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Climate change restricts and alters the distribution range of plant species. Predicting potential distribution and population dynamics is crucial to understanding species' geographical distribution characteristics to harness their economic and ecological benefits. This study uses Dendrocalamus brandisii as the research subject, aiming to accurately reveal the impact of climate change on this plant. The findings offer important insights for developing practical conservation and utilization strategies, and guidance for future introduction and cultivation. The MaxEnt model was optimized using regularization multiplier (RM) and feature combination (FC) from the 'Kuenm' package in R language, coupled with ArcGIS for modeling 142 distribution points and 29 environmental factors of D. brandisii. This article explored the key environmental factors influencing the potential suitable regions for D. brandisii, and predicted trends in habitat changes under SSPs2.6 and SSPs8.5 climate scenarios for the current era, the 2050s, 2070s, and 2090s. (1) The results show that when FC = QPH and RM = 1, the AUC = 0.989, indicating that the model prediction is accurate with the lowest complexity and overfitting. The key environmental factors affecting its primary suitable distribution, determined by jackknife training gain and single-factor response curve, are the precipitation of warmest quarter (bio18), the temperature seasonality (bio4), the minimum average monthly radiation (uvb-4), and elevation (Elev), contributing 93.6% collectively. It was established that the optimal range for D. brandisii is precipitation of warmest quarter of between 657 and 999 mm, temperature seasonality from 351% to 442%, minimum average monthly radiation from 2420 to 2786 J/m2/day, at elevation from 1099 to 2217 m. (2) The current potential habitat distribution is somewhat fragmented, covering an area of 92.17 × 104 km2, mainly located in southwest, south, and southeast China, central Nepal, southern Bhutan, eastern India, northwestern Myanmar, northern Laos, and northern Vietnam. (3) In future periods, under different climate scenario models, the potential habitat of D. brandisii will change in varying degrees to become more fragmented, with its distribution center generally shifting westward. The SSP8.5 scenario is not as favorable for the growth of D. brandisii as the SSPs2.6. Central Nepal, southern Bhutan, and the southeastern coastal areas of China have the potential to become another significant cultivation region for D. brandisii. The results provide a scientific basis for the planning of priority planting locations for potential introduction of D. brandisii in consideration of its cultivation ranges. [ABSTRACT FROM AUTHOR]

Cycads hold important economic and conservation value. Some species are extensively used in landscaping, while others are endangered and legally protected. The Australian cycad-attacking weevil, Siraton internatus, is notably destructive, occasionally causing infestations and invasions across various countries. This study simulated habitat suitability for S. internatus to assess its potential invasion and the impact of climate change. Habitat suitability was evaluated under current climate and four climate change scenarios over two time frames (2050 and 2090). Furthermore, we investigated the threat posed by S. internatus to cycad reserves, using Taiwanese reserves as a representative case. Our MaxEnt predictions demonstrated high accuracy, meeting multiple evaluation criteria. We explored the potential distribution of S. internatus within Australia and internationally, identifying suitable habitats in Africa, the Americas, Asia, and Europe. The case study highlighted the low habitat suitability within the two Taiwanese cycad reserves, which is projected to decrease to unsuitable levels under future climate change scenarios for this weevil species. Moreover, our results revealed that suitable habitat for S. internatus is projected to contract globally under all climate scenarios and time periods, but expansion in Chile and the southern Himalaya (e.g., Nepal). This study provides valuable insights into cycad conservation and pest invasion risks. The results support both global and local efforts to manage the invasion threats from this destructive Australian cycad-attacking weevil species. It also accentuates the urgency for continuous biosecurity inspections and prevention of exporting mature cycad caudexes from Australia. [ABSTRACT FROM AUTHOR]

Exploring the geographical distribution of forestry pests is crucial for formulating pest management strategies. Cyrtotrachelus buqueti (Guer) stands out as one of the primary pests among China's forestry hazards. This study employs the MaxEnt model, along with 19 bioclimatic variables and habitat characteristics, to predict the current and future distribution of C. buqueti under three typical emission scenarios for 2050 and 2070 (2.6 W/m2 (SSP1-2.6), 7.0 W/m2 (SSP3-7.0), and 8.5 W/m2 (SSP5-8.5)). Among the 19 bioclimatic variables, BIO 14 (precipitation of the driest month), BIO 8 (mean temperature of the wettest quarter), Elev, slope, and aspect were identified as significant contributors. These five variables are critical environmental factors affecting the suitability of habitats for C. buqueti and are representative of its potential habitat. The results indicate that C. buqueti predominantly inhabits southern regions such as Chongqing, Guizhou, Yunnan, Sichuan, Guangxi, Shaanxi, Hubei, Hainan, and Taiwan. Among them, Chongqing, Guizhou, and Yunnan are the primary distribution areas of high suitability. In the future, the centroid's movement direction will generally shift southward, with an expansion trend observed in the distribution areas of each province. This study enhances researchers' understanding of forestry pest dynamics and promotes proactive management strategies to mitigate their impact on forest ecosystems and agricultural productivity. [ABSTRACT FROM AUTHOR]

[Objective] The aims of this study are to explore the effects of different climatic factors (temperature and precipitation) on the grassland Net Primary Productivity (NPP) of the Qinghai-Tibet Plateau (QTP) under the two climate scenarios SSP126 and SSP585, and to clarify the main climatic factors affecting the spatial and temporal distribution characteristics of grassland NPP on the QTP, so as to provide a theoretical basis for the comprehensive management of vegetation ecosystem on the QTP in the future. [Methods] Based on the daily value meteorological data and soil type data of 48 meteorological stations on the QTP during the base period (1971—2020) and the site management data required by the model, the model parameters were calibrated by using the DAILY CENTURY (DAYCENT) model, and the measured NPP values and MOD17A3 remote sensing data of corresponding stations in the literature, and the results were evaluated by means of the root-mean-square error, determinability coefficient, efficiency coefficient and other statistical indexes. Secondly, a one-way nested BCC-CSM1.1 climate model was used to predict the NPP of grassland on the QTP under the future climate SSP126 and SSP585 scenarios, and the prediction results were statistically analyzed on spatial and temporal scales. [Results] (1) Under the SSP126 and SSP585 scenarios, the NPP of the QTP shows the decreasing distribution from southeast to northwest, and the area with higher NPP value was mainly located in the southeast of the QTP, while the area with lower NPP value will be located in the west of the QTP. (2) Under the SSP126 scenario, the NPP of grassland on the QTP generally shows the downward trend, and the largest fluctuation range will be in the long-term period (2081—2100), with a decrease rate of 12.1%. Under the SSP585 scenario, NPP generally presents a steady upward trend, with the highest growth rate of 12% in the near future (2021—2040) . (3) Compared with the baseline period (1971—2021), under the SSP126 and SSP585 scenarios, the NPP of more than 85% grassland on the QTP shows the decreasing trend, and the NPP value of SSP126 and SSP585 will decrease by 28% and 23%, respectively, compared with the baseline period (1971—2020) . [Conclusion] Under the two different climate scenarios, the correlation between temperature and NPP is higher than that of precipitation. Low temperature and lack of rainfall are the main reasons for the decrease of grassland NPP in the northwest of the QTP. [ABSTRACT FROM AUTHOR]

Global climate change poses a great impact on crop growth, development and yield. Soybean production in Northeast China, which is one of the traditional dominant soybean production areas in China, is of great significance for developing the domestic soybean industry and reducing dependence on imported soybeans. Therefore, it is crucial to evaluate the impacts of future climate change on soybean yield in Northeast China, and to propose reasonable adaptation measures. In this study, we took Fujin city of Heilongjiang province in Northeast China as an example, and used the CROPGRO-soybean model in DSSAT (Decision Support System for Agrotechnology Transfer) to simulate the impacts of future climate change on soybean yield in the four periods of the 2020s (2021-2030), 2030s (2031-2040), 2040s (2041-2050) and 2050s (2051-2060) under two representative concentration pathway (RCP) scenarios (RCP4.5 and RCP8.5), and further determine the best agronomic management practices. The results showed that the calibrated and validatedmodel is suitable for simulating soybean in the study area. By analyzing the meteorological data under future climate scenarios RCP4.5 and RCP8.5 from the PRECIS regional climate model, we found that the average temperature, cumulative precipitation and cumulative solar radiation would mostly increase during the growing season in Fujin city of Heilongjiang province. Combined with the model simulation results, it is shown that under the effect of CO2 fertilization, future climate change will have a positive impact on soybean yield. Compared to the baseline (1986-2005), the soybean yield would increase by 0.6% (7.4%), 3.3% (5.1%), 6.0% (16.8%) and 12.3% (20.6%) in the 2020s, 2030s, 2040s and 2050s under RCP4.5 (RCP8.5). Moreover, the optimal sowing dates and the optimal supplemental irrigation amount under RCP4.5 (RCP8.5) are May 10 (May 5) and 50 mm (40mm), respectively. Under future climate conditions, the agronomic management practices, such as advancing the sowing date and supplementary irrigation in the key stage of soybean growth would increase soybean yield and make soybean growth more adaptable to future climate change. [ABSTRACT FROM AUTHOR]

Agroclimatic regionalization is an effective way to utilize agricultural climate resources in a reasonable manner. Accurate and objective agroclimatic regionalization plays a great directive role in ecological layout and decision support for agriculture farming. The purpose of this article was to investigate the influence of climate change on agroclimatic regions in China. Following the same regionalization principle concept as the theory and techniques of agroclimatic regionalization, three agroclimatic regions, the Tibetan High Cold Region (THCR), Northwest Arid Region (NAR), and Eastern Monsoon Region (EMR) were identified in China. The changes in the three agroclimatic regions were analyzed from 1961 to 2020 in the past and from 2006 to 2060 in the future. Future changes in agroclimatic regions were estimated from 2006 to 2030 and from 2031 to 2060 under scenarios RCP2.6, RCP4.5, and RCP8.5 for greenhouse gas emissions. The borders of agoclimatic regions had changed as a result of global climate warming and precipitation variability. There was a surplus in the geographical displacement and range of agroclimatic region borders in 1991 to 2020, especially when compared to those in 1961–1990. The TCHR exhibited significant spatial variation, with its northeast corner shifting nearly 170 km to the southwest. In the future, the area ratio of the THCR will be 26% by 2031–2060 under the RCP8.5 scenario, with the highest decline (1% compared to that in the reference period of 1981–2005), while under the RCP4.5 and RCP2.6 scenarios it will stabilize the area ratio at 27%. The eastern boundary of the NAR will migrate south or east, while the center boundary will rise northward. Under all three climatic scenarios, the area ratio will be 25% (down 1%). The range of the EMR will expand. The area ratio of the EMR will increase by 1% under the RCP2.6 and RCP4.5 scenarios and by 2% under the RCP8.5 scenario. Overall, our study may facilitate an in-depth understanding of agroclimatic regions changes and thus provide a scientific reference for the distribution of agricultural production and sustainable development under climate change in China. [ABSTRACT FROM AUTHOR]

The ongoing climate change‐induced shifts in flowering phenology have emerged as a consequential force impacting biodiversity and ecosystems. Despite the globally recognized significance of flowering phenology as a key reproductive attribute, studies in subtropical regions have been relatively fewer, particularly in comparison to temperate and cold regions. Additionally, the nuanced response of deciduous and evergreen plants to climate change remains insufficiently explored. In addressing this gap, we built a phenological model and a generalized linear mixed effect model to assess the differential responses of key flowering phenological traits, that is, first flowering date (FFD), peak flowering date (PFD), end of flowering date (EFD), and flowering duration (FD), to climate factors (temperature and precipitation) between deciduous and evergreen plants. We observed distinct responses in flowering phenological traits to climate change between deciduous and evergreen plants. Specifically, the advancement of FFD, PFD, and EFD in deciduous in response to temperature rise exceeded that in evergreen plants. FD in evergreen plants exhibited a stronger extension to temperature increase compared to deciduous. Conversely, the phenological change of evergreen plants in response to decreasing precipitation was greater than that of deciduous ones. Since temperature is a decisive climatic factor in affecting phenological changes, climate change‐induced advances in flowering phenology of deciduous plants are still larger than evergreen plants. Projections from our phenological model under future climate scenarios (SSP 1‐2.6 and SSP 5‐8.5) indicate a continuous enlargement of difference in flowering phenology between deciduous and evergreen plants, with this trend escalating into the future (2100>2070>2050>2030). The larger extension in FD of evergreens to climate change suggests a potential increase in their proportion within subtropical forest communities relative to deciduous plants. These insights contribute significantly to our understanding of the intricate dynamics of climate‐induced changes in subtropical plant ecosystems. [ABSTRACT FROM AUTHOR]

Climate change restricts and alters the distribution range of plant species. Predicting potential distribution and population dynamics is crucial to understanding species’ geographical distribution characteristics to harness their economic and ecological benefits. This study uses Dendrocalamus brandisii as the research subject, aiming to accurately reveal the impact of climate change on this plant. The findings offer important insights for developing practical conservation and utilization strategies, and guidance for future introduction and cultivation. The MaxEnt model was optimized using regularization multiplier (RM) and feature combination (FC) from the ‘Kuenm’ package in R language, coupled with ArcGIS for modeling 142 distribution points and 29 environmental factors of D. brandisii. This article explored the key environmental factors influencing the potential suitable regions for D. brandisii, and predicted trends in habitat changes under SSPs2.6 and SSPs8.5 climate scenarios for the current era, the 2050s, 2070s, and 2090s. (1) The results show that when FC = QPH and RM = 1, the AUC = 0.989, indicating that the model prediction is accurate with the lowest complexity and overfitting. The key environmental factors affecting its primary suitable distribution, determined by jackknife training gain and single-factor response curve, are the precipitation of warmest quarter (bio18), the temperature seasonality (bio4), the minimum average monthly radiation (uvb-4), and elevation (Elev), contributing 93.6% collectively. It was established that the optimal range for D. brandisii is precipitation of warmest quarter of between 657 and 999 mm, temperature seasonality from 351% to 442%, minimum average monthly radiation from 2420 to 2786 J/m2/day, at elevation from 1099 to 2217 m. (2) The current potential habitat distribution is somewhat fragmented, covering an area of 92.17 × 104 km2, mainly located in southwest, south, and southeast China, central Nepal, southern Bhutan, eastern India, northwestern Myanmar, northern Laos, and northern Vietnam. (3) In future periods, under different climate scenario models, the potential habitat of D. brandisii will change in varying degrees to become more fragmented, with its distribution center generally shifting westward. The SSP8.5 scenario is not as favorable for the growth of D. brandisii as the SSPs2.6. Central Nepal, southern Bhutan, and the southeastern coastal areas of China have the potential to become another significant cultivation region for D. brandisii. The results provide a scientific basis for the planning of priority planting locations for potential introduction of D. brandisii in consideration of its cultivation ranges.

The three leading modes of the North Atlantic atmospheric circulation explain about 70% of the winter climate variability. Although climate models generally can capture these modes, biases may induce large uncertainties in regional climate predictions. Here, we evaluate the leading winter modes simulated by CMIP5‐PMIP3 and CMIP6‐PMIP4 models from the last millennium to future scenarios in comparison with historical reanalysis and paleo‐reconstructions. The models generally have a good representation of the average spatial pattern of the North Atlantic Oscillation (NAO) while showing a larger spread in performance for the East Atlantic and Scandinavian patterns. In contrast to historical reanalysis, the simulated NAO pattern tends to be rather stationary under various climate states over the years 861–2100. Such underestimated spatial variability in the simulated NAO is directly related to the biased spatial shifts in NAO‐related regional temperature and precipitation changes, inducing uncertainties in climate projections over the North Atlantic sector. Plain Language Summary: The North Atlantic Oscillation (NAO) is the most dominant weather pattern over the North Atlantic‐European sector with profound influence on regional climate. The NAO pattern describes the changes in a low‐pressure system over Iceland and a high‐pressure system over the Azores, which are commonly known as the NAO centers of action. However, the NAO centers of action are not always fixed in these two locations, but show spatial movement over time. In this study, we compare the winter weather patterns over the North Atlantic region from the last millennium to future scenarios in climate models with reanalysis products and paleoclimate reconstructions. We find that models generally can capture the spatial structure of the NAO very well but with little changes in its location and shape over time in contrast to historical reanalysis. These biased NAO patterns in climate models can shift the modeled regional temperature and precipitation patterns associated with the NAO. This remains a challenge for the reliability of future climate projections over the North Atlantic region. Key Points: We compare the spatial variability of modeled North Atlantic climate modes over the years 861–2100 with reanalysis and reconstructed dataThe North Atlantic Oscillation (NAO) in 13 Coupled Model Intercomparison Project‐Paleoclimate Modelling Intercomparison Project models shows underestimated spatial variability compared to historical reanalysisProjections of regional temperature and precipitation may show biased patterns due to the underestimated spatial shifts in simulated NAO [ABSTRACT FROM AUTHOR]

Fruit growing, especially in family farming, has a significant income potential in small areas, but climate change is a major challenge. This study aimed to quantify the irrigation requirements for citrus, papaya, mango and passion fruit, in the Vão do Paranã region, Goiás state, Brazil. The climate data encompassed the observed periods from 1961 to 2020 and future scenarios from 2021 to 2100. The irrigation demand was obtained from the daily water balance, while the reference evapotranspiration (ETo) was estimated using the Penman-Monteith method and then compared with an artificial intelligence tool. The future scenarios indicated a higher increase for air temperature and a lower increase for rainfall. The ETo levels raised from 1,528 mm year-1, in 1991-2020, to 1,614-1,656 mm year-1, in 2021-2050. The artificial intelligence performance was limited in the ETo estimation, with a mean absolute error of 0.71 mm day-1 and an “r” value of 0.59, when considering the air temperature as the input variable. For the 2021-2050 period, when compared with 1991-2020, there was an increase in irrigation demand, in which, under the extreme scenario, the citrus demand reached 690 mm year-1(+11 %); papaya (+10 %) and passion fruit (+5 %) surpassed 800 mm year-1; and mango reached 491 mm year-1 (+14 %). An increase in demand for irrigation was observed, with management alternatives in association with strategies for maximum cultivation area based on water supply being recommended.

In recent years, Meloidogyne enterolobii has emerged as a major parasitic nematode infesting many plants in tropical or subtropical areas. However, the regions of potential distribution and the main contributing environmental variables for this nematode are unclear. Under the current climate scenario, we predicted the potential geographic distributions of M. enterolobii worldwide and in China using a Maximum Entropy (MaxEnt) model with the occurrence data of this species. Furthermore, the potential distributions of M. enterolobii were projected under three future climate scenarios (BCC-CSM2-MR, CanESM5 and CNRM-CM6-1) for the periods 2050s and 2090s. Changes in the potential distribution were also predicted under different climate conditions. The results showed that highly suitable regions for M. enterolobii were concentrated in Africa, South America, Asia, and North America between latitudes 30°S to 30°N. Bio16 (precipitation of the wettest quarter), bio10 (mean temperature of the warmest quarter), and bio11 (mean temperature of the coldest quarter) were the variables contributing most in predicting potential distributions of M. enterolobii. In addition, the potential suitable areas for M. enterolobii will shift toward higher latitudes under future climate scenarios. This study provides a theoretical basis for controlling and managing this nematode.

Exploring the geographical distribution of forestry pests is crucial for formulating pest management strategies. Cyrtotrachelus buqueti (Guer) stands out as one of the primary pests among China’s forestry hazards. This study employs the MaxEnt model, along with 19 bioclimatic variables and habitat characteristics, to predict the current and future distribution of C. buqueti under three typical emission scenarios for 2050 and 2070 (2.6 W/m2 (SSP1-2.6), 7.0 W/m2 (SSP3-7.0), and 8.5 W/m2 (SSP5-8.5)). Among the 19 bioclimatic variables, BIO 14 (precipitation of the driest month), BIO 8 (mean temperature of the wettest quarter), Elev, slope, and aspect were identified as significant contributors. These five variables are critical environmental factors affecting the suitability of habitats for C. buqueti and are representative of its potential habitat. The results indicate that C. buqueti predominantly inhabits southern regions such as Chongqing, Guizhou, Yunnan, Sichuan, Guangxi, Shaanxi, Hubei, Hainan, and Taiwan. Among them, Chongqing, Guizhou, and Yunnan are the primary distribution areas of high suitability. In the future, the centroid’s movement direction will generally shift southward, with an expansion trend observed in the distribution areas of each province. This study enhances researchers’ understanding of forestry pest dynamics and promotes proactive management strategies to mitigate their impact on forest ecosystems and agricultural productivity.

Abstract The three leading modes of the North Atlantic atmospheric circulation explain about 70% of the winter climate variability. Although climate models generally can capture these modes, biases may induce large uncertainties in regional climate predictions. Here, we evaluate the leading winter modes simulated by CMIP5‐PMIP3 and CMIP6‐PMIP4 models from the last millennium to future scenarios in comparison with historical reanalysis and paleo‐reconstructions. The models generally have a good representation of the average spatial pattern of the North Atlantic Oscillation (NAO) while showing a larger spread in performance for the East Atlantic and Scandinavian patterns. In contrast to historical reanalysis, the simulated NAO pattern tends to be rather stationary under various climate states over the years 861–2100. Such underestimated spatial variability in the simulated NAO is directly related to the biased spatial shifts in NAO‐related regional temperature and precipitation changes, inducing uncertainties in climate projections over the North Atlantic sector.

There is no doubt that during recent years, the developing countries are in urgent demand of energy, which means the energy generation and the carbon emissions increase accumulatively. The 40 % of the global energy consumption per year comes from the building stock. Considering the predictions regarding future climate due to climate change, a good understanding on the energy use due to future climate is required. The aim of this study was to evaluate the impact of future weather in the heating demand and carbon emissions for a group of buildings at district level, focusing on two areas of London in the United Kingdom. The methodological approach involved the use of geospatial data for the case study areas, processed with Python programming language through Anaconda and Jupyter notebook, generation of an archetype dataset with energy performance data from TABULA typology and the use of Python console in QGIS to calculate the heating demand in the reference weather data, 2050 and 2100 in accordance with RCP 4.5 and RCP 8.5 scenarios. A validated model was used for the district level heating demand calculation. On the one hand, the results suggest that a mitigation of carbon emissions under the RCP4.5 scenario will generate a small decrease on the heating demand at district level, so slightly similar levels of heating generation must continue to be provided using sustainable alternatives. On the other hand, following the RCP 8.5 scenario of carbon emission carrying on business as usual will create a significant reduction of heating demand due to the rise on temperature but with the consequent overheating in summer, which will shift the energy generation problem. The results suggest that adaptation of the energy generation must start shifting to cope with higher temperatures and a different requirement of delivered energy from heating to cooling due to the effect of climate change.

Agroclimatic regionalization is an effective way to utilize agricultural climate resources in a reasonable manner. Accurate and objective agroclimatic regionalization plays a great directive role in ecological layout and decision support for agriculture farming. The purpose of this article was to investigate the influence of climate change on agroclimatic regions in China. Following the same regionalization principle concept as the theory and techniques of agroclimatic regionalization, three agroclimatic regions, the Tibetan High Cold Region (THCR), Northwest Arid Region (NAR), and Eastern Monsoon Region (EMR) were identified in China. The changes in the three agroclimatic regions were analyzed from 1961 to 2020 in the past and from 2006 to 2060 in the future. Future changes in agroclimatic regions were estimated from 2006 to 2030 and from 2031 to 2060 under scenarios RCP2.6, RCP4.5, and RCP8.5 for greenhouse gas emissions. The borders of agoclimatic regions had changed as a result of global climate warming and precipitation variability. There was a surplus in the geographical displacement and range of agroclimatic region borders in 1991 to 2020, especially when compared to those in 1961–1990. The TCHR exhibited significant spatial variation, with its northeast corner shifting nearly 170 km to the southwest. In the future, the area ratio of the THCR will be 26% by 2031–2060 under the RCP8.5 scenario, with the highest decline (1% compared to that in the reference period of 1981–2005), while under the RCP4.5 and RCP2.6 scenarios it will stabilize the area ratio at 27%. The eastern boundary of the NAR will migrate south or east, while the center boundary will rise northward. Under all three climatic scenarios, the area ratio will be 25% (down 1%). The range of the EMR will expand. The area ratio of the EMR will increase by 1% under the RCP2.6 and RCP4.5 scenarios and by 2% under the RCP8.5 scenario. Overall, our study may facilitate an in-depth understanding of agroclimatic regions changes and thus provide a scientific reference for the distribution of agricultural production and sustainable development under climate change in China.

The impact of climate change on surface runoff and soil moisture in the source region of the Yellow River is analyzed, which will provide a scientific basis for the rational use and protection of water resources in the source area. In this paper, the SWAT hydrological model was coupled with the Coupled Model Intercomparison Project (CMIP) to predict future changes in surface runoff and soil moisture in the source region of the Yellow River. The prediction of surface runoff and soil moisture in the Yellow River Basin was analyzed by a linear regression model. The SWAT model rate had a calibration period R2 of 0.876 and a validation period R2 of 0.972. The trend of surface runoff and annual mean temperature in the source region of the Yellow River from 2011 to 2022 showed an overall increasing trend, and soil moisture showed a general decreasing trend. 2011–2022 trends between surface runoff and annual mean temperature in the source region of the Yellow River showed a highly significant difference, indicating that surface runoff flow was significantly influenced by temperature. The difference between the trends in soil moisture and the annual mean temperature was highly significant. The surface runoff fluctuated greatly in different years, and the surface runoff changed greatly in different scenarios of CMIP5 (RCP2.6, RCP4.5, and RCP8.5). For all three climate change scenarios, the surface runoff displayed a downward trend. The surface runoff showed a similar uneven distribution for all scenarios on a yearly cycle. Under the three climate scenarios, the runoff was highest between May and August, with a slowly increasing trend from January to April and a slightly decreasing trend from September to December. The interannual and interannual distribution of soil water was basically consistent with the distribution of surface runoff, and there was an overall trend in the length of all soil water reduction scenarios. Surface runoff and soil moisture are and will be greatly affected by climate change (mainly temperature and precipitation). Under the three climate scenarios, the precipitation increases to some extent, but the surface runoff and soil moisture will both decrease, which may be attributed to the greater evaporation than the precipitation. [ABSTRACT FROM AUTHOR]

Soil organic carbon (SOC) is crucial for the quality and productivity of terrestrial ecosystems and its sequestration plays an important role in mitigating climate change. Understanding the effects of agricultural management under future climate on the SOC balance helps decision making in environmental policies. Thereby, grasslands will play a key role, since future climate change may prolong the vegetation period.We used 24 representative grassland sites in Germany to assess the SOC balance obtained from the CANDY model in relation to ten management regimes, 18 future climate change scenarios and different soil types. Simulations were conducted over a period of 110 years.For most of the selected grassland sites an increase in both air temperature and precipitation was observed in the future climate. The effect of management on the SOC balance largely exceeded the effect of soil type and climate. An increasing management intensity (i.e. three to five cuts) generally increased the SOC balance, while extensive management (i.e. two or fewer cuts) lead to SOC losses. The seasonal variation of precipitation was the most important climate metric, with increased SOC sequestration rates being observed with increasing growing season precipitation. Clay soils had the potential for both highest gains and highest losses depending on management and precipitation. Given an overall lower SOC storage potential in sands and loams, the SOC balance in those soil types varied the least in response to climate change.We conclude that fostering SOC sequestration is possible in grassland soils by increasing management intensity, which involves increased fertilizer input and field traffic. This however may stand in conflict with other policy aims, such as preserving biodiversity. Multicriterial assessments are required to estimate the nett greenhouse gas balance and other aspects associated with these management practices at a farm scale.

In the context of the future global warming trend, the complexity and diversity of climate change will have varying effects on grassland productivity. However, the response of grassland productivity to various climatic changes remains unknown. We investigated the impact of climate change on the grassland NPP of the Qinghai-Tibt Plateau(QTP), estimated grassland productivity over the QTP from 1978 to 2021, and predicted grassland NPP under the future climate scenarios SSP245 and SSP585 (SSP stands for Shared Socioeconomic Pathways). According to the SSP245 (2021 −2100) scenario, global average temperatures will rise by 2.7 °C by the end of the century. However, under the SSP585 (2021 −2100) scenario, the rise will be 4.4 °C). The simulation results were analyzed using spatiotemporal statistics and compared with the base period (1978 −2021). The results revealed the following: (1) In both the SSP245 and SSP585 scenarios, the grassland NPP of the QTP decreased from southeast to northwest, with higher NPP values primarily located in the east and south of the QTP and lower NPP values concentrated in the northwest. (2) Compared to the base period, the NPP of grassland in the QTP showed a downward trend under the SSP245 and SSP585 scenarios, and the NPP values of SSP245 and SSP585 decreased by 13 % and 23 %, respectively, compared to the base period (1978 −2021). (3) Temperature and precipitation were negatively correlated with NPP in the SSP245 scenario, while under the SSP585 scenario, both temperature and precipitation positively correlated with NPP. The temperature correlated more strongly with NPP than precipitation in future climate scenarios. Therefore, we proposed strengthening grassland ecosystem protection measures in the QTP, adopting corresponding protection mechanisms based on climate change and vegetation growth environment in different regions, and implementing key protection measures in the northwest of the QTP to increase vegetation coverage and enhance man-made effects to offset the negative impact of climate change.

BACKGROUND: Climate change is the main cause of biotic and abiotic stresses in plants and affects yield. Therefore, we sought to carry out a study on future changes in the agroclimatic conditions of banana cultivation in Brazil. The current agroclimatic zoning was carried out with data obtained from the National Institute of Meteorology related to mean air temperature, annual rainfall, and soil texture data in Brazil. The global climate model BCC‐CSM1.1 (Beijing Climate Center‐Climate System Model, version 1.1), adopted by the Intergovernmental Panel on Climate Change, corresponding to Representative Concentration Pathways (RCPs) 2.6, 4.5, 6.0, and 8.5 for the period 2050 (2041–2060) and 2070 (2061–2080), obtained through the CHELSA V1.2 platform, was chosen for the climate projections of the Coupled Model Intercomparison Project 5. Matrix images at a depth of 5–15 cm, obtained through the product of the SoilGrids system, were used for the texture data. ArcGIS version 10.8 was used to construct the maps. RESULTS: Areas favorable to the crop plantation were classified as suitable when air temperature TAIR was between 20 and 29 °C, annual rainfall RANNUAL between 1200 and 1900 mm, and soil clay content CSOIL between 30 and 55%. Subsequently, the information was reclassified, summarizing the classes into preferential, recommended, little recommended, and not recommended. The current scenario shows a preferential class of 8.1%, recommended of 44.6%, little recommended of 47.1%, and not recommended of 0.1% for the Brazilian territory. CONCLUSION: The results show no drastic changes in the total area regarding the classes, but there is a migration from these zones; that is, from tropical to subtropical and temperate regions. RCP 8.5–2070 (2061–2080) showed trends with negative impacts on arable areas for banana cultivation at the end of the century. © 2022 Society of Chemical Industry. [ABSTRACT FROM AUTHOR]

The general objective of this research was to propose a new fire risk index, specifically the Fire Risk Atlantic Forest (FIAF) index in Itatiaia National Park (PNI). The data were collected from two levels (2 and 10 m) of a micrometeorological tower, with a time series on an hourly scale, daily from 2018 to 2021. Two multiple regression models were generated for the two collection levels (FIAF 2 and 10 m) and, based on the statistical criteria and the choice of the best model, a future simulation was generated using the scenario SSP 4.5 for 2022 to 2050. The correlation matrix between the data from the FIAF and fire foci models was also analyzed. The FIAF model was compared with the traditional models already used in Brazil, such as the Angström indices, Monte Alegre Formula (FMA), and the improved Monte Alegre Formula (FMA+) models. The results showed that the FIAF model at 10 m overestimated the results observed mainly during the dry season. The FIAF 2 m model presented the highest correlation with a fire foci value greater than 0.74. In the future simulation, the years that presented the highest extreme risks were: 2025, 2035, 2041, and 2049. Thus, it is possible to state that the FIAF model at the 2 m level was the best model for predicting the degree of fire risk in the PNI. [ABSTRACT FROM AUTHOR]

Green orange is a characteristic fruit and plays an important role in local agricultural production in Hainan island. However, few researches have been available regarding the climatic suitability of green orange planting in the island. Based on the last 40a (1980-2019) daily meteorological data, topographical and other factors from 19 meteorological stations in the main island of Hainan island, a spatial analysis model is established to comprehensively determine the climate suitability index for green orange planting in the island. With climate suitability model, this study constructed the suitability models of temperature, sunshine and precipitation, as well as comprehensive suitability model for green orange planting. The methods of geographic information system (GIS) and natural point break were used in finely zoning green orange planting climate suitability. With the model simulation data of RCP4.5 climate scenario, this study assessed the trend of green orange planting suitability zone in the next 30y (2020-2049) under future climate change scenario. The results show that, the most suitable area for green orange planting is mainly distributed in the central region, with the subtotal area being 0.87×104km2, and the climate suitability index ranging from 0.9-1.0; the suitable area is mainly distributed in the eastern local region and most of the central and western regions, with the subtotal area being 1.83×104km2, and the climate suitability index being 0.7-0.9; the subsuitable area is mainly located in the western coastal, central and western high altitude regions, with subtotal area being 0.51×104km2, and the climate suitability index varying from 0.4-0.7; the unsuitable area is mainly distributed in the central mountainous region, with the subtotal area being 0.17×104km2, and the climate suitability index ranging from 0-0.4. Under the future climate scenario, the area with suitable temperature and precipitation will have large change, namely, the area with suitable temperature will gradually shrink from the surrounding to the central regions, the area with suitable precipitation will gradually move from the eastern to the central regions. The most climatic suitable area for future green orange planting will mainly distribute in the most parts of Qiongzhong, Tunchang and Baoting counties, western parts of Wanning, central and eastern parts of Baisha county in the island. [ABSTRACT FROM AUTHOR]

Addressing resilience, sustainability, and water resource conservation has become increasingly important in the modern world. Challenges arise due to periodic droughts, climate change, and seasonal variability in areas with limited freshwater availability. Therefore, implementing and promoting water reuse is essential. Rainwater harvesting (RWH) is one such alternative, offering benefits in conserving water resources and mitigating droughts while reducing urban flooding and costs by generating alternative lower-cost water sources. Providing users with knowledge of available volumes for harvesting, including homeowners and governmental entities, is key to encouraging this practice. Hydrological data and geographic information systems are fundamental for managing, designing, and projecting rainwater harvesting practices. However, no tools currently integrate this information at multiple scales with current and future climate scenarios. This research aimed to develop a multi-scale assessment tool named H2O HARVEST, for evaluating the availability and potential of rainwater harvesting. Additional benefits of the H2O HARVEST app include aiding decision-making by national governmental entities and analyzing potential future scenarios for homeowner users. The app also provides regulatory policy information at the state level. We offer an app with the necessary capabilities to bridge the technology gap and promote rainwater harvesting practices. Our research demonstrated that RWH has the potential to be a sustainable water reuse practice. For more than 50% of the states, the RWH could supply at least 50% of the water demand. The regions of the US with the greatest potential are the Central and East. [Display omitted] • Rainwater harvesting availability and optimal system capacity. • Multi-scale assessment tool: H2O HARVEST – an interactive application. • Analysis of future scenarios for RWH. • Analysis of RWH and public supply. • Policy and regulations of rainwater harvesting (RWH) in the Continental U.S. [ABSTRACT FROM AUTHOR]

Background: Anopheles gambiae (An. gambiae) is considered the most effective malaria vector worldwide and is widely distributed in Africa. The purpose of this study is to determine the potential impact of climate change on An. gambiae and predict the present and future potential suitable habitat globally. Methods: In this study, environmental variables, global occurrence data of An. gambiae, and the maximum entropy model (MaxEnt) were used to evaluate the contribution of environmental factors and predict the habitat suitability of An. gambiae under the current and future scenarios. Results: Among all environmental variables, isothermality (Bio3, 34.5%) contributed the most to An. gambiae distribution. Under current climate conditions, the potential suitable areas for An. gambiae are mainly located near the equator (approximately 30°N-30°S), with a total area of 16.58 million km2, including central and northern South America, a fraction of areas near the equator of North America, central and southern Africa, some tropical regions of southern Asia, and small areas of Oceania. The areas of potential suitable habitats would be reduced to varying degrees in future climate scenarios. Conclusions: Potential suitable habitats for An. gambiae may not be limited to Africa. Necessary surveillance and preventive measures should be undertaken in high-risk regions, including those outside Africa, to monitor and control the spread of An. gambiae.

Wetland-dependent birds are considered to be at particularly high risk for negative climate change effects. Bioclimatic models are widely used tools for assessing potential responses of species to climate change. We predicted current and future distributions of American Flamingo (Phoenicopterus ruber; Family Phoenicopteridae), a resident species in Cuba, using ecological niche models in combination with climate data in Maxent software. We predicted four potential future distributions in Cuba under two emissions scenarios in both 2050 and 2070, combining three Global Circulation Models. Bioclimatic variables that contributed the most to modelled distributions of American Flamingo were mean diurnal temperature range [mean of monthly (max temp - min temp)], temperature seasonality, and precipitation of wettest quarter. Our results predict that the current distribution of American Flamingo will be reduced by 38% in a changing future climate under the most pessimistic scenario of 2070. The potential suitable habitat of American Flamingo under this same scenario would be 51% excluded from the National System of Protected Areas of Cuba. Species distribution modeling can inform the current and future management of the American Flamingo throughout Cuba, and our findings suggest a strong conservation strategy is needed to conserve American Flamingo populations under a changing climate. [ABSTRACT FROM AUTHOR]

Understanding present and future impacts of drought on the terrestrial carbon budget is of great significance to the evaluation of terrestrial ecosystem disturbance and terrestrial carbon sink. Here, we evaluate the effect of vegetation net primary productivity (NPP) associated with drought through the difference between the mean NPP in the drought and normal years during a specific time period (30 years). Then, the NPP effects in different vegetation types and climatic zones under baseline stage (1981–2010) and future climate scenarios (RCP2.6, RCP4.5, and RCP8.5) is assessed. The results indicate that the negative NPP extremes are captured in most regions, except for the high‐latitude in the Northern Hemisphere. The NPP loss caused by extreme droughts in 2071–2100 is largest under RCPs, followed by the effects of severe and moderate droughts. Regionally, central United States, southern Africa, central Asia, India, Amazon tropical rainforest, and Australia are projected to experience a significant increase in negative NPP extremes and most of these regions are in arid and semi‐arid and tropical rain forest areas. In contrast, tropical Asia suffers little drought effects. For different vegetation, Evergreen Broadleaf Forest, Closed Shrubland, Open Shrubland, Croplands, and Grassland are the most affected by drought. The largest NPP loss occurs in most part of regions under RCP4.5 scenario, not RCP8.5. Climate change is projected to play the largest role in aggravating the risk of drought‐induced NPP reduction. And meanwhile, the adverse effects of drought on vegetation may be resisted through rational fertilizer utilization and land management in future. Plain Language Summary: Drought is already the most widespread factor affecting terrestrial net primary productivity (NPP) via direct physiological effects, such as water limitation and heat stress. Nevertheless, the effects of drought on terrestrial ecosystems under future climate change are still highly uncertain. In this study, we assess and compare the present and future impact of drought on vegetation net primary productivity. The results suggest that global drought events are projected to be intensified and frequent in the coming decades. Drought‐related NPP reduction is prevalent especially in the arid and semi‐arid areas and tropical regions at the end of 21st century. Extreme drought depresses NPP most under RCPs, followed by severe and moderate droughts. For vegetation, the adverse impact on NPP induced by drought under RCPs is increasingly significant in Evergreen Broadleaf Forest, Grassland, Savanna, and Cropland. Climate change is projected to play the largest role in aggravating the risk of drought‐induced NPP reduction. These results highlight the growing vulnerability of ecosystem productivity to droughts, implying increased adverse impacts of these climate extremes on terrestrial carbon sinks. Key Points: Net primary productivity (NPP) reduction associated with drought is prevalent especially in the arid and semi‐arid areas and tropical regionsAdverse impact of drought on NPP under RCPs is large in Evergreen Broadleaf Forest, Shrubland, Grassland, and CroplandThe largest NPP loss occurs under RCP4.5. Climate change plays the largest role in aggravating the risk of drought‐induced NPP reduction [ABSTRACT FROM AUTHOR]

Climate change is one of the most pressing global issues of the twenty-first century. This phenomenon has an increasingly severe impact on water resources and crop production. The main purpose of this study is to evaluate the impact of climate change on water resources, crop production, and agricultural sustainability in an arid environment in Iran. To this end, the study constructs a new integrated climate-hydrological-economic model to assess the impact of future climate change on water resources and crop production. Furthermore, the agricultural sustainability is evaluated using the multicriteria decision making (MCDM) technique in the context of climate change. The findings regarding the prediction of climate variables show that the minimum and maximum temperatures are expected to increase by about 5.88% and 6.05%, respectively, while precipitation would decrease by approximately 30.68%. The results of the research reveal that water availability will decrease by about 13.79–15.45% under different climate scenarios. Additionally, the findings show that in the majority of cases crop production will reduce in response to climate scenarios so that rainfed wheat will experience the greatest decline (approximately 59.95%). The results of the MCDM model show that climate change can have adverse effects on economic and environmental aspects and, consequently, on the sustainability of the agricultural system of the study area. Our findings can inform policymakers on effective strategies for mitigating the consequences of climate change on water resources and agricultural production in dry regions. [ABSTRACT FROM AUTHOR]

Abstract Understanding present and future impacts of drought on the terrestrial carbon budget is of great significance to the evaluation of terrestrial ecosystem disturbance and terrestrial carbon sink. Here, we evaluate the effect of vegetation net primary productivity (NPP) associated with drought through the difference between the mean NPP in the drought and normal years during a specific time period (30 years). Then, the NPP effects in different vegetation types and climatic zones under baseline stage (1981–2010) and future climate scenarios (RCP2.6, RCP4.5, and RCP8.5) is assessed. The results indicate that the negative NPP extremes are captured in most regions, except for the high‐latitude in the Northern Hemisphere. The NPP loss caused by extreme droughts in 2071–2100 is largest under RCPs, followed by the effects of severe and moderate droughts. Regionally, central United States, southern Africa, central Asia, India, Amazon tropical rainforest, and Australia are projected to experience a significant increase in negative NPP extremes and most of these regions are in arid and semi‐arid and tropical rain forest areas. In contrast, tropical Asia suffers little drought effects. For different vegetation, Evergreen Broadleaf Forest, Closed Shrubland, Open Shrubland, Croplands, and Grassland are the most affected by drought. The largest NPP loss occurs in most part of regions under RCP4.5 scenario, not RCP8.5. Climate change is projected to play the largest role in aggravating the risk of drought‐induced NPP reduction. And meanwhile, the adverse effects of drought on vegetation may be resisted through rational fertilizer utilization and land management in future.

The impact of climate change on surface runoff and soil moisture in the source region of the Yellow River is analyzed, which will provide a scientific basis for the rational use and protection of water resources in the source area. In this paper, the SWAT hydrological model was coupled with the Coupled Model Intercomparison Project (CMIP) to predict future changes in surface runoff and soil moisture in the source region of the Yellow River. The prediction of surface runoff and soil moisture in the Yellow River Basin was analyzed by a linear regression model. The SWAT model rate had a calibration period R2 of 0.876 and a validation period R2 of 0.972. The trend of surface runoff and annual mean temperature in the source region of the Yellow River from 2011 to 2022 showed an overall increasing trend, and soil moisture showed a general decreasing trend. 2011–2022 trends between surface runoff and annual mean temperature in the source region of the Yellow River showed a highly significant difference, indicating that surface runoff flow was significantly influenced by temperature. The difference between the trends in soil moisture and the annual mean temperature was highly significant. The surface runoff fluctuated greatly in different years, and the surface runoff changed greatly in different scenarios of CMIP5 (RCP2.6, RCP4.5, and RCP8.5). For all three climate change scenarios, the surface runoff displayed a downward trend. The surface runoff showed a similar uneven distribution for all scenarios on a yearly cycle. Under the three climate scenarios, the runoff was highest between May and August, with a slowly increasing trend from January to April and a slightly decreasing trend from September to December. The interannual and interannual distribution of soil water was basically consistent with the distribution of surface runoff, and there was an overall trend in the length of all soil water reduction scenarios. Surface runoff and soil moisture are and will be greatly affected by climate change (mainly temperature and precipitation). Under the three climate scenarios, the precipitation increases to some extent, but the surface runoff and soil moisture will both decrease, which may be attributed to the greater evaporation than the precipitation.

Climate change has caused habitat fragmentation and reduced connectivity. The Fen River Basin in Shanxi Province, China is an important habitat for the central population of the brown-eared pheasant (BEP). The effects of climate change need to be considered in the conservation planning of BEP habitats. We used a species dispersion model to determine the BEP core habitat and graph theory to explore the connectivity of the BEP's main habitats. The pinch point areas of BEP dissemination were determined by circuit theory. Least-cost pathways were used to identify the critical corridors for BEP dissemination. A gap analysis was conducted to estimate the efficiency of BEP conservation measures. Under the future climate scenarios, BEP habitats decreased by between 54.69% and 97.63%, and the connectivity of the main habitats was reduced by a similar magnitude. The BEP core habitat shifted to the southwestern region under the influence of climatic conditions. Currently, 90.84% of the species' critical habitat remains unprotected. Due to climate change, the core habitat in the future was projected to differ from the current protected area. Enhancing the protection of the pinch point region may aid in the restoration of habitat connectivity. [ABSTRACT FROM AUTHOR]

Climate change has been projected to impact negatively on African agricultural systems. However, there is still an insufficient understanding of the possible effects of climate change on crop yields in Africa. In this study, a previously calibrated Environmental Policy Integrated Climate (EPIC) model was used to assess the effects of future climate change on maize (Zea mays L.) yield in the Eastern Cape Province of South Africa. The study aimed to compare maize yields obtained from EPIC simulations using baseline (1980–2010) weather data with maize yields obtained from EPIC using statistically downscaled future climate data sets for two future periods (mid-century (2040–2069) and late century (2070–2099)). We used three general circulation models (GCMs): BCC-CSM1.1, GFDL-ESM2M and MIROC-ES under two Representative Concentration Pathways (RCPs), RCP 4.5 and RCP 8.5, to drive the future maize yield simulations. Simulation results showed that for all three GCMs and for both future periods, a decrease in maize production was projected. Maize yield was projected to decrease by as much as 23.8% for MIROC, RCP 8.5, (2070–2099). The temperature was projected to rise by over 50% in winter under RCP 8.5 for both future periods. For both future scenarios, rainfall was projected to decrease in the summer months while increasing in the winter months. Overall, this study provides preliminary evidence that local farmers and the Eastern Cape government can utilise to develop local climate change adaptation strategies. [ABSTRACT FROM AUTHOR]

• Hygrothermal load change under different future climate scenarios was evaluated. • Hot and humid regions may have more months with high or critical moisture loads. • The hygrothermal resilience of lightweight steel-framed wall assemblies was assessed. • Indoor cooling and dehumidification loads may increase significantly. • Ventilated rainscreens and external insulation could reduce the impacts. Ensuring the hygrothermal performance of lightweight steel-framed (LSF) wall assemblies is critical for the sustainability of LSF buildings in hot-humid regions; however, climate change poses new challenges. Therefore, this study aimed to bridge the gap in understanding the characteristics of hygrothermal load changes and the hygrothermal resilience of different LSF wall assemblies. Future climate data for each decade of this century (from 2030 to 2090), under various carbon emission scenarios, were generated based on a typical meteorological year (TMY) for hot-humid areas to evaluate the impact of climate change on hygrothermal loads. The hygrothermal resilience of four typical LSF wall assemblies, with either ventilated rainscreen cladding or face-sealed stucco cladding, with or without external insulation, was evaluated through simulation and comparative analysis. The results indicated that the thermal load would increase with increasing carbon emissions and over time, with the annual average external temperature peaking at 25.8 °C by the end of this century. The frequency of months with a high or critical moisture load would increase, although the moisture load may fluctuate across scenarios and years. Consequently, the cooling and dehumidification loads may increase by 52–60 % and 40–88 %, respectively. The hygrothermal risk within the walls may increase, with maximum increases of 2.8–8.0 % in the annual average relative humidity, 2.7–3.0 °C in the annual average temperature, and 10–35 % in the wetness duration. However, the impacts could be reduced by using ventilated rainscreens and external insulation, with further precautions needed to mitigate the increasing hygrothermal risk in areas with thermal bridges and rainwater leakage, as well as at the outer interface of the external insulation. These findings could provide a reference for the design of LSF buildings adapted to climate change in hot-humid areas. [ABSTRACT FROM AUTHOR]

Understanding forest understorey community response to environmental change, including management actions, is vital given the understorey's importance for biodiversity conservation and ecosystem functioning. The Natural World Heritage Hyrcanian temperate forests (Iran) provide an ideal template for furnishing an appreciation of how management actions can mitigate undesired climate change effects, due to the forests' broad environmental gradients, isolation from colonization sources and varied light environments. We used records of 95 understorey plant species from 512 plots to model their probability of occurrence as a function of contemporary climate and soil variables, and canopy cover. For 65 species with good predictive accuracy, we then projected two climate scenarios in the context of either increasing or decreasing canopy cover, to assess whether overstorey management could mitigate or aggravate climate change effects. Climate variables were the most important predictors for the distribution of all species. Soil and canopy cover varied in importance depending on understorey growth form. Climate change was projected to negatively affect future probabilities of occurrence. However, management, here represented by canopy cover change, is predicted to modify this trajectory for some species groups. Models predict increases in light‐adapted and generalist forbs with reduced canopy cover, while graminoids and ferns still decline. Increased canopy cover is projected to buffer an otherwise significant decreasing response of cold‐adapted species to climate change. However, increasing canopy cover is not projected to buffer the predicted negative impact of climate change on shade‐adapted forest specialists. Inconsistent responses of different species and/or growth forms to climate change and canopy cover reflect their complicated life histories and habitat preferences. Canopy cover management may help prevent the climate change induced loss of some important groups for biodiversity conservation. However, for shade‐adapted forest specialists, our results imply a need to adopt other conservation measures in the face of anticipated climate change. [ABSTRACT FROM AUTHOR]

Ixodes cookei Packard, the groundhog tick or woodchuck tick, is the main known vector of Powassan virus (POWV) disease in North America and an ectoparasite that infests diverse small- and mid-size mammals for blood meals to complete its life stages. Since I. cookei spends much of its life cycle off the host and needs hosts for a blood meal in order to pass to the next life stage, it is susceptible to changes in environmental conditions. We used a maximum-entropy approach to ecological niche modeling that incorporates detailed model-selection routes to link occurrence data to climatic variables to assess the potential geographic distribution of I. cookei under current and likely future climate conditions. Our models identified suitable areas in the eastern United States, from Tennessee and North Carolina north to southern Canada, including Nova Scotia, New Brunswick, eastern Newfoundland and Labrador, southern Quebec, and Ontario; suitable areas were also in western states, including Washington and Oregon and restricted areas of northern Idaho, northwestern Montana, and adjacent British Columbia, in Canada. This study produces the first maps of the potential geographic distribution of I. cookei. Documented POWV cases overlapped with suitable areas in the northeastern states; however, the presence of this disease in areas classified by our models as not suitable by our models but with POWV cases (Minnesota and North Dakota) requires more study. [ABSTRACT FROM AUTHOR]

The general objective of this research was to propose a new fire risk index, specifically the Fire Risk Atlantic Forest (FIAF) index in Itatiaia National Park (PNI). The data were collected from two levels (2 and 10 m) of a micrometeorological tower, with a time series on an hourly scale, daily from 2018 to 2021. Two multiple regression models were generated for the two collection levels (FIAF 2 and 10 m) and, based on the statistical criteria and the choice of the best model, a future simulation was generated using the scenario SSP 4.5 for 2022 to 2050. The correlation matrix between the data from the FIAF and fire foci models was also analyzed. The FIAF model was compared with the traditional models already used in Brazil, such as the Angström indices, Monte Alegre Formula (FMA), and the improved Monte Alegre Formula (FMA+) models. The results showed that the FIAF model at 10 m overestimated the results observed mainly during the dry season. The FIAF 2 m model presented the highest correlation with a fire foci value greater than 0.74. In the future simulation, the years that presented the highest extreme risks were: 2025, 2035, 2041, and 2049. Thus, it is possible to state that the FIAF model at the 2 m level was the best model for predicting the degree of fire risk in the PNI.

Climate change is one of the most pressing global issues of the twenty-first century. This phenomenon has an increasingly severe impact on water resources and crop production. The main purpose of this study is to evaluate the impact of climate change on water resources, crop production, and agricultural sustainability in an arid environment in Iran. To this end, the study constructs a new integrated climate-hydrological-economic model to assess the impact of future climate change on water resources and crop production. Furthermore, the agricultural sustainability is evaluated using the multicriteria decision making (MCDM) technique in the context of climate change. The findings regarding the prediction of climate variables show that the minimum and maximum temperatures are expected to increase by about 5.88% and 6.05%, respectively, while precipitation would decrease by approximately 30.68%. The results of the research reveal that water availability will decrease by about 13.79–15.45% under different climate scenarios. Additionally, the findings show that in the majority of cases crop production will reduce in response to climate scenarios so that rainfed wheat will experience the greatest decline (approximately 59.95%). The results of the MCDM model show that climate change can have adverse effects on economic and environmental aspects and, consequently, on the sustainability of the agricultural system of the study area. Our findings can inform policymakers on effective strategies for mitigating the consequences of climate change on water resources and agricultural production in dry regions.

Climate change has caused habitat fragmentation and reduced connectivity. The Fen River Basin in Shanxi Province, China is an important habitat for the central population of the brown-eared pheasant (BEP). The effects of climate change need to be considered in the conservation planning of BEP habitats. We used a species dispersion model to determine the BEP core habitat and graph theory to explore the connectivity of the BEP’s main habitats. The pinch point areas of BEP dissemination were determined by circuit theory. Least-cost pathways were used to identify the critical corridors for BEP dissemination. A gap analysis was conducted to estimate the efficiency of BEP conservation measures. Under the future climate scenarios, BEP habitats decreased by between 54.69% and 97.63%, and the connectivity of the main habitats was reduced by a similar magnitude. The BEP core habitat shifted to the southwestern region under the influence of climatic conditions. Currently, 90.84% of the species’ critical habitat remains unprotected. Due to climate change, the core habitat in the future was projected to differ from the current protected area. Enhancing the protection of the pinch point region may aid in the restoration of habitat connectivity.

Climate change has been projected to impact negatively on African agricultural systems. However, there is still an insufficient understanding of the possible effects of climate change on crop yields in Africa. In this study, a previously calibrated Environmental Policy Integrated Climate (EPIC) model was used to assess the effects of future climate change on maize (Zea mays L.) yield in the Eastern Cape Province of South Africa. The study aimed to compare maize yields obtained from EPIC simulations using baseline (1980–2010) weather data with maize yields obtained from EPIC using statistically downscaled future climate data sets for two future periods (mid-century (2040–2069) and late century (2070–2099)). We used three general circulation models (GCMs): BCC-CSM1.1, GFDL-ESM2M and MIROC-ES under two Representative Concentration Pathways (RCPs), RCP 4.5 and RCP 8.5, to drive the future maize yield simulations. Simulation results showed that for all three GCMs and for both future periods, a decrease in maize production was projected. Maize yield was projected to decrease by as much as 23.8% for MIROC, RCP 8.5, (2070–2099). The temperature was projected to rise by over 50% in winter under RCP 8.5 for both future periods. For both future scenarios, rainfall was projected to decrease in the summer months while increasing in the winter months. Overall, this study provides preliminary evidence that local farmers and the Eastern Cape government can utilise to develop local climate change adaptation strategies.

Invasive alien plant species (IAPS) have had a significant impact on agriculture in many countries in the world. Climatic suitability maps can be used to assess the vulnerability of agricultural areas for potential plant invasions. We used climatic suitability heat maps of IAPS to examine how potential climatic suitability for multiple IAPS invasion changes in eight agricultural land-use types in Sri Lanka under climate change scenarios for 2050. The findings of the study revealed that all evaluated agricultural land-use types are potentially vulnerable to invasion by different numbers of IAPS. In the majority of land-use types, the climatic suitability for multiple IAPS was predicted to increase under climate change scenarios. Out of all land-use types, coconut grown areas are expected to have the greatest suitability increase for more than three IAPS. Nearly all paddy lands were predicted to become suitable for two serious aquatic invaders (Eichhornia crassipes and Salvinia molesta) under current climatic conditions. However, a noticeable reduction in climatic suitability for IAPS was predicted in paddy lands by 2050. The study provides useful information that can be used by policy-makers to develop effective control and management strategies against the establishment of IAPS in agricultural land-uses in Sri Lanka.

In the context of global warming, agricultural production and social and economic development are significantly affected by drought. The future change of climate conditions is uncertain; thus, it is of great importance to clarify the aspects of drought in order to define local and regional drought adaptation strategies. In this study, the meteorological data from 1976 to 2005 was used as a historical reference, and nine Global Climate Models (GCMs), downscaling to meteorological stations from 2039 to 2089, were used as future climate data. Based on Penman–Monteith, the reference crop Evapotranspiration (ET0) and Standardized Precipitation Evapotranspiration Index (SPEI) of the reference crop in three emission scenarios of RCP2.6, RCP4.5, and RCP8.5, under future climate conditions, were calculated. A non-parameter Mann–Kendall trend test was performed on temperature, precipitation, ET0, and SPEI to analyze the drought spatiotemporal distribution traits under upcoming climate scenarios. The results showed that, under future climate conditions, SPEI values in most areas of the Huang-Huai-Hai region would continuously increase year by year, and drought would be alleviated to some extent at the same pace. However, with the increase of greenhouse gas concentration in the emission scenarios, SPEI values continued to decline. In the RCP8.5 scenario, the area of severe drought was large. To sum up, in the future climate scenario, the degree of drought in the Huang-Huai-Hai region will be alleviated to some extent with the increase of rainfall, but with the increase of greenhouse gas concentration, the degree of drought will be further intensified, posing a huge challenge to agricultural water use in the region. This study provides a theoretical foundation for alleviating drought in the Huang-Huai-Hai region in future climate scenarios.

Wind-driven rain (WDR) is the primary source of building moisture, and it has a direct impact on the durability and thermal performance of building envelopes. To suppress the impact of WDR on buildings under climate change, this study analyzed three representative concentration pathways (RCP) scenarios (RCP2.6, RCP4.5, and RCP8.5) based on the climate data of Meteonorm and assesses China's forecasted WDR for the next 80 years. The WDR exposure maps and the WDR increase maps of China's three future emission scenarios in 2050, 2080, and 2100 were plotted, and the rate of increase of WDR was calculated. A total of ten representative cities in China were selected based on their WDR exposure. Using the ASHRAE 160-2021 standard, WDR patterns of 16 orientations of building facades in the ten selected cities in 2050, 2080, and 2100 under three future climate scenarios were obtained. The results show that China's overall WDR exposure will increase under the three future climate scenarios, with the increase under the RCP4.5 scenario being the largest. From a regional perspective, southeastern China is exposed to severe WDR in current scenarios and may also be exposed to the same in future scenarios; the absolute increase of WDR is also the largest. There are certain differences in WDR exposure and increase of WDR exposure in cities in different climate zones in China, and the increase in coastal areas is significantly greater than that in inland areas. Particularly, in Shanghai, the exposure of WDR evidently increases both in summer and winter. • Wind-driven rain index under three RCP scenarios are projected. • Increase in wind-driven rain is more pronounced in coastal areas than inland. • Increase rate of wind-driven rain index under RCP 4.5 is the largest. • Wind-driven rain's seasonal change in summer and winter should be further studied. [ABSTRACT FROM AUTHOR]

In Canada, climate change is expected to increase the extreme precipitation events by magnitude and frequency, leading to more intense and frequent river flooding. In this study, we attempt to map the flood hazard and damage under projected climate scenarios (2050 and 2080). The study was performed in the two most populated municipalities of the Petite Nation River Watershed, located in southern Quebec (Canada). The methodology follows a modelling approach, in which climate projections are derived from the Hydroclimatic Atlas of Southern Quebec following two representative concentration pathways (RCPs) scenarios, i.e., RCP 4.5 and RCP 8.5. These projections are used to predict future river flows. A frequency analysis was carried out with historical data of the peak flow (period 1969–2018) to derive different return periods (2, 20, and 100 years), which were then fed into the GARI tool (Gestion et Analyse du Risque d’Inondation). This tool is used to simulate flood hazard maps and to quantify future flood risk changes. Projected flood hazard (extent and depth) and damage maps were produced for the two municipalities under current and for future scenarios. The results indicate that the flood frequencies are expected to show a minor decrease in peak flows in the basin at the time horizons, 2050 and 2080. In addition, the depth and inundation areas will not significantly change for two time horizons, but instead show a minor decrease. Similarly, the projected flood damage changes in monetary losses are projected to decrease in the future. The results of this study allow one to identify present and future flood hazards and vulnerabilities, and should help decision-makers and the public to better understand the significance of climate change on flood risk in the Petite Nation River watershed.

The Baltic Sea is a shallow, semi-enclosed brackish sea suffering like many other coastal seas from eutrophication caused by human impact. Hence, nutrient load abatement strategies are intensively discussed. With the help of a high-resolution, coupled physical-biogeochemical circulation model we investigate the combined impact of changing nutrient loads from land and changing climate during the 21st century as projected from a global climate model regionalized to the Baltic Sea region. Novel compared to previous studies are an extraordinary spin-up based upon historical reconstructions of atmospheric, nutrient load and runoff forcing, revised nutrient load scenarios and a comparison of nutrient load scenario simulations with and without changing climate. We found in almost all scenario simulations, with differing nutrient inputs, reduced eutrophication and improved ecological state compared to the reference period 1976-2005. This result is a long-lasting consequence of ongoing nutrient load reductions since the 1980s. Only in case of combined high-end nutrient load and climate scenarios, eutrophication is reinforced. Differences compared to earlier studies are explained by the experimental setup including nutrient loads during the historical period and by the projected nutrient loads. We found that the impact of warming climate may amplify the effects of eutrophication and primary production. However, effects of changing climate, within the range of considered greenhouse gas emission scenarios, are smaller than effects of considered nutrient load changes, in particular under low nutrient conditions. Hence, nutrient load reductions following the Baltic Sea Action Plan will lead to improved environmental conditions independently of future climate change. [ABSTRACT FROM AUTHOR]

To improve the scientific management capacity of water resources in Songhua River Harbin Section, SWAT model was used to simulate the hydrology change in the mid and late 21st Century. Results show that SWAT model performed good for both the calibration and validation periods. Three Global Climate Model (GCMs) in scenario SRES A1B were chosen as the future Climate Scenarios. Multi-year mean temperature in the mid and late 21st Century of three GCMs was all significantly increased. Precipitation in mid 21st Century was increased for Canadian Centre for Climate Modelling and Analysis (CCCMA) and Model for Interdisciplinary Research on Climate (MIROC). Precipitation in late 21st Century was all increased for all the three GCMs. Due to the change of temperature and precipitation, flow in CCCMA and Geophysical Fluid Dynamics Laboratory (GFDL) show a downward trend in future, GFDL has the most reduce rate. The flow of GFDL will be reduced to 1256m³/s and 1342m³/s in mid and late 21st Century respectively. Possibility of flow reduction in future will lead to the shortage of water resources. Therefore, the study area was divided into low, medium and high area according to their ability to provide water resources. Different areas can make more reasonable water arrangement. This study predicts the changes of water resources in future. The results provide technical guidance for the regional water resources management and utilization. [ABSTRACT FROM AUTHOR]