Shared Socioeconomic Pathways Scenarios Research Articles

Analyzing inflow to Faye reservoir sensitivity to climate change using CMIP6 and random forest algorithm

ABSTRACT In the era of Climate Change and Climate Variability (CC and CV), renewable energy sources such as Hydropower (HP) have a significant role to play in mitigation. However, inflow to reservoir which is the key fuel for HP generation is vulnerable to CC and CV. Thus, there is a need to investigate the potential impacts of CC and CV on HP systems in the future. This study attempts to assess the potential impacts of CC and CV on the Faye reservoir inflow using the Random Forest (RF) algorithm. For this purpose, bias-adjusted precipitation and temperature data of thirteen climate model outputs and their ensemble mean from Coupled Model Inter-comparison Project Phase 6 (CMIP6) under three Shared Socioeconomic Pathways scenarios (SSP1-2.6; SSP2-4.5 and SSP5-8.5) were used as predictors. The potential changes in reservoir inflows were evaluated in the near (2025–2049), mid (2050–2074) and far (2075–2099) futures relative to the reference period (1990–2014). The results show the good performance of the RF algorithm in simulating reservoir inflows with Cor > 0.6 for all models. The annual inflows to the Faye reservoir are noted to increase in the future compared to the reference period despite the potential decrease in future precipitation probably due to land use/cover change. For the ensemble mean of models, this projected increase is estimated to around 16%, 23% and 10%, respectively under the SSP1-2.6, SSP2-4.5 and SSP5-8.5 scenarios for all projection periods. The largest annual increase is noted under the SSP2-4.5 scenario while the lowest increase is noted under the SSP5-8.5 scenario for all projection periods. This study could help the small dam managers better consider the implications of CC and CV on inflow management.

Optimal reliability ensemble averaging approach for robust climate projections over China

AbstractAccurate simulation and reliable projection of temperature and precipitation over China under climate change is important for proposing adaptation measures for future natural ecosystems. This study proposes a novel method to construct an optimal reliability ensemble averaging (REA) subset from the Coupled Model Intercomparison Project Phase 6 (CMIP6) based on their historical performance in simulating temperature and precipitation across different subregions. The optimal REA ensemble outperforms the multi‐model ensemble mean (MMEM) and single optimal model in reproducing the spatial patterns of historical annual mean temperature and precipitation over China from 1985 to 2014. Under the examined Shared Socioeconomic Pathway scenarios (SSP1‐2.6, SSP2‐4.5 and SSP5‐8.5), the REA projects persistent warming and increased precipitation towards the end of the 21st century, intensifying under higher emissions. Nationwide mean temperature rises of 1.39, 2.69 and 5.05°C, and precipitation increases of 9%, 10% and 20% are projected in the long‐term (2081–2100) relative to 1995–2014 under SSP1‐2.6, SSP2‐4.5 and SSP5‐8.5 scenarios, respectively. Northwestern China and the Tibetan Plateau are expected to experience amplified warming and precipitation increases, respectively. Compared to the MMEM, the REA generally indicates reduced warming but larger precipitation increases, especially over the Tibetan Plateau under higher‐emissions scenarios. The REA exhibits lower projection uncertainty than the MMEM for both temperature and precipitation, primarily attributed to reduced internal variability. The novel optimal framework for REA shows the potential for extracting robust regional climate information applicable to different subregions of China. This study may contribute to new comprehension of future climate change over China.

Spatially explicit downscaling and projection of population in mainland China

Spatially explicit population data is critical to investigating human-nature interactions, identifying at-risk populations, and informing sustainable management and policy decisions. Existing long-term global population data are generally limited in three key ways: 1) they were estimated with simple scaling or trend extrapolation methods which are not able to capture detailed population variation spatially and temporally; 2) the rate of urbanization and the spatial patterns of settlement changes were not fully considered in their population redistribution; and 3) the spatial resolution is generally coarse. To address these limitations, we proposed a framework for large-scale spatially explicit downscaling of populations from census data and projection of future population distributions under different Shared Socio-economic Pathways (SSP) scenarios with distinctive changes in urban extent patterns. We separated the urban and rural population numbers before downscaling and included the spatial dynamic changes in urban extent during future projection. Treating urban and rural populations as distinct but interconnected entities, we constructed a random forest model to downscale historical populations and designed a gravity-based population potential model to project future population changes at the grid level. This work unleashes a new capacity for getting accurate spatially explicit demographic change with an unprecedented combination of temporal, spatial, and SSP scenario dimensions, paving the way for cross-disciplinary studies on long-term socio-environmental interactions.

Predicting the Spatial Distribution of the Mangshan Pit Viper (Protobothrops mangshanensis) under Climate Change Scenarios Using MaxEnt Modeling

This study explores the critical issue of understanding the potential impacts of climate change on the habitat suitability of the highly endangered forest-dwelling Mangshan pit viper (Protobothrops mangshanensis) in China. Through the application of the MaxEnt model, high-resolution bioclimatic datasets, and species occurrence data, the research aims to elucidate the spatial and temporal dynamics of P. mangshanensis distribution from the present to the years 2050 and 2070. Through the integration of three climate models from the Coupled Model Intercomparison Project Phase 6 (CMIP6) and exploring different shared socioeconomic pathway (SSP) scenarios (SSP126, SSP370, and SSP585), the study seeks to provide comprehensive insights into the potential variations in habitat suitability under diverse future climate conditions. The methodology employed involves the construction of the MaxEnt model utilizing the BioClim dataset and 83 species occurrence points. The SSP scenarios mentioned above represent future climate change scenarios, and the accuracy of the model is evaluated using the area under the receiver-operating characteristic (ROC) curve (AUC). Key findings reveal that the MaxEnt model exhibits high accuracy (AUC = 0.998), pinpointing the current suitable habitat for P. mangshanensis to be confined to the Mangshan area within the Nanling Mountains, covering an approximate area of 1023.12 km2. However, projections based on future climate scenarios suggest notable shifts in habitat suitability dynamics. While potential suitable habitats may emerge in the northwest of the current range, the existing suitable habitats are anticipated to undergo significant reduction or even complete disappearance. Notably, precipitation during the driest month emerges as a critical determinant influencing the distribution of the species. In conclusion, the study underscores the exacerbating impact of climate change on habitat deterioration and survival risks for P. mangshanensis, emphasizing the urgent need for conservation measures to safeguard the remaining suitable habitats for this endangered species. The implications of these findings are far-reaching, with the anticipated contraction of the snake’s range potentially leading to its disappearance and increased habitat fragmentation. By shedding light on the potential distributional changes of P. mangshanensis in Mangshan, the research provides valuable insights for informing targeted conservation strategies and policy interventions aimed at mitigating the adverse effects of climate change on endangered species.

Predicting Current and Future Habitat Suitability of an Endemic Species Using Data-Fusion Approach: Responses to Climate Change

Fritillaria imperialis L., an indicator plant species in Iran, is facing threats and its population has declined in recent years. To provide insights into the drivers affecting its loss, this research aims to identify the effects of three groups of factors, including climate, soil, and physiographic variables, on the current and future spatial distributions of F. imperialis. For this purpose, we used five machine learning algorithms as well as an ensemble forecasting of species distribution approach to explain the geographical distributions of the species as a function of these factors. In addition, we used two shared socio-economic pathways scenarios – SSP 1-2.6 and SSP 5-8.5 – to project the future distributions of F. imperialis in 2030, 2050, 2070, and 2090. Based on evaluation indices, area under the ROC curve (AUC) and true skill statistic (TSS), the Random Forest (RF) model generated the strongest prediction of the distribution of F. imperialis (TSS>0.9 and AUC>0.9). No significant difference observed among the three datasets (climate-only variables, climate + physiography variables, and climate + physiography + soil variables) in terms of AUC values. In models using climate + physiography + soil datasets, soil electrical conductivity, clay, and pH emerged as the most important variables affecting the growth and development of F. imperialis while climate factors played a lesser role in its distribution. Future projections revealed different patterns when using the optimistic (SSP 1-2.6) and pessimistic (SSP 5-8.5) socio-economic pathway scenarios and either the climate only or climate + physiography models. The climate + physiography + soil model produced similar prediction patterns for the scenarios. The climate-only models predicted larger areas suitable for crown imperial in the future than did the climate + physiography + soil model. These results emphasize the consideration of factors beyond climate scenarios when modeling biological responses to global warming and regional climate change.

A CMIP6 multi-model based analysis of potential climate change effects on watershed runoff using SWAT model: A case study of kunhar river basin, Pakistan

The hydrological regimes of watersheds might be drastically altered by climate change, a majority of Pakistan's watersheds are experiencing problems with water quality and quantity as a result precipitation changes and temperature, necessitating evaluation and alterations to management strategies. In this study, the regional water security in northern Pakistan is examined about anthropogenic climate change on runoff in the Kunhar River Basin (KRB), a typical river in northern Pakistan using Soil and Water Assessment tool (SWAT) and flow durarion curve (FDC). Nine general circulation models (GCMs) were successfully utilized following bias correction under two latest IPCC shared socioeconomic pathways (SSPs) emission scenarios. Correlation coefficients (R2), Nash-Sutcliffe efficiency coefficients (NSE), and the Percent Bias (PBIAS) are all above 0.75. The conclusions demonstrate that the SWAT model precisely simulates the runoff process in the KRB on monthly and daily timescales. For the two emission scenarios of SSP2-4.5 and SSP5-8.5, the mean annual precipitation is predicted to rise by 3.08 % and 5.86 %, respectively, compared to the 1980–2015 baseline. The forecasted rise in mean daily high temperatures is expected to range from 2.08 °C to 3.07 °C, while the anticipated increase in mean daily low temperatures is projected to fall within the range of 2.09 °C–3.39 °C, spanning the years 2020–2099. Under the two SSPs scenarios, annual runoff is estimated to increase by 5.47 % and 7.60 % due to climate change during the same period. Future socioeconomic growth will be supported by a sufficient water supply made possible by the rise in runoff. However, because of climate change, there is a greater possibility of flooding because of increases in both rainfall and runoff. As a result, flood control and development plans for KRB must consider the climate change's possible effects. There is a chance that the peak flow will move backwards relative to the baseline.

Projecting Global Mercury Emissions and Deposition Under the Shared Socioeconomic Pathways

AbstractMercury (Hg) is a naturally occurring element that has been greatly enriched in the environment by human activities like mining and fossil fuel combustion. Despite commonalities in some carbon dioxide (CO2) and Hg emission sources, the implications of long‐range climate scenarios for anthropogenic Hg emissions have yet to be explored. Here, we present comprehensive projections of anthropogenic Hg emissions extending to the year 2300 and evaluate impacts on global atmospheric Hg deposition. Projections are based on four Shared Socioeconomic Pathways (SSPs) ranging from sustainable reductions in resource and energy intensity to rapid economic growth driven by abundant fossil fuel exploitation. There is a greater than two‐fold difference in cumulative anthropogenic Hg emissions between the lower‐bound (110 Gg) and upper‐bound (235 Gg) scenarios. Hg releases to land and water are approximately six times those of direct emissions to air (600–1,470 Gg). At their peak, anthropogenic Hg emissions reach 2,200–2,600 Mg a−1 sometime between 2010 (baseline) and 2030, depending on the SSP scenario. Coal combustion is the largest determinant of differences in Hg emissions among scenarios. Decoupling of Hg and CO2 emission sources occurs under low‐to mid‐range scenarios, though contributions from artisanal and small‐scale gold mining remain uncertain. Future Hg emissions may have lower gaseous elemental Hg (Hg0) and higher divalent Hg (HgII), resulting in a higher fraction of locally sourced Hg deposition. Projected reemissions of previously deposited anthropogenic Hg follow a similar temporal trajectory to primary emissions, amplifying the benefits of primary Hg emission reductions under the most stringent mitigation scenarios.

Floating Ports as Support for Port Relocation Measures on Sea Level Rise

Ports are one of the structures where the effects of global warming are most severe and intense in atmospheric, oceanic, and geographical terms. According to the Intergovernmental Panel on Climate Change (IPCC)'s assessment reports, although it is possible to slow down global warming by reducing greenhouse gas (GHG) emissions, it is not foreseen to stop global warming and sea level rise (SLR) in any scenario. The rising sea level, an inevitable consequence of global warming, is a clear threat to conventional port facilities. In summary, SLR triggered by climate change, which is today's hot topic, may cause conventional port infrastructures to be flooded and lose their functionality. To cope with this threat, port facility planning, and design stages must be carried out by referring to the updated threshold values in Shared Socioeconomic Pathway (SSP) scenarios defined by the Working Groups of the IPCC. However, the uncertainty about the scale, timing, and location of SLR makes definitive solution-oriented approaches more prominent. One of these approaches is floating port structures. This study aims to reveal the role of floating port structures in the implementation of the relocation measure emphasized in the IPCC Sixth Assessment Report (AR6) for conventional ports under the threat of SLR. Initially, in this study, regions with higher SLR risk were identified by considering SSP scenarios contributed by Sixth Phase of the Coupled Model Intercomparison Project (CMIP6) data. Afterwards, the dynamic downscaling model was used to determine the regions with higher regional sea level rise (RSLR) risk and the Marine Traffic database was used to determine the ports in these regions. Thus, it is evaluated whether floating ports can be a suitable alternative in the relocation decision of ports under SLR risk. It is expected that maritime transport will be maintained at adequate security and operational levels by revealing the pros and cons of floating ports.

Evaluating climate change scenarios in the white volta basin: A statistical bias-correction approach

This study provides a critical assessment of future climate scenarios in the White Volta Basin (WVB), an area heavily reliant on groundwater resources. With monthly model results for two Shared Socioeconomic Pathway Scenarios (SSP2-4.5 and 5–8.5), seven (7) Coupled Model Intercomparison Project Phase 6 (CMIP6) models with spatial resolution ranging from 1.125° × 2.8° were assessed. The study also considered, three 30-year time intervals, 1971–2013 for the Baseline (historical) climate, the 2020s (2020–2040) 2050s (2041–2070), and the 2080s (2071–2075) for the future climate scenarios. The Climate Change for Watershed Modeling (CMhyd) software was used for bias correction of these models, with observational data sourced from the National Centers for Environmental Prediction's Climate Forecast System Reanalysis (NCEP CFSR) and 12 gridded climate stations. The bias-corrected models validated using R2, NSE, RMSE, PBIAS, and additional metrics, demonstrated good calibration results compared to observed data. Precipitation ensembles showed 97–99% R2, 94–99% NSE, 70–485 mm RMSE, and -9-5% PBIAS. Maximum and minimum bias corrected temperatures performance varied from 95 to 99% R2, 92–99% NSE, 0.01–0.07 RMSE, and 0.01–0.23 PBIAS, Overall, precipitation levels are expected to decline for SSP2-4.5, while under SSP5-8.5 are expected to increase until the 2080s across all scenarios in comparison to the baseline period. Maximum temperature will be considerably high under SSP5-8.5, with an estimated increase of around 4.3 °C/year in comparison to the reference period. The monthly average variation in the maximum temperature ranges from 0.62 to 2.43 0Cunder the SSP5-8.5 scenario. These findings reveal the potential impacts of climate change on agricultural productivity, groundwater recharge, and crucial information for the development of Ghana's National Determined Contributions (NDCs) and National Adaptation Plans (NAPs). Thus, emphasizing the need for comprehensive adaptation strategies to mitigate the climate change impact on water resources and ecosystem services.

Projected changes in daily precipitation, temperature and wet‐bulb temperature across Arizona using statistically downscaled CMIP6 climate models

AbstractTo evaluate future changes in the climate system, the outputs from coarse‐resolution global climate models (GCMs) need to be downscaled to a finer scale, making them more directly applicable for impact assessment. Here we focus on examining the projected changes of three key climate variables (precipitation, air temperature, and wet bulb temperature) across Arizona (south‐western United States). We use daily outputs from GCMs from the sixth phase of the Coupled Model Intercomparison Project (CMIP6) and bias correct and downscale them to a 4‐km resolution. Through leave‐one‐out cross‐validation, we compare various bias correction methods and identify that the empirical quantile mapping approach performs the best regardless of the climate variable. Then, we analyse the projected bias‐corrected outputs for two future periods (Mid‐of‐Century: 2015–2048; End‐of‐Century: 2067–2100) with respect to the 1981–2014 period, under four shared socioeconomic pathway scenarios (SSP1‐2.6, SSP2‐4.5, SSP3‐7.0 and SSP5‐8.5). Our results show that Arizona's climate is projected to become overall warmer and wetter, more so towards the end of this century and for higher emission scenarios. Additionally, our findings project an increase in wet bulb temperature and cooling degree days, implying the ongoing warming climate's potential impacts on public health and the economy. These results provide a baseline understanding of climate change impacts in the state and highlight the projected changes in the future in response to the climate scenarios.

An analysis of the expected wave conditions in the Mediterranean Sea in the context of global warming

The main objective of the work proposed herewith is to assess the climate change impact on sea state conditions in the Mediterranean Sea under Representative Concentration Pathways and Shared Socio-economic Pathways scenarios and its expected dynamics in the near future. To perform this, the analysis is structured in two-time intervals, each one covering 30 years. The first period represents the recent past, while the second comprises the near future (2041–2070) under the RCP8.5 and SSP5-8.5 scenarios. A wave modelling system based on the SWAN (Simulating WAves Nearshore) model is implemented and validated in the target area using ERA5 wind data to drive the wave model for the recent past. Then, for the same period, simulations with the wave modelling system forced by the Regional Climate Model wind fields are carried out. For the near future, wind fields under the above-mentioned scenarios are considered to drive the wave model. Various analyses and comparisons are performed to identify the climate change impacts on the wave climate and wave power, to evaluate the differences between the results obtained for each scenario, and also the evolution of the characteristics of the extreme values in the Mediterranean Sea basin. No high differences between the average values of significant wave height and wave power obtained under the two scenarios along the entire basin of the Mediterranean Sea were found. In the marginal seas sometimes different trends appear, especially in the Aegean and Tyrrhenian Seas. Regarding the 99th percentile of Hs, under the SSP5-8.5 scenario, they are slightly higher than those under the RCP8.5 scenario.

Synoptic circulation factors associated with wintertime high-PM2.5 concentrations in seoul, Republic of Korea: Their interpretations and applications

Many studies have explored atmospheric circulation fields associated with high particulate matter (PM) events, but only a few studies have evaluated the relative importance of meteorological variables in the circulation fields. In this study, we applied factor analysis method to examine meteorological variables related to the synoptic pattern of high-PM2.5 episodes (i.e., days with daily averaged PM2.5 concentration exceeding 35 μg m−3) in Seoul, Republic of Korea, during the winter season of 2004–2018. Here, we highlight three independent key factors (F1, F2, and F3) associated with high-PM2.5 concentrations. F1 is related to temperature (T) and meridional wind (V) at 850 hPa and 1000 hPa (i.e., T850, T1000, V850, and V1000) and temperature and geopotential height (Z) at 500 hPa (i.e., T500 and Z500). It is the most strongly correlated with T850, indicating an increase in thermal stability over Korea during high-PM2.5 events due to vertically reduced atmospheric ventilation. Meanwhile, F2 is significantly related to the geopotential height and zonal wind (U) at 850 hPa and 1000 hPa (i.e., Z850, Z1000, U850, and U1000). This indicates a dynamically stable condition due to the anomalous easterlies caused by the vertically elongated anticyclone over the northern part of the Korean Peninsula. Finally, F3 is positively associated with zonal and meridional winds at 500 hPa (i.e., U500 and V500). This means that relatively strong winds in the low-to-mid troposphere contribute to high-PM2.5 concentrations by transporting pollutants from the industrial regions of eastern China to Korea. We also examined the possible influence of climate change on high-PM2.5 concentrations based on the aforementioned three factors. According to the various shared socioeconomic pathway (SSP) scenarios, F1 increases significantly for all SSP scenarios compared to F2 and F3, indicating that synoptic patterns related to F1 occur frequently, leading to a favorable condition for high-PM2.5 concentrations. Thus, our finding suggests that climate change alone could worsen air quality in the future even without changes in direct PM2.5 emissions.

Bio‐ORACLE v3.0. Pushing marine data layers to the CMIP6 Earth System Models of climate change research

AbstractMotivationImpacts of climate change on marine biodiversity are often projected with species distribution modelling using standardized data layers representing physical, chemical and biological conditions of the global ocean. Yet, the available data layers (1) have not been updated to incorporate data of the Sixth Phase of the Coupled Model Intercomparison Project (CMIP6), which comprise the Shared Socioeconomic Pathway (SSP) scenarios; (2) consider a limited number of Earth System Models (ESMs), and (3) miss important variables expected to influence future biodiversity distributions. These limitations might undermine biodiversity impact assessments, by failing to integrate them within the context of the most up‐to‐date climate change projections, raising the uncertainty in estimates and misinterpreting the exposure of biodiversity to extreme conditions. Here, we provide a significant update of Bio‐ORACLE, extending biologically relevant data layers from present‐day conditions to the end of the 21st century Shared Socioeconomic Pathway scenarios based on a multi‐model ensemble with data from CMIP6. Alongside, we provide R and Python packages for seamless integration in modelling workflows. The data layers aim to enhance the understanding of the potential impacts of climate change on biodiversity and to support well‐informed research, conservation and management.Main Types of Variable ContainedSurface and benthic layers for, chlorophyll‐a, diffuse attenuation coefficient, dissolved iron, dissolved oxygen, nitrate, ocean temperature, pH, phosphate, photosynthetic active radiation, total phytoplankton, total cloud fraction, salinity, silicate, sea‐water direction, sea‐water velocity, topographic slope, topographic aspect, terrain ruggedness index, topographic position index and bathymetry, and surface layers for air temperature, mixed layer depth, sea‐ice cover and sea‐ice thickness.Spatial Location and GrainGlobal at 0.05° resolution.Time Period and GrainDecadal from present‐day to the end of the 21st century (2000–2100).Major Taxa and Level of MeasurementMarine biodiversity associated with surface and epibenthic habitats.Software FormatA package of functions developed for Python and R software.

Assessment of the impact of climate change on streamflow of Ganjiang River catchment via LSTM-based models

Study regionGanjiang River catchment, China. Study focusQuantifying the effects of the impacts climate change on streamflow is of great importance for regional water resources management. In this study, four LSTM-based models, i.e., LSTM, Stack-LSTM, Bi-LSTM and CNN-LSTM, were constructed to assess hydrological changes under future climate change based on the bias-corrected Coupled Model Intercomparison Project Phase 6 meteorological data from four shared socioeconomic pathways (SSPs). New hydrological insights for the study region(1) The Bi-LSTM achieves the best streamflow simulation performance during the validation period of 2006–2016, followed by the LSTM, Satck-LSTM and CNN-LSTM. The climate will get warmer and wetter in future scenarios with mean annual precipitation and temperature increasing by 3.0–6.2% and 8.3–13.4% (compared to baseline period), respectively. (2) The predicted streamflow will have a decrease of 1.5–16.5% during 2026–2075 under all SSP scenarios. The results of LSTM, Stack-LSTM, and Bi-LSTM models show a decrease of 0.27–50% in November to March, indicating that the dry season will become even drier in the future. These results reveal that the hydrological regime of the Ganjiang River is likely to change and will be characterized by greater seasonal uncertainty and more potential for extreme events due to significant warming and wetting over the future periods.

Suitable areas for temperate fruit trees in a Brazilian hotspot area: Changes driven by new IPCC scenarios

Chilling accumulation and sufficient water availability are mandatory requirements for most temperate fruit trees (TFT) productivity. Given the strong dependence of these crops on air temperature and water availability, it is imperative to analyze the impacts of climate change on TFT, especially in areas considered persistent climate change hotspots. This study analyzed the impacts of climate change on suitable areas for planting TFT in a Brazilian state considered a hotspot for future climate changes - Minas Gerais, using an Agroclimatic Zoning (AZ) method, and outlined adaptation measures for the sustainable cultivation of the main TFT under future climate scenarios. Daily near-surface air temperature (minimum and maximum) and precipitation data were used from 10 Earth System Models (ESMs) from the NASA Earth Exchange Global Daily Downscaled Projections (NEX-GDDP), which are derived from the Coupled Model Intercomparison Project Phase 6 (CMIP6), in two Shared Socioeconomic Pathways scenarios (SSP2–4.5 and SSP5–8.5). The AZ method was applied for the current climate simulations (1995–2014), as well as for projections by the near (2041–2060) and far future (2081 – 2100) climate conditions considering two base temperature values (Tb = 7ºC and 13ºC). Given intense and persistent increases in air temperature, water deficit, and reduced chilling accumulation, TFT plantations will be restricted and economically unfeasible for about 69% (SSP2.4–5) to 86% (SSP5.8–5) of Minas Gerais in the far future for both Tb values. Only ∼6% (near future) and ∼2% (far future) of the study area located in a small portion of the Southern region of Minas Gerais will be suitable for TFT plantations. However, the future of TFT plantations in places classified as regular will depend on adopting adaptation measures to reduce vulnerability to these crops, including low-chill genotypes selection, improvements in breeding programs, using strategies to break dormancy artificially, and intensifying irrigation with less water-demanding techniques.

Assessing Hydropower Potential under Shared Socioeconomic Pathways Scenarios Using Integrated Assessment Modelling

The world is facing a global sustainability crisis affecting environmental systems and society. Addressing these issues requires a multi-dimensional approach that can integrate energy, water, and environment Systems, as well as provide scientific policy advice. In this study, an updated version of an Integrated Assessment Model (IAM) was used, together with new data compatible with Shared Socioeconomic Pathways (SSPs) projections, to significantly improve the work developed before. SSP climate data (temperature, precipitation, and total radiative forcing) and socioeconomic data (population and GDP) were loaded into the IAM, together with different scenario parameters. By analyzing varying socioeconomic scenarios, mitigation efforts, and adaptation strategies, this study assesses their impact on primary energy demand and, consequently, their impact on hydropower potential production. Our results show diverse energy paths, strongly dependent on the future scenario. Energy demand could increase up to 160%; however, several projections foresee a decline in hydropower production to minus 46% due to both climate change and socioeconomic transformation. Our findings highlight the importance of considering a range of potential future scenarios in energy planning and policy development. The varied outcomes across the considered scenarios emphasize the need for flexibility in strategies to accommodate for uncertainties and address the challenges posed by divergent trajectories in hydropower use and renewable energy shares.

Current and future potential distribution of Culex (Melanoconion) (Diptera: Culicidae) of public health interest in the Neotropics.

Anthropogenic activities are altering ecosystem stability and climate worldwide, which is disturbing and shifting arbovirus vector distributions. Although the overall geographic range of some epidemiologically important species is recognized, the spatiotemporal variation for other species in the context of climate change remains poorly understood. Here we predict the current potential distribution of 9 species of Culex (Melanoconion) based on an ecological niche modeling (ENM) approach and assess spatiotemporal variation in future climate change in the Neotropics. The most important environmental predictors were the mean temperature of the warmest season (27 °C), precipitation during the driest month (50 mm), and precipitation during the warmest season (>200 mm). The best current model for each species was transferred to the future general circulation model IPSL-CM6A-LR, using 2 shared socioeconomic pathway scenarios (ssp1-2.6, ssp5-8.5). Under both scenarios of climatic change, an expansion of suitable areas can be observed followed by a strong reduction for the medium-long future under the worst scenario. The multivariate environmental similarity surface analysis indicated future novel climates outside the current range. However, none of the species would occur in those areas. Even if many challenges remain in improving methods for forecasting species responses to global climate change and arbovirus transmission, ENM has strong potential to be applied to the geographic characterization of these systems. Our study can be used for the monitoring of Culex (Melanoconion) species populations and their associated arboviruses, contributing to develop region-specific public health surveillance programs.

Assessment and Prediction of Future Climate Change in the Kaidu River Basin of Xinjiang under Shared Socioeconomic Pathway Scenarios

Xinjiang, located in the arid region of the northwest, is one of the areas most sensitive to global changes. The Kaidu River Basin, situated in the heart of Xinjiang, is one of the sources of China’s largest inland river—the Tarim River. The Kaidu River not only bears the responsibility for supplying water for industrial use and agricultural production and people’s daily life in the basin, but also plays a crucial role in ecological water supply to the Tarim River. Studying and analyzing the characteristics and trends of meteorological condition in the future under climate change can provide important references and a basis for a deeper understanding of changes in the hydrological process and water resources in the basin. Therefore, this paper selects seven precipitation bias correction methods and four temperature bias correction methods to adjust the precipitation and temperature output data of eight general circulation models of the Sixth Coupled Model Intercomparison Project (CMIP6) within the Kaidu River Basin. The applicability of different bias correction methods in the study area is evaluated, and based on the corrected future meteorological data and calculated extreme meteorological index, the trends of meteorological data (precipitation, temperature) in the future period (2025–2050) under four SSP scenarios (SSP1-2.6, SSP2-4.5, SSP3-7.0, SSP5-8.5) in the Kaidu River Basin are analyzed. The results show that: (1) Different types of bias correction methods have different correction focus and effects; their reflections on evaluation indicators are also different. (2) In the future period (2025–2050), the annual precipitation and average temperature in the Kaidu River Basin are higher than those in the historical period (1975–2014). The average annual temperature shows an upward trend in the future, but the annual precipitation shows a downward trend in the future except for the SSP2-4.5 scenario. (3) Compared with the historical period, the extreme precipitation in the future period under the SSP1-2.6, SSP2-4.5, and SSP5-8.5 scenarios is higher than that in the historical period, and the number of rainless days decreases. In the future, under the SSP1-2.6 and SSP5-8.5 scenarios, the probability of meteorological drought events occurring due to high temperatures in the basin may further increase, while under the SSP2-4.5 scenario, the situation of high temperatures and heavy rain in the basin may continue to increase.

Integrating spatiotemporal co-evolution patterns of land types with cellular automata to enhance the reliability of land use projections

Land use and land cover change (LUCC) simulation aids the interpretation of the causes and consequences of future landscape dynamics under various scenarios, which in turn supports policy decisions. The essence of LUCC simulation lies in representing complex spatiotemporal associations among land types, including competitions and interactions. Currently, analyses of complex spatiotemporal LUCC associations mainly focus on the spatial configuration of land use while ignoring the intricate spatiotemporal co-evolution patterns of land types. Therefore, by integrating spatiotemporal co-evolution pattern mining (STC) in a future land use simulation (FLUS) model, a land use change simulation model named STC-FLUS was developed in this study. The proposed model is innovative because it can accurately quantify the spatiotemporal co-evolution patterns of land types, which can be effectively incorporated into LUCC simulations. A set of simulations indicate that the STC-FLUS model is more accurate than the classical FLUS model, with a figure of merit score of 0.135 compared with 0.114. Simulation results under five localized shared socioeconomic pathway scenarios from 2020 to 2040 demonstrate that the proposed model is effective for future LUCC simulation under a set of development scenarios. We conclude that spatiotemporal co-evolution patterns of land types can enhance the reliability of land use projections. Moreover, the STC-FLUS model can serve as a useful tool to understand future land use dynamics.

Divergent trajectories of future global gross primary productivity and evapotranspiration of terrestrial vegetation in Shared Socioeconomic Pathways

Understanding the future trends of carbon and water fluxes between terrestrial ecosystems and the atmosphere is crucial for predicting Earth's climate dynamics. This study employs an advanced numerical approach to project global gross primary productivity (GPP) and evapotranspiration (ET) from 2001 to 2100 under various climate scenarios based on Shared Socioeconomic Pathways (SSPs). To improve predictions of vegetation dynamics, we introduce a novel model (CoLM-PVPM), an enhancement of the Common Land Model version 2014 (CoLM2014), incorporating a prognostic vegetation phenology model (PVPM). Compared to CoLM2014 that relies on satellite-based leaf area index (LAI) inputs, CoLM-PVPM predicts LAI time series using climate variables. Model validation using historical data from 2001 to 2010 demonstrates PVPM in capturing spatiotemporal variations in satellite LAI. Our modeling results indicate that annual averaged LAI and total GPP increase under SSP1–2.6 but decrease under SSP2–4.5, SSP3–7.0, and SSP5–8.5 by 2100. By comparison, annual total ET consistently increases under all SSP scenarios by 2100. Global annual averaged LAI is highly correlated with annual total GPP in all scenarios, while its correlation with annual total ET weakens in SSP2–4.5, SSP3–7.0, and SSP5–8.5. Global annual total vapor pressure deficit (VPD) and precipitation are highly correlated with annual total ET in all scenarios. As emission levels increase, the negative correlation between annual total VPD and GPP strengthens, while the correlation between annual total precipitation and GPP weakens. This research presents an improved model for predicting terrestrial vegetation processes and underscores the importance of low carbon emission scenarios in maintaining carbon-water balances in specific regions.