The Integration of Climate Data into the SAVi model

The document details how climate indicators from the Copernicus Climate Data Store (CDS) are integrated into the SAVi tool to improve the analysis of infrastructure resilience. It covers a literature review of climate impacts on energy, irrigation, wastewater, buildings, roads, and nature-based infrastructure, providing specific equations to link climate variables to asset performance. The report includes simulation results for Johannesburg, South Africa, demonstrating how climate variables like temperature and precipitation affect load factors, thermal efficiency, and maintenance costs, ultimately impacting the economic viability and societal costs of these assets.

• Wind power generation is sensitive to wind speed; in Scotland, wind speed variations between -20 and +20 m/s impact turbine output by ranges from -26.06% to +13.66%.
• Solar PV efficiency is negatively impacted by temperature; an increase of 1°C can decrease efficiency by up to 2% in some studies, while an increase of 1 unit (W/m2) of irradiance increases output by 5%.
• Hydropower potential is highly sensitive to precipitation and river discharge, where a 1% change in these variables typically results in a 1% change in power generation.
• Gas turbine power output decreases as ambient temperature rises; for every 1 Kelvin increase above ISO conditions, a turbine can lose 0.1% in thermal efficiency and 1.47 MW of gross power output.
• Coal power plant load factors are negatively affected by moisture; a 1% increase in total moisture can reduce the plant load factor by 4%.
• Nuclear power plant efficiency and output decrease with rising cooling water temperatures; an increase of 1°C can lead to a 0.12% to 0.16% decrease in thermal efficiency and a 0.39% to 0.45% decrease in net power output.
• Electrical grid capacity and transformer efficiency decline with temperature; network capacity falls by approximately 1% for each 1°C increase.
• Crop yields for major staples decrease with temperature increases: wheat by 6.0%, rice by 3.2%, maize by 7.4%, and soybean by 3.1% per 1°C increase in global mean temperature.
• In Johannesburg, under the IPSL RCP 8.5 scenario, net irrigation requirements per hectare for maize are projected to increase by 23% by the 2090–2100 decade compared to 1980–1990.
• Wastewater treatment nutrient removal is temperature-dependent; for nitrogen, a change of -1.5 to +5°C can impact removal efficiency by -6% per 1°C decrease in influent sewage temperature.
• Rainwater harvesting potential in Johannesburg is significantly higher when using CDS climate data compared to constant seasonal values, with average annual yields of 3,068.6 litres/m2 versus 134.7 litres/m2.
• Rooftop solar PV generation in Johannesburg is projected to decrease by 5.4% in load factor due to temperature increases, resulting in a cumulative reduction of 90.21 GWh over 80 years for 1 MW of capacity.
• In Johannesburg, heating degree days (HDD) are forecasted to decline by 82% over 80 years, while cooling degree days (CDD) will increase from zero to 204 days per year.
• Road maintenance costs increase with precipitation; for unpaved roads, maintenance costs increase by 0.8% for every 1% increase in maximum monthly precipitation.
• Weather conditions increase road accident probability: a 100 mm increase in monthly rainfall increases probability by 0.2%–0.3%, and a 1°C increase in monthly temperature increases it by 1%–2%.
• Urban green infrastructure can significantly reduce stormwater runoff; for example, green roofs in Chicago retained over 75% of the volume from a one-inch storm.
• Vegetation cover reduces urban air temperatures; an average reduction of 1.635°C is observed for a 25% increase in vegetation cover.
• In Johannesburg, increasing vegetation cover to 75% could potentially mitigate temperature-related labour productivity impacts entirely.

APA

Perera, O. (2020). The Integration of Climate Data into the SAVi model. International Institute for Sustainable Development. https://www.iisd.org/system/files/2020-09/intregration-climate-data-savi-model.pdf

Chicago

Perera, Oshani. The Integration of Climate Data into the SAVi model. International Institute for Sustainable Development, 2020. https://www.iisd.org/system/files/2020-09/intregration-climate-data-savi-model.pdf.

Wikipedia

{{cite report |last1=Perera |first1=Oshani |title=The Integration of Climate Data into the SAVi model |publisher=International Institute for Sustainable Development |date=September 2020 |url=https://www.iisd.org/system/files/2020-09/intregration-climate-data-savi-model.pdf |access-date=23 October 2026 |via=Climate Insights Directory}}

BibTeX

@techreport{perera2020integration, author = {Perera, Oshani}, title = {{The Integration of Climate Data into the SAVi model}}, institution = {International Institute for Sustainable Development}, year = {2020}, month = sep, url = {https://www.iisd.org/system/files/2020-09/intregration-climate-data-savi-model.pdf}, urldate = {2026-10-23}, note = {Indexed by Climate Insights Directory} }