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In recent years, the frequency of floods has escalated due to global warming and human activities. Addressing this challenge, our study investigates how future climate change scenarios will affect flood susceptibility in the Tajan watershed, northern Iran. The primary objective is to quantify and map the evolving risk of flooding in this region under different future climate scenarios. We applied machine learning techniques, coupled model intercomparison project phase 6 (CMIP6) climatic models, and remote sensing to achieve this goal. The CanESM5 climate model was chosen for its accuracy among four global climate models in CMIP6 to estimate future precipitation trends under shared socioeconomic pathways (SSP 2.6, 4.5, and 8.5) over two-time horizons: future (2020–2060) and far future (2061–2100). These scenarios encompass various influential factors, such as greenhouse gas emissions, urbanization, deforestation, and socioeconomic development, which played crucial roles in modulating flood susceptibility. Flood susceptibility maps were generated considering future precipitation patterns and scenarios using random forest (RF) and support vector machine (SVM) algorithms, 432 flood locations, and 15 flood influencing factors. The accuracy of our prediction was validated through multiple statistical measures, including the area under the receiver operating characteristic (AUC-ROC) curve. The results indicated that the proposed models performed well, with the RF model (AUC=0.91) demonstrating higher accuracy compared to the SVM model (AUC=0.85). From a spatial perspective, increased future precipitation under all SSP scenarios enhances the likelihood of flood occurrences in the central and downstream regions. In the far future, intensified precipitation due to changes in regional topography and climate, coupled with higher greenhouse gas concentrations, is expected to heighten flood risks, especially at higher altitudes. We hope that our study findings will inform effective flood risk management strategies and adaptation plans in response to climate-induced flood risks, both in our study area and in similar regions globally. [ABSTRACT FROM AUTHOR]

Estimating potential changes in future flood patterns based on anticipated changes in hydrological characteristics within the basin is crucial for mitigating flood damage and managing flood risk. In this study, nonparametric probability models are used to estimate future rainfall patterns in Seoul under the GCM‐based climate change scenarios (CCS), and the estimated future daily rainfall data was temporally downscaled to hourly units using the KNNR‐GA technique. Changes in flood hazard and runoff characteristics of the target area based on the estimated future rainfall data are quantitatively assessed. The results highlight that under CCS, flood runoff may increase further into the future, resulting in more significant changes in flood patterns and accelerating the increase in flood hazard. The delta change factor of flood risk indicators increased relatively significantly in more severe CCS. This study also proposed a process to estimate future flood runoff and mitigation effects according to CCS by reflecting various flood mitigation measures in the urban drainage system model. These findings can offer valuable insights for setting the direction of current and future mitigation measures. [ABSTRACT FROM AUTHOR]

This paper presents a method to analyze and improve the set of equations constituting a rainfall‐runoff model structure based on a combination of a data assimilation algorithm and polynomial updates to the state equations. The method, which we have called "Data Assimilation Informed model Structure Improvement" (DAISI) is generic, modular, and demonstrated with an application to the GR2M model and 201 catchments in South‐East Australia. Our results show that the updated model generated with DAISI generally performed better for all metrics considered included Kling‐Gupta Efficiency, NSE on log transform flow and flow duration curve bias. In addition, the elasticity of modeled runoff to rainfall is higher in the updated model, which suggests that the structural changes could have a significant impact on climate change simulations. Finally, the DAISI diagnostic identified a reduced number of update configurations in the GR2M structure with distinct regional patterns in three sub‐regions of the modeling domain (Western Victoria, central region, and Northern New South Wales). These configurations correspond to specific polynomials of the state variables that could be used to improve equations in a revised model. Several potential improvements of DAISI are proposed including the use of additional observed variables such as actual evapotranspiration to better constrain internal model fluxes. Plain Language Summary: This paper presents a data‐driven method to improve rainfall‐runoff models used to generate future water resources scenario in climate change studies. The method, which we have called "Data Assimilation Informed model Structure Improvement" (DAISI) is generic, modular, and demonstrated with an application to monthly streamflow simulations over a large data set of catchments in South‐East Australia. Our results show that DAISI improves model performance for a wide range of metrics and increases the sensitivity of the model to climate inputs, which is critical in climate change scenarios. Finally, the improvements identified by DAISI take a simple mathematical form with distinct regional patterns in three sub‐regions of the study domain (Western Victoria, central region, and Northern New South Wales). Several improvements of DAISI are discussed including the inclusion of additional observed variables such as evapotranspiration to better constrain model simulations. Key Points: Data Assimilation Informed model Structure Improvement method diagnoses hydrological model structures by combining data assimilation with a polynomial update of state equationsThe method was applied to the GR2M rainfall‐runoff model with significantly improved streamflow simulations in 201 Australian catchmentsThe method identified updates to state equations with marked regional characteristics that could guide future improvement of GR2M [ABSTRACT FROM AUTHOR]

Abstract Estimating potential changes in future flood patterns based on anticipated changes in hydrological characteristics within the basin is crucial for mitigating flood damage and managing flood risk. In this study, nonparametric probability models are used to estimate future rainfall patterns in Seoul under the GCM‐based climate change scenarios (CCS), and the estimated future daily rainfall data was temporally downscaled to hourly units using the KNNR‐GA technique. Changes in flood hazard and runoff characteristics of the target area based on the estimated future rainfall data are quantitatively assessed. The results highlight that under CCS, flood runoff may increase further into the future, resulting in more significant changes in flood patterns and accelerating the increase in flood hazard. The delta change factor of flood risk indicators increased relatively significantly in more severe CCS. This study also proposed a process to estimate future flood runoff and mitigation effects according to CCS by reflecting various flood mitigation measures in the urban drainage system model. These findings can offer valuable insights for setting the direction of current and future mitigation measures.

Context: Developing ecological networks (ENs) is a widely acknowledged conservation strategy for mitigating habitat fragmentation and ecosystem degradation. Therefore, it is crucial to assess the sustainability of the ENs before or after their development in order to maintain their functions and ecosystem service. While most previous studies have explored ENs based on ecosystem service evaluation and structure construction, the functions and structures of EN have rarely been integrally assessed under climate change scenarios. Objective: Taking the Yangtze River Delta urban agglomeration as the study area, we aim to assess the future sustainability of the current EN under multiple climate change scenarios by integrating its functions and structures. Methods: Ten scenarios were constructed, including a current scenario and nine future climate scenarios. Ecological sources were derived from the importance of the ecosystem service, and were used to develop EN by using the Linkage mapper toolbox. We then used the range difference between current and future ecological sources to indicate the functional sustainability of the current EN. The NetworkX was used to assess the EN structural stability by integrating the EN functional sustainability. Results: The capacity of 6.23% of the current ecological sources is projected to decline in efficiently providing ecosystem services by 2050 under the selected nine future climate scenarios, and these functional degradations will also lead to a 33.55% decrease in the EN structural stability. Poor, low, and medium functional sustainable sources will be mostly located in forests and water bodies of the central YRDUA with a small average patch area, while high functional sustainable sources will be mainly distributed in the southwestern mountainous regions and water areas in the north-central region with a larger average patch area. Conclusion: Our study provides a prospective assessment of EN, which is particularly crucial for enhancing ecological strategies and ensuring landscape sustainability. Constructing future climate scenarios and integrally assessing EN functional sustainability and structural stability can provide effective information for long-term EN management. [ABSTRACT FROM AUTHOR]

Water shortage consider on of the main threats facing the agriculture, mainly in the Mediterranean area. So that there is a great need to apply new methods to water resource management. The crop models are used to achieve this objective. Tomato is a significant vegetable crop globally and represent an important part of horticultural production with 180 million tons produced on over five million hectares even though few studies have validated the AquaCrop model, especially in Egypt. This study was conducted in a protected cultivation experimental farm, Agricultural Research Center (ARC), Dokki, Giza, Egypt during the winter seasons of 2019/2020 and 2020/2021. Different irrigation levels (IL): 55%, 70%, 85%, 100%, and 115% of evapotranspiration (Eto) were applied on tomato. Plant growth parameters, relative chlorophyll content (SPAD), yield, fruit quality and plant nutrients (NPK) were recorded at both seasons. Also, the aforementioned irrigation levels were used to validate the AquaCrop model on different climate change scenarios on tomato productivity in 2050 and 2100. The findings revealed that the highest plant growth parameters were obtained in 85% and 100% Eto as compared to all treatments at both seasons. In contrast, the 55%of Eto obtained the lowest values of all plant growth parameters. The number of fruits/plant, early yield, and total yield of 100% Eto were ranked secondly. Fruits quality was significantly affected by the tested ILs. The highest values of TSS, firmness and vit C of tomato fruits were obtained by 55% followed by 70% Eto. The lowest proline content was recorded at 115% of Eto in both seasons. The content of proline in plants of 70% Eto ranked secondly after 55% of Eto in both seasons. The results of AquaCrop model (Version 7.0) revealed that the crop productivity decreased by 4% and 33% of RCP4.5 and RCP8.5 scenarios, respectively, of the years 2050, 14% and 44% for the same scenarios, respectively, of the year 2100.

The Alligator Gar (Atractosteus spatula) is a very dangerous species that has an extremely destructive impact on the ecological environment in aquatic areas. As a kind of ornamental fish, it is spreading rapidly all over the world. In order to avoid or delay the spread of Alligator Gar in China, it is urgent to further monitor and clarify its distribution range within the country. In this study, a maximum entropy model (MaxEnt) and geographical information system (ArcGIS) were used to identify suitable regions under climate change scenarios in China, and the significance of environmental factors that shape this species' distribution were evaluated. According to the geographical distribution data of Alligator Gar and selected environmental variables, the potential habitat area of Chinese Alligator Gar under four climate scenarios in the present (2000s) and future (2050s and 2070s) was predicted. (1) The main environmental variables affecting the potential geographical distribution of Alligator Gar are temperature factor variables (the mean temperature of the warmest quarter and the mean temperature of the coldest quarter), precipitation factor variables (precipitation in the driest month and in the driest quarter), and altitude. (2) Under the current climate conditions, the suitable breeding areas of Alligator Gar are mainly concentrated in the Guangxi Zhuang Autonomous Region, Guangdong Province, and Hainan Province. Under the four future climate scenarios, the distribution area of the total suitable area of Alligator Gar will gradually decrease, and the suitable area of Alligator Gar will spread to high latitudes. [ABSTRACT FROM AUTHOR]

Water shortage consider on of the main threats facing the agriculture, mainly in the Mediterranean area. So that there is a great need to apply new methods to water resource management. The crop models are used to achieve this objective. Tomato is a significant vegetable crop globally and represent an important part of horticultural production with 180 million tons produced on over five million hectares even though few studies have validated the AquaCrop model, especially in Egypt. This study was conducted in a protected cultivation experimental farm, Agricultural Research Center (ARC), Dokki, Giza, Egypt during the winter seasons of 2019/2020 and 2020/2021. Different irrigation levels (IL): 55%, 70%, 85%, 100%, and 115% of evapotranspiration (Eto) were applied on tomato. Plant growth parameters, relative chlorophyll content (SPAD), yield, fruit quality and plant nutrients (NPK) were recorded at both seasons. Also, the aforementioned irrigation levels were used to validate the AquaCrop model on different climate change scenarios on tomato productivity in 2050 and 2100. The findings revealed that the highest plant growth parameters were obtained in 85% and 100% Eto as compared to all treatments at both seasons. In contrast, the 55% of Eto obtained the lowest values of all plant growth parameters. The number of fruits/plant, early yield, and total yield of 100% Eto were ranked secondly. Fruits quality was significantly affected by the tested ILs. The highest values of TSS, firmness and vit C of tomato fruits were obtained by 55% followed by 70% Eto. The lowest proline content was recorded at 115% of Eto in both seasons. The content of proline in plants of 70% Eto ranked secondly after 55% of Eto in both seasons. The results of AquaCrop model (Version 7.0) revealed that the crop productivity decreased by 4% and 33% of RCP4.5 and RCP8.5 scenarios, respectively, of the years 2050, 14% and 44% for the same scenarios, respectively, of the year 2100. [ABSTRACT FROM AUTHOR]

With the increase in global temperature, the global change situation dominated by climate warming is becoming more and more serious. Climate change will cause differences in the suitable areas of species in different periods. Ginkgo biloba L., a rare and endangered wild plant protected at the national level in China, is the oldest relict plant in the world. Because of severe climate change, only China's wild Ginkgo biloba has been preserved, yet China's wild Ginkgo biloba population is facing extinction risk. Ginkgo biloba has rich ornamental value, application value, economic value, medicinal value and ecological value. Not only can it produce economic and ecological benefits, but it can also produce huge social benefits. Based on the data of Ginkgo biloba sample distribution, bioclimatic variables and soil variables, this paper uses the MaxEnt model to simulate Ginkgo biloba suitable area under current and future different climate scenarios, and analyzes the changes in the potential suitable area of Ginkgo biloba in the future through ArcGIS 10.6. The results are as follows: (1) the results simulated by the MaxEnt model are AUC > 0.9, showing that the simulation results have a high accuracy; (2) the min temperature of the coldest month, precipitation of the wettest month, elevation, and temperature seasonality are the main environmental variables affecting the change in the Ginkgo biloba suitable area; (3) under future climate scenarios, the suitable area of Ginkgo biloba is predicted to expand in the future, covering most of the south and some northeast regions, and moderate temperature and precipitation changes under climate change are conducive for the growth of Ginkgo biloba; and (4) in the future, the distribution center of the suitable area will move to the northeast. According to the conclusions in this paper, it is expected to provide theoretical reference for cultivation and management, sustainable utilization and solution of ecological environment problems of Ginkgo biloba. [ABSTRACT FROM AUTHOR]

Climate change jeopardizes species survival, particularly for endangered species. This risk extends to the endangered Chinese endemic tree Tapiscia sinensis. The factors underpinning T. sinensis's habitat distribution are poorly understood, and its potential response to future climate scenarios remains unclear. With six shortlisted climate factors and 117 occurrence records, we modeled T. sinensis's potential distribution across China using the BIOCLIM model. We applied principal component analysis to examine the primary climate factors restricting its geographical range. The findings indicate that T. sinensis' range is principally located in China's middle subtropical climatic zone at low–mid altitudes. The principal component analysis identified two critical factors representing temperature and precipitation. Temperature was the most critical factor limiting T. sinensis distribution, especially the effect of temperature seasonality and isothermality. The habitat suitability model generated by BIOCLIM under current climate conditions demonstrated strong concordance between the predicted suitable areas and the present actual distribution range. These results verify that the model can reliably identify habitats conducive to T. sinensis growth and survival. However, under a hypothetical future climate scenario of doubled atmospheric CO2 concentrations for 2100, the model indicates a precipitous reduction and fragmentation in the areas with excellent suitability conditions. This predicted decline highlights the considerable threats posed by climate change to the long-term survival of this endangered species in China. Our habitat modeling yields critical insights that inform the development of science-based strategies and best practices to improve conservation management plans for research, protection, nursery cultivation, and sustainable planting in China. Habitat suitability knowledge could aid introduction and cultivation efforts for T. sinensis globally in places with analogous climates. [ABSTRACT FROM AUTHOR]

Climate change caused by global warming has altered temporal-spatial distribution as well as rate and form of precipitation, the magnitude of floods, annual precipitation ​​in rivers, seasonal variation of probable maximum precipitation and flood, water quality, evaporation rate, concentrations of nutrients in aquifers, etc. the Atmosphere-Ocean Coupled General Circulation Model (AOGCM) is currently the most reliable tool to study the effects of climate change on different systems. This model simulates climate parameters. Estimation of probable maximum precipitation (PMP) is an important and practical research method that not only identifies behavior of extreme rainfall in climatology, but also helps hydrologists to design various large water control structures, especially dams. Climate change affects PMP in the coming periods. Consequently, PMP estimates will be modified by hydrologists.

Agriculture plays an important role in Senegalese economy and annual early warning predictions of crop yields are highly relevant in the context of climate change. In this study, we used three main machine learning methods (support vector machine, random forest, neural network) and one multiple linear regression method, namely Least Absolute Shrinkage and Selection Operator (LASSO), to predict yields of the main food staple crops (peanut, maize, millet and sorghum) in 24 departments of Senegal. Three combination of predictors (climate data, vegetation data or a combination of both) are used to compare the respective contribution of statistical methods and inputs in the predictive skill. Our results showed that the combination of climate and vegetation with the machine learning methods gives the best performance. The best prediction skill is obtained for peanut yield likely due to its high sensitivity to interannual climate variability. Although more research is needed to integrate the results of this study into an operational framework, this paper provides evidence of the promising performance machine learning methods. The development and operationalization of such prediction and their integration into operational early warning systems could increase resilience of Senegal to climate change and contribute to food security. [ABSTRACT FROM AUTHOR]

Many species of Alternaria are important pathogens that cause plant diseases and postharvest rots. They lead to significant economic losses in agriculture and affect human and animal health due to their capacity to produce mycotoxins. Therefore, it is necessary to study the factors that can result in an increase in A. alternata. In this study, we discuss the mechanism by which phenol content protects from A. alternata, since the red oak leaf cultivar (containing higher phenols) showed lower invasion than the green one, Batavia, and no mycotoxin production. A climate change scenario enhanced fungal growth in the most susceptible cultivar, green lettuce, likely because elevated temperature and CO2 levels decrease plant N content, modifying the C/N ratio. Finally, while the abundance of the fungi was maintained at similar levels after keeping the lettuces for four days at 4 °C, this postharvest handling triggered TeA and TEN mycotoxin synthesis, but only in the green cultivar. Therefore, the results demonstrated that invasion and mycotoxin production are cultivar- and temperature-dependent. Further research should be directed to search for resistant cultivars and effective postharvest strategies to reduce the toxicological risk and economic losses related to this fungus, which are expected to increase in a climate change scenario. [ABSTRACT FROM AUTHOR]

The distribution shift of forest species due to the fact of climate change may negatively affect ecosystem services including the production of medicinal resources. Climate change impact assessments of habitat range changes are essential to sustainably manage forest resources. A change in the habitat suitability due to the fact of climate change was predicted for Angelica gigas, which has high economic value among forest medicinal resources in South Korea. The habitat suitability was predicted by an ensemble species distribution model that combined the results of nine single algorithm models using the committee averaging method. A total 168 occurrence data and 10 environmental variables were used for the single algorithm models. The area under the receiver operation characteristic curve (AUC) and true skill statistic (TSS) were applied to evaluate the models' performance, and the contribution of the environmental variables was calculated as an important value for each single algorithm model. Climate change scenarios were projected to predict future habitat suitability. The future suitable habitat for A. gigas was gradually reduced to the high mountain regions of the eastern part of South Korea regardless of the climate change scenarios. The main environmental variable was the annual mean temperature, and the rise in temperature due to the fact of climate change was found to have a negative effect on the habitat suitability for A. gigas. The decline in the habitat suitability for A. gigas, a major forest medicinal resource, is expected to result in the reduction in its production. Therefore, it is required to establish adaptation measures to mitigate the negative impact of this decrease, such as protecting the natural habitats of A. gigas. [ABSTRACT FROM AUTHOR]

The Alligator Gar (Atractosteus spatula) is a very dangerous species that has an extremely destructive impact on the ecological environment in aquatic areas. As a kind of ornamental fish, it is spreading rapidly all over the world. In order to avoid or delay the spread of Alligator Gar in China, it is urgent to further monitor and clarify its distribution range within the country. In this study, a maximum entropy model (MaxEnt) and geographical information system (ArcGIS) were used to identify suitable regions under climate change scenarios in China, and the significance of environmental factors that shape this species’ distribution were evaluated. According to the geographical distribution data of Alligator Gar and selected environmental variables, the potential habitat area of Chinese Alligator Gar under four climate scenarios in the present (2000s) and future (2050s and 2070s) was predicted. (1) The main environmental variables affecting the potential geographical distribution of Alligator Gar are temperature factor variables (the mean temperature of the warmest quarter and the mean temperature of the coldest quarter), precipitation factor variables (precipitation in the driest month and in the driest quarter), and altitude. (2) Under the current climate conditions, the suitable breeding areas of Alligator Gar are mainly concentrated in the Guangxi Zhuang Autonomous Region, Guangdong Province, and Hainan Province. Under the four future climate scenarios, the distribution area of the total suitable area of Alligator Gar will gradually decrease, and the suitable area of Alligator Gar will spread to high latitudes.

With the increase in global temperature, the global change situation dominated by climate warming is becoming more and more serious. Climate change will cause differences in the suitable areas of species in different periods. Ginkgo biloba L., a rare and endangered wild plant protected at the national level in China, is the oldest relict plant in the world. Because of severe climate change, only China’s wild Ginkgo biloba has been preserved, yet China’s wild Ginkgo biloba population is facing extinction risk. Ginkgo biloba has rich ornamental value, application value, economic value, medicinal value and ecological value. Not only can it produce economic and ecological benefits, but it can also produce huge social benefits. Based on the data of Ginkgo biloba sample distribution, bioclimatic variables and soil variables, this paper uses the MaxEnt model to simulate Ginkgo biloba suitable area under current and future different climate scenarios, and analyzes the changes in the potential suitable area of Ginkgo biloba in the future through ArcGIS 10.6. The results are as follows: (1) the results simulated by the MaxEnt model are AUC > 0.9, showing that the simulation results have a high accuracy; (2) the min temperature of the coldest month, precipitation of the wettest month, elevation, and temperature seasonality are the main environmental variables affecting the change in the Ginkgo biloba suitable area; (3) under future climate scenarios, the suitable area of Ginkgo biloba is predicted to expand in the future, covering most of the south and some northeast regions, and moderate temperature and precipitation changes under climate change are conducive for the growth of Ginkgo biloba; and (4) in the future, the distribution center of the suitable area will move to the northeast. According to the conclusions in this paper, it is expected to provide theoretical reference for cultivation and management, sustainable utilization and solution of ecological environment problems of Ginkgo biloba.

Climate change jeopardizes species survival, particularly for endangered species. This risk extends to the endangered Chinese endemic tree Tapiscia sinensis. The factors underpinning T. sinensis’s habitat distribution are poorly understood, and its potential response to future climate scenarios remains unclear. With six shortlisted climate factors and 117 occurrence records, we modeled T. sinensis’s potential distribution across China using the BIOCLIM model. We applied principal component analysis to examine the primary climate factors restricting its geographical range. The findings indicate that T. sinensis’ range is principally located in China’s middle subtropical climatic zone at low–mid altitudes. The principal component analysis identified two critical factors representing temperature and precipitation. Temperature was the most critical factor limiting T. sinensis distribution, especially the effect of temperature seasonality and isothermality. The habitat suitability model generated by BIOCLIM under current climate conditions demonstrated strong concordance between the predicted suitable areas and the present actual distribution range. These results verify that the model can reliably identify habitats conducive to T. sinensis growth and survival. However, under a hypothetical future climate scenario of doubled atmospheric CO2 concentrations for 2100, the model indicates a precipitous reduction and fragmentation in the areas with excellent suitability conditions. This predicted decline highlights the considerable threats posed by climate change to the long-term survival of this endangered species in China. Our habitat modeling yields critical insights that inform the development of science-based strategies and best practices to improve conservation management plans for research, protection, nursery cultivation, and sustainable planting in China. Habitat suitability knowledge could aid introduction and cultivation efforts for T. sinensis globally in places with analogous climates.

Invasive alien plants pose a serious threat to native ecosystems and their biodiversity. To control their spread and damage, it is critical to clarify the potential distribution patterns of the invaders at the regional scale and their response to climate change. Mimosa bimucronata (DC.) Kuntze, a shrub extremely tolerant of stressful habitat conditions, is used as firewood and hedgerow in the tropics. Since its introduction to China in the 1950s, it has established a sustainable population and become an aggressive invasive species. Due to serious ecological threats in South China, it has been listed as a Key Management Invasive Alien Species. To understand its invasibility in China, two species distribution models (SDMs), BIOCLIM and DOMAIN, were employed to evaluate the main environmental factors regulating its geographical distribution under the current climate and a future climate change scenario with double CO2 concentration. We found an extensive spread of current suitability habitats in South China, concentrated mainly in the tropical-subtropical provinces of Guangdong, Guangxi, Hainan, and Fujian. The complex interplay of local bioclimatic factors shaped the species distribution. Under the current climate, the predicted range was somewhat larger than the current one. Under the climate change scenario, suitable habitats for Mimosa bimucronata would remain similar to the current one. However, its invasiveness in the current biogeographical range would be more serious. The suitability habitats would expand and shift to the Southeastern coastal and Northern parts of South China under future climate change. We suggest adopting early monitoring and eradication measures in potential invasion areas and practical measures to contain its spread. The results could provide a conceptual basis to formulate prevention and control strategies to improve future containment of its invasion.

Venerable trees are important natural resources and cultural heritage, offering historical, ecological, social and economic value. However, global warming and anthropogenic activities have threatened their welfare and survival. A comprehensive understanding of their current and future spatial patterns, vis-á-vis environmental conditions, can inform the co-management of sustainable resource use and conservation. We employed the existing spatial occurrence data and environmental variables (bioclimate and elevation) to simulate the optimal habitats for venerable trees in China's Sichuan Province. We evaluated the current and future climate scenarios of 2100 with double CO2 concentration. The BIOCLIM and QGIS spatial analyses assessed the primary factors of geographical distribution. The results identified 10,720 venerable trees from 123 species, 81 genera and 42 families. Cupressus funebris dominated, with the maximum importance value, followed by Ginkgo biloba, Ficus virens var. sublanceolata, and Phoebe zhennan. The elevation distribution of tree abundance and species richness demonstrated a unimodal pattern, skewing to the low-elevation end, with a concentration in the 600–1500 m low-medium altitude. The majority of trees and excellent habitats were found in eastern Sichuan with a less harsh terrain and climate. The bio3 (isothermality) and bio7 (temperature annual range) factors significantly influenced tree occurrence. Temperature imposed a greater effect on distribution than moisture under the current climate scenario. For the future climate-change scenario, the suitable habitats were predicted to maintain an overall stable pattern, with largely contiguous expansions of better habitats. However, climate warming would shrink the excellent habitats on the plains. The findings can inform strategies and guidelines for venerable-tree conservation in Sichuan. Furthermore, vulnerable areas could be identified. The future range expansion sites could be enlisted to cultivate new trees to replenish the venerable-tree pool. Habitat patches that remain sustainable could provide refugia with the potential for protected-area designation. [ABSTRACT FROM AUTHOR]

To estimate groundwater resources under changing climate is one of the important issues for Ulaanbaatar City in the Tuul river basin of Mongolia. The main water supply is provided from groundwater and demand has been increasing due to the rapid growth of population and economic development. There have not been any complete studies to assess climate change impact on groundwater resources for Ulaanbaatar city. Therefore, in this study we proposed to estimate future potential resources of the groundwater from the main wellfields in the city using the AnAqSim (Analytic Aquifer Simulator) model. The model calibration was performed on 10 wellfields during the reference period from 1960 to 2015. Based on the reliable calibration results for the natural conditions, the impact of climate change on groundwater resources was assessed to use the projected HadCM3 scenario for the periods 2046–2065 and 2080–2099. The results of the study contribute to a water management plan for the city to recommend seasonal abstraction. HIGHLIGHTS AnAqSim is one of the potential tools to assess climate change impact on groundwater resources.; The groundwater resources for the ten wellfields of the Upper Tuul river basin are reasonably modeled and re-estimated using the AnAqSim.; The wellfields of the upper and downstream side of the Upper Tuul river will be more sensitive to climate change.;

Because of multifunctional weirs installed as part of large river regulation works in Korea, water quality problems have arisen from environmental changes in tandem with decreased flow rates. However, there has been limited research into the green algae removal effect, water quality improvement in congested waters, dam and weir operations, and consequential riverbed changes. Studies regarding outflow in a basin, the application and development of sediment load output analysis methods, feasibility of related dam operations, and riverbed patterns have been separately performed. However, basins and rivers should be analyzed by an integrated method instead of an individual one. Therefore, in the present study, the effect of congestion on a river connected to a dam/weir and estuary bank was analyzed based on climate change scenarios HadGEM3-RA RCP 4.5 and 8.5, with the aim of integrating individual studies using watershed and river models. Flow was controlled by dam- and weir-related discharge simulations. Variations in the riverbed caused by the transfer of suspended load in the downstream region were analyzed for both long and short durations. The results of this analysis suggest that given future climate change scenarios, the width of the river and riverbed variations in the riverbed are expected to rise. HIGHLIGHTS Impact of long-term geometric variation in rivers.; Analysis of climate change scenarios.; Integrate watershed and river.; Variations in the riverbed for both long and short durations.; Analysis of both watershed runoff and sediment transport in a river.;

Study region: Sangdo-dong, Dongjak-gu, which is classified as inland water disaster risk zone within Seoul, Republic of Korea. Study focus: This study evaluated the amount of flooding that could occur in the carbon-neutral era, and optimized low-impact development (LID) technology for an urban flood adaptation. The optimal scenario of LID technologies was proposed for the Sangdo-dong area at the urban level, and the amount of runoff and flood reduction was evaluated. New hydrological insights: The LID controls provided by the storm water management model were used to analyze the flood damage that may occur under RCP 2.6 scenario. The evaluation simulated eight combinations of three optimized techniques: green roofs, permeable pavements, and vegetative swales to construct evidence-based data for the use by decision makers. In Scenario 8, which was evaluated as the most efficient, both peak and total flooding could be reduced by 55–60% respectively. This study not only evaluate the runoff but also the flooding value reflecting the interactions among LID-pipe networks. The LID installation area and runoff damage scale are inversely proportional. In contrast, as total flooding considers the capacity of the sewage network, a non uniform graph can be seen compared to the runoff. Results of this study can be used as guidance for LID evidence-based design and construction as well as strategies for decision maker.

Abstract Background Several water resources projects are under planning and implementation in the Baro-Akobo basin. Currently, the planning and management of these projects is relied on historical data. So far, hardly any study has addressed water resources management and adaptation measures in the face of changing water balances due to climate change in the basin. The main bottleneck to this has been lack of future climate change scenario base data over the basin. The current study is aimed at developing future climate change scenario for the basin. To this end, Regional Climate Model (RCM) downscaled data for A1B emission scenario was employed and bias corrected at basin level using observed data. Future climate change scenario was developed using the bias corrected RCM output data with the basic objective of producing baseline data for sustainable water resources development and management in the basin. Result The projected future climate shows an increasing trend for both maximum and minimum temperatures; however, for the case of precipitation it does not manifest a systematic increasing or decreasing trend in the next century. The projected mean annual temperature increases from the baseline period by an amount of 1 °C and 3.5 °C respectively, in 2040s and 2090s. Similarly, evapotranspiration has been found to increase to an extent of 25% over the basin. The precipitation is predicted to experience a mean annual decrease of 1.8% in 2040s and an increase of 1.8% in 2090s over the basin for the A1B emission scenario. Conclusion The study resulted in a considerable future change in climatic variables (temperature, precipitation, and evapotranspiration) on the monthly and seasonal basis. These have an implication on hydrologic extremes-drought and flooding, and demands dynamic water resources management. Hence the study gives a valuable base information for water resources planning and managers, particularly for modeling reservoir inflow-climate change relations, to adapt reservoir operation rules to the real-time changing climate.

Climate change led an increase in the frequency of severe droughts, the effects of which are exacerbated by a lack of water storage. Despite increasing research on global-scale total water storage (TWS) change and drought prediction, basin-scale long-term hydrological drought, TWS change, and El Nino–Southern Oscillation (ENSO) influence in East Africa (EA) remain unexplored. In this paper, we used multiple approaches, of ensemble machine learning (ML) and weighted averaging, to conduct a basin-scale exploratory analysis of future hydrological drought and TWS change for the period 2025–2099. Climate change will affect future hydrological conditions in the basins, with maximum severity values of −90.3, −726.6, and −2567.7 Gt in the Tana, Turkana, and Victoria basins, respectively, under RCP6.0. Similarly, we found maximum severities of −187.5 and −277.7 Gt in the Abaya-Chamo and Tanganyika basins, respectively, under RCP 2.6. The most severe El Nino drought and exceptional La Nina conditions in the basins were found under both RCP scenarios. To ensure future water security in EA, sustainable water resource management strategies that can mitigate the impacts of extreme climate events in the future are urgently needed. [ABSTRACT FROM AUTHOR]

The distribution shift of forest species due to the fact of climate change may negatively affect ecosystem services including the production of medicinal resources. Climate change impact assessments of habitat range changes are essential to sustainably manage forest resources. A change in the habitat suitability due to the fact of climate change was predicted for Angelica gigas, which has high economic value among forest medicinal resources in South Korea. The habitat suitability was predicted by an ensemble species distribution model that combined the results of nine single algorithm models using the committee averaging method. A total 168 occurrence data and 10 environmental variables were used for the single algorithm models. The area under the receiver operation characteristic curve (AUC) and true skill statistic (TSS) were applied to evaluate the models’ performance, and the contribution of the environmental variables was calculated as an important value for each single algorithm model. Climate change scenarios were projected to predict future habitat suitability. The future suitable habitat for A. gigas was gradually reduced to the high mountain regions of the eastern part of South Korea regardless of the climate change scenarios. The main environmental variable was the annual mean temperature, and the rise in temperature due to the fact of climate change was found to have a negative effect on the habitat suitability for A. gigas. The decline in the habitat suitability for A. gigas, a major forest medicinal resource, is expected to result in the reduction in its production. Therefore, it is required to establish adaptation measures to mitigate the negative impact of this decrease, such as protecting the natural habitats of A. gigas.

Many species of Alternaria are important pathogens that cause plant diseases and postharvest rots. They lead to significant economic losses in agriculture and affect human and animal health due to their capacity to produce mycotoxins. Therefore, it is necessary to study the factors that can result in an increase in A. alternata. In this study, we discuss the mechanism by which phenol content protects from A. alternata, since the red oak leaf cultivar (containing higher phenols) showed lower invasion than the green one, Batavia, and no mycotoxin production. A climate change scenario enhanced fungal growth in the most susceptible cultivar, green lettuce, likely because elevated temperature and CO2 levels decrease plant N content, modifying the C/N ratio. Finally, while the abundance of the fungi was maintained at similar levels after keeping the lettuces for four days at 4 °C, this postharvest handling triggered TeA and TEN mycotoxin synthesis, but only in the green cultivar. Therefore, the results demonstrated that invasion and mycotoxin production are cultivar- and temperature-dependent. Further research should be directed to search for resistant cultivars and effective postharvest strategies to reduce the toxicological risk and economic losses related to this fungus, which are expected to increase in a climate change scenario.

With the continuous warming of the global climate, the ecological environment and water resources in the Yangtze River Basin are apt to be affected. The analysis and study of the future precipitation change trend in the region will help to provide a theoretical basis for water resources management and ecological protection in the Yangtze River Basin. Based on the observed precipitation data and precipitation data output from 13 global climate models of the Sixth International Coupled Model Comparison Program (CMIP6) with bias correction, this paper evaluates the precipitation simulation ability of climate models in the Yangtze River Basin from 1995 to 2014. The temporal and spatial variation trends of precipitation in 2021-2040 (near term), 2041-2070 (middle term), and 2071-2100 (long term) are analyzed under four SSP scenarios (SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5). The results show that: ① The model data with bias correction can simulate the characteristics of precipitation well in the Yangtze River Basin on the spatial and temporal scale, which is close to the observed values. ② The annual precipitation in the Yangtze River Basin will increase significantly with the increase in the radiation forcing level. Compared with the historical period (1995-2014), the annual precipitation growth in each scenario is relatively flat in the near term, and the precipitation growth is the largest in the long term. In general, seasonal precipitation shows the largest increase in winter, and the increase in spring and summer precipitation is relatively gentle. In autumn, except for the negative value of the precipitation change rate in the near term under the scenario SSP3-7.0, the other scenarios and periods are slowly increasing with lower change rates. ③ Spatially, the regions with larger annual precipitation increase are concentrated on the source region of the Yangtze River and the middle and upper reaches of the Yangtze River with relatively less precipitation. The high value center of spring precipitation increase is in the source area and the north of the middle and upper reaches, while the precipitation change rate is low in the south of the basin. The precipitation increment in summer and autumn is relatively low, and the near term and middle-term precipitation in the northern part of the middle and upper reaches is lower than that in the historical period. In winter, the precipitation in the whole basin has increased, which shows that the precipitation change rate in the north of the Yangtze River Basin is the largest, and it is smaller in the south. [ABSTRACT FROM AUTHOR]

Copyright of Arid Zone Research / Ganhanqu Yanjiu is the property of Arid Zone Research Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Despite research into the response of ammonia (NH3) volatilization in farmland to various meteorological factors, the potential impact of future climate change on NH3 volatilization is not fully understood. Based on a database consisting of 1063 observations across China, nonlinear NH3 models considering crop type, meteorological, soil and management variables were established via four machine learning methods, including support vector machine, multi-layer perceptron, gradient boosting machine and random forest (RF). The RF model had the highest R² of 0.76 and the lowest RMSE of 0.82 kg NH3-N ha-1, showing the best simulation capability. Results of model importance indicated that NH3 volatilization was mainly controlled by total input of N fertilizer, followed by meteorological factors, human managements and soil characteristics. The NH3 emissions of China's cereal production (paddy rice, wheat and maize) in 2018 was estimated to be 3.3 Mt NH3-N. By 2050, NH3 volatilization will increase by 23.1-32.0% under different climate change scenarios (Representative Concentration Pathways, RCPs), and climate change will have the greatest impact on NH3 volatilization in the Yangtze river agro-region of China due to high warming effects. However, the potential increase in NH3 volatilization under future climate change can be mitigated by 26.1-47.5% through various N fertilizer management optimization options. [ABSTRACT FROM AUTHOR]

Copyright of Chinese Journal of Applied Ecology / Yingyong Shengtai Xuebao is the property of Chinese Journal of Applied Ecology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This study proposes a novel approach that expands the existing QDM (quantile delta mapping) to address spatial bias, using Kriging within a Bayesian framework to assess the impact of using a point reference field. Our focus here is to spatially downscale daily rainfall sequences simulated by regional climate models (RCMs), coupled to the proposed QDM‐spatial bias‐correction, in which the distribution parameters are first interpolated onto a fine grid (rather than the observed daily rainfall). The proposed model is validated through a cross‐validatory (CV) evaluation using rainfall data from a set of weather stations in South Korea and climate change scenarios simulated by three alternate RCMs. The results demonstrate the efficacy of the proposed model to simulate the bias‐corrected daily rainfall sequences over large regions at fine resolutions. A discussion of the potential use of the proposed approach in the field of hydrometeorology is also offered. Plain Language Summary: Climate models can simulate biased representations of atmospheric processes, necessitating procedures for correction before use in hydrological applications. Such spatial bias can be caused for many reasons, one of which is the use of point data in establishing a spatial reference field to compare model simulations against. The most straightforward way to address this bias is to interpolate the locally observed data at the weather station onto a fine grid and use as a reference. Alternatively, one can define a bias‐correction model that accounts for the systematic impact induced by the use of point data, of special importance when the point data field is sparse and unevenly distributed. Here, we develop a novel approach to better address spatial bias using the Bayesian Kriging model. The results demonstrate the efficacy of the proposed model to simulate the bias‐corrected daily rainfall sequences over large regions at fine resolutions. Key Points: All parameters for the spatial downscaling and bias‐correction can be simultaneously estimated and interpolated at the desired pointsThe parameter interpolation is more effective than that of the precipitation in the context of spatial downscaling and bias‐correctionThe Bayesian Kriging based SD‐QDM can reproduce spatial dependency in the interpolated parameters associated with bias‐correction [ABSTRACT FROM AUTHOR]

The tropical fire ant (TFA, Solenopsis geminata) is an aggressive fire ant species that can cause health problems in humans and damages ecosystems. The TFA can be found across the world, from North and South America to Africa, Asia, and Oceania; furthermore, it has been introduced into new areas by human transport or natural flights. In this study, species distribution modeling was applied to the TFA for the first time, and its potential distribution as a response to climate change was evaluated. This study used CLIMEX as the climate-specific species distribution modeling tool with the distribution data for TFA, current global meteorological data, and two types of climate change scenarios. Thus, although the climatic suitability of the TFA was assumed to decrease with climate change, its distribution limit was expected to increase. In addition, the difference between potential distributions predicted using Special Report on Emissions Scenarios A1B and Special Report on Emissions Scenarios A2 increased over time. In conclusion, even with an overall decrease in climatic suitability, in the future, the TFA will still be able to invade a new area that it cannot inhabit under the current climatic scenario. As the first study that predicts TFA distribution using a species distribution model, we expect that this study will provide the basic information for further TFA modeling and for setting up quarantine and control measures.

Venerable trees are important natural resources and cultural heritage, offering historical, ecological, social and economic value. However, global warming and anthropogenic activities have threatened their welfare and survival. A comprehensive understanding of their current and future spatial patterns, vis-á-vis environmental conditions, can inform the co-management of sustainable resource use and conservation. We employed the existing spatial occurrence data and environmental variables (bioclimate and elevation) to simulate the optimal habitats for venerable trees in China’s Sichuan Province. We evaluated the current and future climate scenarios of 2100 with double CO2 concentration. The BIOCLIM and QGIS spatial analyses assessed the primary factors of geographical distribution. The results identified 10,720 venerable trees from 123 species, 81 genera and 42 families. Cupressus funebris dominated, with the maximum importance value, followed by Ginkgo biloba, Ficus virens var. sublanceolata, and Phoebe zhennan. The elevation distribution of tree abundance and species richness demonstrated a unimodal pattern, skewing to the low-elevation end, with a concentration in the 600–1500 m low-medium altitude. The majority of trees and excellent habitats were found in eastern Sichuan with a less harsh terrain and climate. The bio3 (isothermality) and bio7 (temperature annual range) factors significantly influenced tree occurrence. Temperature imposed a greater effect on distribution than moisture under the current climate scenario. For the future climate-change scenario, the suitable habitats were predicted to maintain an overall stable pattern, with largely contiguous expansions of better habitats. However, climate warming would shrink the excellent habitats on the plains. The findings can inform strategies and guidelines for venerable-tree conservation in Sichuan. Furthermore, vulnerable areas could be identified. The future range expansion sites could be enlisted to cultivate new trees to replenish the venerable-tree pool. Habitat patches that remain sustainable could provide refugia with the potential for protected-area designation.

According to the SRES A1B climate change scenario, by the end of the 21st century temperature in Poland will increase by 2–4°C, no increase in precipitation totals is predicted. This will rise crop irrigation needs and necessity to develop irrigation systems. Due to increase in temperature and needs of sustainable agriculture development some changes in crop growing structure will occur. An increase interest in high protein crops cultivation has been noted last years and further extension of these acreage is foreseen. Identifying the future water needs of these plants is crucial for planning and implementing sustainable agricultural production. In the study, the impact of projected air temperature changes on soybean water needs, one of the most valuable high-protein crops, in 2021–2050 in the Kuyavia region in Poland was analysed. The calculations based on meteorological data collected in 1981–2010 were considered as the reference period. Potential evapotranspiration was adopted as a measure of crop water requirements. The potential evapotranspiration was estimated using the Penman–Monteith method and crop coefficient. Based on these estimations, it was found that in the forecast years the soybean water needs will increase by 5% in the growing period (from 21 April to 10 September), and by 8% in June–August. The highest monthly soybean water needs increase (by 15%) may occur in August. The predicted climate changes and the increase in the arable crops water requirements, may contribute to an increase in the irrigated area in the Kuyavia region and necessity of rational management of water resources. [ABSTRACT FROM AUTHOR]

Climate warming is having profound effects on the hydrological cycle by increasing atmospheric demand, changing water availability, and snow seasonality. Europe suffered three distinct heat waves in 2019, and 11 of the 12 hottest years ever recorded took place in the past two decades, which will potentially change seasonal streamflow patterns and long‐term trends. Central Europe exhibited six dry years in a row since 2014. This study uses data from a well‐documented headwater catchment in Central Europe (Lysina) to explore hydrological responses to a warming climate. We applied a lumped parameter hydrologic model Brook90 and a distributed model Penn State Integrated Hydrologic Model (PIHM) to simulate long‐term hydrological change under future climate scenarios. Both models performed well on historic streamflow and in agreement with each other according to the catchment water budget. In addition, PIHM was able to simulate lateral groundwater redistribution within the catchment validated by the groundwater table dynamics. The long‐term trends in runoff and low flow were captured by PIHM only. We applied different EURO‐CORDEX models with two emission scenarios (Representative Concentration Pathways RCP 4.5, 8.5) and found significant impacts on runoff and evapotranspiration (ET) for the period of 2071–2100. Results from both models suggested reduced runoff and increased ET, while the monthly distribution of runoff was different. We used this catchment study to understand the importance of subsurface processes in projection of hydrologic response to a warming climate. [ABSTRACT FROM AUTHOR]

Seagrasses play a vital role in structuring coastal marine ecosystems, but their distributional range and genetic diversity have declined rapidly in recent decades. To improve conservation of seagrass species, it is important to predict how climate change may impact their ranges. Such predictions are typically made with correlative species distribution models (SDMs), which can estimate a species' potential distribution under present and future climatic scenarios given species' presence data and climatic predictor variables. However, these models are typically constructed with species‐level data, and thus ignore intraspecific genetic variability, which can give rise to populations with adaptations to heterogeneous climatic conditions. Here, we explore the link between intraspecific adaptation and niche differentiation in Thalassia hemprichii, a seagrass broadly distributed in the tropical Indo‐Pacific Ocean and a crucial provider of habitat for numerous marine species. By retrieving and re‐analysing microsatellite data from previous studies, we delimited two distinct phylogeographical lineages within the nominal species and found an intermediate level of differentiation in their multidimensional environmental niches, suggesting the possibility for local adaptation. We then compared projections of the species' habitat suitability under climate change scenarios using species‐level and lineage‐level SDMs. In the Central Tropical Indo‐Pacific region, models for both levels predicted considerable range contraction in the future, but the lineage‐level models predicted more severe habitat loss. Importantly, the two modelling approaches predicted opposite patterns of habitat change in the Western Tropical Indo‐Pacific region. Our results highlight the necessity of conserving distinct populations and genetic pools to avoid regional extinction due to climate change and have important implications for guiding future management of seagrasses. [ABSTRACT FROM AUTHOR]

Climate change causing an increase of frequency and magnitude of heat waves has a huge impact on the urban population worldwide. In Indonesia, the Southeast Asian country in the tropical climate zone, the increasing heat wave duration due to climate change will be also magnified by projected rapid urbanization. Therefore, not only climate change mitigation measures but also adaptation solutions to more frequent extreme weather events are necessary. Adaptation is essential at local levels. The projected increase of the heat wave duration will trigger greater health-related risks. It will also drive higher energy demands, particularly in urban areas, for cooling. New smart solutions for growing urbanization for reducing urban heat island phenomenon are critical, but in order to identify them, analyzing the changing magnitude and spatial distribution of urban heat is essential. We projected the current and future spatial variability of heat stress index in three cities in Indonesia, namely, Medan, Surabaya, and Denpasar, under climate change and land-cover change scenarios, and quantified it with the Universal Thermal Climate Index (UTCI) for two periods, baseline (1981–2005) and future (2018–2042). Our results demonstrated that currently the higher level of the UTCI was identified in the urban centers of all three cities, indicating the contribution of urban heat island phenomenon to the higher UTCI. Under climate change scenarios, all three cities will experience increase of the heat, whereas applying the land-cover scenario demonstrated that in only Medan and Denpasar, the UTCI is likely to experience a higher increase by 3.1°C; however, in Surabaya, the UTCI will experience 0.84°C decrease in the period 2018–2042 due to urban greening. This study advanced the UTCI methodology by demonstrating its applicability for urban heat warning systems and for monitoring of the urban green cooling effect, as well as it provides a base for adaptation measures’ planning.

Background: Several water resources projects are under planning and implementation in the Baro-Akobo basin. Currently, the planning and management of these projects is relied on historical data. So far, hardly any study has addressed water resources management and adaptation measures in the face of changing water balances due to climate change in the basin. The main bottleneck to this has been lack of future climate change scenario base data over the basin. The current study is aimed at developing future climate change scenario for the basin. To this end, Regional Climate Model (RCM) downscaled data for A1B emission scenario was employed and bias corrected at basin level using observed data. Future climate change scenario was developed using the bias corrected RCM output data with the basic objective of producing baseline data for sustainable water resources development and management in the basin. Result: The projected future climate shows an increasing trend for both maximum and minimum temperatures; however, for the case of precipitation it does not manifest a systematic increasing or decreasing trend in the next century. The projected mean annual temperature increases from the baseline period by an amount of 1 °C and 3.5 °C respectively, in 2040s and 2090s. Similarly, evapotranspiration has been found to increase to an extent of 25% over the basin. The precipitation is predicted to experience a mean annual decrease of 1.8% in 2040s and an increase of 1.8% in 2090s over the basin for the A1B emission scenario. Conclusion: The study resulted in a considerable future change in climatic variables (temperature, precipitation, and evapotranspiration) on the monthly and seasonal basis. These have an implication on hydrologic extremes-drought and flooding, and demands dynamic water resources management. Hence the study gives a valuable base information for water resources planning and managers, particularly for modeling reservoir inflow-climate change relations, to adapt reservoir operation rules to the real-time changing climate. [ABSTRACT FROM AUTHOR]

Global warming changes the characteristics of regional climate and crop growth environments and affects human production and life in the long term. In this study, historical snowfall observation data from 199 conventional meteorological stations in the Huaihe River Basin were analyzed with the climate trend method, the nonparametric Mann-Kendall (M-K) test and the sliding t test to determine the variability and abrupt changes in climate trends in the Huaihe River Basin during 1951–2018. Then, the Coupled Model Intercomparison Project phase 6 datasets were used to analyze the evolutionary trend of snowfall under four climate change scenarios in 2015–2065. The results show the following: 1) Due to climate warming, both the historical data and the simulated data under different climate change scenarios show declining snowfall in the Huaihe River Basin. 2) Based on the analysis of historical data from meteorological stations, the snow events had a clear latitudinal distribution—the higher the latitude, the lower the occurrence of snow events. In contrast, as shown by the analysis results obtained under different climate change scenarios, the snow mass flux had a clear longitudinal distribution—the higher the longitude, the higher the snow mass flux. 3) In Scenario SSP370, 2050 is important because the snowfall changes from decreasing to increasing, and in 2065, the snowfall in most areas of the basin is greater than that in 2050. Furthermore, the snowfall along the southeastern coast of the basin has the greatest variability.

The present study was conducted to predict the current and future potential distribution of a tree species Pterocarpus marsupium Roxb. in Ranchi, Eastern India using ecological niche modeling. Nine environmental variables comprising of isothermality, precipitation of wettest and warmest quarter, annual temperature range, soil type, human influence index, elevation, slope and land use cover were used to model the distribution of the species. Climatic variables governed the predicted distribution of the species as they contributed 56.7% as compared to the other nonclimatic variables (43.3%). Northern parts exhibited the most suitable niche of the species as compared to south‐east and central parts that showed low probability of occurrence due to high disturbances caused by rapid urbanization as well as over exploitation of the species for timber, edible and medicinal uses. The average test area under the receiver operating curve (AUC) (0.921) as well as the partial AUC indicated good model performance. The projected change scenarios of RCP2.6, RCP4.5 and RCP6.0 for the year 2050 reveal that the climatically suitable areas will be drastically reduced in Ranchi. The population of the species is declining due to its exploitation from natural habitats for timber and medicinal uses and is listed as near‐threatened by the current IUCN‐Red lists. The findings of this paper will help to identify the potential habitats for further conservation of this near‐threatened species in the changing climatic conditions and increasing anthropogenic pressure. [ABSTRACT FROM AUTHOR]

Abstract After the publication of various multi-regional input–output (MRIO) databases over the past years and related environmental and socio-economic footprint analyses, the interest in these global value chain analyses is ever increasing. In order to provide forward-looking analysis of policy impacts, it is necessary to take MRIO data one step further, projecting them into the future. This paper introduces a simple approach to implementing existing climate change scenarios, such as the IEA energy technology perspective scenarios, in MRIO models. Rather than forecasting the world economy, the methodology is based on a mix of econometric estimations on the demand side and using specific information regarding technology development and its classical implementation in input–output tables. We apply this “what if” scenario approach to the most recent version of the MRIO system EXIOBASE. We compare the development of consumption- and production-based CO2 emissions up to 2030. As an additional example, we show that the energy dependency of Europe is reduced in the 2-degree scenario compared to the 6-degree scenario, while the material dependency is higher. We discuss the major shortcoming of the model, the assumption of constant shares if no better information is available, and suggest that this actually is an advantage for deducing policy implications.