Representative Concentration Pathway Scenarios Research Articles

Potential future changes in soil carbon dynamics in the Ziwuling Forest, China under different climate change scenarios

Soil carbon (C) cycling processes in terrestrial ecosystems are significantly influenced by global changes, and soil microorganisms play a crucial role in soil organic carbon (SOC) and its feedbacks to climate change. To investigate the potential future changes in soil C dynamics under different scenarios in the Ziwuling Forest region, China, we conducted a soil observation and sampling experiment from April 2021 to July 2022. By utilizing a microbial ecological model (MEND), we aimed to predict the future dynamics of soil C under different scenarios in the area. Our results demonstrate that under the RCP2.6 (Representative Concentration Pathway) scenario, SOC showed a rapid increase, SOC under the RCP2.6 scenario will be significantly higher than those under the RCP4.5 scenario and RCP8.5 scenario in the topsoil and whole soil. Furthermore, the positive correlation between total litter carbon (LC) and SOC under the RCP2.6 scenario highlights the potential role of total litter carbon in driving SOC dynamics. Our study also revealed that the low greenhouse gas (GHG) emission scenario favors the accumulation of SOC in the study area, while the high GHG emission scenario leads to greater soil carbon loss. Overall, these results underscore the importance of considering the impact of climate change, especially global warming, on soil ecosystems in the future. Protecting the soil ecosystem of the Loess Plateau is critical for maintaining soil carbon sinks, preventing soil erosion, and improving and regulating the surrounding environmental climate.

Global warming is projected to lead to increased freshwater growth potential and changes in pace of life in Atlantic salmon Salmo salar.

Global warming has been implicated in widespread demographic changes in Atlantic salmon Salmo salar populations, but projections of life-history responses to future climate change are lacking. Here, we first exploit multiple decades of climate and biological data from the Burrishoole catchment in the west of Ireland to model statistical relationships between atmospheric variables, water temperature, and freshwater growth of juvenile Atlantic salmon. We then use this information to project potential changes in juvenile growth and life-history scheduling under three shared socioeconomic pathway and representative concentration pathway scenarios from 1961 to 2100, based on an ensemble of five climate models. Historical water temperatures were well predicted with a recurrent neural network, using observation-based atmospheric forcing data. Length-at-age was in turn also well predicted by cumulative growing degree days calculated from these water temperatures. Most juveniles in the Burrishoole population migrated to sea as 2-year-old smolts, but our future projections indicate that the system should start producing a greater proportion of 1-year-old smolts, as increasingly more juveniles cross a size-based threshold in their first summer for smoltification the following spring. Those failing to cross the size-based threshold will instead become 2-year-old smolts, but at a larger length relative to 2-year-old smolts observed currently, owing to greater overall freshwater growth opportunity. These changes in age- and size-at-seaward migration could have cascading effects on age- and size-at-maturity and reproductive output. Consequently, the seemingly small changes that our results demonstrate have the potential to cause significant shifts in population dynamics over the full life cycle. This workflow is highly applicable across the range of the Atlantic salmon, as well as to other anadromous species, as it uses openly accessible climate data and a length-at-age model with minimal input requirements, fostering improved general understanding of phenotypic and demographic responses to climate change and management implications.

Changes in the Urban Hydrological Cycle of the Future Using Low-Impact Development Based on Shared Socioeconomic Pathway Scenarios

Representative Concentration Pathway (RCP) scenarios have been used for various studies in the field of climate change. In this regard, the Shared Socioeconomic Pathway (SSP) scenario has been newly introduced to examine climate change impacts, but relevant research is still insufficient. For this reason, new SSP scenarios with a combination of Low-Impact Development (LID) techniques are applied to predict rainfall-runoff efficiency and hydrological variation. The inter-model variability in the monthly average precipitation for each GCM according to new SSP scenarios under future climate was investigated. Based on the RCP 4.5 and RCP 8.5 scenarios, the results show precipitation changes with an increase of 4.8% and 12.3%, respectively. Furthermore, precipitation projections under SSP2-4.5 and SSP5-8.5 scenarios are predicted to increase by 13.9% and 20.6%, respectively, indicating that the magnitude of precipitation increases with new climate change scenarios. The Storm Water Management Model (SWMM) during the future period indicated that LID applications will reduce runoff compared with scenarios with no LID application. In particular, the introduction of permeable pavement and infiltration trenches revealed the best runoff reduction performance among the combinations of LID techniques considered. In addition, this study projected changes in the urban hydrological cycle for the climate over the next 30 years to reflect the implementation of urban hydrological cycle plans, which take approximately 10 years. Overall, it was found that, in the future, LID applications will contribute to improving the sustainability of the urban hydrological cycle of the study area. The results of our study can provide future directions for water management strategies in Korea.

Assessing the impact of RCP4.5 and RCP8.5 scenarios on landslide susceptibility mapping using support vector machine: A case study of Penang Island, Malaysia

This paper investigates the climate change influence on landslide susceptibility mapping (LSM) using a case study conducted on Penang Island in Malaysia, a region prone to landslides. This study was carried out due to limited research assessing the climate change effect on LSM, considering rainfall and temperature. The study employs climate factors, including temperature and rainfall, alongside 12 causative factors in a Support Vector Machine (SVM) model to develop LSM. The Statistical Downscaling Model (SDSM) is used to derive climate change projections under two Representative Concentration Pathways (RCP) scenarios (RCP4.5 and RCP8.5). Data preparation and normalization are performed using ArcGIS 10.7. Based on the results, future annual rainfall and daily temperatures are expected to rise under both scenarios, with RCP8.5 exhibiting more significant climatic changes. The LSM zonation is impacted more significantly under RCP8.5 due to the severity of climate change. LSM under an observation period achieves the best results (area under the curve (AUC) = 85.75, average statistical index (SI) = 94.48%, kappa = 0.885), followed by LSM under RCP4.5 (AUC = 84.38, average SI = 93.54%, kappa = 0.865) and LSM under RCP8.5 (AUC = 84.13, average SI = 93.34%, kappa = 0.860), demonstrating their reliability and adequate performance. These LSMs can assist local authorities in designating critical areas for monitoring and implementing an early-warning system to respond more effectively to landslide risks caused by climate change. However, the study's limitation is considering only two climate scenarios (RCP4.5 and RCP8.5). Future research should encompass a broader range of climate scenarios to develop the most reliable LSM, given the high uncertainty associated with climate change.

Dynamic simulation and projection of land use change using system dynamics model in the Chinese Tianshan mountainous region, central Asia

A critical step in alleviating the contradiction between human activities and land systems is to project and analyze land use change traits in various scenarios to furnish a basis for formulating economic development and ecological conservation strategies. However, few studies have examined land use change using the system dynamics (SD) model and patch-generating land use simulation (PLUS) model in the Chinese Tianshan mountainous region (CTMR) affected by different economic growth patterns and climate change contexts. Therefore, based on the Shared Socioeconomic Pathways and Representative Concentration Pathways (SSPs-RCPs) scenarios, we attempted to construct a regional SD model (RSDM) including population, economic, land, and climate subsystems. Then, the coupled model by combining RSDM and PLUS was employed to simulate and project land use/cover changes (LUCC) in the CTMR at the regional level to explore the spatial distribution of land and its pattern of change in different climate contexts. The SD model is an effective method that can simulate the nonlinear behavior of a complex system and predict its evolution through the interactions and feedback relationships between different influencing factors. The PLUS model is an effective tool that can be used to simulate the evolution of land patches and capture the extent to which the driving factors contribute to LUCC. The relative errors were less than 5%, and the total accuracy of PLUS model was 91.77%. The above results demonstrated the effectiveness of RSDM and PLUS model in modeling LUCC across the CTMR. From 2005 to 2020, there was an expansion trend in the area of forest and construction land as well as in the area of cultivated land, while the grassland area displayed a significant decline. By 2040, the area of unused land, grassland, and water is expected to demonstrate a decreasing trend while other land types increase, with construction land showing the most significant increase of 101.37% under the SSP585 scenario. It is anticipated to expand mainly to cultivated land and grassland around cities. The cultivated land is expected to primarily encroach on the regions of unused land and grassland under the three scenarios, reaching the expansion demand. As opposed to the scenarios of SSP126 and SSP245, the SSP585 scenario distribution of the cultivated land patches will be more compact and denser. The SSP126 and SSP245 scenarios will exhibit similar patterns of future spatial distribution of land. The SSP585 scenario is anticipated to display marked differences. According to the three scenarios, grassland degradation will be severe and require increased grassland protection. The findings can offer novel perspective concepts with regard to future ecological and environmental management, judicious distribution of land resources, and sustainable progression in the CTMR.

Water quantity–quality assessment in the transboundary river basin under climate change: a case study

Abstract This study combined hydrological and water quality simulation models with a water resources planning model to project future water supply conditions under the dam construction in the Harirud River, located at the Afghanistan, Turkmenistan, and the Iran border. The sustainability requirements and possible conflicts among riparian countries were assessed under climate change and future development in Afghanistan's upstream. The water quantity and quality of the Doosti Dam Basin on the Harirud River were investigated based on a contemporary time (1955–2015) to predict the future condition (2020–2099). The representative concentration pathway scenarios were applied based on five bias-corrected climate models. Results showed that most areas of the study area experienced an increase in temperature (1.5–3.8°C) and a decrease in precipitation (19–24%). The Doosti Dam inflow decreased by about 70% after the Salma Dam construction, and the reliability and sustainability of agricultural water supply in Iran and Turkmenistan will reduce to less than 3% under the representative concentration pathways 8.5 climate change scenario. The results show that the Doosti Dam is not a reliable source to supply the domestic water demand of Mashhad, the second most important city in Iran.

Climate Risk and Vulnerability Assessment of Georgian Hydrology under Future Climate Change Scenarios

The Climate Risk and Vulnerability Assessment (CRVA) is a systematic process used to identify gaps in regional climate adaptation strategies. The CRVA method assesses regional vulnerability, adaptation capacity, exposure, and sensitivity to climate change to support improved adaptation policies. This CRVA study assesses Georgia’s climate exposure, geographic sensitivity, and socio-economic sensitivity by focusing on the impacts of climate change on regional hydrology. The projected change in climate extreme indices, defined by the Expert Team on Climate Change Detection and Indices (ETCCDI), is assessed against the 1961–1990 baseline under future Representative Concentration Pathway (RCP) scenarios. These indices encompass various climate factors such as the maximum daily temperature, warmth duration, total precipitation, heavy and extreme precipitation, maximum 5-day precipitation, and consecutive drought duration. This evaluation helps us understand the potential climate exposure impacts on Georgia. The climate-induced geographic sensitivity is examined based on water stress, drought risk, and changes in soil productivity using the Normalized Difference Vegetation Index (NDVI). The climate-induced socio-economic sensitivity is determined using the Gross Domestic Product per capita (GDP), Human Development Index, Education Index, and population density. The highest vulnerability to climate change was found in the Kakheti and Kvemo Kartli regions, with the vulnerability index values ranging from 6 to 15, followed by Mtskheta-Mtianeti, Samtskhe–Javakheti, and Shida Kartli with vulnerability index values ranging from 2 to 8. The location of these regions upstream of the Alazani-Iori, Khrami-Debeda, and Mktvari river basins indicates that the country’s water resources are vulnerable to climate change impacts in the future under the RCP 4.5 and 8.5 scenarios.

Stratospheric Sulfate Aerosols Impacts on West African Monsoon Precipitation Using GeoMIP Models

AbstractStratospheric Aerosol Geoengineering (SAG) is proposed to offset global warming; however, the use of this approach can an impact on the hydrological cycle. We used simulations from Coupled Model Intercomparison Project (CMIP5) and Geoengineering Model Intercomparison Project (G3 simulation) to analyze the impacts of SAG on precipitation (P) and to determine its responsible causes in West Africa and Sahel region. CMIP5 historical data were first validated, the results obtained are consistent with observational data. Under the Representative Concentration Pathway scenario RCP4.5, a slight increase is found in the West Africa Region relative to present‐day climate. The dynamic processes especially, the monsoon shifts are responsible for this precipitation change. Under RCP4.5, during the monsoon period, reductions in P are 0.86%, 0.80% relative to the present‐day climate in the Northern and Southern Sahel, respectively, while precipitation is increased by 1.04% in the West African Region. Under SAG, we find a 3.71% decrease of precipitation in the West African Region while the precipitation decrease is 17.4% and 8.47% respectively in the North Sahel and South Sahel. This decrease in monsoon precipitation is mainly explained by changes in dynamics, which lead to weakened monsoon circulation and a shift in the distribution of monsoon precipitation. This result suggests that SAG deployment to balance all warming can be harmful to rainfall in WAR if the amount of SO2 to be injected into this tropical area is not taken into consideration.

Northward range expansions are not the full story: A case study of Sabellaria alveolata in Great Britain

Warming oceans are causing changes in the distribution of numerous species. If greenhouse gas production continues to follow current trends, further distribution changes will result. It is often assumed that range shifts into higher latitudes may mitigate threats to susceptible species. Sabellaria alveolata is an important intertidal ecosystem engineer and protected by the UK Biodiversity Action Plan. In this study we created a habitat suitability model for S. alveolata around Great Britain and quantified changes in coastal suitability with increasing sea surface temperatures (SST) using Representative Concentration Pathway (RCP) scenarios modelled by the International Panel on Climate Change. The model included maximum and minimum SST, substrate, slope, kinetic energy due to waves and currents, photosynthetically active radiation, and the coefficient of light attenuation. The model performed very well (AUC = 0.932, 50.2% of deviance explained) and all variables explaining comparable deviance (up to 10%). Recent SST was then combined with predicted SST increases, leading to suitable habitat locations expanding across the southern and western GB coastlines and resulting in larger patches, hence potential for connectivity. Some northern extension was observed; however, this was limited and patchy. The greatest extensions were seen on the south coast, with most of the south coast becoming a continuous network with excellent suitability. With an increase of 1.2 °C, the extent of coast in the excellent suitability class was increased by 5.75% compared to 2020, and by 38.1% with 3.2 °C. Despite the importance of winter temperature in driving S. alveolata distribution, over-riding environmental constraints prevented any major northward migration. The assertion that potential for range shifts to higher latitudes may mitigate the effects of climate change depends on niche availability which may not always be realised.

Simulation of Soil Organic Carbon Dynamics in Postfire Boreal Forests of China by Incorporating High-Resolution Remote Sensing Data and Field Measurement

Soil organic carbon (SOC) is an important component of the ecosystem carbon pool, and fire is one of the important disturbances in forest ecosystems. With global warming, there has been a gradual increase in boreal forest fires, which has a nonnegligible impact on the SOC dynamics in forests. The CENTURY model was employed in our study to simulate the changes in SOC stocks in boreal forests of the Great Xing’an Mountains, China under different fire severity conditions. Fire severity was represented by the metric of difference normalized burn ratio (dNBR) derived from 30-m Landsat-8 imageries. Changes in forest SOC stocks following fire disturbance were predicted under four future Representative Concentration Pathway (RCP) scenarios (RCP2.6, RCP4.5, RCP6.0, and RCP8.5). We found that the CENTURY model had good results in simulating the SOC stocks in the postfire of China’s boreal forests. Forest SOC dynamics responded differently to fire severities and the larger SOC loss was associated with increasing fire severity. Importantly, a feedback mechanism was found between climate change and SOC stocks, which reduces SOC stocks with increasing temperatures. High-severity forest fires tended to cause serious damage to the SOC pool and delay forest SOC recovery time; after such events, forest SOC stocks cannot be fully recovered to the prefire levels (6.74% loss). In addition, higher CO2 emissions and warmer temperatures significantly affected the recovery of SOC stocks after fire disturbance, resulting in larger SOC losses. Overall, we projected losses of 10.14%, 12.06%, 12.41%, and 15.70% of SOC stocks after high-severity fires in four RCP scenarios, respectively. Our findings emphasize the importance of fire disturbance and climate change on future dynamics of SOC stocks in China’s boreal forests, providing a scientific basis for future boreal forest management and fire management.

Significance of Direct and Indirect Impacts of Temperature Increase Driven by Climate Change on Threat to Oilseed Rape Posed by Sclerotinia sclerotiorum.

Sclerotinia stem rot, caused by Sclerotinia sclerotiorum, threatens oilseed rape (Brassica napus) crops internationally. The development of this disease is strongly controlled by the weather, which is why global climate change is likely to influence its spread and impact. Climate change may affect the pathogen directly or indirectly via the host plant. This study investigates the potential impact of climate warming on Sclerotinia stem rot severity in oilseed rape in Poland. The aim of this investigation was to assess the relationship between the direct impact (DI) and indirect impact (II) of climate change on disease severity using the 4.5 and 8.5 representative concentration pathways (RCPs). Under the RCP4.5 scenario, nearly 60% of the simulations performed for 16 regions in four periods (2020-2039, 2040-2059, 2060-2079, 2080-2099) showed reductions in disease severity in comparison to those conducted for 1986-2005, while under RCP 8.5, this reduction was generated for nearly 90% of the cases. The effect of the RCP scenario on clustering the regions according to the value of Sclerotinia stem rot severity was also investigated. The simulations revealed that, for all periods, the lowest disease severities are expected for Zachodniopomorskie and Pomorskie. The results obtained also show the superior effects of the II over the DI on Sclerotinia stem rot severity in the future. Under the RCP4.5 scenario, the rate of IIs was greater than that of DIs for 10 regions, while under RCP8.5, this relationship was registered for 16 regions. These outcomes result from the acceleration of the oilseed rape flowering period triggered by expected temperature increases. The novelty of this study lies in a detailed analysis of the relationships between the DI and II of climate change, expressed numerically, for 16 regions in Poland. The obtained results highlight the role of the indirect impact in shaping disease severity and indicate that it should be incorporated into assessment methods of climate change effects alongside the direct impact.

Reference evapotranspiration projections in Southern Spain (until 2100) using temperature-based machine learning models

This study firstly examines the performance of temperature-based machine learning models for estimating reference evapotranspiration (ET0), an essential parameter for water management in agriculture, ecosystems, and hydrology. Data from 122 Automated Weather Stations (AWS) across different regions in Southern Spain has been studied and four machine learning models have been developed and assessed: Multilayer Perceptron (MLP), Extreme Learning Machine (ELM), Random Forest (RF), and Support Vector Machine (SVM). The results indicate that all machine learning models outperform the traditional Hargreaves-Samani method in estimating ET0. Specifically, ELM performed, on average, as the best in terms of Global Performance Indicator (GPI) for those locations situated in the first region (GPI = 0.1860), while MLP outperformed the rest for those located in the second (GPI = 0,1162). Besides, the configuration using minimum, mean and maximum air temperature (Tx, Tm, Tn, respectively), the diurnal temperature range (DTR), and Extraterrestrial solar radiation features (Ra) was found to be the fittest for the second region (GPI = 0.0734) and that using Tx, Tn, Tm and Ra in the first one in (GPI = 0.1938). Once the models were validated, they were applied to future 5 km gridded projection datasets, using different Representative Concentration Pathway (RCP) scenarios, in order to estimate ET0 up to the year 2100. In general, the projected ET0 was found to increase significantly in the future, with Mann Kendall Z values that ranged from 7.11 to 10.37 in the RCP4.5 scenario and from 10.84 to 12.57 in the RCP8.5 scenario. Thus, the ET0 is expected to increase from 1300 to 1600 mm to 1500–1700 mm using the RCP4.5 and to 1900 mm using the RCP8.5 in Andalusia, with the highest increase occurring in the south coastal region. This study provides important insights into the application of machine learning models to estimate ET0 and its implications for future water management strategies.

Exploring the historical and future spatial interaction relationship between urbanization and ecosystem services in the Yangtze River Basin, China

Diagnosing the spatial interaction between urbanization and ecosystem services (ES) is of guiding significance for regional sustainable development. The complex spatial interactions mechanism between different types of urbanization and ES during historical and future periods were rarely explored at the whole watershed scale. To fill this gap, we systematically evaluated, predicted and compared the complex spatial interaction relationship between different types of urbanization and ES in the Yangtze River Basin (YRB) during 1990–2019 and in 2050 under different Shared Socioeconomic Pathways (SSPs) and Representative Concentration Pathways (RCPs) scenarios. Results showed that economic and land urbanization in the YRB will increase sharply during 1990–2050, while the population urbanization will drop slightly during 2019–2050. More than 59% of the YRB will experience an increase in total ecosystem services (TES) in SSP1-2.6 and SSP2-4.5 scenarios during 2019–2050. The average coupling coordination degree (CCD) between urbanization and TES in the YRB ranged 0.16–0.61 during 1990–2050, with a gradual increase from severe uncoordinated to basic coordination, and experienced a transition from lagging urbanization to lagging TES. More than 64% and 38% of the YRB will achieve strong decoupling between economic and population urbanization and TES during 1990–2050, respectively, which largely occur in the upper reaches, while over 34% of the YRB belong to weak decoupling between land urbanization and TES except for SSP1-2.6, which cluster in the middle and lower reaches. Moreover, we found that SSP1-2.6 will be the most promising scenario to achieve a win-win situation between different types of urbanization and TES, while prominent conflict between them will likely emerge in SSP5-8.5 scenario. The findings can contribute to further understanding the spatial interaction relationship between urbanization and ES, and provide support for ecosystem protection and sustainable urban planning.

Future distribution patterns of nine cuttlefish species under climate change

While most coastal communities are expected to, or have been, negatively impacted by climate change, cephalopods have generally thrived with shifting ocean conditions. However, whilst benefitting from the same physiological flexibility that characterizes cephalopods in general, cuttlefish have depth constraints imposed by the presence of a cuttlebone and are limited to specific locations by their particularly low vagility. To evaluate the potential effects of marine climate change on cuttlefish, Species Distribution Models (SDM) were applied to nine species of genus Sepiidae to assess potential changes to their future distribution (2050 and 2100), under four representative concentration pathway (RCP) scenarios (i.e., RCP 2.6, 4.5, 6.0, and 8.5; CMIP5). We show that future cuttlefish habitat suitability and distribution will potentially decrease. The species with the most extreme impacts, Doratosepion braggi (Verco, 1907), was observed to decline as much as 30.77% in average habitat suitability (from present 55.26% to 24.48% at RCP 8.5 in 2100), to Sepia officinalis Linnaeus, 1758 with a low maximum decrease of 1.64% in average habitat suitability (from present 59.62% to 57.98% at RCP 8.5 in 2100). Increases in habitat suitability were projected mostly at higher latitudes, while habitat decrease was predicted for the tropical regions and lower latitudinal limits of species’ distributions. As their habitats decrease in terms of habitat suitability, cuttlefish may not benefit from future changes in climate. Additionally, as potential “sea canaries” for coastal ecosystems, we may see many species and habitats from these systems affected by climate change, particularly in tropical regions.

Future projections of extreme precipitation in Tropical America and Panama under global warming based on 150‐year continuous simulations using 20‐km and 60‐km atmospheric general circulation models

AbstractThis study explores future changes in extreme precipitation across tropical America and Panama using 150‐year continuous experiments run on the Meteorological Research Institute atmospheric general circulation model at horizontal resolutions of 20 km (AGCM20) and 60 km (AGCM60). Tropical America is a warming hotspot where the mean seasonal precipitation amounts are decreasing, but annual maximum daily precipitation amounts are following an increasing trend. In Panama, future climates simulated using AGCM20 and AGCM60 show an increase in December–January–February (DJF) precipitation over eastern Panama, and a decrease in June–July–August (JJA) precipitation over western areas near the Pacific coast. Annual maximum daily precipitation increases on the eastern side of the country in both models. The mean annual maximum daily precipitation for tropical America shows decadal oscillations, similar to the global mean but with smaller amplitude variations. The mean annual maximum daily precipitation for Panama shows large oscillations, even for the 10‐year running mean. For tropical America, the mean annual maximum daily precipitation has a linear relationship with surface air temperature that is independent of the Representative Concentration Pathway scenarios, but this is not the case for Panama. Temporal correlations between the tropical America mean annual maximum daily precipitation and annual mean surface air temperature in the historical experiments have a geographical distribution similar to that of the sea surface temperature anomalies during negative El Niño/Southern Oscillation events, but not in the future experiments. The correlation between the Panama mean annual maximum daily precipitation and surface air temperature in both the historical and future experiments has a geographical distribution similar to that of the temporal correlation for the tropical America mean in the historical experiment. The annual maximum daily precipitation in Panama is projected to increase during negative El Niño events in the future, as observed in the historical climate.

Blue and green water accounting for climate change adaptation in a water scarce river basin

Understanding how climate change influences the portioning of rainfall into blue water (BW) and green water (GW) is vital for establishing adaptive water management strategies in water-scarce regions. This study presents a conceptual framework to investigate climate change impacts on blue and green water accounting by integrating a hydrological model with a multi-criteria decision-making approach to prioritize watersheds for climate change adaptation. A Soil and Water Assessment Tool (SWAT) is adopted to investigate the hydrologic variability of BW and GW for baseline (1995–2019) and future (2020–2100) periods in a semi-arid Dharoi catchment of the Sabarmati River basin, India. The future climate data, derived from five general circulation models, are ensembled using a multi-model ensemble mean approach under two RCP (representative concentration pathways) scenarios. Several non-parametric tests are employed to ascertain statistically significant changes in the hydrologic variables between future and baseline periods. Further, Analytic Hierarchy Process (AHP) is applied considering seven attributes related to climate change, land use, and demographic parameters for watershed prioritization. Higher weights are assigned to the climate change attributes, followed by land use and demographic attributes. The SWAT results indicate a decrease in rainfall, BW, and GWS (green water storage) and an increase in temperature and GWF (green water flow) while traversing from the beginning of 2040 till the end of the 21st century under both RCPs. Thus, 2040 is established as a ‘tipping point’ for climate-induced changes in the Dharoi catchment, wherein a distinct reversal in the hydroclimatic regime is evident before and after 2040. Further, significant warming is observed by the end of 21st century under RCP4.5 and RCP8.5, contributing to a reduction in rainfall (up to 24.8%) and a decline in BW (up to 78.8%) across two-thirds of the watersheds in the Dharoi catchment. The AHP analysis identifies three out of ten watersheds as vulnerable and assigned ‘high priority’ for implementing adaptation measures to counter climate change-induced water scarcity.

Mapping the shared socio-economic pathways onto the Nature Futures Framework at the global scale

AbstractThe Nature Futures Framework (NFF) was developed for the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services (IPBES) to explore scenarios that represent a diversity of positive relationships between humans and nature. Widely used in global environmental assessments, the shared socio-economic pathways (SSPs) in combination with the representative concentration pathways (RCPs) were developed for climate change assessments. However, the relationship at a global level between the SSP–RCP scenario outcomes and the framing of the NFF around three value perspectives—Nature for Nature, Nature for Society, and Nature as Culture—has not been established. Here, we demonstrate a method to map onto the NFF value perspectives results from alternative SSP scenarios, each paired with an RCP consistent with the SSP storyline. For each of the NFF value perspectives, multiple elements were identified, each represented by one or more nature-focused indicators. Values for these indicators, for the different SSP scenario outcomes, were derived from an existing application of a global land system model, LandSyMM. A score for each indicator is estimated by comparing the indicator values against a normative target range. We find that only SSP1 provides greater benefits for Nature as Culture and Nature for Society relative to a 2010 baseline. Overall, the SSP scenarios provide fewer benefits for Nature for Nature, consistent with a bias towards the provision of material over non-material ecosystem services. The results demonstrate that the SSP–RCP scenario framing captures some, but not all, of the dimensions of nature and that alternative scenario framings, such as the NFF, are needed to study a broader range of biodiversity and ecosystem related questions as well as exploring positive futures.

Global Temperature Projections from a Statistical Energy Balance Model Using Multiple Sources of Historical Data

Abstract This paper estimates a two-component energy balance model as a linear state-space system (EBM-SS model) using historical data. It is a joint model for the temperature in the mixed layer, the temperature in the deep ocean layer, and radiative forcing. The EBM-SS model allows for the modeling of nonstationarity in forcing and the incorporation of multiple data sources for the unobserved processes. We estimate the EBM-SS model using historical datasets at the global level for the period 1955–2020 by maximum likelihood. We show in the empirical estimation and in simulations that using multiple data sources for the unobserved processes reduces parameter estimation uncertainty. When fitting the EBM-SS model to six observational global mean surface temperature (GMST) anomaly series, the GMST projections under representative concentration pathway scenarios are comparable to those from Coupled Model Intercomparison Project models. The results show that a simple statistical climate model estimated on the historical period can produce GMST projections compatible with output from large-scale Earth system models. Significance Statement We develop a statistical model to understand Earth’s energy balance and how it impacts temperature across the planet’s surface and deep oceans. Unlike previous approaches, ours is fully statistical. This means that we use maximum likelihood to calculate the estimates and evaluate the level of uncertainty in the model’s parameters. By applying the model to historical data from 1955 to 2020, we are able to make predictions about the average global surface temperature that are consistent with those from the Coupled Model Intercomparison Project. Our findings support current predictions about global temperatures using a straightforward statistical climate model, grounded in historical data.

Land Use Change and Landscape Ecological Risk Prediction in Urumqi under the Shared Socio-Economic Pathways and the Representative Concentration Pathways (SSP-RCP) Scenarios

Understanding land use/cover change (LUCC) and landscape ecological risk change in the context of future climate warming can help adjust socio-economic development policies, optimize regional ecological security patterns, and promote green and low-carbon development on the one hand and provide important supplements and improvements for research in related fields on the other. Taking Urumqi as the study area, based on the Coupled Model Intercomparison Project Phase 6 (CMIP6) multi-modal ensemble data, we used the coupled system dynamics (SD) model and patch-generation land use simulation (PLUS) model to simulate land use change under three SSP-RCP scenarios in 2020–2060, and we predicted trends of landscape ecological risk change in this 40-year period by using the landscape ecological risk index (LERI). The results indicate that woodland and grassland significantly increase under the SSP126 scenario. Unused land is larger in the SSP245 scenario. The expansion trend of construction land toward cultivated land is most obvious in the SSP585 scenario; additionally, the area of water increases more distinctly in this scenario. The overall landscape ecological risk under the three SSP-RCP scenarios is reduced to different degrees; in particular, the risk level of urban built-up areas and nature reserves decreases remarkably, and the area of the highest risk zones of unused land is also gradually narrowed. By 2060, the average LERI under the SSP126 scenario is the lowest. The study findings can help relevant departments formulate reasonable urban development plans, which are of great theoretical and practical significance for guaranteeing regional ecological security.

Effects of projected wave climate changes on the sizing and performance of OWCs: a focus on the Mediterranean and Atlantic European coastal waters

Reliable estimations of the Annual Energy Production (AEP) achievable with a certain Wave Energy Converter (WEC) are among the fundamental elements for a sound evaluation of the related Levelized Cost of Energy (LCOE) that plays a crucial role in the investment decision-making process. The lack of reliability in estimates of the device productivity can be a result of the uncertainty in the assessment of the available wave energy resource since the geometry of the device which maximizes the AEP is dependent on the specific wave climate at the foreseen installation site.
 The Climate Data Store of the Copernicus Climate Change Service delivers projections of the wave climate along the 20 m bathymetric contours of the whole European coastlines, covering the period 2040-2100, under two Representative Concentration Pathway (RCP) scenarios (RCP4.5 and RCP8.5) [1].
 This work addresses the effect of such long-term wave climate changes on the optimal sizing and performances of Oscillating Water Column (OWC) WECs to be installed along the Atlantic European coastline, expanding a previously published work in which the Mediterranean coastline was investigated [2]. The capture width of the OWC WEC under different wave conditions is computed using an empirical model [3] capable of predicting the device performance with acceptable accuracy and limited computational time.
 The results show that the optimal geometry of the OWC WEC varies significantly in the different geographical locations, and that the long-term changes in the wave energy resource could cause a slight modification of the optimal geometry in each potential installation site. The AEP of the OWC WEC in the projected wave climate scenario can be significantly different compared to the present one, potentially contributing to reducing the LCOE of this wave energy conversion technology in some geographical locations.
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