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Model–data comparisons of plant physiological processes provide an understanding of mechanisms underlying vegetation responses to climate. We simulated the physiology of a piñon pine–juniper woodland (Pinus edulis–Juniperus monosperma) that experienced mortality during a 5 yr precipitation-reduction experiment, allowing a framework with which to examine our knowledge of drought-induced tree mortality. We used six models designed for scales ranging from individual plants to a global level, all containing state-of-the-art representations of the internal hydraulic and carbohydrate dynamics of woody plants. Despite the large range of model structures, tuning, and parameterization employed, all simulations predicted hydraulic failure and carbon starvation processes co-occurring in dying trees of both species, with the time spent with severe hydraulic failure and carbon starvation, rather than absolute thresholds per se, being a better predictor of impending mortality. Model and empirical data suggest that limited carbon and water exchanges at stomatal, phloem, and below-ground interfaces were associated with mortality of both species. The model–data comparison suggests that the introduction of a mechanistic process into physiology-based models provides equal or improved predictive power over traditional process-model or empirical thresholds. Both biophysical and empirical modeling approaches are useful in understanding processes, particularly when the models fail, because they reveal mechanisms that are likely to underlie mortality. We suggest that for some ecosystems, integration of mechanistic pathogen models into current vegetation models, and evaluation against observations, could result in a breakthrough capability to simulate vegetation dynamics.

• A valid representation of intra-annual wood formation processes in global vegetation models is vital for assessing climate change impacts on the forest carbon stock. Yet, wood formation is generally modelled with photosynthesis, despite mounting evidence that cambial activity is rather directly constrained by limiting environmental factors. • Here, we apply a state-of-the-art turgor-driven growth model to simulate 4 yr of hourly stem radial increment from Picea abies (L.) Karst. and Larix decidua Mill. growing along an elevational gradient. For the first time, wood formation observations were used to validate weekly to annual stem radial increment simulations, while environmental measurements were used to assess the climatic constraints on turgor-driven growth. • Model simulations matched the observed timing and dynamics of wood formation. Using the detailed model outputs, we identified a strict environmental regulation on stem growth (air temperature > 2°C and soil water potential > −0.6 MPa). Warmer and drier summers reduced the growth rate as a result of turgor limitation despite warmer temperatures being favourable for cambial activity. • These findings suggest that turgor is a central driver of the forest carbon sink and should be considered in next-generation vegetation models, particularly in the context of global warming and increasing frequency of droughts.

Within the field of species distribution modelling an apparent dichotomy exists between process-based and correlative approaches, where the processes are explicit in the former and implicit in the latter. However, these intuitive distinctions can become blurred when comparing species distribution modelling approaches in more detail. In this review article, we contrast the extremes of the correlative—process spectrum of species distribution models with respect to core assumptions, model building and selection strategies, validation, uncertainties, common errors and the questions they are most suited to answer. The extremes of such approaches differ clearly in many aspects, such as model building approaches, parameter estimation strategies and transferability. However, they also share strengths and weaknesses. We show that claims of one approach being intrinsically superior to the other are misguided and that they ignore the process—correlation continuum as well as the domains of questions that each approach is addressing. Nonetheless, the application of process-based approaches to species distribution modelling lags far behind more correlative (process-implicit) methods and more research is required to explore their potential benefits. Critical issues for the employment of species distribution modelling approaches are given, together with a guideline for appropriate usage. We close with challenges for future development of process-explicit species distribution models and how they may complement current approaches to study species distributions.

Process‐based land surface models (LSMs) are widely used for global water cycle and runoff assessments, but when integrated with hydrodynamic models, the streamflow simulations exhibit significant uncertainties in uncalibrated mode, limiting their effectiveness in local hydrology applications. The calibration of LSMs against observed streamflow across large basins and regions is computationally prohibitive and sometimes degrades performance of other variables. In contrast, deep learning models, particularly Long‐Short Term Memory (LSTM) networks, have shown promising results in streamflow simulations, but they are often limited by poor reproducibility of other water cycle variables. This study presents a hybrid modeling framework that integrates process‐based models with deep learning to improve daily streamflow simulations without requiring basin‐specific calibration. The framework is showcased on a national scale using a multi‐model hydrologic ensemble from the Indian Land Data Assimilation System (ILDAS). It is integrated with a proposed two‐stage post‐processor, which pairs a residual error prediction LSTM with an auto‐regressive meta‐learning LSTM to predict 1‐day ahead streamflow. Trained on multi‐decadal data from 220 catchments across India, the framework improves Kling‐Gupta Efficiency in 208 catchments, raising the national median from 0.18 (uncalibrated) to 0.60. It also reduced peak flow timing error and peak mean absolute percentage error by 25% in 135 catchments. During monsoon and post‐monsoon periods, residual error interquartile range (IQR) decreased by 66.3% and 81.7%, respectively. This approach has the potential to integrate LSMs with deep learning for more accurate and locally relevant streamflow predictions, while enhancing other water cycle variables through methods like data assimilation.

Dynamic vegetation models provide process-based explanations of the dynamics and the distribution of plant ecosystems. They offer significant advantages over static, correlative modelling approaches, particularly for ecosystems that are outside their equilibrium due to global change or climate change. A persistent problem, however, is their parameterization. Parameters and processes of dynamic vegetation models (DVMs) are traditionally determined independently of the model, while model outputs are compared to empirical data for validation and informal model comparison only. But field data for such independent estimates of parameters and processes are often difficult to obtain, and the desire to include better descriptions of processes such as biotic interactions, dispersal, phenotypic plasticity and evolution in future vegetation models aggravates limitations related to the current parameterization paradigm. In this paper, we discuss the use of Bayesian methods to bridge this gap. We explain how Bayesian methods allow direct estimates of parameters and processes, encoded in prior distributions, to be combined with inverse estimates, encoded in likelihood functions. The combination of direct and inverse estimation of parameters and processes allows a much wider range of vegetation data to be used simultaneously, including vegetation inventories, species traits, species distributions, remote sensing, eddy flux measurements and palaeorecords. The possible reduction of uncertainty regarding structure, parameters and predictions of DVMs may not only foster scientific progress, but will also increase the relevance of these models for policy advice.

Water temperature forecasting in lakes and reservoirs is a valuable tool to manage crucial freshwater resources in a changing and more variable climate, but previous efforts have yet to identify an optimal modeling approach. Here, we demonstrate the first multi‐model ensemble (MME) reservoir water temperature forecast, a forecasting method that combines individual model strengths in a single forecasting framework. We developed two MMEs: a three‐model process‐based MME and a five‐model MME that includes process‐based and empirical models to forecast water temperature profiles at a temperate drinking water reservoir. We found that the five‐model MME improved forecast performance by 8%–30% relative to individual models and the process‐based MME, as quantified using an aggregated probabilistic skill score. This increase in performance was due to large improvements in forecast bias in the five‐model MME, despite increases in forecast uncertainty. High correlation among the process‐based models resulted in little improvement in forecast performance in the process‐based MME relative to the individual process‐based models. The utility of MMEs is highlighted by two results: (a) no individual model performed best at every depth and horizon (days in the future), and (b) MMEs avoided poor performances by rarely producing the worst forecast for any single forecasted period (<6% of the worst ranked forecasts over time). This work presents an example of how existing models can be combined to improve water temperature forecasting in lakes and reservoirs and discusses the value of utilizing MMEs, rather than individual models, in operational forecasts. Key Points Aggregated lake temperature forecast skill was higher for multi‐model ensemble (MME) forecasts than individual model forecasts Including baseline empirical models (day‐of‐year, persistence) with process models improved MME forecast performance MME forecasts improved forecast skill by “hedging,” as no individual model performed best at all horizons or depths

This work was supported by National Basic Research Program of China (973) (2013CB430402), the National Natural Science Foundation for Innovative Research Group (No. 51721093), the National Natural Science Foundation of China (No. 51279007) and the Fundamental Research Funds for the Central Universities.

Applications of process‐based models (PBM) for predictions are confounded by multiple uncertainties and computational burdens, resulting in appreciable errors. A novel modeling framework combining a high‐fidelity PBM with surrogate and machine learning (ML) models is developed to tackle these challenges and applied for streamflow prediction. A surrogate model permits high computational efficiency of a PBM solution at a minimum loss of its accuracy. A novel probabilistic ML model partitions the PBM‐surrogate prediction errors into reducible and irreducible types, quantifying their distributions that arise due to both explicitly perceived uncertainties (such as parametric) or those that are entirely hidden to the modeler (not included or unexpected). Using this approach, we demonstrate a substantial improvement of streamflow predictive accuracy for a case study urbanized watershed. Such a framework provides an efficient solution combining the strengths of high‐fidelity and physics‐agnostic models for a wide range of prediction problems in geosciences. Plain Language Summary This study proposes a new framework that combines three different modeling techniques to make flood forecasting more accurate. The framework combines the strengths of (a) complex models (or process‐based models, PBMs) based on our understanding of relevant processes that can reproduce measurable quantities; (b) simpler models that are designed to mimic PBM's solutions—known as surrogate models—and make predictions within a few seconds; and (c) machine learning models that can detect relationships among variables using only data, improve the accuracy of prediction, and provide estimates of prediction uncertainty. The framework is tested in an urbanized watershed and shows a significant improvement in both computational efficiency and accuracy of streamflow prediction. Ultimately, the proposed framework is a novel powerful solution that combines the latest advances in different types of modeling approaches to solve prediction problems in geosciences. Its adaptability and efficiency make it suitable for a wide range of situations. Key Points While PBMs are physics‐based, the complexity of uncertainties and the high computational burden have limited their utility for predictions The developed novel framework integrates process‐based models, surrogate, and machine learning (ML) models to predict ensemble flood attributes with error quantification A novel probabilistic ML model partitions the errors into reducible and irreducible types, also quantifying their distributions

Climate change, increasing atmospheric CO2, and land use change have altered biogeochemical and hydrologic cycles world-wide, with grassland systems being particularly vulnerable to resulting vegetation shifts (Komatsu et al., 2019). Therefore, incorporating plant community dynamics into ecosystem models is critical for accurate forecasting of ecosystem responses to global change (Levine, 2016). Process-based ecosystem models, which simulate the biogeochemical transfers of mass and energy among biota, the subsurface, and atmosphere, require representation of dynamic composition of organisms within ecosystems. For example, these models simulate leaf and plant-level characteristics, such as electron transport rate and allometry of carbon (C) allocation, to predict how net primary productivity and other ecosystem processes respond to abiotic drivers. These models are particularly useful in scaling from organismal to ecosystem levels but are still underdeveloped in their ability to capture community change, especially in grassland ecosystems. To represent compositional changes, these models must simulate competition, mortality, establishment, and reproduction of plant populations within communities. Yet, current ecosystem modeling approaches to forecast plant community change have derived from studies of forested systems and are either too coarse to capture fine-scale community dynamics (e.g. dynamic global vegetation models (DGVMs)) or too complex to be used at large spatial scales (e.g. forest gap models).

Summary Climate models predict that the frequency, intensity and duration of drought events will increase in tropical regions. Although water withdrawal from deep soil layers is generally considered to be an efficient adaptation to drought, there is little information on the role played by deep roots in tropical forests. Tropical Eucalyptus plantations managed in short rotation cycles are simple forest ecosystems that may provide an insight into the water use by trees in tropical forests. The contribution made by water withdrawn from deep soil layers to the water required for evapotranspiration was quantified daily from planting to harvesting age for a Eucalyptus grandis stand using a soil water transfer model coupled with an ecophysiological forest model (MAESPA). The model was parameterized using an extensive data set and validated using time series of the soil water content down to a depth of 10 m and water‐table level, as well as evapotranspiration measured using eddy covariance. Fast root growth after planting provided access to large quantities of water stored in deep soil layers over the first 2 years. Eucalyptus roots reached the water‐table at a depth of 12 m after 2 years. Although the mean water withdrawal from depths of over 10 m amounted to only 5% of canopy transpiration from planting to a harvesting age of 5 years, the proportion of water taken up near the water‐table was much higher during dry periods. The water‐table rose from 18 to 12 m below‐ground over 2 years after the harvest of the previous stand and then fell until harvesting age as evapotranspiration rates exceeded the annual rainfall. Deep rooting is an efficient strategy to increase the amount of water available for the trees, allowing the uptake of transient gravitational water and possibly giving access to a deep water‐table. Deep soil layers have an important buffer role for large amounts of water stored during the wet season that is taken up by trees during dry periods. Our study confirms that deep rooting could be a major mechanism explaining high transpiration rates throughout the year in many tropical forests. Lay Summary