Tag: Climate Change Downscaling Techniques

Abstract

Extreme precipitation events can cause severe floods that pose significant risks to human lives, properties, and ecosystems. Therefore, understanding how climate change may affect the characteristics of these events is crucial for developing effective adaptation and mitigation strategies. In this study, we investigated the effect of climate change on the extreme characteristics through the concept of Intensity-Duration-Frequency curves. For this purpose, annual maximum precipitation series derived from five climate models from Coupled Model Intercomparison Project phase 6 were used to develop the historical (1965–2014) and future (2051–2100) curves for 12 major cities in Iran. By applying the pooling data method, the changes in intensity and frequency of the extreme precipitation with duration of 24-h, 48-h, and 72-h were assessed for three scenarios of SSP1-2.6, SSP2-4.5, and SSP3-7.0. The results indicate that most stations will experience more intense (up to 20%) and frequent (up to 8 times) extreme precipitation events under projected climate change scenarios, especially for the SSP3-7.0 scenario. However, these results varied across cities. The findings of this study provide valuable insights into the potential impacts of climate change on flood risk management in Iran and suggest the need for appropriate adaptive strategies.

Abstract

Although it is unanimous among scientists and researchers that the food production chain is a substantial contributor to environmental challenges, so far, no food ingredient has been evaluated for its environmental performance relative to planetary boundaries. Given this, this study conducts an analysis oriented to food key ingredients considered essential in human nutrition, namely: proteins, β-carotene as a precursor of vitamin A, and polyunsaturated fatty acids (docosahexaenoic and eicosapentaenoic acids), using life cycle assessment (LCA) linked to the planetary boundary structure, under nine indices: climate change, biosphere integrity, global biogeochemical fluxes, stratospheric ozone depletion, ocean acidification, global freshwater use, land use change, chemical pollution, and atmospheric aerosol loading. Protein sources from animals such as beef, pork, and poultry, β-carotene from palm oil and synthetic routes, and fatty acids from fish oil were also compared to alternative sources from microalgae-based ingredients. The results show that protein ingredients of animal origin and alternatives have largely contributed to the disruption of planetary boundaries. However, the worst environmental performance for protein ingredients studied was attributed to bovine protein, matching three risk indices (climate change, ecotoxicity, and photochemical ozone formation) out of the nine evaluated. On the other hand, among fine chemical food ingredients, only vitamin A from palm oil, which is mostly found in a risk and uncertainty zone, when compared to conventional synthetic processes and microalgae-based; these, in turn, operate fully within safe limits. In contrast, only one planetary index is assigned to the uncertainty zone for polyunsaturated fatty acids from fish oil, the others operate in safe zones equally for microalgae-based processes. Therefore, the conclusions highlight major challenges the food production chain faces to achieve safe and sustainable food. These results guide critical food groups and environmental indicators to prioritize in future efforts to reduce environmental impact.

Abstract

It is crucial to investigate the characteristics of fire danger in the areas around Beijing to increase the accuracy of fire danger monitoring, forecasting, and management. Using meteorological data from 17 national meteorological stations in the areas around Beijing from 1981–2021, this study calculated the fire weather index (FWI) and analyzed its spatiotemporal characteristics. It was found that the high and low fire danger periods were in April–May and July–August, with spatial patterns of “decrease in the northwest–increase in the southeast” and a significant increase throughout the areas around Beijing, respectively. Next, the contributions of different meteorological factors were quantified by the multiple regression method. We found that during the high fire danger period, the northern and southern parts were affected by precipitation and minimum relative humidity, respectively. However, most areas were influenced by wind speed during the low fire danger period. Finally, comparing with the FWI characteristics under different SSP scenarios, we found that the FWI decreased during high fire danger period and increased during low fire danger period under different SSP scenarios (i.e., SSP245, SSP585) for periods of 2021–2050, 2071–2100, 2021–2100, except for SSP245 in 2071–2100 with an increasing trend both in high and low fire danger periods. This study implies that there is a higher probability of FWI in the low fire danger period, threatening the ecological environment and human health. Therefore, it is necessary to enhance research on fire danger during the low fire danger period to improve the ability to predict summer fire danger.

Abstract

Low-impact development (LID) practices are critical for mitigating urban stormwater runoff and alleviating flood risks. The strategic placement of LID facilities is paramount to optimizing their efficacy within urban landscapes. This study conducts a comprehensive bibliometric analysis of LID-related literature over the past decade, utilizing data visualization tools to elucidate key disciplines, publication trends, and the prevalence of various optimization algorithms. We delve into the application of multi-objective optimization (MOO) algorithms in LID facility layout, examining their practical applications, theoretical underpinnings, and case studies. The paper also scrutinizes the strengths and limitations of these algorithms, proposing future research trajectories that leverage MOO to enhance LID’s role in urban stormwater management.

Abstract

Climate change poses significant challenges to agricultural systems in the Mediterranean region, with Italy being significantly affected. This literature scoping review aims to examine existing research on the impact of climate change on yield and income on the three agri-food value chains in Italy: viticulture, fruit and vegetables, and dairy cattle. By analysing the available literature, this study seeks to outline the pros and cons, knowledge gaps, and potential areas for future research. A systematic search of scientific databases was conducted to identify relevant articles published between 2000 and 2022. The search terms included climate change, agriculture, Italy, yield, income, and related keywords. Articles were screened based on predetermined inclusion and exclusion criteria, resulting in a final selection of studies. Quantitative information was collected and organized into descriptive tables. The review encompassed 44 studies that investigated the impact of climate change on yield and income in various agricultural sectors across different regions of Italy. The findings indicate that climate change is already impacting crop productivity and income levels, with increased temperature, changes in precipitation patterns, and extreme weather events being identified as the primary drivers. Additionally, disparities were observed between different agricultural regions, crops, and farming systems, highlighting the need for location-specific and crop-specific assessments. The scoping review provides a prospective overview of the existing literature on climate change impacts on yield and income within the Italian agriculture system. It underscores the urgency for targeted adaptation strategies to minimize the negative consequences of climate change. Further research should focus on understanding the complex interactions between climate change, agricultural practices, socio-economic factors, and policy interventions to develop context-specific solutions for sustainable agriculture in Italy.

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Abstract

Rice is the staple food of more than half of the world’s population, especially in Asia, where rice provides more than 50% of the caloric supply for at least 520 million people, most of them are either extremely impoverished or poor. Information on rice production is thus essential for agricultural management and the formulation of food security policies. The objective of this research is to develop an approach combining remote sensing and artificial intelligence (AI) techniques with a crop growth model for enhancing yield estimation and crop management in Taiwan. The data processing involves three main steps: (1) data pre-processing to generate model inputs, (2) crop yield modeling through assimilating satellite-derived leaf area index (LAI) into a crop growth model using the AI particle swarm optimization (PSO) algorithm, and (3) model validation. The assimilation process was performed using a cost function based on the difference between remotely-sensed and simulated LAI values. The optimization process began with an initial parameterization and appropriately adjusted input parameters in the model. The fitness value derived from a cost function was determined using the PSO. The results of yield estimates obtained from the crop growth model based on optimized inputs were evaluated using the government’s yield statistics, revealing close agreement between these two datasets. The root mean square percentage error (RMSPE) and the mean absolute percentage error (MAPE) for the first crop were 19.8% and 17.1%, and the values for the second crop were 8.4% and 6.3%, respectively. The relative percentage error (RPE) values of 18.5% and − 5.1%, respectively, showed a slight overestimate and underestimate for the first and second crops.

Abstract

This study employs machine learning algorithms to construct Multi Model Ensembles (MMEs) based on Regional Climate Models (RCMs) within the Esca River basin in the Pyrenees. RCMs are ranked comprehensively based on their performance in simulating precipitation (pr), minimum temperature (tmin), and maximum temperature (tmax), revealing variability across seasons and influenced by the General Circulation Model (GCM) driving each RCM. The top-ranked approach is used to determine the optimal number of RCMs for MME construction, resulting in the selection of seven RCMs. Analysis of MME results demonstrates significant improvements in precipitation on both annual and seasonal scales, while temperature-related enhancements are more subtle at the seasonal level. The effectiveness of the ML–MME technique is highlighted by its impact on hydrological representation using a Temez model, yielding outcomes comparable to climate observations and surpassing results from Simple Ensemble Means (SEMs). The methodology is extended to climate projections under the RCP8.5 scenario, generating more realistic information for precipitation, temperature, and streamflow compared to SEM, thus reducing uncertainty and aiding informed decision-making in hydrological modeling at the basin scale. This study underscores the potential of ML–MME techniques in advancing climate projection accuracy and enhancing the reliability of data for basin-scale impact analyses.

Abstract

Sturgeons are ancient fish, with 27 species distributed in the Northern Hemisphere. This review first touches upon the significance of sturgeons in the context of their biological, ecological, and economic importance, highlighting their status as “living fossils” and the challenges they face in genomic research due to their diverse chromosome numbers. This review then discusses how omics technologies (genomics, transcriptomics, proteomics, and metabolomics) have been used in sturgeon research, which so far has only been done on Acipenser species. It focuses on metabolomics as a way to better understand how sturgeons work and how they react to their environment. Specific studies in sturgeon metabolomics are cited, showing how metabolomics has been used to investigate various aspects of sturgeon biology, such as growth, reproduction, stress responses, and nutrition. These studies demonstrate the potential of metabolomics in improving sturgeon aquaculture practices and conservation efforts. Overall, the review suggests that metabolomics, as a relatively new scientific tool, has the potential to enhance our understanding of sturgeon biology and aid in their conservation and sustainable aquaculture, contributing to global food security efforts.

Abstract

The role of China is increasingly pivotal in climate change mitigation, and the formulation of energy conservation and emission reduction policies requires city-level information. The effectiveness of national policy implementation is contingent upon the support and involvement of local governments. Accurate data on final energy consumption is vital to formulate and implement city-level energy transitions and energy conservation and emission reduction policies. However, there is a dearth of data sources pertaining to China’s city-level final energy consumption. To address these gaps, we developed computational modeling techniques along with top-down and downscaling methods to estimate China’s city-level final energy consumption. In this way, we compiled a final energy consumption inventory for 331 Chinese cities from 2005 to 2021, covering seven economic sectors, 30 fossil fuels, and four clean power sources. Moreover, we discussed the validity of the estimation results from multiple perspectives to enhance estimation accuracy. This dataset can be utilized for analysis in various cutting-edge research fields such as energy transition dynamics, transition risk management strategies, and policy formulation processes.

Abstract

From all kinds of scientific investigations and research it is said that the climate change impact will strongly affect the monsoon and the rainfall patterns in India. A catchment wise assessment is needed to understand the real impact on water management aspects related to water availability and floods. The study has been undertaken to gauge the forthcoming patterns of precipitation variability across upper Chambal River catchment area up to Gandhi Sagar Dam. The SDSM was harnessed to refine the results from GCMs spanning three projected timeframes: (2006–2036), (2037–2067), and (2068–2098) for the RCPs 2.6, 4.5 and 8.5. The outcomes of the study paint a picture of an impending "wetter" monsoon season on a monthly scale and Specifically under the CanESM2 (RCP 2.6) there is an overall annual precipitation increase of 0.22%, 11.21% and 5.65% during timeframe for the years 2006–2036, 2037–2067 and 2068–2098 respectively. These findings suggest that as we move into the future more substantial rainfall during the monsoon season compared to other times of the year is expected. The study also indicates a significant uptick of daily extreme rainfall occurrences. It has been observed that there is a substantial percentage hike in the extreme event frequency as compared to the baseline period (1983–1995) are primarily observed during months outside the monsoon period specially those proceeding and succeeding the monsoon period.