Explore the vast range of titles available.

Environmental change is accelerating in the 21st century, but how multiple drivers may interact to alter forest resilience remains uncertain. In forests affected by large high-severity disturbances, tree regeneration is a resilience linchpin that shapes successional trajectories for decades. We modeled stands of two widespread western U.S. conifers, Douglas-fir (Pseudotsuga menziesii var. glauca), and lodgepole pine (Pinus contorta var. latifolia), in Yellowstone National Park (Wyoming, USA) to ask (1) What combinations of distance to seed source, fire return interval, and warming-drying conditions cause postfire tree-regeneration failure? (2) If postfire tree regeneration was successful, how does early tree density differ under future climate relative to historical climate? We conducted a stand-level (1 ha) factorial simulation experiment using the individual-based forest process model iLand to identify combinations of fire return interval (11–100 yr), distance to seed source (50–1,000 m), and climate (historical, mid-21st century, late-21st century) where trees failed to regenerate by 30-yr postfire. If regeneration was successful, we compared stand densities between climate periods. Simulated postfire regeneration were surprisingly resilient to changing climate and fire drivers. Douglas-fir regeneration failed more frequently (55%) than lodgepole pine (28% and 16% for non-serotinous and serotinous stands, respectively). Distance to seed source was an important driver of regeneration failure for Douglas-fir and non-serotinous lodgepole pine; regeneration never failed when stands were 50 m from a seed source and nearly always failed when stands were 1 km away. Regeneration of serotinous lodgepole pine only failed when fire return intervals were ≤20 yr and stands were far (1 km) from a seed source. Warming climate increased regeneration success for Douglas-fir but did not affect lodgepole pine. If regeneration was successful, postfire density varied with climate. Douglas-fir and serotinous lodgepole pine regeneration density both increased under 21st-century climate but in response to different climate variables (growing season length vs. cold limitation). Results suggest that, given a warmer future with larger and more frequent fires, a greater number of stands that fail to regenerate after fires combined with increasing density in stands where regeneration is successful could produce a more coarse-grained forest landscape.

We present a probability based theoretical scheme for building process‐based models of uncertain hydrological systems, thereby unifying hydrological modeling and uncertainty assessment. Uncertainty for the model output is assessed by estimating the related probability distribution via simulation, thus shifting from one to many applications of the selected hydrological model. Each simulation is performed after stochastically perturbing input data, parameters and model output, this latter by adding random outcomes from the population of the model error, whose probability distribution is conditioned on input data and model parameters. Within this view randomness, and therefore uncertainty, is treated as an inherent property of hydrological systems. We discuss the related assumptions as well as the open research questions. The theoretical framework is illustrated by presenting real‐world and synthetic applications. The relevant contribution of this study is related to proposing a statistically consistent simulation framework for uncertainty estimation which does not require model likelihood computation and simplification of the model structure. The results show that uncertainty is satisfactorily estimated although the impact of the assumptions could be significant in conditions of data scarcity. Key Points Unified approach to hydrological modeling and uncertainty assessment Uncertainty assessment for models of any complexity Stochastic process‐based modelling of uncertain systems

Regulated rivers can experience sharp temperature variations induced by intermittent hydropower production (thermopeaking). To mitigate ecological impacts, dam operators need to assess the impacts of hydropeaking on stream temperature, and to test scenarios that might reduce them. While stream temperature modeling has been investigated in numerous studies, few have systematically assessed how integrated processes and their representation affect model performance, and models capable of capturing both sub‐hourly variations and long‐term thermal dynamics remain a challenge. Herein, a stream temperature model within the HEC‐RAS platform was used to model the thermal regime of a regulated river in Switzerland, with a 10‐min timestep over the annual time‐scale and for a 22‐km long reach; and for which we had installed a network of stream temperature sensors. While the initial model demonstrated an acceptable performance at the yearly scale (Mean Absolute Error: 0.78–2.10°C and Kling‐Gupta Efficiency: 0.55–0.85), this was not the case at the daily or seasonal time‐scales. Two model corrections were found to be crucial; (a) the correction of potential incoming solar radiation for local shading; and (b) the representation of the heat flux linked to water‐sediment exchanges. With these two corrections, the annual performance improved (MAE: 0.48–0.83°C and KGE: 0.85–0.93) as did the daily and seasonal performance. Although physically based, the model required calibration, underscoring the importance of high‐quality in situ temperature data. The resulting model proves effective for practical applications in hydropower mitigation and river temperature management under complex flow regimes.

A monthlong time series of wave, current, salinity, and suspended‐sediment measurements was made at five sites on a transect across the Mouth of Columbia River (MCR). These data were used to calibrate and evaluate the performance of a coupled hydrodynamic and wave model for the MCR based on the Delft3D modeling system. The MCR is a dynamic estuary inlet in which tidal currents, river discharge, and wave‐driven currents are all important. Model tuning consisted primarily of spatial adjustments to bottom drag coefficients. In combination with (near‐) default parameter settings, the MCR model application is able to simulate the dominant features in the tidal flow, salinity and wavefields observed in field measurements. The wave‐orbital averaged method for representing the current velocity profile in the wave model is considered the most realistic for the MCR. The hydrodynamic model is particularly effective in reproducing the observed vertical residual and temporal variations in current structure. Density gradients introduce the observed and modeled reversal of the mean flow at the bed and augment mean and peak flow in the upper half of the water column. This implies that sediment transport during calmer summer conditions is controlled by density stratification and is likely net landward due to the reversal of flow near the bed. The correspondence between observed and modeled hydrodynamics makes this application a tool to investigate hydrodynamics and associated sediment transport. Key Points Investigate the hydrodynamic processes at the mouth of the Columbia River Present the Mega Transect data set Present a validation of the Delft3D flow‐wave model

The hypoxic zone in the northern Gulf of Mexico is among the most dramatic examples of impairments to aquatic ecosystems. Despite having attracted substantial attention, management of this environmental crisis remains challenging, partially due to limited monitoring to support model development and long-term assessments. Here, we leverage new geostatistical estimates of hypoxia derived from nearly 150 monitoring cruises and a process-based model to improve characterization of controlling mechanisms, historic trends, and future responses of hypoxia while rigorously quantifying uncertainty in a Bayesian framework. We find that November–March nitrogen loads are important controls of sediment oxygen demand, which appears to be the major oxygen sink. In comparison, only ~23% of oxygen in the near-bottom region appears to be consumed by net water column respiration, which is driven by spring and summer loads. Hypoxia typically exceeds 15,600 km² in June, peaks in July, and declines below 10,000 km² in September. In contrast to some previous Gulf hindcasting studies, our simulations demonstrate that hypoxia was both severe and worsening prior to 1985, and has remained relatively stable since that time. Scenario analysis shows that halving nutrient loadings will reduce hypoxia by 37% with respect to 13,900 km² (1985–2016 median), while a +2°C change in water temperature will cause a 26% hypoxic area increase due to enhanced sediment respiration and reduced oxygen solubility. These new results highlight the challenges of achieving hypoxia reduction targets, particularly under warming conditions, and should be considered in ecosystem management.

Over the last centuries, human activities have exerted increasing pressures on the environment, leading to drastic alterations in the functioning of freshwater bodies (e.g., eutrophication). Global biogeochemical models have proven crucial to investigate interactions between humans, hydrology, and water quality of surface fresh waters. However, most do not account for high‐resolution spatial and temporal variability within watersheds, and they typically lack any representation of benthic dynamics that can drive pollution legacy effects. We present here the Integrated Model to Assess the Global Environment‐Dynamic Global Nutrient Model (IMAGE‐DGNM), which couples global, spatially explicit hydrology and integrated assessment models with process‐based biogeochemistry in surface fresh waters. The new Dynamic In‐Stream Chemistry (DISC) module calculates advective transport from headwaters to estuaries, processes in the water column and in bed sediments, as well as the exchanges between these two compartments. As application examples of IMAGE‐DGNM, we simulate sediment dynamics and nitrogen cycling in two large river basins. We assess in‐stream concentration time series at specific locations, and identify governing processes in transfers along the aquatic continuum. Results highlight the importance of benthic dynamics in watersheds highly perturbed by damming. The implementation of such dynamics within IMAGE‐DGNM allows for including the temporal effect of pollution legacies in large scale water quality studies and shifts in pollutant speciation along river continua. This new framework therefore incorporates new features for large basin to global scale studies that are crucial to better predict the effects on receiving ecosystems and evaluate future environmental management pathways. Plain Language Summary Humans have strongly modified the functioning of the Earth's surface fresh waters, through pollution emissions and infrastructure, which has led to widespread ecological deterioration. Over the past decades, our understanding of the interactions between humans and the environment has been translated into models to investigate future sustainable pathways. However, large‐scale water quality models are usually too coarse to identify spatiotemporal pollution hotspots within river networks. Most of all, they lack any representation of pollution remobilization from bed sediments, which can delay the response to mitigation measures. To bridge these gaps, we developed a new tool simulating in‐stream pollutant transfer and transformation processes, allowing for the assessment of changes in the water quality over time within whole river networks. This tool is applicable globally and is applied here to two large watersheds to simulate sediment and nitrogen dynamics. Our results show that including processes in bed sediments is crucial to correctly assess water quality in heavily dammed river networks, such as the Mississippi. The new tool is of great importance for future projections and policy development. It will allow for identifying efficient mitigation options, pinpointing vulnerable areas within global river networks, and assessing the time needed for freshwaters ecosystems' recovery. Key Points IMAGE‐DGNM is a new spatially explicit, globally applicable framework that dynamically simulates in‐stream biogeochemical processes Simulated compounds, transformation and exchange processes, and their parameterization are user defined IMAGE‐DGNM can be used for hindcasting and projections, including on long‐term pollution legacy effects in fresh waters

Endodormancy and the related chilling requirement synchronize the seasonal development of trees from the boreal and temperate regions under the climatic conditions prevailing at their native growing sites. The phenomenon of endodormancy has been known at the whole-plant level for 100 years, and in the last couple of decades, insights into the physiological and molecular basis of endodormancy and its release have also been obtained. Intriguingly, recent studies have shown experimentally that subtropical trees also show endodormancy and a chilling requirement. Motivated by the climatic differences between the subtropical and more northern zones, here we address the similarities and differences in endodormancy between trees growing in the subtropical zone and those growing in more northern zones.

Green manure (GM) enhances the ecological services in agricultural ecosystems, including soil health and carbon sequestration. However, its effect on regional methane (CH 4 ) emissions from paddy fields is unclear. Here we clarify the impacts of GM rotation by combining process-based modeling with microbial gene abundance information and coordinated distributed observations at 14 sites in southern China. We found that GM management, including application rate and rotation year, mainly affects CH 4 emissions in GM-rice systems by impacting soil biotic factors, which explain 78.4% of the variation (p < 0.001). The most influential factor is the ratio of soil CH 4 production to oxidation gene abundances (R 2 = 0.510; p < 0.001), which decreases with GM rotation year due to increased activity of methane-oxidizing soil microbes (p < 0.001), indicating that CH 4 emissions from GM-rice systems decrease with increased GM rotation year. By incorporating these microbial mechanisms as quantitative parameters in process-based model, we project that approximately 76% of the paddy rice areas in southern China, which have relatively low GM biomass and baseline CH 4 emissions, can achieve reductions in CH 4 emissions through nearly 15 years of GM crop rotation. This study indicates that CH 4 emissions from GM-rice rotations with appropriate GM application rate over the long term will not significantly increase, resolving the contradictions in previous research

Warming in permafrost regions stimulates carbon (C) release through decomposition, but increasing atmospheric CO2 and available soil nitrogen enhance plant productivity at the same time. To date, a large uncertainty in the regional C dynamics still remains. Here we use a process‐based biogeochemical model by considering C exposure from thawed permafrost and observational data to quantify permafrost C emissions and ecosystem C budget in northern high latitudes in the 21st century. Permafrost degradation will make 119.3 Pg and 251.6 Pg C available for decomposition by 2100 under the Shared Socioeconomic Pathway (SSP)126 and SSP585, respectively. However, only 4–8% of the newly thawed permafrost C is expected to be released into the atmosphere by 2100. Cumulatively, permafrost degradation will reduce ecosystem C stocks by 3.37 Pg and 15.37 Pg under the SSP126 and SSP585, respectively. Additionally, CO2 fertilization effects would stimulate plant productivity and increase ecosystem C stocks substantially. The combined effects of climate change, CO2 fertilization, and permafrost degradation on C fluxes are typically more profound than any single factor, emphasizing the intricate interplay between these elements in shaping permafrost C‐climate feedbacks. Our study suggests that the majority of the thawed C will remain sequestered in previously frozen layers in this century, posing a significant challenge to climate change mitigation efforts once any process accelerates the decomposition of this huge amount of thawed C. Plain Language Summary Amplified warming in permafrost areas accelerates permafrost degradation, thereby exposing vast quantities of previously frozen carbon (C) that has the potential to strongly feedback to global climate upon decomposition. Nevertheless, the amount of C that would be released into the atmosphere as a result of permafrost thawing remains uncertain. To refine our predictions of permafrost C loss, we leveraged observational data to constrain C exposure from thawed permafrost and simulated its decomposition by accounting for varying soil conditions at different depths. Our findings indicate that 119.3 and 251.6 billion tons of previously frozen C would be subject to microbial decomposition by 2100 under the Shared Socioeconomic Pathway (SSP) 1–2.6 and SSP 5–8.5, respectively. However, the majority of this newly thawed C is likely to remain sequestered in deep soil layers this century, with only a minor fraction (4%–8%) decomposing and releasing into the atmosphere. A potential mitigating factor is the enhanced plant C assimilation due to rising atmospheric CO2 concentrations. Our research underscores the significant threat that the substantial amount of newly thawed C poses to climate change mitigation efforts, particularly if any process accelerates the decomposition of organic C in deep soil layers. Key Points Most newly thawed permafrost C would be retained in deep layers in the 21st century Permafrost degradation would reduce ecosystem C stocks Climate change, CO2 fertilization, and permafrost degradation collectively affect ecosystem C cycling

Air chemistry is affected by the emission of biogenic volatile organic compounds (BVOCs), which originate from almost all plants in varying qualities and quantities. They also vary widely among different crops, an aspect that has been largely neglected in emission inventories. In particular, bioenergy‐related species can emit mixtures of highly reactive compounds that have received little attention so far. For such species, long‐term field observations of BVOC exchange from relevant crops covering different phenological phases are scarcely available. Therefore, we measured and modeled the emission of three prominent European bioenergy crops (maize, ryegrass, and oil‐seed rape) for full rotations in north‐eastern Germany. Using a proton transfer reaction–mass spectrometer combined with automatically moving large canopy chambers, we were able to quantify the characteristic seasonal BVOC flux dynamics of each crop species. The measured BVOC fluxes were used to parameterize and evaluate the BVOC emission module (JJv) of the physiology‐oriented LandscapeDNDC model, which was enhanced to cover de novo emissions as well as those from plant storage pools. Parameters are defined for each compound individually. The model is used for simulating total compound‐specific reactivity over several years and also to evaluate the importance of these emissions for air chemistry. We can demonstrate substantial differences between the investigated crops with oil‐seed rape having 37‐fold higher total annual emissions than maize. However, due to a higher chemical reactivity of the emitted blend in maize, potential impacts on atmospheric OH‐chemistry are only 6‐fold higher. Plain Language Summary For evaluating the air quality, it is important to know what kind of chemical compounds are emitted from plants into the atmosphere. Such emissions vary widely by plant type and species, including agricultural crops. These differences have not been sufficiently accounted for because long‐term field observations from relevant crops are scarcely available. Therefore, we measured and modeled the emission of three prominent European crops (maize, ryegrass, and oil‐seed rape) for full rotations in north‐eastern Germany. Using the measurements for parametrization, we simulated each measured compound individually and also evaluated the importance of these emissions for air chemistry. We can now demonstrate substantial differences between the investigated crops. For example, on an annual basis, oil‐seed rape emitted 37‐fold more overall emissions than maize, but since the emitted compounds are less reactive, its effect on air chemistry is only 6‐fold higher. Key Points Emissions differ greatly between crop species in pattern and strength and also vary with weather conditions and phenological development Potential impacts on air chemistry vary strongly with species and depend on compound reactivity in addition to source strength of emissions Data suggest that models should better consider growth developmental stages in order to better represent the seasonality of crop emissions