Reviews and syntheses: A scoping review evaluating the potential application of ecohydrological models for northern peatland restoration
-
Published:2024-07-09
Issue:13
Volume:21
Page:3143-3163
-
ISSN:1726-4189
-
Container-title:Biogeosciences
-
language:en
-
Short-container-title:Biogeosciences
Author:
Silva Mariana P.ORCID, Healy Mark G., Gill LaurenceORCID
Abstract
Abstract. Peatland restoration and rehabilitation action has become more widely acknowledged as a necessary response to mitigating climate change risks and improving global carbon storage. Peatland ecosystems require restoration time spans of the order of decades and, thus, cannot be dependent upon the shorter-term monitoring often carried out in research projects. Hydrological assessments using geospatial tools provide the basis for planning restoration works as well as analysing associated environmental influences. “Restoration” encompasses applications to pre-restoration and post-restoration scenarios for both bogs and fens, across a range of environmental impact fields. The aim of this scoping review is to identify, describe, and categorize current process-based modelling uses in peatlands in order to investigate the applicability and appropriateness of ecohydrological and/or hydrological models for northern peatland restoration. Two literature searches were conducted using the entire Web of Science database in September 2022 and August 2023. Of the final 211 papers included in the review, models and their applications were categorized according to this review's research interests in seven distinct categories aggregating the papers' research themes and model outputs. Restoration site context was added by identifying 229 unique study site locations from the full database, which were catalogued and analysed against raster data for the Köppen–Geiger climate classification scheme. A majority of northern peatland sites were in temperate oceanic zones or humid continental zones that experienced snow. Over one in five models from the full database of papers were unnamed and likely intended for single use. Key themes emerging from topics covered by papers in the database included the following: modelling restoration development from a bog growth perspective, the prioritization of modelling greenhouse gas (GHG) emissions dynamics as a part of policymaking, the importance of spatial connectivity within or alongside process-based models to represent heterogeneous systems, and the increased prevalence of remote sensing and machine learning techniques to predict restoration progress with little physical site intervention. Models are presented according to their application to peatlands or broader ecosystem and organized from most to least complex. This review provides valuable context for the application of ecohydrological models in determining strategies for peatland restoration and evaluating post-intervention development over time.
Funder
Environmental Protection Agency
Publisher
Copernicus GmbH
Reference117 articles.
1. Acharya, S., Kaplan, D. A., Jawitz, J. W., and Cohen, M. J.: Doing ecohydrology backward: Inferring wetland flow and hydroperiod from landscape patterns, Water Resour. Res., 53, 5742–5755, https://doi.org/10.1002/2017WR020516, 2017. 2. Apori, S. O., Mcmillan, D., Giltrap, M., and Tian, F.: Mapping the restoration of degraded peatland as a research area: A scientometric review, Front. Environ. Sci., 10, 942788, https://doi.org/10.3389/fenvs.2022.942788, 2022. 3. Bacon, K. L., Baird, A. J., Blundell, A., Bourgault, M.-A., Chapman, P. J., Dargie, G., Dooling, G. P., Gee, C., Holden, J., Kelly, T., McKendrick-Smith, K. A., Morris, P. J., Noble, A., Palmer, S. M., Quillet, A., Swindles, G. T., Watson, E. J., and Young, D. M.: Questioning ten common assumptions about peatlands, Mires Peat, 19, 1–23, https://doi.org/10.19189/MaP.2016.OMB.253, 2017. 4. Baird, A. J., Morris, P. J., and Belyea, L. R.: The DigiBog peatland development model 1: rationale, conceptual model, and hydrological basis, Ecohydrology, 5, 242–255, https://doi.org/10.1002/eco.230, 2012. 5. Ball, J., Gimona, A., Cowie, N., Hancock, M., Klein, D., Donaldson-Selby, G., and Artz, R. R. E.: Assessing the Potential of using Sentinel-1 and 2 or high-resolution aerial imagery data with Machine Learning and Data Science Techniques to Model Peatland Restoration Progress – a Northern Scotland case study, Int. J. Remote Sens., 44, 2885–2911, https://doi.org/10.1080/01431161.2023.2209916, 2023.
|
|