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Project properties |
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| Title | Modelling the greenhouse gas exchange of the Dutch peatlands |
| Group | Water Systems and Global Change |
| Project type | thesis |
| Credits | 36 |
| Supervisor(s) | Laurent Bataille
Bart Kruijt Ronald Hutjes Laura van der Poel Hong Zhao Ruchita Ingle Iwan Supit Spyros Paparrizos |
| Examiner(s) | Fulco Ludwig |
| Contact info | laurent.bataille@wur.nl |
| Begin date | 2024/01/01 |
| End date | |
| Description | The peatlands of the Netherlands are under pressure from drainage and subsidence and thus emit large amounts of CO2. Mitigation measures proposed include raising water tables and restoring wetlands, reducing peat oxidation, and enhancing methane emissions. We develop methods to better understand this delicate balance and directly monitor CO2 and CH4 emissions using Eddy-Covariance. The projects proposed aim to develop a comprehensive understanding of the carbon balance within these ecosystems through a combination of advanced Eddy-Covariance techniques and detailed modelling efforts. The overarching objective is to refine and apply methodologies for accurately assessing and managing the carbon dynamics in these critical landscapes.
Projects and possibilities for students: -Estimating Harvest and Biomass Production (2024-04-DD): We focus on the carbon budget specific to the Frisian fen-meadows, emphasizing the need to model seasonal biomass production effectively. Students will engage in calibrating the LINGRA grass growth model using local measurements and remote sensing data (e.g., Sentinel2, Groenmonitor). This effort aims to close the gaps in the annual carbon budgets by incorporating harvest data and emissions from fertilization practices. -Soil Biogeochemical Mapping vs Fluxes Models (2024-07-DD): This project compares traditional soil biogeochemical maps with those derived from data-driven models informed by Eddy-Covariance measurements. The goal is to validate these models against biogeochemical parameters (e.g., C:N ratios, soil organic matter) and improve them based on the spatial correlation analysis. -Characterizing Soil Moisture Dynamics via remote Sensing (2024-04-DD): This project focuses on the soil moisture characterization of Dutch peatlands through remote sensing and aims to integrate top-soil moisture proxies with field measurements. Emphasis will be placed on radar remote sensing technologies, and these data will be compared with ground observations from the NOBV measurement network. Might involve broader collaboration with specialized departments or institutes. -Combine Eddy-Covariance Measurements with Planet Datasets in the Dutch Wet Nature (2024-04-DD): In a specific case study set in the recently restored natural areas of Drenthe, students will investigate vegetation dynamics using high-resolution Planet datasets. The project aims to link these observations with carbon flux measurements obtained through Eddy-Covariance and footprint analysis, focusing on the yearly and seasonal changes in vegetation. -Climate Scenarios and Peatland Modelling (2024-07-DD): This research aims to project the responses of Dutch peatlands to various future climate scenarios. It involves developing data-driven models to understand how changes in climate, vegetation cover, and water management strategies might affect these landscapes. Required skills include data analysis, climate modelling, and an understanding of vegetation dynamics and hydrological processes. |
| Used skills | - Python/R
- Time-series analysis - Spatial analysis - Data processing & analysis - Machine Learning & Deep-learning - Fundamentals in GIS and remote-sensing - Radar remote sensing - Fundamentals in Crop Modelling |
| Requirements | - Programming knowledge is a plus but not a must.
- General knowledge on measurement methods appreciated |