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Natural hazard processes, as an inherent component of mountain environments, react sensitively to global warming. The main drivers of these changes are alterations in the amount, intensity or type of precipitation, glacier melting, or thawing of permafrost ice. The hazard responses can involve a change in hazard intensity or frequency (increasing or decreasing), a shift in their location or, a shift from one type of hazard to another. As climate change impacts vary in space and time, this variability must be considered when planning measures to protect populations and infrastructure from hazardous processes. To support this, we developed a method for assessing the climate sensitivity of small individual rock releases and larger rockfall processes. The method is based on a fuzzy logic approach and uses highly resolved climate scenario data, allowing application on a regional or even larger scale. The application in a study area of 700 km2 in the central Valais (Switzerland) shows that the impacts of climate change on natural hazard processes can vary quite substantially across small spatial scales. Generally, an increase in rockfall frequency and magnitude is simulated under future warming scenarios, especially at higher altitudes. However, at lower elevations and on south-exposed slopes, a decrease in freeze-thaw cycles leads to a decrease in material availability. This knowledge is essential in discussions of how climate change should be considered in hazard and disaster management. • Climate change impact on natural hazards varies essentially at small spatial scales. • Increase in rockfall frequency and magnitude under future warming scenarios • Decrease in rockfall frequency at lower altitudes due to less freeze-thaw cycles • Depicting high variation in altitude by using high resolution climate scenarios • Fuzzy logic is well suited to depict complex system behavior. [ABSTRACT FROM AUTHOR]

[Display omitted] • New index method for groundwater vulnerability assessment is applied. • Ecohydrogeological indicator link groundwater and groundwater-dependent ecosystems. • Method sensitivity verified by vulnerability assessment of two catchment areas. • Vulnerable groundwater in rivers valleys correspond with the occurrence of GDEs. Methods for assessing the groundwater-dependent ecosystems (GDEs) risk remain underexplored, particularly concerning the threat of groundwater pollution. This article presents the application of the DIRECT index – a multicriteria indicator – designed for the coupled assessment of groundwater vulnerability to pollution (GV) and the risk affecting GDEs. This ecological indicator modifies the DRASTIC index to enable the assessment of GDEs pollution risk and provide a more holistic approach. The assessment of GV and GDEs risk was conducted in areas with varying hydrogeological and geomorphological conditions within a temperate climate zone in Poland (Central Europe). In the reference scenario, GV was categorized as high and very high, constituting 53% and 41% in the Świder and Wisłok study areas, respectively. Spatial variability in GV was noted, with the highest values observed in river valleys where the majority of GDEs are located. The proportion of areas with very high and high-risk GDEs accounted for 73% and 44% of the total GDEs area in the study regions, respectively. Additionally, 59% and 45% of all springs in these areas were situated in zones deemed to have extremely high and high GDEs risk, respectively. A further objective of this study was to project how GV and the associated risk to GDEs might evolve up to the years 2050 and 2100. Four climate change scenarios were examined under the RCP4.5 and RCP8.5 emission pathways. The projections indicated that climate change effects, primarily increases in groundwater recharge, could elevate the pollution risk to GDEs by 2050, with a potential slight reduction by 2100. The application of the DIRECT index facilitated a scenario-based assessment of GV coupled with an evaluation of pollution risk to GDEs. This approach should prove beneficial for managing this risk in the context of climate change and aid in preserving the overall ecological status of GDEs. [ABSTRACT FROM AUTHOR]

• A mixed physical barrier (MPB) was used to control SWI in layered aquifers. • The MPB outperformed the single cut-off wall by up to 55 %. • This barrier system showed its effectiveness in all the layered cases studied. • The MPB proved effective, achieving a SWI reduction of up to 70%. Mixed physical barriers (MPB), which combine a cutoff wall and subsurface dam, were applied in heterogeneous coastal aquifer settings, and their ability to prevent seawater intrusion (SWI) was tested. The performance of MPB was examined in a typical stratified aquifer using laboratory experiments and numerical modelling. The performance of the MPB was compared to that of a single cut-off wall by measuring the percentage of reduction of the intrusion length. SEAWAT was used for validation purposes and to further evaluate the effectiveness of MPB in five additional realistic heterogeneous configurations. Experimental results show that the MPB effectively reduced the saltwater wedge length by up to 71%. The MPB outperformed the single cut-off wall by up to 55 %. Also, numerical results show that the MPB proved to be effective, achieving a SWI length reduction of up to 69% in the scenario regardless of the aquifer layering structures. Comparable performance was observed when the high K layer was confined between two low K layers or when the aquifer presented a monotonically increasing/decreasing K pattern. The findings of this study provide insights into the applicability of the MPB in realistic heterogeneous coastal aquifers. [ABSTRACT FROM AUTHOR]

Climate change impacts on probable maximum precipitation (PMP), the theoretical greatest rainfall depth for a specific area at the city level, are still unclear. In this study, we evaluate future changes in 24-h PMP in Hong Kong by statistically downscaling the outputs of five CMIP6 Global Climate Models (GCMs) under three Shared Socioeconomic Pathways (SSP) scenarios (i.e., SSP126, SSP370, and SSP585). The downscaling results demonstrate the methods' satisfactory ability to estimate PMP values in the reference period (1986–2014). The projections exhibit increases in annual maximum daily precipitation in the future (2071–2099) with the largest average increment of 28.8% in SSP585. The multi-model ensemble mean shows the increases in the 100-year return period annual maximum daily dew point temperature (Wm) and the dew point temperature of annual maximum daily precipitation (Ws) in the future. Meanwhile, larger changes in Wm relative to Ws lead to a rise in the moisture maximization ratio (Rm). With the combined effects of the changes in precipitation and dew point temperature, the 24-h PMP estimates corresponding to 10 km2 in the SSP126, SSP370, and SSP585 scenarios are expected to increase, reaching 1724.6 mm (30.8% increase), 1435 mm (8.3%), and 1788.1 mm (35.6%), respectively. • Probable Maximum Precipitation (PMP) in Hong Kong in future climate is estimated. • PMP estimates computed from statistically downscaled GCMs match the observations. • Future increases in annual maximum daily precipitation are projected. • Future changes in precipitation and dew point temperature increase PMP estimates. [ABSTRACT FROM AUTHOR]

Proper strategy for managing disposable meal boxes is crucial to achieve low-carbon waste systems. A life cycle assessment is conducted on the systems with incineration, landfilling and recycling, to compare the environmental impacts of managing waste polypropylene (PP) meal boxes and the alternative, polylactic acid (PLA) ones. Recycling PP meal boxes provides 1.81 tCO 2 -eq savings per ton of recycled waste to the climate change impact, but incinerating the residues contributes 1.96 tCO 2 -eq loads per ton of incinerated waste. In contrast, incinerating and landfilling one ton of waste PLA meal boxes contribute 0.45 tCO 2 -eq savings and 1.88 tCO 2 -eq loads, respectively. Using PLA meal boxes to replace PP ones is more suitable for the regions with high incineration ratio and low recycling ratio. With cleaner energy, recycling PP meal boxes becomes more valuable than energy recovery. This study calls for a conditional reconsideration of the strategical trade-off from long-term and environmental perspectives. [Display omitted] [ABSTRACT FROM AUTHOR]

• Agricultural total factor productivity (ATFP) in China increased fast but unevenly. • Climate change reduced ATFP growth in major agro-production areas in 1981–2019. • Temperature change affects ATFP more significantly than precipitation change. • Future climate change would negatively affect ATFP growth in southeast China. • Agricultural adaptation to climate should consider ATFP but not only production. Agricultural total factor productivity (ATFP) is a crucial measure that determines the aggregate agricultural output per unit of aggregate input. ATFP is sensitive to climate change; however, the impacts of climate change on ATFP have rarely been investigated despite its significance. In this study, we employ the Malmquist index methods to calculate the ATFP from 1981 to 2019 based on detailed census data at the prefecture level across China. Furthermore, we project the ATFP in the period of 2020–2060 under the Societal Development Pathway (SSP) 1–2.6 and SSP3-7.0. The results showed that, during 1981–2019, the ATFP of China increased significantly, ranging from 0.74 to 38.95. The joint changes in temperature and precipitation benefited ATFP growth in some prefectures in northwestern, northeastern and southern China but reduced it in the Huang-Huai-Hai Region. During 2031–2060, ATFP is projected to be more positively affected by climate change in northern China than in southern China. Some regions such as the Northwest Arid Region, most of the Northeast China and Southwest China Region will benefit from climate change. However, some major agricultural production areas including the Huang-Huai-Hai Region and southeast China are expected to be negatively affected by climate change. Our findings highlight the need to consider the impacts of climate change on ATFP when developing climate adaptation strategies in agriculture. [ABSTRACT FROM AUTHOR]

A continued upward trend in global greenhouse gas emissions is estimated to see average temperatures rise by 2.7 °C before 2100. This warming effect presents risks to global infrastructure and built assets that should be identified to minimise negative consequences on inhabitants. For higher education estates, a key challenge is to maintain high indoor environmental quality standards whilst mitigating increased cooling loads under future climates. Findings from this meta-analysis suggest that existing passive cooling mechanisms may be insufficient to tolerate predicted increases in summertime temperatures, even in cooler UK climates. Across typologies, peak electricity demand for mechanically cooled higher education buildings was estimated to increase the most for halls of residences (4–27 %) and the least for laboratory buildings (0–5%) by 2080. Under a high emission scenario, the increase in total annual energy consumption by 2050 varies widely across studies (+5–33 %), although almost all cases predict a greater increase in cooling energy consumption than decrease in heating energy consumption. Probabilistic climate projections are the predominant source of uncertainty for predictions of energy demand, with the difference between low and high emission scenarios contributing to 34–44 % of variability in predicted annual cooling energy consumption in 2050. Further research is warranted to identify the most likely indicators of future building performance across a range of university building typologies. This work provides recommendations on expanding the evidence basis through development of standardised climate change impact assessments. [Display omitted] • Existing passive cooling mechanisms insufficient to avoid overheating under future climates. • Relative cooling load increases outweigh decreases in heating loads in almost all predictions. • Climate change impact modelling approaches vary widely, limiting inter-study comparisons. • Key considerations and recommendations are provided for a more robust, standardised framework. [ABSTRACT FROM AUTHOR]

[Display omitted] • Material-focused strategy achieves 54% more carbon credit than the energy-focused. • Carbon credit from biogas energy recovery drop by 23% and 38% in 2030 and 2060. • Carbon credit from biogas material recovery boost by 35% and 59% in 2030 and 2060. • Material-focused biogas strategies better align with climate change goals. Two novel biogas upgrading strategies that recover high-value chemicals or CO 2 liquid fertilizer from biogas besides biomethane were evaluated from the view of global warming potential (GWP) through life cycle assessment in comparison with conventional approaches. Results show that the scenarios producing biomethane with nano calcium carbonate or CO 2 liquid fertilizer from biogas present significantly lower GWP (–3.4 kgCO 2 -eq/Nm3-biogas and –4.4 kgCO 2 -eq/Nm3-biogas, respectively), compared to combined heat and power scenario (–2.4 kgCO 2 -eq/Nm3-biogas) and biogas upgrading by high pressure water scrubbing scenario (–1.3 kgCO 2 -eq/Nm3-biogas). The carbon sequestration and utilization from CO 2 -rich water significantly enhanced carbon reduction in overall biogas management. Furthermore, considering cleaner electricity in the future, strategies focusing on managing biogas for materials will align more with climate change goals than energy-focused strategies. This study provides insight for decision-makers in developing roadmaps for carbon reduction pathways in biogas-relating sectors. [ABSTRACT FROM AUTHOR]

Fluoride (F) exhibits toxicity in excess but provides health benefits in moderate concentrations. While extensive research has focused on elevated F levels in groundwater, studies are scarce on F concentrations in surface water, a crucial drinking water source. In this study, we compiled a comprehensive database encompassing three decades of oral epidemiological data from over 140,000 Chinese children (6–17 years) and F concentrations in 160 freshwater lakes and reservoirs across China to investigate the health risks, environmental drivers, and future evolution of F in surface water. We found that insufficient F intake through surface water does not cause dental fluorosis but rather dental caries in children. East and South China regions bear the highest health burdens, quantified in Disability-adjusted life years (DALYs), reflecting the extensive use of surface water for drinking purposes. Multiple linear regression analysis indicated that soil & geology largely control F concentrations, with precipitation exerting a significant negative effect, accounting for 16.5% of the explained variance. As China anticipates increased precipitation in the future, a potentially elevated risk of fluorine deficiency in South and East China demands vigilant attention. [Display omitted] • The dose-response curve based on fluoride intake via drinking water was developed. • Fluorine deficiency was prevalent in Chinese residents with surface water sources. • More than half of the attributable DALYs were concentrated in South and East China. • A regression model attributed national-scale lake F variability to 5 key predictors. • Climate change may exacerbate fluorine deficiency in children of South and East China. [ABSTRACT FROM AUTHOR]

To get a full understanding of the impacts of global change on the catchment-scale sediment balance, models are needed that combine hillslope soil erosion processes with channel morphodynamics. Here we present a modification to the SPHY-MMF model that includes a novel channel morphodynamics module, which determines erosion and deposition in rills and channels. We applied the model to a Mediterranean study area in southeast Spain, in which we show that channel erosion contributes substantially (35%–40%) to the total sediment yield, highlighting the importance of accounting for channel erosion in catchment-scale sediment budget estimations. The climate change scenarios show that the different erosional processes (i.e. sheet, rill, channel) are projected to decrease or increase, depending on the projected change in annual and extreme precipitation. From this we conclude that interactions between different erosional and depositional processes should be considered when studying the impact of global change on the catchment-scale sediment balance. [Display omitted] • We present a novel channel morphodynamics module for the SPHY-MMF model. • The new module determines erosion and deposition in rills and channels. • We assessed the impacts of land use and climate change on the sediment balance. • Channel erosion contributes substantially (35%–40%) to the catchment sediment yield. • Differences in future annual and extreme precipitation control the sediment balance. [ABSTRACT FROM AUTHOR]

Accurate estimation of reference evapotranspiration (ET 0) is very important in hydrological cycle research, and is essential in agricultural water management and allocation. The application of the standard model (FAO-56 Penman-Monteith) to estimate ET 0 is restricted due to the absence of required meteorological data. Although many machine learning algorithms have been applied in modeling ET 0 with fewer meteorological variables, most of the models are trained and tested using data from the same station, their performances outside the training station are not evaluated. This study aims to investigate generalization ability of the random forest (RF) algorithm in modeling ET 0 with different input combinations (refer to different circumstances in missing data), and compares this algorithm with the gene-expression programming (GEP) method using the data from 24 weather stations in a karst region of southwest China. The ET 0 estimated by the FAO-56 Penman-Monteith model was used as a reference to evaluate the derived RF-based and GEP-based models, and the coefficient of determination (R 2), Nash-Sutcliffe coefficiency of efficiency (NSCE), root of mean squared error (RMSE), and percent bias (PBIAS) were used as evaluation criteria. The results revealed that the derived RF-based generalization ET 0 models are successfully applied in modeling ET 0 with complete and incomplete meteorological variables (R 2, NSCE, RMSE and PBIAS ranged from 0.637 to 0.987, 0.626 to 0.986, 0.107 to 0.563 mm day−1, and −2.916% to 1.571%, respectively), and seven RF-based models corresponding to different incomplete data circumstances are proposed. The GEP-based generalization ET 0 models are also proposed, and they produced promising results (R 2, NSCE, RMSE and PBIAS ranged from 0.639 to 0.944, 0.636 to 0.942, 0.222 to 0.555 mm day−1, and −1.98% to 0.248%, respectively). Although the RF-based ET 0 models performed slightly better than the GEP-based models, the GEP approach has the ability to give explicit expressions between the dependent and independent variables, which is more convenient for irrigators with minimal computer skills. Therefore, we recommend applying the RF-based models in water balance research, and the GEP-based models in agricultural irrigation practice. Moreover, the models performance decreased with periods due to climate change impact on ET 0. At last, both of the two methods have the ability to assess the importance of predictors, the order of the importance of meteorological variables on ET 0 in Guangxi is: sunshine duration, air temperature, relative humidity, and wind speed. [ABSTRACT FROM AUTHOR]

An innovative approach of using agro-ecological zones (AEZs), instead of using political boundaries, has been adopted for climate change impact analysis on rice production of India. The analysis has been carried out by using a process-based Crop Simulation Model (CSM)-CERES-Rice fed with improved state of art bias corrected climate projections from eight Global Climate Models (GCMs) for four expected climatic scenarios- Representative Concentration Pathways (RCP 2.6, 4.5, 6.0 and 8.5). Using weather-soil-crop information along with year-wise effect of CO 2 increase assumption for different RCPs as input to the crop model, simulations were performed for the base period (1976–2005) as well as three future periods (2020s: 2006–2035, 2050s: 2036–2065, and 2080s: 2066–2095) for insight understanding of climate change impact on rice yield. Model simulated rice yields of future periods were compared with that of the base period to quantify the climate change impact. Results based on multi-GCM ensemble show expected increase in rice yield of most of the AEZs in RCP 2.6 but as on moving towards RCP 8.5 through RCP 4.5 and 6.0, the positive impact on rice yield in RCP 2.6, in major rice producing zones, is expected to mitigate and lead to the negative impact by 2080s. Large spatiotemporal variability is expected in most of the zones with humongous variability in arid and hilly zones. The overall change in spatial rice yield in India taking all used GCM-RCP combinations in consideration is expected to vary from 1.2 to 8.8%, 0.7 to 12.6% and −2.9 to 17.8% due to the expected climate change in 2020s, 2050s and 2080s, respectively. • Climate change impact on AEZs' rice yield is estimated by using multi-GCMs ensemble. • Major rice producing AEZs may experience negative impact of climate change by the end of 21st century. • Increasing yield in majority of AEZs till 2050s is expected to decrease in 2080s. [ABSTRACT FROM AUTHOR]

Abstract Rivers are among the ecosystems most sensitive to climate change. Whilst methods quantifying the impact and uncertainty of climate change on flow regime are well-established, the impact on hydroecological response is not well understood. Typically, investigative methods are qualitative in nature or follow quantitative methods of limited scope, whilst the effect of uncertainty is frequently minimised. This paper proposes a coupled hydrological and hydroecological modelling framework to assess the impact of climate change on hydroecological response quantitatively. The characterisation and reduction of modelling uncertainties was critical to the development of the framework. The ability of the framework is illustrated through application to a case study river, the River Nar, Norfolk, England, using the UKCP09 probabilistic climate projections (high emissions scenario, SRES A1F1). The results show that, by the 2050s, a reduction in instream biodiversity is virtually certain if future emissions follow the assumptions of SRES A1F1. Disruption to the natural low flow processes, essential to ecosystem functioning, is also indicated. These findings highlight the importance of the framework in water resources adaptation, particularly with respect to future environmental flows management. Highlights • We propose a modelling framework to assess hydroecological response to climate change. • Characterisation and reduction of uncertainty considered throughout. • Provides quantifiable measure of the uncertainty in the hydroecological projections. • Case study sees reduced instream biodiversity and disruption to flow processes. [ABSTRACT FROM AUTHOR]

Abstract Using projections of surface solar radiation and temperature from 23 CMIP5 global climate models for two climate change scenarios (RCP4.5 & 8.5) we quantify the average change in PV electricity production expected in the years 2060–2080 compared to the present (2007–2027). We upsample daily radiation data to hourly resolution with a sinusoidal diurnal cycle model and split it into direct and diffuse radiation with the semi-empirical BRL model as input to a PV electricity generation model. Locally, changes in PV potential from −6% to +3% in annual and −25% to +10% in monthly means are shown. These projections are combined with a PV deployment scenario and show countries benefitting from increased PV yields include Spain, France, Italy and Germany. We also calculate uncertainties when calculating PV yield with input data at daily or lower resolution, demonstrating that our method to derive synthetic hourly profiles should be of use for other researchers using input data with low temporal resolution. We conclude that PV is an attractive and no-regrets investment in Europe irrespective of future climate change, and can continue to play a key role in energy system decarbonisation. Highlights • We simulate future PV potential based on daily CMIP5 radiation and temperature data. • PV output varies locally by −6% to +3% on annual and −25% to +10% on monthly scales. • Southern Europe sees increased PV output, while northern Europe sees a decrease. • Synthetic hourly irradiance based on daily data is necessary for accurate results. • PV is a no-regrets investment in Europe irrespective of climate change. [ABSTRACT FROM AUTHOR]

Abstract In warming Europe, we are witnessing a growth in urban population with aging trend, which will make the society more exposed and vulnerable to extreme weather events. In the period 1950–2015 the occurrence of extreme heat waves increased across European capitals. As an example, in 2010 Moscow was hit by the strongest heat wave of the present era, killing more than ten thousand people. The cold extremes will have decreasing tendency as global warming progresses, however due to higher variability of future climates, the cold wave hazard may remain locally important threat. Moreover, the heat and cold-related mortality will be enhanced with foreseen demographic evolution in European cities. Here we focus on larger metropolitan areas of European capitals (EU28 plus Moscow, Oslo and Zurich). By using an ensemble of eight EURO-CORDEX models under the RCP8.5 scenario, we detected heat waves and cold waves events by deployment of Heat Wave Magnitude Index daily and its cold wave counterpart. We introduce a ranking procedure based on ensemble predictions using the median of metropolitan grid cells for each capital, and population density as a proxy to quantify the future exposure. All the investigated European metropolitan areas will be more vulnerable to extreme heat in the coming decades. Based on the impact ranking, results reveal that cold waves will represent some threat in mid of the century but they will not be the major threat in any of European capitals, and that they are projected to completely vanish by the end of this century. On the contrary, in near, but even more so in distant future, extreme heat events in European capitals will be not exclusive to traditionally exposed areas such as the Mediterranean and the Iberian Peninsula. The ranking of European capitals based on their exposure to extreme heat is of paramount importance to decision makers in order to mitigate the heat related mortality, especially with the foreseen increase of global mean temperature. Furthermore, this simple comparative indicator helps communicating the global, complex and impersonal issue of climate change locally thus contributing to raise awareness and call for action. [ABSTRACT FROM AUTHOR]

Abstract This study investigates how Swedish municipalities work to reduce the climate change impact of building construction. It focuses on current practices related to promoting the use of sustainable construction materials and on barriers to environmental requirements in construction, in particular environmental performance requirements based on LCA procedures. Municipalities were surveyed about the existence of municipal policies dealing with environmental issues in construction, the knowledge level about these issues, and the measures and requirements used to promote materials with low climate change impact. The survey was followed by semi-structured interviews about current practices and barriers to environmental requirements in construction. Results show that large municipalities are more likely to have dedicated policies and implement more measures than their smaller counterparts. However, willingness to implement future measures and knowledge of sustainable construction do not vary significantly with municipality population. Efforts are often limited to procurement, municipal construction projects and discussions with stakeholders. When requirements are set, they are almost always based on prescribing a technical solution (e.g. use of timber) rather than assessing environmental performance (e.g. calculating greenhouse gases emissions with a LCA tool). Measures that municipalities can take as public authorities are restricted by the law, which remains ambiguous as to the legality of environmental performance requirements. Legal issues, limited knowledge and resources appear to be the main barriers to environmental performance requirements in construction. A strategy is proposed to overcome these issues. After setting appropriate targets and securing resources, the municipality should establish dialogue with developers and constructors. It should then require them to provide inventory data for new construction projects. The municipality should make sure appropriate guidance and standardised methods are available. Information requirements should be strengthened over time, and practitioners should progressively become familiar with data collection and LCA methods. When data and knowledge gaps are filled, the municipality can set environmental performance requirements for new buildings. Highlights • A survey reviews how municipalities reduce the climate impact of construction. • Interviews investigate barriers to environmental performance requirements. • Large municipalities have more resources to mobilize but still face barriers. • Barriers relate to knowledge, data, resources, the law, and national guidance. • A suggested strategy is to improve dialogue and use information requirements. [ABSTRACT FROM AUTHOR]

The response of rice growth and yield to climate variations in different climate zones and the underlying mechanisms need to be better understood. The objectives were to investigate the variations of rice growth and yield with climate variables in different climate zones across China in the past decades and their relations with varietal traits, to gain insights into rice response and adaptation mechanisms to climate variations. Based on long-term experimental data during 2004–2016 at five representative stations across China, the first-order difference, Mann-Kendall test, multi-collinearity detection, Pearson correlation, stepwise linear regression and structural equation modeling were used. Temperature and precipitation during the growing seasons generally increased although solar radiation declined. Rice yield was more closely associated with temperature during the vegetative growth period at Yanting station, rainfall during the whole growth period at Changshu station and solar radiation during the reproductive growth period at Shenyang station, respectively. Early-season rice yield was more related to solar radiation during the vegetative growth period at Qianyanzhou and Taoyuan stations. Late-season rice yield was more correlated with rainfall during the reproductive growth period at Qianyanzhou station and maximum temperature during the reproductive growth period at Taoyuan station, respectively. Based on the coefficient of determination, climate change and traits explained 13–85% and 47–95% yield variability, respectively. Rice yield response to climate variation diverges strongly among climate zones across China and is subject to trait variation. This study provides new insights into the mechanisms underlying rice response and adaptation to climate changes, supporting the need for trait-based adaptation strategies to develop new cultivars. • Impacts of climate variations on crop traits are lacking. • A significant increase in temperature and a decline in radiation in past decades. • Climate and traits accounted for 13–85% and 47–95% of interannual yield variations. • Yield response to climate variation is trait-sensitive and differs in climate zones. • Environment-specific ideotypic traits-based adaptation strategies are promising. [ABSTRACT FROM AUTHOR]

In July 2021, intense rainfall in parts of Western Europe was followed by unprecedented flash flooding. The flooding killed many people, mostly in Germany, which was subsequently blamed on a lack of preparation by either authorities or the broader populace. Knowledge about people's awareness of a hazard, its impacts and possible adaptation can help improve hazard management. We used social media data to assess situational awareness, sentiments and behaviour before, during and after the flood. We analysed nearly 58,000 German Twitter (now X) messages about the flood using machine learning-based unsupervised topic modelling. We showed that message frequency translated into four phases with message content suggesting sender priorities shifted in each phase of the flood. Besides messages with weather updates, correlated topics included 'Solidarity, recovery and aid' and 'Grief and empathy' (together 22% of all tweets) and a set of four topics about climate change attribution, extreme weather, long-term flood protection measures and politics (together 38% of all tweets). Many topics depended on the phase of the flood and on the distance from the affected areas. For those near the affected areas, tweets about evacuation (in peak phase) and damage assessment (in recovery phase) were particularly prominent. The high engagement on Twitter to seek weather information might indicate awareness of the extreme climatic conditions before the flood, but the severity was unexpected. Social media data provided unprecedent citizen-generated real-time information. We discuss how rapid automated analysis could contribute to disaster communication and risk mitigation. [ABSTRACT FROM AUTHOR]

Robust crop yield projections under future climates are fundamental prerequisites for reliable policy formation. Both process-based crop models and statistical models are commonly used for this purpose. Process-based models tend to simplify processes, minimize the effects of extreme events, and ignore biotic pressures, while statistical models cannot deterministically capture intricate biological and physiological processes underpinning crop growth. We attempted to integrate and overcome shortcomings in both modelling frameworks by integrating the dynamic linear model (DLM) and random forest machine learning model (RF) with nine global gridded crop models (GGCM), respectively, in order to improve projections and reduce uncertainties of maize (Zea mays L.) and soybean (Glycine max [L.] Merrill) yield projections. Our results demonstrated substantial improvements in model performance accuracy by using RF in concert with GGCM across China's maize and soybean belt. This improvement surpasses that achieved using DLM. For maize, the GGCM+RF models increased the r values from 0.15 to 0.61–0.64–0.77 and decreased nRMSE from approximately 0.20 to 0.50–0.13–0.17 compared with using GGCM alone. For soybean, the models increased r from 0.37 to 0.70–0.54–0.70 and decreased nRMSE from 0.17 to 0.35–0.17–0.20 compared with using GGCM alone. The main factors influencing maize yield changes included chilling days (CD), crop pests and diseases (CPDs), and drought, while for soybean the primary influencing factors included CPD, tropical days (based on exceeding a maximum temperature), and drought. Our approach decreased uncertainties by 33–78% for maize and by 56–68% for soybean. The main source of uncertainty for GGCM was the crop model. For GGCM+RF, the main source of uncertainty for the 2040–2069 period was the global climate model, while the main source of uncertainty for the 2070–2099 period was the climate scenario. Our results provide a novel, robust, and pragmatic framework to constrain uncertainties in order to accurately assess the impact of future climate change on crop yields. These results could be used to interpret future ensemble studies by accounting for uncertainty in crop and climate models, as well as to assess future emissions scenarios. • Machine learning integration with crop models improves maize and soybean yield projections. • Investigating crop yield responses to extreme climate events, pests, and diseases. • Employing machine learning in combination with crop models considerably reduces overall crop yield projection uncertainty. • Crop models are the dominant source of uncertainty in crop yield projections. • For hybrid models, GCM and SSP become the main source of uncertainty, particularly during the period of 2070–2099. [ABSTRACT FROM AUTHOR]

The Mediterranean Sea represents a natural laboratory to infer the possible impacts of climate change and ocean acidification. In this article, we report the deteriorating ability of sea urchin larvae (Paracentrotus lividus) to cope with toxicity of a reference contaminant (Cu EC 50) over the past 20 years and assessed the influence of 5 environmental factors from satellite measurements. This timeframe was divided in before and after January 2016 (46.57 μg/L vs 28.56 μg/L respectively, p < 0.001). In the second subset of data, correlation of the biological variable with CO 2 and pH strengthened compared to the first part (rCO 2 -EC 50 : −0.21 vs −0.83 and rpH-EC 50 : 0.25 vs 0.87 respectively), with a causal link starting from one year and ending 4 months prior to EC 50 measurements. Considering the continuous increase in CO 2 concentrations recorded recently, this study could reveal a rapid deterioration of the health condition of this population of sea urchins in a coastal ecosystem. NIn this study, EC 50 values of the last two decades originating from the same natural population of sea urchins in the Mediterranean Sea have been correlated to 5 principal environmental factors measured with satellite observation. A causality link has also been assessed. [Display omitted] • Climate change and ocean acidification affect reproductive health of sea urchins. • Larvae ability to cope with copper deteriorated in the past 20 years. • Contribution of CO2, pH and temperature worsened in the past 7 years. [ABSTRACT FROM AUTHOR]

Regional Climate Models (RCMs) generate past and future climate simulations that serve as input for subsequent modelling of impact projections. The most recent coordinated regional climate modelling initiative, CORDEX, provides RCM data for Europe with increased spatial resolution (12.5 km for the CORDEX EUR-11 ensemble) and based on the new Representative Concentration Pathways (RCPs). RCM data from the previous initiative ENSEMBLES had a spatial resolution of 25 km and was based on the SRES emission scenarios. In this contribution we explore the development from ENSEMBLES to CORDEX in a hydrological impact study for the Upper Danube, focussing on the representation of past climate and projections of the future for climate and resulting river discharge. We replicated a hydrological modelling framework, including RCM downscaling and bias correction, that used data of 21 ENSEMBLES RCMs under SRES A1B emission scenario ( Kling et al., 2012 ), and now applied CORDEX EUR-11 data of 32 RCM simulations under RCP4.5 and RCP8.5 emission scenarios. Results with CORDEX show a small improvement in the representation of historical precipitation, but a decline in the accuracy of historical temperature simulations, despite the increase in spatial model resolution. The tendency of ENSEMBLES climate projections is reproduced with the CORDEX RCMs, albeit with slightly higher precipitation in the CORDEX data, yielding a less pronounced reduction in future mean annual discharge for the Upper Danube at Vienna. The previously identified change in discharge seasonality – increasing winter discharge and decreasing summer discharge – is confirmed with the new CORDEX data. Updating climate impact simulations in the presented rigorous replication framework allows analysing specific new information in the latest generation climate model data as well as crystallizing essential expectable patterns of change that need to be addressed in adaptation strategies. [ABSTRACT FROM AUTHOR]

The relation between climate change and its potential effects on the stability of slopes remains an open issue. For rainfall induced landslides, the point consists in determining the effects of the projected changes in the duration and amounts of rainfall that can initiate slope failures. We investigated the relationship between fine-scale climate projections obtained by downscaling and the expected modifications in landslide occurrence in Central Italy. We used rainfall measurements taken by 56 rain gauges in the 9-year period 2003–2011, and the RainFARM technique to generate downscaled synthetic rainfall fields from regional climate model projections for the 14-year calibration period 2002–2015, and for the 40-year projection period 2010–2049. Using a specific algorithm, we extracted a number of rainfall events, i.e. rainfall periods separated by dry periods of no or negligible amount of rain, from the measured and the synthetic rainfall series. Then, we used the selected rainfall events to forcethe Transient Rainfall Infiltration and Grid-Based Regional Slope-Stability Model TRIGRS v. 2.1. We analyzed the results in terms of variations (or lack of variations) in the rainfall thresholds for the possible initiation of landslides, in the probability distribution of landslide size (area), and in landslide hazard. Results showed that the downscaled rainfall fields obtained by RainFARM can be used to single out rainfall events, and to force the slope stability model. Results further showed that while the rainfall thresholds for landslide occurrence are expected to change in future scenarios, the probability distribution of landslide areas are not. We infer that landslide hazard in the study area is expected to change in response to the projected variations in the rainfall conditions. We expect our results to contribute to regional investigations of the expected impact of projected climate variations on slope stability conditions and on landslide hazards. [ABSTRACT FROM AUTHOR]

Different aspects of climate change, such as increased temperature, changed rainfall and higher atmospheric CO 2 concentration, all have different effects on crop yields. Process-based crop models are the most widely used tools for estimating future crop yield responses to climate change. We applied APSIM crop simulation model in a dry Mediterranean climate with Jordan as sentinel site to assess impact of climate change on wheat production at decadal level considering two climate change scenarios of representative concentration pathways (RCP) viz. , RCP4.5 and RCP8.5. Impact of climatic variables alone was negative on grain yield but this adverse effect was negated when elevated atmospheric CO 2 concentrations were also considered in the simulations. Crop cycle of wheat was reduced by a fortnight for RCP4.5 scenario and by a month for RCP8.5 scenario at the approach of end of the century. On an average, a grain yield increase of 5 to 11% in near future i.e. , 2010s–2030s decades, 12 to 16% in mid future i.e. , 2040s–2060s decades and 9 to 16% in end of century period can be expected for moderate climate change scenario (RCP4.5) and 6 to 15% in near future, 13 to 19% in mid future and 7 to 20% increase in end of century period for a drastic climate change scenario (RCP8.5) based on different soils. Positive impact of elevated CO 2 is more pronounced in soils with lower water holding capacity with moderate increase in temperatures. Elevated CO 2 had greater positive effect on transpiration use efficiency (TUE) than negative effect of elevated mean temperatures. The change in TUE was in near perfect direct relationship with elevated CO 2 levels (R 2 > 0.99) and every 100-ppm atmospheric CO 2 increase resulted in TUE increase by 2 kg ha − 1 mm − 1 . Thereby, in this environment yield gains are expected in future and farmers can benefit from growing wheat. [ABSTRACT FROM AUTHOR]

Extreme climatic events are likely to become more frequent owing to global warming. This may put additional stress on critical infrastructures with typically long life spans. However, little is known about the risks of multiple climate extremes on critical infrastructures at regional to continental scales. Here we show how single- and multi-hazard damage to energy, transport, industrial, and social critical infrastructures in Europe are likely to develop until the year 2100 under the influence of climate change. We combine a set of high-resolution climate hazard projections, a detailed representation of physical assets in various sectors and their sensitivity to the hazards, and more than 1100 records of losses from climate extremes in a prognostic modelling framework. We find that damages could triple by the 2020s, multiply six-fold by mid-century, and amount to more than 10 times present damage of €3.4 billion per year by the end of the century due only to climate change. Damage from heatwaves, droughts in southern Europe, and coastal floods shows the most dramatic rise, but the risks of inland flooding, windstorms, and forest fires will also increase in Europe, with varying degrees of change across regions. Economic losses are highest for the industry, transport, and energy sectors. Future losses will not be incurred equally across Europe. Southern and south-eastern European countries will be most affected and, as a result, will probably require higher costs of adaptation. The findings of this study could aid in prioritizing regional investments to address the unequal burden of impacts and differences in adaptation capacities across Europe. [ABSTRACT FROM AUTHOR]

Standardized drought indices have been traditionally used to identify and assess droughts because of their simplicity and flexibility to compare the departure from normal conditions across regions at different timescales. Nevertheless, the statistical foundation of these indices assumes stationarity for certain aspects of the climatic variables, which could no longer be valid under climate change. This contribution provides a framework to analyze the impact of climate change on meteorological and hydrological droughts, considering shifts in precipitation and temperature, adapted to a Mediterranean basin. For this purpose, droughts are characterized through a combination of relative standardized indices: Standardized Precipitation Index (rSPI), Standardized Precipitation Evapotranspiration Index (rSPEI) and a Standardized Flow Index (rSFI). The uncertainty and the stationarity of the distribution parameters used to compute the drought indices are assessed by bootstrapping resampling techniques and overlapping coefficients. For the application of the approach to a semiarid Mediterranean basin (Jucar River Basin), the Thornthwaite scheme was modified to improve the representation of the intra-annual variation of the potential evapotranspiration and low flow simulation in hydrological modelling was improved for a better characterization of hydrological droughts. Results for the Jucar basin show a general increase in the intensity and magnitude of both meteorological and hydrological droughts under climate change scenarios, due to the combined effects of rainfall reduction and evapotranspiration increase. Although the indicators show similar values for the historical period, under climate change scenarios the rSPI could underestimate the severity of meteorological droughts by ignoring the role of temperature. [ABSTRACT FROM AUTHOR]

[Display omitted] • All year climate change impact in an urban catchment is evaluated using SWMM. • Average annual runoff and seasonal extreme flows are expected to increase. • An overall increase in the number of flow events is modelled, notably in winter. • A holistic stormwater management is needed to mitigate and adapt to climate change. • The benefit of using convection-permitting climate models is evident. Urbanization coupled with climate change is expected to put pressure on the urban stormwater network. To predict and mitigate the effects of these trends, accurate modeling of urban stormwater changes is required at scales and resolutions meaningful to stormwater management. Although numerous studies have analyzed the effect of climate change on urban flooding risk using the event-based approach, none have incorporated the continuous modelling approach to investigate the whole spectrum of changes in an urban catchment. This study analyzes seasonal changes in future urban hydrological behavior using a mini-ensemble of six state-of-the-art climate model projections and a calibrated hydrological-hydraulic Storm Water Management Model (SWMM). The modelling results show future changes in seasonal and monthly hydrological behavior. The notable winter warming is the major driver in the future snow processes, resulting in considerably less snow days and an increase in the flow events frequency during the winter months. The modelling results also suggest an increase in the annual maximum hourly flow in all seasons, with the clearest trend modelled in winter. Monthly average runoff during the cold period is modelled to increase, while no clear trends are detected for the rest of the year. There is a clear added benefit in using convection-permitting regional climate models throughout the year. Overall, the climate change mitigation and adaption strategies in urban catchments should focus more on the whole spectrum of changes rather than only on urban pluvial flooding risk. These findings call for a transition from traditional to a more advanced stormwater management. [ABSTRACT FROM AUTHOR]

Hydrological impact assessments are increasingly performed at fine spatial and temporal resolutions in order to resolve local-scale changes under a future climate. Apart from the uncertainty represented by different climate models, emission scenarios and post-processing methods, the local-scale internal variability of the climate can be a major source of uncertainty for hydrological projections. To assess the latter at the catchment scale, this paper presents a methodology which is particularly suitable for spatially distributed hydrological models. An ensemble of daily precipitation and daily mean temperature realizations on a high-resolution grid is simulated from stochastic weather generators (WGs) trained on historical data and equipped with climate change information obtained from a regional climate model. Based on the resulting simulated daily runoff data, the significance of changes in the runoff regime is assessed using a statistical hypothesis test, and the variability contributed by the two input variables is quantified using the analysis of variance (ANOVA). As a proof of concept, simulations on a 1-km grid over a period of 19 years are carried out for nine catchments in central Norway. Significant changes in runoff regimes are found, indicating that the trends introduced in the WGs are not overwhelmed by the local-scale internal variability. Variability in the runoff simulations varies substantially throughout the year; it is highest in periods with potential snowmelt and lowest during winter. Temperature is the dominant source of variability in the colder months (November–March) due to its influence on rainfall and snowmelt. High variability in May–June is contributed comparably by both temperature and precipitation. In summer and early autumn the runoff variability is precipitation dominated. The results are in line with findings in the literature where the runoff variability is driven by the large-scale internal climate variability. This indicates that ignoring the local-scale internal variability may yield an underestimation of the overall variability in runoff projections and projected changes. • Gridded climate input data simulated using stochastic weather generators. • Significance of changes in runoff regime assessed using a statistical test. • Runoff variability due to input variables quantified using ANOVA. • Results in line with findings based on large-scale internal climate variability. • Runoff projections need to consider local-scale internal variability of climate. [ABSTRACT FROM AUTHOR]

Leaf rust is responsible for significant wheat yield losses. Its occurrence and severity have increased in recent years, partly because of warmer climate. It is therefore critical to understand and anticipate the effects of climate change on leaf rust. Direct climate effects and indirect effects via host plants that provide a biophysical environment for disease development were both considered. The coupled STICS-MILA model simulates both crop and pathogen dynamics in a mechanistic way and their interaction is managed by two sub-models: one calculating the microclimate within the canopy and the other converting numbers of spores and lesions to affected surfaces. In this study, STICS-MILA was first calibrated and evaluated using leaf rust severity observed at various sites in France for multiple years. STICS-MILA was then run on three contrasting French sites under 2.6, 4.5 and 8.5 RCP future climate scenarios. Results focused firstly on changes in disease earliness and intensity, secondly on disease dynamics, particularly the synchronism between plant and disease developments, and finally on elementary epidemic processes. The calibration and evaluation of STICS-MILA revealed a high sensitivity to the initial amount of primary inoculum (a forcing variable in STICS-MILA) and thus the need to properly simulate the summering and overwintering pathogen survival. The simulations in the context of future climate showed a significant change in host-pathogen synchronism: in the far future, according to RCP 4.5 and 8.5 scenarios, disease onset is expected to occur not only with an advance of around one month but also at an earlier developmental stage of wheat crops. This positive effect results from rising temperatures, nevertheless partly counter-balanced during spring by lower wetness frequency. The crop growth accelerates during juvenile stages, providing a greater support for disease development. The resulting microclimate shortens latency periods and increases infection and sporulation efficiencies, thus causing more infectious cycles. An increase of final disease severity is thus forecasted with climate change. [ABSTRACT FROM AUTHOR]

• Most existing climate niche models predict species realized niche. • A fundamental climate niche model was developed based on forest occurrence data. • A novel method for climate niche model optimization was devised. • This modeling approach is likely applicable to other species. • Projected fundamental niches can improve conservation and assisted migration planning. Species climate niche models (CNMs) have been widely used for assessing climate change impact, developing conservation strategies and guiding assisted migration for adaptation to future climates. However, the CNMs built based on species occurrence data only reflect the species' realized niche, which can overestimate the potential loss of suitable habitat of existing forests and underestimate the potential of assisted migration to mitigate climate change. In this study, we explored building a fundamental climate niche model using widely available species occurrence data with two important forest tree species, lodgepole pine (Pinus contorta Dougl. ex Loud.) and Douglas-fir (Pseudotsuga menziesii Franco.), which were introduced to many countries worldwide. We first compared and optimized three individual modeling techniques and their ensemble by adjusting the ratio of presence to absence (p/a) observations using an innovative approach to predict the realized climate niche of the two species. We then extended the realized climate niches to their fundamental niches by determining a new cut-off threshold based on species occurrence data beyond the native distributions. We found that the ensemble model comprising Random Forest and Maxent had the best performance and identified a common cut-off threshold of 0.3 for predicting the fundamental climate niches of the two species, which is likely applicable to other species. We then predicted the fundamental climate niches of the two species under current and future climate conditions. Our study demonstrated a novel approach for predicting species' fundamental climate niche with high accuracy using only species occurrence data, including both presence and absence data points. It provided a new tool for assessing climate change impact on the future loss of existing forests and implementing assisted migration for better adapting to future climates. [ABSTRACT FROM AUTHOR]

• Field experiments representing climate change in Central Europe are scarce. • A space-for-time approach simulating a temperature increase of 2.5–3°C was tested. • Increased temperature and drought stress caused a marked decrease in yield. • Better-adapted cultivars were identified. • Traits contributing to better adaptation were identified. Climate change in Central Europe is expected to influence crop growth through frequent drought and heat extremes. Wheat contributing about 20% of the total dietary calories and proteins worldwide, is sensitive to heat and drought, especially during its reproductive stages. To develop strategies to improve climate resilience in wheat, a comprehensive field evaluation was carried out under drier and warmer field conditions in Moldova from 2016 to 2019 for forty winter wheat varieties from Germany and Eastern Europe. The grain yield of German cultivars grown in Moldova ranged from 4.46–6.58 t/ha from 2016 to 2019 and was 40% lower compared to the same cultivars grown in Southern Germany. This is significantly more than the reduction predicted by current process-based crop yield models considering only seasonal temperature effects and not combined drought effects. Among all the wheat varieties tested in Moldova, Eastern European varieties and German hybrids achieved the best yield performance across the years. The lower grain yield of German wheat varieties was mainly due to the reduction in grain filling duration, which led to a decrease in the grain filling rate and thousand-grain weight. A higher grain filling rate contributed to a higher grain yield in the Eastern European varieties. These results suggest that well-adapted varieties with a faster rate of development and an earlier start of grain filling have a notable advantage under drought and heat conditions, providing a useful genetic source to increase heat and drought tolerance in future plant breeding programs. The space-for-time approach used in this study allows for the investigation of combined heat and drought, reflecting anticipated or future climate conditions without taking into account CO 2 effects. [ABSTRACT FROM AUTHOR]

Climate models project higher temperatures and a substantial net water deficit during the summer months over Germany until the end of the 21st century. The agricultural sector is particularly vulnerable to changing weather and climate conditions. In this study, we examine the role of irrigation for potato yields as a climate change adaptation measure in Northeast Lower Saxony (Germany). The region represents the largest irrigated area in Germany and is one of the main growing areas of potatoes. We develop a statistical multivariate regression model to investigate whether regional climate and irrigation data at the monthly and county level are suitable to describe potato yield variability from 1978 to 2018. Irrigation is estimated based on the climatic water balance and crop water demand. These estimates are calibrated against irrigation data from local agencies to account for realistic monthly water withdrawals. We analyze future yield changes based on climate model projections for the 21st century and different irrigation scenarios. We find that the potato yield will decrease significantly by 18% on average until 2050 with climate projection uncertainty of ±6.4% under the high emission scenario RCP 8.5 and without irrigation. Irrigation at current levels could compensate for about 54% of these yield losses. Doubling the amount of irrigation minimizes yield losses by as much as 80%. Under the low (middle) emission scenario RCP 2.6 (RCP 4.5), potato yield are projected to decrease moderately by 4.5% ± 3.4% (5.4% ± 2.8%) with irrigation at current levels. We show that the yield losses mainly originate from higher temperatures during the summer. Our analysis indicates that the capacity of irrigation to minimize yield losses is limited. Strengthening climate change mitigation efforts may be more effective in preventing yield losses. However, the effect of elevated CO 2 might buffer the high yield losses, especially in RCP 8.5. The studies supports farmers and decision makers in assessing future risks in potato yields. It can guide policymakers in allocating economic resources to cope with climate change. [Display omitted] • Potato yields in Germany will decrease sharply under climate change for RCP8.5. • Irrigation at current levels mitigates about half of the yield losses. • Irrigation potential is limited because it can not buffer high temperatures. • Climate change mitigation and other adaption actions need to complement irrigation. • The effect of elevated CO2 might buffer high yield losses, especially in RCP8.5. [ABSTRACT FROM AUTHOR]

• The energy availability and mitigation effects were assessed for perennial grasses in 2050. • The bioenergy production can up to 24.33 EJ and satisfy 34.15% of the global bioenergy supply. • A newly developed framework was implemented to assess the climate change impacts of bioenergy. • The highest mitigation effect can be 5800.06–6716.64 mt CO2 eq by substituting coal-fired power. To adopt the Paris Agreement, perennial grasses become a favorable energy resource. To analyze the contribution of perennial grasses-derived bioenergy to carbon neutrality in 2050, the biomass availability was estimated by land availability and areal biomass yields under different climate change scenarios. The climate change impacts of bioenergy were assessed by integrating biomass regrowth and decomposition, and atmospheric carbon cycle models. Analysis revealed that the global biomass yields would be the highest under SSP4–6.0 (1,683.78–1,922.05 Mt), and the associated bioenergy could satisfy 13.35%–34.15% of the global bioenergy demand. The global climate change impacts were 3.44 (SSP5–8.5)–385.58 (SSP4–6.0) Mt CO 2 eq, and the mitigation effect was 19.72–6,716.64 Mt CO 2 eq. The production of combined heat and power yielded the highest energy output, and bio-power had the largest mitigation effect by substituting coal-fired power. The improvement in technology and grass yield can significantly enhance the mitigation effect. [Display omitted] [ABSTRACT FROM AUTHOR]

A water and energy budget-based distributed hydrological model with improved snow physics (WEB-DHM-S) was applied to elucidate the impact of climate change on mountain snow hydrology in the Shubuto River basin, Hokkaido, Japan. The simulated spatial distribution of snow cover was evaluated using the Moderate Resolution Imaging Spectroradiometer (MODIS) 8-day maximum snow-cover extent (MOD10A2) product, which revealed the model’s capability for capturing the spatiotemporal variations in snow cover within the study area. Four Atmosphere Ocean General Circulation Models (AOGCMs) were selected and the SRESA1B emission scenario of the Intergovernmental Panel on Climate Change was used to describe climate predictions in the basin. All AOGCMs predict a future decrease in snowmelt contribution to total discharge 11–22% and an average decrease in SWE of 36%, with a shift in peak SWE by 4–14 days. The shift in runoff regime is broadly consistent between the AOGCMs with snowmelt-induced peak discharge expected to occur on average about two weeks earlier in the future hydrological year. The warming climate will drive a shift in runoff regime from a combined rainfall- and snowmelt-driven regime to one with a reduced contribution from snowmelt. The results of the study revealed that the model could be successfully applicable on the basin scale to simulate river discharge and snow processes and to investigate the effect of climate change on hydrological processes. This research contributes to improve the understanding of basin hydrological responses and the pace of change associated with climate variability. [ABSTRACT FROM AUTHOR]

The impact of livestock production on the environment is already well acknowledged. When non-ruminants are considered, the major contributors to products' environmental impacts are related to feed crop production. Feed crop production sustainability is therefore especially relevant in addressing the sustainability and mitigation potential of non-ruminant production. Here, the climate change impact and water scarcity (WS) impact of the current Finnish feed crop production was assessed. The impact of methodological differences on the climate change impact of national-, regional-, and farm-level feed crop production were investigated for utilising the European Commission's Product Environmental Footprint (PEF) guidance and the guidelines provided by Intergovernmental Panel on Climate Change (IPCC). Feed crops selected for the assessment were considered as typical for Finnish pork and broiler chicken production, and included wheat (T. aestivum), oat (A. sativa), barley (H. vulgare), turnip rape (B. rapa ssp. oleifera), pea (P. sativum), and faba bean (V. faba). The farmgate climate change impact varied from turnip rape's 1.22 kg CO 2 eq./kg to faba bean's 0.37 kg CO 2 eq./kg, while cereals resulted in 0.47–0.63 kg CO 2 eq./kg, and pea 0.49 kg CO 2 eq./kg according to IPCC methods. Similarly, with PEF methods, the results varied from turnip rape's 1.18 kg CO 2 eq./kg to 0.31 kg CO 2 eq./kg of faba bean, cereals resulted in 0.42–0.58 kg CO 2 eq./kg, and pea 0.43 kg CO 2 eq./kg. The absolute difference between methods ranged between −0.045 and −0.068 kg CO 2 eq./kg. The analysis showed that even if the relative error varied from 3% to 19% between methods, the relative order was the same, independent of the methods. The farmgate WS impact of Finnish feed crops was 0.23 m3 eq./kg for turnip rape, 0.05 m3 eq./kg for faba bean, 0.08 m3 eq./kg for barley, 0.08 m3 eq./kg for oat, 0.09 m3 eq./kg for wheat, and 0.06 m3 eq./kg for pea. It was observed that the PEF and IPCC methods functioned similarly in distinguishing better and worse performing crop production between cases. It was shown that the major contributors to feed crop production in Finland were energy and input production, N 2 O from N inputs, and peat soil degradation. The amount of N 2 O emissions from peat soil degradation especially demonstrated high variability and was therefore identified to have high mitigation potential. The investigation demonstrated the importance of including N 2 O emissions in the widest form when finding production hotspots. • Finnish feed crop products' carbon footprint and water scarcity were assessed. • IPCC and PEF methods were compared for climate change impact. • PEF method functioned similarly to IPCC method in distinguishing well performing production from poorly performing. • Better resolution was achieved when N 2 O emissions were included in widest form. [ABSTRACT FROM AUTHOR]

Under changing climate and burgeoning food production demands, climate-smart agriculture (CSA) practices are the need of the hour. Physically-based crop models have been designed, which require large sets of input variables, besides being timeworn, tedious, and not user-friendly in the current situation. There is a rapid advancement in the modeling approaches in agricultural practices. But, still, there is a shortage of comprehensive information related to the recent technological advancement in the modeling applications toward CSA and its prospects. The paper reviews, critically assess, and discusses the present state-of-art modeling technologies related to the CSA. Special emphasis is placed on highlighting the current research trends in the different crop simulation models and their CSA applications. The assessment and analysis of the combined implications of crop simulation and hydrological models form another vital area of our review. Subsequently, the suitability and future scope of AI in crop simulation models is thoroughly assessed. Finally, the ways to address future challenges for CSA through the models and AI-based approaches are evaluated. We have selected and critically reviewed the literature to deliberate on the various aspects of modeling and their impact on CSA. Overall, we attempted to distinctly bring out the different latest and advanced technologies and their potential roles in modeling exercises towards CSA. The current review also collates and presents new and innovative modeling approaches toward CSA with techno-feasible solutions. The review involves the simulation-optimization framework for coupling hydrological and climate models with crop models that presently have the utmost importance for the CSA. Concurrently it also highlights the overarching significance of different models and suggests refined solutions that enable better crop and environment estimation, field management, and decision-making. [Display omitted] • Reviewed existing and emerging technologies for strengthening agro-based modeling. • Chronological trends in the development of agroecological models are examined. • Review offers an ideal comparative analysis of modeling approaches. • Depicts the application of hydrological models with the crop models in agriculture. • Applications and drawbacks of different modeling approaches are delineated. [ABSTRACT FROM AUTHOR]

Energy consumption and thermal comfort in buildings are heavily affected by weather conditions. This study investigated the impact of climate change on thermal comfort conditions and on heating and cooling energy demand in dwellings in three cities in Brazil. Scenario A2 of the Intergovernmental Panel on Climate Change was selected to be used in the study. To quantify the impact, the Climate Change World Weather File Generator was used to produce weather data for future typical meteorological years, such as 2020, 2050 and 2080. The EnergyPlus computer programme was used to estimate the indoor air temperature and the annual heating and cooling energy demand in the future. In order to maintain the energy consumption in the houses at the level it is nowadays, passive design strategies such as solar shading, low absorptance and thermal insulation were assessed. Results show that there will be an increase in the annual energy demand ranging from 19%–65% among the three cities in 2020; 56%–112% in 2050; and 112%–185% in 2080. In the coldest city, the annual heating energy demand will decrease by 94% in 2080 due to an increase in the average temperature and global solar radiation. The use of passive design strategies may reduce up to 50% the future annual cooling and heating energy demand in houses in Brazil. [ABSTRACT FROM AUTHOR]

The water content of wetlands represents a key driver of their hydrological services and it is highly dependent on short- and long-term weather conditions, which will change, to some extent, under evolving climate conditions. The impact on stream flows of this critical dynamic component of wetlands remains poorly studied. While hydrodynamic modelling provide a framework to describe the functioning of individual wetland, hydrological modelling offers the opportunity to assess their services at the watershed scale with respect to their type ( i.e. , isolated or riparian). This study uses a novel approach combining hydrological modelling and limited field monitoring, to explore the effectiveness of wetlands under changing climate conditions. To achieve this, two isolated wetlands and two riparian wetlands, located in the Becancour River watershed within the St Lawrence Lowlands (Quebec, Canada), were monitored using piezometers and stable water isotopes (δD – δ18O) between October 2013 and October 2014. For the watershed hydrology component of this study, reference (1986–2015) and future meteorological data (2041–2070) were used as inputs to the PHYSITEL/HYDROTEL modelling platform. Results obtained from in-situ data illustrate singular hydrological dynamics for each typology of wetlands ( i.e., isolated and riparian) and support the hydrological modelling approach used in this study. Meanwhile, simulation results indicate that climate change could affect differently the hydrological dynamics of wetlands and associated services ( e.g., storage and slow release of water), including their seasonal contribution ( i.e., flood mitigation and low flow support) according to each wetland typology. The methodological framework proposed in this paper meets the requirements of a functional tool capable of anticipating hydrological changes in wetlands at both the land management scale and the watershed management scale. Accordingly, this framework represents a starting point towards the design of effective wetland conservation and/or restoration programs. [ABSTRACT FROM AUTHOR]

Summary Impact studies of the hydrological response of future climate change are important for the water authorities when risk assessment, management and adaptation to a changing climate are carried out. The objective of this study was to model the combined effect of land use and climate changes on hydrology for a 486 km 2 catchment in Denmark and to evaluate the sensitivity of the results to the choice of hydrological model. Three hydrological models, NAM, SWAT and MIKE SHE, were constructed and calibrated using similar methods. Each model was forced with results from four climate models and four land use scenarios. The results revealed that even though the hydrological models all showed similar performance during calibration, the mean discharge response to climate change varied up to 30%, and the variations were even higher for extreme events (1th and 99th percentile). Land use changes appeared to cause little change in mean hydrological responses and little variation between hydrological models. Differences in hydrological model responses to land use were, however, significant for extremes due to dissimilarities in hydrological model structure and process equations. The climate model choice remained the dominant factor for mean discharge, low and high flows as well as hydraulic head at the end of the century. [ABSTRACT FROM AUTHOR]

In climate change research ensembles of climate simulations are produced in an attempt to cover the uncertainty in future projections. Many climate change impact studies face difficulties using the full number of simulations available, and therefore often only subsets are used. Until now such subsets were chosen based on their representation of temperature change or by accessibility of the simulations. By using more specific information about the needs of the impact study as guidance for the clustering of simulations, the subset fits the purpose of climate change impact research more appropriately. Here, the sensitivity of such a procedure is explored, particularly with regard to the use of different climate variables, seasons, and regions in Europe. While temperature dominates the clustering, the resulting selection is influenced by all variables, leading to the conclusion that different subsets fit different impact studies best. [ABSTRACT FROM AUTHOR]

Copious precipitation in the Meghna Basin has remarkable consequences for floods in the north-eastern region of Bangladesh. In addition, due to climate change more frequent extreme precipitation may also increase the frequency and intensity of flash floods. In this study, a hydrologic model H08 has been applied over the basin at a relatively fine grid resolution (10 km). The impacts of climate change are assessed by using the super-high-resolution MRI-AGCM3.2S with A1B scenario through three time-slice experiments; the base-period (1979–2003), the near-future (2015–2039), and the far-future (2075–2099) periods. This study investigates the spatio-temporal changes and the changes in the frequency of precipitation and runoff with different magnitude ranges and finds the implications for water resources management under climate change. Results show that, (a) the projected maximum increment of mean annual precipitation (runoff) is +23% (+34%) and +31% (+39%) during the near-future and the far-future, respectively, (b) the projected increment of median value of monthly discharges at basin outlet is significantly high in wet period (May-July), ranging from 38 to 44% and 25–104% in the near-future and the far-future, respectively. Moreover, the monsoonal peaks are expected to come about 1 ∼ 1.5 month earlier, which ultimately lead to higher possibility of earlier flash flood in the future. This anticipated higher increment in peak discharge as well as future shifting of seasonal cycle of river discharge will have significant implications for agriculture and flood disaster management. Findings will also contribute to enhance water resource management in the basin and improve the design of adaptive measures. [ABSTRACT FROM AUTHOR]

• Climate change impact on groundwater levels (GWL) varies spatially. • The climate change impact response reflects the dominating hydrological processes. • An ensemble of climate and land surface models shows the uncertainty of future GWL. • Climate models are the main source of uncertainty for predicting future GWL. • Uncertainty from the land surface models increases for high GWL and near the river. This paper presents the first study assessing the climate change impact on groundwater levels of the Zagreb alluvial aquifer in Croatia by coupling climate projections under RCP8.5 for the period 2040–2070 with local scale groundwater flow modelling. As a novelty in groundwater modelling of climate change impacts, this study utilizes an ensemble of five climate models and two land surface models for providing projections of relevant boundary conditions to the groundwater model. The groundwater model is used for predicting climate driven changes in groundwater levels in the aquifer system influenced by both changes in local groundwater recharge and changes in river-flow. Both boundary conditions are obtained from the land surface models. In addition, the uncertainty contributions to groundwater levels from both climate models and land surface models were quantified using variance decomposition. The results revealed that the spatial pattern of changes in groundwater levels can be related to different processes influencing groundwater dynamics. In areas dominated by a strong groundwater-surface water interaction, close to the Sava river, the multi-model ensemble mean change in groundwater levels is negligible for low and average water levels. However, for high water levels, an increase was identified. In contrast, a decline in groundwater levels prevails in all areas where aquifer recharge is largely driven by rainfall infiltration. For the variance decomposition analysis, climate models were identified as the main source of uncertainty for groundwater levels. However, the uncertainty contribution of the choice of land surface model to the impact simulations was found to be more important in the area affected by a strong groundwater–surface water interaction and for periods of high groundwater levels. Also, specific local issues related to low and high groundwater levels that are associated with extreme hydrological regimes were found to exacerbate under climate projections. [ABSTRACT FROM AUTHOR]

Knowledge about climate change impacts on species distribution at national scale is critical to biodiversity conservation and design of management programs. Although China is a biodiversity hot spot in the world, potential influence of climate change on Chinese protected birds is rarely studied. Here, we assess the impact of climate change on 108 protected bird species and nature reserves using species distribution modeling at a relatively fine spatial resolution (1 km) for the first time. We found that a large proportion of protected species would have potential suitable habitat shrink and northward range shift by 77-90 km in response to projected future climate change in 2080. Southeastern China would suffer from losing climate suitability, whereas the climate conditions in Qinghai-Tibet Plateau and northeastern China were projected to become suitable for more protected species. On average, each protected area in China would experience a decline of suitable climate for 3-4 species by 2080. Climate change will modify which species each protected area will be suitable for. Our results showed that the risk of extinction for Chinese protected birds would be high, even in the moderate climate change scenario. These findings indicate that the management and design of nature reserves in China must take climate change into consideration. [ABSTRACT FROM AUTHOR]

Agricultural vulnerability to climate change is likely to vary considerably between agro-environmental regions. Exemplified on Austrian cropland, we aim at (i) quantifying climate change impacts on agricultural vulnerability which is approximated by the indicators crop yields and gross margins, (ii) developing robust crop production portfolios for adaptation, and (iii) analyzing the effect of agricultural policies and risk aversion on the choice of crop production portfolios. We have employed a spatially explicit, integrated framework to assess agricultural vulnerability and adaptation. It combines a statistical climate change model for Austria and the period 2010–2040, a crop rotation model, the bio-physical process model EPIC (Environmental Policy Integrated Climate), and a portfolio optimization model. We find that under climate change, crop production portfolios include higher shares of intensive crop management practices, increasing average crop yields by 2–15% and expected gross margins by 3–18%, respectively. The results depend on the choice of adaptation measures and on the level of risk aversion and vary by region. In the semi-arid eastern parts of Austria, average dry matter crop yields are lower but gross margins are higher than in western Austria due to bio-physical and agronomic heterogeneities. An abolishment of decoupled farm payments and a threefold increase in agri-environmental premiums would reduce nitrogen inputs by 23–33%, but also crop yields and gross margins by 18–37%, on average. From a policy perspective, a twofold increase in agri-environmental premiums could effectively reduce the trade-offs between crop production and environmental impacts. [ABSTRACT FROM AUTHOR]

Summary Climate change impact assessment studies in water resources section demand the simulations of climatic variables at coarser scales from dynamic General Circulation Models (GCMs) to be mapped to even finer scales. Related studies in this area have mostly been relying on statistical techniques for downscaling variables to finer resolution. This demands a careful selection of a suitable downscaling model, to alleviate the downscaling uncertainty. In this study, it is proposed to develop a dynamic framework for downscaling purpose by integrating the frequently used techniques, K-Nearest Neighbour (KNN) and Support Vector Machine (SVM). In order to give flexibility in future predictors–predictand relationships and to account the sensitivity in model parameters, it is also proposed to generate an ensemble of outputs by identifying various plausible model parameter combinations. The performance of this framework for downscaling daily precipitation values at different locations is compared with simple KNN and SVM models. The proposed hybrid model is found to be better in capturing various characteristics of daily precipitation than individual models, especially in simulating the extremes, both in magnitude and duration. The mean ensemble is found to be efficient than single best simulation with optimum parameter combinations. The efficacy of hybrid SVM–KNN ensemble downscaling model is established through detailed investigations. The future downscaled projection for mid-century and late century employing this hybrid model indicates an increased variability in future precipitation, though the intensity varies for various locations. The developed methodology hence ensures lesser downscaling uncertainty and also eliminates the inherent assumption of relationship stationarity considered in many downscaling models. [ABSTRACT FROM AUTHOR]

Increased climate variability and higher mean temperatures are expected across many world regions, both of which will contribute to more frequent extreme high temperatures events. Empirical evidence increasingly shows that short episodes of high temperature experienced around flowering can have large negative impacts on cereal grain yields, a phenomenon increasingly referred to as heat stress. Crop models are currently the best tools available to investigate how crops will grow under future climatic conditions, though the need to include heat stress effects has been recognized only relatively recently. We reviewed literature on both how key crop physiological processes and the observed yields under production conditions are impacted by high temperatures occurring particularly in the flowering and grain filling phases for wheat, maize and rice. This state of the art in crop response to heat stress was then contrasted with generic approaches to simulate the impacts of high temperatures in crop growth models. We found that the observed impacts of heat stress on crop yield are the end result of the integration of many processes, not all of which will be affected by a “high temperature” regime. This complexity confirms an important role for crop models in systematizing the effects of high temperatures on many processes under a range of environments and realizations of crop phenology. Four generic approaches to simulate high temperature impacts on yield were identified: (1) empirical reduction of final yield, (2) empirical reduction in daily increment in harvest index, (3) empirical reduction in grain number, and (4) semi-deterministic models of sink and source limitation. Consideration of canopy temperature is suggested as a promising approach to concurrently account for heat and drought stress, which are likely to occur simultaneously. Improving crop models’ response to high temperature impacts on cereal yields will require experimental data representative of field production and should be designed to connect what is already known about physiological responses and observed yield impacts. [ABSTRACT FROM AUTHOR]

When estimating climate change impact on crop yield, a typical assumption is constant elasticity of yield with respect to a climate variable even though the elasticity may be inconstant. After estimating both constant and inconstant elasticities with respect to temperature and precipitation based on provincial panel data in China 1980–2008, our results show that during that period, the temperature change contributes positively to total yield growth by 1.3% and 0.4% for wheat and rice, respectively, but negatively by 12% for maize. The impacts of precipitation change are marginal. We also compare our estimates with other studies and highlight the implications of the inconstant elasticities for crop yield, harvest and food security. We conclude that climate change impact on crop yield would not be an issue in China if positive impacts of other socio-economic factors continue in the future. [ABSTRACT FROM AUTHOR]

Wheat has been documented to be vulnerable to climate change in broad regions of the world including China. Adaptation to future climate change by breeding climate resilient cultivars is essential. However the precise information as to where, when, and what cultivar traits should be applied to adapt to climate change in the coming decades has not been available. In this study, we developed novel hybrid assessment models by incorporating a process-based crop model and machine learning algorithms based on a large number of cultivars field experiments data. The models were applied to assess the impact of climate change on wheat productivity and to identify the timescale of wheat cultivar adaptation in the major wheat cultivation regions across China. Wheat yield was projected to decrease on average by 6.3% (9.4%) in the 2050 s under RCP 4.5 (8.5), relative to the baseline period (1986–2005), without the CO 2 effect. By contrast, it was projected to increase on average by 5.7% (8.1%) in the 2050 s with the CO 2 effect, across the regions and cultivar-maturing traits in China. Solar radiation, precipitation, temperature, cultivar-maturing traits, and CO 2 are critical factors affecting wheat productivity in the major wheat cultivation regions. About 44% (39%) and 68% (57%) of wheat planting grids would require cultivar renewal before 2050 (2040) under RCP 4.5 and 8.5 emission scenario, respectively, at a medium risk level without the CO 2 effect. The cultivars with a long reproductive growth duration, high photosynthetic efficiency and large harvest index would be generally promising although there are specific traits desirable for certain regions. This study developed a novel framework to identify the precise information on where, when, and what cultivar traits should be applied for wheat to adapt to future climate change, helping the stakeholders to cope with climate change timely and precisely. Timescale of wheat cultivar adaptation under RCPs 4.5 and 8.5. The time plotted in the figure is mean time when cultivar adaptation is required for the grid without the CO 2 fertilization effect. Grey areas indicate the areas where cultivar adaptation is not needed by 2050 under the RCPs. [Display omitted] • Develop novel hybrid assessment models by incorporating crop model and machine learning. • Identify where, when, and what cultivar traits should be applied to adapt to future climate change. • Impacts of future climate change on wheat productivity would depend on regions and cultivar traits. • 39% and 57% of wheat planting grids would require cultivar renewal before 2040 under RCP 4.5 and 8.5. • Region-specific wheat cultivars ideotypic traits are identified. [ABSTRACT FROM AUTHOR]

In arid regions, water resources are under immense pressure and multiple stresses. Excessive use of water resources without proper management occurs in the present situation, and it will be intensified under future climate change conditions. Finding a good strategy for water resources management is essential to preserve sustainability in such watersheds. In this study, the first purpose is to optimize water use for the agricultural crops in the Eshtehard watershed in Iran, where the aquifer is a strategic resource for irrigated agriculture and is facing intensive drawdown because of over-extraction. A hydro-agronomic simulation-optimization approach is used for agronomy and groundwater management in which the agricultural production and water level variation are simulated by linking SWAT and MODFLOW, and the optimal crops pattern is searched for by the PSO algorithm. The crops' area and their irrigation water consumption are the decision variables with the objective of maximizing the net benefit gained from the crops' yield while the groundwater drawdown is limited to a pre-defined stability level. For the following purpose, the climate change impact on the optimal crop pattern across the watershed under two representative concentration pathways scenarios (RCPs 2.6 and 8.5) downscaled by the data of the HadGEM2-ES General Circulation Model for the near future is evaluated. Results show that irreparable damage to groundwater depletion is reduced in the optimum state, and lower stress is imposed on the aquifer under the climate change impacts by executing the optimum crop pattern. To see this, a part of the winter wheat and cotton crops' area must be replaced by winter barley. Also, the crop's irrigation amount must be decreased for winter wheat, winter barley and cotton by 45%, 7%, and 10%, respectively, which reduces groundwater extraction in the optimum situation. • A hydro-agronomic simulation-optimization approach for agronomy-groundwater management by linking SWAT and MODFLOW and PSO. • Evaluating the climate change impact on the optimal crop pattern across the watershed under two RCPs for the near future. • Decreasing the irreparable damage for groundwater depletion by using the optimized pattern in the climate change condition. • Reducing groundwater extraction by replacing a part of winter wheat and cotton crops' area with winter barley. [ABSTRACT FROM AUTHOR]