Biophysical and Human Controls of Land Productivity under Global Change : Development and Demonstration of Parsimonious Modelling Techniques
(2016)- Abstract
- Net primary production (NPP) serves as an indicator for plant-based resources such as food, timber and biofuel for human appropriation. It is defined by the annual production of plant matter and is mainly controlled by climate and human activities. Climate change in combination with human activities is altering NPP. As the controls of NPP are expected to further change in the future, it is vital to investigate alterations in NPP and their magnitudes. The impacts of climate change and human activities on NPP can be explored in integrated assessment (IA) frameworks, where sectoral models are coupled and interact rapidly. For such frameworks, parsimonious models are desired because they enable rapid estimates and facilitate easy model... (More)
- Net primary production (NPP) serves as an indicator for plant-based resources such as food, timber and biofuel for human appropriation. It is defined by the annual production of plant matter and is mainly controlled by climate and human activities. Climate change in combination with human activities is altering NPP. As the controls of NPP are expected to further change in the future, it is vital to investigate alterations in NPP and their magnitudes. The impacts of climate change and human activities on NPP can be explored in integrated assessment (IA) frameworks, where sectoral models are coupled and interact rapidly. For such frameworks, parsimonious models are desired because they enable rapid estimates and facilitate easy model coupling for explorations of multiple global change scenarios (i.e. large volumes of data).
This thesis aims to advance parsimonious modelling techniques for quantifying current and future NPP on land. This is accomplished by developing and testing rapid models that facilitate easy model coupling to explore the impacts of multiple global change scenarios on NPP. The model development is based on the meta-modelling concept, which can be applied to simplify the dynamic vegetation model LPJ-GUESS in a parsimonious model. For this, multiple climate change and [CO2] perturbations are applied to LPJ-GUESS to simulate NPP. The NPP data are then used to define biophysically motivated relationships between NPP and the driving climate variables along with [CO2]. The relationships are then combined in a synergistic function – the meta-model. Thereafter, the meta-models are assessed for their performance in estimating NPP by comparing them to LPJ-GUESS NPP simulations, to independent field observations and to NPP experiments under enriched [CO2] on biome level. The results provide confidence in the modelled NPP estimates for the most productive biomes, which are important for global quantifications of NPP. The meta-models capture NPP enhancement under enhanced [CO2] adequately in the majority of the studied biomes. Finally, the NPP meta-models are coupled with other sectoral models in two IA modelling-frameworks in order to explore the impacts of global change on ecosystem indicators. The first framework enables an IA of climate change impacts and vulnerabilities for a range of sectors on the European level. This thesis conducts a sensitivity analysis on the effects of climatic and socio-economic change drivers on model outputs related to key sectors. This provides better quantification and increased understanding of the complex relationships between input and output variables in IA modelling-frameworks. The second framework addresses the NPP supply-demand balance in the Sahel region by coupling two sectoral models in order to analyze the timings and geographies of NPP shortfalls in the 21st century Sahel under global change. The results show consistent regional NPP shortfalls in the Sahel for the majority of global change scenarios.
Overall, the parsimonious modelling techniques developed in this thesis contribute with rapid NPP estimates on the biome and global scale. BME NPP estimates agree reasonably well with NPP observations in the majority of biomes (especially in the most productive biomes). This thesis demonstrates that NPP meta-models facilitate easy model coupling for exploring the impacts of global change on human-environmental systems in IA modelling-frameworks. (Less) - Abstract (Swedish)
- Nettoprimärproduktionen (NPP), är källan till den mat vi äter, virket vi bygger hus av och veden vi eldar med. NPP spelar också en stor roll i den globala kolcykeln, genom fotosyntesen så binder växterna koldioxid från luften och producerar blad och stammar. Hur mycket växterna producerar styrs till stor utsträckning av klimatet samt hur mycket koldioxid som finns i atmosfären. Detta gör att de förändringar som vi står inför, klimatförändringarna, som till stor del kan härledas till en ökad koldioxidhalt i atmosfären är intressanta att studera. Mänskliga aktiviteter så som jord- och skogsbruk, har också en stor inverkan på NPP. Då den globala befolkningen spås öka till 9 miljarder fram till 2050, så kommer också trycket på Jordens... (More)
- Nettoprimärproduktionen (NPP), är källan till den mat vi äter, virket vi bygger hus av och veden vi eldar med. NPP spelar också en stor roll i den globala kolcykeln, genom fotosyntesen så binder växterna koldioxid från luften och producerar blad och stammar. Hur mycket växterna producerar styrs till stor utsträckning av klimatet samt hur mycket koldioxid som finns i atmosfären. Detta gör att de förändringar som vi står inför, klimatförändringarna, som till stor del kan härledas till en ökad koldioxidhalt i atmosfären är intressanta att studera. Mänskliga aktiviteter så som jord- och skogsbruk, har också en stor inverkan på NPP. Då den globala befolkningen spås öka till 9 miljarder fram till 2050, så kommer också trycket på Jordens ekosystem att öka genom större uttag av NPP från skogar och jordbruk. Det är av stor vikt för mänskligheten att vi kan studera hur samspelet mellan hur vi sköter de ekosystem som vi är så beroende av och klimatförändringarna för att kunna ta adekvata beslut inför framtiden. För att studera detta samspel så krävs Integrerade Modeller (IM), modeller som binder samman ekosystemen och de mänskliga systemen.
För att kunna modellera detta kopplade system så krävs det förenklingar, och då framtiden är osäker, så krävs det att man utforskar många olika scenarier. För att kunna göra det, är det viktigt att dessa modeller är så snabba att man kan utföra en stor mängd simuleringar.
I den här avhandlingen så kommer jag att presentera förenklade metoder för att uppskatta NPP i naturlig vegetation baserat på en begränsad mängd klimatvariabler (t.ex. årlig medelnederbörd eller årligt temperaturmaximum) och jämföra det med mer komplexa modeller som i större utsträckning förlitar sig på detaljerad klimatdata.
Dessa förenklade modeller bygger på det så kallade metamodelleringskonceptet, där man genom att fånga hur de mest elementära processerna påverkas av en liten mängd yttre variabler kan skapa snabba responsmodeller. Jag utvärderar här, både hur de komplexa och förenklade modellerna lyckas simulera observationer från fältförsök utförda i olika ekosystemtyper runt om Jorden samt hur väl dessa modeller fångar ökningen i NPP på grund av den ökade koldioxidhalten från så kallade FACE-experiment (Free-Air Carbon Experiment).
Jag kommer också att visa hur dessa förenklade ekosystemproduktionsmodeller kan kopplas till modeller som simulerar mänskliga system. I två fallstudier, en i Europa och en i Sahelregionen i Afrika, så studeras dessa intergrerade system. I studien för Sahel, så studeras de kombinerade effekterna av klimatförändringar och populationsökningar på tillgången till NPP för mänsklig konsumtion.
(Less)
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/4a57a53c-d271-484e-b8d0-481869fc55d7
- author
- Sallaba, Florian LU
- supervisor
-
- Jonathan Seaquist LU
- Stefan Olin LU
- Veiko Lehsten LU
- opponent
-
- Reader Metzger, Marc J., University of Edinburgh, United Kingdom
- organization
- publishing date
- 2016-11
- type
- Thesis
- publication status
- published
- subject
- keywords
- Ecosystem Modelling, Meta-modelling, Net Primary Production, Global Change, Model coupling
- pages
- 234 pages
- publisher
- Lund University, Faculty of Science, Department of Physical Geography and Ecosystem Science
- defense location
- Geocentre I, lecture hall “Världen”, Sölvegatan 10, Lund
- defense date
- 2016-12-20 10:00:00
- ISBN
- 978-91-85793-70-9
- 978-91-85793-69-3
- project
- Biophysical and Human Controls of Land Productivity under Global Change. Development and Demonstration of Parsimonious Modelling Techniques.
- language
- English
- LU publication?
- yes
- id
- 4a57a53c-d271-484e-b8d0-481869fc55d7
- date added to LUP
- 2016-11-22 19:21:20
- date last changed
- 2020-09-23 11:54:08
@phdthesis{4a57a53c-d271-484e-b8d0-481869fc55d7,
abstract = {{Net primary production (NPP) serves as an indicator for plant-based resources such as food, timber and biofuel for human appropriation. It is defined by the annual production of plant matter and is mainly controlled by climate and human activities. Climate change in combination with human activities is altering NPP. As the controls of NPP are expected to further change in the future, it is vital to investigate alterations in NPP and their magnitudes. The impacts of climate change and human activities on NPP can be explored in integrated assessment (IA) frameworks, where sectoral models are coupled and interact rapidly. For such frameworks, parsimonious models are desired because they enable rapid estimates and facilitate easy model coupling for explorations of multiple global change scenarios (i.e. large volumes of data). <br/>This thesis aims to advance parsimonious modelling techniques for quantifying current and future NPP on land. This is accomplished by developing and testing rapid models that facilitate easy model coupling to explore the impacts of multiple global change scenarios on NPP. The model development is based on the meta-modelling concept, which can be applied to simplify the dynamic vegetation model LPJ-GUESS in a parsimonious model. For this, multiple climate change and [CO2] perturbations are applied to LPJ-GUESS to simulate NPP. The NPP data are then used to define biophysically motivated relationships between NPP and the driving climate variables along with [CO2]. The relationships are then combined in a synergistic function – the meta-model. Thereafter, the meta-models are assessed for their performance in estimating NPP by comparing them to LPJ-GUESS NPP simulations, to independent field observations and to NPP experiments under enriched [CO2] on biome level. The results provide confidence in the modelled NPP estimates for the most productive biomes, which are important for global quantifications of NPP. The meta-models capture NPP enhancement under enhanced [CO2] adequately in the majority of the studied biomes. Finally, the NPP meta-models are coupled with other sectoral models in two IA modelling-frameworks in order to explore the impacts of global change on ecosystem indicators. The first framework enables an IA of climate change impacts and vulnerabilities for a range of sectors on the European level. This thesis conducts a sensitivity analysis on the effects of climatic and socio-economic change drivers on model outputs related to key sectors. This provides better quantification and increased understanding of the complex relationships between input and output variables in IA modelling-frameworks. The second framework addresses the NPP supply-demand balance in the Sahel region by coupling two sectoral models in order to analyze the timings and geographies of NPP shortfalls in the 21st century Sahel under global change. The results show consistent regional NPP shortfalls in the Sahel for the majority of global change scenarios.<br/>Overall, the parsimonious modelling techniques developed in this thesis contribute with rapid NPP estimates on the biome and global scale. BME NPP estimates agree reasonably well with NPP observations in the majority of biomes (especially in the most productive biomes). This thesis demonstrates that NPP meta-models facilitate easy model coupling for exploring the impacts of global change on human-environmental systems in IA modelling-frameworks.}},
author = {{Sallaba, Florian}},
isbn = {{978-91-85793-70-9}},
keywords = {{Ecosystem Modelling; Meta-modelling; Net Primary Production; Global Change; Model coupling}},
language = {{eng}},
publisher = {{Lund University, Faculty of Science, Department of Physical Geography and Ecosystem Science}},
school = {{Lund University}},
title = {{Biophysical and Human Controls of Land Productivity under Global Change : Development and Demonstration of Parsimonious Modelling Techniques}},
url = {{https://lup.lub.lu.se/search/files/17400331/Dissertation_paper1_2_Florian_Sallaba.pdf}},
year = {{2016}},
}