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opencanada-crownSep 2024View details →

Montreal climate data for building simulations with urban heat island effects and nature-based solutions

As cities face rising temperatures, increased frequency of extreme weather events, and altered precipitation patterns, buildings are subjected to increasing energy demand, heat stress, thermal comfort issues, and decreased service life. Therefore, evaluating building performance under changing climate conditions is essential for building sustainable and resilient communities. Unique climate characteristics of cities, such as the urban heat island effect, are not well simulated by global or regional climate models, and is therefore often not included in typical building analyses. Consequently, a computationally efficient approach is used to generate “urbanized” climate data, derived from regional climate models, to prepare building simulation climate data that incorporate urban effects. We demonstrate this process using existing climate data for Montreal airport’s weather station and extend it to prepare projections for scenarios where nature-based solutions, such as increased greenery and albedo, were implemented. We find significant improvements in the representation of the urban heat island and subsequent cooling effects of nature-based solutions in the urbanized climate data. This dataset allows building practitioners to evaluate building performance under historical and potential future changes in climate, considering the complex interactions within the urban canopy and the implementation of mitigation efforts such as nature-based solutions. This dataset contains hourly historical and future weather files for use in building simulations for the city of Montreal, Canada. While similar weather files are usually based on measurements taken at a city's nearby airport, the current dataset utilizes a novel statistical-dynamical downscaling technique which involves the use of the dynamical Weather Research and Forecasting (WRF) model combined with a statistical approach and climate projections from an ensemble of 15 Canadian Regional Climate Model 4 (CanRCM4) to generate urban climate data which includes the effects of the urban heat island and different nature-based solutions (NBS) as mitigation strategies (such as increasing surface albedo and greenery). Additionally, different levels of implementation of these mitigation strategies were produced, for example, when the albedo is increased to 0.40 (ALBD40) and 0.80 (ALBD80), and similarly for the green and combined scenarios, GRN40, GRN80, COMB40, and COMB80. The URBAN scenario is considered the control case where the urban heat island effects are accounted for in the data, but the NBS scenarios are not yet implemtned.  The data are stored in large CSV files, where the rows consists of all 15 realizations of the CanRCM4 ensemble and the variables make up the columns. For example, each 31-year period is repeated 15 times, once for each of the RCM realizations. Therefore, there are 4,073,400 (15x31x8760) rows in each file. We recommend viewing the data using packages from Python or R.     The historical and future global warming thresholds and their corresponding time periods are as follows: <table> <tbody> <tr> <td> Global Warming Scenario </td> <td> Time Period </td> </tr> <tr> <td> Historical </td> <td> 1991-2021 </td> </tr> <tr> <td> Global Warming 0.5ºC </td> <td> 2003-2033 </td> </tr> <tr> <td> Global Warming 1.0ºC </td> <td> 2014-2044 </td> </tr> <tr> <td> Global Warming 1.5ºC </td> <td> 2024-2054 </td> </tr> <tr> <td> Global Warming 2.0ºC </td> <td> 2034-2064 </td> </tr> <tr> <td> Global Warming 2.5ºC </td> <td> 2042-2072 </td> </tr> <tr> <td> Global Warming 3.0ºC </td> <td> 2051-2081 </td> </tr> <tr> <td> Global Warming 3.5ºC </td> <td> 2064-2094 </td> </tr> </tbody> </table>   The following variables are included in the files: <table> <tbody> <tr> <td>Variable</td> <td>Description</td> </tr> <tr> <td>RUN</td> <td>Run number (R1-R15) of Canadian Regional Climate Model, CanRCM4 large ensemble associated with the selected reference year data</td> </tr> <tr> <td>YEAR</td> <td>Year associated with the record</td> </tr> <tr> <td>MONTH</td> <td>Month associated with the record</td> </tr> <tr> <td>DAY</td> <td>Day of the month associated with the record</td> </tr> <tr> <td>HOUR</td> <td>Hour associated with the record</td> </tr> <tr> <td>YDAY</td> <td>Day of the year associated with the record</td> </tr> <tr> <td>DRI_kJPerM2</td> <td>Direct horizontal irradiance in kJ/m2 (total from previous HOUR to the HOUR indicated)</td> </tr> <tr> <td>DHI_kJperM2</td> <td>Diffused horizontal irradiance in kJ/m2 (total from previous HOUR to the HOUR indicated)</td> </tr> <tr> <td>DNI_kJperM2</td> <td>Direct normal irradiance in kJ/m2 (total from previous HOUR to the HOUR indicated)</td> </tr> <tr> <td>GHI_kJperM2</td> <td>Global horizontal irradiance in kJ/m2 (total from previous HOUR to the HOUR indicated)</td> </tr> <tr> <td>TCC_Percent</td> <td>Instantaneous total cloud cover at the HOUR in % (range: 0-100)</td> </tr> <tr> <td>RAIN_Mm</td> <td>Total rainfall in mm (total from previous HOUR to the HOUR indicated)</td> </tr> <tr> <td>WDIR_ClockwiseDegFromNorth</td> <td>Instantaneous wind direction at the HOUR in degrees (measured clockwise from the North)</td> </tr> <tr> <td>WSP_MPerSec</td> <td>Instantaneous wind speed at the HOUR in meters/sec</td> </tr> <tr> <td>RHUM_Percent</td> <td>Instantaneous relative humidity at the HOUR in %</td> </tr> <tr> <td>TEMP_K</td> <td>Instantaneous temperature at the HOUR in Kelvin</td> </tr> <tr> <td>ATMPR_Pa</td> <td>Instantaneous atmospheric pressure at the HOUR in Pascal</td> </tr> <tr> <td>SnowC_Yes1No0 </td> <td>Instantaneous snow-cover at the HOUR (1 - snow; 0 - no snow)</td> </tr> <tr> <td>SNWD_Cm</td> <td>Instantaneous snow depth at the HOUR in cm</td> </tr> </tbody> </table>

opencanada-crownSep 2024View details →

Toronto climate data for building simulations with urban heat island effects and nature-based solutions

As cities face rising temperatures, increased frequency of extreme weather events, and altered precipitation patterns, buildings are subjected to increasing energy demand, heat stress, thermal comfort issues, and decreased service life. Therefore, evaluating building performance under changing climate conditions is essential for building sustainable and resilient communities. Unique climate characteristics of cities, such as the urban heat island effect, are not well simulated by global or regional climate models, and is therefore often not included in typical building analyses. Consequently, a computationally efficient approach is used to generate “urbanized” climate data, derived from regional climate models, to prepare building simulation climate data that incorporate urban effects. We demonstrate this process using existing climate data for Toronto airport’s weather station and extend it to prepare projections for scenarios where nature-based solutions, such as increased greenery and albedo, were implemented. We find significant improvements in the representation of the urban heat island and subsequent cooling effects of nature-based solutions in the urbanized climate data. This dataset allows building practitioners to evaluate building performance under historical and potential future changes in climate, considering the complex interactions within the urban canopy and the implementation of mitigation efforts such as nature-based solutions. This dataset contains hourly historical and future weather files for use in building simulations for the city of Toronto, Canada. While similar weather files are usually based on measurements taken at a city's nearby airport, the current dataset utilizes a novel statistical-dynamical downscaling technique which involves the use of the dynamical Weather Research and Forecasting (WRF) model combined with a statistical approach and climate projections from an ensemble of 15 Canadian Regional Climate Model 4 (CanRCM4) to generate urban climate data which includes the effects of the urban heat island and different nature-based solutions (NBS) as mitigation strategies (such as increasing surface albedo and greenery). Additionally, different levels of implementation of these mitigation strategies were produced, for example, when the albedo is increased to 0.40 (ALBD40) and 0.80 (ALBD80), and similarly for the green and combined scenarios, GRN40, GRN80, COMB40, and COMB80. The URBAN scenario is considered the control case where the urban heat island effects are accounted for in the data, but the NBS scenarios are not yet implemtned.  The data are stored in large CSV files, where the rows consists of all 15 realizations of the CanRCM4 ensemble and the variables make up the columns. For example, each 31-year period is repeated 15 times, once for each of the RCM realizations. Therefore, there are 4,073,400 (15x31x8760) rows in each file. We recommend viewing the data using packages from Python or R.     The historical and future global warming thresholds and their corresponding time periods are as follows: <table> <tbody> <tr> <td> Global Warming Scenario </td> <td> Time Period </td> </tr> <tr> <td> Historical </td> <td> 1991-2021 </td> </tr> <tr> <td> Global Warming 0.5ºC </td> <td> 2003-2033 </td> </tr> <tr> <td> Global Warming 1.0ºC </td> <td> 2014-2044 </td> </tr> <tr> <td> Global Warming 1.5ºC </td> <td> 2024-2054 </td> </tr> <tr> <td> Global Warming 2.0ºC </td> <td> 2034-2064 </td> </tr> <tr> <td> Global Warming 2.5ºC </td> <td> 2042-2072 </td> </tr> <tr> <td> Global Warming 3.0ºC </td> <td> 2051-2081 </td> </tr> <tr> <td> Global Warming 3.5ºC </td> <td> 2064-2094 </td> </tr> </tbody> </table>   The following variables are included in the files: <table> <tbody> <tr> <td>Variable</td> <td>Description</td> </tr> <tr> <td>RUN</td> <td>Run number (R1-R15) of Canadian Regional Climate Model, CanRCM4 large ensemble associated with the selected reference year data</td> </tr> <tr> <td>YEAR</td> <td>Year associated with the record</td> </tr> <tr> <td>MONTH</td> <td>Month associated with the record</td> </tr> <tr> <td>DAY</td> <td>Day of the month associated with the record</td> </tr> <tr> <td>HOUR</td> <td>Hour associated with the record</td> </tr> <tr> <td>YDAY</td> <td>Day of the year associated with the record</td> </tr> <tr> <td>DRI_kJPerM2</td> <td>Direct horizontal irradiance in kJ/m2 (total from previous HOUR to the HOUR indicated)</td> </tr> <tr> <td>DHI_kJperM2</td> <td>Diffused horizontal irradiance in kJ/m2 (total from previous HOUR to the HOUR indicated)</td> </tr> <tr> <td>DNI_kJperM2</td> <td>Direct normal irradiance in kJ/m2 (total from previous HOUR to the HOUR indicated)</td> </tr> <tr> <td>GHI_kJperM2</td> <td>Global horizontal irradiance in kJ/m2 (total from previous HOUR to the HOUR indicated)</td> </tr> <tr> <td>TCC_Percent</td> <td>Instantaneous total cloud cover at the HOUR in % (range: 0-100)</td> </tr> <tr> <td>RAIN_Mm</td> <td>Total rainfall in mm (total from previous HOUR to the HOUR indicated)</td> </tr> <tr> <td>WDIR_ClockwiseDegFromNorth</td> <td>Instantaneous wind direction at the HOUR in degrees (measured clockwise from the North)</td> </tr> <tr> <td>WSP_MPerSec</td> <td>Instantaneous wind speed at the HOUR in meters/sec</td> </tr> <tr> <td>RHUM_Percent</td> <td>Instantaneous relative humidity at the HOUR in %</td> </tr> <tr> <td>TEMP_K</td> <td>Instantaneous temperature at the HOUR in Kelvin</td> </tr> <tr> <td>ATMPR_Pa</td> <td>Instantaneous atmospheric pressure at the HOUR in Pascal</td> </tr> <tr> <td>SnowC_Yes1No0 </td> <td>Instantaneous snow-cover at the HOUR (1 - snow; 0 - no snow)</td> </tr> <tr> <td>SNWD_Cm</td> <td>Instantaneous snow depth at the HOUR in cm</td> </tr> </tbody> </table>

opencc-by-4.0Jun 2023View details →

Data created in a stakeholder analysis of selected cases of Nature-Based Solutions in Scotland

These data represent the results of a stakeholder analysis of selected cases of catchment-related 'Nature-Based Solutions' (NbS) in Scotland.  This arose from a desk-based study to examine publicly-accessible website content and associated reports to assess the actors involved in Nature-Based Solutions (NbS), their attributes, and relationship to six NbS related initiatives in Scotland.  These data were collected and analysed November 2022-March 2023. Please see the first tab for more information about the study, and the content on each of the tabs of the spreadsheet. This was carried out as part of investigation into Scaling and Mainstreaming Nature-based Solutions: WP4 of Project JHI-D2-2 in the 2022-2027 Strategic Research Programme funded by the Scottish Government.  Please visit https://www.hutton.ac.uk/research/projects/scaling-and-mainstreaming-nature-based-solutions for more information about this project and a report which accompanies and used these data.

openother-atSep 2023View details →

UrbAlytics - Remote Sensing tools for Urban Heat Island Assessment and Climate Change Adaptation through Nature-Based Solutions

Urban Heat Island (UHI) is considered one of the significant problems posed to human beings due to the urbanization and industrialization of human civilization. The leading causes of UHI are the vast amounts of heat urban structures produce as they absorb and re-radiate solar radiation and anthropogenic heat sources. The issue mainly affects cities or metropolises with a vast population and a thriving economy. The problem will worsen significantly in the future due to the predicted three billion people living in urban areas worldwide. Due to the severity of the problem, accessing up-to-date information layers that can support city planners and decision-makers in the context of climate resilience is a demanding problem nowadays. UrbAlytics is an experimental sub-project of the H2020-funded project <a href="https://ai4copernicus-project.eu/">AI4Copernicus</a> that aims to bridge Artificial Intelligence with Earth Observations, producing information layers that can support city planners and decision-makers in the context of climate resilience and related challenges in urban areas. This research investigates, thanks to the joint expertise of the partners <a href="https://www.latitudo40.com/">Latitudo 40</a> and <a href="https://www.landsrl.com/land-research-lab">LAND Research Lab®</a>, the Urban Heat Island (UHI) effect, evaluating its impacts on cities, assessing Ecosystem Services provided by Blue and Green Infrastructures and proposing a set of Nature-Based Solutions (NBS) for climate adaptation and extreme heat mitigation.  The dataset This dataset is the tool's output of a fully automated workflow realized during the project and tested for the cities of Milan and Naples, pilot users of the experiment. The choice of Milan and Naples allows for different readiness levels, data availability, and urban-climatic conditions. For each city, the dataset contains the following layers for the analysis period 2018-2022.     HEATWAVE POTENTIAL RISK (HPR) Risk Assessment mapping concerning extreme heat, considering the severity of the heat island phenomenons, the exposure of sensitive age groups and the vulnerability due to city morphology and surface materials. The risk assessment is the first step in defining a methodology that aims to assess the effectiveness of mitigation and adaptation strategies to climate extremes. It's a value in [0,1], where the higher the value higher the risk.     MICROCLIMATIC PERFORMANCE INDEX (MPI) The role of vegetation in the city in abating the Heat Island effect has been widely demonstrated. In this context, deploying Urban Green Infrastructure is recognized as one of the most important strategies to mitigate UHI and promote a resilient city environment. The significance of the mitigation role of the Heat Island phenomenon that vegetation assumes makes it necessary to map Urban Green Infrastructure to estimate a cooling potential. Estimating the microclimatic performance of urban vegetation is crucial to plan adaptation and mitigation actions for the UHI effect. In this work, up-to-date Tree Cover Density and Land Cover maps have been produced using machine learning applied to Sentinel-2 satellite imagery. Those maps have been interpolated and combined, creating 20 Blue and Green Infrastructures classes. Each category's microclimatic performance score was attributed based on evapotranspiration potential, shading and albedo. The output is a map with integer values in [1, 20], where the lower the value higher the microclimatic performance.      PARK COOL ISLANDS (PCI) Park Cool Islands layer identifies the most performing areas during extreme summer heatwaves, according to their size and relevant characteristics, providing reliable information to citizens and urban planners about the safest and coolest areas during extreme heatwaves. Since the green areas' type and composition can influence their cooling effects, we considered both the size and composition of urban parks to identify the most performing green areas in terms of the Park Cool Island effect.  The layer distinguishes between major and minor Park Cool Islands. Major PCI includes areas covered by at least 50% of tree canopy coverage and bigger than 2 hectares with an estimated cooling distance of 300 m buffer. Minor PCI includes green areas whose surface is between 1 and 2 hectares as well as those green areas bigger than 2 hectares but covered by less than 50% of tree canopy coverage, with an estimated cooling distance of 100 m buffer.   Contact Information If you would like further information about the dataset or if you experience any issues downloading files, please contact us <a href="mailto:giovanni.giacco@latitudo40.com">giovanni.giacco@latitudo40.com</a>, <a href="mailto:giulia.castellazzi@landsrl.com">giulia.castellazzi@landsrl.com</a>

opencc-by-4.0Sep 2023View details →

Nature-based solutions are critical for putting Brazil on track towards net-zero emissions by 2050

This dataset supports the findings of the article "Nature-based solutions are critical for putting Brazil on track towards net-zero emissions by 2050" from Soterroni et al. (2023) accepted in Global Change Biology.

ClinicalTrials.gov32/100

Nature-based Solutions for Work-related Stress

ClinicalTrials.gov study NCT04111796. IPD Sharing: NO. Countries: 1. Publications: 1.

closedIPD-NOFeb 2026View details →

dryad32/100

Excel code for: Trading off nature for nature-based solutions: The bioeconomics of forest management for wildlife, timber and carbon

Open the record for dataset details and reuse information.

publicMay 2024View details →

dryad32/100

Biodiversity-productivity relationships are key to nature-based climate solutions

Open the record for dataset details and reuse information.

publicApr 2021View details →

opencc-by-4.0Oct 2020View details →

Figure 2 from: Balzan MV, Tomaskinova J, Collier MJ, Dicks L, Geneletti D, Grace M, Longato D, Sadula R, Stoev P, Sapundzhieva A (2020) Building capacity for mainstreaming nature-based solutions into environmental policy and landscape planning. Research Ideas and Outcomes 6: e58970. https://doi.org/10.3897/rio.6.e58970

Figure 2 (a) Assessing the relationship between green infrastructure cover (GI) in each local council and average ES capacity and (b) population density (Adapted from: Balzan 2017).

opencc-by-4.0Nov 2020View details →

Figure 1 from: Sapundzhieva A, Balzan MV, Tomaskinova J, De Santis L, Collier MJ, Williams J, Dicks L, Grace M, Geneletti D, Demirova I (2020) ReNature: creating the first nature-based solutions compendium in the Mediterranean. Research Ideas and Outcomes 6: e59646. https://doi.org/10.3897/rio.6.e59646

Figure 1 The ReNature nature-based compendium flyer was sent and handed out to key stakeholders to invite them to submit case-studies for inclusion in the online compendium.

Supplementary material 10 from: Tomaskinova J, Dicks L, Collier M, Geneletti D, Grace M, Longato D, Sadula R, Stoev P, Sapundzhieva A, Balzan MV (2020) Capacity-building and networking events for nature-based solutions and re-naturing in Malta. Research Ideas and Outcomes 6: e60893. https://doi.org/10.3897/rio.6.e60893

How can we save bees through sustainable farming

Supplementary material 8 from: Tomaskinova J, Dicks L, Collier M, Geneletti D, Grace M, Longato D, Sadula R, Stoev P, Sapundzhieva A, Balzan MV (2020) Capacity-building and networking events for nature-based solutions and re-naturing in Malta. Research Ideas and Outcomes 6: e60893. https://doi.org/10.3897/rio.6.e60893

Nature-based solutions: not just a city thing!

Supplementary material 7 from: Tomaskinova J, Dicks L, Collier M, Geneletti D, Grace M, Longato D, Sadula R, Stoev P, Sapundzhieva A, Balzan MV (2020) Capacity-building and networking events for nature-based solutions and re-naturing in Malta. Research Ideas and Outcomes 6: e60893. https://doi.org/10.3897/rio.6.e60893

Nature-based solutions and environmental justice

Supplementary material 6 from: Tomaskinova J, Dicks L, Collier M, Geneletti D, Grace M, Longato D, Sadula R, Stoev P, Sapundzhieva A, Balzan MV (2020) Capacity-building and networking events for nature-based solutions and re-naturing in Malta. Research Ideas and Outcomes 6: e60893. https://doi.org/10.3897/rio.6.e60893

Designing NbS in cities: What do planners need to know?

Supplementary material 5 from: Tomaskinova J, Dicks L, Collier M, Geneletti D, Grace M, Longato D, Sadula R, Stoev P, Sapundzhieva A, Balzan MV (2020) Capacity-building and networking events for nature-based solutions and re-naturing in Malta. Research Ideas and Outcomes 6: e60893. https://doi.org/10.3897/rio.6.e60893

The ecological foundations: biodiversity and its relationship to ecological function

Supplementary material 4 from: Tomaskinova J, Dicks L, Collier M, Geneletti D, Grace M, Longato D, Sadula R, Stoev P, Sapundzhieva A, Balzan MV (2020) Capacity-building and networking events for nature-based solutions and re-naturing in Malta. Research Ideas and Outcomes 6: e60893. https://doi.org/10.3897/rio.6.e60893

Using evidence in decision-making

Supplementary material 3 from: Tomaskinova J, Dicks L, Collier M, Geneletti D, Grace M, Longato D, Sadula R, Stoev P, Sapundzhieva A, Balzan MV (2020) Capacity-building and networking events for nature-based solutions and re-naturing in Malta. Research Ideas and Outcomes 6: e60893. https://doi.org/10.3897/rio.6.e60893

Ecosystem services in Polish urban areas

Supplementary material 1 from: Tomaskinova J, Dicks L, Collier M, Geneletti D, Grace M, Longato D, Sadula R, Stoev P, Sapundzhieva A, Balzan MV (2020) Capacity-building and networking events for nature-based solutions and re-naturing in Malta. Research Ideas and Outcomes 6: e60893. https://doi.org/10.3897/rio.6.e60893

Biodiversity, green infrastructure and ecosystem services: an introduction

Supplementary material 2 from: Tomaskinova J, Dicks L, Collier M, Geneletti D, Grace M, Longato D, Sadula R, Stoev P, Sapundzhieva A, Balzan MV (2020) Capacity-building and networking events for nature-based solutions and re-naturing in Malta. Research Ideas and Outcomes 6: e60893. https://doi.org/10.3897/rio.6.e60893

Identification of trees in a Maltese urban context