Representative Concentration Pathways Research Articles

Analyzing energy consumption due to occupant interaction with manual and automatic window blinds in multiple climates

Proper daylight management through automated or manual blind systems can enhance user comfort and energy conservation. Furthermore, the influence of weather conditions, coupled with occupant behavior, significantly impacts energy consumption, rendering weather forecasts invaluable in conducting individual building energy simulations to identify energy-efficient strategies for the future. Also, I wonder if it is possible to move some items from room 308 until you can provide items from other resources. Future weather conditions and their impact on building performance can be evaluated using Representative Concentration Pathways (RCP) scenarios provided by the Intergovernmental Panel on Climate Change (IPCC). This research aimed to compare the energy usage of manual and automated window blinds with internal blinds in mid-size office buildings across four main climate zones (2B Hot-Dry; 2A Hot-Humid; 7 Very Cold; 4A Mixed-Humid) in the USA under current and future climate change scenarios (RCP 4.5, 8.5). Utilizing simulation as the methodology and comparing the energy use resulting from the RCP 4.5 and 8.5 scenarios revealed that the most significant decrease in heating energy usage was happening in the Hot-Humid zone with Automated blinds, which is a more efficient alternative than manual Blinds. In contrast, the Very Cold zone experienced almost no changes in either heating or cooling energy consumption. In Climate Zones 2B Hot-Dry and 7 Very Cold, rising temperatures lead to increased cooling energy requirements and reduced heating demands due to climate changes. Climate Zone 4A Mixed-Humid saw reduced energy consumption for cooling but increased usage for heating, particularly under RCP 8.5. In Zone 2A Hot-Humid, a high demand for heating energy was found.

Assessing Change Impacts on Flood and Drought hazard in Thua Thien Hue province using Standardized Precipitation Index (SPI)

The objective of this study is to comprehensively analyze short- and long-term drought and flood occurrences, providing valuable insights into the potential impact of climate change in the future. Our analysis, utilizing the SPI-6 and SPI-12 indices, focuses on historical data spanning from 1979 to 2019, revealing noteworthy patterns. Notable periods of severe drought include September 1982 to August 1983, September 2004 to August 2005, and October 2014 to August 2015. Conversely, instances of elevated humidity and extreme inundation were recorded from October 1980 to August 1981, October 1995 to June 1997, November 1999 to October 2000, November 2007 to October 2008, October 2011 to August 2012, and January 2017 to August 2018. These findings are consistent with historical records of drought and flood events in Thua Thien Hue Province over the past four decades. Incorporating the SPI-6 and SPI-12 indices alongside rainfall forecasts based on updated climate change and sea-level rise scenarios for 2020 in Vietnam, our study extends its projections to the period from 2020 to 2035. These projections indicate a significant departure from typical annual conditions, with both the severity and frequency of drought and flood events expected to increase. Specifically, Thua Thien Hue weather stations estimate a flood frequency of 14% (SPI-12) and 16% (SPI-6), equivalent to approximately one dry month every seven months. High humidity events are projected to occur at an 18% frequency, roughly once every 5-6 months. Analysis of future scenarios under RCP4.5 and RCP8.5 reveals varying levels of risk associated with drought and flood occurrences. Generally, the risk of flooding is slightly lower than that of drought, with only marginal differences observed in scenario RCP8.5. When considering seasonal rainfall patterns—where half of the year experiences a dry season and the other half is rainy—the incidence of drought surpasses that of high humidity in scenario RCP4.5. In contrast, in scenario RCP8.5, the likelihood of flooding outweighs that of drought. It’s noteworthy that historically, mountainous areas within Thua Thien Hue Province have experienced fewer droughts in comparison to coastal regions. Furthermore, the severity of drought and flood events is more pronounced in scenario RCP8.5, particularly concerning flood events. This underscores that despite higher total rainfall in RCP8.5, leading to more severe inundation events, the distribution exhibits unevenness marked by extreme conditions, including more severe drought events.

Near-, medium- and long-term impacts of climate change on the thermal energy consumption of buildings in Finland under RCP climate scenarios

The building sector is critical in climate change mitigation by reducing its energy consumption. At the same time, warming climate requires adaptation measures from buildings, making it necessary to study the future thermal energy demand of buildings. This paper studies the impact of climate change on the thermal energy demand of buildings in a heating dominated climate of Finland, under “representative concentration pathway” (RCP) climate scenarios in near- (2030), medium- (2050) and long-term (2080) horizon. Additionally, the impact of the thermal performance level of the buildings is investigated. The thermal energy demand calculation is based on overarching standards from the European “energy performance of buildings” directive. The weather scenarios present both typical and extreme weather years, applied through morphing and future weather data from the Finnish Meteorological Institute. The results show that thermal energy demand in Finland in 2080 can decrease up to 74.6kWh/m2. In addition, the decrease in heating demand will be higher than the increase in cooling demand. Cooling can become the dominant peak in long-term evaluations, especially in the South (Vantaa) with passive buildings under RCP8.5 climate scenario. Renovation is the most efficient action to decrease energy intensity, the earliest it is done the better.

Climate change in the Biebrza Basin—Projections and ecohydrological implications

Over the last decades observed climate change in Poland had a significant impact on the condition and functioning of the environment. Thus, it is crucial to analyze further future changes to be able to cope with the potential effects of changing climate conditions. In this study, we aimed to assess the impact of projected climate change on meteorological and hydrological conditions in the Biebrza Basin. We analyzed seasonal and annual changes in air temperature, precipitation, streamflow and flood characteristics using the hydrological Soil and Water Assessment Tool (SWAT) model. We examined projected changes for two future time horizons (2024–2050 and 2074–2100) under the Representative Concentration Pathways (RCP) 4.5 and 8.5 using an ensemble of nine EUROCORDEX model scenarios. Climate change projections indicated an increase in precipitation by up to +17 % (+117 mm) and air temperature by up to 3.8 °C by the end of the 21st century. In the analyzed flow gauges a considerable increase in low and mean flows is projected in the future. High flows are projected to slightly decrease for Sztabin, remain at a similar level for Dębowo and slightly increase for Osowiec and Burzyn. Flood area and volume will slightly increase in future horizons. The greatest increase in flood duration (by up to 16 days) is projected for RCP8.5 by the end of the 21st century. It seems, that the expected hydrological conditions, both in the short and long term, will become more stable and improve the conditions for the development of wetlands.

Evolution of ozone-precursor sensitivity in the Guangdong-Hong Kong-Macao Greater Bay Area of China under the backdrop of climate change

China is facing the dual challenges of worsening ozone (O3) pollution and climate change. Better understanding the cause of O3 pollution and its response to climate change is crucial for developing an effective O3 control strategy in the coming years. In this study, a localized three-dimensional numerical modeling system was applied to investigate the long-term evolution of ozone-precursor sensitivity (OPS) over the Guangdong-Hong Kong-Macao Greater Bay Area (GBA) during September 2030, 2040 and 2050 under different climate scenarios, including four Representative Concentration Pathway (RCP), the Nationally Determined Contribution (NDC), and carbon neutrality (NEU). RCP emissions were corrected both temporally and spatially by bounding with the actual emissions in 2020 to reduce the uncertainty of future emission estimation. The atmospheric oxidative capacity-based HO2/OH indicator was used to quantify the variations of OPS. The percentage of NOx-limited area over the GBA will increase by 9.8%, 14.8% and 16.9% under RCP2.6, RCP4.5 and RCP8.5 in 2050, comparing with those in 2020. NOx-limited area under NEU showed a greater increase by 23.1%, as a result of larger reduction degree of NOx than VOCs. This highlights that deep NOx emission reduction, mostly driven by the structural adjustments in industry, energy, and transportation under the “2060 carbon neutrality” strategy, will accelerate OPS transition into NOx-limited and ensure the gradual improvement of O3 pollution. NOx emission control in some urban areas still in the VOCs-limited OPS in 2050 under NEU, e.g., Guangzhou and Hong Kong, need to be strengthened to speed up O3 mitigation. The findings of this study would improve our understanding on the long-term trends of OPS in the GBA under the backdrop of climate change, and provide guidance for the synergistic reduction of greenhouse gases and air pollutants in the future.

A causal physics-informed deep learning formulation for groundwater flow modeling and climate change effect analysis

In this study, we propose and test a formulation for building causal physics-informed hybrid models over traditional physics-informed hybrid models (H-HBVo) and a convolutional neural network (1D-CNN) in groundwater level (GWL) modeling. Two types of models are built based on our formulation and named H-HBV and H-Lin, because they integrate two different hydrological models. The comparison is made in terms of performance and ability to learn cause-and-effect relationships between inputs and outputs. The novelty of this study lies in the CRC (Causal Relationship Constraints) conditions, derived from a mathematical development, that are imposed on the layers of the physics-informed hybrid model to force it to learn causal relationships. The results showed that the 1D-CNN algorithm performed slightly better than the hybrid algorithms, and that the H-Lin and H-HBV algorithms together achieved slightly better results than the traditional hybrid algorithm, H-HBVo. It is also obtained that the algorithms subjected to our formulations (H-Lin and H-HBV) are the ones with satisfactory causality properties and that the integration of domain knowledge is not sufficient to obtain causal and interpretable models within the framework of hybrid algorithms. Subsequently, an analysis of the effect of climate change is carried out for Quebec (Canada) using H-HBV models. The results show that the RCP and SSP scenarios give fairly similar results, i.e. an increase in GWL between 2011 and 2090 with median amplitudes varying between + 5.6 and + 7 cm. In some cases, the amplitude of increase can exceed between + 14 and + 23 cm, and even reach between + 35 and + 70 cm. The results of the climate change analysis are consistent with changes in vertical inflow and potential evapotranspiration, the input variables, for the different climate scenarios, confirming the robustness of our formulation.

Modelling Habitat Suitability and Distribution of the Endemic Mindanao Horned Frog (Pelobatrachus stejnegeri) and its Response to Changing Climate

Climate change is already affecting biodiversity, with special concern to endemics whose range is restricted and limited. This study focuses on the Mindanao horned frog (Pelobatrachus stejnegeri), an endemic species to the Philippines, susceptible to climate-induced habitat changes. Using MaxEnt species distribution model (SDM), the current and future (year 2050 projections) habitat suitability and distribution of P. stejnegeri were modelled. Results showed that annual mean temperature, elevation, and annual precipitation were the environmental variables having the highest influence on P. stejnegeri's distribution. The model predicts a significant range contraction under representative concentration pathways (RCP) future scenarios (RCP 2.6 and RCP 8.5), with a more pronounced decrease in distribution (31.72%) under the high emission scenario (RCP 8.5). These findings emphasize the vulnerability of P. stejnegeri to climate change and highlight the importance of integrating SDM into conservation and management strategies to protect endemic species under changing climatic conditions.

A climate-induced tree species bottleneck for forest management in Europe.

Large pulses of tree mortality have ushered in a major reorganization of Europe's forest ecosystems. To initiate a robust next generation of trees, the species that are planted today need to be climatically suitable throughout the entire twenty-first century. Here we developed species distribution models for 69 European tree species based on occurrence data from 238,080 plot locations to investigate the option space for current forest management in Europe. We show that the average pool of tree species continuously suitable throughout the century is smaller than that under current and end-of-century climate conditions, creating a tree species bottleneck for current management. If the need for continuous climate suitability throughout the lifespan of a tree planted today is considered, climate change shrinks the tree species pool available to management by between 33% and 49% of its current values (40% and 54% of potential end-of-century values), under moderate (Representative Concentration Pathway 2.6) and severe (Representative Concentration Pathway 8.5) climate change, respectively. This bottleneck could have strong negative impacts on timber production, carbon storage and biodiversity conservation, as only 3.18, 3.53 and 2.56 species of high potential for providing these functions remain suitable throughout the century on average per square kilometre in Europe. Our results indicate that the option space for silviculture is narrowing substantially because of climate change and that an important adaptation strategy in forestry-creating mixed forests-might be curtailed by widespread losses of climatically suitable tree species.

Climate projections of the Adriatic Sea: role of river release

The Adriatic Sea, characterized by unique local features in comparison to the broader Mediterranean Sea, stands out as a highly susceptible region to climate change. In this context, our study involves a focused climate downscaling approach, concentrating on the Adriatic water cycle. This encompasses integrated modeling at the mesoscale, covering the atmosphere, hydrology, and marine general circulation. The study period spans from 1992 to 2050, considering the high emission scenario RCP8.5. We aim at evaluating how the river release projection affects the local density stratification and the sea level rise. Indeed, the river release is found to decrease by approximately 35% in the mid-term future and condition the stratification of the water column with differences between the Northern and Southern sub-basins. The projected runoff decrease has a major impact on the Northern sub-basin, where the stratification is haline-dominated and the foreseen salinization prevails on the heating through the whole water column. Conversely, the runoff decrease has a lower impact on the Southern sub-basin, where the future changes of other mechanisms may play a major role, e.g., the changing properties of the Mediterranean water entering the Otranto Strait and the foreseen heating prevails on the salinization from the intermediate to deep water column. The study provides the first evidence of how the decreasing river discharge locally reduces the density stratification, increases the dense water, and mitigates the sea level rise in the Northern Adriatic Sea, thus acting in the opposite direction to the global warming. To minimize uncertainty in coastal ocean projections around the world, it is essential that the climate downscaling integrates high-resolution hydrology and hydrodynamics models to correctly reproduce the link between surface buoyancy and stratification and the resulting dynamics.

China's future wind energy considering air density during climate change

To assess the impact of air density on future wind resources under representative concentration pathway (RCP) scenarios (RCP2.6, RCP4.5, and RCP8.5), the Coordinated Regional Downscaling Experiment-East Asia project was utilized to simulate the future air density characteristics of China (2021–2100). Empirical air density distributions were calculated, and fourteen probability density functions were fitted to the empirical air density distributions, determining the optimal fitted distribution functions. In this study, the effect of air density on the annual energy production and wind power density was quantified. The results indicated that only approximately 4 % of regional air density values reaches the standard air density under the three climate scenarios. The seven parameter Generalized Extreme Value-Generalized Extreme Value distribution, six parameter Gamma-Generalized Extreme Value distribution, and six parameter Exponentiated Weibull- Generalized Extreme Value distribution matched the empirical distributions well under RCP2.6, RCP4.5, and RCP8.5, respectively. Annual energy production and wind power density decreased by more than 15 % after considering air density. Under the different RCP scenarios (RCP2.6, RCP4.5, and RCP8.5), the errors of annual energy production and wind power density gradually escalate. In the Tibetan Plateau region, accounting for air density results in an overestimation of annual energy production by over 40 % and wind power density by more than 35 %. The findings of this study offer guidance for future wind energy resource planning, enabling optimal utilization of wind energy resources.

Influence of the solar penetration depth and heat-fluxes on the sea surface temperature using an ocean mixed layer model

Solar penetration depth is a major factor in modulating the Sea Surface Temperature (SST) and hence influences the air-sea interface processes. This study investigates the influence of solar penetration depth and heat-fluxes on the SST at three RAMA (Research Moored Array for African-Asian-Australian Monsoon Analysis and Prediction) buoy locations (along 90°E at 15°N, 12°N, and 8°N) in the Bay of Bengal (BoB) using the One-Dimensional Price-Weller-Pinkel (1D-PWP) model. The results show that SST has strong dependence on the solar penetration depths with the shortwave component of solar insolation outweighing its longwave component. We found that spatio-temporally varying penetration depths refine the simulated SST, when compared against constant penetration depths. These spatio-temporally varying penetration depths (ranging from 10 to 75 m for the shortwave component; with higher values during monsoon) can be attributed to spatio-temporally changing biological parameters, wind, precipitation, and suspended particulate matter. Upon examination, an antiphase relationship between the penetration depths and total chlorophyll, total primary production, & total phytoplankton was found. Upon including the effect of horizontal advection and vertical entrainment in the 1D-PWP model results, the SST predictions improved albeit by a small amount. Furthermore, the effects of increased heat-fluxes, under the different Representative Concentration Pathway (RCP) equivalent scenarios, coupled with varying penetration depths were estimated. It was found that for the spatio-temporally varying penetration depths, the mean (maximum) increment was 0.52 °C (0.92 °C) under the highest emission scenario, i.e., RCP8.5, across all the locations. The results of the study can potentially be used further for 3D ocean modeling studies, and the characterization of air-sea interaction during monsoon and cyclones in the BoB.

Prosopis juliflora (Swartz) DC. invasion in protected areas of India under climate change

Globally invasive alien plants pose a serious threat to biodiversity and are invading protected areas. However, information regarding the potential risk of invasive alien species in protected areas is very limited in India. Therefore, the aim of the study was to examine the potential risks of P. juliflora invasions under projected climate change. We used P. juliflora climate suitability maps based on our previous study using the MaxEnt species distribution model and investigated how P. juliflora invasion may change inside and outside the protected areas by 2050 under two climate change scenarios, representative concentration pathways (RCP) 2.6 and 8.5. The findings of the study suggest that the overall area of P. juliflora suitability in the protected areas will increase by 2050. Further, we found that several protected areas containing relatively high vertebrate diversity are located in climatically suitable areas for P. juliflora. The study findings encourage management efforts to manage the spread of P. juliflora invasion in the selected protected areas of India to protect and conserve the threatened vertebrates' habitats.

Influence of teleconnections on observations and projections of hydroclimatic extremes caused by tropical cyclones in the arid climate of Baja California Sur

This study focuses on identifying modulations by large-scale synoptic, inter-annual, and decadal oscillations on the extreme rainfall in the state of Baja California Sur, and provides statistical models to forecast future evolution. The region is arid, with 70% of precipitation from July to October, and is affected by tropical systems that may lead to moderate and even intense precipitation. Seven clusters were obtained using the Ward method applied to quality-controlled climatological data from 1950 to 2014. Normalized extreme precipitation (95th percentile) shows an overall increase in the last decades (1995-2004 and 2005-2014), with total values much larger than in any of the previous 50 years. Multivariate linear models (MLMs) were developed based on indices for the Pacific Decadal Oscillation (PDO) and El Niño-Southern Oscillation (ENSO) in Region 3.4, which were shown to modulate extreme precipitation. The MLM based on PDO, ENSO, and the fraction of tropical cyclones (TC) within a radius of 300 km to the peninsula (M4), has a better correlation with observed rainfall than the historical simulations of the Coupled-Model Inter-comparison Project version 5 (CMIP5) models; moreover, M4 outperforms all other MLMs in six of the seven clusters. Projections were evaluated based on the MLMs and CMIP5 simulations under scenarios RCP4.5 and RCP8.5 for mid- and long-term horizons. Model M4 projects more extreme events than CMIP5, and all MLM projects negative trends in extreme precipitation from 2041 to 2100 under RCP8.5. This study provides valuable information on future extreme precipitation in an arid region in the presence of steep topography, which could result in potential damage to ecosystems and infrastructure.

Quantifying the influence of climate change on streamflow of Rietspruit sub-basin, South Africa

Abstract This study integrated climate projections from five global climate models (GCMs) into the soil and water assessment tool to evaluate the potential impact of climate alterations on the Rietspruit River sub-basin under two representative concentration pathways (RCP4.5 and 8.5). The model's performance was evaluated based on the coefficient of determination (R2), percent bias (PBIAS), Nash–Sutcliffe efficiency (NSE), probability (P)-factor and correlation coefficient (R)-factor. Calibration results showed an R2 of 0.62, NSE of 0.60, PBIAS of 20, P-factor of 0.86 and R-factor of 0.91, while validation produced an R2 of 0.64, NSE of 0.61, PBIAS of 40 and P-factor of 0.85 and R-factor of 1.22. Precipitation is predicted to increase under both RCPs. Maximum temperature is projected to increase under both RCPs, with a major increase in the winter months. Minimum temperatures are projected to decrease under RCP4.5 in the near (−0.99 °C) and mid (−0.23 °C) futures, while the far future is projected to experience an increase of 0.14 °C. Precipitation and temperature changes correspond to increases in streamflow by an average of 53% (RCP4.5) and 47% (RCP8.5). These results indicate a need for an integrated approach in catchment water resource management amid potential climate and land use variations.

The incorporation of strontium and barium into the otoliths of the flounder Paralichthys olivaceus at early life stages demonstrates resilience to ocean acidification.

Ocean acidification could modify the bioavailability and chemical properties of trace elements in seawater, which could affect their incorporation into the calcareous structures of marine organisms. Fish otoliths, biomineralized ear stones made by aragonite, are suspended within the endolymph fluid of teleosts, indicating that the elemental incorporation of otoliths might also be susceptible to ocean acidification. In this study, we evaluated the combined effects of CO2-induced ocean acidification (pH 8.10, 7.70, and 7.30, corresponding to ocean acidification scenarios under the representative concentration pathway 8.5 model as projected by the Intergovernmental Panel on Climate Change) and water elemental concentrations of strontium (Sr) and barium (Ba; low, medium, and high) on elemental incorporation into otoliths of the flounder Paralichthys olivaceus at early life stages. Our results revealed that the elemental incorporation of Sr and Ba into otoliths was principally dependent on the corresponding water elemental concentrations rather than on ocean acidification. Moreover, the partition coefficients (DMe) of Sr and Ba may stabilize after dynamic equilibrium is reached as the water elemental concentration increases, but are not affected by ocean acidification. Therefore, the incorporation of Sr and Ba into otoliths of the flounder at early life stages may not serve as an effective indicator of ocean acidification. In other words, the findings suggest that ocean acidification does not impact the incorporation of Sr and Ba incorporation into otoliths when tracing the temperature or salinity experiences of the flounder. Our findings will provide new knowledge for understanding the potential ecological effects of ocean acidification on the recruitment dynamics of fish species.

Influence of Regional Temperature Anomalies on Strawberry Yield: A Study Using Multivariate Copula Analysis

A thorough understanding of the impact of climatic factors on agricultural production is crucial for improving crop models and enhancing predictability of crop prices and yields. Fluctuations in crop yield and price can have significant implications for the market sector and farming community. Given the projected increase in frequency and intensity of extreme events, reliable modelling of cropping patterns becomes essential. Temperature anomalies are expected to play a prominent role in future extreme events, emphasizing the need to comprehend their influence on crop yield. Forecasting extreme yield, which encompasses both the highest and lowest levels of agricultural production within a given time period, along with peak crop prices representing the highest market values, poses greater challenges in forecasting compared to other values. Probability-based predictions, accounting for uncertainty and variability, offer a more accurate approach for extreme value estimation and risk assessment. In this study, we employ a multivariate analysis based on vine copula to explore the interdependencies between temperature anomalies and daily strawberry yield in Santa Maria, California. By considering the maximum and minimum daily yields each month, we observe an increased probability of yield loss with rising temperature anomalies. While we do not explicitly consider the specific impacts of temperature anomalies under individual Representative Concentration Pathway (RCP) scenarios, our analysis is conducted within the broader context of the current global warming scenario. This allows us to capture the overall anticipated effects of regional temperature anomalies on agriculture. The findings of this study have potential impacts and consequences for understanding the vulnerability of agricultural systems and improving crop model predictions. By enhancing our understanding of the relationships between temperature anomalies and crop yield, we can inform decision-making processes related to the impact of climate change on agriculture. This research contributes to the ongoing efforts in improving agricultural sustainability and resilience in the face of changing climatic conditions.

A machine learning approach to mapping suitable areas for forest vegetation in the eThekwini municipality

Driven by climate change, global forests are undergoing significant transformations in growth, ecology, and distribution, necessitating informed restoration and conservation strategies, particularly in the eThekwini Municipality where anthropogenic activities exacerbate these trends. Modelling current forest suitability (2023) utilized bioclimatic variables from the WorldClim dataset, alongside elevation and slope from the Shuttle Radar Topography Mission (SRTM) dataset, with remote sensing data acquired from Landsat 9 and Sentinel 2A. Future forest suitability (2021–2040) was projected also using bioclimatic variables from two Global Climate Models (GCMs) under four WorldClim Shared Socioeconomic Pathway (SSP)-based Representative Concentration Pathway (RCP) scenarios. Employing Random Forests (RF), Light Gradient Boosting (LightGBM), and Artificial Neural Networks (ANN), data processing was carried out using Google Earth Engine (GEE), QGIS and Python, with model accuracy primarily assessed using the Receiver Operating Characteristic (ROC) curves and the Area Under the ROC Curve (AUC). LightGBM demonstrated superior performance, achieving AUCs of 96.88% and 93.75% for current and future suitability mapping, respectively, with annual precipitation and vegetation changes identified as crucial variables. Currently, 30% of the municipality's land is deemed suitable, primarily concentrated in the central region. Future projections highlight the mountainous north-western region as most suitable, notably under the SSP370 scenario with a projected suitable area of 63%. Strategic recommendations include prioritizing reforestation efforts, engaging private landowners, exploring urban reforestation opportunities, and implementing continuous monitoring for adaptive management, thereby enhancing carbon sequestration, biodiversity conservation, and ecosystem resilience. This study provides valuable insights for informed decision-making in forest restoration and conservation, despite inherent uncertainties.

Forecasting water scarcity with an optimized Soil and Water Assessment Tool (SWAT) model under representative concentration pathway (RCP) scenarios

Abstract Population growth and climate change have increased the demand for water resources, and there is a need to predict water scarcity effectively. The soil and water assessment tool model has been optimized in this work to estimate water scarcity under several representative concentration pathway scenarios. To improve the Soil and Water Assessment Tool model, a new version of the dwarf mongoose optimization technique named Improved Dwarf Mongoose Optimization has been developed. The findings showed that the optimized Soil and Water Assessment Tool model performs better than other optimization models in terms of water scarcity prediction accuracy. The findings indicated that the improved Soil and Water Assessment Tool model can be used to predict water scarcity in other locations and provide valuable insights into the future of water resource management. According to the results, water availability also decreased in the representative concentration pathways, but the trend declined more in the representative concentration pathways 8.5 scenarios. Critical times of water scarcity have been discovered that can guide water management techniques. The results of this study can help policymakers make informed choices about water management and develop adaptive methods to mitigate the effects of water scarcity.

The Impact of Global Warming on Residential Air Conditioning Ownership and Usage in Mauritius, a Small Island Developing State

Acknowledging the increasing demand for Air Conditioning (AC) usage, understanding how AC demand will evolve under the impact of climate change in the tropical island of Mauritius is very important. This paper presents the results of a survey focused on the AC usage patterns and adoption for Mauritius. The data are further used to develop a logit model to predict how AC ownership will change for 2050 and 2070 under Representative Concentration Pathway (RCP) 4.5 and 8.5. The model predicts that in 2070 under the worst-case scenario, RCP 8.5, 95% of the population will have 50% probability of owning an AC compared to 77% of the population presently. It was further estimated that 22% of the whole population will likely be operating their AC through the year. This paper points out the need for urgent strategies for promoting more sustainable cooling in Mauritius and the importance of further research on cooling options.

Probabilistic projections of temperature and rainfall for climate risk assessment in Vietnam

ABSTRACT In this study, we developed a probabilistic model using the surrogate mixed model ensemble (SMME) method to project temperature and rainfall in Vietnam under the Representative Concentration Pathway (RCP) 4.5 and 8.5 scenarios. The SMME model combines patterns from 31 global climate models participating in the Coupled Model Intercomparison Project Phase 5 (CMIP5) and their weighted model surrogates. Testing for the period of 2006–2018 demonstrated the SMME's ability to encompass observed temperature and rainfall changes. By the end of the 21st century, there is a 5% probability of average temperature increase exceeding 6.29 °C, and a 95% probability of minimum temperature increasing by more than 2.21 °C during 2080–2099 under RCP8.5 compared to 1986–2005. Meanwhile, rainfall is projected to slightly increase, with an average rise of 6.12% at the 5% probability level. The study also quantified the contributions of uncertainty sources – unforced, forced, and scenario-related – to the projection results, revealing that unforced uncertainty dominates the total signal at the beginning of the 21st century and gradually decreases, while forced uncertainty remains relatively moderate but increases gradually over time. As we approach the end of the century, scenario uncertainty dominates, accounting for 75–80% of the total signal.