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CoastalDEM v2.1 is a high-resolution global digital elevation model (DEM) designed to improve the accuracy of coastal flood and sea level rise assessments. By employing a convolutional neural network (CNN) trained on NASA's ICESat-2 satellite lidar data and using NASADEM as a base, it significantly reduces the positive vertical bias common in satellite radar DEMs, particularly in densely populated urban areas where buildings often distort elevation readings. Validation against ICESat-2 and airborne lidar data from the US and Australia indicates that CoastalDEM v2.1 outperforms other leading global models like TanDEM-X and MERIT, especially in the critical elevation band below 5 meters.

The paper introduces EconWEAP, a model that integrates the State Wide Economic Agricultural Production Model (SWAP) and the Water Evaluation and Planning (WEAP) system to better reflect physical and behavioral responses to climate change. By using a step function approximation of water demand curves from SWAP within an iterative search process, the model estimates crop land allocation based on economic value and water availability. Application of EconWEAP to California's Central Valley demonstrated a significant increase in agricultural revenues compared to using WEAP alone, achieved through the economically optimal allocation of water while maintaining the same hydrology.

The document describes a water resource systems model for the Apalachicola–Chattahoochee–Flint, Alabama–Coosa–Tallapoosa, and Tombigbee River basins. The model integrates electricity generation, agricultural, and municipal water use to assess impacts on streamflow and water temperatures, specifically addressing thermal pollution from power plant cooling. Validation against 1980–2010 data shows the model robustly represents monthly average streamflow and water temperatures.

The paper details a new soil moisture module for the DO3SE model, designed to address a limitation in regional risk assessments where soil water deficits were previously assumed not to limit ozone flux. The module uses the Penman-Monteith energy balance method and empirical data on pre-dawn leaf water status to estimate the biological control of transpiration, testing four different methods ranging from simple soil water content relations to complex models incorporating hydraulic resistance and plant capacitance.

The authors developed the MILLENNIA model to address shortcomings in global soil models, specifically the lack of long-term non-equilibrium soil organic carbon accumulation and dynamic hydrological processes. Unlike traditional C-pool models, MILLENNIA uses peat cohorts and variable water table depth to drive carbon dynamics. Validation against a UK peatland site demonstrates that the model's predictions for soil organic carbon accumulation and peat ages align well with observations.