Kathrin Fuchs

Last Name

Fuchs

First Name

Kathrin

ORCID

0000-0003-1776-283X

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Publications 1 - 10 of 16

Soziale Nachhaltigkeit in der Landwirtschaft: eine Medienanalyse
Janker, Judith; Fuchs, Kathrin; Krutli, Pius (2019)
Agrarforschung Schweiz
Gap-filling eddy covariance methane fluxes: Comparison of machine learning model predictions and uncertainties at FLUXNET-CH4 wetlands
Irvin, Jeremy; Zhou, Sharon; McNicol, Gavin; et al. (2021)
Agricultural and Forest Meteorology
Time series of wetland methane fluxes measured by eddy covariance require gap-filling to estimate daily, seasonal, and annual emissions. Gap-filling methane fluxes is challenging because of high variability and complex responses to multiple drivers. To date, there is no widely established gap-filling standard for wetland methane fluxes, with regards both to the best model algorithms and predictors. This study synthesizes results of different gap-filling methods systematically applied at 17 wetland sites spanning boreal to tropical regions and including all major wetland classes and two rice paddies. Procedures are proposed for: 1) creating realistic artificial gap scenarios, 2) training and evaluating gap-filling models without overstating performance, and 3) predicting half-hourly methane fluxes and annual emissions with realistic uncertainty estimates. Performance is compared between a conventional method (marginal distribution sampling) and four machine learning algorithms. The conventional method achieved similar median performance as the machine learning models but was worse than the best machine learning models and relatively insensitive to predictor choices. Of the machine learning models, decision tree algorithms performed the best in cross-validation experiments, even with a baseline predictor set, and artificial neural networks showed comparable performance when using all predictors. Soil temperature was frequently the most important predictor whilst water table depth was important at sites with substantial water table fluctuations, highlighting the value of data on wetland soil conditions. Raw gap-filling uncertainties from the machine learning models were underestimated and we propose a method to calibrate uncertainties to observations. The python code for model development, evaluation, and uncertainty estimation is publicly available. This study outlines a modular and robust machine learning workflow and makes recommendations for, and evaluates an improved baseline of, methane gap-filling models that can be implemented in multi-site syntheses or standardized products from regional and global flux networks (e.g., FLUXNET).
Are there memory effects on greenhouse gas emissions (CO2, N2O and CH4) following grassland restoration?
Merbold, Lutz; Decock, Charlotte; Eugster, Werner; et al. (2021)
Biogeosciences
A 5-year greenhouse gas (GHG) exchange study of the three major gas species (CO2, CH4 and N2O) from an intensively managed permanent grassland in Switzerland is presented. Measurements comprise 2 years (2010 and 2011) of manual static chamber measurements of CH4 and N2O, 5 years of continuous eddy covariance (EC) measurements (CO2–H2O – 2010–2014), and 3 years (2012–2014) of EC measurement of CH4 and N2O. Intensive grassland management included both regular and sporadic management activities. Regular management practices encompassed mowing (three to five cuts per year) with subsequent organic fertilizer amendments and occasional grazing, whereas sporadic management activities comprised grazing or similar activities. The primary objective of our measurements was to compare pre-plowing to post-plowing GHG exchange and to identify potential memory effects of such a substantial disturbance on GHG exchange and carbon (C) and nitrogen (N) gains and losses. In order to include measurements carried out with different observation techniques, we tested two different measurement techniques jointly in 2013, namely the manual static chamber approach and the eddy covariance technique for N2O, to quantify the GHG exchange from the observed grassland site. Our results showed that there were no memory effects on N2O and CH4 emissions after plowing, whereas the CO2 uptake of the site considerably increased when compared to pre-restoration years. In detail, we observed large losses of CO2 and N2O during the year of restoration. In contrast, the grassland acted as a carbon sink under usual management, i.e., the time periods 2010–2011 and 2013–2014. Enhanced emissions and emission peaks of N2O (defined as exceeding background emissions 0.21 ± 0.55 nmol m−2 s−1 (SE = 0.02) for at least 2 sequential days and the 7 d moving average exceeding background emissions) were observed for almost 7 continuous months after restoration as well as following organic fertilizer applications during all years. Net ecosystem exchange of CO2 (NEECO2) showed a common pattern of increased uptake of CO2 in spring and reduced uptake in late fall. NEECO2 dropped to zero and became positive after each harvest event. Methane (CH4) exchange fluctuated around zero during all years. Overall, CH4 exchange was of negligible importance for both the GHG budget and the carbon budget of the site. Our results stress the inclusion of grassland restoration events when providing cumulative sums of C sequestration potential and/or global warming potential (GWP). Consequently, this study further highlights the need for continuous long-term GHG exchange observations as well as for the implementation of our findings into biogeochemical process models to track potential GHG mitigation objectives as well as to predict future GHG emission scenarios reliably.
Multimodel Evaluation of Nitrous Oxide Emissions From an Intensively Managed Grassland
Fuchs, Kathrin; Merbold, Lutz; Buchmann, Nina; et al. (2020)
Journal of Geophysical Research: Biogeosciences
Process‐based models are useful for assessing the impact of changing management practices and climate on yields and greenhouse gas (GHG) emissions from agricultural systems such as grasslands. They can be used to construct national GHG inventories using a Tier 3 approach. However, accurate simulations of nitrous oxide (N2O) fluxes remain challenging. Models are limited by our understanding of soil‐plant‐microbe interactions and the impact of uncertainty in measured input parameters on simulated outputs. To improve model performance, thorough evaluations against in situ measurements are needed. Experimental data of N2O emissions under two management practices (control with typical fertilization versus increased clover and no fertilization) were acquired in a Swiss field experiment. We conducted a multimodel evaluation with three commonly used biogeochemical models (DayCent in two variants, PaSim, APSIM in two variants) comparing four years of data. DayCent was the most accurate model for simulating N2O fluxes on annual timescales, while APSIM was most accurate for daily N2O fluxes. The multimodel ensemble average reduced the error in estimated annual fluxes by 41% compared to an estimate using the Intergovernmental Panel on Climate Change (IPCC)‐derived method for the Swiss agricultural GHG inventory (IPCC‐Swiss), but individual models were not systematically more accurate than IPCC‐Swiss. The model ensemble overestimated the N2O mitigation effect of the clover‐based treatment (measured: 39–45%; ensemble: 52–57%) but was more accurate than IPCC‐Swiss (IPCC‐Swiss: 72–81%). These results suggest that multimodel ensembles are valuable for estimating the impact of climate and management on N2O emissions.
Improvement of modeling plant responses to low soil moisture in JULESvn4.9 and evaluation against flux tower measurements
Harper, Anna B.; Williams, Karina E.; McGuire, Patrick C.; et al. (2021)
Geoscientific Model Development
Drought is predicted to increase in the future due to climate change, bringing with it myriad impacts on ecosystems. Plants respond to drier soils by reducing stomatal conductance in order to conserve water and avoid hydraulic damage. Despite the importance of plant drought responses for the global carbon cycle and local and regional climate feedbacks, land surface models are unable to capture observed plant responses to soil moisture stress. We assessed the impact of soil moisture stress on simulated gross primary productivity (GPP) and latent energy flux (LE) in the Joint UK Land Environment Simulator (JULES) vn4.9 on seasonal and annual timescales and evaluated 10 different representations of soil moisture stress in the model. For the default configuration, GPP was more realistic in temperate biome sites than in the tropics or high-latitude (cold-region) sites, while LE was best simulated in temperate and high-latitude (cold) sites. Errors that were not due to soil moisture stress, possibly linked to phenology, contributed to model biases for GPP in tropical savanna and deciduous forest sites. We found that three alternative approaches to calculating soil moisture stress produced more realistic results than the default parameterization for most biomes and climates. All of these involved increasing the number of soil layers from 4 to 14 and the soil depth from 3.0 to 10.8 m. In addition, we found improvements when soil matric potential replaced volumetric water content in the stress equation (the “soil14_psi” experiments), when the critical threshold value for inducing soil moisture stress was reduced (“soil14_p0”), and when plants were able to access soil moisture in deeper soil layers (“soil14_dr*2”). For LE, the biases were highest in the default configuration in temperate mixed forests, with overestimation occurring during most of the year. At these sites, reducing soil moisture stress (with the new parameterizations mentioned above) increased LE and increased model biases but improved the simulated seasonal cycle and brought the monthly variance closer to the measured variance of LE. Further evaluation of the reason for the high bias in LE at many of the sites would enable improvements in both carbon and energy fluxes with new parameterizations for soil moisture stress. Increasing the soil depth and plant access to deep soil moisture improved many aspects of the simulations, and we recommend these settings in future work using JULES or as a general way to improve land surface carbon and water fluxes in other models. In addition, using soil matric potential presents the opportunity to include plant functional type-specific parameters to further improve modeled fluxes.
Evaluating the Potential of Legumes to Mitigate N2O Emissions From Permanent Grassland Using Process‐Based Models
Fuchs, Kathrin; Merbold, Lutz; Buchmann, Nina; et al. (2020)
Global Biogeochemical Cycles
A potential strategy for mitigating nitrous oxide (N2O) emissions from permanent grasslands is the partial substitution of fertilizer nitrogen (Nfert) with symbiotically fixed nitrogen (Nsymb) from legumes. The input of Nsymb reduces the energy costs of producing fertilizer and provides a supply of nitrogen (N) for plants that is more synchronous to plant demand than occasional fertilizer applications. Legumes have been promoted as a potential N2O mitigation strategy for grasslands, but evidence to support their efficacy is limited, partly due to the difficulty in conducting experiments across the large range of potential combinations of legume proportions and fertilizer N inputs. These experimental constraints can be overcome by biogeochemical models that can vary legume‐fertilizer combinations and subsequently aid the design of targeted experiments. Using two variants each of two biogeochemical models (APSIM and DayCent), we tested the N2O mitigation potential and productivity of full factorial combinations of legume proportions and fertilizer rates for five temperate grassland sites across the globe. Both models showed that replacing fertilizer with legumes reduced N2O emissions without reducing productivity across a broad range of legume‐fertilizer combinations. Although the models were consistent with the relative changes of N2O emissions compared to the baseline scenario (200 kg N ha−1 yr−1; no legumes), they predicted different levels of absolute N2O emissions and thus also of absolute N2O emission reductions; both were greater in DayCent than in APSIM. We recommend confirming these results with experimental studies assessing the effect of clover proportions in the range 30–50% and ≤150 kg N ha−1 yr−1 input as these were identified as best‐bet climate smart agricultural practices.
Modelling biological N fixation and grass-legume dynamics with process-based biogeochemical models of varying complexity
Fitton, Nuala; Bindi, Marco; Brilli, Lorenzo; et al. (2019)
European Journal of Agronomy
Grasslands comprised of grass-legume mixtures could become a substitute for nitrogen fertiliser through biological nitrogen fixation (BNF) which in turn can reduce nitrous oxide emissions directly from soils without negative impacts on productivity. Models can test how legumes can be used to meet environmental and production goals, but many models used to simulate greenhouse gas (GHG) emissions from grasslands have either a poor representation of grass-legume mixtures and BNF, or poor validation of these features. Our objective is to examine how such systems are currently represented in two process-based biogeochemical models, APSIM and DayCent, when compared against an experimental dataset with different grass-legume mixtures at three nitrogen (N) fertiliser rates. Here, we propose a novel approach for coupling DayCent, a single species model to APSIM, a multi-species model, to increase the capability of DayCent when representing a range of grass-legume fractions. While dependent on specific assumptions, both models can capture the key aspects of the grass-legume growth, including biomass production and BNF and to correctly simulate the interactions between changing legume and grass fractions, particularly mixtures with a high clover fraction. Our work suggests that single species models should not be used for grass-legume mixtures beyond about 30% legume content, unless using a similar approach to that adopted here.
Multifunctionality of permanent grasslands: ecosystem services and resilience to climate change
Buchmann, Nina; Fuchs, Kathrin; Feigenwinter, Iris; et al. (2019)
Grassland Science in Europe ~ Improving sown grasslands through breeding and management
High-resolution modelling of interactions between soil moisture and convective development in a mountain enclosed Tibetan Basin
Gerken, Tobias; Babel, Wolfgang; Herzog, Michael; et al. (2015)
Hydrology and Earth System Sciences
The Tibetan Plateau plays a significant role in atmospheric circulation and the Asian monsoon system. Turbulent surface fluxes and the evolution of boundary-layer clouds to deep and moist convection provide a feedback system that modifies the plateau's surface energy balance on scales that are currently unresolved in mesoscale models. This work analyses the land surface's role and specifically the influence of soil moisture on the triggering of convection at a cross section of the Nam Co Lake basin, 150 km north of Lhasa using a cloud-resolving atmospheric model with a fully coupled surface. The modelled turbulent fluxes and development of convection compare reasonably well with the observed weather. The simulations span Bowen ratios of 0.5 to 2.5. It is found that convective development is the strongest at intermediate soil moisture. Dry cases with soils close to the permanent wilting point are moisture limited in convective development, while convection in wet soil moisture cases is limited by cloud cover reducing incoming solar radiation and sensible heat fluxes, which has a strong impact on the surface energy balance. This study also shows that local development of convection is an important mechanism for the upward transport of water vapour, which originates from the lake basin that can then be transported to dryer regions of the plateau. Both processes demonstrate the importance of soil moisture and surface–atmosphere interactions on the energy and hydrological cycles of the Tibetan Plateau.
Livestock enclosures in drylands of Sub-Saharan Africa are overlooked hotspots of N2O emissions
Butterbach-Bahl, Klaus; Gettel, Gretchen; Kiese, Ralf; et al. (2020)
Nature Communications
Sub-Saharan Africa (SSA) is home to approximately ¼ of the global livestock population, which in the last 60 years has increased by factors of 2.5–4 times for cattle, goats and sheep. An important resource for pastoralists, most livestock live in semi-arid and arid environments, where they roam during the day and are kept in enclosures (or bomas) during the night. Manure, although rich in nitrogen, is rarely used, and therefore accumulates in bomas over time. Here we present in-situ measurements of N2O fluxes from 46 bomas in Kenya and show that even after 40 years following abandonment, fluxes are still ~one magnitude higher than those from adjacent savanna sites. Using maps of livestock distribution, we scaled our finding to SSA and found that abandoned bomas are significant hotspots for atmospheric N2O at the continental scale, contributing ~5% of the current estimate of total anthropogenic N2O emissions for all of Africa.

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