Search

In the last 20 years, research on the observed and projected impacts of climate change on cultural heritage has led to significant developments regarding damage quantification and risk assessment, which unfortunately are not yet exhaustively transferred to practical applications and to the sector of policy and decision making. One of the major reasons for this still lacking alignment remains with the inadequate handover of quantitative data, which is a prerequisite for the development of measures and strategies for the mitigation of the impacts and risk reduction. In this paper, we focus on the methods and approaches put in place for the production of projections providing quantitative assessments of climate change-induced impacts in the near and far future (up to the 21st century) on outdoor built heritage mainly constituted by stone and stone-like materials. Our critical study found that different approaches have been applied for quantifying slow cumulative damage due to the ongoing variations of climate and air pollution parameters and to risk assessment caused by hydrometeorological extreme events induced by variations of temperature and precipitation. There is clear evidence that efforts are still needed for directing research to provide concrete solutions and tools addressed to meet the requirements of stakeholders and to solve the existing challenges in the field: selected effective models and tools are illustrated. The discussion is structured in order to highlight the driving role of research in supporting the definition of priorities for heritage managers and the development of strategies by decision and policy makers for the prevention and safeguarding of cultural heritage at risk. [ABSTRACT FROM AUTHOR]

Climate change (CC) impacts on hydrology pose significant global concerns due to their effects on water availability, thereby impacting various human activities reliant on this essential resource. This study assesses the influence of CC on the water supply in the Angostura-Bolivia basin. We employed the RegCM4 system, which develops its own regional climate models (RCMs) tailored to the Angostura basin using specific convective schemes, diverging from reliance on pre-existing RCMs, like those provided by CORDEX. Methodologically, the study involves hydrometeorological data collection and analysis, utilizing dynamic and statistical downscaling methods to refine the RCMs derived from ERA-Interim reanalysis data. Subsequently, precipitation and temperature projections are generated under CC scenarios (RCP 4.5 and RCP 8.5) for both near (2045–2055) and far (2065–2075) future periods, compared to the historical period (1981–2010). The final stage employs the HydroBID system to project future runoff, considering both perturbed and unperturbed hydrometeorological data under CC effects. The analysis of flow duration curves for 50%, 75%, and 90% exceedance probabilities reveals a significant reduction in flows across all scenarios, indicating a noteworthy impact on water availability. These findings underscore the urgency of comprehending and adapting to CC on hydrology, emphasizing the critical importance of sustainable water resource management amidst evolving climatic conditions. [ABSTRACT FROM AUTHOR]

Storm surges and waves are key climate-driven parameters affecting the design and operation of ports and other infrastructure on the coast. Reliable predictions of future storm surges and waves are not yet available for the west coast of Canada, and this data gap hinders effective climate risk assessment, planning and adaptation. This paper presents numerical simulations of storm surges and waves in British Columbia coastal waters under a future climate (Representative Concentration Pathway) scenario (RCP8.5). The numerical models were first forced by wind and surface pressure fields from the ERA-5 global reanalysis, and calibrated and validated using historical wave and water level records. The models were then driven by atmospheric data from four regional climate models (RCMs) to investigate potential changes in the frequency and magnitude of storm surges and extreme waves over the 21st century. The model outputs were analyzed to determine the potential impacts of climate change on storm surges and wave effects at key ports and transportation assets in western Canada. The study is the first of its kind to utilize unstructured, computational models to simulate storm surges and waves for the entire western Canada coastal region, while maintaining the high spatial resolution in coastal sub-basins needed to capture local dynamic responses. [ABSTRACT FROM AUTHOR]

This work presents new climate and emissions scenarios to investigate changes on future meteorology and air quality in the U.S. Here, we employ a dynamically downscaled Weather Research and Forecasting model coupled with chemistry (WRF/Chem) simulations that use two Intergovernmental Panel on Climate Change scenarios (i.e., A1B and B2) integrated with explicitly projected emissions from a novel Technology Driver Model (TDM). The projected 2046–2055 emissions show widespread reductions in most gas and aerosol species under both TDM/A1B and TDM/B2 scenarios over the U.S. The WRF/Chem simulations show that under the combined effects of the TDM/A1B climate and emission changes, the maximum daily average 8-h ozone (MDA8 h O3) increases by ~3 ppb across the U.S. mainly due to widespread increases in near-surface temperature and background methane concentrations, with some contributions from localized TDM emission changes near urban centers. For the TDM/B2 climate and emission changes, however, the MDA8 h O3 is widely decreased, except near urban centers where the relative TDM emission changes and O3 formation regimes leads to increased O3. The number of O3 exceedance days (i.e., MDA8 h O3 > 70 ppb) for the entire domain is significantly reduced by a grid cell maximum of up to 43 days (domain average ~0.5 days) and 62 days (domain average ~2 days) for the TDM/A1B and TDM/B2 scenarios, respectively, while in the western U.S., larger O3 increases lead to increases in nonattainment areas, especially for the TDM/A1B scenario. The combined effects of climate and emissions (for both A1B and B2 scenarios) will lead to widespread decreases in the daily 24-h average (DA24 h) PM2.5 concentrations, especially in the eastern U.S. (max decrease up to 93 µg m−3). The PM2.5 changes are dominated by decreases in anthropogenic emissions for both the TDM/A1B and TDM/B2 scenarios, with secondary effects on decreasing PM2.5 from climate change. The number of PM2.5 exceedance days (i.e., DA24 h PM2.5 > 35 µg m−3) is significantly reduced over the eastern U.S. under both TDM/A1B and B2 scenarios, which suggests that both climate and emission changes may synergistically lead to decreases in PM2.5 nonattainment areas in the future. [ABSTRACT FROM AUTHOR]

Land evaporation (LET) is an important variable in climate change, water cycle and water resources management. Mastering the projected changes in LET is significant for crop water requirements and the energy cycle. The global climate model (GCM) is a vital tool for future climate change research. However, the GCMs have low spatial resolution and relatively high errors. We use a deep learning (DL)-based model to deal with this problem. The DL approach can downscale the model data and merge simultaneously. We applied the DL approach to a suit of models from the Coupled Model Intercomparison Project 6th edition (CMIP6) LET data. From the result of all the evaluation metrics, the DL merged data greatly improved in both spatial and time dimensions. The mean RMSE is 5.85 mm and the correlation is 0.95 between the DL merged data and reference data (historical reliable evaporation data). The future LET evidently increases in four scenarios (SSP1–2.6, SSP2–4.5, SSP3–7.0, and SSP5–8.5), and the upward intensity rises from the low to high emission scenarios. The highest increasing regions are in the Tibet Plateau and the south of China and the trend is larger than 10 mm/decade in the high scenarios. From the seasonal point of view, the increasing trend in spring and summer is far larger than for autumn and winter. The Tibet Plateau and the northeast of China have the largest upward trend in the spring of SSP5–8.5, higher than 1.6 mm/decade. [ABSTRACT FROM AUTHOR]

Satellite-based normalized difference vegetation index (NDVI) time series data are useful for monitoring the changes in vegetation ecosystems in the context of global climate change. However, most of the current NDVI products cannot effectively reconcile high spatial resolution and continuous observations in time. Here, to produce a global-scale, long-term, and high-resolution NDVI database, we developed a simple and new data downscaling approach. The downscaling algorithm considers the pixel-wise ratios of the coefficient of variation (CV) between the coarse- and fine-resolution NDVI data and relative changes in the NDVI against a baseline period. The algorithm successfully created a worldwide monthly NDVI database with 250 m resolution from 1982 to 2018 by translating the fine spatial information from MODIS (Moderate-resolution Imaging Spectroradiometer) data and the long-term temporal information from AVHRR (Advanced Very High Resolution Radiometer) data. We employed the evaluation indices of root mean square error (RMSE), mean absolute error (MAE), and Pearson's correlation coefficient (Pearson's R) to assess the accuracy of the downscaled data against the MODIS NDVI. Both the RMSE and MAE values at the regional and global scales are typically between 0 and 0.2, whereas the Pearson's R values are mostly above 0.7, which implies that the downscaled NDVI product is similar to the MODIS NDVI product. We then used the downscaled data to monitor the NDVI changes in different plant types and places with significant vegetation heterogeneity, as well as to investigate global vegetation trends over the last four decades. The Google Earth Engine platform was used for all the data downscaling processes, and here we provide a code for users to easily acquire data corresponding to any part of the world. The downscaled global-scale NDVI time series has high potential for the monitoring of the long-term temporal and spatial dynamics of terrestrial ecosystems under changing environments. [ABSTRACT FROM AUTHOR]

Localized climate is sensitive to terrain, underlying surface material, building distribution, green coverage and CO2 emissions. The Regional Climate Model (RegCM) was used to make a statistical detailed analysis of the climate change data in a specific study area to obtain fine-scale distribution of climatic elements data over time. The effects of climate change factors on height growth trends of a climate-sensitive tree species (Cyclobalanopsis glauca) were simulated based on historical climate base line data (1961–2010) and future climate change (2010–2100) predictions. Cyclobalanopsis glauca growth trends were simulated and analyzed by using a nonlinear mixed effect model (NLME). The results showed that under the RCP8.5 emissions scenario, the growth promotion effect on the height growth of Cyclobalanopsis glauca will be obvious. Under RCP4.5 and RCP2.6 emissions scenarios, although the inhibition intensity is not exactly the same, height growth will still be inhibited to a certain extent, which may lead to the gradual extinction of this species, affecting the composition of dominant tree species in the study area. The results indirectly reflect the impact of climate change on tree species diversity in the future. [ABSTRACT FROM AUTHOR]