Tag: Climate Change Downscaling Techniques

Abstract

Climate change is a pressing global issue that has far-reaching effects on the environment, including its impact on infrastructure such as roads and pavements. As temperatures rise, it is essential to consider selecting asphalt binder performance grades to ensure the long-lasting and reliable performance of asphalt pavements. In Sharjah, the penetration grading system guides the selection of asphalt binder grade (grade 60/70). However, this grading system is empirical in nature and doesn’t take into account local temperature fluctuations. The superpave performance grade (PG) system considers explicitly local temperature records while allowing the incorporation of future temperature changes. The main goal of this study project is to assess the impact of future temperature changes on the required asphalt binder PG in Sharjah and the consequent impact on pavement performance simulated by AASHTOWare Pavement ME Design. Future temperature records were predicted via the MRI-CGCM3 model considering two RCPs (4.5 and 8.5) and two future periods (near and far). The historical temperature records revealed a PG 70–10 at a 98% reliability level for Sharjah compared to a PG 64–16, equivalent to the currently used 60/70 penetration grade. Future temperature changes yielded a higher performance grade of PG 76–10 for the far future, considering RCP 8.5. The performance simulations emphasized the importance of carefully selecting the asphalt binder PG, as this decision has been shown to increase the life span of asphalt pavement.

Abstract

Hydrological models serve as valuable instruments for assessing the impact of climate change on water resources and agriculture as well as for developing adaptation measures. In Yemen, climate change and variability are imposing a significant impact on the most important sectors such as agriculture and economy. The current study evaluates the influence of future climate on hydrology and water balance components in Yemen’s highlands using a semi-distributed physical-based hydrologic model Soil Water Assessment Tool (SWAT) and employing high-resolution climate projections. The SWAT was calibrated and verified using observed streamflow data from 1982 to 2000 in three large catchments. Ground data from 24 stations and statistically downscaled future climate data for the period 2010–2100 under RCP2.6 and RCP8.5 are used. SWAT performance was assessed using multiple statistical methods, which revealed the commendable performance of SWAT during the calibration (average NSE = 0.80) and validation (NSE = 0.72) periods. The outcome indicates an increase in future seasonal and annual rainfall, maximum temperature, and minimum temperature in the 2020s and the 2080s under both RCP2.6 and RCP8.5 scenarios. This projected increase in the rainfall and the local temperature will result in increased averages of surface runoff, evapotranspiration, soil water, and groundwater recharge in the representative three catchments up to 6.5%, 21.1%, 7.6%, and 6.4%, respectively. Although, the projected increase in the water balance components will benefit the agriculture and water sector, specific adaptation measures will be crucial to mitigate potential flood impacts arising from the increased precipitations as well as to minimize the consequences of the increased temperature. Likewise, demand for supplementary irrigation is expected to increase to offset the higher evapotranspiration rates in the future.

Abstract

Global wheat production has faced, and will persist in encountering many challenges. Therefore, developing a dynamic cultivation approach generated through modeling is crucial to coping with the challenges in specific districts. The modeling can contribute to achieving global objectives of farmers’ financial independence and food security by enhancing the cropping systems. The current study aims to assess the effects of cultivars and sowing windows intricately on irrigated wheat production using the two models from Coupled Model Intercomparison Project Phase 6 (CMIP6), including ACCES-CM2 and HadGEM31-LL under two shared socioeconomic pathways (SSP245, and SSP585). A two-year on-farm experiment was conducted for parametrization and validation of the APSIM-Wheat model at two locations. The model reasonably simulated the days to anthesis, maturity, biomass production, and yield within all cultivars. The normalized root-mean-square error (RMSE) of the phenological stages was simulated and measured values were 5% and 2–4%, while the index of agreement (IOA) was in the range of 0.84–0.88 and 0.95–0.97. An acceptable agreement of the simulated biomass (RMSE = 5–7% and 0.91–0.78) and yield (RMSE = 6–11% and IOA = 0.70–0.94) was identified in the model. Afterward, the LARS-WG model generated the baseline (2000–2014) based on the weather data at the sites and projected the models for the near (2030–2049) and remote future (2050–2070). The models revealed that not only the average maximum and minimum temperatures will rise by 1.85 °C and 1.62 °C which will exacerbate the reference evapotranspiration (ET₀), but also the precipitation and solar radiation will reach + 58%, and + 0.25 Mj m⁻². Our results clearly showed that precipitation volume over the growing seasons would elevate approximately two times as much as the baseline in the future, while there is a significant decrease in water productivity (WP) and yield from the intensive ET₀. Based on the wheat simulation, the short-duration cultivar (Kalate) combined with the postponed planting (16-Dec) was determined as a practical alternative; nonetheless, both WP and yield significantly decreased by 40% and 7%, respectively (p < 0.05). In conclusion, identifying and analyzing future farming conditions (e.g., agro-climate, soil and crop management data) would provide a perception of the forthcoming scenarios. When applied, this knowledge can potentially mitigate the adverse impacts of climate change on global wheat production.

Abstract

Argentina is a country with a variety of climates, where an increase in mean and extreme temperatures is currently on-going, demanding regional climate information to design and implement effective strategies for climate change adaptation. In this regard, the use of empirical statistical downscaling (ESD) procedures can help provide tailored climate information. In this work, a set of ESD models were tested and applied to generate plausible regional climate projections for daily maximum and minimum temperatures (Tx, Tn) in Argentina. ESD models were applied to an ensemble of CMIP5 and CMIP6 global circulation models (GCMs) to downscale historical and future RCP8.5 and SSP585 scenarios. The plausibility of the ESD projections was analysed by comparing them with their driving GCMs and with CORDEX regional climate models (RCMs). Generally, all ESD models added value during the historical period, in mean values as well as in extreme indices, especially for Tx. The climate projections depicted an extended signal of warming (both in the mean and in the frequency of extremes), consistent between all simulations (GCMs, RCMs and ESD) and strongest over northern Argentina. ESD models showed potential to produce plausible projections, although, depending on the technique considered (for Tx) and the predictor configurations (for Tn), differences in the change rates were identified. Nevertheless, the uncertainty in future changes was considerably reduced by RCMs and ESD when compared to their driving GCMs. Overall, this study evidences the potential of ESD in a climate change context and contributes to the assessment of the uncertainty on the future Argentine climate.

Abstract

The climate change assessment in the context of urban areas is very crucial for policy making regarding hazard mitigation and citizen’s health, especially for the smart cities. The past climate assessment and present climate monitoring is somewhere easy, but the projection of future climate at city level is very difficult as most climate models fail to resolve the cities spatially. Over the highly populated areas like the region of Gangetic Plains the precise city specific climate projection becomes more important. The present study aims to project the future temperature, covering both minimum temperature (Tmin) and maximum temperature (Tmax) and analyse the extreme indices over smart cities in the Gangetic Plains, as one of the pioneer works using CMIP6 model’s downscaled outputs using SDSM model. The study reveals that these smart cities are likely to experience warmer and more extreme temperatures in the upcoming decades. The future temperature projections were generated under two emission scenarios (SSP245 and SSP585), and for near future (2030–2065) and far future (2066–2100) periods. The drastic change in minimum temperature (Tmin) was observed over New Delhi, Prayagraj, Kolkata, and Lucknow by the end of the century under SSP585. Four extreme temperature indices were also analyzed for future time series: (1) TXgt40(No. of days Tmax > 40ºC); (2) TNlt10(No. of days when Tmin < 10ºC); (3) TX90p (Percentage of days when Tmax > 90th percentile); and (4) TN10P (Percentage of days when Tmin < 10th percentile). The increasing trend of warm temperature Indices and decreasing trend of cool temperature indices were observed over all the stations. The drastic change in extreme temperature indices may have a significant effect on urban climate, it could impact public health by increasing the incidence of heat-related illnesses such as heat stress or heat exhaustion. The present study can be also utilized as a probable baseline for assessing the extreme climate conditions in the future. As this study is one of the very first attempts under the aspect of smart cities and thus it may help in developing early warning systems for smart cities in the Gangetic Plain.

Abstract

Assessing and mapping flood risks are fundamental tools that significantly contribute to the enhancement of flood management strategies. Identifying areas that are susceptible to floods and devising strategies to reduce the risk of waterlogging is of utmost importance. In the present study, an integrated approach, combining advanced remote sensing technologies, Geographic Information Systems (GIS), and analytic hierarchy process (AHP), was adopted in the Patan district of Gujarat, India, with a coastline spanning over 1600 km, to evaluate the numerous variables that contribute to the risk of flooding and waterlogging. After evaluating the flood conditioning factors and their respective weights using the analytic hierarchy process (AHP), the results were processed in GIS to accurately delineate areas that are prone to flooding. The results highlighted exceptional precision in identifying vulnerable areas, allowing for a thorough evaluation of the impact severity. The integrated approach yields valuable insights for multi-criteria assessments. The findings indicate that a significant portion of the district’s land, precisely 8.94%, was susceptible to very high- risk of flooding, while 27.76% were classified as high-risk areas. Notably, 35.17% of the region was identified as having a moderate level of risk. Additionally, 20.96% and 7.15% were categorized as low-risk and very low-risk areas, respectively. Overall, the study highlights the need for proactive measures to mitigate the impact of floods on vulnerable communities. The research findings were verified by conducting ground truth and visual assessments using microwave satellite imagery (Sentinel-1). The aim of this validation was to test the accuracy of the study in identifying waterlogged agricultural areas and their extent based on AHP analysis. The ground verification and analysis of satellite images confirmed that the model accurately identified approximately 74% of the area categorized under high and very high flood vulnerability to be waterlogged and flooded. This research can provide valuable assistance to policymakers and authorities responsible for flood management by gathering necessary information about floods, including their intensity and the regions that are most susceptible to their impact. Additionally, it is crucial to implement corrective measures to improve soil drainage in vulnerable areas during heavy rainfall events. Prioritizing the adoption of sustainable agricultural practices and improving land use are also crucial for environmental conservation.

Abstract

Purpose

Reducing the environmental impacts of food systems has become a growing concern for public authorities. This study aims to adapt the territorial LCA framework (T-LCA) to local food system assessment to identify territorial hotspots of a food system in relation to its land use functions. To achieve this goal, the T-LCA must be enhanced by overcoming previously exposed limitations.

Methods

Deriving from the T-LCA framework, the methodology used in this paper assesses all territorial food-producing, processing, and consuming activities. The methodological developments suggest addressing its three principal methodological limitations by (i) using agricultural statistics to estimate the local consumption and thus account for intra-territorial flows, (ii) proposing novel agri-food land use functions related to a local food system, and (iii) developing a simplified framework for sensitivity analysis (SA) through detection of the most uncertain and influential data followed by a once-at-a-time (OAT) approach to improve the uncertainty related to the substantial number of data involved in meso-level LCAs. The methodology is applied to a case study in France using the Environmental Footprint (EF) 3.0 method.

Results and discussion

The results indicate that intra-territorial flow analysis effectively distinguishes between local and imported flows, identifying their primary environmental hotspots. Despite the significant impact of imported flows, export-oriented livestock production emerges as the principal hotspot of the studied food system. Integrating agri-food land use functions into LCA is crucial for linking the activities with higher environmental impact contributions and their territorial functions. This is the case of animal husbandry which is the main environmental hotspot and one of the principal local economic activities. Finally, the sensitivity analysis reveals a low sensitivity of the overall results to the most influential and uncertain parameters.

Conclusions

These findings confirm the interest in further developing territorial LCA methodologies and adapting them to various contexts to determine the principal environmental burdens of local systems and improve territorial land planning. This study also proposes various research perspectives to confirm and enhance the robustness of T-LCA frameworks, including the development of regional life cycle inventories.

Abstract

The Gabès region in southeastern Tunisia faces significant water stress due to limited water resources, with groundwater being the primary source for various human activities. The ongoing and future effects of global warming and climate change, characterized by rising temperatures and fluctuations in rainfall patterns, are set to exacerbate water scarcity and degrade the quality of this region's water resources. This study aimed to assess the impact of global warming and climate change on natural groundwater recharge in an unconfined aquifer system within the Gabès region utilizing the Markov chains methodology. To do so, future climatic parameters specific to the study area were projected by downscaling a general circulation model (GCM). Over the temporal period from 2020 to 2100, various climate change scenarios, including A1, A2, and B2, were employed to estimate the evolution of groundwater levels throughout the region. Upon analyzing historical data and employing the Markov chain method to forecast future scenarios, the downscaling modeling approach proved to be effective in predicting climate parameters such as average temperature, precipitation, and wind speed for the designated time frame. The results were visually represented as piezometric maps representing the different scenarios. It is important to note that there is predicted to be a general decline in precipitation, with an anticipated average decrease of 40 mm/year by the 2100s, particularly when considering potential increases in water abstraction rates. Consequently, groundwater recharge is expected to decrease, leading to a noticeable drawdown in groundwater levels across all three scenarios. This study sheds light on the critical implications of climate change for the Gabès region, highlighting the urgent need for sustainable water resource management and conservation measures to mitigate the adverse effects of decreasing groundwater availability.

Abstract

Grain aeration is an environmentally friendly technique commonly used to preserve the grains of cereals. However, this technique could be disrupted by climate change, due to anomalies of the air temperature and relative humidity, which would impact its effectiveness, especially in hot climate regions, such as northern Tunisia. This study examines the future potential of grain aeration over a 30-year projection period (2041–2070), comparing it with the current state (2015–2020) and focusing on the critical storage period (from July to October) in northwestern Tunisia. To assess the effectiveness of the aeration technique, we used the global climate models CNRM-CM5.1 and ESM2M, in combination with the regional climate model SMHI, considering two projection scenarios RCP4.5 and RCP8.5 to predict ambient air temperature and relative humidity. The grain aeration simulator AERO was coupled with two process control strategies, AERO2 and the timer controller, to predict grain moisture content, temperature, and suitable aeration times. The results show that the current state, as well as projections based on the RCP4.5 and RCP8.5 scenarios, allow for safe grain storage with moisture levels between 12% and 13% relative humidity throughout the storage period. However, simulations indicate that climate change could reduce the favorable aeration hours, with an average decrease of 23.44 h compared with the current situation with high variability of suitable hours, reflecting the warming trend and prolonged warm periods. This reduction varies significantly due to global warming and the extended warm period. During the 2645 h of storage, the projection shows an average standard deviation of 34.12 h. However, reality indicates an average standard deviation of less than 1. The impact of climate change will reduce the use of ambient air aeration, necessitating careful short-term and long-term planning. To prevent the deterioration of grain quality during storage, it is essential to incorporate the climate change component into expert grain stock management systems and harness the potential of sunlight for grain preservation through chilling aeration.

Graphical abstract

Abstract

The Gabès region in southeastern Tunisia faces significant water stress due to limited water resources, with groundwater being the primary source for various human activities. The ongoing and future effects of global warming and climate change, characterized by rising temperatures and fluctuations in rainfall patterns, are set to exacerbate water scarcity and degrade the quality of this region's water resources. This study aimed to assess the impact of global warming and climate change on natural groundwater recharge in an unconfined aquifer system within the Gabès region utilizing the Markov chains methodology. To do so, future climatic parameters specific to the study area were projected by downscaling a general circulation model (GCM). Over the temporal period from 2020 to 2100, various climate change scenarios, including A1, A2, and B2, were employed to estimate the evolution of groundwater levels throughout the region. Upon analyzing historical data and employing the Markov chain method to forecast future scenarios, the downscaling modeling approach proved to be effective in predicting climate parameters such as average temperature, precipitation, and wind speed for the designated time frame. The results were visually represented as piezometric maps representing the different scenarios. It is important to note that there is predicted to be a general decline in precipitation, with an anticipated average decrease of 40 mm/year by the 2100s, particularly when considering potential increases in water abstraction rates. Consequently, groundwater recharge is expected to decrease, leading to a noticeable drawdown in groundwater levels across all three scenarios. This study sheds light on the critical implications of climate change for the Gabès region, highlighting the urgent need for sustainable water resource management and conservation measures to mitigate the adverse effects of decreasing groundwater availability.