Significance: The significance of forest mortality on ecosystem services, and water, carbon, and nutrient cycling is indubitable. While there is a general agreement that climate change-induced heat and drought stress is expected to intensify forest mortality, the concurrent influence of changes in atmospheric humidity and CO ₂ concentration remains unclear. Here, the response of mortality risk to projected climate change is evaluated in 13 biomes across the globe. Our results show that increasing specific humidity and CO ₂ concentration partially offset the intensification of risk by changing precipitation and air temperature. The risk response is also mediated by plant hydraulic traits. The study provides a mechanistic foundation for estimating future responses of forest mortality risk, which can facilitate ecosystem management.

The National Water Model (NWM) is a key tool for flood forecasting, planning, and water management. Key challenges facing the NWM include calibration and parameter regionalization when confronted with big data. We present two novel versions of high‐resolution (∼37 km²) differentiable models (a type of hybrid model): one with implicit, unit‐hydrograph‐style routing and another with explicit Muskingum‐Cunge routing in the river network. The former predicts streamflow at basin outlets whereas the latter presents a discretized product that seamlessly covers rivers in the conterminous United States (CONUS). Both versions use neural networks to provide a multiscale parameterization and process‐based equations to provide a structural backbone, which were trained simultaneously (“end‐to‐end”) on 2,807 basins across the CONUS and evaluated on 4,997 basins. Both versions show great potential to elevate future NWM performance for extensively calibrated as well as ungauged sites: the median daily Nash‐Sutcliffe efficiency of all 4,997 basins is improved to around 0.68 from 0.48 of NWM3.0. As they resolve spatial heterogeneity, both versions greatly improved simulations in the western CONUS and also in the Prairie Pothole Region, a long‐standing modeling challenge. The Muskingum‐Cunge version further improved performance for basins >10,000 km². Overall, our results show how neural‐network‐based parameterizations can improve NWM performance for providing operational flood predictions while maintaining interpretability and multivariate outputs. The modeling system supports the Basic Model Interface (BMI), which allows seamless integration with the next‐generation NWM. We also provide a CONUS‐scale hydrologic data set for further evaluation and use.

Nature-based solutions (NbSs) for water involve using or mimicking natural processes to contribute to the improved management of water. Although NbSs are gaining a significant amount of scientific attention, to ensure their wide usage for enhancing groundwater recharge, there is a need for clear documentation outlining their benefits and barriers. In this study, a systematic literature review was carried out to evaluate the application of NbSs for managing groundwater recharge. First, NbS approaches were classified into two broad groups: managed aquifer recharge (MAR) and ancillary recharge methods (ARMs). MAR includes all activities that intentionally enhance the recharge of an aquifer for later recovery, while ARMs include all the remaining NbSs wherein recharge enhancement is a secondary goal. In 50 out of 61 reviewed studies, MAR was reported to be successful in increasing recharge. However, in the remaining studies, reductions in recharge rates were reported. Most of the NbSs that failed to improve groundwater recharge were from the ARMs group. This group had little consensus among studies regarding the effectiveness of NbSs on groundwater recharge. In this study, we also identified opportunities and challenges, such as gaps in our knowledge of NbSs’ effectiveness, their assessment in long-term, cost–benefit analysis and scalability. Addressing these challenges will further enhance the efficiency of NbSs, which indeed is a promising alternative for enhancing groundwater resources.

The timing and amount of snowmelt from forested watersheds is influenced by the total radiation reaching the forest floor. In order to understand and predict the impact of natural or anthropogenic changes in vegetation density on snow energetics, it is important to first quantify the relationship between vegetation density and net snow cover radiation. A physically based forest radiation model (FoRM) was developed to quantify net radiation on the forest floor and its dependence on vegetation density in midlatitude coniferous forests. For clear sky conditions, net radiation on the horizontal forest floor frequently exhibits a nonmonotonic decreasing then increasing trend with increasing vegetation density. The relationship turns monotonically increasing when cloudiness is considered. Variations in slope and aspect were also found to influence the behavior of radiation in relation to changing vegetation density. Net radiation increases with increasing slope for southerly aspects and decreases with northerly facing aspects. For clear sky conditions on south facing slopes, the optimal vegetation density at which radiation is minimized increases both with slope and toward more southerly aspects. In contrast, on north facing slopes, the vegetation density where net radiation is minimized decreases with increasing slope angle. When sky cloudiness is considered, for increasing south facing slopes, the relationship becomes relatively complex with the appearance of a local minimum at intermediate vegetation densities. The results have implications for prediction of snowmelt dynamics in complex terrain and development of forest thinning strategies to modulate snowmelt timing in mountainous environments.