Your browser does not allow JavaScript!
JavaScript is necessary for the proper functioning of this website. Please enable JavaScript or use a modern browser.
Open Science Slovenia
Open Science
DiKUL
slv
|
eng
Search
Browse
New in RUL
About RUL
In numbers
Help
Sign in
Evaluating five forest models using multi-decadal inventory data from mountain forests
ID
Irauschek, Florian
(
Author
),
ID
Barka, Ivan
(
Author
),
ID
Bugmann, Harald
(
Author
),
ID
Courbaud, Benoit
(
Author
),
ID
Elkin, Che
(
Author
),
ID
Hlásny, Tomáš
(
Author
),
ID
Klopčič, Matija
(
Author
),
ID
Mina, Marco
(
Author
),
ID
Rammert, Werner
(
Author
),
ID
Lexer, Manfred J.
(
Author
)
URL - Source URL, Visit
https://doi.org/10.1016/j.ecolmodel.2021.109493
PDF - Presentation file,
Download
(1,21 MB)
MD5: 4F4D17FFEE0EE5A2F209E7B109560160
Image galllery
Abstract
Forest ecosystem models, being widespread science tools and used for forest management decision support are usually evaluated individually against field data sets, while model intercomparison and joint evaluation studies are rare. We tested five forest models according to a harmonized protocol against data from nine forest compartments in the Sn%žnik region, in Slovenia. The suite of models included stand- and landscape-scale, empirical- and process-based models used across Europe. The test dataset originated from inventory data covering 50 years (tree measurements 1963, 1983 and 2013) and included annual harvesting records at tree level. Uncertainties in data and forest conditions were considered by defining 12 scenarios varying initial regeneration, browsing pressure and harvest modalities. We evaluated the models` ability to initialize forest conditions accurately, whether management interventions could be implemented based on harvest records, and how well basal area and diameter structure could be predicted. Simulation results for basal area development showed good to satisfactory performance for all models, at which SAMSARA2, SIBYLA and PICUS showed the best agreement. Comparison of simulated and observed diameter distributions showed good performance of ForClim, PICUS, SAMSARA2 and SIBYLA. Model output variability was between 6% and 24%, indicating the relevance to consider uncertainties that can be attributed to specific sources. There was no clear hierarchy between more empirical or more process-based models regarding accuracy of stand development projections. The cohort-based landscape model LandClim showed the lowest stand-level accuracy and scenario sensitivity, but results nevertheless qualified it for complementary application at landscape scale. Within individual-based models, spatially explicit models seemed to be more suitable for heterogeneous mixed mountain forests. The findings demonstrated the usefulness of inventory datasets for model testing and intercomparison.
Language:
English
Keywords:
model intercomparison
,
tree growth
,
tree mortality
,
forest management
,
ungulate browsing
,
forest inventory data
Work type:
Article
Typology:
1.01 - Original Scientific Article
Organization:
BF - Biotechnical Faculty
Publication status:
Published
Publication version:
Version of Record
Submitted for review:
21.07.2020
Article acceptance date:
18.02.2021
Publication date:
03.03.2021
Year:
2021
Number of pages:
11 str.
Numbering:
Vol. 445, article 109493
PID:
20.500.12556/RUL-133322
UDC:
630*52:630*45
ISSN on article:
0304-3800
DOI:
10.1016/j.ecolmodel.2021.109493
COBISS.SI-ID:
85939203
Publication date in RUL:
22.11.2021
Views:
1368
Downloads:
184
Metadata:
Cite this work
Plain text
BibTeX
EndNote XML
EndNote/Refer
RIS
ABNT
ACM Ref
AMA
APA
Chicago 17th Author-Date
Harvard
IEEE
ISO 690
MLA
Vancouver
:
Copy citation
Share:
Record is a part of a journal
Title:
Ecological modelling
Shortened title:
Ecol. model.
Publisher:
Elsevier
ISSN:
0304-3800
COBISS.SI-ID:
26792960
Secondary language
Language:
Slovenian
Keywords:
primerjava modelov
,
rast dreves
,
mortaliteta
,
gospodarjenje z gozdovi
,
objedanje
,
gozdna inventura
Similar documents
Similar works from RUL:
Similar works from other Slovenian collections:
Back