The effect of rainfall variability on Nitrogen dynamics in a small agricultural catchment

Wang, Qiaoyu; Yang, Jie; Heidbüchel, Ingo; Xu, Teng; Lu, Chunhui

doi:https://doi.org/10.5194/egusphere-2025-676

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https://doi.org/10.5194/egusphere-2025-676

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25 Mar 2025

| 25 Mar 2025

Status: this preprint is open for discussion and under review for Hydrology and Earth System Sciences (HESS).

The effect of rainfall variability on Nitrogen dynamics in a small agricultural catchment

Qiaoyu Wang, Jie Yang, Ingo Heidbüchel, Teng Xu, and Chunhui Lu

Abstract. Throughout history, extreme storms and droughts have had serious impacts on society and ecosystems globally. Rainfall variability in particular has been identified as a primary performance of climate change. However, so far little has been done to explore the effect of rainfall variability on water quality. This study is aimed at investigating the effect of rainfall variability on nitrogen (N) dynamics and its potentially negative influence on water quality. The transport of water and nitrate was simulated for a small agricultural catchment in Central Germany using the fully coupled surface-subsurface model HydroGeoSphere. Rainfall time series with specific climatic characteristics were generated using a stochastic rainfall generator. N transformation and transport were compared for four scenarios (with high, normal, low annual precipitation amounts, and low annual precipitation amounts coupled with reduced plant uptake, respectively) in order to identify the impact of inter-annual rainfall variability on N dynamics. The results suggest that higher annual precipitation amounts can enhance the transformation and transport of nitrogen. Lower annual precipitation amounts are conducive to nitrogen retention. Nonetheless, when vegetation suffers from drought stress, the retention capacity will decline markedly, suggesting that vegetation plays a vital role in N dynamics under extreme droughts. The linear regressions between selected parameters of the rainfall generator and N loads / fluxes were analyzed to elucidate the impact of intra-annual rainfall variability on N dynamics. The results indicate that wet / dry conditions and different dry-wet patterns caused by the distribution of storm durations and inter-storm periods over the course of a year can significantly affect N loads and in-stream nitrate concentration, respectively. In the warm season, droughts prompt the accumulation of SON, but drying-wetting cycles can enhance the extensive transformation of SON. In-stream nitrate concentration dramatically elevates during the rewetting period after the drought. High mean rainfall intensity contributes not only to the transformation of N when mineralization is not limited by low temperatures, but also to the plant absorption of inorganic nitrogen in the growing season. There is merely small effect of mean rainfall intensity on stream water quality. Overall, the study clarifies the effect of rainfall variability on N dynamics in a small agricultural catchment, which provides theoretical support to formulate fertilization strategies and protect aquatic ecosystems under climate change in the future.

Received: 13 Feb 2025 – Discussion started: 25 Mar 2025

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RC1: 'Comment on egusphere-2025-676', Anonymous Referee #1, 22 Apr 2025 reply

This manuscript presents a numerical modeling study exploring how inter- and intra-annual precipitation variability affects nitrogen (N) loads and fluxes in a catchment. The topic is timely and important for water quality and environmental management. The paper is generally well written, though some grammatical edits are needed. The methods and results are mostly clear and logically presented. The conclusions are relevant and likely to be of interest to scientists and resource managers focused on mitigating nutrient pollution.
Primary suggestions are to:
(1) Clarify and narrow the research focus: While the broad importance of precipitation variability is well established in the introduction, the final introductory paragraph should better define the unique contribution this work makes to the literature. Consider making the statement of objectives more specific and clarifying how the objectives fill gaps left by other recent studies.
(2) Differentiate from previous work: The study references and builds on previous work, particularly Wang et al. (2023). However, the manuscript does not always make it clear where prior work ends and the current study begins. The manuscript also refers the reader to the previous studies for some crucial details (e.g. calibration to stream nitrate concentrations), which are not easy to find in the previous studies. I recommend adding further descriptions of the related studies at this site and delineating which analyses, model developments, and findings are new in this study.
(3) Clarify model-data connection: A clearer connection could be made between the numerical model and real-world observations. The use of simple abstractions in the nutrient transport component of the model is reasonable, but also requires careful consideration of (a) how well the simplified processes representations mimic actual processes, (b) the uncertainty of the parameter estimates, and (c) the accuracy of the model in terms of reproducing observations. Otherwise, there is a risk of circularity: the model is built around certain processes and parameterizations, and then used to test the importance of those same processes and parameters.
Line-by-Line comments:
Line 14: “performance” is wrong word. “effect”?
34: SON not defined at this point
39 – “a small effect”
58 – not clear what “their” refers to
59 – grammar problem
72 –“a major”
59-78 – the paragraph starts with climate variability and ends with nitrate. I recommend keeping to one topic per paragraph.
99-100 sentence fragment
105 – “it is”
107 – The objectives are somewhat broad. It has been established that precipitation variability can affect N dynamics, and it would help to be more specific in this paragraph about the aspects of variability being tested and what if anything has been done to address them previously. In other words, how the specific objectives of this study relate to gaps in knowledge left by previous studies?
163: cross section is not discernable and it is not clear what is the source of the saturation values
164: this figure is mostly recycled from Wang et al., 2023 but no citation is given.
169: Please clarify if/how these data are used in the current study.
192 – delete “in details”
231 – what does this mean that the calibrated model was “verified” over the entire simulation period?
240 – meaning unclear “delineated corresponding to the reality”
245-6: “in route” grammar
258-260: meaning unclear.
320: “validation” might not be the right word (not the same as calibration).
323: Wang et al 2023 refers readers to Yang 2018 for more details and is not an easy source of information about the estimation of the N cycling parameters, uncertainty of those parameters or the quality of fit to the data. These are crucial aspects of the calibrated model and should be presented clearly and succinctly for the readers.
325: “impermeable for nitrate” (and water?)
333-375: probably don’t need this much detail about the rainfall generator
336: “a stochastic model”
Table 1 – This table has too many numbers and variables for readers to easily absorb. Consider replacing with a schematic, examples, or another simpler figure or table.
399-401 – grammar problem, meaning is lost
458 – Why is soil denitrification lumped with GW denitrification? Are they expected to be similar?
503 – These figures are confusing because the response variables (SON, SIN, LEA, Cq) are not on the z-axes.
564: 5.1 section title: consider being more specific about what increased rainfall does to the N dynamics
608: It seems notable that the high flows during the 2018 to 2019 drought are as high as the high flows from 2014-2018, and the main difference during 2018-2019 seems to be in the low flow periods.
670: 5.6. Consider discussing: Data limitations, uncertainty of parameters, model process representations
Table S1 – van Meter reference is missing date; bibliography is not included in this document

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Citation: https://doi.org/10.5194/egusphere-2025-676-RC1

Qiaoyu Wang, Jie Yang, Ingo Heidbüchel, Teng Xu, and Chunhui Lu

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https://doi.org/10.5194/egusphere-2025-676-supplement

Qiaoyu Wang, Jie Yang, Ingo Heidbüchel, Teng Xu, and Chunhui Lu

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Short summary

Extreme storms and droughts had profound impacts on water quality. We adapted a stochastic rainfall generator to examine how rainfall changes affect the transformation and transport of nitrogen (N) and its potential effects on water quality. We found that annual precipitation is an important factor impacting the transport of N. Wet/dry conditions of a year can significantly affect the transformation of N. Different dry-wet patterns during a year can change water quality in terms of nitrate.


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