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Flooding is one of the most serious and frequent natural hazards affecting human life, property, and the environment. This study develops and tests a deep learning approach for large‐scale spatial flood modeling, using Convolutional Neural Network (CNN) and optimized versions combined with the Gray Wolf Optimizer (GWO) or the Imperialist Competitive Algorithm (ICA). With Sweden as an application case for nation‐wide flood susceptibility mapping, this modeling approach considers ten geo‐environmental input factors (slope, elevation, aspect, plan curvature, length of slope, topographic wetness index, distance from river, distance from wetland, rainfall, and land use). The GWO and ICA optimization improves model prediction by 12% and 8%, respectively, compared with the standalone CNN model performance. The results show 40% of the land area, 45% of the railroad, and 43% of the road network of Sweden to have high or very high flood susceptibility. They also show the aspect to have the highest input factor impact on flood susceptibility prediction while, for example, rainfall ranks only seven of the total 10 considered geo‐environmental input factors. In general, accurate nation‐wide flood susceptibility prediction is essential for guiding flood management and mitigation efforts. This study's approach to such prediction has emerged as well‐performing and cost‐effective for the case of Sweden, calling for further application and testing in other world regions. Plain Language Summary: Floods have the potential to negatively affect human wellbeing, infrastructures and the natural environment. Identifying regions prone to flooding is essential in avoiding such catastrophes. In the current study, standard and optimized convolutional neural network models are used to generate maps identifying the regions of Sweden with highest probability of flooding. Various topographic, hydrological, and anthropogenic factors are taken into account for the modeling. The analysis reveals numerous areas in Sweden prone to flooding, especially in the northern, central, and southeastern parts of the country. Malmo (the third largest city in Sweden) and some areas of Stockholm (capital) are the cities most susceptible to flooding. Additionally, considerable extensions of Sweden's roadways and railways might be impacted by floods. Accurate flood susceptibility mapping is required to assist policymakers and urban planners in implementing measures aimed at mitigating flood vulnerability. Furthermore, the findings show the ability of deep learning models for detecting flood‐susceptible areas, which can be implemented worldwide to improve flood management strategies and protect lives. Key Points: Deep learning model of convolutional neural network (CNN) was optimized and improved with gray wolf optimizer (GWO) and imperialist competitive algorithm (ICA) to detect flood‐prone areasDuring both the training and testing phases, the CNN‐GWO model demonstrated superior performance compared to CNN‐ICA and the standalone CNN modelThe results show 40% of the land area, 45% of the railroad, and 43% of the road network of Sweden to have high or very high flood susceptibility [ABSTRACT FROM AUTHOR]

On‐going climatic changes and land‐use changes may impact water storage dynamics within wetlandscapes (defined as the entire hydrological catchments of interconnected wetland systems). These dynamics are closely linked to many wetland ecosystem services including flood buffering, nutrient retention and biodiversity support. Here, we investigate if and how current water storage dynamics can differ between wetlands within the same wetlandscape. Based on continuous monitoring of water levels in multiple wetlands throughout a growing season (spring, summer, autumn) in Vattholma, Sweden, we show that there are two distinct storage behaviours depending on the position of the wetland within the landscape. Headwater wetland regions were active in temporary storage of surplus water from regular summer rains while water levels of downstream wetlands dropped to seasonal low values without responding to individual summer precipitation events. Thereby, the downstream wetlands maintained capacity to buffer extreme floods. We also found that headwater wetlands were associated with complex and patchy inundation, which causes habitat conditions to vary over short time scales both within and among these wetlands, in contrast to the prolonged low‐water state of the downstream wetlands. These differences between headwater‐downstream wetlands imply that the functionality of an entire wetlandscape cannot be assessed by simple extrapolation of data from monitoring stations that typically are located downstream of headwater regions. Increased understanding of these differences can support wetland management practices that target location‐specific nature‐based solutions and ecosystem services. [ABSTRACT FROM AUTHOR]

Distinction between active and legacy sources of nutrients is needed for effective reduction of waterborne nutrient loads and associated eutrophication. This study quantifies main typological differences in nutrient load behaviour versus water discharge for active and legacy sources. This quantitative typology is used for source attribution based on monitoring data for water discharge and concentrations of total nitrogen (TN) and total phosphorous (TP) from 37 catchments draining into the Baltic Sea along the coastline of Sweden over the period 2003–2013. Results indicate dominant legacy source contributions to the monitored loads of TN and TP in most (33 of the total 37) study catchments. Dominant active sources are indicated in 1 catchment for TN, and mixed sources are indicated in 3 catchments for TN, and 4 catchments for TP. The TN and TP concentration contributions are quantified to be overall higher from the legacy than the active sources. Legacy concentrations also correlate well with key indicators of human activity in the catchments, agricultural land share for TN (R2 = 0.65) and population density for TP (R2 = 0.56). Legacy‐dominated nutrient concentrations also change more slowly than in catchments with dominant active or mixed sources. Various data‐based results and indications converge in indicating legacy source contributions as largely dominant, mainly anthropogenic, and with near‐zero average change trends in the present study of catchments draining into the Baltic Sea along the coastline of Sweden, as in other parts of the world. These convergent indications emphasize needs to identify and map the different types of sources in each catchment, and differentiate strategies and measures to target each source type for possible achievement of shorter‐ and longer‐term goals of water quality improvement. [ABSTRACT FROM AUTHOR]

Hydrochemical constituents in streams may originate from currently active sources at the surface and/or legacy sources from earlier surface inputs, waste deposits and land contamination. Distinction and quantification of these source contributions are needed for improved interpretation of tracer data and effective reduction of waterborne environmental pollutants. This article develops a methodology that recognizes and quantifies some general mechanistic differences in stream concentration and load behavior versus discharge between such source contributions. The methodology is applied to comparative analysis of stream concentration data for chloride (Cl−), copper (Cu), lead (Pb), and zinc (Zn), and corresponding data for water discharge, measured over the period 1990–2018 in multiple hydrological catchments (19 for Cl−, 11 for Cu and Zn, 10 for Pb) around the major Lake Mälaren in Sweden. For Cl−, the average load fraction of active sources is quantified to be 19%, and the average active and legacy concentration contributions as 2.9 and 11 mg/L, respectively. For the metals, the average active load fractions at outlets are 1%–3% over all catchments and 9%–14% in the relatively few catchments with mixed metal sources. Average active and legacy concentration contributions are 0.14 and 3.2 μg/L for Cu, 0.05 and 1.5 μg/L for Pb, and 1.4 and 12 μg/L for Zn, respectively. This multi‐catchment analysis thus indicates a widespread prevalence of legacy sources, with greater legacy than active concentration contributions for both Cl− and the metals, and active contributions playing a greater role for chloride than for the metals. The relatively simple first‐order methodology developed and applied in the study can be used to screen commonly available stream monitoring data for possible distinction of active and legacy contributions of any hydrochemical constituent in and across various hydrological catchment settings. [ABSTRACT FROM AUTHOR]

This study quantifies the separate and combined effects of spatial and temporal variability for waterborne solute transport through catchments. The questions addressed regard whether, when and why the different types of variability may dominate catchment-scale transport. We utilize a versatile numerical solute transport code with a particle-based Monte Carlo time-domain random walk method to simulate waterborne transport through a generic catchment. Simulations are concretized and exemplified using data on spatiotemporal flow-transport variability from direct stream discharge observations and independently calculated advective solute travel time distributions for catchments within the water management district Northern Baltic Proper in Mid-Eastern Sweden. A main conclusion is that the projections of catchment mass loading based on spatial variability alone are robust estimates of long-term average solute transport development. This is especially true when annually aggregated mass load rather than finer temporal resolution of mass flux is considered. Temporal variability yields short-term fluctuations around the long-term average solute breakthrough development, and earlier or later arrival than the latter, depending on the timing and duration of solute input relative to the temporal flow variability. The exact temporal characteristics of future solute breakthrough are thus fundamentally uncertain, but their statistical expectation may be well quantified by accounting only for spatial variability. Copyright © 2015 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

The Budyko framework characterizes landscape water cycles as a function of climate. We used this framework to identify regions with contrasting hydroclimatic change during the past 50 years in Sweden. This analysis revealed three distinct regions: the mountains, the forests, and the areas with agriculture. Each region responded markedly different to recent climate and anthropogenic changes, and within each region, we identified the most sensitive subregions. These results highlight the need for regional differentiation in climate change adaptation strategies to protect vulnerable ecosystems and freshwater resources. Further, the Budyko curve moved systematically towards its water and energy limits, indicating augmentation of the water cycle driven by changing vegetation, climate and human interactions. This finding challenges the steady state assumption of the Budyko curve and therefore its ability to predict future water cycles. Copyright © 2013 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

This study uses GIS-based modeling of incoming solar radiation to quantify fine-resolved spatiotemporal responses of year-round monthly average temperature within a field study area located on the eastern coast of Sweden. A network of temperature sensors measures surface and near-surface air temperatures during a year from June 2011 to June 2012. Strong relationships between solar radiation and temperature exhibited during the growing season (supporting previous work) break down in snow cover and snowmelt periods. Surface temperature measurements are here used to estimate snow cover duration, relating the timing of snowmelt to low performance of an existing linear model developed for the investigated site. This study demonstrates that linearity between insolation and temperature 1) may only be valid for solar radiation levels above a certain threshold and 2) is affected by the consumption of incoming radiation during snowmelt. [ABSTRACT FROM AUTHOR]

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Abstract: Deep saline aquifers have large capacity for geological CO2 storage, but are generally not as well characterized as petroleum reservoirs. We here aim at quantifying effects of uncertain hydraulic parameters and uncertain stratigraphy on CO2 injectivity and migration, and provide a first feasibility study of pilot-scale CO2 injection into a multilayered saline aquifer system in southwest Scania, Sweden. Four main scenarios are developed, corresponding to different possible interpretations of available site data. Simulation results show that, on the one hand, stratigraphic uncertainty (presence/absence of a thin mudstone/claystone layer above the target storage formation) leads to large differences in predicted CO2 storage in the target formation at the end of the test (ranging between 11% and 98% of injected CO2 remaining), whereas other parameter uncertainty (in formation and cap rock permeabilities) has small impact. On the other hand, the latter has large impact on predicted injectivity, on which stratigraphic uncertainty has small impact. Salt precipitation at the border of the target storage formation affects CO2 injectivity for all considered scenarios and injection rates. At low injection rates, salt is deposited also within the formation, considerably reducing its availability for CO2 storage. [Copyright &y& Elsevier]

The article focuses on the scale dependence and the effects of model resolution on the measurements of advective travel times and their distributions in the catchment areas in Norrstroöm and Forsmark, Sweden. It states that the advective travel times of the drainage basin in Norrstroöm and the two catchment show clear scale dependence. It says that the local dispersion and molecular diffusion in mobile water contributes to the spreading of solute in travel times.

The zinc load from the Dalãlven River to the Baltic Sea is greater than for any other watercourse in Sweden. This paper investigates zinc mass flows into and through the Dalälven River from major mining areas within its drainage basin. Reported zinc mass flow data for this river are re-analyzed using an input-output flow analysis approach. Results show major inconsistencies in previous data interpretations which totally neglected possible zinc mass load contributions from the groundwater system to the river. This paper quantifies significant subsurface zinc load contributions that are consistent with all independently available data. Furthermore, a possible explanation for why these subsurface contributions may have been missed in previous studies and by the prevailing Swedish environmental monitoring system is provided. The study indicates that the input-output flow analysis approach may be generally useful for identifying and quantifying diffuse, unmonitored and uncertain pollutant load contributions from ground- to surface water systems. [ABSTRACT FROM AUTHOR]

We analyze and compare simulations and controlling processes of the past 60 years and possible future short- and long-term development of phosphorus and nitrogen loading from the Swedish Norrström drainage basin to the Baltic Sea under different inland source management scenarios. Results indicate that both point and agricultural source inputs may need to be decreased by at least 40% in order to reach a long-term sustainable 30% reduction of anthropogenic coastal nitrogen loading, as required by national environmental goals. A corresponding 20% anthropogenic phosphorus load reduction goal may be reached in the short term by analogous combined 40% source input reduction, but appears impossible to maintain as a long-term achievement by inland source abatement only. In general, realistic quantification of the slow subsurface nutrient transport and accumulation-release dynamics may be essential for accurately predicting and managing nutrient loading to surface and coastal waters. [ABSTRACT FROM AUTHOR]

Looks into the importance of cost effective coastal water management policies for the alternative distributions of stochastic water pollution in Sweden. Conduction of research on water resources protection and remediation; Efficiency of the management of coastal water supply; Assessment on the damage of the resources by pollution.

• Drought indicator prediction is assessed with various machine learning models. • The temporal method is most effective but requires large computing power. • The multi-features method achieves great performance in large scale prediction. • Soil moisture is the most readily modeled and best predicted drought indicator. Agricultural drought can severely reduce crop yields, lead to large economic losses and health impacts. Combined climate and land use variations determine key indicators of agricultural drought, including soil moisture and the Palmer drought severity index (PDSI). This study investigated the use of machine learning (ML) methods for predicting these indicators over Sweden, spanning steep climate and land use gradients. Three data arrangement methods (multi-features, temporal, and spatial) were used and compared in combination with seven ML/deep learning (DL) models (random forest (RF), decision tree, multivariate linear regression, support vector regression, autoregressive integrated moving average (AMIRA), artificial neural network, and convolutional neural network). Seven investigated features, obtained from Google Earth Engine, were used in the ML/DL modeling (soil moisture, PDSI, precipitation, evapotranspiration, elevation, slope and soil texture). The temporal ARIMA model (found most suitable for local scale prediction) and the multi-features RF model (more suitable for national-scale prediction) emerged as best performing for soil moisture prediction (with MAE of 9.1 and 11.95, and R2 of 0.79 and 0.59, respectively). All models generally performed better in predicting the soil moisture than the PDSI indicator of drought. For drought indicator prediction and mapping, previous-year average monthly soil moisture emerged as the most important feature, combined with the four additional corresponding features of PDSI, precipitation, evapotranspiration and elevation. [ABSTRACT FROM AUTHOR]

Summary: Phosphorous (P) transport and retention is investigated here based on data from a 3-year field study of five drainage ditch reaches at Färingsö, west of Stockholm, Sweden. Results show that P retention rate in the drainage reaches greatly depends on and decreases with increasing water flow and watercourse depth. This supports previous simulation indications and implies that changing drainage reach depth and flow conditions may considerably change P loading to downstream waters. In deeper drainage reaches with tile-drained fields in their catchment areas, which are common in Nordic lowlands, particulate P amounted to about 80% of total P, with about half of this fraction being bio-available. As much as 70% of that particulate P was retained in a reach section that was flow- and depth-restricted by heavy vegetation. Retained P in sediments was found to be mostly bound to Fe and/or Al. Some of that PAl–Fe fraction may after dissolution become more permanently retained by re-binding to Ca. [Copyright &y& Elsevier]

Ongoing urban expansion may degrade natural resources, ecosystems, and the services they provide to human societies, e.g., through land use and water changes and feedbacks. In order to control and minimize such negative impacts of urbanization, best practices for sustainable urban development must be identified, supported, and reinforced. To accomplish this, assessment methods and tools need to consider the couplings and feedbacks between social and ecological systems, as the basis for improving the planning and management of urban development. Collaborative efforts by academics, urban planners, and other relevant actors are also essential in this context. This will require relevant methods and tools for testing and projecting scenarios of coupled social-ecological system (CSES) behavior, changes, and feedbacks, in support of sustainable development of growing cities. This paper presents a CSES modeling approach that can provide such support, by coupling socio-economically driven land use changes and associated hydrological changes. The paper exemplifies and tests the applicability of this approach for a concrete case study with relevant data availability, the Tyresån catchment in Stockholm County, Sweden. Results show that model integration in the approach can reveal impacts of urbanization on hydrological and water resource, and the implications and feedbacks for urban societies and ecosystems. The CSES approach introduces new model challenges, but holds promise for improved model support towards sustainable urban development. • We present a tool for planning support by coupling social-ecological systems. • The tool helps identify and understand change drivers and feedbacks. • The tool was successfully tested in an urban-hydrological system in Sweden. • Model coupling can support better decisions for sustainable development. [ABSTRACT FROM AUTHOR]

Summary: During the 20th century, Sweden underwent a persistent agricultural development. In this study, we use and combine historical hydroclimatic and agricultural data to investigate how this large scale change of land use, and subsequent intensification of crop production, affected regional hydrology in two adjacent Swedish drainage basins. We find a main increase of evapotranspiration (ET) as cultivated area and/or crop production increased during the period 1901–1940. Thereafter, ET stabilized at a new higher level. Comparison between the data given, water balance constrained ET quantification (ETwb), and a range of different comparative estimates of purely climate driven ET (ETclim) shows that only 31% of the steep 1901–1940 increase of ETwb can be explained by climate change alone. The remaining 69% of this ETwb shift, which occurred in both investigated drainage basins, is instead explainable to large degree by the regional land use conversion from seminatural grasslands to cultivated land and associated enhanced productivity of herbaceous species. [Copyright &y& Elsevier]

Hydroclimatic change may affect the range of some infectious diseases, including tularemia. Previous studies have investigated associations between tularemia incidence and climate variables, with some also establishing quantitative statistical disease models based on historical data, but studies considering future climate projections are scarce. This study has used and combined hydro-climatic projection outputs from multiple global climate models (GCMs) in phase six of the Coupled Model Intercomparison Project (CMIP6), and site-specific, parameterized statistical tularemia models, which all imply some type of power-law scaling with preceding-year tularemia cases, to assess possible future trends in disease outbreaks for six counties across Sweden, known to include tularemia high-risk areas. Three radiative forcing (emissions) scenarios are considered for climate change projection until year 2100, incuding low (2.6 Wm -2 ), medium (4.5 Wm -2 ), and high (8.5 Wm -2 ) forcing. The results show highly divergent changes in future disease outbreaks among Swedish counties, depending primarily on site-specific type of the best-fit disease power-law scaling characteristics of (mostly positive, in one case negative) sub- or super-linearity. Results also show that scenarios of steeper future climate warming do not necessarily lead to steeper increase of future disease outbreaks. Along a latitudinal gradient, the likely most realistic medium climate forcing scenario indicates future disease decreases (intermittent or overall) for the relatively southern Swedish counties Örebro and Gävleborg (Ockelbo), respectively, and disease increases of considerable or high degree for the intermediate (Dalarna, Gävleborg (Ljusdal)) and more northern (Jämtland, Norrbotten; along with the more southern Värmland exception) counties, respectively.

Human-induced urban growth and sprawl have implications for greenhouse gas (GHG) emissions that may not be included in conventional GHG accounting methods. Improved understanding of this issue requires use of interactive, spatial-explicit social-ecological systems modeling. This paper develops a comprehensive approach to modeling GHG emissions from urban developments, considering Stockholm County, Sweden as a case study. GHG projections to 2040 with a social-ecological system model yield overall greater emissions than simple extrapolations in official climate action planning. The most pronounced difference in emissions (39% higher) from energy use single-residence buildings resulting from urban sprawl. And this difference is not accounted for in the simple extrapolations. Scenario results indicate that a zoning policy, restricting urban development in certain areas, can mitigate 72% of the total emission effects of the model-projected urban sprawl. The study outcomes include a decision support interface for communicating results and policy implications with policymakers.

Hydroclimatic changes may be particularly pronounced in high-latitude regions and can influence infectious diseases, jeopardizing regional human and animal health. In this study, we consider the example of tularemia, one of the most studied diseases in high-latitude regions, which is likely to be impacted by large regional hydroclimatic changes. For this disease case, we use a validated statistical model and develop a method for quantifying possible hydroclimatically driven shifts in outbreak conditions. The results show high sensitivity of tularemia outbreaks to certain combinations of hydroclimatic variable values. These values are within the range of past regional observations and may represent just mildly shifted conditions from current hydroclimatic averages. The methodology developed also facilitates relatively simple identification of possible critical hydroclimatic thresholds, beyond which unacceptable endemic disease levels may be reached. These results call for further research on how projected hydroclimatic changes may affect future outbreaks of tularemia and other infectious diseases in high-latitude and other world regions, with particular focus on critical thresholds to high-risk conditions. More research is also needed on the generality and spatiotemporal transferability of statistical disease models.

In this study, we investigate long-term hydroclimatic changes and their possible relation to regional changes in climate, land-use and water-use over the twentieth century in the transboundary Sava River Catchment (SRC) in South Eastern Europe. In a hydropower dominated part of the SRC, unlike in an unregulated part, we find increase in average annual evapotranspiration and decrease in temporal runoff variability, which are not readily explainable by observed concurrent climate change in temperature and precipitation and may be more related to landscape-internal change drivers. Among the latter investigated here, results indicate hydropower developments as most closely related to the found hydroclimatic shifts, consistent with previous such indications in studies of Swedish hydropower catchments. Overall, the present results have quantitatively framed the recent history and present state of hydroclimate in the SRC, of relevance for water resources in several countries and for a majority of their populations. This provides a useful basis for further assessment of possible future hydroclimatic changes, under different scenarios of climate change and land/water-use developments in the region.

The goals for water-quality and ecosystem integrity are often defined relative to "natural" reference conditions in many water-management systems, including the European Union Water Framework Directive. This paper examines the difficulties created for water management by using "natural" as the goal. These difficulties are articulated from different perspectives in an informal (fictional) conversation that takes place after a workshop on reference conditions in water-resources management. The difficulties include defining the natural state and modeling how a system might be progressed toward the natural, as well as the feasibility and desirability of restoring a natural state. The paper also considers the appropriateness for developing countries to adopt the use of natural as the goal for water management. We conclude that failure to critically examine the complexities of having "natural" as the goal will compromise the ability to manage the issues that arise in real basins by not making the ambiguities associated with this "natural" goal explicit. This is unfortunate both for the western world that has embraced this model of "natural as the goal" and for the developing world in so far as they are encouraged to adopt this model.

Environmental policies for water quality and ecosystem management do not commonly require explicit stochastic accounts of uncertainty and risk associated with the quantification and prediction of waterborne pollutant loads and abatement effects. In this study, we formulate and investigate a possible environmental policy that does require an explicit stochastic uncertainty account. We compare both the environmental and economic resource allocation performance of such an uncertainty-accounting environmental policy with that of deterministic, risk-prone and risk-averse environmental policies under a range of different hypothetical, yet still possible, scenarios. The comparison indicates that a stochastic uncertainty-accounting policy may perform better than deterministic policies over a range of different scenarios. Even in the absence of reliable site-specific data, reported literature values appear to be useful for such a stochastic account of uncertainty.

We simulate and analyze long-term dynamics of coastal nitrogen (N) loading and the inland source changes and processes that may have determined its development over the past 60-year period and may govern its possible future responses to various N source management scenarios. With regard to processes, the results show that average basin-scale N delivery fractions to the coast may not be representative of the coastal impacts of either diffuse or point inland sources. The effects of inland source changes may be greatly redistributed in space-time and delayed by slow N transport and mass transfer processes in the subsurface water system of coastal catchments. Extrapolation of current N transport-attenuation conditions for quantification of future abatement effects may therefore be misleading if the extrapolation models do not realistically represent delayed long-term influences of slow subsurface processes. With regard to policy, the results show that and why national Swedish and international Baltic Sea region policies for coastal N load abatement may be difficult or impossible to achieve by inland source abatement only. Large mitigation of both point and diffuse sources may be necessary to achieve targeted coastal N load reductions fast and maintain them also in the long term.

In mine water pollution abatement, it is commonly assumed that known mine waste sites are the major pollution sources, thus neglecting the possibility of significant contribution from other old and diffuse sources within a catchment. We investigate the influence of different types of pollution source uncertainty on cost-effective allocation of abatement measures for mine water pollution. A catchment-scale cost-minimization model is developed and applied to the catchment of the river Dalälven, Sweden, in order to exemplify important effects of such source uncertainty. Results indicate that, if the pollution distribution between point and diffuse sources is partly unknown, downstream abatement measures, such as constructed wetlands, at given compliance boundaries are often cost-effective. If downstream abatement measures are not practically feasible, the pollution source distribution between point and diffuse mine water sources is critical for cost-effective solutions to abatement measure allocation in catchments. In contrast, cost-effective solutions are relatively insensitive to uncertainty in total pollutant discharge from mine water sources.

Eutrophication problems in coastal and marine waters worldwide emphasize the significance, for the scientific community as well as the whole society, of relevant quantification of catchment-scale nitrogen transport from land to coast. Different catchment-scale nitrogen budget models use, and base management recommendations on, quite different process representations of and spatial resolution approaches to in-stream nitrogen attenuation. We compare three different spatial resolution approaches to modeling nitrogen loss rates in streams of the same drainage basin. Results show that commonly used spatial model aggregation may lead to artificial decrease of calibrated nitrogen loss rates with increasing stream depth (or flow), in addition to any such dependences that may prevail in independently measurable reality. Coastal nitrogen impact predictions and practical management implications of large-scale model aggregation of nitrogen attenuation rates may further differ considerably from those based on rates from finer resolution modeling or independent measurements.