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Balkovič J, Madaras M, Skalský R, Folberth C, Smatanová M, Schmid E, van der Velde M, Kraxner F, Obersteiner M. Verifiable soil organic carbon modelling to facilitate regional reporting of cropland carbon change: A test case in the Czech Republic. JOURNAL OF ENVIRONMENTAL MANAGEMENT 2020; 274:111206. [PMID: 32818829 DOI: 10.1016/j.jenvman.2020.111206] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/04/2020] [Revised: 07/08/2020] [Accepted: 08/05/2020] [Indexed: 06/11/2023]
Abstract
Regional monitoring, reporting and verification of soil organic carbon change occurring in managed cropland are indispensable to support carbon-related policies. Rapidly evolving gridded agronomic models can facilitate these efforts throughout Europe. However, their performance in modelling soil carbon dynamics at regional scale is yet unexplored. Importantly, as such models are often driven by large-scale inputs, they need to be benchmarked against field experiments. We elucidate the level of detail that needs to be incorporated in gridded models to robustly estimate regional soil carbon dynamics in managed cropland, testing the approach for regions in the Czech Republic. We first calibrated the biogeochemical Environmental Policy Integrated Climate (EPIC) model against long-term experiments. Subsequently, we examined the EPIC model within a top-down gridded modelling framework constructed for European agricultural soils from Europe-wide datasets and regional land-use statistics. We explored the top-down, as opposed to a bottom-up, modelling approach for reporting agronomically relevant and verifiable soil carbon dynamics. In comparison with a no-input baseline, the regional EPIC model suggested soil carbon changes (~0.1-0.5 Mg C ha-1 y-1) consistent with empirical-based studies for all studied agricultural practices. However, inaccurate soil information, crop management inputs, or inappropriate model calibration may undermine regional modelling of cropland management effect on carbon since each of the three components carry uncertainty (~0.5-1.5 Mg C ha-1 y-1) that is substantially larger than the actual effect of agricultural practices relative to the no-input baseline. Besides, inaccurate soil data obtained from the background datasets biased the simulated carbon trends compared to observations, thus hampering the model's verifiability at the locations of field experiments. Encouragingly, the top-down agricultural management derived from regional land-use statistics proved suitable for the estimation of soil carbon dynamics consistently with actual field practices. Despite sensitivity to biophysical parameters, we found a robust scalability of the soil organic carbon routine for various climatic regions and soil types represented in the Czech experiments. The model performed better than the tier 1 methodology of the Intergovernmental Panel on Climate Change, which indicates a great potential for improved carbon change modelling over larger political regions.
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Affiliation(s)
- Juraj Balkovič
- International Institute for Applied Systems Analysis, Ecosystems Services and Management Program, Schlossplatz 1, A-2361, Laxenburg, Austria; Faculty of Natural Sciences, Comenius University in Bratislava, Ilkovičova 6, 842 15, Bratislava, Slovak Republic.
| | - Mikuláš Madaras
- Crop Research Institute, Division of Crop Management Systems, Drnovská 507/73, 161 06, Praha 6 - Ruzyně, Czech Republic.
| | - Rastislav Skalský
- International Institute for Applied Systems Analysis, Ecosystems Services and Management Program, Schlossplatz 1, A-2361, Laxenburg, Austria; National Agricultural and Food Centre, Soil Science and Conservation Research Institute, Trenčianska 55, 821 09, Bratislava, Slovak Republic.
| | - Christian Folberth
- International Institute for Applied Systems Analysis, Ecosystems Services and Management Program, Schlossplatz 1, A-2361, Laxenburg, Austria.
| | - Michaela Smatanová
- Central Institute for Supervising and Testing in Agriculture, Hroznová 63/2, 656 06, Brno, Czech Republic.
| | - Erwin Schmid
- Institute for Sustainable Economic Development, University of Natural Resources and Life Sciences, Vienna, Feistmantelstrasse 4, 1180, Vienna, Austria.
| | | | - Florian Kraxner
- International Institute for Applied Systems Analysis, Ecosystems Services and Management Program, Schlossplatz 1, A-2361, Laxenburg, Austria.
| | - Michael Obersteiner
- International Institute for Applied Systems Analysis, Ecosystems Services and Management Program, Schlossplatz 1, A-2361, Laxenburg, Austria; Environmental Change Institute, University of Oxford, South Parks Road, Oxford, OX1 3QY, United Kingdom.
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Hanan EJ, Tague C, Choate J, Liu M, Kolden C, Adam J. Accounting for disturbance history in models: using remote sensing to constrain carbon and nitrogen pool spin-up. ECOLOGICAL APPLICATIONS : A PUBLICATION OF THE ECOLOGICAL SOCIETY OF AMERICA 2018; 28:1197-1214. [PMID: 29573305 DOI: 10.1002/eap.1718] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/03/2017] [Revised: 02/07/2018] [Accepted: 02/21/2018] [Indexed: 06/08/2023]
Abstract
Disturbances such as wildfire, insect outbreaks, and forest clearing, play an important role in regulating carbon, nitrogen, and hydrologic fluxes in terrestrial watersheds. Evaluating how watersheds respond to disturbance requires understanding mechanisms that interact over multiple spatial and temporal scales. Simulation modeling is a powerful tool for bridging these scales; however, model projections are limited by uncertainties in the initial state of plant carbon and nitrogen stores. Watershed models typically use one of two methods to initialize these stores: spin-up to steady state or remote sensing with allometric relationships. Spin-up involves running a model until vegetation reaches equilibrium based on climate. This approach assumes that vegetation across the watershed has reached maturity and is of uniform age, which fails to account for landscape heterogeneity and non-steady-state conditions. By contrast, remote sensing, can provide data for initializing such conditions. However, methods for assimilating remote sensing into model simulations can also be problematic. They often rely on empirical allometric relationships between a single vegetation variable and modeled carbon and nitrogen stores. Because allometric relationships are species- and region-specific, they do not account for the effects of local resource limitation, which can influence carbon allocation (to leaves, stems, roots, etc.). To address this problem, we developed a new initialization approach using the catchment-scale ecohydrologic model RHESSys. The new approach merges the mechanistic stability of spin-up with the spatial fidelity of remote sensing. It uses remote sensing to define spatially explicit targets for one or several vegetation state variables, such as leaf area index, across a watershed. The model then simulates the growth of carbon and nitrogen stores until the defined targets are met for all locations. We evaluated this approach in a mixed pine-dominated watershed in central Idaho, and a chaparral-dominated watershed in southern California. In the pine-dominated watershed, model estimates of carbon, nitrogen, and water fluxes varied among methods, while the target-driven method increased correspondence between observed and modeled streamflow. In the chaparral watershed, where vegetation was more homogeneously aged, there were no major differences among methods. Thus, in heterogeneous, disturbance-prone watersheds, the target-driven approach shows potential for improving biogeochemical projections.
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Affiliation(s)
- Erin J Hanan
- Department of Civil and Environmental Engineering, Washington State University, Pullman, Washington, 99164, USA
| | - Christina Tague
- Department of Environmental Science, Policy and Management, University of California Santa Barbara, Santa Barbara, California, 93106, USA
| | - Janet Choate
- Department of Environmental Science, Policy and Management, University of California Santa Barbara, Santa Barbara, California, 93106, USA
| | - Mingliang Liu
- Department of Civil and Environmental Engineering, Washington State University, Pullman, Washington, 99164, USA
| | - Crystal Kolden
- Department of Geography, University of Idaho, Moscow, Idaho, 83844, USA
| | - Jennifer Adam
- Department of Civil and Environmental Engineering, Washington State University, Pullman, Washington, 99164, USA
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Lo YH, Blanco JA, Canals RM, González de Andrés E, San Emeterio L, Imbert JB, Castillo FJ. Land use change effects on carbon and nitrogen stocks in the Pyrenees during the last 150 years: A modeling approach. Ecol Modell 2015. [DOI: 10.1016/j.ecolmodel.2015.06.005] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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Lugato E, Panagos P, Bampa F, Jones A, Montanarella L. A new baseline of organic carbon stock in European agricultural soils using a modelling approach. GLOBAL CHANGE BIOLOGY 2014; 20:313-326. [PMID: 23765562 DOI: 10.1111/gcb.12292] [Citation(s) in RCA: 21] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/20/2013] [Accepted: 06/04/2013] [Indexed: 06/02/2023]
Abstract
Proposed European policy in the agricultural sector will place higher emphasis on soil organic carbon (SOC), both as an indicator of soil quality and as a means to offset CO2 emissions through soil carbon (C) sequestration. Despite detailed national SOC data sets in several European Union (EU) Member States, a consistent C stock estimation at EU scale remains problematic. Data are often not directly comparable, different methods have been used to obtain values (e.g. sampling, laboratory analysis) and access may be restricted. Therefore, any evolution of EU policies on C accounting and sequestration may be constrained by a lack of an accurate SOC estimation and the availability of tools to carry out scenario analysis, especially for agricultural soils. In this context, a comprehensive model platform was established at a pan-European scale (EU + Serbia, Bosnia and Herzegovina, Croatia, Montenegro, Albania, Former Yugoslav Republic of Macedonia and Norway) using the agro-ecosystem SOC model CENTURY. Almost 164 000 combinations of soil-climate-land use were computed, including the main arable crops, orchards and pasture. The model was implemented with the main management practices (e.g. irrigation, mineral and organic fertilization, tillage) derived from official statistics. The model results were tested against inventories from the European Environment and Observation Network (EIONET) and approximately 20 000 soil samples from the 2009 LUCAS survey, a monitoring project aiming at producing the first coherent, comprehensive and harmonized top-soil data set of the EU based on harmonized sampling and analytical methods. The CENTURY model estimation of the current 0-30 cm SOC stock of agricultural soils was 17.63 Gt; the model uncertainty estimation was below 36% in half of the NUTS2 regions considered. The model predicted an overall increase of this pool according to different climate-emission scenarios up to 2100, with C loss in the south and east of the area (involving 30% of the whole simulated agricultural land) compensated by a gain in central and northern regions. Generally, higher soil respiration was offset by higher C input as a consequence of increased CO2 atmospheric concentration and favourable crop growing conditions, especially in northern Europe. Considering the importance of SOC in future EU policies, this platform of simulation appears to be a very promising tool to orient future policymaking decisions.
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Affiliation(s)
- Emanuele Lugato
- European Commission, Joint Research Centre, Institute for Environment and Sustainability, Via E. Fermi, Ispra, VA, 2749 I-21027,, Italy
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