Simulating damage for wind storms in the land surface model ORCHIDEE-CAN (revision 4262)

Chen, Yiying; Gardiner, Barry; Pasztor, Ferenc; Blennow, Kristina; Ryder, James; Valade, Aude; Naudts, Kim; Otto, Juliane; McGrath, Matthew J.; Planque, Carole; Luyssaert, Sebastiaan

doi:10.5194/gmd-11-771-2018

Cited by 39 publications

(27 citation statements)

References 89 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Table 3), tree mortality from ephemeral insect and pathogen outbreaks, which, at least in some regions, might be similar in magnitude to tree mortality from fire (Kautz et al, 2018) and liable to intensify with global warming (Seidl et al, 2017), is not to our knowledge part of any operational global model. Stand-replacing windthrow events, which are the main natural disturbance in parts of temperate and tropical forests (Negrón-Juárez et al, 2018;Seidl et al, 2014), are another example of a key process missing in current models (but see Chen et al, 2018). Accounting for such disturbances through a process-oriented modelling approach (Chen et al, 2018;Dietze and Matthes, 2014;Huang et al, 2019;Landry et al, 2016) remains highly challenging in the absence of sufficient quantitative data on cause and effect.…”

Section: Processes Causing Tree Mortality (H4)mentioning

confidence: 99%

Understanding the uncertainty in global forest carbon turnover

et al. 2020

View full text Add to dashboard Cite

Abstract. The length of time that carbon remains in forest biomass is one of the largest uncertainties in the global carbon cycle, with both recent historical baselines and future responses to environmental change poorly constrained by available observations. In the absence of large-scale observations, models used for global assessments tend to fall back on simplified assumptions of the turnover rates of biomass and soil carbon pools. In this study, the biomass carbon turnover times calculated by an ensemble of contemporary terrestrial biosphere models (TBMs) are analysed to assess their current capability to accurately estimate biomass carbon turnover times in forests and how these times are anticipated to change in the future. Modelled baseline 1985–2014 global average forest biomass turnover times vary from 12.2 to 23.5 years between TBMs. TBM differences in phenological processes, which control allocation to, and turnover rate of, leaves and fine roots, are as important as tree mortality with regard to explaining the variation in total turnover among TBMs. The different governing mechanisms exhibited by each TBM result in a wide range of plausible turnover time projections for the end of the century. Based on these simulations, it is not possible to draw robust conclusions regarding likely future changes in turnover time, and thus biomass change, for different regions. Both spatial and temporal uncertainty in turnover time are strongly linked to model assumptions concerning plant functional type distributions and their controls. Thirteen model-based hypotheses of controls on turnover time are identified, along with recommendations for pragmatic steps to test them using existing and novel observations. Efforts to resolve uncertainty in turnover time, and thus its impacts on the future evolution of biomass carbon stocks across the world's forests, will need to address both mortality and establishment components of forest demography, as well as allocation of carbon to woody versus non-woody biomass growth.

show abstract

Section: Processes Causing Tree Mortality (H4)mentioning

confidence: 99%

Understanding the uncertainty in global forest carbon turnover

et al. 2020

View full text Add to dashboard Cite

show abstract

“…The TC was added as it is reasonable that wind-affected areas will provide a higher degree of brightness and lower degree of greenness with respect to undisturbed areas (Baumann et al, 2014). Several machine learning algorithms were employed, including Random Forest, Extremely Randomized Forest, Gradient Boosting Machines, Deep Neural Networks and Stacked Ensemble, all trained and cross-validated based on K-fold validation with K = 5 (Click et al, 2016). Results, based on the best performing classification model (Random Forest), provided very promising accuracy, with an F1 score of 0.97, 27 false positives and 1 false negative over 915 pixels used for testing (507 undamaged and 408 damaged).…”

Section: Remote Sensing Detection and Attribution Of Wind Disturbancesmentioning

confidence: 99%

A spatially explicit database of wind disturbances in European forests over the period 2000–2018

Forzieri

Pecchi

Girardello

et al. 2020

Earth Syst. Sci. Data

Self Cite

View full text Add to dashboard Cite

Abstract. Strong winds may uproot and break trees and represent a major natural disturbance for European forests. Wind disturbances have intensified over the last decades globally and are expected to further rise in view of the effects of climate change. Despite the importance of such natural disturbances, there are currently no spatially explicit databases of wind-related impact at a pan-European scale. Here, we present a new database of wind disturbances in European forests (FORWIND). FORWIND is comprised of more than 80 000 spatially delineated areas in Europe that were disturbed by wind in the period 2000–2018 and describes them in a harmonized and consistent geographical vector format. The database includes all major windstorms that occurred over the observational period (e.g. Gudrun, Kyrill, Klaus, Xynthia and Vaia) and represents approximately 30 % of the reported damaging wind events in Europe. Correlation analyses between the areas in FORWIND and land cover changes retrieved from the Landsat-based Global Forest Change dataset and the MODIS Global Disturbance Index corroborate the robustness of FORWIND. Spearman rank coefficients range between 0.27 and 0.48 (p value < 0.05). When recorded forest areas are rescaled based on their damage degree, correlation increases to 0.54. Wind-damaged growing stock volumes reported in national inventories (FORESTORM dataset) are generally higher than analogous metrics provided by FORWIND in combination with satellite-based biomass and country-scale statistics of growing stock volume. The potential of FORWIND is explored for a range of challenging topics and scientific fields, including scaling relations of wind damage, forest vulnerability modelling, remote sensing monitoring of forest disturbance, representation of uprooting and breakage of trees in large-scale land surface models, and hydrogeological risks following wind damage. Overall, FORWIND represents an essential and open-access spatial source that can be used to improve the understanding, detection and prediction of wind disturbances and the consequent impacts on forest ecosystems and the land–atmosphere system. Data sharing is encouraged in order to continuously update and improve FORWIND. The dataset is available at https://doi.org/10.6084/m9.figshare.9555008 (Forzieri et al., 2019).

show abstract

“…(iv) Although abrupt disturbances such as fires, pests and storms are increasingly being simulated by land-surface models (Chen et al, 2018;Yue et al, 2014) these functionalities are at present of limited use for benchmarking against TRW data.…”

Section: (Iii)mentioning

confidence: 99%

Using the International Tree-Ring Data Bank (ITRDB) records as century-long benchmarks for land-surface models

Jeong¹,

Barichivich²,

Peylin³

et al. 2020

Preprint

Self Cite

View full text Add to dashboard Cite

Abstract. The search for a long-term benchmark for land-surface models (LSM) has brought tree-ring data to the attention of the land-surface community as they record growth well before human-induced environmental changes became important. The most comprehensive archive of publicly shared tree-ring data is the International Tree-ring Data Bank (ITRDB). Many records in the ITRDB have, however, been collected almost exclusively with a view on maximizing an environmental target signal (e.g. climate), which has resulted in a biased representation of forested sites and landscapes and thus limits its use as a data source for benchmarking. The aim of this study is to propose advances in land-surface modelling and data processing to enable the land-surface community to re-use the ITRDB data as a much-needed century-long benchmark. Given that tree-ring width is largely explained by phenology, tree size, and climate sensitivity, LSMs that intend to use it as a benchmark should at least simulate tree phenology, size-dependent growth, differently-sized trees within a stand, and responses to changes in temperature, precipitation and atmospheric CO2 con¬cen¬tra¬tions. Yet, even if LSMs were capable of accurately simulating tree-ring width, sampling biases in the ITRDB need to be accounted for. This study proposes two solutions: exploiting the observation that the variation due to size-related growth by far exceeds the variation due to environmental changes; and simulating a size-structured population of trees. Combining the proposed advances in modelling and data processing resulted in four complementary benchmarks - reflecting different usage of the information contained in the ITRDB - each described by two metrics rooted in statistics that quantify the performance of the benchmark. Although the proposed benchmarks are unlikely to be precise, they advance the field by providing a much-needed large-scale constraint on changes in the simulated maximum tree diameter and annual growth increment for the transition from pre-industrial to present-day environmental conditions over the past century. Hence, the proposed benchmarks open up new ways of exploring the ITRDB archive, stimulate the dendrochronological community to refine its sampling protocols to produce new and spatially unbiased tree-ring networks, and help the modelling community to move beyond the short-term benchmarking of LSM.

show abstract

Simulating damage for wind storms in the land surface model ORCHIDEE-CAN (revision 4262)

Cited by 39 publications

References 89 publications

Understanding the uncertainty in global forest carbon turnover

Understanding the uncertainty in global forest carbon turnover

A spatially explicit database of wind disturbances in European forests over the period 2000–2018

Using the International Tree-Ring Data Bank (ITRDB) records as century-long benchmarks for land-surface models

Contact Info

Product

Resources

About