Search Results (8)

In recent decades, accelerated urban growth in Arequipa has led to the loss of more than 40% of riparian vegetation and increased ecological fragmentation in the Chili River valley. This transformation has degraded water quality and limited equitable access to green and public spaces. Therefore, this research aims to design a Green Corridor along the Chili River as an ecosystem-based strategy to enhance social connectivity and ecological resilience in Arequipa, Peru. The methodology combined an extensive literature review, a comparative analysis of international case studies, and a territorial diagnosis supported by geospatial and climatic data. The process is supported by digital tools such as Google Earth Pro 2025, AutoCAD 2024, SketchUp Pro 2023, and solar simulations with Ladybug-Grasshopper, complemented by data from SENAMHI, SINIA, and the Solar Atlas of Peru. The results propose a resilient green corridor integrating passive and active sustainability strategies, including 40 photovoltaic panels, 44 solar luminaires, biodigesters producing between 90 and 150 kWh per month, and phytotechnologies capable of absorbing 75,225 kg of CO₂ annually, based on WHO conversion factors adapted to high-altitude conditions. The proposal employs eco-efficient materials such as reforested eucalyptus wood and volcanic sillar, creating recreational and productive spaces that promote social cohesion and circular economy. In conclusion, this study demonstrates the potential of ecosystem-based design to regenerate arid urban riverbanks, harmonizing environmental sustainability, social inclusion, and cultural identity. Thus, the Chili River corridor is consolidated as a replicable model of green-blue infrastructure for Andean cities, aligned with Sustainable Development Goals 6, 7, 11, 12, 13, and 15. Full article

This study assessed the impact of 39 active and passive energy efficiency measures on the energy demand of a prototype dwelling, modeled through parametric simulations in DesignBuilder across nine climatic zones in Chile, classified according to the Köppen system. Each measure was evaluated individually (single-measure scenarios); three variation levels were evaluated to quantify their relative influence on energy demand. Results indicate that passive strategies are more effective in cold and humid climates, where increasing wall insulation thickness reduced energy demand by up to 45%, and improving airtightness achieved a 43% reduction. In contrast, in tundra climates or areas with high thermal variability, some measures, such as green façades or overhangs, increased energy demand by up to 49% due to the loss of useful solar gains. In desert climates, characterized by high diurnal temperature variation, thermal mass played a more significant role: high-inertia walls without additional insulation outperformed lightweight EPS-based solutions. The findings suggest that measure selection must be climate-adapted, prioritizing high-impact passive strategies and avoiding one-size-fits-all solutions. This work provides quantitative evidence to inform residential thermal design and support climate-sensitive energy efficiency policies. This study delivers a single-measure comparative atlas; future research should integrate multi-measure optimization together with comfort/cost metrics. Full article

Urban areas are increasingly challenged by the combined effects of climate change, rapid population growth, and high energy demand. The integration of renewable energy systems, such as photovoltaic (PV) panels, and nature-based solutions, such as green roofs, represents a key strategy for sustainable urban development. This study evaluates the spatial potential for PV and green roof implementation in Iași, Romania, using moderate to high-resolution geospatial datasets, including the ALOS AW3D30 Digital Surface Model (DSM) and the Copernicus Urban Atlas 2018, processed in ArcMap 10.8.1 and ArcGIS Pro 2.6.0. Solar radiation was computed using the Area Solar Radiation tool for the average year 2023, while roof typology (flat vs. pitched) was derived from slope analysis. Results show significant spatial heterogeneity. The Copou neighborhood has the highest PV-suitable roof share (73.6%) and also leads in green roof potential (46.6%). Integrating PV and green roofs can provide synergistic benefits, improving energy performance, mitigating urban heat islands, managing stormwater, and enhancing biodiversity. These findings provide actionable insights for urban planners and policymakers aiming to prioritize green infrastructure investments and accelerate the local energy transition. Full article

As global temperatures continue to rise despite international mitigation efforts, geoengineering has emerged as a potential avenue for climate intervention. One of the most promising and ambitious concepts is the Planetary sunshade—a large-scale structure located at Lagrange Point L1, designed to reduce solar irradiance by physically blocking or redirecting incoming photons. This paper presents a structural design solution for this ambitious system, focusing on deployable mechanisms, frame architecture, and sail configurations that enable rapid mass production and deployment of solar sails components. The design process follows the European Cooperation for Space Standardization (ECSS) methodology through its early-phase stages, utilizing weighted decision matrices for concept selection and material evaluation. Finite element analysis (FEA) was used to validate structural integrity under Atlas V launch and operational conditions. The final design features a 1297 m² sail composed of four triangular segments, deployed via booms and stowed using a vertical folding pattern around a central spool. The booms incorporate arch-shaped cross-sections to enhance stiffness. This configuration achieves a radius expansion ratio of 25 and a sail efficiency factor of 0.5, ensuring survivability under Atlas V launch loads. Full article

Space-based atmospheric backscatter lidars provide critical information about the vertical distribution of clouds and aerosols, thereby improving our understanding of the climate system. They are additionally useful for detecting hazards to aviation and human health, such as volcanic plumes and man-made pollution events. The Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP, 2006–2023), Cloud-Aerosol Transport System (CATS, 2015–2017), and Advanced Topographic Laser Altimeter System (ATLAS 2018–present) are three such lidars that operated within the past 20 years. The signal-to-noise ratio (SNR) for these lidars is significantly lower in daytime data compared with nighttime data due to the solar background signal increasing the detector response noise. Averaging horizontally across profiles has been the standard way to increase SNR, but this comes at the expense of resolution. Modern, deep learning-based denoising algorithms can be applied to improve the SNR without coarsening resolution. This paper describes how one such model architecture, Dense Dense U-Net (DDUNet), was trained to denoise CATS 1064 nm raw signal data (photon counts) using artificially noised nighttime data. Simulated CATS daytime 1064 nm data were then created to assess the model’s performance. The denoised simulated data increased the daytime SNR by a factor of 2.5 (on average) and decreased minimum detectable backscatter (MDB) to ~$7.3\times {10}^{-4}$ km⁻¹sr⁻¹, which is lower than the CALIOP 1064 nm night MDB value of $8.6\times {10}^{-4}$ km⁻¹sr⁻¹. Layer detection was performed on simulated 2 km horizontal resolution denoised and 60 km averaged data. Despite the finer resolution input, the denoised layers had more true positives, fewer false positives, and an overall Jaccard Index of 0.54 versus 0.44 when compared to the layers detected on averaged data. Layer detection was also performed on a full month of denoised daytime CATS data (Aug. 2015) to detect layers for comparison with CATS standard Level 2 (L2) product layers. The detection on the denoised data yielded 2.33 times more, higher-quality bins within detected layers at 2.7–33 times finer resolution than the CATS L2 products. Full article

Yield estimations at global or regional spatial scales have been compromised due to poor crop model calibration. A methodology for estimating the genetic parameters related to grain growth and yield for the CERES-Wheat crop model is proposed based on yield gap concept, the GLUE coefficient estimator, and the global yield gap atlas (GYGA). Yield trials with three durum wheat cultivars in an experimental farm in northern Greece from 2004 to 2010 were used. The calibration strategy conducted with CERES-Wheat (embedded in DSSAT v.4.7.5) on potential mode taking into account the year-to-year variability of relative yield gap Yrg (YgC_adj) was: (i) more effective than using the average site value of Yrg (YgC_unadj) only (the relative RMSE ranged from 10 to 13% for the YgC_adj vs. 48 to 57% for YgC_unadj) and (ii) superior (slightly inferior) to the strategy conducted with DSSAT v.4.7.5 (DSSAT v.3.5—relative RMSE of 5 to 8% were found) on rainfed mode. Earlier anthesis, maturity, and decreased potential yield (from 2.2 to 3.9% for 2021–2050, and from 5.0 to 7.1% for 2071–2100), due to increased temperature and solar radiation, were found using an ensemble of 11 EURO-CORDEX regional climate model simulations. In conclusion, the proposed strategy provides a scientifically robust guideline for crop model calibration that minimizes input requirements due to operating the crop model on potential mode. Further testing of this methodology is required with different plants, crop models, and environments. Full article

In the context of climate change and growing energy demand, solar technologies are considered promising solutions to mitigate Greenhouse Gas (GHG) emissions and support sustainable adaptation. In Greece, solar power is the second major renewable energy, constituting an increasingly important component of the future low-carbon energy portfolio. In this work, we propose the use of a high-resolution regional climate model (Weather Research and Forecasting model, WRF) to generate a solar climate atlas for the near-term climatological future under the Representative Concentration Pathway (RCPs) 4.5 and 8.5 scenarios. The model is set up with a 5 × 5 km² spatial resolution, forced by the ERA-INTERIM for the historic (1980–2004) period and by the EC-EARTH General Circulation Models (GCM) for the future (2020–2044). Results reaffirm the high quality of solar energy potential in Greece and highlight the ability of the WRF model to produce a highly reliable future climate solar atlas. Projected changes between the annual historic and future RCPs scenarios indicate changes of the annual Global Horizontal Irradiance (GHI) in the range of ±5.0%. Seasonal analysis of the GHI values indicates percentage changes in the range of ±12% for both scenarios, with winter exhibiting the highest seasonal increases in the order of 10%, and autumn the largest decreases. Clear-sky fraction f_clear projects increases in the range of ±4.0% in eastern and north continental Greece in the future, while most of the Greek marine areas might expect above 220 clear-sky days per year. Full article

A monthly mean land and snow Bidirectional Reflectance Distribution Function (BRDF) atlas for visible and near infrared parts of the spectrum has been developed for Radiative Transfer for Television Infrared Observation Satellite (TIROS) Operational Vertical sounder (TOVS) (RTTOV). The atlas follows the methodology of the RTTOV University of Wisconsin infrared land surface emissivity (UWIREMIS) atlas, i.e., it combines satellite retrievals and a principal component analysis on a dataset of hyper-spectral surface hemispherical reflectance or albedo. The current version of the BRDF atlas is based on the Collection 5 of the Moderate Resolution Imaging (MODIS) MCD43C1 Climate Modeling Grid BRDF kernel-driven model parameters product. The MCD43C1 product combines both Terra and Aqua satellites over a 16-day period of acquisition and is provided globally at 0.05° of spatial resolution. We have improved the RTTOV land surface BRDF atlas by using the last Collection 6 of MODIS product MCD43C1. We firstly found that the MODIS C6 product improved the quality index of the BRDF model as compared with that of C5. When compared with clear-sky top of atmosphere (TOA) reflectance of Spinning Enhanced Visible and InfraRed Imagers (SEVIRI) solar channels over snow-free land surfaces, we showed that the reflectances are simulated with an absolute accuracy of 3% to 5% (i.e., 0.03–0.05 in reflectance units) when either the satellite zenith angle or the solar zenith angle is below 70°, regardless of the MODIS collection. For snow-covered surfaces, we showed that the comparison with in situ snow spectral albedo is improved with C6 with an underestimation of 0.05 in the near infrared. Full article