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the Creative Commons Attribution 4.0 License.
| 03 Jun 2024
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
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 - AC1: 'Reply on RC1', Roger Curcoll Masanes, 15 Nov 2024
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RC2: 'Comment on egusphere-2024-1370', Scott Chambers, 22 Aug 2024
Review of “Estimation of seasonal methane fluxes over a Mediterranean rice paddy area using the Radon Tracer Method (RTM)” by Curcoll et al.
This paper seeks to estimate the methane flux, and its seasonal variability, from a particular rice paddy region on a coastal river delta, using the “local scale” nocturnal-accumulation implementation of the radon tracer method (RTM). The writing is clear and the manuscript well structured. As outlined in the introduction, characterisation of agricultural methane emissions is an important step toward ratifying the Paris agreement.
Despite the importance of the intended goal, I cannot recommend publication of this manuscript in its current form because I am not convinced that the RTM, in the way it has been applied, is an appropriate approach to achieve the stated project goals (i.e., targeting the specific rice paddy area). While seasonal methane fluxes are indeed derived, I believe it is unlikely that they are truly representative of only (or predominantly) the ~200 km2 of rice paddies in this region. I outline my concerns in more detail below.
General comments
The radon tracer method as intended to be applied in this study treats the nocturnal boundary layer as a simple box and seeks to use changes in concentration of radon and a companion species (methane, CH4) within this box over a collection of single nights, along with simulated radon emission rates from the bottom surface of this box, to infer the flux of the companion species each night over the same spatial region.
There are some necessary assumptions for this technique to be applied:
- Concentration changes within this box should be a function of only the average flux magnitudes over the base of the box (i.e., local flux contributions), and changes in height of the box.
- For advective effects to be ignored, the flux of each gas should be similarly distributed and homogeneous across the base of this box.
Regarding assumption 1:
It has been well established (e.g., Sesana et al 2003; Chambers et al 2015) that significant accumulation of trace gases in the nocturnal boundary layer, driven specifically by local sources, only occurs when wind speeds are less than about 1.5 m.s-1. At higher wind speeds, observed concentration changes at a given location become increasingly dominated by advection from non-local sources. Under such conditions, confusion can arise between the “local scale” nocturnal accumulation implementation of the RTM as described by Levin et al (1999, 2011), and the “regional scale” implementation of the RTM as described by Biraud et al (2000). According to Figure S4 b, only a relatively small fraction of wind speeds in the nocturnal windows used for the RTM in this study are below 2 m.s-1. Given the 6-hour nocturnal window applied here for the RTM, and maximum acceptable wind speed of < 2 m.s-1 to observe a local influence, the length of the “box” contributing to the observed flux signal would be around 43 km (if only the low wind speeds of this study were considered). The rice paddy region being investigated (based on Fig S1) appears to have dimensions of only around 20 km (east-west) x 12 km (north-south). Based on the location of the measurement site, the rice paddy field fetch in the dominant wind directions (NW and SSE) is only 2 – 10 km. So, even in the most ideal, low wind scenarios, the contribution of the rice paddy area to the overall signal observed is a low fraction.
Regarding assumption 2:
Given that the best-case scenario base length of the idealised “box” from the measurement point is around 43 km, and (a) the site is coastal, and (b) the rice paddy fields are of limited spatial extent, and periodically flooded, neither fluxes of radon or CH4 over this distance (in any direction from the measurement site) would approximately homogeneous or similarly distributed.
If only stable nocturnal conditions with wind speeds less than or equal to 0.5 m.s-1 were targeted, this would limit the local fetch contribution over the 6-hour nocturnal accumulation window to around 10 km, which may work for wind directions west through south, but this would severely limit the amount of data available for analysis.
Specific comments
L44: The RTM requires that both gas source functions are similarly distributed over the region in question. For the ~5 months of the year that the rice paddies are flooded, there is little impediment to CH4 emission (via diffusion, ebullition, and transport through aerenchyma). This is not the case for radon emission. When the soil profile is flooded, radon generated in the saturated soil matrix below the soil surface is unlikely to make it into the atmosphere before decaying. The much smaller fraction of radon generated at or near the soil surface is not produced in sufficient quantities for bubble transport, so diffusion is its only pathway (which would likely take of order 2-3 days to get through the 8 – 15 cm of standing water). Presumably this flux would only be marginally higher than typical open water radon fluxes, and therefore much lower than anything predicted by the European flux maps. Beyond the edges of the rice paddies there is the opposite problem. Radon fluxes would be higher from the unsaturated soils, and CH4 emissions lower.
L72: Clearly the modelled radon fluxes for this region do not represent completely inundated conditions (which is the case for the region of interest for 5 months of the year). Presumably they are representing seasonal changes in soil moisture for uncultivated land with a particular Ra-226 content. [I noticed later that this is acknowledged in Figure 11a – but I assume that the modelled fluxes were still used for this study, which would not correctly represent the study region].
Fig 2: In the month of land preparation (and period of straw incorporation), is an increase in local radon flux expected due to the tilling (and associated increase in porosity / exposed surface area)? According to Figure 4 observed radon concentrations peak at these times. The authors might check back trajectories to see whether there is a notable difference in airmass time over land for these periods, or whether there might be a local change in radon flux.
Section 2.2: the station is not situated well for RTM observations. Fetch in the prevailing wind direction (NW) is limited to around 2 km, wind from the longest fetch region (W) is uncommon, and immediately to the S-SE of the site is a large body of standing water (before the rice paddies continue). Even when the fields are not inundated, this setting would yield large spatial gradients in radon flux. Furthermore, for the period when the fields were inundated, if measurements *were* targeting just the rice paddy fields, the radon flux from this region (within typical measurement error) would be essentially zero.
L165-166: The authors claim that the RTM here is applied over the footprint of the study area. The study area (200 km2) measures roughly 20 x 12 km in dimensions. According to Fig S4b, wind speeds in the nocturnal RTM window reach as high as 22 m.s-1, which would cover a distance of 475 km over the 6-hour nocturnal window. Even the ‘mid-range’ nocturnal wind speed used of 5 m.s-1 would cover a distance of almost 110 km in this time. Based on these values, the CH4 flux signal retrieved by this method from the actual intended study region would only constitute a small fraction of the result (i.e., observations would be dominated by advection from non-local regions).
L170-171: For wind speeds well over 2 m.s-1, and large spatial variability in the radon flux in the vicinity of the measurement site, it is not possible to make the assumption of negligible advection effects.
L176-177: The “local scale” nocturnal accumulation method of RTM application to derive local fluxes only make sense under conditions of nocturnal stability (not when the nocturnal atmosphere is near-neutral or well mixed). If nocturnal stability criteria are included in the selection of appropriate conditions under which to apply the RTM, these are usually also an effective filter of rainfall events. Meaning that, over the 6-hour nocturnal window, there would be less chance of rainfall events, and less chance of variable radon fluxes over the nocturnal window period each night.
L188: The challenge here is knowing the footprint area represented by the measurements, and how this relates to the actual region of interest.
L190-191: Why is an influence area of 70 km x 70 km used, when the actual dimensions of the study area are 20 km x 12 km? Application of equation (3) assumes similar distribution of both fluxes over the region of interest, and homogeneity of the respective fluxes over this region, which is clearly not the case when air masses leave the rice fields to the west or cross the coast to the ocean in any other direction. Based on Fig S3 f, if the boxes represent hourly intervals, this still indicates a contributing fetch region over the 6-hour nocturnal window that is over 3 times the scale of the intended measurement region. Calculating an average radon flux over a region of that size and heterogeneity, will not result in a value representative of the intended study region.
L200-206: No wind speed or stability criterion are used in the selection of nights on which to apply the RTM, when stable conditions are in fact the most necessary criterion for applying the nocturnal accumulation form of the RTM to retrieve local scale fluxes. Also, somewhat dangerously, a strong linear correlation between radon and CH4 is not – on its own – a reliable indicator of a time representing a good local flux measurement (this approach is more like that applied by Biraud et al., 2000 for regional RTM flux estimates). Such conditions can arise under strong advection (high winds) and be completely unrelated to the local flux. Similar arguments can be made regarding positive concentration gradients if they are taken in isolation (e.g., without also considering the nocturnal stability state).
L209-212: A problem with using models to estimate footprint areas for RTM calculations is that stable nocturnal conditions are a requirement for applying the nocturnal accumulation version of the RTM, and these are the conditions under which models have the poorest performance. Mixing depths, wind speeds and footprint regions tend to be significantly exaggerated.
L213-221: If eqn (3) is being used to derive CH4 fluxes from the rice paddy region, then only radon fluxes representative of this region are meaningful, and conditions need to be selected to avoid the measurement footprint significantly exceeding the bounds of the study region. Do the radon flux maps account for the complete inundation of the rice fields for almost half of the year? [I now see Figure 11a confirms that they don’t]
L257-260: This implies that a representative radon flux for the 20 km x 12 km study region is being derived based on a land fetch of ~60 km or more. Presumably most of this land is not inundated for 5 months of the year? Also, if radon fluxes from this fetch are contributing to the observations, doesn’t this also apply to the CH4 fluxes? (which does not match the study goals)
L328: Based on the wind speeds at this site (and daytime minimum radon concentrations in December, Fig 5), the higher December observed average radon concentration would most likely be fetch related, rather than due to a sudden change in local radon flux for a single month.
L354: Northwestern
Fig S4 b (and Fig 7c): Most likely any RTM results derived for nocturnal wind speeds > 2 m.s-1 will not closely represent what is actually happening over the rice paddies. This appears to be the case for a large fraction of the dataset.
L364-365: As previously mentioned, the overestimation of wind speeds by the model at night will lead to exaggerated footprint estimates.
L429-430: At night, under near-neutral or stable conditions, a measurement height of 10 m a.g.l., is not a guarantee of a “very local” fetch when wind speeds exceed 1.5-2 m.s-1.
L432-433: Considering the dominant wind directions within the nocturnal window for RDM application (NW or SSE), it would be uncommon for air masses of a given event to spend more than 15 – 20% of their time over the intended study region. I don’t believe that this is selective enough to achieve the study goal.
L433-434: There are a lot of coastal areas in the oceanic fetch region when the wind direction is from the NW – is it not the case that coastal regions can be sources of methane? Of course, coastal oceanic radon fluxes are also higher than open ocean radon fluxes, but they are still MUCH lower than any terrestrial radon fluxes.
L455-457: Use 10-day back trajectories and calculate average time over land for air masses within the ABL (or residual layer) – i.e., below ~2000 m – and compare this information with the monthly radon plot of Figure 4. If they show similar trends, then fetch effects are a greater influence on the observed radon concentration than seasonal changes in the local radon flux.
L501-503: Based on Fig S3, even air masses arriving at the measurement site from the ocean (NE) could have been over other land regions within the last 1-4 days. This means, they could likely still have significant correlated events of radon and CH4 from prior land contact at the time they cross the local coast of the measurement site. So it is not safe to assume that the Rn and CH4 content of an airmass from the coast, that then crosses a limited extent of rice paddies, is only representative of exchanges from the rice paddy region.
L530-532: Under nocturnal conditions, with moderate to high wind speeds, the assumption of the measurement fetch being limited to a few km is not valid.
References
Biraud and co-authors: European greenhouse gas emissions estimated from continuous atmospheric measurements and radon 222 at Mace Head, Ireland, JGR. Atmos., 105(D1), 1351–1366, doi:10.1029/1999JD900821, 2000.
Chambers and co-authors, 2015. On the use of radon for quantifying the effects of atmospheric stability on urban emissions. Atmos. Chem. Phys. 15, 1175-1190.
Levin and co-authors: Verification of German methane emission inventories and their recent changes based on atmospheric observations, JGR. Atmos., 104(D3), 3447–3456, doi:10.1029/1998JD100064, 1999.
Levin and co-authors: Verification of greenhouse gas emission reductions: the prospect of atmospheric monitoring in polluted areas, Philos. Trans. R. Soc. A Math. Phys. Eng. Sci., 730 369(1943), 1906–1924, doi:10.1098/rsta.2010.0249, 2011.
Sesana, L., Caprioli, E., and Marcazzan, G. M.: Long period study of outdoor radon concentration in Milan and correlation between its temporal variations and dispersion properties of atmosphere, J. Environ. Radioactiv., 65, 147–160, doi:10.1016/S0265-931X(02)00093-0, 2003.
Citation: https://doi.org/10.5194/egusphere-2024-1370-RC2 - AC2: 'Reply on RC2', Roger Curcoll Masanes, 15 Nov 2024
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RC3: 'Comment on egusphere-2024-1370', Dafina Kikaj, 24 Sep 2024
The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2024/egusphere-2024-1370/egusphere-2024-1370-RC3-supplement.pdf
- AC3: 'Reply on RC3', Roger Curcoll Masanes, 15 Nov 2024
Interactive discussion
Status: closed
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RC1: 'Comment on egusphere-2024-1370', Fabian Maier, 24 Jul 2024
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 - AC1: 'Reply on RC1', Roger Curcoll Masanes, 15 Nov 2024
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RC2: 'Comment on egusphere-2024-1370', Scott Chambers, 22 Aug 2024
Review of “Estimation of seasonal methane fluxes over a Mediterranean rice paddy area using the Radon Tracer Method (RTM)” by Curcoll et al.
This paper seeks to estimate the methane flux, and its seasonal variability, from a particular rice paddy region on a coastal river delta, using the “local scale” nocturnal-accumulation implementation of the radon tracer method (RTM). The writing is clear and the manuscript well structured. As outlined in the introduction, characterisation of agricultural methane emissions is an important step toward ratifying the Paris agreement.
Despite the importance of the intended goal, I cannot recommend publication of this manuscript in its current form because I am not convinced that the RTM, in the way it has been applied, is an appropriate approach to achieve the stated project goals (i.e., targeting the specific rice paddy area). While seasonal methane fluxes are indeed derived, I believe it is unlikely that they are truly representative of only (or predominantly) the ~200 km2 of rice paddies in this region. I outline my concerns in more detail below.
General comments
The radon tracer method as intended to be applied in this study treats the nocturnal boundary layer as a simple box and seeks to use changes in concentration of radon and a companion species (methane, CH4) within this box over a collection of single nights, along with simulated radon emission rates from the bottom surface of this box, to infer the flux of the companion species each night over the same spatial region.
There are some necessary assumptions for this technique to be applied:
- Concentration changes within this box should be a function of only the average flux magnitudes over the base of the box (i.e., local flux contributions), and changes in height of the box.
- For advective effects to be ignored, the flux of each gas should be similarly distributed and homogeneous across the base of this box.
Regarding assumption 1:
It has been well established (e.g., Sesana et al 2003; Chambers et al 2015) that significant accumulation of trace gases in the nocturnal boundary layer, driven specifically by local sources, only occurs when wind speeds are less than about 1.5 m.s-1. At higher wind speeds, observed concentration changes at a given location become increasingly dominated by advection from non-local sources. Under such conditions, confusion can arise between the “local scale” nocturnal accumulation implementation of the RTM as described by Levin et al (1999, 2011), and the “regional scale” implementation of the RTM as described by Biraud et al (2000). According to Figure S4 b, only a relatively small fraction of wind speeds in the nocturnal windows used for the RTM in this study are below 2 m.s-1. Given the 6-hour nocturnal window applied here for the RTM, and maximum acceptable wind speed of < 2 m.s-1 to observe a local influence, the length of the “box” contributing to the observed flux signal would be around 43 km (if only the low wind speeds of this study were considered). The rice paddy region being investigated (based on Fig S1) appears to have dimensions of only around 20 km (east-west) x 12 km (north-south). Based on the location of the measurement site, the rice paddy field fetch in the dominant wind directions (NW and SSE) is only 2 – 10 km. So, even in the most ideal, low wind scenarios, the contribution of the rice paddy area to the overall signal observed is a low fraction.
Regarding assumption 2:
Given that the best-case scenario base length of the idealised “box” from the measurement point is around 43 km, and (a) the site is coastal, and (b) the rice paddy fields are of limited spatial extent, and periodically flooded, neither fluxes of radon or CH4 over this distance (in any direction from the measurement site) would approximately homogeneous or similarly distributed.
If only stable nocturnal conditions with wind speeds less than or equal to 0.5 m.s-1 were targeted, this would limit the local fetch contribution over the 6-hour nocturnal accumulation window to around 10 km, which may work for wind directions west through south, but this would severely limit the amount of data available for analysis.
Specific comments
L44: The RTM requires that both gas source functions are similarly distributed over the region in question. For the ~5 months of the year that the rice paddies are flooded, there is little impediment to CH4 emission (via diffusion, ebullition, and transport through aerenchyma). This is not the case for radon emission. When the soil profile is flooded, radon generated in the saturated soil matrix below the soil surface is unlikely to make it into the atmosphere before decaying. The much smaller fraction of radon generated at or near the soil surface is not produced in sufficient quantities for bubble transport, so diffusion is its only pathway (which would likely take of order 2-3 days to get through the 8 – 15 cm of standing water). Presumably this flux would only be marginally higher than typical open water radon fluxes, and therefore much lower than anything predicted by the European flux maps. Beyond the edges of the rice paddies there is the opposite problem. Radon fluxes would be higher from the unsaturated soils, and CH4 emissions lower.
L72: Clearly the modelled radon fluxes for this region do not represent completely inundated conditions (which is the case for the region of interest for 5 months of the year). Presumably they are representing seasonal changes in soil moisture for uncultivated land with a particular Ra-226 content. [I noticed later that this is acknowledged in Figure 11a – but I assume that the modelled fluxes were still used for this study, which would not correctly represent the study region].
Fig 2: In the month of land preparation (and period of straw incorporation), is an increase in local radon flux expected due to the tilling (and associated increase in porosity / exposed surface area)? According to Figure 4 observed radon concentrations peak at these times. The authors might check back trajectories to see whether there is a notable difference in airmass time over land for these periods, or whether there might be a local change in radon flux.
Section 2.2: the station is not situated well for RTM observations. Fetch in the prevailing wind direction (NW) is limited to around 2 km, wind from the longest fetch region (W) is uncommon, and immediately to the S-SE of the site is a large body of standing water (before the rice paddies continue). Even when the fields are not inundated, this setting would yield large spatial gradients in radon flux. Furthermore, for the period when the fields were inundated, if measurements *were* targeting just the rice paddy fields, the radon flux from this region (within typical measurement error) would be essentially zero.
L165-166: The authors claim that the RTM here is applied over the footprint of the study area. The study area (200 km2) measures roughly 20 x 12 km in dimensions. According to Fig S4b, wind speeds in the nocturnal RTM window reach as high as 22 m.s-1, which would cover a distance of 475 km over the 6-hour nocturnal window. Even the ‘mid-range’ nocturnal wind speed used of 5 m.s-1 would cover a distance of almost 110 km in this time. Based on these values, the CH4 flux signal retrieved by this method from the actual intended study region would only constitute a small fraction of the result (i.e., observations would be dominated by advection from non-local regions).
L170-171: For wind speeds well over 2 m.s-1, and large spatial variability in the radon flux in the vicinity of the measurement site, it is not possible to make the assumption of negligible advection effects.
L176-177: The “local scale” nocturnal accumulation method of RTM application to derive local fluxes only make sense under conditions of nocturnal stability (not when the nocturnal atmosphere is near-neutral or well mixed). If nocturnal stability criteria are included in the selection of appropriate conditions under which to apply the RTM, these are usually also an effective filter of rainfall events. Meaning that, over the 6-hour nocturnal window, there would be less chance of rainfall events, and less chance of variable radon fluxes over the nocturnal window period each night.
L188: The challenge here is knowing the footprint area represented by the measurements, and how this relates to the actual region of interest.
L190-191: Why is an influence area of 70 km x 70 km used, when the actual dimensions of the study area are 20 km x 12 km? Application of equation (3) assumes similar distribution of both fluxes over the region of interest, and homogeneity of the respective fluxes over this region, which is clearly not the case when air masses leave the rice fields to the west or cross the coast to the ocean in any other direction. Based on Fig S3 f, if the boxes represent hourly intervals, this still indicates a contributing fetch region over the 6-hour nocturnal window that is over 3 times the scale of the intended measurement region. Calculating an average radon flux over a region of that size and heterogeneity, will not result in a value representative of the intended study region.
L200-206: No wind speed or stability criterion are used in the selection of nights on which to apply the RTM, when stable conditions are in fact the most necessary criterion for applying the nocturnal accumulation form of the RTM to retrieve local scale fluxes. Also, somewhat dangerously, a strong linear correlation between radon and CH4 is not – on its own – a reliable indicator of a time representing a good local flux measurement (this approach is more like that applied by Biraud et al., 2000 for regional RTM flux estimates). Such conditions can arise under strong advection (high winds) and be completely unrelated to the local flux. Similar arguments can be made regarding positive concentration gradients if they are taken in isolation (e.g., without also considering the nocturnal stability state).
L209-212: A problem with using models to estimate footprint areas for RTM calculations is that stable nocturnal conditions are a requirement for applying the nocturnal accumulation version of the RTM, and these are the conditions under which models have the poorest performance. Mixing depths, wind speeds and footprint regions tend to be significantly exaggerated.
L213-221: If eqn (3) is being used to derive CH4 fluxes from the rice paddy region, then only radon fluxes representative of this region are meaningful, and conditions need to be selected to avoid the measurement footprint significantly exceeding the bounds of the study region. Do the radon flux maps account for the complete inundation of the rice fields for almost half of the year? [I now see Figure 11a confirms that they don’t]
L257-260: This implies that a representative radon flux for the 20 km x 12 km study region is being derived based on a land fetch of ~60 km or more. Presumably most of this land is not inundated for 5 months of the year? Also, if radon fluxes from this fetch are contributing to the observations, doesn’t this also apply to the CH4 fluxes? (which does not match the study goals)
L328: Based on the wind speeds at this site (and daytime minimum radon concentrations in December, Fig 5), the higher December observed average radon concentration would most likely be fetch related, rather than due to a sudden change in local radon flux for a single month.
L354: Northwestern
Fig S4 b (and Fig 7c): Most likely any RTM results derived for nocturnal wind speeds > 2 m.s-1 will not closely represent what is actually happening over the rice paddies. This appears to be the case for a large fraction of the dataset.
L364-365: As previously mentioned, the overestimation of wind speeds by the model at night will lead to exaggerated footprint estimates.
L429-430: At night, under near-neutral or stable conditions, a measurement height of 10 m a.g.l., is not a guarantee of a “very local” fetch when wind speeds exceed 1.5-2 m.s-1.
L432-433: Considering the dominant wind directions within the nocturnal window for RDM application (NW or SSE), it would be uncommon for air masses of a given event to spend more than 15 – 20% of their time over the intended study region. I don’t believe that this is selective enough to achieve the study goal.
L433-434: There are a lot of coastal areas in the oceanic fetch region when the wind direction is from the NW – is it not the case that coastal regions can be sources of methane? Of course, coastal oceanic radon fluxes are also higher than open ocean radon fluxes, but they are still MUCH lower than any terrestrial radon fluxes.
L455-457: Use 10-day back trajectories and calculate average time over land for air masses within the ABL (or residual layer) – i.e., below ~2000 m – and compare this information with the monthly radon plot of Figure 4. If they show similar trends, then fetch effects are a greater influence on the observed radon concentration than seasonal changes in the local radon flux.
L501-503: Based on Fig S3, even air masses arriving at the measurement site from the ocean (NE) could have been over other land regions within the last 1-4 days. This means, they could likely still have significant correlated events of radon and CH4 from prior land contact at the time they cross the local coast of the measurement site. So it is not safe to assume that the Rn and CH4 content of an airmass from the coast, that then crosses a limited extent of rice paddies, is only representative of exchanges from the rice paddy region.
L530-532: Under nocturnal conditions, with moderate to high wind speeds, the assumption of the measurement fetch being limited to a few km is not valid.
References
Biraud and co-authors: European greenhouse gas emissions estimated from continuous atmospheric measurements and radon 222 at Mace Head, Ireland, JGR. Atmos., 105(D1), 1351–1366, doi:10.1029/1999JD900821, 2000.
Chambers and co-authors, 2015. On the use of radon for quantifying the effects of atmospheric stability on urban emissions. Atmos. Chem. Phys. 15, 1175-1190.
Levin and co-authors: Verification of German methane emission inventories and their recent changes based on atmospheric observations, JGR. Atmos., 104(D3), 3447–3456, doi:10.1029/1998JD100064, 1999.
Levin and co-authors: Verification of greenhouse gas emission reductions: the prospect of atmospheric monitoring in polluted areas, Philos. Trans. R. Soc. A Math. Phys. Eng. Sci., 730 369(1943), 1906–1924, doi:10.1098/rsta.2010.0249, 2011.
Sesana, L., Caprioli, E., and Marcazzan, G. M.: Long period study of outdoor radon concentration in Milan and correlation between its temporal variations and dispersion properties of atmosphere, J. Environ. Radioactiv., 65, 147–160, doi:10.1016/S0265-931X(02)00093-0, 2003.
Citation: https://doi.org/10.5194/egusphere-2024-1370-RC2 - AC2: 'Reply on RC2', Roger Curcoll Masanes, 15 Nov 2024
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RC3: 'Comment on egusphere-2024-1370', Dafina Kikaj, 24 Sep 2024
The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2024/egusphere-2024-1370/egusphere-2024-1370-RC3-supplement.pdf
- AC3: 'Reply on RC3', Roger Curcoll Masanes, 15 Nov 2024
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