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Key message: PDG-Arena, a new individual- and process-based ecophysiological model, was developed to simulate the functioning of mixed-species forests. It was evaluated using annual growth data on beech-fir stands of the French Prealps. Context: In the context of the ongoing climate and biodiversity crises, mixed forest stands are increasingly considered as a sustainable management alternative to monospecific and even-aged stands. Aims: We developed a new individual- and process-based forest growth model, PDG-Arena, to simulate mixed forest growth and functioning, and test ecophysiological interactions among trees in mixed stands. Methods: The model builds upon the validated ecophysiological stand-scale model CASTANEA and integrates tree competition for light and water. We evaluated the performance of PDG-Arena by comparing the simulated growth with annual radial growth data from 37 common beech and silver fir monospecific and mixed plots in the French Prealps. Results: PDG-Arena performed slightly better than CASTANEA when simulating even-age and monospecific forests (r2 of 32.1 versus 29.5%). When using structure-diverse and species-diverse inventories, PDG-Arena performed better than CASTANEA in pure beech (38.3 versus 22.9%) and mixed stands (40.5 versus 36.3%), but not in pure fir stands (39.8 versus 42.0%). The new model also showed a significant positive effect of species mixing on gross primary production (+ 5.5%), canopy absorbance (+ 11.1%), and transpiration (+ 15.8%) in the tested stands. Conclusions: Our results show that tree-level process-based models such as PDG-Arena, formally simulating interspecific interactions, can serve as a valuable tool to understand and simulate the carbon, radiative, and water dynamics of mixed stands. [ABSTRACT FROM AUTHOR]

Second-growth forest establishment over abandoned agricultural lands has been envisaged as a nature-based solution to reduce atmospheric CO2 through carbon sequestration. However, we still have a very limited knowledge about the climate sensitivity of these second-growth forests in comparison to forests with a more continuous land-use history. Here, we compare the climate sensitivity of recently established forests (post-1956) and long-existing forests (pre-1956), by analyzing their responses to climate variability at 456 inventory plots in NE Spain. We analyzed remotely sensed estimates of leaf area index (LAI) and normalized difference vegetation index (NDVI) from 2000 to 2022, relating yearly variation patterns with annual meteorological drought intensity. Then, we used process-based simulations of forest functioning to explore whether different responses of both forest types to climate variability are consistent with differences in root allocation patterns modulated by land-use legacies. Although recent- and long-existing forests had a very similar structure (that is, similar LAI), NDVI variation in recent-established forests was more strongly related to meteorological drought indices, recording steeper NDVI decreases during drier periods, but also higher NDVI increases during rainy years. This pattern is consistent with differences in root allocation between the two forest types, as simulated and remotely sensed forest responses to climate exhibited higher correlation when we inputted recent-established forest with shallower root systems or lower fine root density in comparison with long-existing forests. A higher climate sensitivity of second-growth forests during drier but also wetter years entails the potential for compensating drought impacts, but it also indicates a long-term higher vulnerability to climatic disturbances. [ABSTRACT FROM AUTHOR]

Abstract Key message PDG-Arena, a new individual- and process-based ecophysiological model, was developed to simulate the functioning of mixed-species forests. It was evaluated using annual growth data on beech-fir stands of the French Prealps. Context In the context of the ongoing climate and biodiversity crises, mixed forest stands are increasingly considered as a sustainable management alternative to monospecific and even-aged stands. Aims We developed a new individual- and process-based forest growth model, PDG-Arena, to simulate mixed forest growth and functioning, and test ecophysiological interactions among trees in mixed stands. Methods The model builds upon the validated ecophysiological stand-scale model CASTANEA and integrates tree competition for light and water. We evaluated the performance of PDG-Arena by comparing the simulated growth with annual radial growth data from 37 common beech and silver fir monospecific and mixed plots in the French Prealps. Results PDG-Arena performed slightly better than CASTANEA when simulating even-age and monospecific forests (r 2 of 32.1 versus 29.5%). When using structure-diverse and species-diverse inventories, PDG-Arena performed better than CASTANEA in pure beech (38.3 versus 22.9%) and mixed stands (40.5 versus 36.3%), but not in pure fir stands (39.8 versus 42.0%). The new model also showed a significant positive effect of species mixing on gross primary production (+ 5.5%), canopy absorbance (+ 11.1%), and transpiration (+ 15.8%) in the tested stands. Conclusions Our results show that tree-level process-based models such as PDG-Arena, formally simulating interspecific interactions, can serve as a valuable tool to understand and simulate the carbon, radiative, and water dynamics of mixed stands.

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 centuryPermafrost degradation would reduce ecosystem C stocksClimate change, CO2 fertilization, and permafrost degradation collectively affect ecosystem C cycling [ABSTRACT FROM AUTHOR]

Globally, the emission of biogenic volatile organic compounds (BVOC) by plants represents the dominant source of volatile organic compounds emitted to the atmosphere. Monoterpenes, as the major BVOC group, can contribute to forming secondary organic aerosols and influence cloud properties. In this study, we developed a process‐based monoterpene module in the Vegetation Integrative SImulator for Trace gases (VISIT) model by considering the production, storage, and emission of monoterpene as three main processes. We further evaluated the modeled monoterpene emissions against the ecosystem‐level observation data at a half‐hour scale at a Japanese larch (Larix kaempferi) forest site on Mt. Fuji, Japan. The VISIT model performed with relatively higher accuracy with a Willmott's index of agreement at 0.61, a mean bias error (MBE) at 0.29, and a root mean squared error (RMSE) at 0.43, comparable to that of Model of Emissions of Gases and Aerosols from Nature model with a Willmott's index of agreement at 0.63, a MBE at 0.40, and a RMSE at 0.54. In a long‐term simulation under high CO2 emission scenarios, the ratio between monoterpene emission and gross primary production exhibited a stronger correlation with CO2 concentration than temperature. Our study provides a process‐based modeling approach for more accurately simulating monoterpene emissions from Japanese larch. Plain Language Summary: This study focuses on constructing the process and improving the accuracy of simulating emissions of biogenic volatile organic compounds (BVOC), specifically monoterpenes, from a Japanese larch forest using the Vegetation Integrative SImulator for Trace gases model. By incorporating a process‐based monoterpene module, the model demonstrated better performance compared to the widely‐used Model of Emissions of Gases and Aerosols from Nature model. The simulated results showed temperature acting as the dominant factor in determining the proportion between gross primary production and monoterpene emission in the short term. In future projections, the analysis emphasized the significant role of long‐term increases in CO2 concentration in determining this proportion. This study provides a valuable tool for simulating site‐based BVOC emissions, contributing to our understanding of the complex interactions between vegetation and the atmosphere. Key Points: The process‐based ecosystem model shows better performance than empirical model in modeling monoterpene emission from a Japanese larchHigh emission of CO2 in future will significantly influence the proportion between gross primary production and monoterpene emission [ABSTRACT FROM AUTHOR]

Abstract 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.

Recent research has shown that deep learning (DL) faces physical realism challenges in predicting runoff responses under climate change, mainly due to DL’s data dependence and lack of process understanding. In this study, a physics-encoded neural network model (dNN) was developed to adress this. dNN enables a fully process-based way to training and prediction by encoding process-based modeling knowledge into the DL architecture, including the water balance principle and causal linkages of catchment hydrological processes. To examine whether dNN can produce reliable runoff responses under warming scenarios, we first conducted regional training for dNN on daily runoff in 29 catchment in California. Two process-based models, EXP-HYDRO and HBV, were then developed as benchmarks. Both dNN and a pure data-driven LSTM were forced under warming scenarios, and the monthly hydrographs and total runoff ratios metrics were evaluated relative to the benchmarks. The results demonstrated: (1) For monthly hydrographs, dNN exhibited advantages in capturing cold-season runoff increase and warm-season recession than LSTM, effectively predicting the changes and trends in monthly runoff under warming scenarios; (2) For total runoff ratios, dNN predicted fewer catchments with increased runoff, indicating it can better maintain the total water budget under warming scenarios; (3) Through the synergy with physics, dNN was able to reasonably infer unobserved snowpack dynamics under warming scenarios. These results highlight the credibility and necessity of considering physics for DL in predicting runoff responses under climate change. Overall, this study provides a promising solution for considering physics in DL to further improve the process understanding in changing environments.

Streamflow prediction (SFP) constitutes a fundamental basis for reliable drought and flood forecasting, optimal reservoir management, and equitable water allocation. Despite significant advancements in the field, accurately predicting extreme events continues to be a persistent challenge due to complex surface and subsurface watershed processes. Therefore, in addition to the fundamental framework, numerous techniques have been used to enhance prediction accuracy and physical consistency. This work provides a well-organized review of more than two decades of efforts to enhance SFP in a physically consistent way using process modeling and flow domain knowledge. This review covers hydrograph analysis, baseflow separation, and process-based modeling (PBM) approaches. This paper provides an in-depth analysis of each technique and a discussion of their applications. Additionally, the existing techniques are categorized, revealing research gaps and promising avenues for future research. Overall, this review paper offers valuable insights into the current state of enhanced SFP within a physically consistent, domain knowledge-informed data-driven modeling framework. [ABSTRACT FROM AUTHOR]

Bayesian network (BN) models are increasingly used as tools to support probabilistic environmental risk assessments (ERAs), because they can better account for uncertainty compared with the simpler approaches commonly used in traditional ERA. We used BNs as metamodels to link various sources of information in a probabilistic framework, to predict the risk of pesticides to aquatic communities under given scenarios. The research focused on rice fields surrounding the Albufera Natural Park (Valencia, Spain), and considered three selected pesticides: acetamiprid (an insecticide), 2‐methyl‐4‐chlorophenoxyacetic acid (MCPA; a herbicide), and azoxystrobin (a fungicide). The developed BN linked the inputs and outputs of two pesticide models: a process‐based exposure model (Rice Water Quality [RICEWQ]), and a probabilistic effects model (Predicts the Ecological Risk of Pesticides [PERPEST]) using case‐based reasoning with data from microcosm and mesocosm experiments. The model characterized risk at three levels in a hierarchy: biological endpoints (e.g., molluscs, zooplankton, insects, etc.), endpoint groups (plants, invertebrates, vertebrates, and community processes), and community. The pesticide risk to a biological endpoint was characterized as the probability of an effect for a given pesticide concentration interval. The risk to an endpoint group was calculated as the joint probability of effect on any of the endpoints in the group. Likewise, community‐level risk was calculated as the joint probability of any of the endpoint groups being affected. This approach enabled comparison of risk to endpoint groups across different pesticide types. For example, in a scenario for the year 2050, the predicted risk of the insecticide to the community (40% probability of effect) was dominated by the risk to invertebrates (36% risk). In contrast, herbicide‐related risk to the community (63%) resulted from risk to both plants (35%) and invertebrates (38%); the latter might represent (in the present study) indirect effects of toxicity through the food chain. This novel approach combines the quantification of spatial variability of exposure with probabilistic risk prediction for different components of aquatic ecosystems. Environ Toxicol Chem 2024;43:182–196. © 2023 The Authors. Environmental Toxicology and Chemistry published by Wiley Periodicals LLC on behalf of SETAC. [ABSTRACT FROM AUTHOR]

Summary: Ericaceous shrubs adapt to the nutrient‐poor conditions in ombrotrophic peatlands by forming symbiotic associations with ericoid mycorrhizal (ERM) fungi. Increased nutrient availability may diminish the role of ERM pathways in shrub nutrient uptake, consequently altering the biogeochemical cycling within bogs.To explore the significance of ERM fungi in ombrotrophic peatlands, we developed the model MWMmic (a peat cohort‐based biogeochemical model) into MWMmic‐NP by explicitly incorporating plant‐soil nitrogen (N) and phosphorus (P) cycling and ERM fungi processes. The new model was applied to simulate the biogeochemical cycles in the Mer Bleue (MB) bog in Ontario, Canada, and their responses to fertilization.MWMmic_NP reproduced the carbon(C)–N–P cycles and vegetation dynamics observed in the MB bog, and their responses to fertilization. Our simulations showed that fertilization increased shrub biomass by reducing the C allocation to ERM fungi, subsequently suppressing the growth of underlying Sphagnum mosses, and decreasing the peatland C sequestration. Our species removal simulation further demonstrated that ERM fungi were key to maintaining the shrub–moss coexistence and C sink function of bogs.Our results suggest that ERM fungi play a significant role in the biogeochemical cycles in ombrotrophic peatlands and should be considered in future modeling efforts. See also the Commentary on this article by Barel & Robroek, 238: 5–7. [ABSTRACT FROM AUTHOR]

Previous quantitative studies of field-scale cyclic steps are mostly based on analysis of field data. Such studies have shed light on the erosion/deposition patterns over these morphological features as well as the magnitudes of the turbidity current parameters back estimated using the measured geometry data. However, it remains unclear to what extent such back estimated hydraulic features and erosion/deposition patterns can be numerically reproduced by process-based numerical models. Here, a two-dimensional layer-averaged fully coupled turbidity current model is applied to forward-model co-evolution of turbidity currents, sediment transport, and cyclic steps in the Rio Muni Basin off the West Africa margin, where nine cyclic steps featuring a transition from the upstream erosion to the downstream deposition were identified. Numerical case studies indicate that large and bankfull turbidity currents do not form the erosion-deposition transition because they are likely to fall into the high-speed regime that favors whole-scale erosion. In this regard, threshold values for the current velocity were derived to distinguish high/low-speed regimes of turbidity currents. It is shown that smaller turbidity currents, which do not follow the bankfull assumption, and, thus, fall into the low-speed turbidity current regime, may produce cyclic steps by current deceleration. While values of the derived physical parameters of the smaller turbidity current differ quantitatively from those of previous back estimated values, their qualitative variation trends are basically the same. • Whether the back estimated flow parameters can predict the evolution of cyclic steps is accessed by a process-based model. • The transition of erosional to depositional cyclic steps in the Rio Muni Basin is attributed to current deceleration. • Large and bankfull turbidity currents favor whole-scale erosion, whilst cyclic steps are likely formed by smaller ones. [ABSTRACT FROM AUTHOR]

Climate warming poses complex challenges for alpine ecosystems on the Qinghai–Tibetan Plateau (QTP), further exacerbated by permafrost degradation. Quantifying the specific ecological impacts of permafrost thaw remains elusive, as ecological variations are also influenced by external climate factors. This study tackles this gap by employing the Noah-MP model to simultaneously simulate permafrost thermal–hydrological dynamics and net primary production (NPP) across the Three River Headwaters Region from 1989 to 2018. Model results were validated against observations. To isolate the ecological effects of permafrost thaw, we implemented a novel relative time transformation on the simulation results. Our analysis reveals a 7.5 × 10 ^4 km ^2 reduction in permafrost coverage during the study period, coinciding with a 1.09 g C m ^−2 yr ^−2 increase in NPP. While precipitation is the primary driver of NPP changes in most years, soil moisture emerges as a crucial factor during permafrost disappearance, when the ground transitions to seasonally frozen ground. Surprisingly, the NPP response to permafrost disappearance exhibited a transient effect, diminishing to negligible levels within five years post-thaw. These findings enhance our understanding of the intricate and dynamic responses of the QTP ecosystem to permafrost degradation under a warming climate.

The formation of fluvial dunes is usually studied by investigating the time development of a small amplitude bottom perturbation of a uniform stream and considering that the dunes originate by the growth of the bottom mode characterized by the largest amplification rate under the assumption of an infinite availability of the mobile sediment (linear stability analysis). Here we undertake the stability analysis investigating the formation of sand dunes in steady currents by accounting for the nonlinear effects of sediment starvation on the formative mechanisms of the bedforms and comparing the theoretical results with laboratory experiments, and an application of a fully nonlinear commercial model of finite amplitude dunes, thus enabling an improved understanding of the genesis of starved fluvial dunes. As the growth of the dunes progressively exposes the motionless substratum, both the stability‐based and the numerical models predict starved dunes characterized by increasing crest‐to‐crest distances. The increase of the crest‐to‐crest distance corresponds to a decrease of the length of individual dunes as well as a growing irregularity in their spacing and morphology. These findings conform with the outcome of physical experiments performed earlier in a laboratory flume and existing measurements of starved fluvial dunes in the field. Plain Language Summary: The genesis of fluvial dunes is a topic of considerable interest to river engineering as dunes are a primary source of flow resistance, regulating water levels and transport processes. The modeling of fluvial dunes is usually performed assuming an infinite availability of the sediment that can be eroded and deposited by the water flow. Field observations and laboratory experiments nevertheless indicate that the supply of sediment affects dune growth and the resulting morphology of the river bed. Here we present a new theoretical model able to reproduce the effects that the lack of sediment has on the growth of dunes and compare it against laboratory experiments, and a numerical commercial model. Our results show that a progressively decrease in sediment supply leads to an increase in dune spacing and a decrease in dune length corresponding to steeper longitudinal profiles. These findings agree with the results of laboratory experiments and existing measurements in the field. As such, this study paves the way to the modeling of fluvial dunes in sediment starved environments and the prediction of their effects on the river flow. Key Points: Our model results indicate that sediment starvation affects fluvial dunes, whose spacing increases as sediment availability decreasesA quasi‐linear and a fully nonlinear model predict larger wavelengths for sediment starved dunes than for their contiguous counterpartsModeling outcome conforms with those of previous flume experiments and existing measurements of starved fluvial dunes in the field [ABSTRACT FROM AUTHOR]

Forest models are instrumental for understanding and projecting the impact of climate change on forests. A considerable number of forest models have been developed in the last decades. However, few systematic and comprehensive model comparisons have been performed in Europe that combine an evaluation of modelled carbon and water fluxes and forest structure. We evaluate 13 widely used, state‐of‐the‐art, stand‐scale forest models against field measurements of forest structure and eddy‐covariance data of carbon and water fluxes over multiple decades across an environmental gradient at nine typical European forest stands. We test the models' performance in three dimensions: accuracy of local predictions (agreement of modelled and observed annual data), realism of environmental responses (agreement of modelled and observed responses of daily gross primary productivity to temperature, radiation and vapour pressure deficit) and general applicability (proportion of European tree species covered). We find that multiple models are available that excel according to our three dimensions of model performance. For the accuracy of local predictions, variables related to forest structure have lower random and systematic errors than annual carbon and water flux variables. Moreover, the multi‐model ensemble mean provided overall more realistic daily productivity responses to environmental drivers across all sites than any single individual model. The general applicability of the models is high, as almost all models are currently able to cover Europe's common tree species. We show that forest models complement each other in their response to environmental drivers and that there are several cases in which individual models outperform the model ensemble. Our framework provides a first step to capturing essential differences between forest models that go beyond the most commonly used accuracy of predictions. Overall, this study provides a point of reference for future model work aimed at predicting climate impacts and supporting climate mitigation and adaptation measures in forests. [ABSTRACT FROM AUTHOR]

Tidal sand waves form a dynamic bed pattern, widely occurring in shallow shelf seas such as the North Sea. Their importance to coastal engineering has inspired many advances in process-based sand wave modelling, aimed at explaining physical mechanisms in the formation stage ('linear regime') and capturing the finite amplitude evolution to equilibrium states ('nonlinear regime'). However, systematic validation of particularly the nonlinear sand wave models is still lacking. Here, we perform a two-step calibration and validation study of a sand wave model (specifically, their linear and nonlinear model versions) against field data from the North Sea. In the first step, the linear model is calibrated by seeking overall values of two uncertain input parameters (slip parameter, wave period) for which the modeled and observed wavelengths show the best agreement. In the second step, using the calibrated input parameters and preferred wavelengths from the linear model, equilibrium heights from the nonlinear sand wave model are validated against the observed sand wave heights. Our results show satisfactory agreement between observed and modeled sand wave lengths (from the linear sand wave model) and a systematic overprediction of sand wave heights (using the nonlinear model). Regression analysis can be used to rescale the nonlinear model results to obtain realistic predictions of sand wave heights. [ABSTRACT FROM AUTHOR]

The stability of estuarine channel-shoal systems is important for port utilization, navigation maintenance, habitat protection and ecosystem service functions. This paper uses the South Channel of the Changjiang (Yangtze River) Estuary as a typical example to investigate the channel-shoal adjustment mechanism and its future trend. The combined approaches of bathymetric data analysis and process-based modeling (Delft3D) are applied. Quantitative analysis of morphological changes indicates that the South Channel experienced remarkable channel-shoal adjustment during 1958–2018. Periodic evolution was identified, including shoal migration, incision and emergence under natural conditions before the mid-1980s. Since then, fluvial sediment decline and local human intervention have interrupted the periodic processes. After 1986, as river sediment discharge started to decline, the South Channel converted to net erosion, and both the mid-channel shoal at the bifurcation node and the tail of the Ruifeng Shoal showed significant scour. Process-based hydrodynamic simulations revealed that the northern rotation of the mainstream downstream of Wusong triggered the erosion of the Ruifeng Shoal, while unordered sand mining at the shoal tail in approximately 2002 enhanced shoal shrinkage. In addition, the self-adjustment of the transverse section shape resulted in abnormal accretion in 2002–2007. Afterward, the South Channel underwent overall erosion as sediment discharge decreased to a low level (<150 Mt/a). Five stages of channel-shoal pattern adjustment and accretion/erosion status during the past 60 years were defined, i.e., the accretion stage (1958–1965), remarkable channel-shoal adjustment stage (1978–1986), slow erosion stage (1986–1997), shoal scour and shrinkage stage (1997–2007) and overall channel-shoal erosion stage (2007–2018). Model prediction of the evolutionary trend indicates that overall erosion within the South Channel is most likely to continue in 2015–2050. Further adjustment of the South Channel under extremely low sediment discharge may threaten the riverbed stability and the sustainable development of this large-scale estuary. Future work on adaptive strategies for varying conditions is recommended. [ABSTRACT FROM AUTHOR]

Although analytical and numerical models have been widely used to explore evolution and equilibrium morphology in tidal environments, less attention has been paid to examining the impact of initial bathymetry on the model outcomes. Here we use two‐dimensional idealized models with contrasting initial bathymetries to study how the interactions between antecedent morphology and tidal exchange processes determine the establishment of an estuarine equilibrium bathymetry, and how these interactions mediate the morphodynamic response to rising sea levels. In all model runs with sandy beds, inter‐tidal zones reach the equilibrium condition first and equilibrium profiles are similar for points close to mean seal level. However, key aspects like channel formation, residence time and energy dissipation do not evolve to the same state and are inherited from the initial bathymetry. This implies that responses to sea‐level rise (SLR) are different as well. Conversely, in cases with mud and sand input at the boundaries, equilibrium occurs more quickly and the equilibrium bathymetry and channel formation are dominated by the boundary mud concentration. General implications of the study are that predictions of coastal response to changes such as SLR depend on initial bathymetric conditions. Plain Language Summary: Tidal environments are an important component of coastal areas because they are located where significant land‐ocean interactions take place. Due to their importance, the equilibria and their fate under sea‐level rise have been widely studied. However, tidal environments are not created equally and their characteristics vary from one to another. This will increase uncertainty in model results that are used to predict the equilibrium bathymetry and their resilience under SLR. Therefore, it is of great importance to examine what are the key factors that can control their geomorphological development. The study aims to examine whether the results are independent from initial conditions or the equilibrium bathymetry is affected by antecedent morphology. We show that in sandy environments or tidal embayments with limited sediment supply, equilibrium bathymetry depends on antecedent morphology whereas in more muddy environments, sediment supply governs equilibrium geometry. Key Points: Equilibrium bathymetry and adaptation timescale depend on initial bathymetry and sediment influxChannels develop differently from different initial bathymetries, changing residence time and energy dissipation rates of the tidal systemIn sandy environments equilibrium depends on antecedent morphology whereas in more muddy environments, sediment supply governs equilibrium geometry [ABSTRACT FROM AUTHOR]

Abstract 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.

For a complex hydrologic system with multiple processes and process interactions, global sensitivity analysis is often used to identify important or influential parameters for model development and improvement. The identification is complicated by process model uncertainty, when a system process can be represented by multiple process models. This study develops a new total‐effect process sensitivity index to identify influential processes under model uncertainty. This is done by extending Sobol's total‐effect parameter sensitivity index for one system model to total‐effect process sensitivity index for multiple system models to account for uncertainty in process models and model parameters. The total‐effect process sensitivity index includes not only the first‐order process sensitivity index for measuring the importance of individual processes but also higher‐order indices that account for process interactions. The total‐effect process sensitivity index can identify an influential process that itself and its interactions with other processes influence a model output. The total‐effect process sensitivity index is applied to two numerical examples: (a) Sobol's G*‐functions with analytical solutions of first‐order and total‐effect process sensitivity indices, and (b) groundwater flow models with interactions between recharge, geology, and snowmelt processes. The second evaluation shows that, due to second‐order and higher‐order process interactions, the first‐order and total‐effect process sensitivity indices give different process ranking. It is thus necessary to estimate both first‐order and total‐effect process sensitivity indices to appreciate the difference between the first‐order impact of a process alone and the overall total‐effect impact of the process itself and its interactions with other processes on a model output. Plain Language Summary: When studying a complex hydrologic system, it is necessary to identify non‐influential processes of the system so that limited resources are not spent on improving our understanding of these processes. On the other hand, it is important to identify influential processes of the system so that limited resources can be efficiently spent on better understanding the influential processes. Identification of the influential and non‐influential processes is difficult when a process can be represented by several plausible process models because it is not always clear which process model to choose. To resolve this issue, we develop a new total‐effect process sensitivity index that considers all the plausible process models without choosing one model and discarding other models. This is done by integrating the model averaging method with the Sobol's total‐effect parameter sensitivity index. We use two numerical examples to verify computer codes and to demonstrate how to use the index to identify influential and non‐influential processes. Applied to groundwater flow modeling, our new index demonstrates that accounting for interactions between recharge, geology, and snowmelt processes gives a ranking of process influence that is different from the ranking of process importance based on the first‐order process sensitivity index. Key Points: A new total‐effect process sensitivity index is derived to account for process interactions under process model and parameter uncertaintyThe total‐effect process sensitivity index has a first‐order term for process importance and higher‐order terms for process interactionsAccounting for process interactions allows for identifying influential system processes and/or screening non‐influential system processes [ABSTRACT FROM AUTHOR]

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 developmentPotential impacts on air chemistry vary strongly with species and depend on compound reactivity in addition to source strength of emissionsData suggest that models should better consider growth developmental stages in order to better represent the seasonality of crop emissions [ABSTRACT FROM AUTHOR]

Predicting soil erosion by water is an essential natural resource management activity. The Water Erosion Prediction Project (WEPP) model is a process-based continuous simulation tool based on erosion mechanics and channel hydraulics. Due to the extensive data requirements, preparing input parameter settings for WEPP can be time-consuming. A Geospatial Interface for WEPP (GeoWEPP) overcame this major disadvantage but had limitations regarding keeping track of operating systems and proprietary software updates. QGeoWEPP is a newly developed open-source QGIS-based GeoWEPP, offering additional novel user-based customizations of model simulations including validation data sets. QGeoWEPP provides a platform for applying WEPP at the hillslope and watershed scales that integrates the whole model application and validation process. QGeoWEPP allows applying WEPP in any study area worldwide with minimum data limitations and more spatial and temporal capabilities in areas such as soil and water conservation, land management, geohazard assessment due to land use and climate change, and many more. • Open-source, process-based GIS interface to model watershed runoff and erosion. • Provides spatial and temporal validation datasets to customize model interface. • Includes statistical evaluation module to evaluate and validate model outputs. • Enables users to include remote sensing derived land use or land cover changes. • Geohazard assessments of terrain changes based on Digital Elevation Models. [ABSTRACT FROM AUTHOR]

• Ungulate browsing can impede successful mixed-species forest development. • Process-based models are able to simulate dynamic species-shifting triggered by species-specific browsing affection. • Shifted biotic competition has a substantial impact on forest structure. • Composition and growth differences already occur in response to slight changes in ungulate density. Ungulate browsing can prevent the successful regeneration and development of forest-owner-intended and climate-resilient admixed tree species, compromising the future provision of multiple ecosystem services. A reduction of the height increment of palatable tree species caused by ungulate browsing results in a competition shift in favor of less palatable species. While this key process of losing admixed tree species is well understood, forest growth models have not yet captured its full complexity. Therefore, our objectives were to simulate (i) the loss of susceptible tree species from different browsing regimes and its impact on stand development, (ii) changes in the height increment of saplings and trees, and (iii) the shifts in social status during stand development without pre-defined limits a sapling can resist browsing. To address these objectives, we used the process-based model iLand to simulate the exposure of Norway spruce, Silver fir, and European beech stands to varying browsing densities. We successfully reproduced expected dynamic shifts in species composition and height development 24 years after establishment, depending on ungulate pressure and species-specific competitive strength. Stand composition and growth differences already occur with slight changes in ungulate density. When saplings remained in stand structure, independent of the number being browsed, our results led to more individuals with substantially smaller dimensions. We found that dynamic tree species shifting can be simulated realistically by a process-based modeling approach, which can be used to show the strong impact of ungulates on stand development. [ABSTRACT FROM AUTHOR]

Tidal sand waves form a dynamic bed pattern, widely occurring in shallow shelf seas such as the North Sea. Their importance to coastal engineering has inspired many advances in process-based sand wave modelling, aimed at explaining physical mechanisms in the formation stage (‘linear regime’) and capturing the finite amplitude evolution to equilibrium states (‘nonlinear regime’). However, systematic validation of particularly the nonlinear sand wave models is still lacking. Here, we perform a two-step calibration and validation study of a sand wave model (specifically, their linear and nonlinear model versions) against field data from the North Sea. In the first step, the linear model is calibrated by seeking overall values of two uncertain input parameters (slip parameter, wave period) for which the modeled and observed wavelengths show the best agreement. In the second step, using the calibrated input parameters and preferred wavelengths from the linear model, equilibrium heights from the nonlinear sand wave model are validated against the observed sand wave heights. Our results show satisfactory agreement between observed and modeled sand wave lengths (from the linear sand wave model) and a systematic overprediction of sand wave heights (using the nonlinear model). Regression analysis can be used to rescale the nonlinear model results to obtain realistic predictions of sand wave heights.

[Display omitted] • A novel process-based deep learning (DL) hydrological model (PRNN-EA-LSTM) is proposed. • PRNN-EA-LSTM exhibits good performance and intelligence for learning and inferring unobserved processes. • The synergy of physics and DL helps PRNN-EA-LSTM improve simulations of the arid catchments. Although deep learning (DL) models, especially long-short-term memory (LSTM), demonstrate greater accuracy than process-based models in rainfall-runoff simulation, the predictions from process-based models are more physical than DL models. The main reason is that DL models have almost no process understanding capabilities like process-based models beyond their fitting capability. In this study, we developed a process-driven DL model under a unified DL architecture to improve the process awareness of DL models. To implement the model, a conceptual hydrological model (EXP-HYDRO) is implanted into a recurrent neural network (RNN) cell as a process driver for providing multi-sub-process variables related to the runoff process, and an Entity-Aware LSTM (EA-LSTM) cell is incorporated as a post-processor layer, resulting in the Process-driven RNN-EA-LSTM (PRNN-EA-LSTM). Under the assistance of the process driver, the model performance of PRNN-EA-LSTM on the 531 catchments from the Catchment Attributes and Meteorology for Large-sample Studies dataset is more robust than the pure DL model, and better than using only EXP-HYDRO as the input of EA-LSTM (i.e., EXP-HYDRO-EA-LSTM). Specifically, the median Nash-Sutcliffe efficiency (NSE) of PRNN-EA-LSTM in local and regional simulation is 0.03 and 0.02 higher than LSTM and 0.01 higher than EXP-HYDRO-EA-LSTM. Additionally, PRNN-EA-LSTM significantly enhances the low flow simulations and reduces the catchments number with negative NSE. This study demonstrates that process-based models can help DL models better represent the rainfall-runoff relationship under a unified architecture. Consequently, integrating the adaptability of process-based models into the DL architecture is anticipated to bolster the process understanding capabilities of DL models. [ABSTRACT FROM AUTHOR]

• Combined the process-based modeling/random forest obtained high spatiotemporal resolution and high precision NEE of coastal wetlands. • Considering tidal influence is necessary for NEE estimation in coastal wetlands. • The net CO 2 uptake ability of plants was S. alterniflora > P. australis > S. salsa. Accurate estimation of the net ecosystem CO 2 exchange (NEE) at regional scales is of great significance for studying the carbon sink potential of coastal wetland ecosystems and their responses to global climate change. However, current NEE estimation methods are mainly developed for terrestrial ecosystems and are therefore unsuitable for NEE estimation with high spatiotemporal resolution estimation in coastal wetlands subjected to sub-daily tidal flooding. In this study, we proposed a high spatiotemporal resolution NEE estimation method for coastal marsh wetlands that properly considered tidal influence by combining the advantages of process-based modeling and machine learning. This method was verified and applied in the Changjiang estuary and Liaohe estuary marsh wetlands based on eddy covariance and environmental measurements, climate reanalysis data, and satellite images. The proposed method had good performance in the NEE estimation of tidal marsh wetlands, with Phragmites australis, Spartina alterniflora , and Suaeda salsa having coefficients of determination (R2) of 0.850, 0.676, and 0.658, respectively, and root mean square error (RMSE) values of 7.211 μmol m−2 s−1, 8.105 μmol m−2 s−1, and 0.109 μmol m−2 s−1, respectively. By integrating the tide level and salinity, the NEE estimation accuracy for each vegetation type was improved. The total annual NEE values of the Changjiang estuary and Liaohe estuary marsh wetlands in 2022 were estimated to be −0.297 and −0.444 Tg C yr−1, respectively. This study demonstrated that integrating process-based model and machine learning estimation can reliably capture the NEE dynamics of coastal wetlands, providing a useful tool to quantify coastal blue carbon potential with high spatiotemporal resolution at large scales. [ABSTRACT FROM AUTHOR]

Boreal forests are often managed to maximize wood production, but other goals, among which climate change mitigation, are increasingly important. Hence, it is necessary to examine synergies and trade-offs between forest production and its potential for carbon sequestration and climate change mitigation in forest stands. To this aim, we develop a novel mass-balanced process-based compartmental model that allows following the carbon path from its photosynthetic fixation until its return to the atmosphere by autotrophic or heterotrophic respiration, or by being burnt as wood product. Following carbon in the system allows to account for how long forest ecosystems and wood products retain carbon away from the atmosphere (i.e., the carbon transit time). As example, we apply the model to four management scenarios, i.e., mixed-aged pine, even-aged pine, even-aged spruce, and even-aged mixed forest, and contrast metrics of performance relative to wood production, carbon sequestration, and climate change mitigation potential. While at the end of an 80 yr rotation the even-aged forests held up to 31% more carbon than the mixed-aged forest, the mixed-aged forest was superior during almost the entire rotation when factoring in the carbon retention time away from the atmosphere, i.e., in terms of climate change mitigation potential. Importantly, scenarios that maximize production or amount of carbon stored in the ecosystems are not necessarily the most beneficial for carbon retention away from the atmosphere. These results underline the importance of considering carbon transit time when evaluating forest management options for potential climate change mitigation. [Display omitted] • We evaluate wood production and climate change mitigation potential of boreal forests. • We combine an ecophysiological growth model with forest inventory tree allometries. • Higher carbon sequestration does not ensure higher climate change mitigation potential. • Potential climate change mitigation depends on carbon time away from the atmosphere. [ABSTRACT FROM AUTHOR]

Sugarcane plays an important role in electricity and sugar production and is a viable biofuel. Developing and optimizing a mechanism that can predict crop growth and yield at different spatiotemporal scales can promote the understanding of the effects of cultivation on the ecosystem, while providing options for optimizing management measures and improving the operational procedures of sugarcane growers. The main objective of this study is to integrate the sugarcane module into the ECOSystem MOdel Simulator (ECOSMOS) model and calibrate a parameter set for sugarcane genotypes groups (using different datasets); the model supports datasets that vary in complexity (from flux tower experiments to operational plots), while accounting for high genotype-by-environment-by-management (GxExM) variability. First, we calibrated the ECOSMOS biophysical and physiological parameters for the sugarcane module using two micrometeorological experimental sites, based on eddy-covariance and biomass measurements. Second, sugarcane genotypes located in different regions of contrasting climate conditions were split into two groups based on their period of harvest, i.e., early or mid-to-late harvest season, and two parameter sets were proposed. The sugarcane module was used to estimate the yield of numerous plots, using two different parameter sets, namely, the general and regionally-specific parameter sets. The model could successfully simulate the biophysical and physiological processes of the biomass of stalks and leaves, energy and carbon fluxes, and soil-water dynamics; for Experimental Site 2, the Nash-Sutcliffe efficiency (NSE) was 0.14–0.86 and the relative root mean square error (RRMSE) was 13–112. However, the generic parameter set did not perform well in all production environments, and the difference between the observed and simulated yields ranged from 0.9 to 14.5 (Mg ha-1). Hence, a novel calibration approach adopted in this study improved the module's accuracy, while improving the performances for all five production environments, with the difference between the observed and simulate yields being 0.3–2.2 (Mg ha-1). Although the two parameter sets can be used as a reference for sugarcane plantations in Brazil, we recommend recalibrating the model (for ensuring higher accuracy) before operational applications. Notably, the ECOSMOS-sugarcane model is emerging as a complex ecosystem model that can support the quantifications and evaluations of the effects of sugarcane plantations on the carbon and water balances in different environmental conditions, particularly in tropical regions. • The ECOSMOS-Sugarcane was updated. • Biophysical and physiological processes were parametrized using flux towers. • Phenological calibrations were done for two groups with different period of harvest. • Re-calibrating two parameters improved the accuracy on simulating the biomass. [ABSTRACT FROM AUTHOR]

Critical to the reliable application of gridded soil moisture products is a thorough assessment of their quality at spatially compatible scales. While previous studies have attempted to evaluate different soil moisture products, comprehensive assessments at the appropriate scale remain challenging and rare. This study explores the potential of the Cosmic-Ray Soil Moisture Observation System (COSMOS) in Germany for effectively mitigating the scale mismatch between soil moisture products and reference measurements. A newly released extensive COSMOS data set provides time series of hectare-scale soil moisture of the main root zone at different locations in Germany and offers a unique opportunity for a comprehensive quality assessment of 15 commonly-used coarse-scale soil moisture products. Those are either satellite-based (AMSR2 LPRM, ASCAT H115/H116, Sentinel-1 SSM, SMAP L3E, SMOS L3, ASCAT/Sentinel-1 SWI, SMAP/Sentinel-1 L2, CCI Combined, and NOAA SMOPS) or model-based (ERA5-Land, GLDAS-Noah, ASCAT H141/H142, GLEAM, SMAP L4, and SMOS L4). We compared the temporal dynamics of the soil moisture products against that of the COSMOS soil moisture estimates at 21 sites of different land cover types over six years (2015–2020), including the drought of 2018. We found that the model-based products generally yield a higher correlation (0.74) and lower unbiased root-mean-square differences (0.05 m 3 m − 3) than the satellite-based products (0.60 and 0.07 m 3 m − 3 , respectively) against the COSMOS data in Germany. Notably, the application of the exponential filter significantly improves the performance of the products. Conversely, deseasonalized time series of all selected products demonstrate lower performances across all COSMOS sites. Most products show a considerable positive bias, which limits their usability for the assessment of absolute soil water storage. We also found that the land cover type, mean annual soil moisture, and vertical support have notable influences on the performance of the soil moisture products. Additionally, the performances of the soil moisture products show seasonal variations, such that both correlation and bias are highest during the summer season. This study highlights the strengths of COSMOS data as a robust reference for evaluating soil moisture products. Additionally, it provides insights on how to assess, interpret, and improve large-scale soil moisture products. • Quality assessment of 15 soil moisture products from satellites/process-based models. • Data from Cosmic-Ray Soil Moisture Observation Systems (COSMOS) solved as reference. • Model-based products generally agree well with COSMOS soil moisture. • Performance influenced by exponential filter, land cover type, average soil moisture. • Best performance on seasonal variation and long-term trends found during summer. [ABSTRACT FROM AUTHOR]

Milk vetch (Astragalus sinicus L.) is leguminous green manure (GM) which produces organic nitrogen (N) for subsequent crops and is widely planted and utilized to simultaneously reduce the use of synthetic N fertilizer and its environmental costs in rice systems. Determination of an optimal N application rate specific to the GM-rice system is challenging because of the large temporal and spatial variations in soil, climate, and field management conditions. To solve this problem, we developed a framework to explore the site-specific N application rate for the low-N footprint rice production system in southern China based on multi-site field experiments, farmer field survey, and process-based model (WHCNS_Rice, soil water heat carbon nitrogen simulator for rice). The results showed that a process-based model can explain >83.3% (p < 0.01) of the variation in rice yield, aboveground biomass, crop N uptake, and soil mineral N. Based on the scenario analysis of the tested WHCNS_Rice model, the simple regression equation was developed to implement site-specific N application rates that considered variations in GM biomass, soil, and climatic conditions. Simulation evaluation on nine provinces in southern China showed that the site-specific N application rate reduced regional synthetic N fertilizer input by 29.6 ± 17.8% and 65.3 ± 23.0% for single and early rice, respectively; decreased their total N footprints (NFs) by 23.4% and 49.3%, respectively; and without reduction in rice yield, compared with traditional farming N practices. The reduction in total NF was attributed to the reduced emissions from ammonia volatilization by 35.2%, N leaching by 28.4%, and N runoff by 32.7%. In this study, we suggested a low NF rice production system that can be obtained by combining GM with site-specific N application rate in southern China. • Site-specific N management practices was developed to achieve the low-N footprint rice system • WHCNS_Rice model can explain >83.3% of the variation in crop and soil variables • Site-specific N management reduced regional synthetic N fertilizer input by 29.6–65.3% • Site-specific N management reduced regional N footprint by 23.4–49.3% [ABSTRACT FROM AUTHOR]

Agriculture is a major source of nitrous oxide (N 2 O) emissions into the atmosphere. However, assessing the impacts of agricultural conservation practices, land use change, and climate adaptation measures on N 2 O emissions at a large scale is a challenge for process-based model applications. Here, we integrated six N 2 O emission algorithms for the nitrification processes and seven N 2 O emission algorithms for the denitrification process into the Soil and Water Assessment Tool-Carbon (SWAT-C). We evaluated the different combinations of methods in simulating N 2 O emissions under corn (Zea mays L.) production systems with various conservation practices, including fertilization, tillage, and crop rotation (represented by 14 experimental treatments and 83 treatment-years) at five experimental sites across the U.S. Midwest. The SWAT-C model exhibited wide variability in simulating daily average N 2 O emissions across treatment-years with different method configurations, as indicated by the ranges of R2, NSE, and BIAS (0.04–0.68, −1.78–0.60, and −0.94–0.001, respectively). Our results indicate that the denitrification process has a stronger impact on N 2 O emissions than the nitrification process. The best performing N 2 O emission algorithms are those rooted in the CENTURY model, which considers soil pH and respiration effects that were overlooked by other algorithms. The optimal N 2 O emission algorithm explained about 63% of the variability of annual average N 2 O emissions, with NSE and BIAS of 0.60 and −0.033, respectively. The model can reasonably represent the impacts of agricultural conservation practices on N 2 O emissions. We anticipate that the improved SWAT-C model, with its flexible configurations and robust modeling and assessment capabilities, will provide a valuable tool for studying and managing N 2 O emissions from agroecosystems. [Display omitted] • We integrated multiple N 2 O emission algorithms into the SWAT-C model. • Model performance was evaluated using data from the US Midwest. • The best-performing algorithm considered soil pH and respiration effects. • The model effectively captured the impacts of agricultural management practices on N 2 O emissions. • The model accurately represented the spatiotemporal variations in N 2 O emissions. [ABSTRACT FROM AUTHOR]

Training walls to improve navigability at river mouths alter natural sediment transport regimes, occasioning morphological changes in the nearby littoral zone. In addition, harbor channels and shorelines are susceptible to on-going changes in global circulation conditions and large-scale engineering interventions. Here we present a process-based modeling approach to determine the relationship between spatial patterns of erosion and deposition, seasonal river discharge, and the geometry of coastal defenses. The study uses the Magdalena River delta as an example, where the redistribution of freshwater and sediments after the construction of the "Tajamar" training wall heralded significant morphological changes in the area. The numerical experiments are used to describe how the erosion and deposition patterns within the estuary and delta of the Magdalena River are linked to the seasonal cycle of the Magdalena River and to the geometry of the hard structures. We demonstrate that the construction of the Tajamar and complementary hard structures would have decreased depositional fluxes in the littoral zone, leading to the observed shoreline retreat and disappearance of an extensive coastal lagoon system. It is shown that an aperture in the training walls may help restore the wetlands without compromising navigability. In addition, projected increases in mean sea level are shown to decrease velocities within the lower estuary, potentially causing increased sedimentation within the channel and more complex conditions for the management of safe navigation over the Magdalena River estuary. • Seawall construction on the Magdalena River, Colombia, resulted in the redistribution of freshwater and sediments. • Numerical experiments provide evidence that shoreline retreat was caused by the altered sedimentation regime. • A diversion system in the training walls may help restore sediment supply to the shorelines without risking navigability. • Sea level rise may increase rates of sedimentation within the estuary. [ABSTRACT FROM AUTHOR]