Climate Scenarios Research Articles

Mapping the Future: Climate-Induced Changes in Aboveground Live-Biomass Carbon Density Across Mexico’s Coniferous Forests

Climate variations in temperature and precipitation significantly impact forest productivity. Precipitation influences the physiology and growth of species, while temperature regulates photosynthesis, respiration, and transpiration. This study developed bioclimatic models to assess how climate change will affect the carbon density of aboveground biomass (cdAGB) in Mexico’s coniferous forests for 2050 and 2070. We used cdAGB data from the National Forest and Soils Inventory (INFyS) of Mexico and 19 bioclimatic variables from WorldClim ver. 2.0. The best predictors of cdAGB were obtained using machine learning techniques with the “caret” library in R. The model was trained with 80% of the data and validated with the remaining 20% using Generalized Linear Models (GLMs). Current cdAGB prediction maps were generated using the best predictors. Future cdAGB was calculated with the average of three general circulation models (GCMs) of future climate projections from the Coupled Model Intercomparison Project Phase 5 (CMIP5), under four Representative Concentration Pathways (RCPs): 2.6, 4.5, 6.0, and 8.5 W/m2. The results indicate cdAGB losses in all climate scenarios, reaching up to 15 Mg C ha−1, and could occur under the RCP 8.5 scenario by 2070 in the central region of the country. Temperature-related variables are more important than precipitation variables. Bioclimatic variables can explain up to 20% of the total variance in cdAGB. The temperature in the study area is expected to increase by 2.66 °C by 2050 and 3.36 °C by 2070, while precipitation is expected to fluctuate by ±10% relative to the current values, which could geographically redistribute the cdAGB of the country’s coniferous forests. These findings underscore the need for forest management to focus not only on biodiversity conservation but also on the carbon storage capacity of these ecosystems.

Tracing the range shifts of African tree ferns: Insights from the last glacial maximum and beyond

African tropical forests are experiencing rapid decline as a result of several factors, including increasing population pressure, recurrent wildfires, selective logging practices, land use changes, intensified agricultural activities, and other social and economic issues. Using MaxEnt, paleoclimatic data, and future climate scenarios, the present study seeks to explore the presence of tree ferns in tropical and Saharan Africa during the Last Glacial Maximum, African Holocene Humid Period (AHHP; ca. 14,500–5000 years ago) and to project the effects of climate change on the distribution of tree ferns in Africa under two future climatic scenarios, Representation Concentration Pathways (RCP) 4.5 and 8.5. Our study reveals that despite a significant increase in precipitation during the AHHP, precipitation distribution was variable and insufficient to support the five tree fern species examined in this study. While some tree fern species have experienced range shifts over time, we found that most of them have maintained their presence within refuge areas that probably endured the late Pleistocene extinction event. These refugia provided a haven for some tree ferns, allowing them to persist and survive amidst challenging and varying environmental conditions. This highlights tree ferns' remarkable adaptability in changing climate as well as the critical importance of these refugial areas in safeguarding their populations during climatic upheaval. Our study further demonstrates that different species respond to climate change differently, with some experiencing minimal range contractions of 2.0 %, up to more than 57.0 % range expansion in other species. Preserving refugia not only safeguards tree fern populations but also contributes to conserving overall forest biodiversity and ecosystem functioning. This knowledge is crucial for implementing targeted conservation actions that promote sustainable forest management and can mitigate the threats posed by climate change and anthropogenic activities in African closed wet forests.

Hydropower potential of the Marsyangdi River and Bheri River basins of Nepal and their sensitivity to climate variables

Abstract. Understanding the hydrology of the Himalayan region and its response to current and future climate scenarios is crucial for identifying the region's future water availability for infrastructural development. For this, the hydropower potential and the impact of future change on the hydrology of the Bheri River basin (BRb) and the Marsyangdi River basin (MRb) were analysed. The Glacio-hydrological Degree-Day Model version 2.0 (GDM V2.0), developed in the PCRaster dynamic modelling framework, was used to simulate the river runoff. Geospatial tools and different criteria were used to assess topographic features, identify suitable places for run-of-river (ROR) hydropower development, and estimate power potential. Eight scenarios with various combinations of temperature and precipitation changes were used to assess the hydropower potential. Increases in temperature by 0.5 and 1 °C (assumed for the near-term and mid-term future) and changes in precipitation of ±10 % and ±20 %, respectively, are used to conduct a sensitivity analysis for the hydropower potential. A total of 116 and 83 suitable sites were identified, and 4242 and 2823 MW power potentials were estimated in the BRb and the MRb, respectively. All the sensitivity scenarios show an increase in hydropower production, except for one with a drier scenario and less precipitation. The integration of a geographic information system (GIS) and a hydrological model helps us to understand the hydrological response to climate variables and its impact on hydropower in the Himalayan region.

Global Warming Will Drive Spatial Expansion of Prunus mira Koehne in Alpine Areas, Southeast Qinghai–Tibet Plateau

Global climate change exerts great effects on plant distributions. However, the response of Prunus mira Koehne, one of the most important species for ecological protection in the southeast of the Qinghai–Tibet Plateau, to climate change remains unclear. To explore the ecological factors affecting the distribution of P. mira in the context of global climate change, the MaxENT model is used to predict suitable habitats for P. mira. Our study indicated that the distribution of Prunus mira Koehn is primarily influenced by temperature rather than precipitation, and warming can facilitate the growth of P. mira. When the temperature seasonality (bio4) ranges from 134 to 576 and the mean temperature of the coldest quarter (bio11) ranges from −2.6 °C to 2.7 °C, it is most conducive to the growth of P. mira. Among the four climate scenarios, the optimal habitat for P. mira is predominantly concentrated in river valley areas and is expected to expand into higher altitude regions, particularly in the north and southeast. SSP245 and SSP370 climate pathways are conducive to the growth and spatial expansion of P. mira. Our findings highlight the significant impact of temperature not precipitation on the distribution of P. mira, and this insight is crucial for the stability and conservation of this ecologically significant plant species.

Predicting the Potential Distribution of Aralia chinensis L. (Wild Vegetable) in China Under Different Climate Change Scenarios.

Global climate change has a main impact on the distribution of plants. Aralia chinensis L. is economically valuable, making it necessary to predict the impact of climate change on its distribution. It is important for researching the effects of climate change on A. chinensis distribution to achieve sustainable utilization. Based on 340 occurrence records of A. chinensis covering all known provinces and 58 environmental factor data, we used MaxEnt to simulate the potential distribution of A. chinensis under current and different future climate scenarios, analyzing the key environmental variables affecting its distribution. The results were as follows: (1) Suitable A. chinensis habitats under current and different future climate scenarios were mainly distributed in the southern region of China, east of the Hu Huanyong line. (2) Annual precipitation, minimum temperature during the coldest month, precipitation during the driest month, and slope were the key environmental variables affecting its potential distribution, and annual precipitation was more important. (3) Suitable habitat areas were projected to increase under different future climate scenarios and expand westward and northward while shrinking in the central regions, such as Hubei and Hunan. The results provide a theoretical reference for the conservation and cultivation of A. chinensis.

Prediction of the potentially suitable areas of Eucommia ulmoides Oliver in China under climate change based on optimized Biomod2 and MaxEnt models.

Eucommia ulmoides Oliver is a medicinal plant of significant economic importance. Its cortex has been employed for centuries to alleviate various conditions such as lumbar pain, knee pain, and osteoporosis. Additionally, E. ulmoides possesses substantial industrial value. With the growing demand for this medicinal herb, ensuring its sustainable supply has become imperative. Climate change has caused habitat restrictions or migration of medicinal plants. Therefore, predicting the impact of climate change on the distribution of E. ulmoides is crucial for its conservation and sustainable use. This study evaluated the potential distribution of E. ulmoides across China under various climate change scenarios since the last interglacial period by modeling suitable areas based on 257 distribution records and 19 major environmental factors related to E. ulmoides. The model selection process initially identified the MaxEnt model as the most suitable. The optimized MaxEnt model, with RM = 2.0 and FC = LQHPT settings, generated the most precise predictions. Results indicate that the minimum temperature of the coldest month, annual mean temperature, and annual precipitation significantly affect the distribution of E. ulmoides. Under current environmental conditions, highly suitable areas for E. ulmoides are found in Southwest and Southeast China, with a total suitable habitat area of 23.12 × 104 km2. However, the range of suitable habitat has shifted due to global warming's negative impact. Under different climate scenarios, suitable areas for E. ulmoides have either increased or decreased, with expansions primarily in high-latitude regions. Future climate scenarios predict shifts in the centroid of suitable E. ulmoides habitat towards Yichang City in Hubei Province. The findings of this study support the development, artificial cultivation, and conservation of E. ulmoides resources.

REGIONÁLNE KLIMATICKÉ SCENÁRE FREKVENCIE VÝSKYTU DNÍ SO ZRÁŽKAMI NA SLOVENSKU

We present scenarios of how frequency of days with precipitation may change over the territory of Slovakia by 2050 and 2080 in the article. Climatic scenarios were created from three regional climate models EURO-CORDEX for the pessimistic emission scenario RCP8.5. We analysed the occurrence of the number of precipitation days for class intervals: 5 – 9.9 mm, 10 – 19.9 mm, 20 – 29.9 mm, 30 – 39.9 mm, as well as for open intervals ≥ 5 mm, ≥ 10 mm, ≥ 20 mm, ≥ 30 mm, and ≥ 40 mm. To correct systematic errors in climate models, we used data (daily precipitation totals) from the national network of rain gauges. The RCM models were corrected in two steps: first in terms of excessive occurrence of so-called “drizzle days”, and then in terms of systematic underestimation of daily sums across the entire distribution function (bias correction). The occurrence of days with daily rainfall exceeding 40 mm for the horizons of 2050 and 2080 is presented in the form of maps. As our analyses revealed, the annual frequency of days with daily rainfall exceeding 40 mm is expected to increase by approximately 20% by the middle of the 21st century and by up to 60% by the end of the century compared to the present. The paper also includes tables with relative changes and absolute frequencies of wet days for 75 selected locations in Slovakia.

Exploring the Impact of Climate Variables and Scenario Simulation on Ecosystem Service Value Profits and Losses in China

Ecosystem restoration can yield multiple benefits, and the quantitative accounting of ecosystem service value (ESV) profits and losses is of significant importance to the economic benefits of ecosystem restoration. This study reveals the dynamic impacts of climate change on ESVs by analyzing the effects of climate variables on ESV profits and losses across different periods and scenarios. The research findings are as follows: (1) From 1990 to 2020, and extending to simulated projections for 2030, China’s ESV exhibits a high distribution pattern in the southern regions. In 2030, under the natural development scenario (NDS), the southwestern region shows a coexistence of high and low ESVs. Under the ecological protection scenario (EPS), ESV in the southwestern region increases, whereas under the urban development scenario (UDS), ESV in the southwest decreases. (2) In both the NDS and UDS, the trends in ESV profits and losses continue from 2010 to 2020. Under the EPS, there is a significant increase in ESV in the southwestern region. The largest contributors to ESV loss are the conversion of grassland to unused land and forest to farmland. The southwestern region shows the most significant spatial differences in ESV profits and losses, with an increase in ESV profits in the northeastern region. In contrast, other regions show no significant spatial differences in ESV profits and losses. (3) From 1990 to 2000, Bio13 (the precipitation of the wettest month) and Bio12 (annual precipitation) had a significant positive impact on ESV profits and losses, indicating that increased precipitation promotes the functioning of ESVs. This study indicates that fluctuations in precipitation and temperature are significant climate factors influencing the value of ESV. Due to climate change, precipitation patterns and temperature swings are now key determinants of ESV changes. By carefully studying ESV profits and losses and their driving factors, this research can serve as the scientific basis for ecosystem restoration and management strategies.

Climate change mitigation and adaptation for rice-based farming systems in the Red River Delta, Vietnam

BackgroundRice is a major contributor to anthropogenic greenhouse gas (GHG) emissions, primarily methane, and at the same time will be negatively impacted by regional climate changes. Identifying rice management interventions to reduce methane emissions while improving productivity is, therefore, critical for climate change mitigation, adaptation, and food security. However, it can be challenging to conduct multivariate assessments of rice interventions in the field owing to the intensiveness of data collection and/or the challenges in testing long-term changes in meteorological and climate conditions. Process-based modeling, evaluated against site-based data, provides an entry point for evaluating the impacts of climate change on rice systems and assessing the impacts, co-benefits, and trade-offs of interventions under historical and future climate conditions.MethodsWe leverage existing site-based management data to model combined rice yields, methane emissions, and water productivity using a suite of process-based coupled crop-soil model experiments for 83 growing sites across the Red River Delta, Vietnam. We test three rice management interventions with our coupled crop-soil model, characterized by Alternate Wetting and Drying (AWD) water management and other principles representing the System of Rice Intensification (SRI). Our simulations are forced with historical as well as future climate conditions, represented by five Earth System Models for a high-emission climate scenario centered on the year 2050. We evaluate the efficacy of these interventions for combined climate change mitigation and adaptation under historical and future climate change.ResultsTwo SRI interventions significantly increased yields (one by over 50%) under historical climate conditions while also reducing (or not increasing) methane emissions. These interventions also increase yields under future climate conditions relative to baseline management practices, although climate change decreases absolute yields across all management practices. Generally, where yield improved, so did crop water-use efficiency. However, impacts on methane emissions were mixed across the sites under future climate conditions. Two of the interventions resulted in increased methane emissions, depending on the baseline management point of comparison. Nevertheless, one intervention reduced (or did not significantly increase) methane under both historical and future climate conditions and relative to all baseline management systems, although there was considerable variation across five selected climate models.ConclusionsSRI management principles combined with high-yielding varieties, implemented for site-specific conditions, can serve climate change adaptation and mitigation goals, although the magnitude of future climate changes, particularly warming, may reduce the efficacy of these interventions with respect to methane reductions. Future work should better bracket important sensitivities of coupled crop-soil models and disentangle which management and climate factors drive the responses shown. Furthermore, future analyses that integrate these findings into socio-economic assessment can better inform if and how SRI/AWD can potentially benefit farmer livelihoods now and in the future, which will be critical to the adoption and scaling of these management principles.

Using surrogate modeling to predict storm surge on evolving landscapes under climate change

Planners managing coastal flood risk under a constrained computational budget face a tradeoff. Simulating many time periods or scenarios limits how many storm simulations can be run on each landscape. In this analysis, we present a deep learning model to predict storm surge as a function of storm parameters but also landscape features and boundary conditions (e.g., sea level). It is trained on peak surge elevations from Advanced Circulation (ADCIRC) hydrodynamic simulations of coastal Louisiana in a 2020 baseline and decadal periods from 2030 to 2070 under two morphological and climate scenarios. Leave-one-landscape-out cross-validation yielded a 0.086-m RMSE and 0.050-m MAE over 90 storms per landscape and 94,013 geospatial locations. A two-sided Kolmogorov-Smirnov test comparing annual exceedance probability (AEP) estimates from the model predictions to ADCIRC simulations rejected the null hypothesis that the predicted and ADCIRC AEP values were drawn from the same distribution only 1.1% of the time.

Evaluating Wheat Cultivation Potential in Ethiopia Under the Current and Future Climate Change Scenarios

Land suitability analyses are crucial for identifying sustainable areas for agricultural crops and developing appropriate land use strategies. Thus, the present study aims to analyze the current and future land suitability for wheat (Triticum aestivum L.) cultivation in Ethiopia. Twelve variables including soil properties, climate variables, and topographic characteristics were used in the evaluation of land suitability. Statistical methods such as Rotated Empirical Orthogonal Functions (REOF), Coefficient of Variation (CV), correlation, and parametric and non-parametric trend analyses were used to analyze the spatiotemporal variability in current and future climate data and identified significant patterns of variability. For future projections of land suitability and climate, this study employed climate models from the Coupled Model Intercomparison Project Phase 6 (CMIP6) framework, downscaled using regional climate model version 4.7 (RegCM4.7) under two different Shared Socioeconomic Pathway (SSP) climate scenarios: SSP1 (a lower emission scenario) and SSP5 (a higher emission scenario). Under the current condition, during March, April, and May (MAM), 53.4% of the country was suitable for wheat cultivation while 44.4% was not suitable. In 2050, non-suitable areas for wheat cultivation are expected to increase by 1% and 6.9% during MAM under SSP1 and SSP5 climate scenarios, respectively. Our findings highlight that areas currently suitable for wheat may face challenges in the future due to altered temperature and precipitation patterns, potentially leading to shifts in suitable areas or reduced productivity. This study also found that the suitability of land for wheat cultivation was determined by rainfall amount, temperature, soil type, soil pH, soil organic carbon content, soil nitrogen content, and elevation. This research underscores the critical importance of integrating spatiotemporal climate variability with future projections to comprehensively assess wheat suitability. By elucidating the implications of climate change on wheat cultivation, this study lays the groundwork for developing effective adaptation strategies and actionable recommendations to enhance management practices. The findings support the county’s commitment to refining agricultural land use strategies, increasing wheat production through suitability predictions, and advancing self-sufficiency in wheat production. Additionally, these insights can empower Ethiopia’s agricultural extension services to guide farmers in cultivating wheat in areas identified as highly and moderately suitable, thereby bolstering production in a changing climate.

Brazil nut journey under future climate change in Amazon.

Climate change is among the principal threats to global terrestrial biodiversity, especially to megadiverse ecosystems such as the Amazon rainforest. In this study, we investigate how it could affect an iconic forest species-Bertholletia excelsa-(the Brazil nut) which has values in multiple dimensions in an Amazonian context. We used an ensemble from various distribution modeling methods designed for four different climate scenarios from CMIP6 by the end of the century. Then, we simulate how spatial dynamics under climate change, including explicitly dispersal events, can affect the persistence, colonization, and potential extinction of Bertholletia excelsa in the future. Our results show that by the end of the century there would be a generalized loss of suitability on the Amazon biome, regardless of the climate scenario evaluated, which could promote a significant loss (up to 94%) of the area available for the species via extinction. Our results also show that, in the future, the species would colonize higher altitudes in search of favorable conditions for its survival. Finally, we detected that areas that had previously become unsuitable because of climate change would have favorable conditions by the end of the century. Such an outcome could be useful in fostering an active restoration agenda that can mitigate the negative effects of climate change on species in this study.

Climate change and plant pathogens: Understanding dynamics, risks and mitigation strategies

AbstractClimate change is reshaping the interactions between plants and pathogens, exerting profound effects on global agricultural systems. Elevated tropospheric ozone levels due to climate change hinder plant photosynthesis and increase vulnerability to biotic invasion. The prevailing atmospheric conditions, including temperature and humidity, profoundly influence fungal pathogenesis, as each stage of a pathogen's life cycle is intricately linked to temperature variations. Likewise, climate change alters bacterial behaviour, fostering increased production of extracellular polysaccharides by plant‐pathogenic bacteria in warmer temperatures. Heat‐adapted bacteria, such as Burkholderia glumea and Ralstonia solanacearum, are emerging as significant global threats as temperature rise. Viruses, too, respond dynamically to climate shifts, with certain species favouring warmer climates for replication, resulting in expanded geographical ranges and modified transmission patterns. Nematodes, formidable constraints in crop production, exhibit temperature‐dependent life cycles and would have potentially accelerated proliferation and distribution as global warming progresses. Molecular‐level changes in pathogenesis, induced by temperature fluctuations, influence various pathogens, thereby impacting their virulence and interactions with host plants. Modelling studies predict changes in disease risks and distributions under future climate scenarios, highlighting the necessity of integrating climate data into crop disease models for accurate forecasts. Mechanistic and observational models illustrate pathogen behaviours amidst changing environmental conditions, providing crucial insights into future disease dynamics. In addition, controlled experiments study disease responses under simulated climate scenarios, underscoring the urgency for comprehensive research to devise effective resistance strategies against severe plant diseases.

Species distribution modeling of North American beavers from the late Pliocene into the future

Abstract Beavers have occurred in North America since at least 7 Ma, but relatively little is known about their distribution across the continent. We modeled distributions of beavers in the late Pliocene (3.3 Ma), Pleistocene (130 ka and 21 ka), and recent Holocene (1970 to 2000) to understand their dispersal across North America, predict future distributions and predict their possible response to future climate and habitat changes. Occurrence data for Castor canadensis were derived from the Global Biodiversity Information Facility. Those data were used with both modern (1970 to 2000) and modeled future (EC-Earth-Veg 2081 to 2100) bioclimatic variables from WorldClim as well as past (Pliocene Marine Isotope Stage M2, Pleistocene Last Interglacial, and Pleistocene Last Glacial Maximum) bioclimatic variables from PaleoClim to model beaver distributions through time. Fossil locality points for Castor extracted from the New and Old Worlds Database of Fossil Mammals (NOW), NEOTOMA Paleoecology Database, and Paleobiology Database were overlain on past projection models to use as validation points. Models were run using MaxEnt with post-processing in ArcGIS. Accuracy for the 5 models ranged between 59.6% and 60.2%. Results for the present model (1970 to 2000) showed habitat suitability in areas beavers inhabit today. During the Pliocene MIS M2 cooling event (3.3 Ma) and Pleistocene Last Glacial Maximum (21 ka), habitat suitability shifted further south into Mexico and peninsular Florida and away from more periglacial northern regions. During the Last Interglacial period (130 ka) and modeled future (2081 to 2100) EC-Earth-Veg 2081 to 2100, habitat suitability was higher in coastal and central regions in North America and lower in southern regions compared to their present distribution. Distributions were most affected by precipitation seasonality, isothermality, and mean annual temperature. High variability in seasonal precipitation and temperatures is likely to influence surface water availability, vegetation type, and riparian vegetation composition, which consequently may reduce available food resources and habitat for beavers. Observed shifts during warmer periods may indicate areas in the late Miocene that facilitated dispersal into North America. Future models using other predicted climatic scenarios and shared socioeconomic pathways may provide better resolution of potential future shifts in beaver distribution with best- and worst-case climate scenarios, thereby permitting at-risk areas to be prioritized for conservation in the face of climate change.

Identification of thresholds and key drivers on water use efficiency in different maize ecoregions in Yellow River Basin of China

Identifying the constrains on water use efficiency (WUE) of crops along a wet-to-dry gradient is important due to irrigation water scarcity, as well as the increasing drought risk under climate change in China. This study coupled five high-resolution climate models from Coupled Model Intercomparison Project Phase 6 (CMIP6) with the Decision Support System for Agrotechnology Transfer (DSSAT)-CERES-Maize model to quantify drought risk and the drivers affecting WUE in five major maize ecoregions of the Yellow River Basin (YRB) under three future scenarios (SSP126, SSP370, and SSP585) for both the historical baseline (1985–2014) and three future periods: 2021–2040 (2030s), 2041–2070 (2050s), and 2071–2100 (2080s). And a bias correction method was implemented for the crop model to analyze optimal WUE thresholds for maize across varying dry-wet gradients. The results indicated that future drought risk will likely persist in the YRB under all scenarios, but with regional differences in drought severity and frequency. The southwestern region (V) experienced the highest frequency of drought (62.50%-SSP126), while the northwestern region (III) exhibited the lowest frequency (33.00%-SSP585) in 2030s, and 83.30% of areas in the southwestern (V) showed significant wetting in the 2080s under SSP126. The bias-corrected CERES-Maize model effectively simulated crop yield and evapotranspiration (ET), resulting in an average reduction of 4.00% and 9.73% in normalized root mean square error (nRMSE) respectively. Distinct WUE thresholds ranging from 1.96 to 8.41 kg ha−1 mm−1 were observed across various scenarios-periods in the maize regions, mostly under slight and moderate dry/wet conditions. Notably, all SSP585 scenarios demonstrated a decrease in WUE thresholds compared to the baseline. Across all scenarios and periods, WUE was mainly driven by yield in the eastern regions (I and II) but by ET in the western regions (III, IV, and V). These findings suggest that regions experiencing varying degrees of drought severity should undergo differentiated management and optimization of agricultural practices to improve WUE under future climate scenarios.

Enhancing Alaskan wildfire prediction and carbon flux estimation: a two-stage deep learning approach within a process-based model

Abstract Wildfires in boreal forests release substantial amounts of carbon into the atmosphere. However, current land-surface models are limited in their representation of fire processes, including their ignition and spread. This study thus developed FireDL, a novel data-driven machine-learning model for the prediction of natural wildfires, and combined it with a land-surface model to better understand the impact of fire on carbon fluxes. FireDL has a two-stage deep learning structure that sequentially combines a long short-term memory (LSTM) algorithm and an artificial neural network (ANN). Preliminary random forest analysis identified fire duration as an important factor in predicting the burned area. Thus, in FireDL, the LSTM algorithm was employed to predict fire occurrence and duration, utilizing lightning, vegetation, and climate datasets. Subsequently, the ANN predicted the total burned area using the LTSM-derived fire duration predictions and climate datasets as input. FireDL produced a robust performance in predicting large fires (>10 000 ha), achieving a correlation coefficient of 0.72. The daily-scaled burned area predictions derived from FireDL were integrated into the Community Land Model version 5—Biogeochemistry (CLM5-BGC) to produce CLM5-BGC-FireDL. This integration considerably improved carbon emission estimations. Notably, the total net ecosystem exchange (NEE) estimated using CLM5-BGC-FireDL in 2019, the year with the highest recorded burned area during our study, was twice that estimated using the standard CLM5-BGC. Discrepancies in the NEE can significantly influence atmospheric CO2 levels, highlighting the importance of our fire prediction model in forecasting the burned area and carbon emissions. The use of FireDL with future climate scenarios is thus anticipated to yield valuable insights into ecosystem management and climate change mitigation strategies.

Exploring the impacts of climate change and identifying potential adaptation strategies for sustainable rice production in Thailand's Lower Chao Phraya Basin through crop simulation modeling.

The lower Chao Phraya River Basin (CPRB) in Thailand, a major rice-producing area, is grappling with increased water scarcity alongside more frequent floods and droughts, necessitating effective adaptation strategies to sustain agricultural productivity. This study assesses the impacts of climate change on rice yield and irrigation water use, using the DSSAT-CERES-Rice model. Based on these findings, potential genotype- and management-based adaptation strategies were recommended. The model was calibrated and evaluated using the data from field experiments conducted at the Asian Institute of Technology, Thailand during 2017-2018 and 2021-2022. The grain yield and irrigation water use between baseline (2010-2022) and future climate periods (early-century: 2023-2040, mid-century: 2041-2070, and late-century: 2071-2100) were compared. Future climate projections were based on five Global Climate Models (GCMs) from the NEX-GDDP-CMIP6 project under three scenarios (SSP126, SSP245, and SSP585). The model calibration and evaluation demonstrated very good performance statistics, with a d-index of 0.85 during both calibration and evaluation. The model simulations indicated that the maximum and minimum temperatures in the lower CPRB are projected to increase by ~ 2 °C and ~ 4 °C in the late century under SSP245 and SSP585, respectively. Consequently, rice yields are projected to decline by up to 33%, and irrigation water usage to increase by 53% under SSP585 by the late century. Based on the findings, the following major genotype- and management-based adaptation strategies are recommended: (1) Developing heat-tolerant rice cultivars to mitigate yield losses under future climate scenarios, (2) Developing rice cultivars with extended grain-filling duration to enhance both irrigation water use and yield, (3) Shifting the planting date 1-2 weeks earlier (from baseline planting date of 20 July), and shifting fertilizer application date 1-2 weeks earlier (from baseline fertilizer application date of 20 September) for the panicle initiation stage to improve yield, and (4) Optimizing irrigation thresholds (remaining soil water at which to irrigate) to reduce irrigation water use without compromising yield. Overall, the findings highlight the importance of genotype improvement and adaptive management practices in mitigating the adverse effects of climate change on rice production in the lower CPRB.

Priority Conservation Area of Quercus mongolica Under Climate Change: Application of an Ensemble Modeling

As the primary secondary tree species in Northeast China, Quercus mongolica possesses significant ecological and economic value. This study employed the Biomod2 platform in conjunction with ArcGIS spatial analysis to assess the potential suitable habitat distribution area of Q. mongolica under current climatic conditions. Furthermore, it forecasted the distribution range and niche changes of potentially suitable habitats for Q. mongolica from 2022 to 2090 and pinpointed the key environmental factors influencing its distribution. The findings reveal that the total potential suitable area for Q. mongolica covers 74,994.792 km2, predominantly spread across Inner Mongolia, Heilongjiang, Jilin, and other regions. The primary determinants of suitable area distribution were peak temperature of hottest month, lowest temperature of coldest month, and altitude. Under future climate scenarios, the potentially suitable habitats of Q. mongolica are anticipated to diminish to varying extents, with the distribution center exhibiting a tendency towards northward migration. Concurrently, the overlap among different climate scenarios is predicted to expand over time. This investigation facilitates a comprehensive understanding of Q. mongolica’s adaptation to climate change, enabling informed adjustments and serving as a valuable reference for the preservation and sustainable management of Q. mongolica populations.

Population exposure to flooding in Small Island Developing States under climate change

Abstract Estimates of current and future population exposure to both coastal and inland flooding do not exist consistently in all Small Island Developing States (SIDS), despite these being some of the places most at risk to climate change. This has primarily been due to a lack of suitable or complete data. In this paper, we utilise a ∼30 m global hydrodynamic flood model to estimate population exposure to coastal and inland flood hazard in all SIDS under present day, as well as under low, intermediate, and very high emissions climate change scenarios (SSP1-2.6, SSP2-4.5 and SSP5-8.5). Our analysis shows that present day population exposure to flooding in SIDS is high (19.5% total population: 100 year flood hazard), varies widely depending on the location (3%–66%), and increases under all three climate scenarios—even if global temperatures remain below 2 °C warming (range in percentage change between present day and SSP1-2.6: −4.5%–44%). We find that levels of flood hazard and population exposure are not strongly linked, and that indirect measures of exposure in common vulnerability or risk indicators do not adequately capture the complex drivers of flood hazard and population exposure in SIDS. The most exposed places under the lowest climate change scenario (SSP1-2.6) continue to be the most exposed under the highest climate change scenario (SSP5-8.5), meaning investment in adaptation in these locations is likely robust to climate scenario uncertainty.

Warming-induced contrasts in snow depth drive the future trajectory of soil carbon loss across the Arctic-Boreal region

The Arctic-Boreal region is projected to experience spatially divergent trends in snow depth following climate change. However, the impact of these spatial trends has remained largely unexplored, despite potentially large consequences for the carbon cycle. To address this knowledge gap, we forced a customised arctic version of the dynamic vegetation model LPJ-GUESS with daily CMIP6 outputs from a global climate model (MRI-ESM2-0) under three climate scenarios. We find that snow depths increased the most in the coldest, northernmost regions, insulating the soil, which led to increased heterotrophic respiration and reduced carbon residence times. We emphasise the need for improved projections of future snow depth - in particular diverging trends across landscapes - to more accurately simulate the strength of Arctic-Boreal carbon feedbacks and their impact on global climate.