An Ecohydrological Perspective on Drought-induced Forest Mortality

Regional‐scale drought‐induced forest mortality events are projected to become more frequent under future climates due to changes in rainfall patterns. The occurrence of these mortality events is driven by exogenous factors such as frequency and severity of drought and endogenous factors such as tree water and carbon use strategies. To explore the link between these exogenous and endogenous factors underlying forest mortality, a stochastic ecohydrological framework that accounts for random arrival and length of droughts as well as responses of tree water and carbon balance to soil water deficit is proposed. The main dynamics of this system are characterized with respect to the spectrum of anisohydric‐isohydric stomatal control strategies. Using results from a controlled drought experiment, a maximum tolerable drought length at the point where carbon starvation and hydraulic failure occur simultaneously is predicted, supporting the notion of coordinated hydraulic function and metabolism. We find qualitative agreement between the model predictions and observed regional‐scale canopy dieback across a precipitation gradient during the 2002–2003 southwestern United States drought. Both the model and data suggest a rapid increase of mortality frequency below a precipitation threshold. The model also provides estimates of mortality frequency for given plant drought strategies and climate regimes. The proposed ecohydrological approach can be expanded to estimate the effect of anticipated climate change on drought‐induced forest mortality and associated consequences for the water and carbon balances

Oak forest carbon and water simulations:Model intercomparisons and evaluations against independent data

Models represent our primary method for integration of small-scale, process-level phenomena into a comprehensive description of forest-stand or ecosystem function. They also represent a key method for testing hypotheses about the response of forest ecosystems to multiple changing environmental conditions. This paper describes the evaluation of 13 stand-level models varying in their spatial, mechanistic, and temporal complexity for their ability to capture intra- and interannual components of the water and carbon cycle for an upland, oak-dominated forest of eastern Tennessee. Comparisons between model simulations and observations were conducted for hourly, daily, and annual time steps. Data for the comparisons were obtained from a wide range of methods including: eddy covariance, sapflow, chamber-based soil respiration, biometric estimates of stand-level net primary production and growth, and soil water content by time or frequency domain reflectometry. Response surfaces of carbon and water flux as a function of environmental drivers, and a variety of goodness-of-fit statistics (bias, absolute bias, and model efficiency) were used to judge model performance. A single model did not consistently perform the best at all time steps or for all variables considered. Intermodel comparisons showed good agreement for water cycle fluxes, but considerable disagreement among models for predicted carbon fluxes. The mean of all model outputs, however, was nearly always the best fit to the observations. Not surprisingly, models missing key forest components or processes, such as roots or modeled soil water content, were unable to provide accurate predictions of ecosystem responses to short-term drought phenomenon. Nevertheless, an inability to correctly capture short-term physiological processes under drought was not necessarily an indicator of poor annual water and carbon budget simulations. This is possible because droughts in the subject ecosystem were of short duration and therefore had a small cumulative impact. Models using hourly time steps and detailed mechanistic processes, and having a realistic spatial representation of the forest ecosystem provided the best predictions of observed data. Predictive ability of all models deteriorated under drought conditions, suggesting that further work is needed to evaluate and improve ecosystem model performance under unusual conditions, such as drought, that are a common focus of environmental change discussions

Increasing Atmospheric Humidity and CO\u3csub\u3e2\u3c/sub\u3e Concentration Alleviate Forest Mortality Risk

Climate-induced forest mortality is being increasingly observed throughout the globe. Alarmingly, it is expected to exacerbate under climate change due to shifting precipitation patterns and rising air temperature. However, the impact of concomitant changes in atmospheric humidity and CO2 concentration through their influence on stomatal kinetics remains a subject of debate and inquiry. By using a dynamic soil–plant–atmosphere model, mortality risks associated with hydraulic failure and stomatal closure for 13 temperate and tropical forest biomes across the globe are analyzed. The mortality risk is evaluated in response to both individual and combined changes in precipitation amounts and their seasonal distribution, mean air temperature, specific humidity, and atmospheric CO2 concentration. Model results show that the risk is predicted to significantly increase due to changes in precipitation and air temperature regime for the period 2050–2069. However, this increase may largely get alleviated by concurrent increases in atmospheric specific humidity and CO2 concentration. The increase in mortality risk is expected to be higher for needleleaf forests than for broadleaf forests, as a result of disparity in hydraulic traits. These findings will facilitate decisions about intervention and management of different forest types under changing climate

Impact of droughts on the carbon cycle in European vegetation : a probabilistic risk analysis using six vegetation models

Peer reviewedPublisher PD

Physiological drought responses improve predictions of live fuel moisture dynamics in a Mediterranean forest.

The moisture content of live fuels is an important determinant of forest flammability. Current approaches for modelling live fuel moisture content typically focus on the use of drought indices. However, these have mixed success partly because of species-specific differences in drought responses. Here we seek to understand the physiological mechanisms driving changes in live fuel moisture content, and to investigate the potential for incorporating plant physiological traits into live fuel moisture models. We measured the dynamics of leaf moisture content, access to water resources (through stable isotope analyses) and physiological traits (including leaf water potential, stomatal conductance, and cellular osmotic and elastic adjustments) across a fire season in a Mediterranean mixed forest in Catalonia, NE Spain. We found that differences in both seasonal variation and minimum values of live fuel moisture content were a function of access to water resources and plant physiological traits. Specifically, those species with the lowest minimum moisture content and largest seasonal variation in moisture (Cistus albidus: 49–137% and Rosmarinus officinalis: 47–144%) were most reliant on shallow soil water and had the lowest values of predawn leaf water potential. Species with the smallest variation in live fuel moisture content (Pinus nigra: 96–116% and Quercus ilex: 56–91%) exhibited isohydric behaviour (little variation in midday leaf water potential, and relatively tight regulation of stomata in response to soil drying). Of the traits measured, predawn leaf water potential provided the strongest predictor of live fuel moisture content (R2 = 0.63, AIC = 249), outperforming two commonly used drought indices (both with R2 = 0.49, AIC = 258). This is the first study to explicitly link fuel moisture with plant physiology and our findings demonstrate the potential and importance of incorporating ecophysiological plant traits to investigating seasonal changes in fuel moisture and, more broadly, forest flammability.This study was made possible thanks to the collaboration of and the staff from the Natural Park of Poblet, P Sopeña, and the technical staff from MedForLab. This study was funded by the Spanish Government (RYC-2012-10970, AGL2015-69151-R). R. H. Nolan was supported with funding from the New South Wales Office of Environment and Heritage, via the Bushfire Risk Management Research Hub. We benefitted from critical comments from J Voltas, JM Moreno and L Serrano and instrument loans from R Savín

A semi-empirical model of boreal-forest gross primary production, evapotranspiration, and soil water — calibration and sensitivity analysis

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A process-based model of conifer forest structure and function with special emphasis on leaf lifespan

We describe the University of Sheffield Conifer Model (USCM), a process-based approach for simulating conifer forest carbon, nitrogen, and water fluxes by up-scaling widely applicable relationships between leaf lifespan and function. The USCM is designed to predict and analyze the biogeochemistry and biophysics of conifer forests that dominated the ice-free high-latitude regions under the high pCO2 “greenhouse” world 290–50 Myr ago. It will be of use in future research investigating controls on the contrasting distribution of ancient evergreen and deciduous forests between hemispheres, and their differential feedbacks on polar climate through the exchange of energy and materials with the atmosphere. Emphasis is placed on leaf lifespan because this trait can be determined from the anatomical characteristics of fossil conifer woods and influences a range of ecosystem processes. Extensive testing of simulated net primary production and partitioning, leaf area index, evapotranspiration, nitrogen uptake, and land surface energy partitioning showed close agreement with observations from sites across a wide climatic gradient. This indicates the generic utility of our model, and adequate representation of the key processes involved in forest function using only information on leaf lifespan, climate, and soils

Modelling tropical forest responses to drought and El Niño with a stomatal optimization model based on xylem hydraulics.

The current generation of dynamic global vegetation models (DGVMs) lacks a mechanistic representation of vegetation responses to soil drought, impairing their ability to accurately predict Earth system responses to future climate scenarios and climatic anomalies, such as El Niño events. We propose a simple numerical approach to model plant responses to drought coupling stomatal optimality theory and plant hydraulics that can be used in dynamic global vegetation models (DGVMs). The model is validated against stand-scale forest transpiration (E) observations from a long-term soil drought experiment and used to predict the response of three Amazonian forest sites to climatic anomalies during the twentieth century. We show that our stomatal optimization model produces realistic stomatal responses to environmental conditions and can accurately simulate how tropical forest E responds to seasonal, and even long-term soil drought. Our model predicts a stronger cumulative effect of climatic anomalies in Amazon forest sites exposed to soil drought during El Niño years than can be captured by alternative empirical drought representation schemes. The contrasting responses between our model and empirical drought factors highlight the utility of hydraulically-based stomatal optimization models to represent vegetation responses to drought and climatic anomalies in DGVMs.This article is part of a discussion meeting issue 'The impact of the 2015/2016 El Niño on the terrestrial tropical carbon cycle: patterns, mechanisms and implications'

Integrating in-situ measurements, land surface models and satellite remote sensing to understand impacts of environmental changes on terrestrial ecosystem processes at multiple scales

How terrestrial ecosystems respond to environmental changes affects the well-being of human society. Thus, extreme climate events, increasing the atmospheric concentration of CO₂, and drastic changes in temperature are sources of major concern. However, our current capacity to understand and predict these responses is still limited because a myriad of physical, chemical, and biological processes are involved. While many mechanistic-based land surface models have been developed, their performances remain relatively poor and require continuous improvement. While ground-based and space-based observational datasets of the surface of the Earth have been available for a long time, their linkages to the functional aspects of the processes in terrestrial ecosystems often are weak. In this study, I used the approach of integrating in-situ measurements, land surface models, and remote sensing by satellites. I hypothesized that, through such integration, the impacts of environmental changes on terrestrial processes at multiple scales could be better understood and even predicted, especially the impacts related to the functions of important ecosystems. I tested this hypothesis at the local, regional, and global scales. At the local scale, i.e., at a Midwest forest site known as the isoprene volcano of the world, I examined the effects of droughts on the emissions of isoprene, which is the most abundant, non-methane, biogenic volatile organic compound. I compared flux tower observations with simulations performed by a state-of-the-art land model (CLM) coupled with the model of emissions of gases and aerosols from Nature version 2.1 (MEGAN2.1), and I used these observations to develop an understanding of how the amount of moisture in the soil and the ambient temperature affect the prediction of isoprene emissions during droughts. I found that temperature had a delaying effect on isoprene emissions, which are sensitive to variations in the moisture content of the soil. Thus, during drought conditions, both the delaying effect and the sensitivity to moisture are overlooked by the model. A better model that does not have these two shortcomings is required for realistic predictions of isoprene emissions. At the regional scale, I investigated the potential of using sun-induced chlorophyll fluorescence (SIF) retrieved from a satellite to monitor vegetation activities in an arid region and a semi-arid region in Australia. I chose these two types of regions for this investigation because the ecosystems in such regions have important effects on the global carbon cycle, while their contributions are poorly constrained in global carbon budgets. I found that SIF was synchronized better with the activity of vegetation than other indices that are commonly used for this purpose. I quantified the relationships between the various activities of plants and the amount and frequency of precipitation, and I was able to demonstrate that, over the region being studied, SIF represented the activity of vegetation in response to the availability of water better than other, remotely-sensed variables. At the global scale, I used multiple model ensembles to determine the climatic and anthropogenic controls on the terrestrial evapotranspiration trends from 1982 to 2010. After climatic influences, increases in CO₂ were found to be the second-most dominant factor that affected the trend of ET. CO₂ causes a decreasing trend in the canopy’s transpiration and ET, and this is especially of concern for tropical forests and high-latitude shrub lands. The increased deposition of nitrogen amplifies the global ET slightly due to enhanced growth of plants. On a global scale, land-use-induced ET responses are minor, but they can be significant locally, particularly over regions with intensive changes in the land-cover. The results of my studies demonstrated that integrating in-situ measurements, models of the surface on the land, and remote sensing using satellites can provide insights regarding the impacts of environmental changes on terrestrial processes at multiple scales. This approach is particularly important when models are imperfect and observations are lacking. My findings indicated ways that future models can be improved and identified key observational needs for the functions of terrestrial ecosystems.Geological Science