Disentangling the impact of catchment heterogeneity on nitrate export dynamics from event to long-term time scales

Winter, Carolin; Lutz, Stefanie; Musolff, Andreas; Kumar, Rohini; Weber, Michael; Fleckenstein, Jan H.

doi:10.1002/essoar.10503228.1

Cited by 11 publications

(28 citation statements)

References 48 publications

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“…The simulated yearly average N surplus (nitrate input + mineralization ‐ plant uptake) from the optimal parameter set is 33.8 kg N ha ‒1 year ‒1 . This is comparable with the calculated N surplus (33 kg N hav ‒1 year ‒1 ) from other studies for the area (Häußermann et al., 2019; Winter et al., 2021). In terms of storage, the mobile N storage (DIN) in the soil and groundwater is around 22.3 kg N ha ‒1 and 4.6 kg N ha ‒1 , respectively.…”

Section: Results and Validationsupporting

confidence: 90%

“…(2021) for the same area also shows an immediate decrease of instream nitrate concentration and loading following a drastic decrease of N input after 1990. In the lower Selke catchment, longer time lags (up to 12 years) were estimated due to drier conditions and deeper aquifers (Winter et al., 2021). This indicates a high variability of N‐related time lags in the region.…”

Section: Results and Validationmentioning

confidence: 73%

“…In a recent study for the study area, Winter et al. (2021) assumed that TTDs follow a lognormal distribution. Parameters of the lognormal were determined from a comparison of the long‐term changes in annual N input and flow normalized nitrate concentrations observed in the output.…”

Section: Results and Validationmentioning

confidence: 99%

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Modeling Nitrate Export From a Mesoscale Catchment Using StorAge Selection Functions

Nguyen

Kumar

Lutz

et al. 2021

Water Resources Research

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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.

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Section: Results and Validationsupporting

confidence: 90%

Section: Results and Validationmentioning

confidence: 73%

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Modeling Nitrate Export From a Mesoscale Catchment Using StorAge Selection Functions

Nguyen

Kumar

Lutz

et al. 2021

Water Resources Research

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“…Three nested sub-catchments for the Selke as well as the Holtemme river system, both part of the Bode, a well-studied river system near the Harz Mountains in central Germany were included additionally (Fig. 1) (Ehrhardt et al, 2019;Rode et al, 2016;Winter et al, 2020;Mueller et al, 2018). All catchments had ~10 years of uninterrupted daily Q data available between 1995 and 2010 (Musolff, 2020).…”

Section: Catchment Selectionmentioning

confidence: 99%

“…To verify the model's ability to reproduce realistic concentration time series and Curvature, modeled and simulated data were compared in the Selke catchment (at Meisdorf gauging station; 282 km², Table 2) where extensive field campaigns and modelling studies have been conducted related to in-stream processes (Rode et al, 2016;Dupas et al, 2017;Yang et al, 2019;Yang et al, 2018). This relatively homogeneous upstream part of the Selke consists of forest and cropland and is characterized by consistent export regimes (Winter et al, 2020). For an input parameter combination (Table C1) set to reasonable values for this catchment (Rode et al, 2016), the land to stream NO 3 − inputs averaged 1.2 kg N day -1 km -2 which is similar to the 1.9 kg N day -1 km -2 reported by Winter et al (2020) for the Selke River (Meisdorf ); and it is well within the general 0.001 to 100 kg N day -1 km -2 range established by Mulholland et al (2008).…”

Section: Catchment Selectionmentioning

confidence: 99%

Comment on hess-2021-16

2021

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Nitrate (NO 3 − ) excess in rivers harms aquatic ecosystems and can induce detrimental algae growths in coastal areas. Riverine NO 3 − uptake is a crucial element of the catchment scale nitrogen balance and can be measured at small spatiotemporal scales while at the scale of entire river networks, uptake measurements are rarely available. Concurrent, low frequency NO 3 − concentration and stream flow (Q) observations at a basin outlet, however, are commonly monitored and can be analyzed in terms of concentration discharge (C-Q) relationships. Previous studies suggest that more positive log(C)-log(Q) slopes under low flow conditions (than under high flows) are linked to biological NO 3 − uptake, creating a bent rather than linear log(C)log(Q) relationship. Here we explore if network scale NO 3 − uptake creates bent log(C)-log(Q) relationships and when in turn uptake can be quantified from observed low frequency C-Q data. To this end we apply a parsimonious mass balance based river network uptake model in 13 mesoscale German catchments (21-1450 km²) and explore the linkages between log(C)log(Q) bending and different model-parameter combinations. The modelling results show that uptake and transport in the river network can create bent log(C)-log(Q) relationships at the basin outlet from log-log linear C-Q relationships describing the NO 3 − land to stream transfer. We find that the bending is mainly shaped by geomorphological parameters that control the channel reactive surface area rather than by the biological uptake velocity itself. Further we show that in this exploratory modelling environment, bending is positively correlated to percentage NO 3 − load removed in the network ( . ) but that network wide flow velocities should be taken into account when interpreting log(C)-log(Q) bending. Classification trees, finally, can successfully predict classes of low (~4 %), intermediate (~32 %) and high (~68 %) . using information on water velocity and log(C)-log(Q) bending. These results can help to identify stream networks that efficiently attenuate NO 3 − loads based on low frequency NO 3 − and Q observations and generally show the importance of the channel geomorphology on the emerging log(C)-log(Q) bending at network scales.and McGlynn, 2018). Increased NO 3 − concentrations in surface waters can induce detrimental algae growths (Beusen et al., 2016;Canfield et al., 2010;Galloway et al., 1995), compromise river ecosystem health and jeopardize drinking water supplies.Since the beginning of the 20 th century, human activities such as agricultural expansion and fossil fuel burning have mobilized additional reactive nitrogen (N), initiating and later exacerbating this problem (Seitzinger et al., 2002). In arable landscapes, which include large parts of Europe, the efficient management of aquatic NO 3 − at network scales is complicated by the spatiotemporal variability of loading patterns and hydrologic regimes as well as the lack of understanding of nutrient pathways, connected transit times and removal processes from in...

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Landscape spatial configuration influences phosphorus but not nitrate concentrations in agricultural headwater catchments

Dupas

Casquin

Durand

et al. 2023

Hydrological Processes

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Landscape organized (or structured) heterogeneity influences hydrological and biogeochemical patterns across space and time. We developed landscape indices that describe the spatial configuration of nutrient sources and sinks as a function of their hydrological distance to the stream (lateral dimension) or to the outlet (longitudinal dimension) and their intersection with flow-accumulation areas. Using monthly nitrate, total phosphorus (TP), soluble reactive phosphorus (SRP) and daily discharge (Q) data from 221 rural catchments (1-300 km 2 ) from 2010-2020, we observed higher variability in flow-weighted mean concentrations in smaller catchments than in larger ones. The variability in landscape configurations also decreased with increasing catchment size. A landscape configuration index, calculated as mean arable land use weighted by spatial data on hydrological distance and flow accumulation, improved prediction of TP and SRP, but not nitrate, compared to the unweighted mean arable land use. We conclude that landscape configuration influences phosphorus transfer more than nitrate transfer, and that flow-accumulation zones and riparian areas are critical source areas for TP and SRP, respectively. By contrast, landscape spatial configuration in the lateral (upslope-downslope) and longitudinal (upstreamdownstream) dimensions did not have an identifiable influence on nutrients temporal dynamics. The indices developed in this study can help design landscapes that minimize diffuse phosphorus losses to streams and show that landscape management is not a first order control for nitrate losses.

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Disentangling the impact of catchment heterogeneity on nitrate export dynamics from event to long-term time scales

Cited by 11 publications

References 48 publications

Modeling Nitrate Export From a Mesoscale Catchment Using StorAge Selection Functions

Modeling Nitrate Export From a Mesoscale Catchment Using StorAge Selection Functions

Comment on hess-2021-16

Landscape spatial configuration influences phosphorus but not nitrate concentrations in agricultural headwater catchments

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