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Accurate snow depth observations are critical to assess water resources. More than a billion people rely on water from snow, most of which originates in the Northern Hemisphere mountain ranges. Yet, remote sensing observations of mountain snow depth are still lacking at the large scale. Here, we show the ability of Sentinel-1 to map snow depth in the Northern Hemisphere mountains at 1 km² resolution using an empirical change detection approach. An evaluation with measurements from ~4000 sites and reanalysis data demonstrates that the Sentinel-1 retrievals capture the spatial variability between and within mountain ranges, as well as their inter-annual differences. This is showcased with the contrasting snow depths between 2017 and 2018 in the US Sierra Nevada and European Alps. With Sentinel-1 continuity ensured until 2030 and likely beyond, these findings lay a foundation for quantifying the long-term vulnerability of mountain snow-water resources to climate change., Remote sensing observations of mountain snow depth are still lacking for the Northern Hemisphere mountains. Here authors use Sentinel-1 satellite radar measurements to assess the snow depth in mountainous areas at 1 km² resolution and show that the Sentinel-1 retrievals capture the spatial variability between and within mountain ranges, as well as their inter-annual differences.

In order to use the best suited snow models to investigate snow conditions at ungauged sites and for a changed climate, we have tested four snow models for 17 catchments in Norway. The Crocus and seNorge models are gridded whereas the Distance Distribution Dynamics (DDD) model with its two versions, DDD_CX and DDD_EB, is catchment based. Crocus and DDD_EB use energy balance for estimating snowmelt and SeNorge and DDD_CX use temperature-index methods. SeNorge has calibrated the temperature-index against observed snowmelt, whereas DDD_CX has calibrated the temperature-index against runoff. The models use gridded temperature and precipitation at 1 h resolution for the period 2013–2016. Crocus needs additional forcing from a numerical weather prediction model, whereas DDD_EB calculates the energy-balance elements by using proxy models forced by temperature and precipitation. The threshold temperature for solid and liquid precipitation is common for all the models and equal to 0.5 °C. No corrections of precipitation or temperature are allowed. The snow simulations are validated against observed snow water equivalent (SWE) and against satellite derived snow covered area (SCA). SeNorge and DDD_EB perform best with respect to both SWE and SCA suggesting model structures suited for describing snow conditions at ungauged sites and for a changed climate.

Climate models show that global warming will disproportionately influence high‐latitude regions and indicate drastic changes in, among others, seasonal snow cover. However, current continental and global simulations covering these regions are often run at coarse grid resolutions, potentially introducing large errors in computed fluxes and states. To quantify some of these errors, we have assessed the sensitivity of an energy‐balance snow model to changes in grid resolution using a multiparametrization framework for the spatial domain of mainland Norway. The framework has allowed us to systematically test how different parametrizations, describing a set of processes, influence the discrepancy, here termed the scale error, between the coarser (5 to 50‐km) and finest (1‐km) resolution. The simulations were set up such that liquid and solid precipitation was identical between the different resolutions, and differences between the simulations arise mainly during the ablation period. The analysis presented in this study focuses on evaluating the scale error for several variables relevant for hydrological and land surface modelling, such as snow water equivalent and turbulent heat exchanges. The analysis reveals that the choice of method for routing liquid water through the snowpack influences the scale error most for snow water equivalent, followed by the type of parametrizations used for computing turbulent heat fluxes and albedo. For turbulent heat exchanges, the scale error is mainly influenced by model assumptions related to atmospheric stability. Finally, regions with strong meteorological and topographic variability show larger scale errors than more homogenous regions.

Snow dynamics play a crucial role in the hydrology of alpine catchments in the Himalaya. However, studies based on in-situ observations that elucidate the energy and mass balance of the snowpack at high altitude in this region are scarce. In this study, we use meteorological and snow observations at two high-altitude sites in the Nepalese Himalaya to quantify the mass and energy balance of the seasonal snowpack. Using a data driven experimental set-up we aim to understand the main meteorological drivers of snowmelt, illustrate the importance of accounting for the cold content dynamics of the snowpack, and gain insight into the role that snow meltwater refreezing plays in the energy and mass balance of the snowpack. Our results show an intricate relation between the sensitivity of melt and refreezing on the albedo, the importance of meltwater refreezing, and the amount of positive net energy used to overcome the cold content of the snowpack. The net energy available at both sites is primarily driven by the net shortwave radiation, and is therefore extremely sensitive to snow albedo measurements. We conclude that, based on observed snowpack temperatures, 21% of the net positive energy is used to overcome the cold content build up during the night. We also show that at least 32–34% of the snow meltwater refreezes again for both sites. Even when the cold content and refreezing are accounted for, excess energy is available beyond what is needed to melt the snowpack. We hypothesize that this excess energy may be explained by uncertainties in the measurement of shortwave radiation, an underestimation of refreezing due to a basal ice layer, a cold content increase due to fresh snowfall and the ground heat flux. Our study shows that in order to accurately simulate the mass balance of seasonal snowpacks in Himalayan catchments, simple temperature index models do not suffice and refreezing and the cold content needs to be accounted for.

Summary Frequently updated maps of snow conditions are useful for many applications, e.g., for avalanche and flood forecasting services, hydropower energy situation analysis, as well as for the general public. Numerical snow models are often applied in snow map production for operational hydrological services. However, inaccuracies in the simulated snow maps due to model uncertainties and the lack of suitable data assimilation techniques to correct them in near-real time may often reduce the usefulness of the snow maps in operational use. In this paper the revised seNorge snow model (v.1.1.1) for snow mapping is described, and a simplified data assimilation procedure is introduced to correct detected snow model biases in near real-time. The data assimilation procedure is theoretically based on the Bayesian updating paradigm and is meant to be pragmatic with modest computational and input data requirements. Moreover, it is flexible and can utilize both point-based snow depth and satellite-based areal snow-covered area observations, which are generally the most common data-sources of snow observations. The model and analysis codes as well as the “R” statistical software are freely available. All these features should help to lower the challenges and hurdles hampering the application of data-assimilation techniques in operational hydrological modeling. The steps of the data assimilation procedure (evaluation, sensitivity analysis, optimization) and their contribution to significantly increased accuracy of the snow maps are demonstrated with a case from eastern Norway in winter 2013/2014.

The conventional climate gridded datasets based on observations only are widely used in atmospheric sciences; our focus in this paper is on climate and hydrology. On the Norwegian mainland, seNorge2 provides high-resolution fields of daily total precipitation for applications requiring long-term datasets at regional or national level, where the challenge is to simulate small-scale processes often taking place in complex terrain. The dataset constitutes a valuable meteorological input for snow and hydrological simulations; it is updated daily and presented on a high-resolution grid (1 km of grid spacing). The climate archive goes back to 1957. The spatial interpolation scheme builds upon classical methods, such as optimal interpolation and successive-correction schemes. An original approach based on (spatial) scale-separation concepts has been implemented which uses geographical coordinates and elevation as complementary information in the interpolation. seNorge2 daily precipitation fields represent local precipitation features at spatial scales of a few kilometers, depending on the station network density. In the surroundings of a station or in dense station areas, the predictions are quite accurate even for intense precipitation. For most of the grid points, the performances are comparable to or better than a state-of-the-art pan-European dataset (E-OBS), because of the higher effective resolution of seNorge2. However, in very data-sparse areas, such as in the mountainous region of southern Norway, seNorge2 underestimates precipitation because it does not make use of enough geographical information to compensate for the lack of observations. The evaluation of seNorge2 as the meteorological forcing for the seNorge snow model and the DDD (Distance Distribution Dynamics) rainfall–runoff model shows that both models have been able to make profitable use of seNorge2, partly because of the automatic calibration procedure they incorporate for precipitation. The seNorge2 dataset 1957–2015 is available at https://doi.org/10.5281/zenodo.845733. Daily updates from 2015 onwards are available at http://thredds.met.no/thredds/catalog/metusers/senorge2/seNorge2/provisional_archive/PREC1d/gridded_dataset/catalog.html.

The seNorge snow model produces daily updated maps (1 km × 1 km resolution) of snow conditions for Norway which are used by the national flood, avalanche and landslide forecasting services, among others. The snow model uses gridded observations of daily temperature and precipitation as its input forcing. In this paper the revisions made to the new seNorge snow model code (v.1.1.1) are described, and a systematic model analysis is performed by first revealing the most influential key parameters by the Extended FAST sensitivity analysis and then estimating their probability distributions by the MCMC simulation method, using 565 observations of snow water equivalent (SWE) and snow density (ρ). The MCMC simulation resulted in rather narrow posterior distributions for the four estimated model parameters, and enhanced the model performance and snow map quality significantly, mainly by removing the known significant overestimation biases in SWE and ρ. In the new model version (v.1.1.1) the Nash–Sutcliffe (NS) model performance values are now well positive (NS = 0.61 for SWE and NS = 0.30 for ρ), in contrast to the much lower negative NS-values of the previous model (v.1.1). Moreover, the model evaluation against approximately 400 000 point measurements of snow depth shows improvement in the simulated percentage of "good match"-stations (76–84% before April, and still 65% at the end of April). Future research efforts should focus on decreasing the variability in the model fit with observations (i.e. model precision) by further improvements in the seNorge snow model and its important fundament, the gridded meteorological input data set used as its forcing.

Process-based multimedia models are frequently used to simulate the long-term impacts of pollutants and to evaluate potential remediation actions that can be put in place to improve or manage polluted marine environments. Many such models are detailed enough to encapsulate the different scales and processes relevant for various contaminants, yet still are tractable enough for analysis through established methods for uncertainty assessment. Inclusion and quantification of the uncertainty associated with local efficacy of remediation actions is of importance when the desired outcome in terms of human health concerns or environmental classification shows a nonlinear relationship with remediation effort. We present an updated fugacity-based environmental fate model set up to simulate the historical fate of polychlorinated dibenzo-p-dioxins and dibenzo-furans (PCDD/Fs) in the Grenland fjords, in Norway. The model is parameterized using Bayesian inference and is then used to simulate the effect of capping parts of the polluted sediments with active carbon. Great care is taken in quantifying the uncertainty regarding the efficacy of the activated carbon cap to reduce the leaching of contaminants from the sediments. The model predicts that by capping selected parts of the fjord, biota will be classified as moderately polluted approximately a decade earlier than a natural remediation scenario. Our approach also illustrates the importance of incorporating uncertainty in local remediation efforts, as the biotic concentrations scale nonlinearly with remediation effort. Integr Environ Assess Manag 2015;11:481–489. © 2015 SETAC

The conventional climate datasets based on observations only are a widely used source of information for climate and hydrology. On the Norwegian mainland, the seNorge datasets of daily mean temperature and total precipitation amount constitute a valuable meteorological input for snow- and hydrological simulations which are routinely conducted over such a complex and heterogeneous terrain. A new seNorge version (seNorge2) has been released recently and to support operational applications for civil protection purposes, it must be updated daily and presented on a high-resolution grid (1 km of grid spacing). The archive goes back to 1957. The seNorge2 statistical interpolation schemes can provide high-resolution fields for applications requiring long-term datasets at regional or national level, where the challenge is to simulate small-scale processes often taking place in complex terrain. The statistical schemes build upon classical spatial interpolation methods, such as Optimal Interpolation and successive-correction schemes, and introduce original approaches. For both temperature and precipitation, the spatial interpolation exploits the concept of (spatial) scale-separation and the first-guess field is derived from the observed data. Furthermore, the geographical coordinates and the elevation are used as complementary information. The evaluation of the seNorge2 products is presented both from a general point of view, through systematic cross-validations, and specifically as the meteorological input in the operational model chains used for snow- and hydrological simulations. The seNorge snow model is used for simulation of snow fields and the DDD (Distance Distribution Dynamics) rainfall-runoff model is the hydrological model used. The evaluation points out important information for the future seNorge2 developments: the daily mean temperature fields constitute an accurate and precise dataset, on average the predicted temperature is an unbiased estimate of the actual temperature and its precision (at grid points) varies between 0.8 °C and 2.4 °C; the daily precipitation fields provide a reasonable estimate of the actual precipitation, the cross-validation shows that on average the precision of the estimates (at grid points) is about ±20 %, though a systematic underestimation of precipitation occurs in data-sparse areas and for intense precipitation. Both the seNorge snow and the DDD models have been able to make profitable use of seNorge2, partly because of the automatic calibration procedure they incorporate for precipitation. The dataset described in this article is available for public download at http://doi.org/10.5281/zenodo.845733.

In order to simulate food web responses of small boreal lakes to changes in thermal stratification due to global warming, a 4 year whole-lake manipulation experiment was performed. Within that time, period lake mixing was intensified artificially during two successive summers. Complementary data from a nearby lake of similar size and basic water chemistry were used as a reference. Phytoplankton biomass and chlorophyll a did not respond to the greater mixing depth but an increase was observed in the proportional abundance of diatoms, and the proportional abundance of cryptophytes also increased immediately after the onset of mixing. Obligate anoxic green sulphur bacteria vanished at the onset of mixing but gradually recovered after re-establishment of hypolimnetic anoxic conditions. No major effect on crustacean zooplankton was found, but their diversity increased in the metalimnion. During the mixing, the density of rotifers declined but protozoan density increased in the hypolimnion. Littoral benthic invertebrate density increased during the mixing due to Ephemeroptera, Asellus aquaticus and Chironomidae, whereas the density of Chaoborus larvae declined during mixing and lower densities were still recorded one year after the treatment. No structural changes in fish community were found although gillnet catches increased after the onset of the study. The early growth of perch (Perca fluviatilis) increased compared to the years before the mixing and in comparison to the reference lake, suggesting improved food availability in the experimental lake. Although several food web responses to the greater mixing depth were found, their persistence and ecological significance were strongly dependent on the extent of the disturbance. To better understand the impacts of wind stress on small lakes, long term whole-lake experiments are needed.

Snow is an important component of water storage in the Himalayas. Previous snowmelt studies in the Himalayas have predominantly relied on remotely sensed snow cover. However this provides no information on the actual amount of water stored in a snowpack i.e. the snow water equivalent (SWE). Therefore, in this study remotely sensed snow cover was combined with in situ meteorological observations and a modified version of the seNorge snow model to estimate climate sensitivity of SWE and snowmelt runoff in the Langtang catchment in Nepal. Landsat 8 and MOD10A2 snow cover maps were validated with in situ snow cover observations provided by surface temperature and snow depth measurements resulting in classification accuracies of 85.7 % and 83.1 % respectively. Optimal model parameter values were obtained through data assimilation of MOD10A2 snow maps and snow depth measurements using an Ensemble Kalman filter. The approach of modelling snow depth in a Kalman filter framework allows for data-constrained estimation of SWE rather than snow cover alone and this has great potential for future studies in the Himalayas. Climate sensitivity tests with the optimized snow model show a strong decrease in SWE in the valley with increasing temperature. However, at high elevation a decrease in SWE is (partly) compensated by an increase in precipitation, which emphasizes the need for accurate predictions on the changes in the spatial distribution of precipitation along with changes in temperature. Finally the climate sensitivity study revealed that snowmelt runoff increases in winter and early melt season (December to May) and decreases during the late melt season (June to September) as a result of the earlier onset of snowmelt due to increasing temperature.

Observed trends in annual maximum snow depth (SD) in Norway are analyzed and examined in the context of changes in winter climate from 1961 until today. Trends are evaluated for the 50-year period (1961–2010) and for three 30-year periods (1961–1990, 1971–2000, 1981–2010). The analyzed dataset is the most extensive and geographically representative for the country so far, and the analysis gives an up-to-date picture of the recent development in snow accumulation. In regions characterized by colder winter climate long-term trends are found to be positive in general, while short-term trends shift from strongly positive in the first period to predominantly negative in the last period. Variation in SD is here mainly linked to variation in precipitation. In regions of warmer winter climate variation in SD is dominated by temperature, and long-term trends are mainly negative. Short-term trends start out weak overall in the first period but become strongly negative most places in the last period. It is likely that, although more and more regions in Norway will experience declining maximum annual SD in a projected wetter and warmer future climate, some inland and higher mountain regions may still accumulate more snow in the coming decades.

Trends in time series of snow water equivalent (SWE) are analyzed for different time periods and as a function of elevation. Since 1914, hydropower companies have measured snow at the expected snow maximum. Most of the measurements are located at high elevations, 700–1,700 meters above sea level (masl), providing a unique dataset in areas poorly covered by meteorological stations. For single stations, significant positive and negative trends are found using the Mann–Kendall trend test. The trends depend on region, observation period and elevation. In southern Norway negative trends are found for sites below 1,350 masl for the periods 1931–1960 and 1991–2009, and below 850 masl for the period 1961–1990. Above these elevations, positive trends are found. For the entire period, 1931–2009, positive trends are found for stations located above 850 masl. These findings can be explained by varying trends in precipitation and temperature in winter and spring. For central and northern Norway, the pattern in trends is similar, although less pronounced. High and low values of the North Atlantic Oscillation (NAO) index correlate differently with SWE. High NAO index is positively correlated with SWE at high elevations and negatively at low elevations. Low NAO index is positively correlated with SWE at all elevations.

The Norwegian guidelines for risk assessment of contaminated sediments are used to identify areas of concern where remediation may be needed to meet the governmental long-term goal of clean fjords and harbors along the Norwegian coastline. By a thorough sensitivity analysis, this study identifies the most influential factors and parameters for the Tier 2A model in this risk guideline, which are used to estimate fluxes of contaminants from sediments due to diffusion and bioturbation (Fdiff), resuspension caused by ship traffic (Fskipnorm), and uptake and predation of benthic biota (Forg). The sensitivity analysis is run for 36 different scenarios combining 3 different sizes of contaminated area, 3 harbor types, and 3 persistent organic pollutants, namely lindane (γ-hexachlorocyclohexane), benzo[a]pyrene, and 2,2′,4,4′,5,5′-hexachlorobiphenyl (PCB-153), as well as the metal mercury (Hg). The most influential parameters vary from scenario to scenario, but generally 5 parameters appear to be particularly influential for the fluxes and transport estimated by the Tier 2A model: flux of organic carbon to sediment (OCsed), factor for increased diffusion due to bioturbation (a), sediment–water partitioning coefficient (Kd), benthic biota–water bioconcentration factor (BCFfisk), and mass of resuspended fine sediment during arrival or departure of a ship (msed). We also quantify which of the 3 fluxes (Fdiff, Fskipnorm, and Forg) dominate in the different scenarios. Our sensitivity analysis results can be used by authorities, problem owners, consultants, and environmental managers involved in contaminated sediment management to gain insight on the key processes and parameters and to focus their site-specific or laboratory-based measurement efforts on the key parameters and thus increase efficiency and reliability in the contaminated sediment risk assessment. Integr Environ Assess Manag 2011;7:657–667. © 2011 SETAC

Freshwater ice plays an important role in physical, biological, and chemical processes affecting cold-region lakes. To examine the potential magnitude of climate-change-related impacts in ice-cover characteristics, particularly its formation, duration, breakup, thickness, and structural composition over North America, this study employs a one-dimensional, process-based, lake simulation model, ‘MyLake’ (Multi-year simulation model for Lake thermo- and phytoplankton dynamics). The model is first calibrated and validated for Baker Lake, Nunavut, Canada, and then used to simulate patterns of ice conditions using a set of hypothetical lakes of varying depth (5, 20, and 40 m) located at a 2° latitude/longitude grid pattern for the major cold-region portion of North America between 40 and 75° latitude. The model is driven by gridded atmospheric forcing data from the North American Regional Reanalysis (NARR) and the Canadian Regional Climate Model (CRCM) with projections of future climate corresponding to the SRES A2 emissions scenario. The NARR-based simulation results for the period 1979–2006 are consistent with observations of lake-ice thickness and phenology obtained from the Canadian Ice Database. The CRCM-based lake-ice simulation results for the baseline (1961–1990) and future (2041–2070) time periods indicate that projected air-temperature warming will advance break-up by 10–20 days and delay freeze-up by 5–15 days, thereby reducing lake-ice duration by about 15–35 days. Lake depth is also found to have significant influence on lake-ice freeze-up dates and maximum thicknesses but not on the break-up dates. Such changes are accompanied by a reduction in ice thickness of 10–30 cm. Cover composition is also altered as a result of changes in ice thickness and snow loading resulting in somewhat greater thicknesses of white-ice (1–5 cm) over most areas except towards the east and west coasts, as well as more southerly latitudes where it slightly decreased. Implications of such cover changes are also discussed. Copyright © 2011 Royal Meteorological Society and Crown in the right of Canada.

Climate change with higher air temperatures and changes in cloud cover, radiation and wind speed alters the heat balance and stratification patterns of lakes. A paired whole-lake thermocline manipulation experiment of a small (0.047 km2) shallow dystrophic lake (Halsjärvi) was carried out in southern Finland. A thermodynamic model (MyLake) was used for both predicting the impacts of climate change scenarios and for determining the manipulation target of the experiment. The model simulations assuming several climate change scenarios indicated large increases in the whole-lake monthly mean temperature (+1.4–4.4 °C in April–October for the A2 scenario), and shortening of the length of the ice covered period by 56–89 days. The thermocline manipulation resulted in large changes in the thermodynamic properties of the lake, and those were rather well consistent with the simulated future increases in the heat content during the summer-autumn season. The manipulation also resulted in changes in the oxygen stratification, and the expansion of the oxic water layer increased the spatial extent of the sediment surface oxic-anoxic interfaces. The experiment also affected several other chemical constituents; concentrations of TotN, NH4 and organic carbon showed a statistically significant decrease, likely due to both unusual hydrological conditions during the experiment period and increased decomposition and sedimentation. Changes in mercury processes and in the aquatic food web were also introduced. In comparison with the results of a similar whole-lake manipulation experiment in a deep, oligotrophic, clear-watered lake in Norway, it is evident that shallow dystrophic lakes, common in the boreal region, are more sensitive to physical perturbations. This means that projected climate change may strongly modify their physical and chemical conditions in the future.

Climate change is expected to alter environmental distribution of contaminants and their bioaccumulation due to changes in transport, partitioning, carbon pathways, and bioaccumulation process rates. Magnitude and direction of these changes and resulting overall bioaccumulation in food webs is currently not known. The present study investigates and quantifies the effect of climate change in terms of increased temperature and primary production (i.e., concentrations of particulate organic carbon, C(POC)), on bioaccumulation of organic contaminants in biota at various trophic levels. The present study covers only parts of the contaminant behavior that is influenced by climate change, and it was assumed that there were no changes in food web structure and in total air and water concentrations of organic contaminants. Therefore, other climate change-induced effects on net bioaccumulation, such as altered contaminant transport and food web structure, should be addressed in future studies. To determine the effect of climate change, a bioaccumulation model was used on the pelagic marine food web of the Arctic, where climate change is expected to occur fastest and to the largest magnitude. The effect of climate change on model parameters and processes, and on net bioaccumulation, were quantified for three modeling substances (gamma-hexachlorocyclohexane [HCH], polychlorinated biphenyl [PCB]-52, and PCB-153) for two possible climate scenarios. In conclusion, increased temperature and C(POC) reduced the overall bioaccumulation of organic contaminants in the Arctic marine food web, with the largest change being for PCB-52 and PCB-153. Reduced bioavailability, due to increased C(POC), was the most influential parameter for the less water soluble compounds. Increase in temperature resulted in an overall reduction in net bioaccumulation.

A lake model (MyLake) has been used to simulate the impacts of the projected future (2071–2100) climate on the thermodynamic properties of a shallow and a deep lake in Finland. The model has been calibrated using a Bayesian Markov chain Monte Carlo (MCMC) simulation method. The model results show, among others, that the largest change in water temperature in a projected future climate occurs in April–May when a 4.6–7.6°C increase in epilimnion and 2.7–4.4°C increase in whole-lake monthly mean temperature occurs compared to the climate of 1961–1990. This corresponds to a large decrease in the probability of ice cover in March–April. In winter and early spring a negative correlation was obtained between lake water and air temperatures in a projected future climate due to the thermal buffering feature of the lake ice and snow cover. The uncertainties connected to the choice of the general circulation model and the boundary condition forcing for the regional atmospheric climate model were often significantly larger than the uncertainties connected to MyLake model parameter values.

Multimedia environmental fate models are useful tools to investigate the long-term impacts of remediation measures designed to alleviate potential ecological and human health concerns in contaminated areas. Estimating and communicating the uncertainties associated with the model simulations is a critical task for demonstrating the transparency and reliability of the results. The Extended Fourier Amplitude Sensitivity Test(Extended FAST) method for sensitivity analysis and Bayesian Markov chain Monte Carlo (MCMC) method for uncertainty analysis and model calibration have several advantages over methods typically applied for multimedia environmental fate models. Most importantly, the simulation results and their uncertainties can be anchored to the available observations and their uncertainties. We apply these techniques for simulating the historical fate of polychlorinated dibenzo-p-dioxins and dibenzofurans (PCDD/Fs) in the Grenland fjords, Norway, and for predicting the effects of different contaminated sediment remediation (capping) scenarios on the future levels of PCDD/Fs in cod and crab therein. The remediation scenario simulations show that a significant remediation effect can first be seen when significant portions of the contaminated sediment areas are cleaned up, and that increase in capping area leads to both earlier achievement of good fjord status and narrower uncertainty in the predicted timing for this.

Uncertainty and sensitivity analysis provide powerful means to enhance the performance of the models, to increase the transparency and credibility of the model results, and to allow the decision-maker to judge whether the model results are sufficiently accurate and precise to support decision-making or not. Uncertainty and sensitivity analysis of numerical models usually require hundreds, thousands, or even more, repeated model runs (e.g., in Monte Carlo simulation) as well as access to change the model parameters and variables on each model run. Consequently, many model codes, although widely used and well-formulated, are not well-suited for uncertainty and sensitivity analysis due to too long model execution time or due to lack of suitable interface to change model parameters and variables in an automated way. In this paper, we describe in details a lake model code, which aims to combine good simulation capabilities with efficient model execution time and easy application of numerical uncertainty and sensitivity analysis techniques. This model code is called MyLake (Multi-year Lake simulation model) and it is a one-dimensional process-based model code for simulation of daily vertical distribution of lake water temperature and thus density stratification, evolution of seasonal lake ice and snow cover, sediment–water interactions, and phosphorus-phytoplankton dynamics. After giving a detailed technical description of the different processes and algorithms included in the current version 1.2 of the MyLake model code, we present some results from a model application example including an Extended Fourier Amplitude Sensitivity Test (Extended FAST) sensitivity analysis and a simple Monte Carlo simulation based uncertainty analysis. Finally we discuss the performance of the MyLake code, especially in connection with numerical uncertainty and sensitivity analysis techniques.

We describe a novel method for statistical analysis of bioassay experiments with natural plankton communities. The procedure allows unsupervised classification of the type of nutrient limitation in factorial experiments with two limiting nutrients, based on objective selection between several generic limitation patterns with direct biological interpretation, using the Akaike Information Criterion (AIC), which balances the concerns of model fit and model robustness. As such, it avoids the interpretation of nuisance parameters related to time effects in classical factorial design analysis of experiments with repeated measurements over time. The proposed limitation patterns discriminate between exclusive and primary limitation, depending on whether the initially nonlimiting nutrient has an effect or not. They also discriminate between two types of colimitation, depending on whether both nutrients only have an effect in combination, or also separately. The latter response is interpreted as the result of different phytoplankton community components being simultaneously limited by different nutrients. The capabilities of the classification procedure is demonstrated on a comprehensive set of 163 bioassay experiments with N and P additions to natural phytoplankton communities from the coast of Finland (NE Baltic Sea).

The paper illustrates and discusses some rather simple and pragmatic means to enhance the quality of model-based knowledge input for decision-making. Firstly, we apply a stepwise description of the modelling process to describe our “case story”, in which we first define modelling objectives and then use the benchmark criteria approach to evaluate model codes and to select an appropriate model code to be set up, analysed, and applied to simulate the long-term (decades) phosphorus–phytoplankton interactions and thus the eutrophication problem in Lake Vansjo-Storefjorden, Norway. Results from the simulations with the MyLake model indicate that at least 50% reduction in phosphorus loads are needed in order to reach the strictest environmental goal in the long-term. We believe that by: (1) following (and documenting) the step-by-step modelling process outlined in this paper, by (2) using available (internet-based) inventories to get an overview of existing model codes, and by (3) performing and documenting the model code selection with help of the benchmark criteria approach, at least some of the barriers to model use in environmental management can be removed.

The thickness of coastal landfast ice in the Sea of Okhotsk has been examined based on field data and thermodynamic modelling. The study sites were Saroma-ko Lagoon, Hokkaido and Kleye Strait, Sakhalin. The ice sheet has a two-layer structure: a granular snow–ice layer on top and a columnar ice layer below. In Saroma-ko Lagoon, the ice grows to 40–50 cm, with snow–ice portion of 10–100%. In Kleye Strait, the ice grows to about 100 cm, with a remarkable addition (on average 24 cm) during mid-March to mid-April due to snow–ice formation. A one-dimensional thermodynamic ice–snow model was calibrated with observed data and used to examine the thickness climatology; the snow component takes into account snow compaction, slush formation due to flooding, melting or rain and snow–ice growth. The model outcome showed reasonably good agreement for both sites. In Saroma-ko Lagoon, the calibration was based on four winters. The maximum annual ice thickness was in the model on average 3 cm below the observed one, 16 cm in the worst case; the model snow thickness was within 10 cm from the observed ones in February; and the date of ice breakup was on average biased late by 5 days and 11 days in the worst case. The model simulations predicted formation of slush layers and their persistency for 1–4 weeks in different winters. Climatological simulation resulted in mean maximum annual ice thickness of 32 cm, of which 15 cm was snow–ice. In Kleye Strait, the calibration was based on one ice season. The maximum annual ice thickness was 7 cm biased down, and the model snow thickness was within 10 cm from the observed level. Climatological simulation resulted in mean maximum annual ice thickness of 108 cm, of which 70 cm was congelation ice and 38 cm was snow–ice, and the ice season lasted from 5 November to 5 June. Thus, slush formation and its freezing are crucial in the study basin.

The narrow slope-confined warm core of the West Spitsbergen Current (WSC) is a major pathway for warm and saline Atlantic Water to enter the Arctic Ocean. Long-term mean northward cooling and freshening rates in the warm core of the WSC are calculated with error ranges, and heat loss estimates are derived. The observed combination of cooling and freshening is not consistent with a hypothesis of pure isopycnal offshore mixing, but rather suggests influence from diapycnal mixing connected, for example, to exchange with shelf waters.

Using data from 1910 to 1997, we study the mean structure as well as the interannual variability in the autumn hydrography of the part of the West Spitsbergen Current (WSC) confined over the upper continental slope and of its continuation, the Svalbard branch. This is known to provide a major pathway for warm and saline Atlantic water to the Arctic Ocean. We focus our study on the area northwest of Svalbard, around 80°N. The time series of subsurface hydrography (100–300 m layer) show interannual variations which are, in the 1980s and 1990s, quite coherent with hydrographic time series obtained upstream in the WSC around 76°N as well as with variations in the large-scale atmospheric patterns, as indicated by the North Atlantic Oscillation (NAO) index. However, in the 1970s, the coherence is poorer. Our results suggest that it may be important to focus on the narrow domain of the part of the WSC confined over the upper continental slope in order to get consistent estimates of the Arctic Ocean inflow signal in the Fram Strait. The warming in this inflow signal (in 1970–1996) does not seem to have been as strong as previously suggested.

Daily maps of snow conditions have been produced in Norway with the seNorge snow model since 2004. The seNorge snow model operates with 1 × 1 km resolution, uses gridded observations of daily temperature and precipitation as its input forcing, and simulates, among others, snow water equivalent (SWE), snow depth (SD), and the snow bulk density (ρ). In this paper the set of equations contained in the seNorge model code is described and a thorough spatiotemporal statistical evaluation of the model performance from 1957–2011 is made using the two major sets of extensive in situ snow measurements that exist for Norway. The evaluation results show that the seNorge model generally overestimates both SWE and ρ, and that the overestimation of SWE increases with elevation throughout the snow season. However, the R2-values for model fit are 0.60 for (log-transformed) SWE and 0.45 for ρ, indicating that after removal of the detected systematic model biases (e.g. by recalibrating the model or expressing snow conditions in relative units) the model performs rather well. The seNorge model provides a relatively simple, not very data-demanding, yet nonetheless process-based method to construct snow maps of high spatiotemporal resolution. It is an especially well suited alternative for operational snow mapping in regions with rugged topography and large spatiotemporal variability in snow conditions, as is the case in the mountainous Norway.

Ecological and human risks often drive the selection of remedial alternatives for contaminated sediments. Traditional human and ecological risk assessment (HERA) includes assessing risk for benthic organisms and aquatic fauna associated with exposure to contaminated sediments before and after remediation as well as risk for human exposure but does not consider the environmental footprint associated with implementing remedial alternatives. Assessment of environmental effects over the whole life cycle (i.e., Life Cycle Assessment, LCA) could complement HERA and help in selecting the most appropriate sediment management alternative. Even though LCA has been developed and applied in multiple environmental management cases, applications to contaminated sediments and marine ecosystems are in general less frequent. This paper implements LCA methodology for the case of the polychlorinated dibenzo-p-dioxins and -furans (PCDD/F)-contaminated Grenland fjord in Norway. LCA was applied to investigate the environmental footprint of different active and passive thin-layer capping alternatives as compared to natural recovery. The results showed that capping was preferable to natural recovery when analysis is limited to effects related to the site contamination. Incorporation of impacts related to the use of resources and energy during the implementation of a thin layer cap increase the environmental footprint by over 1 order of magnitude, making capping inferior to the natural recovery alternative. Use of biomass-derived activated carbon, where carbon dioxide is sequestered during the production process, reduces the overall environmental impact to that of natural recovery. The results from this study show that LCA may be a valuable tool for assessing the environmental footprint of sediment remediation projects and for sustainable sediment management. This journal is © The Royal Society of Chemistry 2011. This is the authors accepted and refereed manuscript to the article.

The formation and break-up of ice-cover are important seasonal events in mid- to high-latitude cold-region lakes. There is increasing concern regarding how climate change will affect lake-water thermal structure and lake-ice characteristics, particularly ice formation, duration, break-up, thickness, and composition. This study employs a one-dimensional process-based multi-year lake ice model, MyLake, to simulate the evolution of the Northern Hemisphere lake-ice and thermal structure patterns under a changing climate. After testing the model on Baker Lake located in Nunavut Canada, large-scale simulations were conducted over the major land masses of the Northern Hemisphere subarctic regions between 40° and 75°N using hypothetical lakes positioned at 2·5° latitude and longitude resolution. For the baseline period of 1960–1999, the lake-ice model was driven by gridded atmospheric forcings from the ERA-40 global reanalysis data set while atmospheric model forcings corresponding to future (2040–2079) climate were obtained by modifying the ERA-40 data according to the Canadian Global Climate Model projection based on the SRES A2 emissions scenario. Analysis of the modelling results indicates that lake-ice freeze-up timing will be delayed by 5–20 days and break-up will be advanced by approximately 10–30 days, thereby resulting in an overall decrease in lake-ice duration by about 15–50 days. Maximum lake-ice thickness will also be reduced by 10–50 cm. The change in maximum snow depth on the lake-ice ranges between − 15 to + 5 cm, while the change in white-ice thickness ranges between − 20 to + 10 cm depending on the geographic location and other climate parameters. The future warming will also result in an overall increase in lake-water temperature, with summer stratification starting earlier and extending later into the year. Copyright © 2011 Crown in the right of Canada. Published by John Wiley & Sons, Ltd.

The objective of this study in River Atna, Norway, was to analyse the spatial and temporal variation in species composition and diversity of the periphyton community in a pristine sub-alpine / boreal watercourse. The variations in the biotic parameters were related to selected environmental factors. We addressed epilithic algae and species living epiphytic on epilithic algae and submerged bryophytes. The sampling sites were located in the alpine, northern boreal, and mid boreal biomes. There was considerable spatial variation in species composition and diversity. This variation showed close correlation with natural gradients in water temperature and nutrient concentration. Three or four periphyton community categories could be distinguished in terms of species composition, diversity, and environmental variables. At high altitudes (1150–740 m a.s.l.) in cold water temperatures and extremely low nutrient contents, there was very low species diversity, only including algae known from ultra oligothrophic cold waters, e.g. Scytonematopsis starmachii (cyanobacteria) and Klebshormidium rivulare (green algae). The second category, at medium altitudes (701-522 m a.s.l.), was characterised by somewhat higher water temperatures and nutrient contents, and the species diversity was higher. This category included algae known from somewhat richer waters, e.g. Stigonema mamillosum (cyanobacteria) and Zygnema spp. (green algae). The third category was located at approx. 522 m a.s.l., had low water temperatures, relatively high alkalinity, and was characterised by Tolypothrix distorta (cyanobacteria) and Ulothrix zonata (green algae). A possible fourth category was found in the lower part of the river (350 m a.s.l.), where periphyton was distinguished by high diversity. At the individual sampling localities, species diversity showed strong seasonal variation, but otherwise high temporal stability. Over the 12 years of observations, there was only a weak temporal trend; towards species initially occurring only at low altitudes and high nutrient content. The combination of high temporal stability and high spatial variability, correlating closely with environmental gradients, is the main reason why periphyton observations have become an important constituent in water quality assessment.

[1] In this study laboratory experiments of sea ice formed on a vertical surface with initial temperature of −30 to −50°C are presented. The ice formation is rapid, and in 300 s >5 mm of sea ice is formed. Ice formation cooled and salinified the water, and induced a vertical down wards flow of ∼5 mm/s with a boundary layer about 5 mm thick. This ice has a structure with columnar crystals that have small circular cross sections (0.2–1.0 mm) and sea ice salinities are between 24 and 32. A simple model approach indicate that the thermal conductivity of such ice is lower than for other types of sea ice.

Rate constant bioaccumulation models are applied to simulate the flow of polychlorinated dibenzo-p-dioxins and dibenzofurans (PCDD/Fs) in the coastal marine food web of Frierfjorden, a contaminated fjord in southern Norway. We apply two different ways to parameterize the rate constants in the model, global sensitivity analysis of the models using Extended Fourier Amplitude Sensitivity Test (Extended FAST) method, as well as results from general linear system theory, in order to obtain a more thorough insight to the system's behavior and to the flow pathways of the PCDD/Fs. We calibrate our models against observed body concentrations of PCDD/Fs in the food web of Frierfjorden. Differences between the predictions from the two models (using the same forcing and parameter values) are of the same magnitude as their individual deviations from observations, and the models can be said to perform about equally well in our case. Sensitivity analysis indicates that the success or failure of the models in predicting the PCDD/F concentrations in the food web organisms highly depends on the adequate estimation of the truly dissolved concentrations in water and sediment pore water. We discuss the pros and cons of such models in understanding and estimating the present and future concentrations and bioaccumulation of persistent organic pollutants in aquatic food webs.

We studied methanotrophic activity in the water column in relation to heterotrophic bacterial production and efflux of methane (CH 4) from the lake surface in a small, stratified, humic, boreal lake (Valkea-Kotinen, southern Finland). During summer and winter stratification, the highest methanotrophic activities were in the metalimnion, where oxygen concentration was ,6 mmol m 23 . During an incomplete spring turnover and summer stratification period, 3‐5 times more CH4 was consumed by methanotrophs in the water column than was released to the atmosphere. The highest CH4 effluxes (1.2‐5.1 mmol m 22 d 21 ) to the atmosphere occurred during the autumnal turnover despite observed methanotrophic activity in the whole water column. In winter, the amount of CH4 consumed by methanotrophs (0.20 mol CH4 m 22 during 6.5 months) was of the same order of magnitude as that during the ice-free period (0.22 mol CH4 m 22 during 5.5 months). Annually ;80% of CH4 diffused from the sediment was consumed by methanotrophs in the water column, and only 20% (0.11 mol CH 4 m 22 yr 21 ) was released to the atmosphere. During the ice-free period, bacterial production measured as [ 14 C]leucine uptake showed a bell-shaped relation to CH4 concentration. The highest production was found in the metalimnion at CH4 concentrations ranging from 5 to 10 mmol m 23 . During summer stratification, net production of methanotrophs corresponded to 23‐47% of total bacterial production, but during the autumn turnover, this proportion was higher (27‐81%), indicating that methanotrophs offer a potentially significant source of carbon to zooplankton in stratified humic lakes.