the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 07 Jul 2025
Investigating Relationships Between Nitrogen Inputs and In-Stream Nitrogen Concentrations and Exports Across Catchments in Victoria, Australia
Abstract. Accurate information on nitrogen (N) inputs to the landscape is crucial for understanding and predicting in-stream N concentrations and downstream N export. However, obtaining detailed catchment-scale data remains challenging due to their spatial and temporal variability. This study developed a statistical model based on mean annual rainfall to estimate fertiliser N inputs for four agricultural land uses in Victoria, Australia. These estimates, along with contributions from biological fixation and atmospheric deposition, were used to (a) examine how N inputs relate to stream total nitrogen (TN) concentrations and export, and (b) assess the influence of rainfall, hydrology, and other catchment characteristics on TN export across 59 diverse catchments. The model revealed a strong positive correlation between average rainfall and fertiliser N input for each land use at the Catchment Management Authority (CMA) (i.e., regional) level, with R2 values ranging from 0.55 to 0.72. Stream TN concentrations were strongly correlated with total N inputs (R2 = 0.72) and fertiliser N inputs (R2 = 0.68). Stream TN export also showed significant relationships with total N inputs (R2 = 0.47) and fertiliser N inputs (R2 = 0.53). The proportion of total N inputs exported varied widely, ranging from 1.4 % to 26 %, with an average of 7 %. This variation was strongly influenced by agricultural activity and hydroclimatic factors. Moreover, the average export proportion was notably lower than values reported for other regions globally, which may reflect Australia’s generally lower N input levels. These findings provide a useful tool for water quality assessment and can guide targeted strategies to reduce nitrogen pollution in streams.
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RC1: 'Comment on egusphere-2025-2456', Anonymous Referee #1, 13 Jul 2025
Review of Babatunde et al., “Investigating Relationships Between Nitrogen Inputs and InStream Nitrogen Concentrations and Exports Across Catchments in Victoria, Australia”
This paper is very clear and nicely written. The analysis is straightforward. The authors discuss the relationship between N loads, referring to the well-known NANI methodology, and riverine N flows across several catchments in Victoria, Australia. The catchments are predominantly forested with some grazing and dairy farming activity. A critical problem is to estimate the components of NANI to compare with statistical estimates of riverine fluxes (using the WRTDS model). NANI includes fertilizer, N fixation, atmospheric deposition and net food/feed inputs. The authors estimate fertilizer by developing regressions against rainfall for several land use categories, which explain about 55-72% of the variability. Atmospheric deposition is considered a small, spatially invariant value, which essentially has no explanatory power. N fixation estimates are based on crop specific estimates made in a previous study. Net food/feed imports were not included because the authors judged that the data were insufficient to estimate livestock feed, and human food imports were ignored, presumably because the human waste stream was considered insignificant (human wastewater sources were also dismissed as insignificant). Thus, the authors are assuming that the only sources of N in this region are due to fertilizer, as estimated by a rainfall proxy in different land use categories, and crop-dependent N fixation rates developed in an early study. The authors are aware of the limitations of the analysis, and explicitly state them in section 4.5. I think it would be useful to either develop proxies for net food/feed inputs (say, based on even rough estimates of livestock numbers and population density as they vary by catchment) or, if this is not possibly, simply frame the analysis as a relationship between rainfall-based estimates of fertilizer N loads and streamflow N, which is effectively what it is.
A few other comments follow below.
160: Given the seemingly strong relationship between elevation and rainfall shown in figure 1, it is surprising that elevation is nowhere mentioned as an explanatory variable, nor included in any tables. Was it investigated?
183: Need to cite a reference for QGIS
239: Adams et al 2014 considered only wet deposition of N and did not distinguish between reduced and oxidized forms. Reduced N (including NH3, NH4+, etc) are well known to be associated with volatile losses of NH3 from agricultural sources including manure from dairy herds and fertilizer applications, which are likely relevant here. (The authors note the significance of manure in the context of the discussion of NOx vs TN in agricultural vs forested land covers around line 488.) While the overall term is likely relatively small compared to other sources, using a constant value across the landscape cannot possibly be meaningful to estimate spatially varying loads in the models developed here. Either do a better job of estimating the true atmospheric deposition component and its spatial variation, or drop it as insignificant.
507: what is “fertiliser additive land use”?
588: Schaefer et al (2009) actually show lower export as a percent of N inputs in some western US watersheds than Schaefer and Alber (2007) show in southeastern US watersheds, presumably because of the relatively dry environments in much of the west which affect N delivery (as you note in section 4.3).
Section 4.3: A potential difficulty with the discussion of TN export as a fraction in inputs is that the estimated total catchment N inputs may be biased because of incomplete estimates. Some discussion of this specific issue is warranted.
Citation: https://doi.org/10.5194/egusphere-2025-2456-RC1 -
RC2: 'Comment on egusphere-2025-2456', Anonymous Referee #2, 19 Aug 2025
The study addresses an important knowledge gap by developing a rainfall-based model to estimate fertilizer N inputs and linking them to stream N dynamics across diverse catchments. Its major strengths—based on long-term multi-site monitoring data from a large-scale empirical approach (59 catchments), pragmatic modeling of fertilizer inputs, and demonstration of scalable relationships between N inputs and stream exports—offer actionable insights for regional water quality policy. While some methodological simplifications exist, the work achieves its core goal of establishing baselines for N budgeting in semi-arid landscapes.
Some comments:
Firstly, the fertilizer model relies solely on mean annual rainfall as a predictor for four distinct agricultural land uses (presumably dairy, cropping, etc.). However, the irrigation is also very important in this arid zone. How much the N input by irrigation? And did irrigation rates could affect this model?
Line 53-54 The phrase "complex to manage and modify" is redundant and awkwardly implies that modifying the models (rather than their operation) is the focus.
Line 57, The comparison is syntactically incomplete. "Unlike process-based models" lacks a clear verb to contrast what those models do differently.
Line 58 Absence of "that" before "N undergoes" creates a grammatical error.
Line 126, The phrase "geographic representation across..." is slightly awkward. The concepts of "data availability" and "representation" are not perfectly parallel. The intent is clear but can be expressed more forcefully and directly.
Line 325 The pronoun "That" is slightly vague. While it logically refers to the data collection period of Gourley et al. (2008-2009), the reference can be made more explicit for immediate clarity.
Line 347, "natural soil N" This term is vague. In soil science, the preferred terms are typically "native soil nitrogen" or "soil nitrogen mineralization."
Line 364, The phrase "cropping land use, including horticulture" is problematic. In standard agricultural classification, "horticulture" (intensive, high-value crops like vegetables and fruits) is often considered distinct from broadacre "cropping" (extensive field crops). Grouping them under one "cropping land use" umbrella is confusing and requires justification, as their nutrient management practices differ vastly.
Line 380, While "data" is technically plural, it is often treated as a singular mass noun in scientific writing, especially when referring to a dataset as a whole. Using the singular verb is more modern and common.
Line 451, The space between the numeral and the percent symbol ("43.4 %") is a typesetting style often required by publishers. However, many style guides (e.g., APA) recommend no space ("43.4%"). The key is to be consistent throughout the manuscript.
Line 597, The verb "indicate" is weak and overused in scientific writing. A more assertive and precise verb would strengthen the opening statement.
Citation: https://doi.org/10.5194/egusphere-2025-2456-RC2
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