Articles | Volume 11, issue 3
https://doi.org/10.5194/gmd-11-1199-2018
https://doi.org/10.5194/gmd-11-1199-2018
Model evaluation paper
 | 
29 Mar 2018
Model evaluation paper |  | 29 Mar 2018

Calibrating the sqHIMMELI v1.0 wetland methane emission model with hierarchical modeling and adaptive MCMC

Jouni Susiluoto, Maarit Raivonen, Leif Backman, Marko Laine, Jarmo Makela, Olli Peltola, Timo Vesala, and Tuula Aalto

Related authors

Forward Model Emulator for Atmospheric Radiative Transfer Using Gaussian Processes And Cross Validation
Otto M. Lamminpää, Jouni I. Susiluoto, Jonathan M. Hobbs, James L. McDuffie, Amy J. Braverman, and Houman Owhadi
Atmos. Meas. Tech. Discuss., https://doi.org/10.5194/amt-2024-63,https://doi.org/10.5194/amt-2024-63, 2024
Preprint under review for AMT
Short summary
Efficient Bayesian inference for large chaotic dynamical systems
Sebastian Springer, Heikki Haario, Jouni Susiluoto, Aleksandr Bibov, Andrew Davis, and Youssef Marzouk
Geosci. Model Dev., 14, 4319–4333, https://doi.org/10.5194/gmd-14-4319-2021,https://doi.org/10.5194/gmd-14-4319-2021, 2021
Short summary
Efficient multi-scale Gaussian process regression for massive remote sensing data with satGP v0.1.2
Jouni Susiluoto, Alessio Spantini, Heikki Haario, Teemu Härkönen, and Youssef Marzouk
Geosci. Model Dev., 13, 3439–3463, https://doi.org/10.5194/gmd-13-3439-2020,https://doi.org/10.5194/gmd-13-3439-2020, 2020
Short summary
Parameter calibration and stomatal conductance formulation comparison for boreal forests with adaptive population importance sampler in the land surface model JSBACH
Jarmo Mäkelä, Jürgen Knauer, Mika Aurela, Andrew Black, Martin Heimann, Hideki Kobayashi, Annalea Lohila, Ivan Mammarella, Hank Margolis, Tiina Markkanen, Jouni Susiluoto, Tea Thum, Toni Viskari, Sönke Zaehle, and Tuula Aalto
Geosci. Model Dev., 12, 4075–4098, https://doi.org/10.5194/gmd-12-4075-2019,https://doi.org/10.5194/gmd-12-4075-2019, 2019
Short summary
HIMMELI v1.0: HelsinkI Model of MEthane buiLd-up and emIssion for peatlands
Maarit Raivonen, Sampo Smolander, Leif Backman, Jouni Susiluoto, Tuula Aalto, Tiina Markkanen, Jarmo Mäkelä, Janne Rinne, Olli Peltola, Mika Aurela, Annalea Lohila, Marin Tomasic, Xuefei Li, Tuula Larmola, Sari Juutinen, Eeva-Stiina Tuittila, Martin Heimann, Sanna Sevanto, Thomas Kleinen, Victor Brovkin, and Timo Vesala
Geosci. Model Dev., 10, 4665–4691, https://doi.org/10.5194/gmd-10-4665-2017,https://doi.org/10.5194/gmd-10-4665-2017, 2017
Short summary

Related subject area

Biogeosciences
Terrestrial Ecosystem Model in R (TEMIR) version 1.0: simulating ecophysiological responses of vegetation to atmospheric chemical and meteorological changes
Amos P. K. Tai, David H. Y. Yung, and Timothy Lam
Geosci. Model Dev., 17, 3733–3764, https://doi.org/10.5194/gmd-17-3733-2024,https://doi.org/10.5194/gmd-17-3733-2024, 2024
Short summary
biospheremetrics v1.0.2: an R package to calculate two complementary terrestrial biosphere integrity indicators – human colonization of the biosphere (BioCol) and risk of ecosystem destabilization (EcoRisk)
Fabian Stenzel, Johanna Braun, Jannes Breier, Karlheinz Erb, Dieter Gerten, Jens Heinke, Sarah Matej, Sebastian Ostberg, Sibyll Schaphoff, and Wolfgang Lucht
Geosci. Model Dev., 17, 3235–3258, https://doi.org/10.5194/gmd-17-3235-2024,https://doi.org/10.5194/gmd-17-3235-2024, 2024
Short summary
Modeling boreal forest soil dynamics with the microbially explicit soil model MIMICS+ (v1.0)
Elin Ristorp Aas, Heleen A. de Wit, and Terje K. Berntsen
Geosci. Model Dev., 17, 2929–2959, https://doi.org/10.5194/gmd-17-2929-2024,https://doi.org/10.5194/gmd-17-2929-2024, 2024
Short summary
Optimal enzyme allocation leads to the constrained enzyme hypothesis: the Soil Enzyme Steady Allocation Model (SESAM; v3.1)
Thomas Wutzler, Christian Reimers, Bernhard Ahrens, and Marion Schrumpf
Geosci. Model Dev., 17, 2705–2725, https://doi.org/10.5194/gmd-17-2705-2024,https://doi.org/10.5194/gmd-17-2705-2024, 2024
Short summary
Implementing a dynamic representation of fire and harvest including subgrid-scale heterogeneity in the tile-based land surface model CLASSIC v1.45
Salvatore R. Curasi, Joe R. Melton, Elyn R. Humphreys, Txomin Hermosilla, and Michael A. Wulder
Geosci. Model Dev., 17, 2683–2704, https://doi.org/10.5194/gmd-17-2683-2024,https://doi.org/10.5194/gmd-17-2683-2024, 2024
Short summary

Cited articles

Arah, J. R. M. and Stephen, K. D.: A Model of the Processes Leading to Methane Emission from Peatland, Atmos. Environ., 32, 3257–3264, 1998.
Aurela, M., Tuovinen, J.-P., and Laurila, T.: Net CO2 exchange of a subarctic mountain birch ecosystem, Theor. Appl. Climatol., 70, 135–148, https://doi.org/10.1007/s007040170011, 2001.
Aurela, M., Riutta, T., Laurila, T., Tuovinen, J.-V., Vesala, T., Tuittila, E.-S., Jinne, J., Haapanala, S., and Laine, J.: CO2 exchange of a sedge fen in southern Finland–the impact of a drought period, Tellus B, 59, 826–837, https://doi.org/10.1111/j.1600-0889.2007.00309.x, 2007.
Bellisario, L. M., Bubier, J. L., Moore, T. R., and Chanton, J. P.: Controls on CH4 emissions from a northern peatland, Global Biogeochem. Cy., 13, 81–91, https://doi.org/10.1029/1998GB900021, 1999.
Bergman, I., Klarqvist, M., and Nilsson, M.: Seasonal variation in rates of methane production from peat of various botanical origins: effects of temperature and substrate quality, FEMS Microbiol. Ecol., 33, 181–189, https://doi.org/10.1111/j.1574-6941.2000.tb00740.x, 2000.
Download
Short summary
Methane is an important greenhouse gas and methane emissions from wetlands contribute to the warming of the climate. Wetland methane emissions are also challenging to estimate. We analyze the performance of a new wetland emission computer model utilizing mathematical methods and using data from a wetland in southern Finland. The analysis helps to explain how wetlands produce methane and how emission modeling can be improved and uncertainties in the emission estimates reduced in future studies.