This study examines how climatic factors influence the predictive power of LEED credits in determining certification outcomes for hotel buildings across the United States. Using data from 259 LEED-NC v2009 certified hotels, project-level information was integrated with 30-year climate normals from the PRISM database and Building America climate zones. A three-step hierarchical linear regression was conducted to identify the LEED credits that most strongly predict total certification points while controlling for project size, certification year, and baseline climatic conditions, and to test whether climatic factors moderate these relationships. Regularized Linear Regression (LASSO) was then applied to address multicollinearity and assess model stability, followed by Support Vector Regression (SVR) to capture potential nonlinear relationships. This integrated methodological framework, combining hierarchical regression for interpretability, LASSO for coefficient stability, and Support Vector Regression for nonlinear verification, provides a novel, multi-dimensional assessment of climate-sensitive credit behavior at the individual credit level. Results show that energy- and site-related credits, particularly Optimize Energy Performance (EA1), On-Site Renewable Energy (EA2), Green Power (EA6), and Alternative Transportation (SS4), consistently dominate LEED performance across all climate zones. In contrast, indoor environmental quality credits exhibit modest but significant climate sensitivity: higher mean temperatures reduce the contribution of Increased Ventilation (EQ2) while slightly enhancing Outdoor Air Delivery Monitoring (EQ1). Cross-model consistency confirms the robustness of these findings. The findings highlight the need for climate-responsive benchmarking of indoor environmental quality credits to improve regional equity and advance the next generation of climate-adaptive LEED standards.

Tropical forests are major contributors to the global carbon budget but are affected by disturbances such as hurricanes, which cause extensive yet spatially variable tree damage and mortality. High-resolution maps of forest aboveground biomass (AGB) and its temporal change aid in quantifying disturbance impacts, assessing resilience, and supporting forest management. This study presents wall-to-wall, high-resolution mapping of pre- and post-hurricane AGB and AGB change across Puerto Rico. The maps represent forest AGB measured 0–2 years before and after two major hurricanes (Irma and Maria), as well as longer-term conditions up to four years post-disturbance. AGB was modeled using Random Forest (RF) algorithms that integrated Forest Inventory and Analysis (FIA) plot data with canopy height and cover derived from discrete-return LiDAR, multi-temporal satellite imagery, and additional geospatial predictors. Model performance was evaluated using a 10% holdout dataset. Predicted versus observed regressions yielded, at 10 m and 90 m spatial resolutions, respectively, r = 0.75 and 0.79 with model residual mean standard deviation (RMSD) = 87.7 and 39.2 Mg ha⁻¹ for pre-hurricane AGB, and r = 0.77 and 0.74 with RMSD = 69.7 and 58.1 Mg ha⁻¹ for post-hurricane AGB. AGB change models at 10 m and 90 m resolutions yielded r = 0.58 and 0.73 with RMSD = 17.0 and 18.7 Mg ha⁻¹, respectively. Ten-fold cross-validation produced stronger correlations and reduced RMSD values. Frequency distributions of mapped pixels of forest AGB and AGB change, in comparison with previously published maps and island-wide field-based estimates, indicate that, although hurricane-driven biomass reductions of up to 20% were recorded in field data, patterns consistent with longer-term recovery from historical deforestation are evident within four years after the hurricanes. The 10 m maps capture fine-scale heterogeneity in canopy damage and regrowth, whereas the 90 m maps emphasize broader regional patterns. This integrated framework provides a transferable approach for monitoring forest structure and biomass dynamics in disturbance-prone tropical ecosystems.

Amazon Basin deforestation has been driven by agricultural expansion and other developments. Interactions between deforestation, fire, climate, and drought have led to changes in precipitation patterns and river discharge. Previous work suggests that these changes are amplified by land use. Therefore, it is important to understand the degree of change in precipitation patterns and rainy season characteristics. We used long term daily gauge measurements and remote sensing data to analyze the variability and seasonality of rainfall patterns over the Amazon Basin. We focused on the PERSIANN-CDR and CHIRPS precipitation datasets from 1983 to 2018 to quantify trends in predefined indices. The indices that were analyzed to assess variability of precipitation are NDD (Number of dry days); NXE (number of extreme events) during both wet and dry seasons; ORS (Onset of Rainy Season); and ERS (End of Rainy Season). We analyzed the trends for statistical significance and spatial similarity to identify hot spots of change. To connect pattern to process, we also simulated the land-atmosphere system using WRF to assess coupling strength and causality. We are running the simulation using CFSR 2010, with grid resolution of 16 km with the convection scheme active to capture small scale convective rainfall. Previous evidence has suggested an increasing trend on NDD during the dry season, a shift to a later onset and later cessation of the rainy season window, and an increasing trend in the NXE during both wet and dry seasons. The significance and spatial distribution of changes may vary over the region, but we anticipate that in the area with the largest percent of deforestation we will see the highest amount of changes in precipitation.

Climatic conditions fundamentally shape building energy loads, yet their influence on energy benchmarking outcomes in certified buildings is often treated implicitly or through coarse climate zoning. This study examines how long-term climatic factors are associated with the achievement of the Optimize Energy Performance credit (EA7) in LEED v4 New Construction projects across the United States. A dataset of 779 certified buildings was analyzed using spatially matched climatological variables, including heating and cooling degree days, solar resource availability, precipitation, wind speed, temperature, and humidity, derived from high-resolution datasets. To capture both interpretable and non-linear climate-energy relationships, a multi-method framework integrating multiple linear regression, Random Forest regression, and hierarchical clustering was employed. Across methods, results consistently show that moisture- and load-related factors, particularly precipitation and wind exposure, are dominant negative correlates of EA7 achievement, while global tilted irradiance is a strong positive contributor. Cooling demand exhibits a modest negative association, whereas heating demand and mean temperature play comparatively limited roles once multiple climatic variables are considered jointly. Random Forest models substantially outperform linear regression, indicating non-linear interactions and threshold effects in climate-energy relationships. Clustering further identifies two distinct climate-performance regimes, separating solar-rich, low-load environments from wetter, wind-exposed climates with systematically lower EA7 outcomes. These findings demonstrate that identical energy credit scores can reflect markedly different levels of technical difficulty under varying climatic conditions. Explicitly linking climate metrics to modeled energy performance outcomes advances climate-aware interpretation of benchmarking results and supports more physically grounded evaluation of building energy performance across regions.