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Unravel the changing contributions of abiotic vs. biotic drivers of ecosystem gas exchange under weather extremes

English title Unravel the changing contributions of abiotic vs. biotic drivers of ecosystem gas exchange under weather extremes
Applicant Buchmann Nina
Number 198094
Funding scheme COST (European Cooperation in Science and Technology)
Research institution Departement Umweltsystemwissenschaften ETH Zürich
Institution of higher education ETH Zurich - ETHZ
Main discipline Other disciplines of Environmental Sciences
Start/End 01.04.2021 - 31.03.2024
Approved amount 237'709.00
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All Disciplines (2)

Discipline
Other disciplines of Environmental Sciences
Environmental Research

Keywords (5)

modelling; gas exchange; climate change; remote sensing; terrestrial ecosystems

Lay Summary (German)

Lead
Extreme Wetterereignisse (z.B. Dürren, Hitzewellen, sehr milde Winter) und langfristige Klimaveränderungen werden den Austausch von Kohlendioxid (CO2) und Wasser (H2O) in terrestrischen Ökosystemen erheblich beeinflussen. Wechselwirkungen und Rückkopplungen zwischen Land und Atmosphäre sind die Folge. Die genaue Kenntnis der Mechanismen, die diesen Veränderungen zugrunde liegen, ist entscheidend, um dringend erforderliche Anpassungs- und Abschwächungsstrategien für die Zukunft zu definieren.
Lay summary
Wir nutzen zwei sich ergänzende Methoden, um die Relevanz von abiotischen gegenüber biotischen Einflussfaktoren zu verstehen: (1) die Eddy-Kovarianz-Methode, die einzige verfügbare Technik, um mit hoher zeitlicher Auflösung die CO2- und H2O-Flüsse zeitgleich mit den abiotischen Einflussfaktoren vor Ort auf Ökosystem-Ebene zu messen; und (2) die Fernerkundung, die Momentaufnahmen der biotischen Einflussfaktoren auf größeren räumlichen Skalen liefert.

Diese räumlich-zeitliche Skalierung des Gasaustauschs von Ökosystemen ist komplex. Daher stellen wir uns drei Forschungsfragen: (1) Wie verändert sich der Einfluss von abiotischen gegenüber biotischen Einflussfaktoren auf den Gasaustausch unter extremen Bedingungen im Vergleich zu heute? (2) Ermöglicht ein integrierter Multi-Daten-Ansatz die Skalierung von vor Ort- Messungen und die zuverlässige Modellierung auch unter extremen Bedingungen? (3) Was sind die Anforderungen an die Beobachtungen und die daraus resultierenden Unsicherheiten?

Wir werden die Vorteile dieses integrierten Multi-Daten-Ansatzes identifizieren und Empfehlungen zur Modellierung ableiten. Zudem erwarten wir eine Einschätzung der Relevanz verschiedener Einflussfaktoren, die uns hilft, Anpassungs- und Abschwächungsstrategien zu entwickeln.

Direct link to Lay Summary Last update: 02.12.2020

Responsible applicant and co-applicants

Employees

Name Institute

Associated projects

Number Title Start Funding scheme
198227 ICOS-CH Phase 3 01.07.2021 Research Infrastructure
200918 Assessment of formal, natural and social insurances: how to cope best with impacts of extreme events on grasslands for sustainable farming systems? 01.04.2022 Project funding
197357 COS and below-canopy CO2 fluxes of two Swiss forests: understanding land-atmosphere ecosystem exchange (COCO) 01.04.2021 Project funding
173691 ICOS-CH Phase 2 01.07.2017 Research Infrastructure
197243 FLUO4ECO - Combining Fluorescence Spectroscopy and Mechanistic Modelling for Advanced Assessments of Ecosystem Photosynthesis 01.03.2021 Project funding

Abstract

Extreme weather events (e.g. droughts, heat waves, very mild winters) and long-term climatic change will substantially affect carbon dioxide (CO2) and water (H2O) exchange of terrestrial ecosystems. Altered interactions and feedbacks at the land-atmosphere interface are the consequence. Exact knowledge of mechanisms underlying these changes is pivotal to define urgently required adaptation and mitigation strategies that allow coping with future weather extremes. Process-based models provide coarse insights in ecosystem functioning based on our understanding of its abiotic and biotic drivers. This understanding relies on two complementary techniques: (1) the eddy covariance (EC) method which is the only available technique to actually measure CO2 and H2O exchange dynamics at high temporal resolution along with abiotic drivers in situ at ecosystem level; and (2) remote sensing (RS), providing complementary temporal snapshot information on biotic drivers at larger spatial scales.Spatio-temporal scaling of such EC- and RS-based gas exchange information is complex since it is determined by several abiotic and biotic drivers (inter)acting at varying time scales. Currently, EC measurements are typically accompanied with detailed observations of abiotic drivers (e.g. soil conditions, meteorology), but assume relatively constant biotic drivers. In the contrary, RS measures changes in biotic drivers (e.g. leaf biochemistry, canopy structure), but the sparse temporal sampling might increasingly limit the sensitivity of RS when dynamics of biotic drivers increase. During weather extremes, the already highly dynamic response of ecosystems further increases with concurrent and interacting changes in abiotic and biotic drivers of ecosystem gas exchange. Due to such inherent limitations of available technology, our capability to observe the response of ecosystems under extreme conditions is compromised and predictions of how terrestrial ecosystems will behave under current and future weather extremes are insufficient. We hypothesize that analysing extreme events with a multi-data approach will gain mechanistic understanding on spatio-temporal contributions of abiotic vs. biotic drivers for ecosystem gas exchange under changing weather and climate. The evaluation of this hypothesis is based on three questions: (1) How does the impact of abiotic vs. biotic drivers on ecosystem gas exchange change under extreme compared to current conditions? (2) Does an integrated multi-data approach allow scaling in situ gas exchange measurements and reliably representing ecosystem gas exchange under extreme conditions? (3) What are observational requirements and resulting uncertainties of such a multi-data approach? We propose the collection of multi-sensor data at three well-instrumented Swiss eco-systems (i.e. mixed forest (Lägeren), coniferous forest (Davos), arable system (Oensingen)). We apply time series analysis to unravel the changing contributions of abiotic vs. biotic factors on ecosystem gas exchange, including already existing long-term data sets. Using a benchmarking approach, we assess the impact of data scarcity and uncertainties in EC, meteorological and RS data for the prediction of ecosystem gas exchange. We identify benefits of this integrated multi-data approach and derive recommendations on best-practise scaling protocols to model ecosystem gas exchange under environmental change and a priority list of essential abiotic and biotic drivers. Our findings are of high importance for the COST Action CA17134 SENSECO, in particular to objectives related to leaf traits (1.1), to vegetation productivity models (2.3), to multi-sensor approaches (3.1), and best-practise examples (4.3). Our contribution will thus be instrumental to SENSECO, while exchanging data, results and protocols within the large network of the COST Action will be mutually beneficial for EcoDrive. Together, we will advance the integrated use of multiple data streams to better understand ecosystem gas exchange.
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