European larch phenology in the Alps: can we grasp the role of ecological factors by combining field observations and inverse modelling?

Migliavacca, Mirco; Cremonese, Edoardo; Colombo, R.; Busetto, Lorenzo; Galvagno, Marta; Ganis, L.; Meroni, Michele; Pari, E.; Rossini, Micol; Siniscalco, Consolata; Cella, Umberto Morra di

doi:10.1007/s00484-008-0152-9

Cited by 61 publications

(60 citation statements)

References 67 publications

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“…For means of simplification, we decided to define a time point in a year when the process of leaf senescence will be completed, independently of climate conditions. This is also in accordance with the findings of Migliavacca et al (2008), who reported that Larix decidua trees of different stands complete leaf senescence roughly at the same time, independently of the senescence curve. We decided to use 1 December as that day.…”

Section: Appendix A: New Plant Physiological Functions and Parameterssupporting

confidence: 93%

“…The fine-tuning of LPJ-GUESS includes a new function to describe the leaf senescence of Larix decidua. Its photosynthetic activity decreases in fall with an s-shaped curve (based on results of Migliavacca et al (2008), see Appendix A). Furthermore, we developed a modified functionality of the plant parameter of the maximum 20-year coldest month mean temperature for establishment.…”

Section: Model Adaptationmentioning

confidence: 99%

“…However, especially for larches, leaf senescence can be a process that lasts for months during which photosynthetic intensity is reduced stepwise. Based on Migliavacca et al (2008), Scherstjanoi et al (2013) included this physiological trait by defining a new phenology type for Larix decidua. The tree species is modeled like a summer-green species, but in autumn the phenological state of the larches will decrease with an s-shaped curve.…”

Section: Appendix A: New Plant Physiological Functions and Parametersmentioning

confidence: 99%

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Application of a computationally efficient method to approximate gap model results with a probabilistic approach

2014

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Abstract.To be able to simulate climate change effects on forest dynamics over the whole of Switzerland, we adapted the second-generation DGVM (dynamic global vegetation model) LPJ-GUESS (Lund-Potsdam-Jena General Ecosystem Simulator) to the Alpine environment. We modified model functions, tuned model parameters, and implemented new tree species to represent the potential natural vegetation of Alpine landscapes. Furthermore, we increased the computational efficiency of the model to enable area-covering simulations in a fine resolution (1 km) sufficient for the complex topography of the Alps, which resulted in more than 32 000 simulation grid cells. To this aim, we applied the recently developed method GAPPARD (approximating GAP model results with a Probabilistic Approach to account for stand Replacing Disturbances) (Scherstjanoi et al., 2013) to LPJ-GUESS. GAPPARD derives mean output values from a combination of simulation runs without disturbances and a patch age distribution defined by the disturbance frequency. With this computationally efficient method, which increased the model's speed by approximately the factor 8, we were able to faster detect the shortcomings of LPJ-GUESS functions and parameters. We used the adapted LPJ-GUESS together with GAPPARD to assess the influence of one climate change scenario on dynamics of tree species composition and biomass throughout the 21st century in Switzerland. To allow for comparison with the original model, we additionally simulated forest dynamics along a north-south transect through Switzerland. The results from this transect confirmed the high value of the GAPPARD method despite some limitations towards extreme climatic events. It allowed for the first time to obtain area-wide, detailed high-resolution LPJ-GUESS simulation results for a large part of the Alpine region.

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Section: Appendix A: New Plant Physiological Functions and Parameterssupporting

confidence: 93%

Section: Model Adaptationmentioning

confidence: 99%

Section: Appendix A: New Plant Physiological Functions and Parametersmentioning

confidence: 99%

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Application of a computationally efficient method to approximate gap model results with a probabilistic approach

2014

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“…It affects an enormous number of people and the biggest area worldwide [1] drawing attention of scientists, economists, policy makers as well as the public [2]. Increasing, but not uniform drought threat is noted on local to global scale, and is driven by prolonged climatic anomaly [3,4] expressed in change in temperature, precipitation and insolation patterns (e.g., [5][6][7][8][9][10][11][12]). …”

Section: Introductionmentioning

confidence: 99%

Alpine Forest Drought Monitoring in South Tyrol: PCA Based Synergy between scPDSI Data and MODIS Derived NDVI and NDII7 Time Series

et al. 2016

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Observed alternation of global and local meteorological patterns governs increasing drought impact, which puts at risk ecological balance and biodiversity of the alpine forest. Despite considerable attention, drought impact on forest ecosystems is still not entirely understood, and comprehensive forest drought monitoring has not been implemented. In this study, we proposed to bridge this gap exploiting a time-domain synergetic use of medium resolution MODSI NDVI (Normalized Difference Vegetation Index) and NDII7 (Normalized Difference Infrared Index band 7) time series as well as on-station temperature and precipitation measures combined in the scPDSI (self-calibrated Palmer Drought Severity Index) datasets. Analysis employed the S-mode Principal Component Analysis (PCA) examined under multiple method settings and data setups. The investigation performed for South Tyrol (2001-2012) indicated prolonged meteorological drought condition between 2003 and 2007, as well as general drying tendencies. Corresponding temporal variability was identified for local mountain forest. The former response was fostered more often by NDII7, which is related to foliage water content, whereas NDVI was more prone to report on an overall downturn and implied drop in forest photosynthetic activity. Among tested approaches, the covariance-matrix based S-mode PCA of z-score normalized vegetation season NDVI and NDII7 time series ensured the most prominent identification of drought impact. Consistency in recognized temporal patterns confirms integrity of the approach and aptness of used remote-sensed datasets, suggesting great potential for drought oriented environmental analyses.

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“…With the advances of computer science and the concern for global warming, more efforts have been made in phenological model development. For the past decades, these efforts range from examining the controls of environmental factors, biological processes, and intrinsic traits [23,24] on phenology, modelling techniques [25][26][27], to ecosystem-level and evolutionary consequences of phenological changes [28,29].…”

Section: Introductionmentioning

confidence: 99%

Spatio-Temporal LAI Modelling by Integrating Climate and MODIS LAI Data in a Mesoscale Catchment

Sun

Schulz

2017

Remote Sensing

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Vegetation is often represented by the leaf area index (LAI) in many ecological, hydrological and meteorological land surface models. However, the spatio-temporal dynamics of the vegetation are important to represent in these models. While the widely applied methods, such as the Canopy Structure Dynamic Model (CSDM) and the Double Logistic Model (DLM) are solely based on cumulative daily mean temperature data as input, a new spatio-temporal LAI prediction model referred to as the Temperature Precipitation Vegetation Model (TPVM) is developed that also considers cumulative precipitation data as input into the modelling process. TPVM as well as CDSM and DLM model performances are compared and evaluated against filtered LAI data from the Moderate Resolution Imaging Spectroradiometer (MODIS). The calibration/validation results of a cross-validation performed in the meso-scale Attert catchment in Luxembourg indicated that the DLM and TPVM generally provided more realistic and accurate LAI data. The TPVM performed superiorly for the agricultural land cover types compared to the other two models, which only used the temperature data. The Pearson's correlation coefficient (CC) between TPVM and the field measurement is 0.78, compared to 0.73 and 0.69 for the DLM and CSDM, respectively. The phenological metrics were derived from the TPVM model to investigate the interaction between the climate variables and the LAI variations. These interactions illustrated the dominant control of temperature on the LAI dynamics for deciduous forest cover, and a combined influence of temperature with precipitation for the agricultural land use areas.

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European larch phenology in the Alps: can we grasp the role of ecological factors by combining field observations and inverse modelling?

Cited by 61 publications

References 67 publications

Application of a computationally efficient method to approximate gap model results with a probabilistic approach

Application of a computationally efficient method to approximate gap model results with a probabilistic approach

Alpine Forest Drought Monitoring in South Tyrol: PCA Based Synergy between scPDSI Data and MODIS Derived NDVI and NDII7 Time Series

Spatio-Temporal LAI Modelling by Integrating Climate and MODIS LAI Data in a Mesoscale Catchment

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