Multi-year observations of the high mountain water cycle in the Langtang catchment, Central Himalaya.

The Langtang catchment is a high mountain, third order catchment in the Gandaki basin in the Central Himalaya (28.2°N, 85.5°E), that eventually drains into the Ganges. The catchment spans an elevation range from 1400 to 7234 m a.s.l. and approximately one quarter of the area is glacierized. Numerous research projects have been conducted in the valley during the last four decades, with a strong focus on the cryospheric components of the catchment water balance. Since 2012 multiple weather stations and discharge stations provide measurements of atmospheric and hydrologic variables. Full weather stations are used to monitor at an hourly resolution all four radiation components (incoming and outgoing shortwave and longwave radiation; SWin/out and LWin/out), air temperature, humidity, wind speed and direction, and precipitation, and cover an elevational range of 3862-5330 m a.s.l. Air temperature and precipitation are monitored along elevation gradients for investigations of the spatial variability of the high mountain meteorology. Dedicated point-scale observations of snow cover, depth and water equivalent as well as ice loss have been carried out over multiple years and complement the observations of the water cycle. All data presented is openly available in a database and will be updated annually.

Snow depth variability in the Northern Hemisphere mountains observed from space.

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.

Simulating the uncertain effect of active carbon capping of a dioxin-polluted Norwegian fjord.

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.

Use of life cycle assessments to evaluate the environmental footprint of contaminated sediment remediation.

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.

Identification of the most influential factors in the Norwegian guidelines for risk assessment of dispersion of contaminants from sediments.

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 (F(diff)), resuspension caused by ship traffic (F(skipnorm)), and uptake and predation of benthic biota (F(org)). 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 (OC(sed)), factor for increased diffusion due to bioturbation (a), sediment-water partitioning coefficient (K(d)), benthic biota-water bioconcentration factor (BCF(fisk)), and mass of resuspended fine sediment during arrival or departure of a ship (m(sed)). We also quantify which of the 3 fluxes (F(diff), F(skipnorm), and F(org)) 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.

Simulating climate change-induced alterations in bioaccumulation of organic contaminants in an Arctic marine food web.

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.

Black carbon-inclusive modeling approaches for estimating the aquatic fate of dibenzo-p-dioxins and dibenzofurans.

A novel black carbon (BC) inclusive modeling tool is applied to estimate the distribution and long-term fate of dibenzo-p-dioxins and dibenzofurans (PCDD/Fs) in the Norwegian Grenland Fjords. Three versions of the model were developed in which sediment-water partitioning was described using (i) an amorphous organic carbon (AOC) partitioning sorption model without BC sorption, (ii) a combined AOC and BC sorption model based on the Freundlich isotherm, and (iii) a combined BC-AOC model based on the Langmuir isotherm. The predictive ability of the three different models was evaluated for 17 PCDD/Fs by comparison of model predictions with observed organic carbon normalized sediment-water partition coefficients (K(TOC)) and with measured concentrations. All three versions of the model were able to predict concentrations that were in reasonable agreement with measured particulate concentrations (i.e., within a factor of 4 of median values). Estimated particulate concentrations were less sensitive to the model choice because the majority of the mass of these hydrophobic chemicals is associated with particulates regardless. However, for estimation of K(TOC) or dissolved water concentrations, both versions of the combined AOC and BC sorption models provided greatly improved estimates compared to the AOC-only model.

Modeling the effects and uncertainties of contaminated sediment remediation scenarios in a Norwegian fjord by Markov chain Monte Carlo simulation.

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.

Flows of dioxins and furans in coastal food webs: inverse modeling, sensitivity analysis, and applications of linear system theory.

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.

Benchmark criteria: a tool for selecting appropriate models in the field of water management.

A milestone in the field of European water protection policy is the European Union's Water Framework Directive (WFD), which came into force in December 2000 and which integrates the management of European waters in many ways. In this study, we start by focusing on management issues connected to the implementation of the WFD and pose a question: "what type of models would be the most suitable for use in the context of the WFD?" With this question in mind, we aim to establish a set of operational and functional selection criteria for (computer) models whose application is intended to support decision-making related to a particular water management issue. These so-called "benchmark criteria" should help water managers and other model users in choosing appropriate models, e.g., for the WFD implementation purposes. We first describe models and their use in general and then propose an approach for setting the benchmark criteria for models, basing it on the concept of uncertainty management, while keeping firmly in mind the important role of citizens and citizen organizations in water management. The suggested benchmark criteria are in the form of 14 questions through which each model can be evaluated. Finally, the process for testing and refining the benchmark criteria is highlighted.