Modeling Nitrate Export From a Mesoscale Catchment Using StorAge Selection Functions

Abstract

StorAge Selection (SAS) functions describe how catchments selectively remove water of different ages in storage via discharge, thus controlling the transit time distribution (TTD) and solute composition of discharge. SAS‐based models have been emerging as promising tools for quantifying catchment‐scale solute export, providing a coherent framework for describing both velocity‐driven and celerity‐driven transport. Due to their application in headwaters only, the spatial heterogeneity of catchment physiographic characteristics, land use management practices, and large‐scale validation have not been adequately addressed with SAS‐based models. Here, we integrated SAS functions into the grid‐based mHM‐Nitrate model (mesoscale Hydrological Model) at both grid scale (distributed model) and catchment scale (lumped model). The proposed model provides a spatially distributed representation of nitrogen dynamics within the soil zone and a unified approach for representing both velocity‐driven and celerity‐driven subsurface transport below the soil zone. The model was tested in a heterogeneous mesoscale catchment. Simulated results show a strong spatial heterogeneity in nitrogen dynamics within the soil zone, highlighting the necessity of a spatially explicit approach for describing near‐surface nitrogen processing. The lumped model could well capture instream nitrate concentration dynamics and the concentration–discharge relationship at the catchment outlet. In addition, the model could provide insights into the relations between subsurface storage, mixing scheme, solute export, and the TTDs of discharge. The distributed model shows results that are comparable to the lumped model. Overall, the results reveal the potential for large‐scale applications of SAS‐based transport models, contributing to the understanding of water quality‐related issues in agricultural landscapes.

Explaining the Variability in High‐Frequency Nitrate Export Patterns Using Long‐Term Hydrological Event Classification

“…As a result from decreased hydrological connectivity, only nitrate sources in close proximity to the stream network and from sources connected via deeper groundwater flow paths are connected to the stream network (Musolff et al., 2015; Stieglitz et al., 2003; J. Yang, Heidbüchel, et al., 2018). These flow paths during the dry period are generally characterized by longer transit times and thus enable more nitrate uptake and removal via denitrification (Ebeling et al., 2021; Ehrhardt et al., 2019; Kumar et al., 2020; Nguyen et al., 2021). As a result from increased biological activity with higher temperatures, nitrate uptake and removal increases, especially in streams and in the riparian zones (Baird et al., 1995; Lutz et al., 2020; Rode, Angelstein et al., 2016), which can lead to reduced nitrate availability compared to colder seasons.…”

Spatial and Temporal Variability in Concentration‐Discharge Relationships at the Event Scale

“…We thus argue that the AGR1 catchment does not have a pronounced shallow near stream source zones of NO 3 ‐N (absence of fertilization) and nor efficient connectivity of hinterland NO 3 ‐N sources (absence of tile drains) exists. The lack of wide buffer zones with restricted fertilization in AGR2 lead to a high abundance of NO 3 ‐N in shallow ground‐ and soil water while denitrification was shown to partly remove NO 3 ‐N in deeper and older groundwater components in this catchment (Nguyen et al., 2021; Yang et al., 2018). The interplay of source distribution with the efficient connectivity by tile drains (Musolff et al., 2015) can explain the observed homogeneous positive event C‐Q slopes and dominance of counterclockwise hysteresis in the AGR2 catchment.…”

Disparate Seasonal Nitrate Export From Nested Heterogeneous Subcatchments Revealed With StorAge Selection Functions

“…The parameter ranges were selected based on previous studies (J. Yang, Heidbüchel, et al, 2018;Neitsch et al, 2011;Nguyen et al, 2021;X. Yang, Jomaa, et al, 2018) and parameter distributions were assumed to be uniform.…”