Future Climate Change Scenarios Research Articles

Can the combining of wetlands with reservoir operation reduce the risk of future floods and droughts?

Abstract. Wetlands and reservoirs are important water flow and storage regulators in a river basin; therefore, they can play a crucial role in mitigating flood and hydrological drought risks. Despite the advancement of river basin theory and modeling, our knowledge is still limited about the extent to which these two regulators could perform such a role, especially under future climate extremes. To improve our understanding, we first coupled wetlands and reservoir operations into a semi-spatially explicit hydrological model and then applied it in a case study involving a large river basin in northeast China. The projection of future floods and hydrological droughts was performed using the hydrological model during different periods (near future: 2026–2050, middle century: 2051–2075, and end century: 2076–2100) under five future climate change scenarios. We found that the risk of future floods and hydrological droughts can vary across different periods – in particular, it will experience relatively large increases and slight decreases. This large river basin will experience flood events of longer duration, with larger peak flows and volume, and of enhanced flashiness compared to the historical period. Simultaneously, the hydrological droughts will be much more frequent, with longer durations and more serious deficits. Therefore, the risk of floods and droughts will, overall, increase further under future climate change even under the combined influence of reservoirs and wetlands. These findings highlight the hydrological regulation function of wetlands and reservoirs and attest that the combining of wetlands with reservoir operation cannot fully eliminate the increasing future flood and drought risks. To improve a river basin's resilience to the risks of future climate change, we argue that the implementation of wetland restoration and the development of accurate forecasting systems for effective reservoir operation are of great importance. Furthermore, this study demonstrated a wetland–reservoir integrated modeling and assessment framework that is conducive to risk assessment of floods and hydrological droughts and that can be used for other river basins in the world.

Applying RUSLE for soil erosion estimation in Romania under current and future climate scenarios

Soil erosion is one of the main land degradation processes, often enhanced by unsuitable human activities. In the context of global climate change, soil erosion also exhibits quantitative and qualitative changes induced by the temporally dynamic erosion factors, namely rainfall erosivity and land use. Our study attempts to forecast the evolution of soil erosion rates under climate change scenarios over the Romanian territory. Firstly, we apply the Revised Universal Soil Loss Equation (RUSLE) methodology to achieve a spatial model of soil erosion for the current climate conditions. Secondly, we use projections of rainfall erosivity to derive soil erosion models for two future climate change scenarios (Representative Concentration Pathway - RCP 4.5 and 8.5) and two periods of time (2041–2060, 2061–2080). Thirdly, we compute the differences between future projected and current erosion rates to identify spatial patterns of soil erosion evolution. The results show that, under the influence of rainfall erosivity dynamics, soil erosion tends to increase during the next decades. Overall, an enhancement of soil erosion rates on 84–90% of the country is expected to take place. Most of the erosion rates will increase slightly, with <1 t ha−1 yr−1, from an overall average of 2.94 t ha−1 yr−1, at present, to 3.67 (2061–2080, RCP 8.5) – 3.74 (2041–2060, RCP 8.5) t ha−1 yr−1 at country level. Locally, the erosion rates may increase with >5–10 t ha−1 yr−1 on areas estimated to cumulate >8000 km2. The most affected areas will be the soils situated in hilly and plateau regions, leading to an enhancement of their fertility degradation.

Evolution Patterns of Cooling Island Effect in Blue–Green Space under Different Shared Socioeconomic Pathways Scenarios

Blue–green space refers to blue space (rivers and lakes) and green space (lawns and trees), which have the cooling island effect and are increasingly acknowledged as a potential and effective way to help alleviate the urban heat island effect. Scientific and flexible blue–green space planning is required, especially for medium- and large-scale urban agglomerations in the face of climate change. However, the temporal evolution and spatial patterns of the cooling island effect in the blue–green space under different future scenarios of climate change have not been fully investigated. This would impede long-term urban strategies for climate change adaptation and resilience. Here we studied the relationship between future climate change and blue–green spatial layout with Weather Research and Forecasting (WRF), based on the numerical simulation data of 15 global climate models under different extreme Shared Socioeconomic Pathway (SSP) scenarios. As a result, future changes in urban cooling island (UCI) magnitudes were estimated between historical (2015–2020) and future timelines: 2030s (2021–2040), 2050s (2041–2060), 2070s (2061–2080), and 2090s (2081–2100). Our results showed different land use types in blue and green space across the study area were predicted to present various changes in the next 80 years, with forest, grassland, and arable land experiencing the most significant land use transfer. The future UCI intensity of cities under SPP5-8.5 (12) was found to be lower than that under SPP2-4.5 (15), indicating that cities may be expected to experience decreases in UCI magnitudes in the future under SSP5-8.5. When there is no expansion of urban development land, we found that the conversion of different land use types into blue and green space leads to little change in future UCI intensity. While the area growth of forests and water bodies is proportional to the increase in UCI, the increase of farmland was observed to have the most significant impact on reducing the amplitude of urban UCI. Given that Huai’an City, Yancheng City, and Yangzhou City have abundant blue–green space, the urban cooling island effect was projected to be more significant than that of other cities in the study area under different SSP scenarios. The simulation results of the WRF model indicate that optimizing the layout of urban blue–green space plays an important role in modulating the urban thermal environment.

Bayesian functional emulation of CO2 emissions on future climate change scenarios

AbstractWe propose a statistical emulator for a climate‐economy deterministic integrated assessment model ensemble, based on a functional regression framework. Inference on the unknown parameters is carried out through a mixed effects hierarchical model using a fully Bayesian framework with a prior distribution on the vector of all parameters. We also suggest an autoregressive parameterization of the covariance matrix of the error, with matching marginal prior. In this way, we allow for a functional framework for the discretized output of the simulators that allows their time continuous evaluation.

Optimizing sowing window for mungbean and its adaptation option for the South-central zone of Bangladesh in future climate change scenario using APSIM model

Optimizing sowing window for mungbean and its adaptation option for the South-central zone of Bangladesh in future climate change scenario using APSIM model

Dendroanatomy of xylem hydraulics in two pine species: Efficiency prevails on safety for basal area growth in drought-prone conditions

Xylem structure optimizes water conductivity while preventing hydraulic failure via embolism resistance, but how this process is modulated by climate variability and how it affects secondary growth in mature trees is still not fully understood, particularly in water-limited environments. Using quantitative wood anatomy techniques, we estimated xylem anatomical proxies for hydraulic efficiency (xylem specific conductivity, Ks) and safety (cell wall reinforcement, wrein) in two western US conifers, Pinus flexilis and Pinus longaeva, at a montane and subalpine location respectively. We built two large datasets (570 rings for P. flexilis, 635 rings for P. longaeva) to investigate 1) the variability of anatomical parameters (i.e lumen diameter, cell wall thickness) and hydraulic proxies along the stem in the five outermost rings (2009–2013); 2) the response of hydraulic proxies to daily climate over a period of 24 years (1990–2013); and 3) the relationship between xylem hydraulic architecture and basal area increment (BAI). Lumen diameter scaled along the stem following a power function, but the scaling patterns of cell wall thickness and hydraulic proxies differed significantly between species. From 1990–2013, Ks decreased in both species, whereas wrein increased only in P. longaeva, while no trends were observed in BAI. Climate sensitivity of Ks peaked over a longer period (84–102 days) compared to wrein (20–55 days), responding to increasing minimum temperature. In both species, Ks was a better predictor of BAI than wrein, indicating that, even under severely water-limited conditions, radial growth is linked to hydraulic efficiency rather than safety. Based on the variability of cell density along the stem, the trade-off between hydraulic efficiency and safety in P. longaeva appeared to be controlled by a strategy of space occupation. Characterizing the mechanistic relationship between xylem anatomy, plant hydraulic functioning, and stem growth is necessary to better understand climate-growth relationships in the western US and species’ growth plasticity under future climate change scenarios.

Time to Step Up Conservation: Climate Change Will Further Reduce the Suitable Habitats for the Vulnerable Species Marbled Polecat (Vormela peregusna).

Habitat loss and human threats are putting the marbled polecat (Vormela peregusna) on the brink of extinction. Numerous recent studies have found that climate change will further deteriorate the living environment of endangered species, leading to their eventual extinction. In this study, we used the results of infrared camera surveys in China and worldwide distribution data to construct an ensemble model consisting of 10 commonly used ecological niche models to specify potential suitable habitat areas for V. peregusna under current conditions with similar environments to the sighting record sites. Changes in the suitable habitat for V. peregusna under future climate change scenarios were simulated using mid-century (2050s) and the end of the century (2090s) climate scenarios provided by the Coupled Model Intercomparison Project Phase 6 (CMIP6). We evaluated the accuracy of the model to obtain the environmental probability values (cutoff) of the V. peregusna distribution, the current distribution of suitable habitats, and future changes in moderately and highly suitable habitat areas. The results showed that the general linear model (GLM) was the best single model for predicting suitable habitats for V. peregusna, and the kappa coefficient, area under the curve (AUC), and true skill statistic (TSS) of the ensemble model all exceeded 0.9, reflecting greater accuracy and stability than single models. Under the current conditions, the area of suitable habitat for V. peregusna reached 3935.92 × 104 km2, suggesting a wide distribution range. In the future, climate change is predicted to severely affect the distribution of V. peregusna and substantially reduce the area of suitable habitats for the species, with 11.91 to 33.55% of moderately and highly suitable habitat areas no longer suitable for the survival of V. peregusna. This shift poses an extremely serious challenge to the conservation of this species. We suggest that attention be given to this problem in Europe, especially the countries surrounding the Black Sea, Asia, China, and Mongolia, and that measures be taken, such as regular monitoring and designating protected areas for the conservation of vulnerable animals.

Impact assessment of climate change on energy performance and thermal load of residential buildings in New Zealand

While it is evident that climate change will have an impact on the energy demand for heating and cooling in buildings, the exact extent of this impact is not yet fully understood. Quantification of future cooling and heating need in buildings provides a basis for taking appropriate measures for building climate change adaptation. The focus of this study is to examine how future climate change scenarios will impact the heating and cooling of residential buildings across different climatic regions in New Zealand. The future weather data under changing climate were generated for six climatic zones of New Zealand employing the statistical downscaling method. The study used various climate change scenarios, which represent concentration pathways (RCPs), to generate weather data. Specifically, the RCP8.5 and RCP4.5 scenarios were employed in the building performance simulations for different prototypes of residential buildings. The results showed there would be a significant change in the thermal performance of residential buildings, with a noticeable increase in cooling load and a decrease in heating load. These changes include a maximum thermal load change of 3 kWh/m2 in Auckland by 2090, 2.7 kWh/m2 in Hamilton, 8.3 kWh/m2 in Wellington, 4.2 kWh/m2 in Rotorua, 11 kWh/m2 in Christchurch, and 11.6 kWh/m2 in Queenstown. The warmer climatic zones are expected to change from a heating dominated to a cooling-dominated zone. The results indicated the importance of considering present and future climatic conditions in design and establishing a foundation for actions for the resilience of buildings to climate change.

Modeling soil accretion and carbon accumulation in deltaic rice fields

Rice cultivation is popular in low-lying areas such as deltas, but climate change threatens the viability of the crop. In recent decades, the resilience of deltas to sea level rise (SLR) has been influenced by the reduction of sediment load from rivers due to the construction of dams, disrupting natural deposition in deltaic plains. Sediment and organic matter accumulation in wetlands are key to vertical accretion in the face of SLR and soil organic carbon (SOC) sequestration. In this sense, deltaic rice fields can retain sediments as well as wetlands and promote SOC sequestration, which is effective in adapting to SLR. In the Ebro Delta, the sediments that reached the fields through irrigation channels were used to build up and form rice fields in the wetlands of the area. We hypothesize that this sedimentation has been key to vertical accretion and SOC sequestration in rice fields. These processes were simulated by developing a process-based cohort model inspired by accretion in marsh equilibrium models (MEM). The model was able to simulate the soil carbon profile of rice fields in the Ebro Delta, based on the soil-accretion concept and considering the spatial heterogeneity of the area. Its predictions of vertical accretion and carbon content were more accurate for mineral and clay-like soils than for organic and sandy soils. Topsoil decomposition rate and organic matter content were the parameters that most influenced predictions of total vertical accretion and final soil organic carbon stock. Simulations were carried out according to future climate change scenarios, considering restoration of river sediment flux, to evaluate effects on SOC sequestration and vertical accretion in rice fields. Results showed that only with significant river sediment restoration did rice fields show positive vertical accretion, which facilitates SOC sequestration.

Seagrass Thalassia hemprichii and associated bacteria co-response to the synergistic stress of ocean warming and ocean acidification

Seagrass meadows play vital ecological roles in the marine ecosystem. Global climate change poses considerable threats to seagrass survival. However, it is unclear how seagrass and its associated bacteria will respond under future complex climate change scenarios. This study explored the effects of ocean warming (+2 °C) and ocean acidification (−0.4 units) on seagrass physiological indexes and bacterial communities (sediment and rhizosphere bacteria) of the seagrass Thalassia hemprichii during an experimental exposure of 30 days. Results demonstrated that the synergistic effect of ocean warming and ocean acidification differed from that of one single factor on seagrass and the associated bacterial community. The seagrass showed a weak resistance to ocean warming and ocean acidification, which manifested through the increase in the activity of typical oxidoreductase enzymes. Moreover, the synergistic effect of ocean warming and ocean acidification caused a significant decrease in seagrass's chlorophyll content. Although the bacterial community diversity exhibited higher resistance to ocean warming and ocean acidification, further bacterial functional analysis revealed the synergistic effect of ocean warming and ocean acidification led to significant increases in SOX-related genes abundance which potentially supported the seagrass in resisting climate stress by producing sulfates and oxidizing hydrogen sulfide. More stable bacterial communities were detected in the seagrass rhizosphere under combined ocean warming and ocean acidification. While for one single environmental stress, simpler networks were detected in the rhizosphere. In addition, the observed significant correlations between several modules of the bacterial community and the physiological indexes of the seagrass indicate the possible intimate interaction between seagrass and bacteria under ocean warming and ocean acidification. This study extends our understanding regarding the role of seagrass associated bacterial communities and sheds light on both the prediction and preservation of the seagrass meadow ecosystems in response to global climate change.

How will future climates in the Pakistani Punjab rice-wheat system affect the optimal agronomic settings, and can adaptation offset losses?

Pakistan is one of the most vulnerable countries in the world to future climatic changes, already with significant water and food sustainability issues. Rice and wheat production in the Pakistani Punjab is central to national food security and hence of particular concern. We sought to understand what optimal rice-wheat (RW) farmer agronomic practices (those which maximise RW system gross margins (GM) and irrigated water productivity (WPi)) look like under each of 6 projected future climate change scenarios for mid-century (2050). We also examined the impact on 2050 RW production if current farmer practices remained unchanged, to gauge the potential benefits of the optimal adaptations. Cropping systems simulation with a validated model (APSIM) enabled analysis of a broad range of current management practice modifications (6 agronomic management settings parameters, including crop sowing dates, varieties, fertiliser rates, and deficit irrigation strategies) in a large Monte-Carlo Matrix (96,768 elements) for the highest-yielding district in the Punjab, Gujranwala. We evaluated annual crop production, GM, WPi, nitrate leaching, and total system evapotranspiration (ET) for all combinations on an annual basis over a 31-year simulation period for each future climate scenario (as well as the historical record) seeking optimal parameter combinations within each. If current farmer practices were to remain unchanged into the future, we found reductions in farmer GM’s by 35–50% by 2050, with WPi decreasing even further (40–60%). Adaptation of current RW management practices is therefore essential. By adopting optimal practices now (derived using the historical climate record) we found that farmers also place themselves well for future climates and our study suggests they can maintain or maybe even increase current GM’s by 5–30% (depending on climate scenario) through smart adaptation. The best agronomic practices in 2050 look almost indistinguishable from the best agronomic practices now, regardless of future climate scenario chosen. However, compared with a hypothetical farmer operating under optimal practices now, our study suggests that a farmer in 2050 will earn considerably less, with gross margins under optimal practices falling by 25–40%, depending on which future climate scenario is considered. Declines are largely due to falling rice yields under future conditions (20–50% across examined climate scenarios), with wheat yields likely to be maintained or even slightly increased. Our study also indicated an increase in ET (10–15%) across all RW practices (regardless of future climate scenario and despite projections for generally reduced rainfall, runoff and drainage), compared with historical conditions. Our study suggests a rice varietal improvement need for heat tolerance, but that current wheat varieties are suitable to 2050.

Assessment of the applicability of large‐scale climate ensemble simulations for analysing future extreme rainfall

AbstractExtreme rainfall events are a major cause of natural disasters and have been projected to increase in frequency and intensity under future climate change scenarios. Previous studies have used outputs of climate model simulations to evaluate changes in future extreme rainfall, but results have been uncertain due to the use of only a limited number of climate simulations. To address this issue, this study aims to evaluate the applicability of the d4PDF database (Database for Policy Decision‐Making for Future Climate Change), produced through large‐scale climate ensemble simulations, for analysing changes in extreme rainfall values between historical and future climatic conditions. A large‐scale sample size of the d4PDF allows the investigation of high‐magnitude rainfalls with long return periods. To utilize the d4PDF as a large‐scale sample for estimating extreme rainfall, the homogeneity between ensembles was assessed using the t test and Mann–Whitney U test, and the historical extreme rainfall with the d4PDF dataset was validated through comparison with observed data and using the Kolmogorov–Smirnov test. It was confirmed that the d4PDF ensembles could be used as a large‐scale sample, and the historical rainfall series of the d4PDF showed a similar distribution pattern to the observed rainfall series. Subsequent investigation of extreme rainfall for future ensembles during 2051–2110 indicated an increase by 20%–30% compared to historical climatic conditions in South Korea due to climate change. The results demonstrate the applicability of large‐scale climate ensemble simulations, such as d4PDF, for analysing changes in extreme rainfall and suggest that such information can be used to counter future extreme rainfall‐related disasters. Therefore, this study contributes to the knowledge gap in this area and highlights the need for further assessment and utilization of large‐scale climate ensemble simulations for future climate change research.

Plant Growth-Promoting Rhizobacteria Improve Rice Response to Climate Change Conditions.

Rice is one of the most important crops in the world and is considered a strategic crop for food security. Furthermore, the excessive use of chemical fertilizers to obtain high yields causes environmental problems. A sustainable alternative includes taking advantage of beneficial bacteria that promote plant growth. Here, we investigate the effect of five bacterial biofertilizers from halophytes on growth, and we investigate photosynthetic efficiency in rice plants grown under saline conditions (0 and 85 mmol L-1 NaCl) and future climate change scenarios, including increased CO2 concentrations and temperature (400/700 ppm and 25/+4 °C, respectively). Biofertilizers 1-4 increased growth by 9-64% in plants grown with and without salt in both CO2- temperature combinations, although there was no significant positive effect on the net photosynthetic rate of rice plants. In general, biofertilizer 1 was the most effective at 400 ppm CO2 and at 700 ppm CO2 +4 °C in the absence of salt. Inocula 1-5 also stimulated plant length at high CO2 levels without salt. Finally, the positive effect of biofertilization was attenuated in the plants grown under the interaction between salt and high CO2. This highlights the significance of studying biofertilization under stress interaction to establish the real potential of biofertilizers in the context of climate change conditions.

An improved deep learning procedure for statistical downscaling of climate data

Recent climate change (CC) scenarios from the Coupled Model Intercomparison Project Phase 6 (CMIP6) have just been released in coarse resolution. Deep learning (DL) based on statistical downscaling has recently been used, but more research is needed, particularly in arid regions, because little is known about their suitability for extrapolating future CC scenarios. Here we analyzed this issue by downscaling maximum, and minimum temperature over the Egyptian domain based on one General Circulation Model (GCM) as CanESM5 and two shared socioeconomic pathways (SSPs) as SSP4.5 and SSP8.5 from CMIP6 using Convolutional Neural Network (CNN) herein after called CNNSD. The downscaled maximum and minimum temperatures based CNNSD was able to reproduce the observed climate over historical and future periods at a finer resolution (0.1°), reducing the biases exhibited by the original scenario. To the best of our knowledge, this is the first time CNN has been used to downscale CMIP6 scenarios, particularly in arid regions. The downscaled analysis showed that maximum and minimum temperatures are expected to rise by 4.8 °C and 4.0 °C, respectively, in the future (2015–2100), compared to the historical period, under the moderate scenario (SSP4.5). Meanwhile, under the Fossil-fueled Development scenario (SSP8.5), these values will rise by 6.3 °C and 4.2 °C, respectively as analyzed by the CNNSD. The developed approach could be used not only in Egypt but also in other developing countries, which are especially vulnerable to climate change and has a scarcity of related research. The established downscaled approach's supply can be used to provide climate services, as a driver for impact studies and adaptation decisions, and as information for policy development. More research is needed, however, to include multi-GCMs to quantify the uncertainties between GCMs and SSPs, improving the outputs for use in climate change impacts and adaptations for food and nutrition security.

Land Use and Climate Change Effects on Streamflow and Nutrient Loads in a Temperate Catchment: A Simulation Study

Climate and land use changes impact catchment hydrology and water quality (WQ), yet few studies have investigated the amount of land use changes required to meet specific WQ targets under future climate projections. The aim of this study was to determine streamflow and nutrient load responses to future land use change (LUC) and climate change scenarios. We hypothesized that (1) increasing forest coverage would decrease nutrient loads, (2) climate change, with higher temperatures and more intense storms, would lead to increased flow and nutrient loads, and (3) LUC could moderate potential nutrient load increases associated with climate change. We tested these hypotheses with the Soil and Water Assessment Tool (SWAT), which was applied to a lake catchment in New Zealand, where LUC strategies with afforestation are employed to address lake WQ objectives. The model was calibrated from 2002 to 2005 and validated from 2006 to 2010 using measured streamflow (Q) and total nitrogen (TN), total phosphorus (TP), nitrate (NO3-N), and ammonium (NH4-N) concentrations of three streams in the catchment. The model performance across the monitored streams was evaluated using coefficient of determination (R2) and Nash–Sutcliffe efficiency (NSE) metrics to provide a basis for model projections. Future scenarios incorporated LUC and climate change (CC) based on the Representative Concentration Pathway 8.5 and were compared to the baseline streamflow and WQ indicators. Consistent with our hypotheses, Q, TN, and TP loads were predicted to decrease with afforestation. Specifically, afforestation of 1.32 km2 in one of the monitored stream sub-catchments (subbasin 3), or 8.8% of the total lake catchment area, would result in reductions of 11.9, 26.2, and 17.7% in modeled annual mean Q, TN, and TP loads, respectively. Furthermore, when comparing simulations based on baseline and projected climate, reductions of 13.6, 22.8, and 19.5% were observed for Q, TN, and TP loads, respectively. Notably, the combined implementation of LUC and CC further decreased Q, TN, and TP loads by 20.2, 36.7, and 28.5%, respectively. This study provides valuable insights into the utilization of LUC strategies to mitigate nutrient loads in lakes facing water quality challenges, and our findings could serve as a prototype for other lake catchments undergoing LUC. Contrary to our initial hypotheses, we found that higher precipitation and temperatures did not result in increased flow and nutrient loading.

The REBURN model: simulating system-level forest succession and wildfire dynamics

BackgroundHistorically, reburn dynamics from cultural and lightning ignitions were central to the ecology of fire in the western United States (wUS), whereby past fire effects limited future fire growth and severity. Over millennia, reburns created heterogenous patchworks of vegetation and fuels that provided avenues and impediments to the flow of future fires, and feedbacks to future fire event sizes and their severity patterns. These dynamics have been significantly altered after more than a century of settler colonization, fire exclusion, and past forest management, now compounded by rapid climatic warming. Under climate change, the area impacted by large and severe wildfires will likely increase — with further implications for self-regulating properties of affected systems. An in-depth understanding of the ecology of reburns and their influence on system-level dynamics provides a baseline for understanding current and future landscape fire-vegetation interactions.ResultsHere, we present a detailed characterization of REBURN — a geospatial modeling framework designed to simulate reburn dynamics over large areas and long time frames. We interpret fire-vegetation dynamics for a large testbed landscape in eastern Washington State, USA. The landscape is comprised of common temperate forest and nonforest vegetation types distributed along broad topo-edaphic gradients. Each pixel in a vegetation type is represented by a pathway group (PWG), which assigns a specific state-transition model (STM) based on that pixel’s biophysical setting. STMs represent daily simulated and annually summarized vegetation and fuel succession, and wildfire effects on forest and nonforest succession. Wildfire dynamics are driven by annual ignitions, fire weather and topographic conditions, and annual vegetation and fuel successional states of burned and unburned pixels.ConclusionsOur simulation study is the first to evaluate how fire exclusion and forest management altered the active fire regime of this landscape, its surface and canopy fuel patterns, forest and nonforest structural conditions, and the dynamics of forest reburning. The REBURN framework is now being used in related studies to evaluate future climate change scenarios and compare the efficacy of fire and fuel management strategies that either enable the return of active fire regimes or depend on fire suppression and wildfire effects on forest burning.

Future effectiveness of passive house design strategies

Passive architectural design strategies have been an effective response to the energy crisis of the 20th century. In temperate climates, their integration results in thermal behaviors that combine indoor comfort and energy efficiency. However, projected future climate change scenarios will not offer the same performances, resulting in such strategies being less effective. The objective of this work is to quantify the relative change in the effectiveness of passive design strategies in dwellings for arid temperate-cold climates (Bwk), taking the city of Mendoza (Argentina) as an example. Regarding future climate projections for warm arid climates (Bwh), using the CMIP5’s RCP8.5 scenario, equivalent to the IPCC’s CMIP6 SSP85 scenario, the results show a 20% decrease in the number of hours in annual comfort, with a 24% increase in the need for passive summer strategies.

Modeling Land Use and Climate Change Effects on Soil Organic Carbon Storage under Different Plantation Systems in Mizoram, Northeast India

Soil carbon sequestration is vital to reduce the atmospheric carbon concentration, which is expected to increase within future climatic scenarios. The present study aims to investigate the effects of land use changes to different plantation systems on SOC stocks, and simulate these changes using the RothC model in Mizoram, India. With this aim, four land uses, viz., secondary forest, oil palm, orange, and arecanut plantations, established on degraded shifting cultivation lands, and a control natural forest were selected for this study. The soils were sampled 0–30 cm in the secondary forest, plantations, and the natural forest, at an interval of five years. Measured SOC stocks were the highest in the secondary forest (67.0 Mg C ha−1) and the lowest under the oil palm plantation (37.4 Mg C ha−1), 10 years after land use conversion. The climate change projections for 2021–2035 and 2036–2050 indicated that temperature and rainfall changes, projected to increase by 0.8 and 2.0 °C, and 5.9 and 5.4%, respectively, will affect SOC stocks in the future differently, depending on the land use and carbon input from vegetation. Baseline climate simulations under land use change showed the highest increase in the SOC stock under the secondary forest (116%), and the lowest in the oil palm plantation (27%). Overall, the model predicted that SOC stocks would increase, but the rate of change (0.23–1.86 Mg C ha−1 yr−1) varied with different land uses, plant species, and land management practices. The model results indicated that restoring secondary forest following the abandonment of a shifting cultivation and orange plantations are the best options to improve SOC stocks within future climate change scenarios. Conversely, arecanut and oil palm need to be reduced because the SOC storage is lower.

Association of Ambient Temperature With the Prevalence of Intimate Partner Violence Among Partnered Women in Low- and Middle-Income South Asian Countries

Intimate partner violence (IPV), including physical, sexual, and emotional violence, constitutes a critical public health problem, particularly in low- and middle-income countries. While climate change could escalate violent events, data quantifying its possible association with IPV are scant. To evaluate the association of ambient temperature with the prevalence of IPV among partnered women in low- and middle-income countries in South Asia, and to estimate the association of future climate warming with IPV. This cross-sectional study used data from the Demographic and Health Survey and included 194 871 ever-partnered women aged 15 to 49 years from 3 South Asian countries (India, Nepal, and Pakistan). The study applied the mixed-effect multivariable logistic regression model to investigate the association of ambient temperature with IPV prevalence. The study further modeled the change in IPV prevalence under various future climate change scenarios. The data included in the analyses were collected from October 1, 2010, to April 30, 2018, and the current analyses were performed from January 2, 2022, to July 11, 2022. Annual ambient temperature exposure for each woman, estimated based on an atmospheric reanalysis model of the global climate. The prevalence of IPV and its types (physical, sexual, and emotional violence) were assessed based on self-reported questionnaires from October 1, 2010, to April 30, 2018, and the changes in the prevalence with climate changes were estimated through the 2090s. The study included 194 871 ever-partnered women aged 15 to 49 years (mean [SD] age, 35.4 [7.6] years; overall IPV prevalence, 27.0%) from 3 South Asian countries. The prevalence of physical violence was highest (23.0%), followed by emotional (12.5%), and sexual violence (9.5%). The annual temperature ranges were mostly between 20 °C and 30 °C. A significant association was found between high ambient temperature and the prevalence of IPV against women, with each 1 °C increase in the annual mean temperature associated with a mean increase in IPV prevalence of 4.49% (95% CI, 4.20%-4.78%). According to the study's projections under the unlimited emissions scenarios (SSPs [shared socioeconomic pathways], as defined by the Intergovernmental Panel on Climate Change] 5-8.5), IPV prevalence would increase by 21.0% by the end of the 21st century, while it would only moderately increase under increasingly stricter scenarios (SSP2-4.5 [9.8%] and SSP1-2.6 [5.8%]). In addition, the projected increases in the prevalence of physical (28.3%) and sexual (26.1%) violence were greater than that of emotional violence (8.9%). In the 2090s, India was estimated to experience the highest IPV prevalence increase (23.5%) among the 3 countries, compared with Nepal (14.8%) and Pakistan (5.9%). This cross-sectional, multicountry study provides ample epidemiological evidence to support that high ambient temperature may be associated with the risk of IPV against women. These findings highlight the vulnerabilities and inequalities of women experiencing IPV in low- and middle-income countries in the context of global climate warming.

Predicting the potential habitat suitability and distribution of “Weed-Onion” (Asphodelus tenuifolius Cavan.) in India under predicted climate change scenarios

Recent shifts in the spectrum of weeds documented throughout the globe in various agroecosystems were purportedly associated with climate change. Consequently, it called for a more thorough investigation of weed vulnerabilities and management in the changing climate scenarios. In the present investigation, we compile geographic occurrence data of an agricultural weed Asphodelus tenuifolius Cavan. using ecospat R package and BIOMOD ensemble forecasting to analyze the existing distribution and anticipate its possible distribution in the future under future climate change scenarios. Nine different algorithms were used and implemented in biomod2 package for modelling the current potential habitat distribution of the selected species across the study region and climatic data was downloaded from WorldClim database. Ensemble modeling showed high habitat suitability of the majority of central, northern, and western Indian regions under current climatic conditions. In addition, by gaining suitable habitats in the future the target species will undergo significant increase in range change. The potential range of habitat suitability for A. tenuifolius is likely to increase from 138.73% to 190.29% in terms of Committee Averaging and 721.42%–1508% in terms of Weighted Mean under Representative Concentration Pathways (RCP 4.5 and RCP 8.5). This ensemble modelling technique will help us to locate the places where its potential range could expand while simultaneously analysing the spatial distributions of field weeds, which may be important for the creation of cutting-edge weed control methods.