Civil and Environmental Engineering

Editors' Note Volume 9 Issue 2

Abstract Soil moisture‐precipitation coupling critically shapes Earth's water and energy cycles, yet it remains difficult to quantify because land‐atmosphere interactions are nonlinear, multivariate, and state‐dependent. This study applies the High‐Dimensional Model Representation (HDMR) framework to observation‐based data sets over the contiguous United States to diagnose temporal SM‐precipitation amount coupling, defined here as the influence of morning land‐atmosphere states on same‐day afternoon rainfall. HDMR decomposes structural, correlative, and cooperative controls of morning precursors and shows that traditional linear approaches, such as correlation analysis, underestimate coupling strength, explaining only 6%–8% of precipitation variance in regions where HDMR identifies soil moisture (SM) contributions of up to about 20%. The first‐order decomposition reveals a direct wet‐soil advantage in the eastern Great Plains, while convective available potential energy (CAPE) over Texas and land‐surface temperature over Louisiana dominate in separate instability‐ and surface‐temperature‐controlled regimes, respectively. Beyond direct effects, the strongest second‐order signal is a localized SM‐CAPE interaction over southern Oklahoma, where dry soils combined with high CAPE enhance afternoon precipitation through a thermodynamic‐triggering pathway. Thus, positive and negative SM effects can coexist over the Great Plains within temporal precipitation‐amount coupling through distinct physical pathways. Finally, using the observation‐based HDMR diagnostics as a benchmark, we evaluate the convection‐permitting CONUS404 simulation. CONUS404 qualitatively reproduces the main functional structures of SMPC, including the wet‐soil first‐order response and dry‐soil‐high‐CAPE synergy, but systematically underestimates their coupling strength. These findings underscore the need for nonlinear diagnostics to improve the representation of sub‐daily land‐atmosphere coupling in weather and climate models. Plain Language Summary Soil moisture plays a critical role in local weather by controlling the release of heat and water vapor into the atmosphere. However, because these land‐atmosphere interactions are complex, standard statistical tools often struggle to measure them accurately. We apply a nonlinear functional decomposition method to observations across the United States to quantify how morning soil conditions influence afternoon rainfall. We then use these results to evaluate whether convection‐permitting models accurately simulate these relationships. We find that soil moisture explains up to 20% of rainfall variability compared to only 6%–8% estimated by traditional methods. While we identify distinct regions where either wet or dry soils favor rainfall, the convection‐permitting model correctly simulates the patterns but systematically underestimates their strength. These results suggest that current models undervalue the land surface's influence on precipitation. Capturing these complex interactions is essential for improving precipitation forecasts and climate projections. Key Points High Dimensional Model Representation disentangles structural, correlative, and cooperative effects in soil moisture‐precipitation coupling Observations reveal wet‐soil coupling in the eastern Great Plains and dry‐soil, thermally driven coupling over southern Oklahoma Convection‐permitting simulations reproduce the spatial sign of coupling but systematically underestimate the coupling strength

We examine electric vehicle (EV) charging location preferences by investigating how drivers allocate monthly charging sessions across home, workplace, and public locations. In contrast to earlier studies that treat charging location as a one-time, discrete choice, we examine preferences for combinations of all three locations over a monthly period. We employ a multiple discrete–continuous extreme value (MDCEV) model using stated preference data from 881 employed EV drivers across the United States. Results show that 88.9% of charging decisions involve multiple locations. Home charging is preferred overall, followed by workplace and public. Drivers with private driveways, single-family homes, or solar panels, as well as older adults have higher home charging preferences, though battery storage moderates the pull of solar toward home. Urban residents rely more on public charging, while workplace charging is highly price-elastic, offering employers a demand management lever. Our study underscores the importance of examining charging behavior as a portfolio allocation decision.

Since 2000, wildfires in the western United States have increased in both frequency and intensity due to hydro-meteorological shifts, prolonged drought, and expanded human activity. Although geostationary systems enable rapid detection and moderate-resolution sensors offer broad coverage, a gap persists for high-spatial-resolution thermal observations that can assess fine-scale fire behavior. The ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS) provides 70-meter land surface temperature (LST) observations with 1–5 days average revisit intervals, providing enhanced spatial detail for active-fire analysis. In this study, we evaluate the capability of ECOSTRESS Level 2 LST data, which uses 5 thermal bands, to characterize wildfire behavior across four California fires: Carr (2018), Kincade (2019), August Complex (2020), and Dixie (2021). We developed a consistent framework to identify hotspots (LST ≥ 60 ° C ), estimate a satellite-derived rate-of-spread (ROS) proxy using the 95th percentile radial expansion from ignition, and assess relationships between mean active-fire temperature and mean post-fire burn severity (dNBR). Across all fires, median hotspot temperatures ranged from 62 to 77 °C, while 95th percentile values ranged from 117 to 179 °C, indicating right-skewed radiometric distributions. The ROS proxy showed directional variability, with median values typically between 0.05 to 3 km day−1 and substantial heterogeneity among quadrants. Regression indicates consistent positive relationships between mean active-fire temperature and mean burn severity, with R 2 values from 0.51 to 0.88. These relationships were stronger in smaller, short-duration fires. Additionally, the ECOSTRESS derived hotspots were validated against Fire Radiative Power (FRP) data derived from VIIRS, and it was demonstrated that ECOSTRESS-derived thermal anomalies are spatially coherent with independently derived FRP intensity patterns. Our findings indicate that ECOSTRESS provides valuable high-spatial-resolution thermal observations that can resolve fire growth patterns and link active-fire thermal dynamics to subsequent burn severity.

Seismic resilience (SR) has emerged as a critical focus in earthquake engineering to evaluate the ability of structures to endure, recover from, and adapt to seismic events. This study presents an entropy-based multi-criteria approach for selecting optimal intensity measures (IMs) to assess SR of structures. Eight representative IMs, derived from time histories and response spectrum are evaluated. Incremental dynamic analysis is conducted on a reinforced concrete structure, using engineering demand parameters such as the maximum inter-story drift and floor acceleration to generate fragility curves via a probabilistic seismic demand model. The optimal IMs are identified through a multi-criteria decision-making process, with scores calculated using the entropy weight method to incorporate factors such as efficiency, proficiency, and uncertainty based on information entropy. An effective SR framework is derived from fragility results. The findings indicate that peak ground velocity and spectral IMs are the most effective, while energy-related IMs underestimate SR. The study highlights the importance of optimizing IMs for more accurate seismic resilience assessments. The proposed entropy-based multi-criteria approach is shown to be both reliable and effective for selecting optimal IMs in this context.