the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Nitrate-nitrogen dynamics in response to forestry harvesting and climate variability: Four years of UV nitrate sensor data in a shallow, gravel aquifer
Abstract. The leaching of inorganic nitrogen can adversely affect groundwater and hydrologically connected streams and rivers. Traditionally, these effects have been assessed using discrete water quality measurements. However, it is difficult to characterise the complex biogeochemical processes that control nitrate-nitrogen dynamics in groundwater using temporally sparse data. In this study, we installed a continuous UV nitrate sensor, downgradient of forestry land use in a shallow, gravel aquifer to understand nitrate-nitrogen dynamics in groundwater. We found that there were two mechanisms of nitrate-nitrogen pulses in groundwater from the upgradient forestry land use. The most prevalent were nutrient losses during winter months when plant uptake is lower. Outside of winter months, we observed a higher nitrate-nitrogen concentration (12 mg L-1) as a result of changing biogeochemical conditions after trees were harvested, compared to 5.9 mg L-1 when there was no harvesting. We used a novel hysteresis approach, comparing nitrate-nitrogen concentrations and groundwater levels after rainfall recharge to understand event scale variability. First flush events in winter had a larger average area (more hysteresis) of 0.65 compared to 0.35 (less hysteresis) for subsequent events. Peak concentrations occurred earlier in events during 2021 (wetter) compared to 2020 (dryer), highlighting slower drainage pathways in years with less recharge. Through this analysis we also found evidence that the mobilisation of nitrate-nitrogen shifted from rainfall recharge to rising groundwater levels after the surface supply was depleted from successive recharge events. Finally, the nitrate-nitrogen load analysis indicates that the leaching and export occurs in pulses, that discrete sampling cannot accurately characterise. For example, in 2021, over 80 percent of the exported load occurred during a quarter of the year and discharged when there were base flow conditions in the nearby Hurunui River. These findings have implications for forestry land management, the understanding of inorganic nitrogen dynamics in groundwater in response to rainfall recharge and can be applied to future climate projections where periods of drought and storm events are more frequent.
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RC1: 'Comment on egusphere-2024-964', Anonymous Referee #1, 18 Jun 2024
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General comments
The manuscript titled "Nitrate-nitrogen dynamics in response to forestry harvesting and climate variability: Four years of UV nitrate sensor data in a shallow, gravel aquifer" provides valuable insights into the nitrate-nitrogen dynamics influenced by forestry cutting, variation in rainfall (recharge) and groundwater level. The authors effectively used continuous UV nitrate sensors to provide 4 years of data at 15-mins intervals, allowing for detailed temporal analysis in quantifying the amount and timing of the groundwater nitrate-nitrogen leaching and its mobilization and transport mechanisms with hysteresis analysis. However, there are areas that need further clarification and improvement. In general,
- Clear research objectives are addressed, but there is no leading research question(s) presented.
- The introduction provides a good overview of the problems and methods but could benefit from highlighting the consequences of disturbance on forestry nutrient leaching, e.g., concluding or comparing the difference in nitrate-nitrogen concentration before and after ecosystem change for the six disturbed forests listed in Table 1 (if data is available in your references).
- Although only the hysteresis analysis for winter months is shown in your supplement, do you have any findings or insights for the correlation between groundwater level and nitrate-nitrogen leaching during droughts?
Specific comments
- Line 160: Add the coordinates to the monitoring site.
- Line 170: How do you account for the effects of nitrogen leaching from irrigated beef on the nitrate-nitrogen concentration in the Hurunui River?
- Line 173, Figure 1: Is it possible to show the location of the lysimeter on the map?
- Line 179: How does the path length setting of the nitrate sensor affect its measurement accuracy? What might be the causes for the overestimation of high nitrate-nitrogen concentrations?
- Line 190: What’s the precision of the TriOS NICO sensor during the study period?
- Line 207-208: How did you adjust/calibrate the UV nitrate sensors?
- Line 213-215: Which land surface recharge model did you use? Is it a 1-D model? What is its principle for calculating land surface recharge? How did you define the values of parameters? From your reference, I learnt that the model is named the soil-water balance model (GDA-LSR). It’s better to introduce a bit of the model and justify model performance because rainfall recharge is one of your main studied variables.
- Line 225: At what depth was the UV nitrate sensor installed in the Hurunui River?
- Line 227: What is the difference between “rainfall recharge” here and that calculated from the land surface model in Line 212?
- Line 241: What’s the HARP (full name?) algorithm, and what do values represent in Line 348, e.g., 0.65, 0.35 and 50% and 55%?
- Line 259: Does “the volume of water moving in a horizontal direction from the aquifer to the Hurunui Rive” refer to the same as groundwater discharge (qi) in Eq. (1)? Can you introduce the equation used for calculating qi?
- Line 275: Add units to each variable used in the equations and provide clear definitions.
- Line 291: Why did the groundwater level remain low and stable from 2020 to June 2021?
- Line 306: Include a statement clarifying the two distinct climate types and how they influenced the rates of nitrate-nitrogen leaching.
- Line 355: What is the residual analysis for and what is meant by “not changed system state” in Fig. 4?
- Line 383: How did you end up with the conclusion of “The annual and quarterly measurements over or underestimated the load depending on whether they captured the winter pulses of nitrate-nitrogen” from the data in Table 3?
- Line 385: “As a sensitivity analysis” for groundwater discharge calculation?
- Line 402: “We infer that the reduced nutrient demand, increased rainfall and favourable soil water balance conditions induced higher nutrient losses from the forest soils during winter”, where the “favourable soil water balance conditions” is a general indication, could you specify what kind of soil water balance conditions based on your findings in this study?
Technical comment
- Line 128: Add a punctuation mark before “Conversely”.
- Line 168: Add a comma before “sections”.
- Line 192: Add a punctuation mark after “(Fig. 2)”.
- Line 215: Add the unit to “soil water capacity (86.0 mm)”.
- Line 252: Add a comma after the “bore”.
- Line 262: Add a comma after the “…bgl)”.
- Line 301: Just some suggestions for better visualisation of Figure 4, (1) Between two consecutive years on x-axis, add scale bars with monthly timestep will help readers to identify the winter and summer months easier; (2) Label each plot with (a)-(g) for future reference; (3) Combine the last two graphs, e.g., plotting the point series data as the secondary y-axis with different colour scheme on the continuous dataset, to compare lab measurements and UV sensor data while saving space.
- Line 311: Figure 5, the well count for red groups seems to be in the reserve order, then it should range from [0%, 100%] from the top to the bottom at the y-axis on the right hand side.
Good luck and look forward to your replies!
Citation: https://doi.org/10.5194/egusphere-2024-964-RC1 -
AC1: 'Reply on RC1', Ben Wilkins, 04 Jul 2024
reply
Thank you very much for your insightful comments on this paper. I think they will improve the paper and make it more readable. I appreciate the time that has been spent reviewing this paper.
General comments
Clear research objectives are addressed, but there is no leading research question(s) presented.
Thank you for pointing this omission out and we agree that clarification on the leading research question is needed. The manuscript will be improved to include a leading research question on utilising high frequency nitrate-nitrogen data to elucidate the drivers of changes in concentration, groundwater level and concentration relationships and more accurately estimate the exported loads.
The introduction provides a good overview of the problems and methods but could benefit from highlighting the consequences of disturbance on forestry nutrient leaching, e.g., concluding or comparing the difference in nitrate-nitrogen concentration before and after ecosystem change for the six disturbed forests listed in Table 1 (if data is available in your references).
I agree that it would be ideal to compare the difference in nitrate-nitrogen before and after ecosystem changes. However, ecosystem change does not always lead to a step change in nitrate-nitrogen concentrations. Results in other published studies are somewhat inconsistent with some studies reporting large changes before and after disturbance, while others report no change or even a decrease in nitrate-nitrogen concentrations. This is not surprising considering the complex biogeochemical processes controlling leaching and varied monitoring frequencies during studies. It does make commenting on the consequences of disturbance on forestry leaching difficult.
Perhaps I could further highlight the idea that the discrete sampling may not capture the dynamic nitrate-nitrogen changes after ecosystem disturbance, so it is difficult to know how representative the before and after concentrations are. Therefore, it is difficult to determine the consequences of disturbance on forestry nutrient leaching. This highlights the benefits of continuous monitoring.
Although only the hysteresis analysis for winter months is shown in your supplement, do you have any findings or insights for the correlation between groundwater level and nitrate-nitrogen leaching during droughts?
I agree that it would be great to have a comparative analysis between hysteresis in winter months and during periods of low recharge. This is somewhat covered in the discussion where hysteresis in 2020 was found to be more varied, perhaps due to the combination of faster travel times after rainfall recharge and slower pathways because of low groundwater levels. Hysteresis type is also discussed. The supplement shows all hysteresis events during the study period.
Specific comments
Line 160: Add the coordinates to the monitoring site.
Agree. Will add well coordinates.
Line 170: How do you account for the effects of nitrogen leaching from irrigated beef on the nitrate-nitrogen concentration in the Hurunui River?
We explain in the discussion that the effect of the nitrate-nitrogen leaching from the irrigated beef and the forestry harvesting cannot be separated in the observed Hurunui River concentration. We know from previous studies that the nutrient leaching from irrigated beef will be greater than forestry leaching and will therefore contribute more to the observed Hurunui River concentration.
Line 173, Figure 1: Is it possible to show the location of the lysimeter on the map?
Yes, we will show the location of the lysimeter on the map.
Line 179: How does the path length setting of the nitrate sensor affect its measurement accuracy? What might be the causes for the overestimation of high nitrate-nitrogen concentrations?
The TriOS NICO specifications indicate that the accuracy of a 5 mm path length is approximately ± 5% + 0.5 mg/L. Thus, at higher nitrate-nitrogen concentrations there is potentially a larger absolute difference in actual and observed concentrations and therefore less accuracy.
There are a number of reasons why the UV nitrate sensor might not be accurate at higher concentrations:
- It is difficult to have a matrix algorithm that represents the constituents that cause interference during pulses of nitrate-nitrogen because of the short time frame that these pulses occur over.
- There is a trade-off of accuracy between (path length and matrix algorithm) accounting for low concentrations/reduced interference effects during most of the year or accounting for high concentrations/higher interference effects during pulses of nitrate-nitrogen. The current set up has higher accuracy during most of the year and loses accuracy during pulse events.
Overall, the variability of the site means that the accuracy of the UV nitrate sensor cannot be optimised for both high and low concentrations of nitrate-nitrogen (see: Pellerin et al., 2013).
Line 190: What’s the precision of the TriOS NICO sensor during the study period?
In-situ, the precision of the nitrate sensor varies based on different conditions and concentrations. Regular maintenance can also preserve precision (Pellerin et al., 2013).
Compared to the nitrate-nitrogen standards where there are no interference effects, the UV nitrate sensor was most precise at lower nitrate-nitrogen concentrations, similar to Figure 3.
Because of the range of factors controlling precision and the difficulty in measuring real world precision, I prefer not putting a number on precision. I think noting that it is something we controlled is sufficient for the deployment.
Line 207-208: How did you adjust/calibrate the UV nitrate sensors?
Propose changing the start of this sentence to: The recorded UV nitrate sensor measurements.
This sentence is about the adjustment of the UV nitrate sensor data.
Some recorded data from the UV nitrate sensor had obvious offsets from the lab measurements for short periods. These were adjusted to the lab measurements.
Line 213-215: Which land surface recharge model did you use? Is it a 1-D model? What is its principle for calculating land surface recharge? How did you define the values of parameters? From your reference, I learnt that the model is named the soil-water balance model (GDA-LSR). It’s better to introduce a bit of the model and justify model performance because rainfall recharge is one of your main studied variables.
Agree, more description on the model and how the parameters were defined is needed.
It is a 1-D model. Recharge is the rainfall volume above the soil water storage capacity minus evaporation, uptake and runoff.
Parameters were determined from the nearby lysimeter, weather station (PET) and mapped soil properties.
Line 225: At what depth was the UV nitrate sensor installed in the Hurunui River?
The sensor is deployed at different depths between warm and cool seasons to account for changes in base flow conditions.
Line 227: What is the difference between “rainfall recharge” here and that calculated from the land surface model in Line 212?
Groundwater levels are the main focus in section 3.4. These give some indication of regional annual recharge. This is to show the differences in conditions over the study period but the recharge has not been calculated. Rather at the site, the recharge has been calculated using the LSR model. We will rewrite the sentence to clarify this distinction.
Line 241: What’s the HARP (full name?) algorithm, and what do values represent in Line 348, e.g., 0.65, 0.35 and 50% and 55%?
Agree, can add the full name here before the acronym.
Hysteresis, Area, Residual and Peak.
The algorithm is an R script, so it is probably better to link the github address where it can be accessed.
In the results section (Line 348) the decimal values indicate the area of the hysteresis curve.
The percentages indicate the time to reach peak groundwater level or peak nitrate-nitrogen concentration along the hysteresis curve during the event. We will add a few additional clarifying phrases to the text.
Line 259: Does “the volume of water moving in a horizontal direction from the aquifer to the Hurunui Rive” refer to the same as groundwater discharge (qi) in Eq. (1)? Can you introduce the equation used for calculating qi?
Agree, can introduce Darcy’s Law here.
Line 275: Add units to each variable used in the equations and provide clear definitions.
Agree, can add variables from the equation to lines 263-265 to show what we used in this study.
Line 291: Why did the groundwater level remain low and stable from 2020 to June 2021?
Agree, can add detail.
There were low groundwater levels due to low rainfall during 2020 and the first half of 2021.
Line 306: Include a statement clarifying the two distinct climate types and how they influenced the rates of nitrate-nitrogen leaching.
Agree, can add detail.
Low rainfall that resulted in declining groundwater levels, followed by a high rainfall storm event with more consistent rainfall following.
Line 355: What is the residual analysis for and what is meant by “not changed system state” in Fig. 4?
Can add (low nitrate-nitrogen concentrations) after pre-event conditions.
The residual analysis is part of the HARP analysis suite to determine if the environment shifts to higher concentrations, or a new system state, such as eutrophication or another biological condition. It was not included in the analysis as it did not apply to the results due to concentrations returning to pre-event conditions.
Line 383: How did you end up with the conclusion of “The annual and quarterly measurements over or underestimated the load depending on whether they captured the winter pulses of nitrate-nitrogen” from the data in Table 3?
Can add figure 2 to this statement as it is a useful visualisation.
If the discrete annual or quarterly measurement captures an elevated nitrate-nitrogen concentration then it is above the integration load estimation, if not then it underestimates the load by not accounting for the export during the pulses of nitrate-nitrogen.
Line 385: “As a sensitivity analysis” for groundwater discharge calculation?
Agree that more detail is needed here.
There are a range of potential conductivity and hydraulic gradient values that could be used to calculate the discharge of groundwater.
We used a range of potential literature values to give some indication of the exported load range. This should be cited and we will amend the text to reflect this.
Line 402: “We infer that the reduced nutrient demand, increased rainfall and favourable soil water balance conditions induced higher nutrient losses from the forest soils during winter”, where the “favourable soil water balance conditions” is a general indication, could you specify what kind of soil water balance conditions based on your findings in this study?
Agree, can be more specific here.
I used this term because studies have shown that higher soil water content induces increased mineralisation of organic nitrogen after rainfall.
Also, that recharge is occurring more consistently in winter, so there is more transport of nitrate-nitrogen to groundwater.
I will split these biological and hydrological components of soil moisture and soil water balance.
Technical comment
- Line 128: Add a punctuation mark before “Conversely”.
- Line 168: Add a comma before “sections”.
- Line 192: Add a punctuation mark after “(Fig. 2)”.
- Line 215: Add the unit to “soil water capacity (86.0 mm)”.
- Line 252: Add a comma after the “bore”.
- Line 262: Add a comma after the “…bgl)”.
- Line 301: Just some suggestions for better visualisation of Figure 4, (1) Between two consecutive years on x-axis, add scale bars with monthly timestep will help readers to identify the winter and summer months easier; (2) Label each plot with (a)-(g) for future reference; (3) Combine the last two graphs, e.g., plotting the point series data as the secondary y-axis with different colour scheme on the continuous dataset, to compare lab measurements and UV sensor data while saving space.
- Line 311: Figure 5, the well count for red groups seems to be in the reserve order, then it should range from [0%, 100%] from the top to the bottom at the y-axis on the right hand side.
I agree with these changes.
For change 7(3), I would prefer keeping the two time series separate, so that they can be easily referred to but I agree that they could be combined to save space if needed.
For change 8, the graph indicates the percentage of wells that are in each category. I think including a more detailed scale bar would be beneficial for readability.
References
Pellerin, B. A., Bergamaschi, B. A., Downing, B. D., Saraceno, J. F., Garrett, J. D., and Olsen, L. D.: Optical techniques for the determination of nitrate in environmental waters: Guidelines for instrument selection, operation, deployment, maintenance, quality assurance, and data reporting, US Geological Survey, 2013.
Citation: https://doi.org/10.5194/egusphere-2024-964-AC1
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