AREAL – cAtchment nitRous oxide Emissions and nitrAte Leaching

• Applicants: Dr. Tobias Houska; Dr. David Kraus
• Subject Area: Soil Sciences ; Hydrogeology, Hydrology, Limnology, Urban Water Management, Water Chemistry, Integrated Water Resources Management ; Ecology of Land Use
• Term: since 2019
• Project identifier: Deutsche Forschungsgemeinschaft (DFG) - Project number 420449836

The accurate estimation of nitrous oxide (N2O) emissions and monitoring of nitrate (NO3) leaching in agricultural catchments are critical for contemporary environmental science and policymaking. These issues contribute to climate change and groundwater pollution, necessitating a thorough understanding of underlying processes to develop effective mitigation strategies. Our research aims to develop a robust upscaling procedure for N2O emissions and NO3 leaching at the catchment scale, where mitigation actions are finally applied. This involves an integrated approach spanning three scientific disciplines: 1. Field and laboratory measurements: Utilizing local chamber-based and laboratory-based measurements to assess microbial N cycling fluxes and process rates, providing essential data for process understanding. 2. Remote sensing: Leveraging satellite data with unprecedented spatiotemporal resolution to gather catchment-scale information on geomorphology, topography, land use, standing biomass, and soil water status, enhancing our understanding of the catchment environment. 3. Modelling: Employing a fusion of machine learning techniques and mechanistic modeling, we aim to integrate all information from the collected datasets, facilitating the upscaling of N2O emissions and NO3 leaching to the entire catchment scale. Our work program comprises two interrelated work packages focusing on data collection and modeling. WP 1 Data Collection: Creation of a comprehensive dataset, including N2O and NH3 emissions, NO3 leaching, soil δ15N isotopic composition, site preference and δ15N-N2O, and lab-based measurements of N process rates such as gross nitrification. This dataset will provide a deeper understanding of microbial N-cycling processes such as nitrification and denitrification and their roles in N2O production and NO3 leaching. Hot spot monitoring: Continuous measurements at model-guided identified N2O emission hot spots, covering potential hot moments such as freeze-thaw periods and fertilization events. WP 2 Modeling: Machine Learning: Extracting knowledge from all collected data to create models predicting N2O emissions and NO3 leaching. Mechanistic modelling: Improving a state-of-the-art biogeochemical model that includes a spatially explicit hydrology model for the lateral flow of water and nutrients. Improving will be particularly based on incorporating isotopic data and an isotopic tracing model. Combining machine learning and mechanistic models to benefit from each other, with mechanistic models enhancing machine learning through providing additional data and machine learning to identify and improve structural deficiencies of the mechanistic model. This interdisciplinary proposal seeks to advance our understanding of N2O emissions and NO3 leaching at the catchment scale, ultimately providing valuable insights for environmental assessment and mitigation strategies in agricultural landscapes.

• DFG Programme: Research Grants