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Abstract Urban regions situated along major river systems are increasingly facing flood risks, driven by the combined effects of rapid urbanization and intensifying climate change. The Quad Cities region, comprising Davenport and Bettendorf in Iowa, and Rock Island, Moline, and East Moline in Illinois, is vulnerable to flood hazards caused by extreme precipitation, fluvial surges, and extensive impervious surfaces. Historical records indicate 10%–20% increase in annual precipitation, with a rise in high‐intensity rainfall. Projections under the SSP5‐8.5 scenario, using statistically downscaled MIROC6 data, predict a continued increase in short‐duration high‐magnitude rainfall events. To quantify flood inundation scenarios, this study developed a coupled hydrologic‐hydraulic (HH) model over a 35.5‐mile Mississippi River corridor. Simulations indicate that, without intervention, flood depths could rise by 20%–45% and the inundation extent of flooding could expand significantly in low‐lying areas of Rock Island and East Moline. To mitigate these risks, the study tested eight nature‐based solutions (NbS), including bioswales, rain gardens, riparian buffers, infiltration trenches, and detention basins. HH modeling showed that the combined implementation of NbS can reduce peak discharge by up to 69.4% and increase water infiltration by over 25%, resulting in an estimated 37% reduction in flooded areas by the end of the century. Through over 30 stakeholder interviews, three public forums, and participatory mapping workshops, residents identified priority flood zones and proposed NbS strategies. This integrated approach helped develop a streamlined framework that combines high‐resolution flood modeling with community‐led planning, creating robust and socially equitable adaptation pathways for riverine urban systems. 

Abstract Evaluation methods for Regional Climate Models (RCMs) commonly rely on point comparisons with observed meteorological fields, which provide limited understanding of the spatial and temporal representation of important factors affecting urban areas in models. These factors are not only complex but also difficult to differentiate, which complicates their analysis. This study thus develops an innovative approach using Empirical Orthogonal Function (EOF) analysis to compare urban heat island and precipitation patterns in RCM simulations with those from observations, taking advantage of the capacity of the method for data disaggregation. The method was tested on summer daily maximum and minimum temperature (T_maxand T_min) and precipitation (P) in the Chicago Metro Area (CMA). Using observed data, the EOF analysis on temperature consistently produced coherent patterns that reflect known impacts of urban environments on climate and weather. EOF evaluation of corresponding 4-km WRF simulations against observations confirmed a strong warm bias (~3°C) for simulated T_minin the urban area, as observed in point comparisons against stations; further analysis, however, suggested that the shape and time behavior of the urban pattern were well represented. EOF analysis on T_max, which showed no problems in the point comparison, revealed important differences in shape (urban area of influence on temperatures) and time [Principal Components (PC) correlation of −0.5] for the urban pattern between datasets, suggesting the need for model improvements. Results showed no systematic urban effects on summer P for the CMA for observations or simulations, but analysis of winter patterns suggested a possible urban enhancement on P over the city. 

Abstract This study focuses on the period from June 26 to 29, 2023, when record‐breaking Canadian wildfires severely impacted air quality in the Midwest United States. Using the Weather Research and Forecasting Model with Chemistry (WRF‐Chem) and four biomass‐burning data sets (Fire Inventory from NCAR version 1, Fire Inventory from NCAR version 2.5, Quick Fire Emissions Data set [QFED], and Regional ABI‐VIIRS Emission), we analyzed aerosol transport from Canada to the US and assessed the model's accuracy in predicting , , and aerosol weather feedback. Model simulations were compared with ground‐based and remote sensing observations as well as field measurements from the Community Research on Climate and Urban Science (CROCUS) project. Our findings show that the movement of a low‐pressure system from the Great Lakes to the Atlantic, combined with the high‐pressure system over the Atlantic, caused the transport of aerosols from Canadian wildfires to the US. Results show WRF‐Chem significantly underestimated key atmospheric components: aerosol optical depth (AOD) by over 50%, by 65%–90% and peak concentrations by 50%–55% across four biomass burning data sets. Additionally, CO and concentrations were underpredicted. The substantial underestimation of led to an overestimation of temperature by up to 3.6C primarily due to excessive downward shortwave radiation, which resulted from the underestimation of direct aerosol effects and an increase in sensible heat flux. Among the biomass‐burning data sets, QFED produced the most accurate AOD and predictions due to improved wildfire emission estimates, leading to a 1.0 to 1.5C reduction in temperature overestimation during the daytime. These findings underscore the need for improving wildfire emission estimates for trace gases and aerosols to enhance air quality and weather feedback predictions. 

Abstract The effects of heat exposure on negative affect are thought to be central to the observed relationships between hot summer days and deleterious outcomes, such as violent crime or mental health crises. As these relationships are likely to be magnified by the effects of climate change, a better understanding of how consistent or variable the effects of hot weather on affective states is required. The current work combines data gathered from an ecological momentary assessment (EMA) study on individuals’ thermal perceptions, comfort, and affective states in outdoor environments during their daily lives with high spatiotemporal resolution climate-modeled weather variables. Using these data, associations between objective weather variables (temperature, humidity, etc.), perceived heat (thermal perception and comfort), and affective states are examined. Overall, objective weather data reasonably predicted perception and comfort, but only comfort predicted negative affective states. The variance explained across individuals was generally very low in predicting negative affect or comfort, but within-person variance explained was high. In other words, while there may be a relatively consistent relationship between temperature and psychological experience for any given person, there are significant individual differences across people. Age and gender were examined as moderators of these relationships, and while gender had no impact, participant age showed several significant interactions. Specifically, while older adults tended to experience more thermal discomfort and perceived higher temperatures as hotter, the relationship between discomfort and negative affect was lower in older adults. Taken together, these results emphasize the importance of thermal discomfort specifically in predicting negative affect, as well as the high inter-individual variability in thermal perceptions and comfort for the same ambient temperatures. 

Abstract The vertical dimensions of urban morphology, specifically the heights of trees and buildings, exert significant influence on wind flow fields in urban street canyons and the thermal environment of the urban fabric, subsequently affecting the microclimate, noise levels, and air quality. Despite their importance, these critical attributes are less commonly available and rarely utilized in urban climate models compared to planar land use and land cover data. In this study, we explicitly mapped theheight oftreesandbuildings (HiTAB) across the city of Chicago at 1 m spatial resolution using a data fusion approach. This approach integrates high-precision light detection and ranging (LiDAR) cloud point data, building footprint inventory, and multi-band satellite images. Specifically, the digital terrain and surface models were first created from the LiDAR dataset to calculate the height of surface objects, while the rest of the datasets were used to delineate trees and buildings. We validated the derived height information against the existing building database in downtown Chicago and the Meter-scale Urban Land Cover map from the Environmental Protection Agency, respectively. The co-investigation on trees and building heights offers a valuable initiative in the effort to inform urban land surface parameterizations using real-world data. Given their high spatial resolution, the height maps can be adopted in physical-based and data-driven urban models to achieve higher resolution and accuracy while lowering uncertainties. Moreover, our method can be extended to other urban regions, benefiting from the growing availability of high-resolution urban informatics globally. Collectively, these datasets can substantially contribute to future studies on hyper-local weather dynamics, urban heterogeneity, morphology, and planning, providing a more comprehensive understanding of urban environments. 

Abstract The accurate modeling of urban microclimate is a challenging task given the high surface heterogeneity of urban land cover and the vertical structure of street morphology. Recent years have witnessed significant efforts in numerical modeling and data collection of the urban environment. Nonetheless, it is difficult for the physical‐based models to fully utilize the high‐resolution data under the constraints of computing resources. The advancement in machine learning (ML) techniques offers the computational strength to handle the massive volume of data. In this study, we proposed a modeling framework that uses ML approach to estimate point‐scale street‐level air temperature from the urban‐resolving meso‐scale climate model and a suite of hyper‐resolution urban geospatial data sets, including three‐dimensional urban morphology, parcel‐level land use inventory, and weather observations from a sensor network. We implemented this approach in the City of Chicago as a case study to demonstrate the capability of the framework. The proposed approach vastly improves the resolution of temperature predictions in cities, which will help the city with walkability, drivability, and heat‐related behavioral studies. Moreover, we tested the model's reliability on out‐of‐sample locations to investigate the modeling uncertainties and the application potentials to the other areas. This study aims to gain insights into next‐gen urban climate modeling and guide the observation efforts in cities to build the strength for the holistic understanding of urban microclimate dynamics.