Climate Change Impact on Riverine Nutrient Load and Land-Based Remedial Measures of the Baltic Sea Action Plan
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To reduce eutrophication of the Baltic Sea, all nine surrounding countries have agreed upon reduction targets in the HELCOM Baltic Sea Action Plan (BSAP). Yet, monitoring sites and model concepts for decision support are few. To provide one more tool for analysis of water and nutrient fluxes in the Baltic Sea basin, the HYPE model has been applied to the region (called Balt-HYPE). It was used here for experimenting with land-based remedial measures and future climate projections to quantify the impacts of these on water and nutrient loads to the sea. The results suggest that there is a possibility to reach the BSAP nutrient reduction targets by 2100, and that climate change may both aggravate and help in some aspects. Uncertainties in the model results are large, mainly due to the spread of the climate model projections, but also due to the hydrological model.
KeywordsNutrient modelling Climate change Remedial measures Baltic Sea basin
The implementation of the Marine Strategy Framework Directive (MSFD; 2008/56/EC) to protect Europe’s oceans and seas is now underway in the EU Commission and the Member States. The Helsinki Commission (HELCOM) is the coordination platform for the MFSD implementation in the Baltic Sea region. The Baltic Sea in Northern Europe is an enclosed sea, receiving fresh-water and waterborne pollution from nine surrounding countries, and another six upstream countries in the drainage basin (Fig S1, Electronic supplementary material). A serious and difficult to mitigate challenge facing the Baltic Sea is eutrophication. The effects of eutrophication include algal blooms, dead-sea beds and reductions in fish stocks, which also are detrimental to the future economic prosperity of the Baltic Sea Region (HELCOM 2007). For this reason, HELCOM commissioned the preparation of the Baltic Sea Action Plan (BSAP), a programme to restore good ecological status of the Baltic Marine Environment by 2021. The BSAP was approved in 2007 by the countries surrounding the Baltic Sea (HELCOM 2007). An important part of this plan is the reduction of nutrient inflow from the drainage basin into the marine environment. Required nutrient reductions have been apportioned to the countries in the basin and these countries are now planning the remedial measures necessary to meet the plan’s requirements (Swedish EPA 2010).
An important factor that remains to be considered, however, is how well the planned nutrient reduction measures improve nutrient inflows into the Baltic Sea in a changed future climate. Nutrient inflows from land to sea are a result of atmospheric deposition, erosion, subsurface leaching from soil, diffusion from river and lake sediments, point-source emissions from industrial and urban sources and biochemical processes in the freshwater system. With the exception of the point-source emissions these factors are weather dependent.
So far, the international strategic agreements on reduction targets for various countries and societal sectors have been based on the results from the NEST model concept (Mörth et al. 2007; Wulff et al. 2009) and the HELCOM pollution load compilations (HELCOM 2005). Recently, the HYPE model (Lindström et al. 2010) was also applied for the entire Baltic Sea basin, i.e. Balt-HYPE (Donnelly et al. 2010; Arheimer et al. 2012). This model simulates water and nutrient concentrations from land to the sea on a daily basis, including major sources and sinks along the flow path. As the HYPE model is process based and driven by daily temperature and precipitation, it reflects the influence of weather and climate on water and nutrient flow. Hence, it can be used for experimenting with impacts of both the remedial measures and the future climate.
Contemporary climate change analysis uses several model concepts in ensemble runs to estimate the uncertainty in the overall conclusions to decision makers (IPCC 2007). A similar ensemble approach is used for meteorological model input to hydrological forecasts (e.g. Norbert et al. 2010; Arheimer et al. 2011a, b). Ensemble means from several hydrological models have actually been found to give better performance than each single model used (Viney et al. 2005), and recently this approach has also been applied to water quality models (Exbrayat et al. 2010). The Balt-HYPE model may contribute to such a model ensemble in the future.
There have been a few previous efforts on dynamic hydrological modelling of the pan-Baltic basin. For instance, Bergström and Carlsson (1994) constructed a model partly based on observations and Graham (1999) set up the HBV hydrological model to calculate monthly inflows to the Baltic Sea. The latter model was also used to evaluate how freshwater inflows to the Baltic Sea might change in a future climate (Graham 2004), and for modelling nitrogen fluxes in the region (Pettersson et al. 2000). None of the mentioned models, however, were ever used for decision support.
The aim of this article is to show that advanced simulation models can provide useful information not only to scientists but also to decision makers who have to take into account future climate and management when considering environmental issues. Nevertheless, all models involve uncertainties and it is therefore suggested that the presented results should be part of an ensemble of predictive models, rather than be the sole basis for strategic decision making in the Baltic Sea region.
demonstrate the Balt-HYPE model concept for estimating water and nutrient loads to the major Baltic Sea basins.
quantify the impacts of climate change on land-based water and nutrient loads.
quantify the effects of suggested remedial measures in present and future climates.
Materials and Methods
The Balt-HYPE Model
The BALTic Sea basin HYdrological Predictions for the Environment (Balt-HYPE) model calculates water and nutrient fluxes with a relatively high resolution from land to the sea. It is based on the HYPE model code (Lindström et al. 2010), which is dynamic, semi-distributed, process-oriented, and based on well-known hydrological and nutrient transport concepts. The model simulates time series of hydrological and nutrient variables for 5128 sub-basins, which are mostly unmonitored. Observed data are used to evaluate model performance at the points in the model where these are available. Major nutrient sources and sinks are included in the concept. In the model, the landscape is divided into classes according to soil type and vegetation. The soil representation is stratified and can be divided up to three layers. Nutrients follow the same soil path as water. The flow paths include surface runoff, macropore flow, tile drainage and groundwater outflow from the individual soil layers. Rivers and lakes are described separately with routines for outlet flow, turnover, sinks and sources. Several processes in the model concept are weather dependent as precipitation and temperature force the dynamics at each time step. For example, weather affects flow paths, detention time, mineralisation, denitrification, plant uptake, erosion, water volumes and fluxes in rather complex interactions. Thus, the model results reflect the effects of climate.
When setting up the HYPE model for a specific region, relevant input data and parameter values are needed. For the Balt-HYPE model, readily available databases covering the entire region were applied (Table S1, Electronic supplementary material). In the model, coefficients are global, or related to specific characteristics of hydrological response units (HRU), i.e. combinations of soil type and land use. The HYPE model has many rate coefficients, constants and parameters, which in theory could be adjusted. For the Balt-HYPE model-parameter values were based on the Swedish application (called S-HYPE; Strömqvist et al. 2012) and then modified using a step-wise, multi-basin calibration technique (Donnelly et al. 2009). This regional calibration included 35 daily river discharge stations and 20 water quality stations, with validation in a further 121 daily discharge stations. The model was not calibrated to individual stations but to give optimal performance across all stations. Although this may give less optimised performance for individual sites, overall it gives a robust parameter setting for predictions in all ungauged basins. The spread of the model performance in the gauged basins may be assumed to be an estimate of the uncertainty in the predictions in the ungauged basins (e.g. Strömqvist et al. 2012). The model was run on a daily time step from 1961 to 2008 to account for variability in weather and water flow, but using nutrient emissions from the 2000’s. Observed data for the period 1996–2005 was used for calibration and validation (Arheimer et al. 2012). The chosen parameter values were assumed to also be valid in the future climate.
The model was used for experimenting with changes in nutrient emissions and climate (i.e. precipitation and temperature). One or a few factors were changed at a time in the predefined system and compared with original simulations to distinguish the net effect of these changes from complex interactions of water and nutrient processes in both the soil and the watercourses.
The remedial measures for wastewater were based on the suggested treatment levels in of the BSAP (HELCOM 2007), which prescribes a treatment efficiency of 90 % for phosphorus (P) and 70–80 % for nitrogen (N) for waste water treatment plants (WWTP) larger than 100 000 person equivalents (p.e.). As for rural population (and WWTP up to 300 p.e), there is a maximum permissible load per capita of 0.65 g P and 10 g N per day prescribed by the BSAP.
Present loads from wastewater were calculated from numbers on untreated sewage discharge and the HELCOM guidelines for reporting to the 2005 pollution load compilations (HELCOM 2011). The efficiency of WWTPs (primary, secondary and tertiary) was estimated according to Mörth et al. (2007), and for each country the share of population connected to each type of treatment was taken from EEA (2010). For countries outside the EU, where data was often unavailable, the same values as for neighbouring countries were assumed. The WWTP’s loads per capita were included in the Balt-HYPE model set-up together with population density (both the urban and the rural fractions), obtained from the HYDE database (Goldewijk et al. 2011). In the experiment, the present treatment levels were adjusted to BSAP prescriptions only where these were not already met.
Recommendations for best agricultural practices are less detailed in the BSAP. It is not well known that how much best agricultural practices might reduce the load from arable land, or how large the actual potential for improvements is in different regions. Analysis of the potential effects of remedial agricultural measures in southern Sweden shows that a combination of the most effective measures could at most reduce the nutrient load to water by 20 % (Arheimer et al. 2005a, b; Larsson et al. 2005). As an example of a very simple agricultural nutrient reduction scenario, best agricultural practices were thus assumed to reduce the load from all arable land across the basin by 20 % for this model experiment. This was done to relate the effect of improved point-source treatment with remedial measures of diffuse sources, which is also suggested by the BSAP. Finally, the combined effects of future climate and remedial measures were tested by changing both the forcing data according to the climate projections and including the remedial measures in the Balt-HYPE model.
Results and Discussion
Balt-HYPE Model Estimation of Nutrient Load
For the entire sea basin, half of the N load reaching the Baltic Sea comes from agriculture. The corresponding figure for P is one-third. The soil of arable land is probably responsible for a large portion of loading in its natural state, i.e. fertile and rich in nutrients, nevertheless the results indicate that remedial measures in the agricultural sector may have a high potential for load reductions. It should be noted that these calculations do not include leakage from the enormous manure storages directly on the soil in the eastern part of the basin, which have been recently discovered. Nor does it include direct emissions from industries to water. In this model version, manure is only included as a fertilizer on arable land with 100 % use efficiency, and the leakage from the storage of this manure is not explicitly accounted for.
Figure 3 shows that the Balt-HYPE model is capable of providing nutrient loads also for more remote upstream countries, such as Czech Republic, Ukraine and Belarus. Such figures are rarely found elsewhere and it should be noted that the Belarus contribution is 6 %, which is more than each of the coastal Baltic states, Estonia, Latvia and Lithuania. Belarus is not yet included in HELCOM but these results indicate that it should not be neglected in future international agreements. For most countries, Balt-HYPE estimated about the same contribution as HELCOM (2010). Russia and Latvia, however, show lower relative contributions which is explained by the separate inclusion of Ukraine and Belarus in the Balt-HYPE source apportionment. HELCOM fully apportions the load from the river Neman to the countries at the river’s outlet.
Looking at the spatial distribution of the load from various sources (Fig. S3, Electronic supplementary material), it is clear that the nutrient load to the entire Baltic Sea mainly originates from arable land, WWTP and rural households in the southern part of the drainage basin. More than half of both the N and the P load enters the Baltic proper. Contributions from forest and to the northern marine basins are small. Nevertheless, these marine waters may be more sensitive to nutrient loads as they are naturally nutrient poor and the ecosystem is adapted to that. This is also reflected in the differences among nutrient targets that HELCOM has setup for the marine basins (HELCOM 2007).
Climate Change Impact on Water and Nutrient Load to the Baltic Sea
The spread in results for change from the various climate projections was of the same magnitude as the ensemble mean. The uncertainty is thus large. For the southeast region, the Hadley GCM-driven simulations indicate drier conditions than those driven by the Echam GCM. It is also interesting to note that the difference in results caused by the climate models is sometimes larger than the difference caused by using various emission rates and initial conditions in the same GCM. It is thus very important to include several model concepts in an ensemble to account for model uncertainty in climate change impact assessments.
The simulations of nutrient loads indicate that a future climate may reduce the inflow of N but slightly raise the inflow of P to the marine basins; however, some of the climate projections indicated the opposite. If the HadleyA1B projection were to eventuate, the Balt-HYPE model suggests that the HELCOM target for N reduction would be fulfilled for the Baltic proper by the impact of climate change alone by 2100. Nevertheless, the spatial variation is large within countries and within river basins (Donnelly et al. 2011) and on the local scale increases in N concentrations are also seen. The processes responsible for the reduced load in a future climate are mainly reduced water flow, increased detention times and elevated temperature, which are factors that increase denitrification and nutrient availability in the soil in the model. N is thus removed naturally during the storage in water compartments along the water flow paths towards the sea. The increase of P is probably caused by increased mineralisation, due to higher temperatures in the model.
Impacts of Remedial Measures
The model results clearly show that remedial measures of WWTP are most efficient for reducing the P loads, while N load must be combated by also reducing the non-point source pollution, especially from arable land. Similar results have been reported from previous integrated catchment studies in the region (Arheimer et al. 2005a, b). However, the assumption of 20 % reduction of agricultural leaching may be very optimistic for arable land all over the Baltic Sea basin. Presumable impact of best practices in agriculture must thus be examined much more in detail using local information for trustworthy impact analysis. When the BSAP has been implemented on a country-wise level, this experiment can be redone using more correct estimates. The total maximum effects of the simulated remedial measures in the experiment were quantified to 86 000 tonnes N and 19 000 tonnes P reductions from the entire Baltic basin load. Hence, this is probably based on an overestimation of the reduction potential for diffuse sources.
Combined Effect of Climate Change and Remedial Measures
Finally, it should be noted again that there is a large spread in model results depending on which climate projection that is used. The effects of remedial measures may either be strengthened or reduced in future climate, depending on which of the four climate projections that are assumed. Each climate projection is considered equally reliable, so this shows that there is a large overall uncertainty involved in the impact assessments of future nutrient load to the Baltic Sea. As for the present climate, the probabilities to reach P targets were in general higher also in a changed climate. For N, the impact of climate change is of the same order as the expected reduction from remedial measures, according to the results of the model experiment. Thus, climate effects need to be accounted for when estimating the long-term effects of the BSAP.
Uncertainties in the Results from the Model Experiments
There are many uncertainties involved in such a complex chain of data transfer among different analysis tools as presented in this experiment. Some uncertainties were recognized during the process, for instance the climate models gave different loads for the control period for each projection, although statistical bias corrections had been applied. The Balt-HYPE model also includes uncertainties, for instance it overestimated nutrient loads to the sea, compared to the few observation sites available, and thus probably underestimated removal processes in rivers and lakes. This version of the HYPE model had a rather simplified routine for N removal in surface waters (Lindström et al. 2010), using the same parameter setting for all kind of water bodies. This was changed in later versions while working on a new setup for Sweden (S-HYPE_20101) as small streams and lakes have higher removal than rivers, which for instance can be seen in the national monitoring data for Sweden. The present Balt-HYPE model thus probably underestimates removal in lakes and creeks in upstream parts of the catchment and overestimates denitrification in large river channels. This error evolves in a changed climate as the removal routine is based on temperature. For the southern parts of the basin, this effect is further enhanced by increased water residence times, which increase the removal efficiency. As such natural reduction of N in the flow paths reduces the effect of measures, this means that the effects of upstream land-based measures are probably overestimated in the model experiment and direct reductions to river channels are underestimated for the future climate. This is one example of uncertainties in the model concept.
Another uncertainty arises from trends in the nutrient storage in soil, which is difficult to validate. Here, the effect was neglected by simulating time slices, but a transient run could also be made to partly quantify the uncertainties arising from this process. A slight change in the soil storage capacity may have a very large effect on the overall transport to the sea, so there is an urgent need for future research on the long-term trends of nutrient storage in the soil, and how these are affected by climate and land use management change. More empirical data are needed to calibrate and validate the model properly, including specific validation of the model to changes in management where this has been monitored. In fact, there is an urgent overall need for validating the ability of various models to reproduce changes in forcing, for example whether or not a model can reproduce long-term trends in nutrient concentrations, observed following a change in agricultural practices. The assumption about model parameters being valid for another climate also needs clarification, although they were robust enough to cover such a large model domain representing various climates at present. There are few publications regarding this sort of validation of internal process descriptions, which would be valuable to complement the more classical evaluation of the model’s ability to reproduce discharge and concentration variations in time and space. It is important to validate models according to their purpose, therefore more field studies and empirical data are necessary.
When applying the same model code for 17 000 sub-basins covering the country of Sweden, it was possible to evaluate model predictions also for ungauged basins, as 90 % of available monitoring sites were not used for calibration (Strömqvist et al. 2012). The Swedish application (S-HYPE) has also been evaluated against independent internal model variables such as snow pack, lake water level and groundwater fluctuation (Arheimer et al. 2011a, b), which also makes that model application more trustworthy. Arheimer et al. (2012) thus compared model results for Sweden using both the S-HYPE and the Balt-HYPE and, in short, that study showed that especially water discharge was much better simulated using the S-HYPE, with most relative errors are <10 % for S-HYPE and <25 % for Balt-HYPE. Both the applications normally reproduced mean concentration for N within 25 % of the observed mean values, while P showed a larger scatter. Differences in model set-up were reflected in the simulation of both the spatial and the temporal dynamics, and the most sensitive data causing this was found to be precipitation/temperature, agriculture and model-parameter values. Hence, the lack of observations (e.g. for the large Vistula River) probably do influence the overall model performance of Balt-HYPE. How this would have affected the outcome of this specific experiment on climate change and remedial measures is yet unknown. To make the results of the experiment presented a bit more robust, only relative figures are given for future changes in this article (Figs. 5, 6, 7). Even though results are uncertain, it cannot be rejected that the outcomes from the present experiment indicate important considerations for managers to be aware of.
It has been questioned whether the largest sources of uncertainty in climate change impact studies originate from the climate models or the impact models, and several on-going EU projects are addressing this issue (e.g. ECLISE, IMPACT2C). A recent uncertainty study using the HBV model in a changing climate for Sweden (Andréasson et al. 2011) showed that the model-parameter values did introduce uncertainties in the results, but not as much as the climate models. It has been argued that it may not even be worth using climate model data in impact assessments as the climate model results are so uncertain (Beven 2011). The spread in results from the different climate projections in this study could support this argument. Nevertheless, new knowledge about the system behaviour was achieved from experimenting with the Balt-HYPE model. It would have been difficult to figure out all possible process interactions and the net effect of such a complex system without applying a numerical model. The model is based on available knowledge and the results gave second thoughts on credibility and process descriptions. Errors and less stable assumptions were identified in the model set-up and parameter values, which increased the overall understanding of water and nutrient fluxes in the region.
The HYPE model introduces the ability to model detailed hydrological processes at high resolution simultaneously and homogenously across many river basins. It is an advantage that the methods and data used are homogenous across political boundaries. Yet, large-scale models are always difficult to validate, for climate, hydrology and chemistry. Ensemble modelling is a way to handle this problem (e.g. Viney et al. 2005; Exbrayat et al. 2010). By including more models in the analysis it is more likely that the dominant processes and initial states (e.g. of soil storages) are accounted for. More water and nutrient models and more climate projections are thus another way to quantify the uncertainty ranges of the results. The Balt-HYPE model should be considered as one such member in a larger model ensemble for strategic decision making in the Baltic Sea region.
In the Baltic Sea basin, there is a large demand for more water quality data and homogeneous input data for more reliable assessments, nutrient modelling and analysis of uncertainties in results.
Climate effects need to be accounted for when estimating the long-term effects of remedial measures. The model results suggest that the total load to the Baltic Sea may decrease for nitrogen and increase for phosphorus in the future. The experiment indicates that impact of climate change may be of the same order of magnitude as the expected nitrogen reductions from the measures simulated.
For the Baltic Sea, the results of the experiment show that both the improved wastewater treatment and the agricultural measures are needed to reach the BSAP target reductions by 2100. Yet, for half of the climate projections, the targets were not reached, and the variation in the quantified impact is large between different climate projections.
Model experiments are useful to analyze complex process interactions and large databases and to merge knowledge from different disciplines. Experimenting with models also increases the system knowledge as errors and less stable assumptions may be identified in the model set-up and parameter values.
Ensemble modelling, which includes several water/nutrient and climate models are recommended to include uncertainties in the decision support, when combating eutrophication in the Baltic region.
The research presented in this study is part of the project ECOSUPPORT (see introductory acknowledgements, this issue). The study was also co-funded by the national research programs CLEO (CLimate change and Environmental Objectives) by the SEPA and HYDROIMPACTS2.0 (Hydrological climate change impact scenarios: developing the tools for a new generation) by the Swedish Research Council Formas. The study was performed at the SMHI Hydrological Research unit, where much work is done in common, and we would especially like to acknowledge contributions from Kristina Isberg, Charlotta Pers, Johan Strömqvist, Jörgen Rosberg and Wei Yang. Finally, we would like to acknowledge the GRDC and BALTEX for providing observed discharge data. Results from the Balt-HYPE model are available for free download at www.smhi.se\balt-hype.
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