Carbon storage and active carbon sequestration within peatlands strongly depend on water table depth and soil moisture availability. With increasing efforts to protect and restore peatland ecosystems, the assessment of their hydrological condition is highly necessary but remains challenging. Synthetic aperture radar (SAR) satellite observations likely offer an efficient way to obtain regular information with complete spatial coverage over northern peatlands. Studies have indicated that both radar backscatter amplitude and phase are sensitive to peatland condition. Very recently, Differential Interferometric Synthetic Aperture Radar (DInSAR) has been reported as being capable of monitoring ground deformation patterns at the millimetre scale, which are a response to peatland hydrological condition. To further investigate the promise of SAR for peatland monitoring, a laboratory-based polarimetric C-band SAR system was used to acquire the dynamic radar behaviour of a 4 m (l) ×1 m (w) × 0.25 m (d) reconstructed peatland. A forced 4-month drought was introduced with very-high-resolution imagery taken every 2 hours, capturing details of the vertical backscatter patterning through the peat at the centimetric scale. The results showed a clear coherent response both in radar backscatter amplitude and phase to change in water table level and soil moisture. Similar responses were seen across all polarizations. Phase demonstrated a coherent and deterministic change across the experiment; the average differential phase increase across all polarizations was 118° for 17 cm of water table drawdown. Interpreted as the physical movement of the surface, this corresponded to 8.3 mm of surface subsidence. Both phase and amplitude changes were near-linear with changes in the water table depth; amplitude showed a correspondingly strong concomitant mean decrease of 7 dB across all polarizations during the experiment. The results demonstrate the close sensitivity of radar backscatter to hydrological patterns in a peatland ecosystem. The phase result, in particular, strongly supports the notion that differential phase from satellites can be utilized to measure ground deformation as a proxy for the hydrological state.

Backscatter (email)

Table (database)

10.1080/01431161.2022.2131478

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Citations (18)

Landscape Ecology (2002)

Alessandro Gimona Richard Birnie

Geostatistics

10.1023/a:1015236110766

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Citations (9)

The potential for modelling peatland habitat condition in Scotland using long-term MODIS data

The Science of The Total Environment (2018)

Rebekka Artz Sally Johnson Patricia Bruneau Andrea J. Britton Ruth J. Mitchell

Moderate-resolution imaging spectroradiometer

Land Cover

10.1016/j.scitotenv.2018.12.327

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Citations (17)

Multi-Scale Modelling of Ecosystem Services – an Iterative Approach

Marie Castellazzi Iain Brown Laura Poggio Alessandro Gimona

Ecosystems are dependent upon the interactions between the natural environment and human factors. Each different ecosystem service is also associated with varying spatial scales related to their functioning and human benefits. Thus, key requirements for an ecosystem-based approach (EBA) are i) multiple scales, especially landscape scale, ii) flexible method for adaptive response and iii) stakeholders participation. Understanding complex inter-relationships between ecosystem functions and their services requires tools to handle change and uncertainty, notably scenarios and sensitivity analysis. We propose an iterative approach, which is multi-scale and user- focused based upon the LandSFACTS toolkit to generate a suite of land use change scenarios. The complexity of the scenarios and knowledge of the ecosystem is evaluated and built up through an iterative procedure with stakeholders using spatio-temporal constraints on land use. This method has been used to link climate change impacts (direct and indirect) with climate change responses (adaptation and mitigation). An example of evaluating alternative scenarios of land use changes through EBA (e.g. for food security, carbon sequestration, woodland habitat network) is presented in this paper at two scales (Tarland sub-catchment and Dee catchment in Scotland, UK).

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Coupled water-carbon modelling at data-limited sites: a new approach to explore current and future agroforestry scenarios in Scotland

Salim Goudarzi Chris Soulsby Jo Smith Jamie Stevenson Alessandro Gimona

Agroforestry has been suggested as a promising Nature-Based Solution (NBS) due to its potential benefits including soil water regulation and carbon storage, both of which are expected to become increasingly more important under current climate projection scenarios. But it is unclear to what degree these benefits: (i) are likely to be realised individually; and (ii) may interact/counteract with one another. While common in the tropics, agroforestry in the UK and other temperate areas is still limited. Especially given the lack of data, predicting adaptability and optimising environmental benefits of agroforestry systems in temperate regions requires a parsimonious and robust coupled water-carbon modelling approach. Soil carbon models typically tend to use simplistic soil moisture accounting (e.g., rainfall minus PET) and could yield considerably different predictions under more realistic soil moisture representations. However, while large-scale surface and above surface satellite datasets are now readily available, below-ground soil moisture datasets are either not available, not as accurate, or not on the same scale. This is particularly an issue in systems involving trees because they impact soils in general, but soil moisture in particular, at depths much greater than those covered by global satellites. Here, we present a new 1D ecohydrological model that encompasses the main soil-tree-atmospheric interactions while only requiring rainfall, potential evapotranspiration and surface soil moisture information for its calibration, making the model well-suited to be applied in conjunction with limited available datasets (e.g., those from satellites). We first demonstrate the ecohydrological model’s performance in profile soil moisture estimation using only surface information in a data-rich site in Scotland. We then couple this new model with the widely used RothC carbon model for an agroforestry site nearby. Our results show that CO2 emission estimates by RothC change considerably when a more realistic soil moisture accounting is incorporated. Finally, we explore these effects under different agroforestry and future (50-year) climate projection scenarios to inform appropriate agroforestry designs.

Carbon fibers

10.5194/egusphere-egu24-19929

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