the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Data-driven modelling of hydraulic-head time series: results and lessons learned from the 2022 Groundwater Time Series Modelling Challenge
Raoul A. Collenteur
Ezra Haaf
Mark Bakker
Tanja Liesch
Andreas Wunsch
Jenny Soonthornrangsan
Jeremy White
Nick Martin
Rui Hugman
Ed de Sousa
Didier Vanden Berghe
Xinyang Fan
Tim J. Peterson
Jānis Bikše
Antoine Di Ciacca
Xinyue Wang
Yang Zheng
Maximilian Nölscher
Julian Koch
Raphael Schneider
Nikolas Benavides Höglund
Sivarama Krishna Reddy Chidepudi
Abel Henriot
Nicolas Massei
Abderrahim Jardani
Max Gustav Rudolph
Amir Rouhani
J. Jaime Gómez-Hernández
Seifeddine Jomaa
Anna Pölz
Tim Franken
Morteza Behbooei
Jimmy Lin
Rojin Meysami
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Nitrate pollution from farming is a global problem. A natural process called denitrification helps remove nitrate but also releases CO2, which contributes to climate change. Our study shows that CO2 from this process in Danish groundwater may be a major overlooked source – similar to other known agricultural CO2 emissions. This highlights the need to update greenhouse gas reporting to better reflect farming’s full climate impact.
1. Implemented groundwater module (gwflow) into SWAT+ for four watersheds with different unique hydrologic features across the United States.
2. Presented methods for sensitivity analysis, uncertainty analysis and parameter estimation for coupled models.
3. Sensitivity analysis for streamflow and groundwater head conducted using Morris method.
4. Uncertainty analysis and parameter estimation performed using an iterative ensemble smoother within the PEST framework.