Heatwave Dynamics and Urban Cooling: From Risk Assessment to Nature-Based Solutions
Heat exposure, social vulnerability, and cooling solutions are usually studied apart. This thesis joins them: a heatwave risk index that combines several heat metrics with social vulnerability and works from Mississippi counties to Kathmandu wards, projections of urban heat for the 50 most populous U.S. cities, the double role of rain during heatwaves, and how much parks, ponds, and forests cool a dense city. Every method uses globally available data, so it can be applied anywhere.
Advisor
Prof. Rocky Talchabhadel
Degree
M.S. in Engineering (Civil), earned en route to the Ph.D.; GPA 4.0
Study areas
Mississippi; 50 U.S. cities; contiguous United States; Kathmandu Valley, Nepal
Papers from the thesis
J2, J4, J6, J8, J11
Chapter 2 · Framework development
Risk
An integrated heat risk framework, from Mississippi counties to Kathmandu wards
Five heatwave indices from ERA5 (1980–2022), heatwave number, mean temperature, hottest day, total days, and longest duration, are combined into one composite heatwave index, then joined with a social vulnerability index into a Heatwave Risk Index. ERA5 was first checked against ground stations in Mississippi, and the framework was then tested at ward level in the data-limited Kathmandu Valley (Figure 1).
Physical heat and social vulnerability overlap in northwest Mississippi: Bolivar County is the top hotspot, with Tallahatchie, Coahoma, and Washington close behind; 18 counties exceed a risk of 0.75 and 36 exceed 0.5.
Counties with higher African American populations and socioeconomic challenges face disproportionate heat risk, which neither dimension alone would reveal.
The same framework runs at county level in Mississippi and at ward level in the Kathmandu Valley.
Future heatwaves across the 50 most populous U.S. cities
Heatwave frequency, duration, and intensity, day and night, are projected for the near (2025–2054) and far (2065–2094) future under SSP2-4.5 and SSP5-8.5 with 17 selected climate models, and overlaid on population (Figure 2).
Heatwave frequency rises by 200–300% by the end of the century under SSP5-8.5.
Coastal cities get more frequent but less intense heatwaves than inland cities.
The Southwest is the most persistent hotspot, but risk escalates across the Southeast and Midwest, and the Miami metropolitan area faces some of the most severe future heatwave conditions.
Figure 2Projected change in annual heatwave number by region: (A, B) daytime; (C, D) nighttime; near and far future.
Across the contiguous United States, heatwaves are traced to how they end: how much rain terminates them, how often it rains during them, and what happens to humidity and the heat index afterwards, before and after 2000 (Figure 3).
Rain ends heatwaves, but it often raises humidity, so perceived heat stress can persist after the temperature drops.
Florida, the Gulf Coast, and the eastern United States see the most frequent rain during heatwaves.
The heat stress index during heatwaves increased after 2000 across the eastern United States: compound heat and humidity risk is growing faster than temperature alone suggests.
Figure 3Difference in average humidity on days after heatwaves, with and without precipitation; inset for major cities.
How much do parks, ponds, and forests cool the Kathmandu Valley?
Landsat 8 land surface temperature for the summers of 2018–2023, OpenStreetMap features (865 forests, 333 parks, and 42 ponds), population, and terrain are combined to measure the cooling of each blue-green space, how far it reaches, and how it depends on the neighborhood around it (Figures 4 and 5).
Cooling grows exponentially with the size of a park, pond, or forest.
Tree canopy matters most: a 1% increase in high canopy area corresponds to about 0.99 °C more cooling in small parks and 1.76 °C in the largest parks.
Large and very large parks cool by about 1.15 °C, but only up to 120 m away; forests reach their peak cooling of 1.2 °C at 300 m.
Clustering separates urbanized, transitional, and vegetation-dominated zones: the same feature cools far more in a green neighborhood than in a dense one, so cooling strategies have to fit the urban fabric.
Figure 4Kathmandu Valley: (A) blue-green spaces; (B) population density; (C) maximum temperature trend; (D) land cover, 2000 and 2019.Figure 5Temperature with distance from the edge: (A–D) parks by size; (E) ponds; (F) forests.
From the demand side of the water balance to the whole of it
Heat is the demand side of the water balance. The Ph.D. widened the view from heat to rainfall, storage, and their extremes, including a 76-year global heatwave record and compound heatwave–drought, with rain during heatwaves as one of several compound hazards. It then asked how open data like these can drive an open, physics-based flood model.