Australia. Methods This paper provides the background to the special issue. Key results Two Peoples Bay Nature Reserve was gazetted in 1967 for the conservation of the native biota, including two species; the Djimaalap/noisy scrub-bird (Atrichornis clamosus) and Ngilgaitch/Gilbert’s potoroo (Potorous gilbertii), both believed extinct for over 100 years before being rediscovered on the Reserve. The Reserve is 4774.7 ha in area, with wetlands, heathlands, granite outcrops, sand dunes, beaches, cliffs, and islands. Since it was established, mean annual rainfall has decreased by 16.8%, mean annual maximum temperature has increased by 0.2°C, and mean annual minimum temperature has increased by 0.7°C. Conclusions The paper poses the question: what do the changes of drier winters, hotter summers, and more extreme weather events mean for managers of conservation areas such as Two Peoples Bay? Implications Changing climate will pose problems for the managers of Two Peoples Bay Nature Reserve in ensuring the conservation of the Djimaalap/noisy scrub-bird and Ngilgaitch/Gilbert’s potoroo.

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 > 0.05) while both historic mean annual maximum and minimum temperature trends showed a statistically significant increase (P<0.05). Projections for 2033 reveal a significant decrease in rainfall (P < 0.05) while both maximum and minimum temperature will remain quasi uniform

Scaling use of the rust fungus Puccinia punctiformis for biological control of Canada thistle (Cirsium arvense (L.) Scop.): First report on a U.S. statewide effort

Canada thistle (Cirsium arvense (L.) Scop., CT) is one of the worst weeds threatening temperate regions of the world. A host-specific rust fungus, Puccinia punctiformis (F. Strauss) Rohl., is known to cause systemic disease of CT, ultimately killing individuals and reducing stand densities. In 2013, it was demonstrated that fall inoculation of rosettes with coarsely ground leaves bearing P. punctiformis telia can successfully initiate epiphytotics. In the same year, a cooperative project between the Colorado Department of Agriculture and United States Department of Agriculture was initiated, in which CT patches across the state of Colorado (USA) were inoculated and tracked over subsequent years for changes in stem density. Here, we report our findings from 8 years (2014–2021) of monitoring effort. At most sites (N = 87), CT stem densities declined, from a mean (±SE) of 87.9 (±6.5) stems to 44.7 (±4.2). These declines however were spatially-autocorrelated, and likely attributable to local growing conditions, as mean annual daily maximum temperature and standard deviation of elevation, as well as climatic conditions around the times of both treatment and monitoring, were found to be important predictors of CT decline. Further, we observed that the amount of inoculum deployed, timing since last release, and method in which it was spread locally at a site were also associated with the magnitude of CT stem decline. These results are indicative of the value of P. punctiformis as a CT biological control agent.

Grid-based climate variability analysis of Addis Ababa, Ethiopia

Climate change is an intricate global environmental concern. However, its impact is more pervasive in developing nations such as Ethiopia. Hence, this manuscript examines temperature variability and the magnitude of change over 38 years in the specific case of Addis Ababa, Ethiopia. Gridded meteorological data consisting of minimum and maximum temperatures on a monthly time scale ranging from 1981 to 2018 was obtained from the National Meteorological Agency of Ethiopia. The coefficient of variation (CV) and standardized anomaly index (SAI) were used to examine the rate and extent of temperature anomalies. Geostatistical models, particularly ordinary kriging, are presented as a means of spatially interpolating temperature data. Modified Mann-Kendall test (MMK), Sen's Slope (SS) estimator, principal component analysis (PCA), and T-test were employed to determine the monthly, annual, and seasonal trends using Geospatial technologies, “R” programming, and statistical software. The findings revealed substantial spatial and temporal variation in Addis Ababa’s annual and seasonal maximum and minimum temperatures. The long-term mean annual maximum and minimum temperatures were 25.8 °C and 12.6 °C, respectively. The monthly, annual, and seasonal temperatures accrued significantly except in the months of January and September. It is noteworthy that the decadal maximum temperature has risen by 2.7 °C, while minimum temperatures have displayed comparatively minor fluctuations. Moreover, the findings also exhibited that the average maximum and minimum temperatures increased by 1.88 °C and 1.72 °C, correspondingly and the highest temperature occurred during the spring (Belg) season. The first two PCAs (Annual and Kiremt Tmax) account for 90% of the temperature variation. In conclusion, the findings underscore the pressing need for the implementation of climate adaptation strategies and policy measures, which can strengthen the city’s resilience to imminent climate change-induced hazards. The mounting temperature presents substantial challenges across various sectors within the city, emphasizing the urgency of preemptive actions to mitigate potential repercussions.

Analysis of projected climate change in sorghum growing semi-arid rift valley of Ethiopia

Global warming projected to have a significant impact on agricultural water availability for crops in Africa in particularly in Ethiopia. Therefore, the study was conducted to analyze the projected change of temperature and rainfall in sorghum growing semi-arid rift valley of Ethiopia. The weather data for 11 stations were generated using AgiMP5 technique for RCP 4.5 and 8.5 and for the period of 2050s and 2080s. MAKESENS employed for the detection of trend of extreme indices. InStat v3.37 were used for the analysis of start, end, and length of growth season. Under RCP 4.5, the projected mean annual minimum air temperatures in CRV, ERVE and NRVE could increase respectively by 1.9 °C (2050s) and 2.6 °C (2080s), 1.8 °C (2050s) and 2.5 °C (2080s) and 1.88 °C (2050s) and 2.69 °C (2080s). Under RCP 8.5, however, with same location it will projected to increase in both time frames (2050s and 2080s) in all studied sites. The mean annual maximum air temperature, projected under RCP 4.5 in the CRV, ERV and NRVE will increase by 1.59 °C (2050s) and 2.18°C (2080s),1.42 °C (2050s) and 2.08 °C (2080s) and 1.46 °C (2050s) and 2.09 °C (2080s) respectively. However, under the RCP 8.5, at same regions it projected to rise in both periods (2050s and 2080s). This will be convoy with increase of the hot and cold extremes’ indices in regions. The mean percentage change of annual rainfall is projected to decrease insignificantly in (0.6-5.5% and 2.6%) and increase (0.85-12.3% and 22.3%) in half of the stations located in CRV and ERVE, whereas, in NRVE it will projected to increase (6.1-14.6%) under RCP 4.5 in all stations in 2050s. Though the annual projected rainfall under RCP 4.5 in 2080s will decline in range of 2.1-10.1%, 3.12-4.5% and 0.9-4.6% at CRV, ERVE and NRVE respectively. In most of the location in CRV and ERVE stations growing season rainfall will projected to decline from 1.45% to 53.8% and 0.8 to 8.8%, whereas, in NRVE it will projected to increase in 9.2 to 19% under RCP 4.5 in period of 2050. The projected SOS will be changed in mixed pattern, whereas and LGP will prolonged in most locations. The findings indicated that there has been a change of projected climate change in semi-arid RV, which will be alter the agricultural practices in depletion of soil moisture. Therefore, in adjusting planting timing and using early/medium maturation sorghum varieties, may be necessary for farmers in the region. Soil and water management needs attention.

Miocene Climatic Optimum fungal record and plant-based CREST climatic reconstruction from southern McMurdo Sound, Antarctica

Abstract. Deep-time palynological studies are necessary to evaluate plant and fungal distribution under warmer-than-present scenarios such as those of the Middle Miocene. Previous palynological studies from southern McMurdo Sound, Antarctica (SMS), have provided unique documentation for Neogene environments in the Ross Sea region during a time of pronounced global warming. The present study builds on these studies and provides a new climate reconstruction using the previously published SMS pollen and plant spore data. Additionally, 44 SMS samples were reanalyzed with a focus on the fungal fraction of the section to evaluate the fungal distribution under warmer than present conditions. The probability-based climate reconstruction technique (CREST) was applied to provide a new plant-based representation of regional paleoclimate for this Miocene Climatic Optimum (MCO) locality. CREST reconstructs a paleoclimate that is warmer and significantly wetter than present in SMS during the MCO, with mean annual precipitation reconstructed at 1147 mm yr−1 (95 % confidence range: 238–2611 mm yr−1) and a maximum mean annual temperature of 10.3 ∘C (95 % confidence range: 2.0–20.2 ∘C) for the warmest intervals of the MCO. The CREST reconstruction fits within the Cfb Köppen–Geiger climate class during the MCO of SMS. This new reconstruction agrees with previous reconstructions using various geochemical proxies. The fungal palynological analyses yielded surprising results, with only a single morphotype recovered, in low abundance, with concentrations ranging up to 199 fungi per gram of dried sediment. The taxa present belongs to the Apiosporaceae family and are known to be adapted to a wide range of climate and environmental conditions. As fungi are depauperate members of the SMS MCO palynofloras and because the one morphotype recovered is cosmopolitan, using the fungi record to confirm a narrow Köppen–Geiger climate class is impossible. Overall, the study demonstrates refinement of plant-based paleoclimatic reconstructions and sheds light on the limited presence of fungi during the MCO in Antarctica.

Evaluation of CORDEX Africa regional climate models performance in simulating climatology of Zarima sub-basin northwestern Ethiopia

Climate models are basic tools to obtain reliable estimates of future climate change and its effects on the water resources and agriculture in given basin. However, all climate models are not equally valuable for all areas. Therefore, determining the most appropriate climate model for a specific study area is essential. This study examines the performance of 10 CORDEX-AFRICA-220 Regional Climate Models (RCMs), three downscaling institutional based ensembles mean (Reg ensemble, CCLM ensemble and REMOO ensemble) and the multi-model ensemble mean. The models were evaluated based on their ability in replicating the seasonal and annual rainfall, minimum and maximum temperature and inter-annual variability for the period of 1986–2005 using statistical metrics such as BIAS, Root Mean Square Error (RMSE), Pearson correlation coefficient (r), coefficient of variation (CV), Kling Gupta Efficiency (KGE) and Taylor diagram. The findings indicated that HadREMOO, MPI-Reg4-7, HadReg4-7, Reg ensemble, and multi-model ensemble mean performed relatively better in representing the mean annual observed rainfall at the Adiramets, Debarik Ketema, Niguse Maystebri, and Zarima stations, respectively. Whereas, NorESM-CCLM, MPI-CCLM, NorESM-Reg4-7, and NorESM-REMOO exhibited a weak performance in reproducing the observed mean annual rainfall at the Adiramets, Debarik Ketema Niguse, Maystebri, and Zarima stations, respectively. Similarly, RCMs generally capture the mean annual maximum temperature of climatic stationsof Zarima subbasin well. Specifically, the MPI-Reg4-7 simulation performs well in representing the mean annual observed maximum temperature at Adiramets and Maytsebri stations, while the Debarik and Ketema Niguse stations exhibit superior performance in the HadReg4-7 simulation and the Zarima station shows better representation in the CCLM ensemble simulations. The majority of the model simulations exhibit good representation of mean annual minimum temperature at Adiramets, Debarik, and Zarima stations. Specifically, CanESM-RCM, HadReg4-7, REMOOensemble, multi-model ensemble, and Regensemble simulations perform better at Adiramets, Debarik, Ketema niguse, Maystebri and Zarima stations respectively. This suggests that these models may have biases or shortcomings in capturing the temperature values in the subbasin. Furthermore, NorESM-CCLM at Adiramets, Ketema niguse, and Zarima stations, NorESM-REMOO at Debarik station, and HadReg4-7 at Maystebri station demonstrate poor performance in representing the observed mean minimum temprature. Majority of the RCMs, all institutional based ensemble means and the multi-model ensemble mean simulations overestimate the observed mean annual rainfall of the Zarima subbasin with minimum bias of 0.02 mm at Ketema niguse HadReg4-7and maximum bias of 2.81 mm at Maytsebri MPI-CCLM simulation. Similarly, HadReg4-7 simulation of Ketama Niguse MPI-CCLM showed a minimum 0.02 mm and Maytsebri simulation kiremit season mean rainfall showed a maximum bias of and 2.99 mm. Regarding mean annual and kiremit season maximum and minimum temperature of the Zarima subbasin were overestimated by majority of the simulation and the ensemble means. The correlation (r) of observed and model simulated mean annual and kiremit season rainfall was strong (0.60–0.79) and very strong (0.80–0.99) in the majority of the simulations except Ketema niguse station mean annual and kiremit season rainfall simulations of MPI-REMOO, NorESM-Reg4-7; Debarik station kiremit season rainfall of NorESM-CCLM and NorESM-REMOO, MPI-Reg4-7 and MPI-REMOO, which showed moderate correlation. The performance of the RCMs, institutional based ensemble means and multi-model ensemble mean were different in statistical metrics (BIAS, RMSE, r, CV and KGE) and Taylor diagram. Among the simulations and ensemble means, the multi-model ensemble mean was superiors in two or more of statistical metrics at each station of the Zarima subbasin except Maytsebri station kiremit season rainfall, where the CCLM ensemble was better. Consistently, the Taylor diagram showed that the multi-model ensemble was better in the replication of the areal annual and kiremit season rainfall, maximum and minimum temperature of the subbasin. This finding evidenced that selecting the best RCMs and ensemble mean is necessary for climate projection and climate change impact assessment study.

Responses of water use efficiency of Chinese fir to mixed planting and meteorological factors

Chinese fir in China are generally inefficient plantations with single species, unreasonable stand density, and low productivity. The introduction of broadleaved species is usually adopted as a strategy to improve Chinese fir plantations. Taking the pure forests and mixed forests of the Guanshan Forest Farm in Jiangxi Province as example, we quantified the intrinsic water-use efficiency (iWUE) of trees based on the stable isotope carbon method, as well as its response to meteorological factors, and investigated the improvement of stand quality after introducing Phoebe zhennan into Chinese fir plantation. The results showed that the basal area increment was 0.23 cm2 in pure forest, being higher than that of 0.19 cm2 in mixed forest. The δ13C and iWUE of pure forest were -27.4‰ and 52.9%, respectively, being lower than those of -26.7‰ and 62.8% in the mixed forest. Tree δ13C in pure forest was more sensitive to changes in mean annual precipitation and mean annual relative humidity, while that in mixed forest was not significantly correlated with meteorological factors. Pure forest iWUE was positively correlated with mean annual temperature, mean annual atmospheric CO2 concentration, and mean annual maximum temperature, and negatively correlated with mean annual precipitation and mean annual relative humidity, while mixed forest iWUE was positively correlated with mean annual atmospheric CO2 concentration only. Our results indicated that pure forests was more sensitive to climate than mixed forests.

Vulnerability of communities' livelihoods to the impacts of climate change in north-western highlands of Tanzania

Climate change has emerged as one of the critical challenges to human well-being. Changing climate places direct and indirect impacts on human activities and is mainly triggered by non-climatic stressors, which are diverse and inherent in many communities. However, there has been diversity in how climate and non-climatic stress factors affect human well-being and thus requiring locally specific contextualisation of how climate change is driving change in many ecosystems. This study analysed climate and non-climate factors for communities' vulnerability and impacts using a landscape approach in selected villages in the western highlands of Tanzania, specifically in Kasulu and Buhigwe districts in Kigoma Region. The aim was to understand communities' vulnerability to the impacts of climate, and non-climate processes, particularly with regard to livelihoods and impacts, particularly in forest resources. Study sites and respondents were selected through simple random and purposive sampling methods. Data were collected through household surveys, key informants interviews, and focus group discussions. Meteorological data were used for the analysis of climatic changes. Quantitative data analysis tools included meteorological statistics software such as CLIDATA, INSTAT, XLSTAT and SPSS. Theme-content analysis was used to analyse qualitative data. Findings indicate inter-annual and seasonal variabilities of rainfall and temperature levels from the normal across the study sites. The study revealed significant increasing trends for mean annual Maximum and Minimum temperature. Significant changes in rainfall patterns were decreasing seasonal and annual rainfall trends, unreliable onset and cessation, and erratic seasonal rainfall. The maximum decline of seasonal rainfall had slope values of −14.51, while the maximum decline of annual rainfall trends had a slope value of −12.76. The minimum decline for seasonal rainfall had slope values of −2.17, and annual rainfall trends had slope values of −1.36. Villages in the highland zone were found to be highly vulnerable to the impacts of climate change and non-climate stress factors, with a 0.61 vulnerability index value. Major climate stress factors for communities' vulnerability included seasonal variability of rainfall and temperature increase, which influenced the increase of pests and diseases and other unfavourable climatic conditions that affect crop production and livestock keeping. The effects, directly and indirectly, influenced the low agricultural yields and decreased income. Non-climate stress factors such as landscape characteristics, mobility, inadequate extension services, and low household asset endowment might have been accelerating communities' vulnerability to the effects of the changing climate. Moreover, the study shows the climate change on a community's resilience. Generally, changes in climate conditions result in many adverse consequences and have been influencing communities' vulnerability.

Modeling the impact of climate change on streamflow responses in the Kessem watershed, Middle Awash sub-basin, Ethiopia

Abstract In this study, we examined how future climate change will affect streamflow responses in the Kessem watershed. Climate variables from SSP2-4.5 and SSP5-8.5 emission scenarios were extracted from GCMs for the 2040s (2031–2060) and 2070s (2061–2090). The bias-corrected precipitation and temperature were converted into streamflow using a calibrated SWAT model. The simulated output of the future streamflow for the periods 2040s and 2070s was compared with the base period (1992–2020) and presented as percentage changes. During calibration and validation, the SWAT model showed Nash–Sutcliffe efficiency (NSE) values of 0.79 and 0.77, as well as coefficient of determination (R2) values of 0.8 and 0.79, demonstrating its capability of simulating streamflow. The annual mean maximum and minimum temperatures are predicted to increase, with a pronounced increase in the minimum temperature for the mid-term and long-term futures under both emission scenarios. As we approach the end of the century, we see an increase in annual mean rainfall and streamflow under the SSP5-8.5 emission scenario. The increment in annual mean rainfall (streamflow) is expected to be 3% (12.5%) and 23% (48.8%) for the 2040s and 2070s, respectively, under the SSP5-8.5 emission scenario.

Effects of climate factors on wheat and maize under different crop rotations and irrigation strategies in the North China Plain

The North China Plain (NCP) is a crucial agricultural region for grain production in China, primarily focusing on wheat and maize cultivation. However, these crop yields are highly vulnerable to fluctuations in temperature and precipitation, with climate change being a significant factor. This study investigates the impact of climate factors on wheat and maize yields in the NCP under various crop rotations and water supply strategies. Using the Agricultural Production Systems sIMulator crop mechanism model, we evaluated the effects of different crop rotations and water supply strategies on wheat and maize yields. A comprehensive analysis of the simulated results determined the yield variation trends and their correlation and sensitivity to different climate factors. The findings revealed that precipitation levels over the past 40 years showed no significant trend, while radiation levels showed a significant decreasing trend, and annual mean maximum and minimum temperatures displayed significant increasing trends. Furthermore, the study found that irrigation practices and crop rotations substantially impact grain yield in the study area, with average yields ranging from 8105.5 kg ha−1 under rainfed conditions to 13 088.8 kg ha−1 under fully irrigated conditions. There was a statistically significant trend of increasing yields for fully irrigated Monocrop-Wheat and decreasing yields for fully irrigated Monocrop-Maize over the past 40 years. Sensitivity analysis also showed that rational rotation and irrigation can reduce grain yield sensitivity to climate change. In conclusion, the prudent use of rotation and irrigation can enhance food production resilience to climate change. These findings have significant implications for developing strategies to optimize crop yields and adapt to climate change in the NCP while considering trade-offs.

Influence of climate factors on population density and damage of the leopard moth, Zeuzera pyrina L., in walnut orchards, Iran.

The effect of climate factors (temperature, humidity, precipitation, and frost days) on the population changes, damage, and infestation area of the leopard moth, Zeuzera pyrina L., was studied during 2006-2018 in four parts of Iran including Saman, Arak, Najaf-abad, and Baft. For trend analysis, the Mann-Kendall test was run on time series data of both climate and pest population. According to the results, the annual mean (Kendall's statistics, T = 0.64 and 0.48), annual minimum (T = 0.60 and 0.42), and January mean (T = 0.64 and 0.61, respectively) temperatures showed increasing trends in Saman and Najaf-abad. Moreover, the annual mean minimum and January temperatures (T = 0.41 and 0.45, respectively) in Arak and the annual mean maximum temperature (T = 0.79) in Baft showed increasing trends. The number of frost days/year (Kendall's statistics, T = -0.63, -0.53, -0.32 and -0.37) and annual mean relative humidity (T = -0.43, -0.63, -0.64 and -0.42, respectively) showed decreasing trends in Saman, Arak, Baft, and Najaf-abad stations. Trend analysis indicated significant increases in the mean number of moths caught (T = 0.59, 0.76 and 0.90), the percentage of infested branches/tree (T = 0.66, 0.58, and 0.90), the number of active holes/tree (T = 0.79, 0.55, and 0.68) and the infested areas (T = 0.99, 0.73, and 0.98, respectively) in Saman, Arak and Najaf-abad stations. According to stepwise regression, the mean temperatures of January, autumn, and winter were the most effective variables for increasing Z. pyrina damage and population, while relative humidity and the number of frost days played the major role in reducing it.

Exploring temperature dynamics in Madhya Pradesh: a spatial-temporal analysis.

Understanding the dynamics of temperature trends is vital for assessing the impacts of climate change on a regional scale. In this context, the present study focuses on Madhya Pradesh state in Central Indian region to explore the spatial-temporal distribution patterns of temperature changes from 1951 to 2021. Gridded temperature data obtained from the Indian Meteorological Department (IMD) in 1° × 1° across the state are utilised to analyse long-term trends and variations in temperature. The Mann-Kendall (MK) test and Sen's slope (SS) estimator were used to detect the trends, and Pettitt's test was utilised for change point detection. The analysis reveals significant warming trends in Madhya Pradesh during the study period during specific time frames. The temperature variables, such as the annual mean temperature (Tmean), maximum temperature (Tmax), and minimum temperature (Tmin), consistently increase, with the most pronounced warming observed during winter. The trend analysis reveals that the rate of warming has increased in the past few years, particularly since the 1990s. However, Pettitt's test points out significant changes in the temperature, with Tmean rising from 25.46 °C in 1951-2004 to 25.78 °C in 2005-2021 (+0.33 °C), Tmax shifting from 45.77 °C in 1951-2010 to 46.24 °C in 2011-2021 (+0.47°C), and Tmin increasing from 2.65 °C in 1951-1999 to 3.19 °C in 2000-2021 (+0.46 °C). These results, along with spatial-temporal distribution maps, shed important light on the alterations and variations in monthly Tmean, Tmax, and Tmin across the area, underlining the dynamic character of climate change and highlighting the demand for methods for adaptation and mitigation.

Predicting and Mapping Dominant Height of Oriental Beech Stands Using Environmental Variables in Sinop, Northern Turkey

The dominant height of forest stands (SDH) is an essential indicator of site productivity in operational forest management. It refers to the capacity of a particular site to support stand growth. Sites with taller dominant trees are typically more productive and may be more suitable for certain management practices. The present study investigated the relationship between the dominant height of oriental beech stands and numerous environmental variables, including physiographic, climatic, and edaphic attributes. We developed models and generated maps of SDH using multilinear regression (MLR) and regression tree (RT) techniques based on environmental variables. With this aim, the total height, diameter at breast height, and age of sample trees were measured on 222 sample plots. Additionally, topsoil samples (0–20 cm) were collected from each plot to analyze the physical and chemical soil properties. The statistical results showed that latitude, elevation, mean annual maximum temperature, and several soil attributes (i.e., bulk density, field capacity, organic carbon, and pH) were significantly correlated with the SDH. The RT model outperformed the MLR model, explaining 57% of the variation in the SDH with an RMSE of 2.37 m. The maps generated by both models clearly indicated an increasing trend in the SDH from north to south, suggesting that elevation above sea level is a driving factor shaping forest canopy height. The assessments, models, and maps provided by this study can be used by forest planners and land managers, as there is no reliable data on site productivity in the studied region.

Species range shifts of notorious invasive fish species in China under global changes: Insights and implications for management

Due to global changes, e.g., climate change and trade globalization, China is facing an increasingly severe threat from invasive freshwater fish species, which have the potential to cause negative impacts across various aspects and pose significant challenges for their eradication once established. Therefore, prioritizing the understanding of invasive species’ potential ranges and their determinants is vital for developing more targeted management strategies. Moreover, it is equally essential to consider the transitory range dynamics of invasive species that reflect changes in habitat availability and accessibility. Here, we used species distribution models (the maximum entropy algorithm) to assess the potential distributions of six notorious invasive fish species (i.e., Coptodon zillii, Cyprinus carpio, Gambusia affinis, Hemiculter leucisculus, Oreochromis mossambicus, and Oreochromis niloticus) in current and future (i.e., the 2030s, 2050s, and 2070s) periods along with their determinants, under two Shared Socio-economic Pathways scenarios (SSP1-2.6 and SSP5-8.5; global climate model: MRI-ESM2-0). Our results showed that the habitat suitability for the six species substantially benefited from temperature conditions (i.e., annual mean temperature or maximum temperature of warmest month). Throughout the given time periods, dramatic range expansions would occur for C. zillii, G. affinis, O. mossambicus, and O. niloticus, ranging from 38.61% to 291.90%. In contrast, the range of C. carpio would change slightly and irregularly, while H. leucisculus would contract marginally, with losses ranging from 1.06% to 12.60%. By the 2070s, species richness of these species would be relatively high in South, Central, and East China and parts of Southwest China. Furthermore, transitory fluctuations in the species ranges for all six species were observed throughout the entire time period (the 2030s–2070s). Given the range shifts for each species during different time periods, as well as time costs and budgets, adaptation strategies should be developed and implemented in the areas where they are most needed in each time period.

Analysis of Trend in Meteorological and Hydrological Time-series using Mann-Kendall and Sen’s Slope Estimator Statistical Test in Akwa Ibom State, Nigeria

The monthly, seasonal, annual, and decadal trends of seven hydro-meteorological variables were analysed for stations in Akwa Ibom State, Nigeria, controlled by the Nigerian Meteorological Agency (NiMet) and the Cross River Basin Development Authority (CRBDA) from 1972 to 2021. At the 5% statistical significance level, the non-parametric Mann-Kendall and Sen's slope estimator techniques were used to detect if there was a positive or negative trend and the magnitude of the trend in hydro-meteorological data. In the present study, there was a significant statistically increasing (positive) trend in mean seasonal and annual rainfall, maximum temperature, minimum temperature, and runoff. However, there was a significant statistically decreasing (negative) trend in average annual relative humidity, solar radiation, and potential evapotranspiration. The magnitudes of the trends were 19.39mm/year, 0.0314oC/year, 0.013oC/year, -0.104%/year, -8.78MJ/m2/year, -1.440mm/year, and 0.028m3/s/year for annual rainfall, maximum temperature, minimum temperature, relative humidity, solar radiation, PET, and runoff, respectively. The rising trends in precipitation, temperature, and runoff in this research area show that this region is subject to climatic variability. The results of the Mann-Kendall and Sen's slope estimator statistical tests revealed the consistency of performance in the detection of the trend for the hydro-meteorological variables.

Evaluation of historical CMIP6 model simulations and future climate change projections in the Baro River Basin

Abstract This study evaluated the performance of five Global Climate Model (GCM) outputs from the Coupled Model Intercomparison Project phase 6 (CMIP6) in reproducing the historical precipitation and temperature. Observational data from the National Meteorological Agency are used for model evaluation and bias correction. Then, the projections from representative GCMs are used to understand the future climate (2031–2060) of the Baro River Basin under two Shared Socioeconomic Pathways (SSP2-4.5 and SSP5-8.5) with respect to the historical datasets (1985–2014). Statistical metrics (percent of bias, root mean square error, and coefficient of determination) are used to assess the model's performance in reproducing precipitation and temperature and Compromise Programming (CP) was used in ranking GCMs. GFDL-CM4, INM-CM5-0, and INM-CM4-8 models for precipitation; CMCC-ESM2, MRI-ESM2-0, and INM-CM4-8 for maximum temperature; and GFDL-CM4, INM-CM4-8, and INM-CM5-0 for minimum temperature were selected based on their better simulation. The projected annual precipitation shows increases of 6% under SSP2-4.5 and 16.46% under SSP5-8.5. The mean annual maximum and minimum temperature show increases of 1.43 and 1.96 °C under SSP2-4.5, and 1.81 and 3.11 °C under SSP5-8.5, respectively. Overall, the ensemble of three models outperforms the ensemble of all models for the Baro River Basin when utilising the representative GCMs.

THE IMPACT OF CLIMATIC FACTORS AND FOOD SECURITY ON DEMOGRAPHIC FERTILITY IN PUNJAB, PAKISTAN THROUGH GEOSPATIAL STRUCTURAL MODELLING (GSSM)

: Punjab is not only the most fertile province of Pakistan but also the most fertile demographically with 3.4 total fertility rate. The fertility rate is an important demographic phenomenon that has significant implications for sustainable development, particularly in agricultural regions such as Punjab, Pakistan. In recent years, scholars and policymakers have increasingly focused on the link between fertility rate and soil fertility. As soil fertility is an important determinant of agricultural productivity and food security. This research article has explored the relationship and impact of the main climatic factors like maximum and minimum temperature, precipitation and Gross Primary Productivity on total fertility rate of districts of Punjab, for the year 2021. Geospatial Structural modeling (GSSM) has been designed to estimate the influence of climatic factors directly on fertility and its indirect influence on fertility through gross primary productivity and child mortality rate on total fertility rate of districts of Punjab. All the said geospatial primary and secondary datasets are analyzed in ArcGIS environment and resultant map are generated. The results shows that there is the direct and significant impact of gross primary productivity and child mortality rate on the total fertility rate. While there is indirect impact of mean annual precipitation, mean annual maximum and minimum temperature on the total fertility rate. Hence, there is negative direct impact of said factors on total fertility rate of the districts of the Punjab

Modelling the Impacts of the Changing Climate on Streamflow in Didesa Catchment, Abay Basin, Ethiopia

The Didesa catchment, which is the second largest subbasin of the Abay basin, significantly contributes to the Blue Nile’s outflow. Understanding the dynamics of water availability under the changing climate in such a basin assists in the proper planning of land use and other development activities. This study presents changes in climatic elements such as rainfall, temperature, and evapotranspiration using observation data and regional climate models (RCMs) under two representative concentration pathways (RCPs) for three future periods. We use a calibrated hydrological model to further assess climate change’s effects on streamflow. We select three RCMs and their ensemble’s mean by evaluating their performance with respect to observations. We apply the modified Mann–Kendall test to detect trends in each dataset. The result shows that annual mean maximum and minimum temperatures increase in the catchment for the 2021–2040, 2041–2070, and 2071–2100 periods as compared to baseline (1989–2018) under both RCP4.5 and RCP8.5 scenarios. Annual mean maximum temperature and potential evapotranspiration experienced a significant decreasing trend during the year from 1989 to 2018. Furthermore, there was an increasing trend in annual rainfall from 1989 to 2018, which could be related to the cooling of sea surface temperature over the equatorial Pacific. We detect an increasing trend in temperature in both scenarios and all periods; however, no clear trend pattern is found in rainfall. The result from hydrological model simulations reveals that the mean monthly streamflow slightly increases in the winter season while it decreases during the main rainy season. Further study of detailed weather systems, which affect the subbasin’s climate, is recommended.

Geo-climatic risk factors for chronic rhinosinusitis in southwest Iran.

Chronic rhinosinusitis (CRS) is a prevalent and disabling paranasal sinus disease associated with some environmental factors. In this study, we evaluated the effect of geo-climatic factors on CRS in a region of southwest Iran. The study mapped the residency addresses of 232 patients with CRS who lived in Kohgiluyeh and Boyer-Ahmad province and had undergone sinus surgery from 2014 to 2019. The effects of Mean Annual Humidity (MAH), Mean Annual Rainfall (MAR), Mean Annual Temperature (MAT), maximum MAT (maxMAT), minimum MAT (minMAT), Mean Annual Evaporation (MAE), wind, elevation, slope, and land cover were assessed on the occurrence of CRS using Geographical Information System (GIS). Statistical analysis was performed using univariate and multivariate binary logistic regression. Patients came from 55 points including villages, towns, and cities. In univariate analysis, climatic factors including MAT (OR = 0.537), minMAT (OR = 0.764), maxMAT (OR = 0.63), MAR (OR = 0.994), and MAH (OR = 0.626) were significantly related to CRS occurrence. Elevation (OR = 0.999), slope (OR = 0.9), and urban setting (OR = 24.667) were the significant determinants among geographical factors when analyzed independently. The multivariate analysis found maxMAT (OR = 0.5), MAR (OR = 0.994), elevation (OR = 0.998), and urban (OR = 16.8) as significant factors affecting CRS occurrence. The urban setting is the most critical factor affecting CRS disease. Cold and dry areas and low attitude are the other risk factors for CRS in Kohgiluyeh and Boyer-Ahmad province, southwest Iran.