Simulation of nitrate reduction in groundwater – An upscaling approach from small catchments to the Baltic Sea basin
Graphical abstract
Introduction
Large nutrient loads have resulted in severe environmental problems in the Baltic Sea during the past decades (Gustafsson et al., 2012). Diffuse losses of nutrients contribute about two thirds of the total nitrogen (N) and phosphorus (P) loads delivered to the sea with the majority of these originating from agricultural sources (HELCOM, 2011). In order to address the eutrophication problem, the Baltic Sea Action Plan (BSAP) was adopted in 2007, setting country specific abatement targets for N and P loads to achieve a “good status” in the Baltic Sea (Backer et al., 2010). Many abatement actions have already been implemented in the Baltic Sea countries, but the targets are still not met and more actions are required (Backer et al., 2010). This paper addresses simulation of the impacts of a novel regulation methodology in decreasing the total N loads to the sea.
The loss of N from agriculture occurs mainly as leaching of excess nitrate from the root zone. Along the flow path from the root zone via groundwater and surface water to the catchment outlet, nitrate can be naturally transformed and thereby removed by redox processes. Nitrate reduction is a microbial process occurring under anoxic conditions in the presence of an electron donor (organic matter, pyrite and Fe(II)) and occurs both in soils, groundwater and surface waters (Appelo and Postma, 2005). This removal of nitrogen by redox reactions is in this study referred to as “N-reduction” and we mainly focus on the N-reduction occurring below the root zone, i.e., in groundwater and surface waters.
Due to geological heterogeneity, N-reduction varies significantly, not only at the Baltic Sea basin scale (Wulff et al., 2014, Højberg et al., 2017) but also at the local-scale within small catchments (Hansen et al., 2014b). Present agricultural regulations to decrease the N-loss from the root zone specify the same abatement requirements for all areas without considering local variations in N-reductions in the groundwater or surface water. This is obviously not very cost-effective (Jacobsen and Hansen, 2016), and it has led to the idea of implementing a spatially targeted regulation focussing on decreasing N-loss from areas with low natural N-reduction, instead of a uniform regulation where all areas have to decrease by the same amount regardless of the natural reduction capacity in the area (Hansen et al., 2017). A spatially targeted regulation can consist of a number of different regulatory measures implemented at or in connection to areas with a low natural N-reduction. These regulatory measures can either be implemented at the source by decreasing N-leaching from the root zone on areas with low N-reduction, e.g. by changing the crop or the management practice on the field, or they can be implemented where water from areas with low N-reduction is discharging into surface waters, e.g. in the form of constructed wetlands that can remove some of the discharging nitrogen (Langergraber et al., 2011).
The effectiveness of regulatory measures for decreasing N-loads can be assessed using hydrological and nutrient flux models (Vagstad et al., 2009). To exploit the local (field and farm-scale) variability in N-reduction, however, requires simulations at scales resolving the field and farm-scale variability. This is extremely data demanding and requires detailed process descriptions within the hydrological models (Hansen et al., 2014b, Karlsson et al., 2016). Modeling of nitrate processes at large scales, such as the Baltic Sea basin, has been carried out by Arheimer et al. (2012) and Wulff et al. (2014) to assess the impacts of international regulations that are applied uniformly across larger regions or even the entire basin. These large-scale models operate with computational units of several hundred km2, making it impossible to explicitly simulate the impacts of local-scale spatially targeted measures, because they cannot spatially resolve the measures and often have inadequate process descriptions, in particular for describing the spatial variability in groundwater processes. Therefore, there is a need to combine the knowledge and results achieved by local- and large-scale modeling in an upscaling procedure.
Upscaling can be done in a variety of ways (Bloschl and Sivapalan, 1995, Refsgaard et al., 1999, Vereecken et al., 2007). The most common upscaling approach in distributed hydrological modeling is the effective parameter approach, which assumes that process equations and system data originating from smaller scales are applicable at a larger scale and that effective parameters exist that can reproduce the mean behavior of the system observed at the larger scale. This assumption is often justifiable (Refsgaard, 1997, Henriksen et al., 2003), while it in some cases has to be rejected (Beven, 1995). Another approach used in distributed models is the distribution function approach, where the statistical distributions, but not the geo-referenced locations, of system data and parameters are represented in the model (Andersen et al., 2001, Herbst and Diekkruger, 2002). The use of hydrological response units in semi-distributed models like HYPE (Arheimer et al., 2012, Stromqvist et al., 2012) can be seen as a kind of statistical distribution function approach, although the continuous properties here are replaced by categorical data such as soil type and land use.
A fundamentally different upscaling approach is the dynamic upscaling approach, where model results from a model with a finer resolution of the computational units are utilized somehow in a large-scale model with coarser resolution. There are only a few examples of this upscaling approach (Bronstert et al., 2007, Hansen et al., 2008). In the study by Bronstert et al. (2007), a HBV model was set up for the Rhine basin. For three meso-scale catchments within the Rhine basin, small-scale models were set up using the distributed physically-based model WASIM-ETH, and simulated stream discharge from these models was used in the calibration of the large-scale HBV model. The effect of land use changes was simulated with the small-scale models, and these results were then used as input to the HBV model in order to simulate land use changes for the entire Rhine basin.
To be able to assess the potential impacts of implementing a spatially targeted regulation of agricultural practices on the N-loads to the Baltic Sea, it will be necessary to use a large-scale model for the entire Baltic Sea Basin and to develop an upscaling methodology to account for the impacts that cannot be handled explicitly in existing large-scale models. The objectives of the present study were i) to introduce a methodology to upscale knowledge from a local-scale model to a large-scale model simulating water and N flows for the entire Baltic Sea basin with a focus on N-reduction in groundwater; and ii) to test it on predicting impacts of a spatially targeted regulation to decrease the N-load to the sea for two study areas. We use the dynamic upscaling approach in a manner similar to Bronstert et al. (2007), extending this approach from only considering river flow to also including N-fluxes and N-reduction. In this paper we thus report the development and testing of the upscaling approach, whereas the full application on the entire Baltic Sea basin will be reported in a second paper.
Section snippets
Local study areas
Two Danish catchments were used to develop the upscaling methodology. The 85 km2 Norsminde catchment (upstream part of the 101 km2 Norsminde Fjord catchment) is located on the east coast of Jutland, and the 486 km2 Odense catchment (upstream of Kratholm) is located on the island of Funen (Fig. 1). Both catchments discharge into fjords, which are ecologically sensitive with respect to nitrogen, and a significant decrease of the N-loads to the fjords is required to obtain good ecological status (
Results
In this section we show first the results from the impact scenario for Norsminde and Odense using the local-scale models (Section 3.1). Based on these impact results the upscaling relationships used in step 3 of the upscaling approach were developed (Section 3.2). Finally, after developing the upscaling relationships, the upscaling approach was tested on the two study areas Norsminde and Odense (Section 3.3).
Upscaling approach – principles
In this paper we have presented a methodology to upscale knowledge on the impact of a spatially targeted regulation of agricultural practices from two local-scale physically based MIKE SHE catchment models to the large-scale and more conceptual E-HYPE model. The future goal is to then use the large-scale model to simulate changes for the entire Baltic Sea basin. We used the local-scale models to develop a relationship between the impact of spatially targeted regulation in a catchment and the
Acknowledgments
This work was carried out in the BONUS Soils2Sea project, which received funding from BONUS, the joint Baltic Sea research and development programme (Art 185), funded jointly from EU's Seventh Programme for research, technological development and demonstration and from Innovation Fund Denmark, The Swedish Environmental Protection Agency, The Polish National Centre for Research and Development, The German Ministry for Education and Research, and The Russian Foundation for Basic Researches
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