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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 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.