Probabilistic Modeling of Long-term Peatland Carbon Dynamics (eb1023)

Basic data for this project

Type of project: Individual project
Duration: 01/09/2021 - 31/08/2024

Description

Peatlands regulate atmospheric greenhouse gas concentrations andthus the global climate. They form one of the largest terrestrial Cstores and current and projected long-term shifts in temperature,precipitation, and nitrogen deposition represent a potential threat tothese functions. Dynamic peatland models (DPM) are needed toacquire a mechanistic understanding of process interactions, topredict long-term changes in C accumulation rates, and to synthesizecontrasting results of individual studies. For about 40 years DPM havecontinuously been improved by including additional processes,temporal dynamics, and spatial heterogeneity. Sensitivity analyses ofDPM have revealed that uncertainties are generally large yet crucialfor a correct interpretation of process interactions. In other disciplines,the application of probabilistic models, uncertainty analysis, anduncertainty reduction via data assimilation has proven usefulextensions of former deterministic models. However, uncertainty hasbarely been quantified and analyzed for probabilistic DPM. To makeDPM more useful, we suggest to develop a probabilistic DPM and toquantify, analyze, and reduce uncertainties in its input data andparameters, using uncertainty analysis and data assimilation. Weexpect that data assimilation can reduce uncertainties especially forlong-term decomposition rates if one synthesizes different existingdata sources (peat core data and litter bag data) and the informationprovided by multiple peat properties at the same time (e.g. C and Ncontent). With this framework, we aim to assess the impactuncertainties have for our understanding of the effects of temperature,precipitation, and nitrogen deposition on peatland C accumulation,identify conditions under which experiments yield contrasting results,and provide strategies for efficient future uncertainty reduction in DPM

Keywords: peatlands; carbon dynamics; uncertainty analysis; data assimilation; climate change; dynamic peatland model; geoinformatics; landscape ecology; hydrology; biogeochemistry