the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Estimation of seasonal methane fluxes over a Mediterranean rice paddy area using the Radon Tracer Method (RTM)
Abstract. The Ebro River Delta, in the northwestern Mediterranean basin, has an extension of 320 km2 and is mainly covered by rice fields. In the framework of the ClimaDat project, the greenhouse gases atmospheric station DEC was installed in this area in 2013. The DEC station was equipped, among others, with a Picarro G2301 instrument and an ARMON (Atmospheric Radon Monitor) to measure both CH4 and CO2, and 222Rn concentrations, respectively.
The variability of methane fluxes over this area and during the different phases of the rice production cycle was evaluated in this study by using the Radon Tracer Method (RTM). The RTM was carried out using: i) nocturnal hourly atmospheric measurements of CH4 and 222Rn between 2013 and 2019; and ii) FLEXPART-WRF back-trajectories coupled with radon flux maps for Europe with a resolution of 0.05º x 0.05º available thanks to the project traceRadon. Prior to the calculation of methane fluxes by RTM, the FLEXPART-WRF model and the traceRadon flux maps were evaluated by modelling atmospheric radon concentrations at DEC station and comparing them with observed data.
RTM based methane fluxes show a strong seasonality with maximums in October (13.9 mg CH4 m-2 h-1), corresponding with the period of harvest and straw incorporation in rice crop fields, and minimums between March and June (0.2 mg CH4 m-2 h‑1 to 0.6 mg CH4 m-2 h-1). The total estimated methane annual emission was about 262.8 kg CH4 ha‑1. These fluxes were compared with fluxes directly measured with static accumulation chambers by other researchers in the same area. Results show a stunning agreement between both methodologies, both having a very similar annual cycle and monthly mean absolute values.
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RC1: 'Comment on egusphere-2024-1370', Fabian Maier, 24 Jul 2024
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Review of Curcoll et al. (2024), „Estimation of seasonal methane fluxes over a Mediterranean rice paddy area using the Radon Tracer Method (RTM)”
In this study, similarities in the nocturnal mixing between radon and methane (CH4) are used in the so-called Radon Tracer Method (RTM) to estimate the seasonal cycle of CH4 emissions over a rice field area on the Spanish Mediterranean coast. It highlights the potential of the RTM to estimate CH4 fluxes within a limited area, and the importance of CH4 emissions from rice fields during harvest and straw incorporation in the fall season. The latter is a valuable finding for improving CH4 emission inventories.
The manuscript is well structured and written, and provides a thorough analysis and discussion of the RTM approach and its results.
I have only minor comments and recommend publication after these have been addressed.
Minor comments:
You nicely illustrate the performance of the WRF-FLEXPART transport model to simulate radon concentrations (e.g. Fig. 9). These results indicate that the transport model overestimates nocturnal mixing in the boundary layer. I’m wondering how this will affect the RTM results, i.e. the seasonal cycle of the CH4 fluxes. Is there a seasonal cycle in how strong the model underestimates nighttime radon concentrations? Maybe you could briefly discuss to what extent an overestimation of the nocturnal mixing has an impact on the nocturnal RTM footprint and thus on the effective radon flux used to estimate the CH4 emissions.
Specific comments:
Fig 2.: Does the flooding with sea water affect the entire ERD, and/or is it only temporary? I would not have expected high local radon emissions in December (cf. p. 14, l. 326-328) if the land is flooded. Please briefly describe what flooding with sea water means (can also be done in the caption of Fig. 2).
p. 13, l. 305-308: Does this mean that the inventory assumes zero methane emissions for rice fields outside the crop cultivation period? Please clarify.
p. 22, l. 401-403: For some events, the model underestimates the measured radon concentrations (e.g. in ~ July, 20). I'm wondering if such biases could be explained by contributions from lateral radon boundary conditions, e.g. if the air masses come from eastern Europe?
Fig. 8: It's quite hard to distinguish the GLDAS & const. curves (at least for color-blind people). Maybe you could use different colors.
p. 23, l. 418-422: Maybe you want also cite Gerbig et al. (2008) here: Gerbig, C., Körner, S., and Lin, J. C.: Vertical mixing in atmospheric tracer transport models: error characterization and propagation, Atmos. Chem. Phys., 8, 591–602, https://doi.org/10.5194/acp-8-591-2008, 2008.
Fig. 11: Could you also show the seasonal cycle of the footprint-weighted radon fluxes (in Fig. 11a), which you are using for the RTM? It would be interesting to see whether the radon fluxes used in the RTM are more similar to the very local or to the regional radon fluxes shown in Fig. 11a.
p. 26, l. 450-451: To assess the reliability of the ERA5 and GLDAS radon flux maps, it might be useful to show here (in Fig. 11b) also the model-data mismatch for afternoon situations only, as you have already shown that the transport model seems to overestimate nocturnal mixing. This could then perhaps allow a better differentiation between deficits in the transport model versus biases in the radon flux maps.
p. 26, l. 455-457: Can you briefly discuss what could cause these larger radon fluxes in December, i.e. which process is not covered by the description of radon exhalation from the soil. This observation could give indications on how to improve the radon flux maps.
p. 29, l. 501-504: In Fig. 5 you show that the CH4 concentrations have a distinct diurnal cycle only between August and November, when the RTM yields elevated CH4 fluxes. Could this finding support your conclusion that, apart from the rice fields, there are no relevant local CH4 emissions, as these would otherwise cause a diurnal cycle in CH4 concentrations, e.g. by accumulation in the nocturnal boundary layer; and that therefore the RTM-based CH4 fluxes describe mainly the emissions from the rice paddies?
p. 30, l. 518-521: Does the 5.9 kg CH4 ha−1 describe the variability of the flux measurements from the different accumulation chambers or is it an estimate for the uncertainty of the annual mean CH4 flux in the ERD, i.e. does it also include the uncertainties of the accumulation chamber method? If the latter is true, I would not call the 5.9 kg CH4 ha−1 a “high uncertainty” (it is only 2%). Please clarify.
p. 31, l. 547-548: The different observation-simulation biases among the months could also be partly due to seasonal differences in the transport model performance (see my first comment).
Technical corrections:
Throughout: You switched between “backtrajectory” and “back trajectory”.
p. 8, l. 177: “may be”
p. 12, l. 287: “where” (lower case)
p. 25, l. 448: delete “it”
p. 28, l. 477: “WRF-GLDAS”
Supplements:
Fig. S2: Is the map shown in panels d-f the 70 km x 70 km window or rather the 150 km x 150 km window? It appears that you are referring to this window as the 150 km x 150 km window in Fig. 3 in the manuscript.
Fig. S3: If you want, you could also mark these synoptic situations in the time series plots in Fig. S5. Then one could directly see the model-data mismatch associated with these synoptic situations. Typo in the caption: “ … the logarithm … “
Citation: https://doi.org/10.5194/egusphere-2024-1370-RC1
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