‘Contractual’ and ‘cooperative’ civic engagement: The emergence and roles of ‘flood action groups’ in England and Wales

Devolution of responsibilities is transforming how flood risk is managed in many countries. Research assessing the emergence and role of a new element in the governance of flood risk management in England explored the numerous ‘flood action groups’ that have developed over the last decade. We identified two broad categories of relationship between the public and authorities. The first displays ‘contractual’ characteristics: a level of protection provided by the authority in exchange for taxes or similar support. The second embodies a ‘collaborative’ relationship: public knowledge, social and financial resources are equal and complementary to those of authority, and seeking ‘collective security’. In general, the former were more successful than the latter, but common lessons were that success in FRM should not be defined purely as the ability to prevent flooding, but as the ability to access a variety of resources across different levels of society at different stages of flood risk management.     

Power analysis as a reflexive scientific tool for interpretation and implementation of the precautionary principle in the European Union

The diversity of interpretation, the subsequent lack of implementation, and the enforcement of the precautionary principle have been important issues in the European environmental discourse for the past five years. The European Commission published a communication on the Commission's interpretation of the precautionary principle on February 2nd, 2000. However, the distinction between precaution and prevention is absent in the EU Commission's interpretation, resulting in the communication's lacking relevance for the precautionary principle. The important consequence of the precautionary concept in policy and decision-making is that it should not be based on an assumed certainty of the certainty of environmental knowledge--but rather on a certainty of the uncertainty of environmental knowledge. In other words, the regulation should, to a greater extent, be based on the management of uncertainty, and risk assessments should explicitly present and discuss related uncertainty and lack of knowledge. The management of uncertainty should be based on setting the acceptable level of risk of accepting a failure to reject the null hypothesis of no adverse effects (beta). This is done by setting the required power (1-beta) according to a socioeconomic cost-benefit analysis. Moreover, the acceptable ecological effect size (A) could also be set a priori which would have implications for the power of a study. Reversal of the burden of proof could be considered in order to resolve possible legal implications for the risk managers.     

Coping with uncertainty: a call for a new science-policy forum

The scientific and policy worlds have different goals, which can lead to different standards for what constitutes "proof" of a change or phenomena, and different approaches for characterizing and conveying uncertainty and risk. These differences can compromise effective communication among scientists, policymakers, and the public, and constrain the types of socially compelling questions scientists are willing to address. In this paper, we review a set of approaches for dealing with uncertainty, and illustrate some of the errors that arise when science and policy fail to coordinate correctly. We offer a set of recommendations, including restructuring of science curricula and establishment of science-policy forums populated by leaders in both arenas, and specifically constituted to address problems of uncertainty.     

New York State urban and rural measurements of continuous PM2.5 mass by FDMS, TEOM, and BAM

Field evaluations and comparisons of continuous fine particulate matter (PM2,5) mass measurement technologies at an urban and a rural site in New York state are performed. The continuous measurement technologies include the filter dynamics measurement system (FDMS) tapered element oscillating microbalance (TEOM) monitor, the stand-alone TEOM monitor (without the FDMS), and the beta attenuation monitor (BAM). These continuous measurement methods are also compared with 24-hr integrated filters collected and analyzed under the Federal Reference Method (FRM) protocol. The measurement sites are New York City (the borough of Queens) and Addison, a rural area of southwestern New York state. New York City data comparisons between the FDMS TEOM, BAM, and FRM are examined for bias and seasonality during a 2-yr period. Data comparisons for the FDMS TEOM and FRM from the Addison location are examined for the same 2-yr period. The BAM and FDMS measurements at Queens are highly correlated with each other and the FRM. The BAM and FDMS are very similar to each other in magnitude, and both are approximately 25% higher than the FRM filter measurements at this site. The FDMS at Addison measures approximately 9% more mass than the FRM. Mass reconstructions using the speciation trends network filter data are examined to provide insight as to the contribution of volatile species of PM2.5 in the FDMS mass measurement and the fraction that is likely lost in the FRM mass measurement. The reconstructed mass at Queens is systematically lower than the FDMS by approximately 10%.     

Adjusting tapered element oscillating microbalance data for comparison with Federal Reference Method PM2.5 measurements in region 5

Continuous monitoring of particulate matter (PM) with a diameter less than 2.5 microm (PM2.5) is quickly gaining acceptance as an alternative means of measuring fine PM in the United States. For this project, data were taken from all monitoring sites within Region 5 that used the tapered element oscillating microbalance (TEOM) for PM2.5 and had a collocated Federal Reference Method (FRM) monitor. Scatter plots of TEOM versus FRM show that for a significant fraction of the observations, an independent factor causes the TEOM to underestimate the FRM value. This underestimation appears to increase as temperature decreases. For this analysis, a linear relationship was fit to the TEOM versus FRM data, allowing a break or knot in the relationship, modeled as a change of slope, at a site-specific temperature. To test whether the models are adequate for adjusting future measurements, models were also developed using the first year of data only, and the remaining observations were used to test the durability of the relationships. For all but one monitor in Minnesota, the models developed for each site had consistently high R2s, were predictive of future measurements, and could be used to derive "FRM-like" results from the TEOM measurements. The temperature knots fitted by the model for individual sites ranged from 12.9 to 20.6 degrees C. Data from all six sites in the state of Michigan were also combined to determine if a single model could be developed for the entire state. While the single model for the state of Michigan worked reasonably well, some of the predicted concentrations at individual sites were systematically underestimating the observed concentrations on more polluted days. The same conclusion was drawn for a Region 5-wide model. This approach was also found to work very well for six individual TEOM monitors in New York State.     

Using continuous PM2.5 monitoring data to report an air quality index

As stated in 40 CFR 58, Appendix G (2000), statistical linear regression models can be applied to relate PM2.5 continuous monitoring (CM) measurements with federal reference method (FRM) measurements, collocated or otherwise, for the purpose of reporting the air quality index (AQI). The CM measurements can then be transformed via the model to remove any bias relative to FRM measurements. The resulting FRM-like modeled measurements may be used to provide more timely reporting of a metropolitan statistical area's (MSA's) AQI. Of considerable importance is the quality of the model used to relate the CM and FRM measurements. The use of a poor model could result in misleading AQI reporting in the form of incorrectly claiming either good or bad air quality. This paper describes a measure of adequacy for deciding whether a statistical linear regression model that relates FRM and continuous PM2.5 measurements is sufficient for use in AQI reporting. The approach is the U.S. Environmental Protection Agency's (EPA's) data quality objectives (DQO) process, a seven-step strategic planning approach to determine the most appropriate data type, quality, quantity, and synthesis for a given activity. The chosen measure of model adequacy is r2, the square of the correlation coefficient between FRM measurements and their modeled counterparts. The paper concludes by developing regression models that meet this desired level of adequacy for the MSAs of Greensboro/Winston-Salem/High Point, NC; and Davenport/Moline/Rock Island, IA/IL. In both cases, a log transformation of the data appeared most appropriate. For the data from the Greensboro/Winston-Salem/High Point MSA, a simple linear regression model of the FRM and CM measurements had an r2 of 0.96, based on 227 paired observations. For the data from the Davenport/Moline/Rock Island MSA, due to seasonal differences between CM and FRM measurements, the simple linear regression model had to be expanded to include a temperature dependency, resulting in an r2 of 0.86, based on 214 paired observations.     

Flood hazards vulnerability and risk of food security in Bait community flood-prone areas of Punjab Pakistan: In SDGs achievement threat

Climate change in the global perspective has increased the occurrence of natural disasters, which subsequently decreased agricultural production and intensified the issue of food security. Developing countries, such as Pakistan, are facing severe food security issues, where most of the population still experiences poverty and hunger in their daily lives. Flood disasters ruin valuable land, cause agricultural production losses, and interrupt livelihood routines as expected household livelihood becomes more vulnerable. This research work focused on investigating the flood hazards vulnerability and risk of food security in the Bait community flood-prone areas of Punjab, Pakistan, with a broader aspect in contrast to previous research work. A constructed food security index composed of several IPCC and FAO factors with correlated dimensions of food security was used for the empirical estimation in this study. A composite food security index was developed through polychoric principal component analysis. To estimate the influence on the overall food security condition in the study area, a food security index was regressed on various independent variables. Estimates of the study indicated that three-fourths of household respondents in the study area are confronted with the issue of food security with changeable scale. Financing schemes, physical assets, and family type illustrated the positive influence on respondents’ food security level, whereas respondents suffering property losses owing to floods had a negative influence. The study findings suggested integrated strategies must be adopted to effectively deal with issues of food security in the scenario of increasing severity of flood disasters. Policymakers and disaster-concerned institutions need to develop disaster risk mitigation strategies by constructing new water reserves and clearing river encroachments to deal with flood disasters. Agricultural research and development authorities need to provide climate friendly seed varieties and promote particular food crops for flood prone areas to ensure food security and reduce livelihood vulnerability, specifically for the flood-prone communities.

     

A comparison of four gravimetric fine particle sampling methods.

A study was conducted to compare four gravimetric methods of measuring fine particle (PM2.5) concentrations in air: the BGI, Inc. PQ200 Federal Reference Method PM2.5 (FRM) sampler; the Harvard-Marple Impactor (HI); the BGI, Inc. GK2.05 KTL Respirable/Thoracic Cyclone (KTL); and the AirMetrics MiniVol (MiniVol). Pairs of FRM, HI, and KTL samplers and one MiniVol sampler were collocated and 24-hr integrated PM2.5 samples were collected on 21 days from January 6 through April 9, 2000. The mean and standard deviation of PM2.5 levels from the FRM samplers were 13.6 and 6.8 microg/m3, respectively. Significant systematic bias was found between mean concentrations from the FRM and the MiniVol (1.14 microg/m3, p = 0.0007), the HI and the MiniVol (0.85 microg/m3, p = 0.0048), and the KTL and the MiniVol (1.23 microg/m3, p = 0.0078) according to paired t test analyses. Linear regression on all pairwise combinations of the sampler types was used to evaluate measurements made by the samplers. None of the regression intercepts was significantly different from 0, and only two of the regression slopes were significantly different from 1, that for the FRM and the MiniVol [beta1 = 0.91, 95% CI (0.83-0.99)] and that for the KTL and the MiniVol [beta1 = 0.88, 95% CI (0.78-0.98)]. Regression R2 terms were 0.96 or greater between all pairs of samplers, and regression root mean square error terms (RMSE) were 1.65 microg/m3 or less. These results suggest that the MiniVol will underestimate measurements made by the FRM, the HI, and the KTL by an amount proportional to PM2.5 concentration. Nonetheless, these results indicate that all of the sampler types are comparable if approximately 10% variation on the mean levels and on individual measurement levels is considered acceptable and the actual concentration is within the range of this study (5-35 microg/m3).     

Particulate matter in California: part 1--Intercomparison of several PM2.5, PM10-2.5, and PM10 monitoring networks

It will be many years before the recently deployed network of fine particulate matter with an aerodynamic diameter less than 2.5 microm (PM2.5) Federal Reference Method (FRM) samplers produces information on nonattainment areas, trends, and source impacts. However, data on PM2.5 and its major constituents have been routinely collected in California for the past 20 years. The California Air Resources Board operated as many as 20 dichotomous (dichot) samplers for PM2.5 and coarse PM (PM10-2.5). The California Acid Deposition Monitoring Program (CADMP) collected 12-h-average PM2.5 and PM10 from 1988 to 1995 at ten urban and rural sites and 24-h-average PM2.5 at five urban sites since 1995. Beginning in 1994, the Children's Health Study collected 2-week averages of PM2.5 in 12 communities in southern California using the Two-Week Sampler (TWS). Comparisons of collocated samples establish relationships between the dichot, CADMP, and TWS samplers and the 82-site network of PM2.5 FRM samplers deployed since 1999 in California. PM mass data from the different monitoring programs have modest to high correlation to FRM mass data, fairly small systematic biases and negative proportional biases ranging from 7 to 22%. If the biases are taken into account, all of the programs should be considered comparable with the FRM program. Thus, historical data can be used to develop long-term PM trends in California.     

Integrating information for better environmental decisions

As more is learned about the complex nature and extent of environmental impacts from progressive human disturbance, scientists, policy analysts, decision makers, educators, and communicators are increasingly joining forces to develop strategies for preserving and protecting the environment. The Eco-Informa Foundation is an educational scientific organization dedicated to promoting the collaborative development and sharing of scientific information. The Foundation participated in a recent international conference on environmental informatics through a special symposium on integrating information for better environmental decisions. Presentations focused on four general themes: (1) remote sensing and data interpretation, including through new knowledge management tools; (2) risk assessment and communication, including for radioactively contaminated facilities, introduced biological hazards, and food safety; (3) community involvement in cleanup projects; and (4) environmental education. The general context for related issues, methods and applications, and results and recommendations from those discussions are highlighted here.     

Using Continuous PM2.5 Monitoring Data to Report an Air Quality Index

Abstract

As stated in 40 CFR 58, Appendix G (2000), statistical linear regression models can be applied to relate PM_2.5 continuous monitoring (CM) measurements with federal reference method (FRM) measurements, collocated or otherwise, for the purpose of reporting the air quality index (AQI). The CM measurements can then be transformed via the model to remove any bias relative to FRM measurements. The resulting FRM-like modeled measurements may be used to provide more timely reporting of a metropolitan statistical area’s (MSA’s) AQI.¹ Of considerable importance is the quality of the model used to relate the CM and FRM measurements. The use of a poor model could result in misleading AQI reporting in the form of incorrectly claiming either good or bad air quality.

This paper describes a measure of adequacy for deciding whether a statistical linear regression model that relates FRM and continuous PM_2.5 measurements is sufficient for use in AQI reporting. The approach is the U.S. Environmental Protection Agency’s (EPA’s) data quality objectives (DQO) process, a seven-step strategic planning approach to determine the most appropriate data type, quality, quantity, and synthesis for a given activity.² The chosen measure of model adequacy is r², the square of the correlation coefficient between FRM measurements and their modeled counterparts. The paper concludes by developing regression models that meet this desired level of adequacy for the MSAs of Greensboro/Winston-Salem/High Point, NC; and Davenport/Moline/Rock Island, IA/IL. In both cases, a log transformation of the data appeared most appropriate. For the data from the Greens-boro/Winston-Salem/High Point MSA, a simple linear regression model of the FRM and CM measurements had an r² of 0.96, based on 227 paired observations. For the data from the Davenport/Moline/Rock Island MSA, due to seasonal differences between CM and FRM measurements, the simple linear regression model had to be expanded to include a temperature dependency, resulting in an r² of 0.86, based on 214 paired observations.     

Recent changes (1973–2014 versus 1903–1972) in the flow regime of the Lower Paraná River and current fluvial pollution warnings in its Delta Biosphere Reserve

Alterations in flow regimes of large rivers may originate or increase risks to ecosystems and humans. The Paraná River basin (South America) undergoes human pressures (e.g., heavy damming in the upper basin, deforestation, and mixed pollution) that may affect the water quantity and quality of its terminal Delta (Argentina). In this study, after applying univariate and multivariate change-point detection and trend analyses to the daily data series of flows incoming to the Delta (Paraná-Santa Fe section), flow characteristics were compared by Indicators of Hydrologic Alteration (IHA) and Environmental Flow Components (EFC). Some flood characteristics were also compared from hydrometric levels in the middle Delta (San Pedro station). Chemical and microbiological water variables in the main rivers of the “Paraná Delta” Biosphere Reserve were examined during two extreme hydrologic years (October 2008 to July 2010) to detect potential risk factors in association with hydrologic conditions. In the Lower Paraná River, a historical period (1903–1972) and two more altered periods (1973–1999 wet period and 2000–2014 dry period) were identified. Flow duration curves evidenced different changes in both altered periods, reflecting the joint effect of climatic variability and human influence. The most evident alterations in the flow regime were the lack of record of the extreme-low-flow component, the attenuation of monthly flow seasonality, and the increase in the number of reversals (dry period) and in the variability of maximum and minimum flow dates. These alterations are consistent with the monthly and daily flow regulation by upstream dams evidenced by available data from the current dry period. In the middle Delta, the marked monthly seasonality in flood days decreased only in the wet period. The proportion between the number of flood days exceeding the evacuation level and that of those exceeding the warning level doubled in the wet period but decreased only slightly in the dry period. In the Delta Reserve rivers, concentrations of Escherichia coli, cadmium, lead, iron, manganese, and ammonium exceeded guideline levels under a severe drought and a dispersal of cyanobacteria appeared under a high-flow pulse in La Niña year. The ammonium concentration exceeded the level for human drink with the overbanking flood stage in El Niño year. These occasional detections pose a potential risk to the aquatic life and, especially, to the inhabitants of the Reserve. Flow duration curves, IHA, and EFC are useful tools to evaluate trends or changes of ecological and social relevance in flow regime characteristics.     

Flood pulse alterations and productivity of the Tonle Sap ecosystem: a model for impact assessment

Tonle Sap Lake is a large and complex data-deficient ecosystem in the Mekong River Basin. Highly valuable in biodiversity and natural livelihoods capital, it is susceptible to degradation when the flood pulse that drives its productivity is altered as a result of hydropower and irrigation development on the Mekong River. To date, there are no tools to assess the consequences of such flood pulse alterations, leaving the Tonle Sap underrated in water-resources use and planning. A combined ecological-hydrodynamic model is presented for the production potential of the Tonle Sap ecosystem and its likely response to hydrological changes.     

Collocated comparisons of continuous and filter-based PM2.5 measurements at Fort McMurray,Alberta, Canada

Collocated comparisons for three PM_2.5 monitors were conducted from June 2011 to May 2013 at an air monitoring station in the residential area of Fort McMurray, Alberta, Canada, a city located in the Athabasca Oil Sands Region. Extremely cold winters (down to approximately ?40°C) coupled with low PM_2.5 concentrations present a challenge for continuous measurements. Both the tapered element oscillating microbalance (TEOM), operated at 40°C (i.e., TEOM₄₀), and Synchronized Hybrid Ambient Real-time Particulate (SHARP, a Federal Equivalent Method [FEM]), were compared with a Partisol PM_2.5 U.S. Federal Reference Method (FRM) sampler. While hourly TEOM₄₀ PM_2.5 were consistently ~20–50% lower than that of SHARP, no statistically significant differences were found between the 24-hr averages for FRM and SHARP. Orthogonal regression (OR) equations derived from FRM and TEOM₄₀ were used to adjust the TEOM₄₀ (i.e., TEOM_adj) and improve its agreement with FRM, particularly for the cold season. The 12-year-long hourly TEOM_adj measurements from 1999 to 2011 based on the OR equations between SHARP and TEOM₄₀ were derived from the 2-year (2011–2013) collocated measurements. The trend analysis combining both TEOM_adj and SHARP measurements showed a statistically significant decrease in PM_2.5 concentrations with a seasonal slope of ?0.15 μg m^?3 yr^?1 from 1999 to 2014.Implications: Consistency in PM_2.5 measurements are needed for trend analysis. Collocated comparison among the three PM_2.5 monitors demonstrated the difference between FRM and TEOM, as well as between SHARP and TEOM. The orthogonal regressions equations can be applied to correct historical TEOM data to examine long-term trends within the network.     

Development and modelling of realistic retrofitted Nature-based Solution scenarios to reduce flood occurrence at the catchment scale

Decentralized Nature-based Solutions such as Urban Green Infrastructures (UGI) are increasingly promoted to reduce flooding in urban areas. Many studies have shown the effectiveness of flood control of UGI at a plot or neighbourhood level. Modelling approaches that extrapolate their flood reducing impact to larger catchment scales are often based on a simplistic assumption of different percentages of UGI implementation. Additionally, such approaches typically do not consider the suitable space for UGI and potential implementation constraints. This study proposes a scenario development and modelling approach for a more realistic upscaling of UGI based on empirical insights from a representative neighbourhood. The results from this study, conducted in the metropolitan area of Costa Rica, show that upscaling the full potential for UGI could significantly reduce surface runoff, peak flows, and flood volumes. In particular, the permeable pavement has the highest potential for flood reducing in public space while cisterns perform best at the property level. These results can guide the formation of policies that promote UGI.Supplementary InformationThe online version of this article (10.1007/s13280-020-01493-8) contains supplementary material, which is available to authorized users.     

Assessment of the Netherlands’ Flood Risk Management Policy Under Global Change

Climate change and sea level rise urge low-lying countries to draft adaption policies. In this context, we assessed whether, to what extent and when the Netherlands’ current flood risk management policy may require a revision. By applying scenarios on climate change and socio-economic development and performing flood simulations, we established the past and future changes in flood probabilities, exposure and consequences until about 2050. We also questioned whether the present policy may be extended much longer, applying the concept of ‘policy tipping points’. Climate change was found to cause a significant increase of flood risk, but less than economic development does. We also established that the current flood risk management policy in the Netherlands can be continued for centuries when the sea level rise rate does not exceed 1.5 m per century. However, we also conclude that the present policy may not be the most attractive strategy, as it has some obvious flaws.     

Estimation of uncertainty arising from different soil sampling devices: the use of variogram parameters

In the frame of the international SOILSAMP project, funded and coordinated by the National Environmental Protection Agency of Italy (APAT), uncertainties due to field soil sampling were assessed. Three different sampling devices were applied in an agricultural area using the same sampling protocol. Cr, Sc and Zn mass fractions in the collected soil samples were measured by k(0)-instrumental neutron activation analysis (k(0)-INAA). For each element-device combination the experimental variograms were calculated using geostatistical tools. The variogram parameters were used to estimate the standard uncertainty arising from sampling. The sampling component represents the dominant contribution of the measurement uncertainty with a sampling uncertainty to measurement uncertainty ratio ranging between 0.6 and 0.9. The approach based on the use of variogram parameters leads to uncertainty values of the sampling component in agreement with those estimated by replicate sampling approach.     

Human Exposure to Sulfates from Coal-Fired Power Plants

Abstract

Increased interest in the health effects of ambient par–ticulate mass (PM) has focused attention on the evaluation of existing mass measurement methodologies and the definition of PM in ambient air. The Rupprecht and Patashnick Tapered Element Oscillating MicroBalance (TEOM^®) method for PM is compared with time–integrated gravimetric (manual) PM methods in large urban areas during different seasons. Comparisons are conducted for both PM₁₀ and PM_2.5 concentrations.

In urban areas, a substantial fraction of ambient PM can be semi–volatile material. A larger fraction of this component of PM₁₀ may be lost from the TEOM–heated filter than the Federal Reference Method (FRM). The observed relationship between TEOM and FRM methods varied widely among sites and seasons. In East Coast urban areas during the summer, the methods were highly correlated with good agreement. In the winter, correlation was somewhat lower, with TEOM PM concentrations generally lower than the FRM. Rubidoux, CA, and two Mexican sites (Tlalnepantla and Merced) had the highest levels of PM₁₀ and the largest difference between TEOM and manual methods.

PM_2.5 data from collocation of 24–hour manual samples with the TEOM are also presented. As most of the semi–volatile PM is in the fine fraction, differences between these methods are larger for PM_2.5 than for PM₁₀.     

Using stochastic risk assessment in setting information priorities for managing dioxin impact from a municipal waste incinerator

The objectives of this study were to assess site-specific carcinogenic risk of incinerator-emitted dioxins in a manner reflecting pollutant transfer across multimedia and multi-pathways. The study used site-specific environmental and exposure information and combined the Monte Carlo method with multimedia modeling to produce probability distributions of risk estimates. The risk estimates were further categorized by contaminated environmental media and exposure pathways that are experienced by human receptors in order to pinpoint significant sources of risk. Rank correlation coefficients were also calculated along with the Monte Carlo sampling to identify key factors that influenced estimation of risk. The results showed that ingestion accounted for more than 90% of the total risk and that risk control on ingestion of eggs, aboveground vegetables, and poultry should receive priority. It was also found that variation of parameters with variability accounted for around 35% of the total risk variance, while uncertainty contributed to the remaining 65%. Intake rates of aboveground vegetables, eggs, and poultry were the key parameters with the largest contribution to variance. In addition, sufficient sampling and analysis of dioxin contents in eggs, aboveground vegetables, poultry, soil, and fruit should be performed to improve risk estimation because the variation in concentrations in these media accounted for the largest overall risk variance. Finally, focus should be placed on reduction of uncertainty associated with the risk estimation through ingestion of aboveground vegetables, eggs, poultry, fruit, and soil because the risk estimates associated with these exposure pathways had the largest variance.     

On the uncertainty and confidence in decision support tools (DSTs) with insights from the Baltic Sea ecosystem

Ecosystems around the world are increasingly exposed to multiple, often interacting human activities, leading to pressures and possibly environmental state changes. Decision support tools (DSTs) can assist environmental managers and policy makers to evaluate the current status of ecosystems (i.e. assessment tools) and the consequences of alternative policies or management scenarios (i.e. planning tools) to make the best possible decision based on prevailing knowledge and uncertainties. However, to be confident in DST outcomes it is imperative that known sources of uncertainty such as sampling and measurement error, model structure, and parameter use are quantified, documented, and addressed throughout the DST set-up, calibration, and validation processes. Here we provide a brief overview of the main sources of uncertainty and methods currently available to quantify uncertainty in DST input and output. We then review 42 existing DSTs that were designed to manage anthropogenic pressures in the Baltic Sea to summarise how and what sources of uncertainties were addressed within planning and assessment tools. Based on our findings, we recommend future DST development to adhere to good modelling practise principles, and to better document and communicate uncertainty among stakeholders.Electronic supplementary materialThe online version of this article (doi:10.1007/s13280-020-01385-x) contains supplementary material, which is available to authorized users.