Tag: Climate Change Downscaling Techniques

Abstract

Purpose

Due to climate change, the severity and length of heat waves are increasing, and this trend is likely to continue while mitigation efforts are insufficient. These climatic events cause overheating inside buildings, which increases mortality. Adaptation measures reduce overheating but induce environmental impacts, including on human health. This study aims to integrate the overheating-related effects on human health in building LCA to provide a design aid combining mitigation and adaptation.

Methods

In a novel approach, an existing building LCA tool is utilised to evaluate life cycle impacts, including damage to human health expressed in DALYs. The overheating risk is then evaluated using an existing dynamic thermal simulation (DTS) tool and prospective climatic data. Overheating is expressed as a degree-hour (DH) indicator, which integrates both the severity (temperature degrees over a comfort threshold) and the duration (hours). By assuming proportionality between DALYs and DH × area in a first step, the 2003 heat wave mortality data, 2003 climatic data, and a simplified model of the national residential building stock were used to identify a characterisation factor, which can then be used to evaluate DALYs corresponding to any building using DH obtained by thermal simulation.

Results

The proposed overheating model not only allows to derive a characterisation factor for overheating to be used in building LCA but also provides practical insights. The first estimation of the characterisation factor is 1.35E-8DALY. DH-1.m-2. The method was tested in a case study corresponding to a social housing apartment building in France built in 1969 without insulation. The thickness of insulation implemented in the renovation works was varied. For this specific case study, the contribution of overheating is significant, ranging from 1.1E-5DALY.m-2.y-1 to 2.2E-5DALY.m-2.y-1, comparable to the contribution of heating. DTS and LCA results found an optimal thickness, minimising the human health indicator in DALYs. This underscores the potential of active cooling to reduce human health impacts, especially if it consumes electricity produced by a photovoltaic system integrated in the building.

Conclusion

Combining DTS and LCA makes it possible to evaluate damage indicators on human health, including building life cycles (e.g., material and energy) and overheating-related impacts. An application on a case study shows this method’s feasibility and gives a first order of magnitude of overheating health impacts induced by buildings. A more sophisticated model could replace the assumed proportionality between DALYs and DH.

Abstract

Surface Air Temperature (SAT) predictions, typically generated by Global Climate Models (GCMs), carry uncertainties, particularly across different greenhouse gas emission scenarios. Machine Learning (ML) techniques can be employed to forecast long-term temperature variations, although this is a challenging endeavour with few drawbacks, such as the influence of scenarios involving greenhouse gas emissions. Therefore, the present study utilized multiple ML approaches such as Artificial Neural Networks (ANN), multiple linear regression, support vector machine and random forest, along with various daily predicted results of GCMs from Coupled Model Intercomparison Project Phase 6 as predictors and the “India Meteorological Department’s” Maximum SAT (MSAT) as the predictand, to predict daily MSAT in the months of March, April and May (MAM) over Andhra Pradesh (AP) for the period 1981–2022. The results show that ANN outperforms other ML techniques in predicting daily MSAT, with a root mean square error of 1.41, an index of agreement of 0.89 and a correlation coefficient of 0.81. The spatial distribution of hot and heat wave days indicates that the Multiple Model Mean (MMM) underestimates these occurrences, with a minimum bias of 9 and 6 days, respectively. In contrast, the ANN model exhibits much smaller biases, with a maximum underestimation of 3 hot and 2 heat wave days. These findings demonstrate that MMM does not capture the maximum temperatures well, resulting in poor predictability. Further, future temperature projections were analysed from 2023 to 2050, which display a gradual increase in mean MSAT during MAM over AP. This research demonstrates the potential of ML techniques to enhance temperature forecasting accuracy, offering valuable insights for climate modeling and adaptation. The results are crucial for stakeholders in agriculture, health, energy, water resources, socio-economic planning, and urban development, aiding in informed decision-making and improving resilience to climate change impacts.

Abstract

Root zone soil moisture (RZSM) is crucial for agricultural water management and land surface processes. The 1 km soil water index (SWI) dataset from Copernicus Global Land services, with eight fixed characteristic time lengths (T), requires root zone depth optimization (T_opt) and is limited in use due to its low spatial resolution. To estimate RZSM at 100-m resolution, we integrate the depth specificity of SWI and employed random forest (RF) downscaling. Topographic synthetic aperture radar (SAR) and optical datasets were utilized to develop three RF models (RF1: SAR, RF2: optical, RF3: SAR + optical). At the DEMMIN experimental site in northeastern Germany, T_opt (in days) varies from 20 to 60 for depths of 10 to 30 cm, increasing to 100 for 40–60 cm. RF3 outperformed other models with 1 km test data. Following residual correction, all high-resolution predictions exhibited strong spatial accuracy (R ≥ 0.94). Both products (1 km and 100 m) agreed well with observed RZSM during summer but overestimated in winter. Mean R between observed RZSM and 1 km (100 m; RF1, RF2, and RF3) SWI ranges from 0.74 (0.67, 0.76, and 0.68) to 0.90 (0.88, 0.81, and 0.82), with the lowest and highest R achieved at 10 cm and 30 cm depths, respectively. The average RMSE using 1 km (100 m; RF1, RF2, and RF3) SWI increased from 2.20 Vol.% (2.28, 2.28, and 2.35) at 30 cm to 3.40 Vol.% (3.50, 3.70, and 3.60) at 60 cm. These negligible accuracy differences underpin the potential of the proposed method to estimate RZSM for precise local applications, e.g., irrigation management.

Abstract

Thermal images play a crucial role in various applications, such as environmental monitoring, energy efficiency, and food safety. However, thermal images are often affected by low spatial resolution, limited accuracy, and noise, which reduce their usefulness and effectiveness. This research paper presents a novel approach for enhancing thermal images and optimizing using Kriging Interpolation KI. The proposed KI method combines a metaheuristic optimization algorithm, Particle Swarm Optimization (PSO), with Kriging, a geostatistical method for interpolation and prediction of spatially continuous variables. The proposed KI method has been evaluated on a set of low-resolution Land surface temperature (LST) images of Moderate Resolution Imaging Spectroradiometer (MODIS) satellite and validated with higher resolution LandSat-8 LST. The use of PSO in combination with Kriging provides a powerful tool for efficient and accurate spatial enhancement of thermal images, allowing for the preservation of important thermal features and details while improving the overall quality of the images. The proposed KI algorithm demonstrated the effectiveness of the approach in enhancing the spatial resolution and accuracy of the MODIS thermal images. The results show that the proposed method outperforms traditional statistical LST image enhancement methods, such as DisTrad, TsHarp, and Regression Tree in terms of spatial resolution and accuracy. The proposed method has potential applications in agricultural, metrological, and environmental applications, where thermal images are used to continuously monitor and control temperature-sensitive data.

Abstract

In an era marked by rapid global changes, the reinforcement and modernization of plant health surveillance systems have become imperative. Sixty-five scientists present here a research agenda for an enhanced and modernized plant health surveillance to anticipate and mitigate disease and pest emergence. Our approach integrates a wide range of scientific fields (from life, social, physical and engineering sciences) and identifies the key knowledge gaps, focusing on anticipation, risk assessment, early detection, and multi-actor collaboration. The research directions we propose are organized around four complementary thematic axes. The first axis is the anticipation of pest emergence, encompassing innovative forecasting, adaptive potential, and the effects of climatic and cropping system changes. The second axis addresses the use of versatile broad-spectrum surveillance tools, including molecular or imaging diagnostics supported by artificial intelligence, and monitoring generic matrices such as air and water. The third axis focuses on surveillance of known pests from new perspectives, i.e., using novel approaches to detect known species but also anticipating and detecting, within a species, the populations or genotypes that pose a higher risk. The fourth axis advocates the management of plant health as a commons through the establishment of multi-actor and cooperative surveillance systems for long-term data-driven alert systems and information dissemination. We stress the importance of integrating data and information from multiple sources through open science databases and metadata, alongside developing methods for interpolating and extrapolating incomplete data. Finally, we advocate an Integrated Health Surveillance approach in the One Health context, favoring tailored and versatile solutions to plant health problems and recognizing the interconnected risks to the health of plants, humans, animals and the environment, including food insecurity, pesticide residues, environmental pollution and alterations of ecosystem services.

Abstract

Pacific islands are widely exposed to several strong wave events all year long. However, comprehensive analyses of coastal vulnerabilities to wave climates and their extremes are often lacking in those islands. In the present paper, the wave climate around the reef of New Caledonia is analyzed using a 28-year simulation performed with the Wave Watch III model, and accounting for realistic wind intensity forcing from tropical cyclones. Four mean wave regimes are defined with clustering methods, and are shown to vary along the reef depending on its main orientation. The western reef is mostly exposed to energetic south-western swells (significant height over 1.5 m, peak period of ~ 12 s) generated in the Tasman Sea that are reinforced during austral winter. The northern sector and the Loyalty Islands, are hit by shorter waves (~ 8 to 9 s period) coming from the south-east to the north-east, with height ranging on average from 0.8 m in the Loyalty Channel to 1.5 m at the northern tip of the Grande Terre reef. These waves mainly result from the south-eastern trade winds that blow over the central south-western Pacific all year long. In austral summer, additional swell remotely generated by both the extra-tropical westerlies and the north-eastern trade winds of the northern hemisphere reach the north-eastern reef of the archipelago. These wave regimes also strongly vary in response to the interannual El Niño-Southern Oscillation. El Niño events tend to increase the frequency of the south-western swell regime in austral spring and fall, and of the south-eastern trade wind waves in austral summer. In contrast, during La Niña, waves generated in the northern hemisphere are more likely to reach New Caledonia all year long. Finally, extreme wave events and their return periods were assessed. Wave amplitude reaching 7 m is estimated to occur every 100 years. On the west and southern tip of the Grande Terre reef, extreme waves are 80% of the time westerly waves generated by storms in the Tasman Sea or in the Coral Sea, while on the eastern reefs (Loyalty Islands and Channel), 70% of the extreme wave episodes are associated to tropical cyclone-induced waves. During La Niña episodes, more tropical cyclones pass by New Caledonia, increasing their contribution to extreme wave events along the western and southern coasts of the island. Conversely, in El Niño conditions, the exposure to tropical cyclone-induced waves is predominantly concentrated on the northeastern side.

Abstract

This paper summarizes recent developments made in terms of structural and non-structural solutions to manage the safety of urbanized slopes. The paper gives an overview of the pioneering effort to integrate the climate modeling chain into landslide susceptibility assessment using TRIGRS, application of virtual reality to improve the landslide risk awareness, the advancement of upstream flexible barrier system and debris flow screens to reduce the entrainment and impact on terminal barriers, and finally, internal seepage-induced progressive failure of reservoir rim slopes. These advancements are done using numerical modeling, simulations of real cases and physical modeling using small- and large-scale models.

Highlights

A new ensemble model was introduced and evaluated for projecting minimum and maximum temperatures in Iran.

Trends in minimum and maximum temperatures in the near term (2021–2040) were obtained using socio-economic scenarios of five models at 95 synoptic stations.

The ensemble technique reduced the error of the models used in projection to an optimal extent.

Abstract

Three Gorges Dam is the largest hydraulic infrastructure in the world, playing a pivotal role in flood mitigation. The hydrological responses of the Three Gorges Reservoir Region (TGRR) to climate change and human activities are unclear, yet critical for the Three Gorges Dam’s flood control and security. We simulated streamflow and water depth by coupling the Variable Infiltration Capacity model and the CaMa-Flood model. Daily discharge at the outlet of TGRR was well modeled with a relative error within 2% and a Nash-Sutcliffe efficiency coefficient of approximately 0.81. However, the flood peak was overestimated by 2.5–40.0% with a peak timing bias ranging from 5 days earlier to 2 days later. Runoff and water depth in the TGRR increased from 2015 to 2018 but decreased during flood seasons. Land use and land cover changes in 2015 (LUCC2015) and 2020 (LUCC2020) were analyzed to quantify their hydrological impacts. During the 2015–2018 period, land use conversion increased in built-up areas (+ 0.6%) and water bodies (+ 0.1%), but decreased in woodland grassland (-0.7%) and cropland (-0.1%). This led to a slight increase in runoff and inflow of less than 4‰ across the TGRR, a 7.70% decrease in average water depth, and a 15.4‰ increase in maximum water depth. Water depths in the TGRR decreased during flood seasons, and increased during non-flood seasons. Increasing water depth was identified in northern TGRR. This study clarifies the historical TGRR’s hydrological features under LUCC and climate changes, aiding regional flood mitigation in the TGRR.

Abstract

High spatio-temporal resolution estimates of electricity consumption are essential for formulating effective energy transition strategies. However, the data availability is limited by complex spatio-temporal heterogeneity and insufficient multi-source feature fusion. To address these issues, this study introduces an innovative downscaling method that combines multi-source data with machine learning and spatial interpolation techniques. The method’s accuracy showed significant improvements, with determination coefficients (R²) increasing by 30.1% and 33.4% over the baseline model in two evaluation datasets. With this advanced model, we estimated monthly electricity consumption across 1 km x 1 km grid for 280 Chinese cities from 2012 to 2019. Our dataset is highly consistent with officially released electricity consumption of different industries (Pearson correlation coefficients within 0.83 - 0.91). Moreover, our data can reflect the electricity consumption patterns of different urban land uses compared to other datasets. This study bridges a significant gap in fine-grained electricity consumption data, providing a robust foundation for the development of sustainable energy policies.