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Computer-based environmental modelling offers an essential aid to understand current catchment dynamics and to investigate the potential effectiveness of remedial actions aimed at improving water quality. The use of the nutrient phosphorus (P), an essential fertilizer element enhancing plant growth, has underpinned global agriculture and food production since the beginning of the 20th century. The discharge of P to surface water is subject to comprehensive regulations worldwide, such as the Clean Water Act (CWA) in the USA, Water Pollution Prevention and Control (WPPC) Law in China and the Water Framework Directive (WFD) in the European Union. Climatic conditions ? in addition to land use, agricultural practices, urban and sewage nutrient inputs ? are key drivers of eutrophication in lakes.5–8 For instance, in a given catchment, air temperature, precipitation, and the morphometry of a lake will determine the extent to which wind-mixing will influence the vertical transfer of P and influence the effect of light on P uptake by phytoplanktons.
Computer-based environmental modelling offers an aid to understanding current catchment dynamics and investigating the potential effectiveness of remedial actions in the context of climate change.
The Vansjo–Hobol catchment (area = 690 km2), also referred to as the Morsa catchment, is located in south-eastern Norway (59°24?N 10°42?E).
The model network consists of four separate models: a climate model, a hydrological model, a catchment model for P, and a lake model. The outputs of the RCMs, together with basin characteristics, were used as inputs for the hydrological PERSiST model to produce daily estimates of runoff, hydrologically effective rainfall and soil moisture deficit.
Daily hydrological outputs from PERSiST, and weather forcing from the RCMs, were used as inputs for INCA-P. For the Vanemfjorden and Storefjorden basins, water chemistry and temperature data were provided by the Vansjo–Hobol monitoring program, conducted by Bioforsk and by the Norwegian Institute for Water Research (NIVA).
Scenarios are valuable to evaluate alternative directions for development and policy implementation.
Three GCMs were used to obtain predictions according to the A1B greenhouse gas emission scenario (2030–2052) of the Intergovernmental Panel on Climate Change (IPCC).27 The A1 scenario family describes a future world of rapid economic and population growth and the introduction of new and more efficient technologies.
Because the RCMs were based on spatial domains much larger than the catchment, they may contain seasonal biases. Three management scenarios were developed together with stakeholders involved in the catchment's land-use and water management. The management scenarios M1 and M2 were either considered with the reference climate (C0) or with future climate change, thus defining 4 storylines which represent the possible combined effects of climate change and management practices in the Vansjo–Hobol catchment (Fig. PERSiST was manually calibrated against the measured stream flow in the Hobol river at the end of reach R4 for the observation period of 1 January 1996 to 3 December 2000. INCA-P (28 parameters varied) was calibrated by using the MCMC-DREAM algorithm described by Starrfelt et al. In addition to the performance metrics described above, “target diagrams”45,46 were used to compare the model's performance with respect to Q, TP, Chl and PO4.
The hydrology of the catchment was well simulated with PERSiST and yielded satisfactory fits to the observed discharge (Fig. The water quality simulated by MyLake during the calibration period for the surface waters of Storefjorden (L1) and Vanemfjorden (L2) is shown in Fig. Despite the low NS metrics reported for INCA-P, three lines of evidence suggest that the model delivers representative TP loads to the lake model: (1) a linear regression of cumulative TP loads estimated from the observed Q and TP vs.
MCMC-DREAM analysis provides information on the sensitivity of the simulations to INCA-P and MyLake parameters.
Several P-mitigation measures have been implemented in the Vansjo–Hobol catchment over recent decades. PERSiST and INCA-P predict that the hydrological response to climate change causes a significant increase in runoff and in the fluxes of TP to the lake basins. In general, any given management scenario resulted in higher TP and Chl concentrations when the climate change was included. The model network was automatically calibrated against time series of hydrological, chemical and biological observations in the inflowing river and in the lake itself using a Markov Chain Monte-Carlo (MCMC) algorithm. Here, we present a novel network of process-based, mass-balance models linking climate, hydrology, catchment-scale P dynamics and lake processes. In the context of climate change, it is becoming increasingly difficult to disentangle the complex climatic effects influencing water quality from the effects of specific measures implemented to improve it.9 A better understanding of the response of specific catchments to both climate and land-use change is needed for a scientifically guided management design to mitigate the impact of these changes on water quality.
Building on previous catchment modelling efforts aiming at predicting P delivery to lakes in agricultural catchments,10–13 we constructed a novel network of chained model to integrate climate, hydrologic, catchment, and in-lake processes. The Hobol River, with a mean discharge of 4.5 m3 s?1, drains a sub-catchment of 301 km2 into Lake Vansjo, the catchment's main lake. 1 Land-use distribution of the Vansjo–Hobol catchment (right panel) and the corresponding schematic representation of the catchment-lake model network (left panel) indicating river reaches (R) modelled with INCA-P and lake basins (L) modelled with MyLake. The model network is first calibrated to present-day observed data, then run with four storylines to simulate conditions in the future.
Previously, external time series of runoff, hydrologically effective rainfall and soil moisture deficits were obtained from rainfall-runoff models such as HBV.33 Here, we use instead the new model PERSiST v. The catchment P-dynamic model INCA-P,15 one of the iterations of the INCA-suite of models, is a process-based, mass balance model that simulates temporal variation in P export from different land-use types within a river system. Water-column sampling was conducted weekly from 1990 to 2004, and bi-weekly from 2004 on, at the deepest-site of both basins whose coordinates are given in Table 1, using a depth-integrating pipe water-column sampler positioned at a 0–4 m depth. Here, we have defined scenarios representing possible futures in global and regional climates and in catchment management.
It is subdivided into groups that describe alternative directions of technological change in the energy system. Consequently, RCM outputs for the Vansjo–Hobol catchment were bias corrected on a monthly basis. As a result, the following scenarios represent realistic actions that the stakeholders have the capacity to implement. The INCA-P and MyLake models were calibrated using a Markov Chain Monte Carlo (MCMC) approach. 4 Observed (symbols) and simulated (solid line) stream flows at the end of R4 using the model PERSiST (panel A), as well as observed and simulated stream flows and TP at the end of R4 using INCA-P (panel B). 5 Calibration performance of MyLake at Storefjorden (L1, left panels) and Vanemfjorden (L2, right panels) for total phosphorus (TP), chlorophyll (Chl), particulate phosphorus (PP) and phosphate (PO4) over the calibration period of 2005–2012. 6) allow for the comparison of the model performance among parameters and stations in a normalized manner, independent of the magnitudes of the simulated values. 6 Target diagram presenting the normalized bias (B*) against the normalized unbiased root mean square difference (RMSD'*) of simulated Q, TP and TP loads for INCA-P at R4 and of simulated TP, PO4, and Chl for MyLake at Vanemfjorden and Storefjorden over the calibration periods.
For INCA-P, of the 28 parameters tested, TP concentrations were the most sensitive to parameters controlling hydrology and erosion across the different land-uses, in particular the soil reactive zone time constant (d?1), which in INCA refers to the amount of water present in the soil and its residence time, the soil erodibility (kg m?2 d?1), the direct runoff time constant, and the base flow index. These measures consist of reduced tillage to control erosion, reduced fertilizer application rate, implementation of vegetated buffer strips along most of the streams in cultivated areas, construction of artificial wetlands, and incremental improvement of WWTP performance.20,21 As a result, TP loads and Chl concentrations steadily decreased throughout the reference period (Fig.
7 Predicted yearly median total P (panel A) and chlorophyll (panel B) at Storefjorden (L1) and Vanemfjorden (L2) by the MyLake model without (C0; storylines 1 and 3) or with climate change predictions made using the HadRm3 (C1), the ECHAM5 (C2) or the BCM models (C3) as climate forcing (storylines 2 and 4) for the river-lake model network.
This result is consistent with observations in Danish lakes5 where higher TP loads were ascribed to climate-induced increases in rainfall.

8 Seasonal range of MyLake-predicted daily TP (panel A) and chlorophyll (panel B) concentrations in the top 4 m of the Storefjorden (L1) and Vanemfjorden (L2) water columns. This is seen for the Storefjorden basin in the years following 2040, for which the detrimental effect of climate change overrides the beneficial effects of the water-quality focus storylines. 8) show that the water quality is much worse during the summer months under all storylines.
Climate projections from three global climate models (GCM: HadRM3, ECHAM5r3 and BCM) were used. This study exemplifies how an objectively calibrated model network allows disentangling the effects of climate change from those of land-use change on lake water quality and phytoplankton growth. When P is delivered to water bodies, negative influences on water quality are likely, and the eutrophication of freshwater and coastal marine ecosystems resulting from increased anthropogenic P loadings is a global problem.3 In lake basins specifically, excess nutrients from both point and nonpoint sources throughout the catchment can give rise to harmful algal blooms, degrade water quality, and create extensive oxygen depletion. At the top of the model chain is a global climate model (GCM) whose output for daily temperature, precipitation and other variables was downscaled to the region. Lake Vansjo has a surface area of 36 km2 and consists of several sub-basins, the two largest being Storefjorden (eastern basin, L1 in Fig.
The hydrological model PERSiST provides input for the catchment model, and the climate models provide forcing for all models. It has been used extensively in Europe and North America to simulate P dynamics in soils and surface waters and to assess the potential effects of climate and land management on surface water quality.7,11–13,15,34,35 We use a recent fully branched version of INCA-P11 (branched-INCA-P v. The biogeochemical processes linking these state variables in the water-column are the mineralisation of DOP and of Chl to PO4, and the removal of PO4 through phytoplankton growth (yielding Chl) or through sorption onto SS (yielding PIP).
The outputs of the R1 to R6 simulations from INCA-P are combined and used as inputs for L1. The land cover of the Vansjo–Hobol catchment is dominated by forestry (78%), agriculture (15%) and water bodies (7%). We combine these climate predictions and management scenarios into storylines, which help convey the output of the simulations into quantitative expectations for future P loadings in the Vansjo catchment (Fig. Storyline 0 represents the reference management focus and reference climate that were compared to observations in calibrating the river-lake model network and deriving model performance metrics.
The A1B sub-scenario, which describes a balance between a growing reliance on fossil energies and an emergence of new technology, assuming that similar improvement rates apply to all energy supply and end-use technologies. Daily resolution scenario data for surface air temperature and precipitation were derived from a sub-set of these regional climate model simulations41 and implemented by scaling the observed weather (1990–2012).
The reference scenario (M0) represents historical riverine nutrient concentrations and current loadings from land-use, fertilization and WWTPs. Storylines 1 and 2 encompass the water-quality focus scenario with and without climate change, respectively, while storylines 3 and 4 encompass the economic focus scenario with and without climate change, respectively. Given the large number of parameters involved in the simulation of 7 river reaches and 2 lake basins using INCA-P and MyLake, probably many alternative sets of parameters could achieve the same degree of fit with the observed data.
The results are reported as the median (solid line), daily quartile statistics sampled from the parameter sets of equal likelihood (continuous area) together with the observations (circles). RMSD'* is calculated as follows: (2)where sgn represents the sign of the standard deviation difference and ?M is the annual standard deviation of the modelled data. The hydrological model HBV,33 previously used in conjunction with INCA-P, yielded similarly satisfactory simulations of flows.17 Although the use of log-transformed values yielded satisfactory fits with respect to NSlog for both Q and TP, the INCA-P calibration against TP measurements is characterized by relatively poor performance metrics (Table 3 and Fig.
The observed P dynamics in both basins display strong seasonal features, with TP, Chl, and PP all reaching maximum values during the summer, when the lake productivities are at their highest.
The RMSD'* calculation involves the multiplication of a term in eqn (2) by the sign of difference between the standard deviation (?) of simulations and observations. Downstream, P speciation predicted by MyLake was the most sensitive to 5 out of the 10 parameters tested: the re-suspension rate of sediments (m d?1), the sinking rate of suspended inorganic particles (m d?1), the algae growth rate (d?1), the heat vertical diffusion coefficient, and the wind sheltering coefficient. The thick solid lines represent the reference conditions and the thin horizontal solid lines indicate the WFD thresholds specific to each basin (see Table 4). The model's handling of phytoplankton growth, which is temperature-driven when neither light nor PO4 is limiting,16 explains this result. The green, yellow and red shaded zones indicate the basin-specific WFD water quality targets for good, moderate and bad water quality status, respectively (see Table 4), while the asterisks indicate the 5th and 95th percentile outliers.
Both TP and Chl reach values above those of the reference storylines, for which no additional P-load reduction was imposed.
The GCM model HadRM3 predicted the highest increase in temperature and precipitation and yielded the highest increase in total phosphorus and chlorophyll concentrations in the lake basin over the scenario period of 2031–2060. The model network can thus support decision-making to achieve good water quality and ecological status. These are used as inputs to a hydrologic rainfall-runoff model (PERSiST14) to produce daily discharge values for rivers, which, in turn, are used as inputs for INCA-P15 to simulate daily fluxes of suspended sediments and P to the lake.
Although PERSiST shares many conceptual characteristics with the HBV model, such as the temperature index representation of snow dynamics and evapotranspiration, it differs in its description of water storage.14 PERSiST uses the same conceptual representation of water storage as the INCA models. In the sediments, mineralisation of organic-P and equilibrium partitioning of PIP to the pore water govern the fluxes of PO4 to the water-column, while resuspension allows Chl and PIP to return to the bottom water. This scenario projects that anthropogenic emission of greenhouse gases (CO2, CH4 and N2O) peaks and begins to decline past the year 2050. The observed temperatures were changed to reflect the increase in both median and variance predicted by the climate models. The sustainable management scenario (M1), referred to as the “water-quality focus”, represents the implementation of measures to further mitigate the risk of eutrophication in the catchment.
The reference storyline represents the present climate conditions combined with the historical management of the catchment.
After calibration, parameter sets from the last iterations were sampled and the model was run for the scenario period and over the whole catchment. R2 values range from 0 to 1, with higher values indicating less error variance, and typically values greater than 0.5 are considered acceptable.
If the model standard deviation is greater than the observation standard deviation, RMSD'* is positive. Conversely, PO4 is at a minimum during the summer, consistent with its uptake by phytoplanktons. As a result, the RMSD'* provides information whether the ? of simulated values is larger or smaller than the ? of the observations. The inner and outer circles indicate ±0.75 and ±1 standard deviations (?) on the X-axis and 75% and 100% B* on the Y-axis, respectively. Previous – although simpler – INCA-P setups calibrated on data from other Norwegian catchments35,47 were also deemed satisfactory when evaluated against fortnightly or monthly TP loads rather than daily TP values. P speciation was moderately sensitive to the sinking rate (m d?1), the sorption coefficient of P onto inorganic particles (mg P m?3), and to the algae mortality rate (d?1), while insensitive to PAR saturation (mol quanta m?2 s?1) and snow albedo.
Imposing the storylines described in Section 2.4 under these historical reference conditions reveals (i) what the water quality status in the Vansjo–Hobol catchment would have been should additional management decisions have been made and (ii) the effect of different climate change scenarios on the water quality.

Recent studies have further highlighted that temperature-mediated P release from lake sediments can increase under a warmer climate,5,6,48 thus promoting algal growth. Nonetheless, and although the effects of climate change are significant, variations in water quality brought about by different management scenarios are always greater than those brought about by climate change (Fig. These thresholds are specific to each lake type, so that the TP and Chl concentrations below which the water quality meets the guidelines are different for Storefjorden and Vanemfjorden (Table 4). Despite the significant impact of climate change on these aspects of water quality, it is minimal when compared to the much larger effect of changes in land-use.
At the end of the model chain is the lake model MyLake.16 Here, we take advantage of these models' matching state variables, spatial scales and temporal resolutions,17 couple them into a network consisting of river stretches and lake basins, and perform automated calibration and uncertainty analysis across the network. Coupling PERSiST with INCA allows a consistent conceptual model of the runoff generation process for both hydrological estimations and water chemistry simulations. INCA-P is so-called semi-distributed, that is, soil properties are spatially averaged within user-defined sub-catchment branches. The MyLake setups L1 and L2 are at the end of the model chain, because the lake Vanemfjorden (L2) discharges into the Oslo fjord.
Together, agricultural practices contribute an estimated 48% of the total P input to the river basin, followed by natural runoff (39%), WWTPs (5%) and scattered dwellings (8%). The assumptions made in defining these scenarios, and the choice made to combine them into storylines, are detailed below. GCM runs, prepared from the results of the ENSEMBLES EU FP6 project,39,40 provided boundary conditions for the RCMs.
Precipitation was scaled using a ratio of change approach, multiplying observation by the ratio of observed (1990–2012) over predicted (2030–2052) precipitation. These measures impose (1) a 10% reduction in the agricultural land, which is then converted to a forest, (2) a 25% decrease in vegetable production, which is then converted to grass production, (3) a 25% decrease in P-based fertilizer applications, and (4) a 90% improvement in the P-removing performance of WWTPs.
RMSE values retain the same units as the constituent being evaluated and can be directly compared with the data (as in Fig. The observed TP values show a high degree of variability from week-to-week, likely due to the integrating nature of the TP parameter. An increase in RMSD'* reflects an increase in the discrepancy between simulations and observations,46 pointing to incommensurability between what is modelled and the available observations, while lower values indicate less residual variance between them.
Thus, during the scenario period, the response of INCA-P to the climate and land-use changes is expected to be reasonable both in magnitude and in direction.
However, the influence of higher temperatures on internal P loadings in Lake Vansjo cannot be ascertained here, because the relevant sediment–water processes are only partly implemented in the MyLake model (see Section 3.4). The results suggest that implementing realistic abatement measures will remain a viable approach to improving water quality in the context of climate change.
The outputs of these model pairs, all based on the A1B scenario of climate change, are hereafter referred to as future climates C1–C3 (Table 2 and Fig. Averaged, monthly local changes in temperature and precipitation predicted by the three RCMs under the A1B scenario for the 2030–2052 period are shown in Fig.
Finally, a less sustainable management scenario (M2), referred to as the “economic focus”, reflects a projected increase in anthropogenic pressure throughout the catchment due to population growth and an intensification of food production. MyLake (10 parameters varied) was calibrated against a time series of measurements in the surface waters of the Vanemfjorden and the Storefjorden basins for the observation period of 1 April 2005 to 1 September 2012. Amongst the three climate models tested, HadRm3 (C1) projected the largest climate change39 and yielded the highest TP and Chl values. In addition, the climate scenario used here, A1B, predicted that greenhouse gas emissions will be curbed by the mid-21st century. Land-use and management regimes had a profound impact on the water quality, more so than the projected climate change under the A1B scenario. In addition, there are six smaller lakes which together represent less than 15% of the lake surface area. 3), whereas the climate condition during the reference period 1990–2012 is referred to as climate C0. Further growth of agricultural and urban activities in the catchment in scenario M2 is imposed as follows: (1) a 10% reduction of forest cover, which is then converted into agricultural lands, (2) a shift of 25% of the grass production to vegetable production, (3) an increase of fertilizer applications by 25%, and (4) a 25% increase in the P load of effluents from scattered dwellings and WWTPs throughout the catchment.
5 shows that MyLake simulations for both basins well captured the seasonal minima in PO4 and maxima in both PP and Chl.
Most likely this was done to the higher amount of precipitation projected by HadRm3, which resulted in higher P loads and runoff from the catchment in INCA-P. Other scenarios, such as those in the A2 and B2 families of scenarios, assume larger increases of greenhouse gas emissions as well as higher increases in temperature and precipitation in Nordic catchments.
Relative to the reference storyline, imposing a water-quality focus (storyline 1) improved the water quality overall by decreasing TP and Chl by 24% and 33%, respectively, in Storefjorden, and by 18% and 23%, respectively, in Vanemfjorden. Overall, HadRm3 predicts average yearly changes in both temperature and precipitation that are greater than those predicted by ECHAM5 or BCM (Table 2). NS ranges between ?? and 1, with a value of 1 being optimal and values between 0.5 and 1 being generally viewed as good. The seasonal trends in Chl, a measure of the abundance of phytoplanktons, are also well captured by the model, with the exception of an algal bloom in the summer of 2006, whose magnitude was not fully captured (Fig. The outcome of our simulations indicates that these climatic conditions would further increase the risk of eutrophication in Nordic lakes, as previously suggested.6,12,49,50 Thus, projected increases of Chl concentrations are likely conservative. Conversely, an economic focus (storyline 3) adversely affected the water quality by increasing TP and Chl by 58% and 59%, respectively, in Storefjorden, and by 44% and 42%, respectively, in Vanemfjorden.
Negative NS values indicate that the mean observed value is a better predictor than the simulated value, pointing to poor model performance. 6 reveals that the simulations are generally unbiased, and that the residual variances increase as we move from INCA-P to MyLake, that is, further along the model network.
It thus follows that storyline 1 represents the best case, while storyline 4 represents the worst case (Fig.
We refer the reader to a study by Moriasi44 for extensive discussion on the procedures used to qualify the calculated values of these statistics.
The algal bloom in the summer of 2008 is reproduced by the model, although also underestimated, despite the high magnitude rain events that occurred throughout the catchment during that year. When compared to the observations, INCA-P simulations are less biased and, on an absolute scale, have a smaller RMSD'* than the simulations generated by MyLake.
In particular, a single bank erosion event in the winter of 2008 resulted in high SS in the river.22 The NS metric is high for simulated Q with PERSiST, but low for simulations of TP with both INCA-P and MyLake (Table 3).
This metric is unforgiving, in that it is strongly affected by simulations that do not match observed peak concentrations.

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