Remote retrieval of near-surface chlorophyll-a (Chla) concentration in small inland waters is challenging due to substantial optical interferences of various water constituents and uncertainties in the atmospheric correction (AC) process. Although various algorithms have been developed to estimate Chla from moderate-resolution terrestrial missions (∼10–60 m), the production of both accurate distribution maps and time series of Chla has proven challenging, limiting the use of remote analyses for lake monitoring. Here, we develop a support vector regression (SVR) model, which uses satellite-derived remote-sensing reflectance spectra (Rrsδ) from Sentinel-2 and Landsat-8 images as input for Chla retrieval in a representative eutrophic prairie lake, Buffalo Pound Lake (BPL), Saskatchewan, Canada. Validated against in situ Chla from seven ice-free seasons (N ∼ 200; 2014–2020), the SVR model outperformed both locally tuned, Rrsδ-fed empirical models (Normalized Difference Chlorophyll Index, 2- and 3-band, and OC3) and Mixture Density Networks (MDNs) by 15–65%, while exhibiting comparable performance to a locally trained MDN, with an error of ∼35%. Comparison of Chla retrieval models, AC processors (iCOR, ACOLITE), and radiometric products (Rayleigh-corrected, surface, and top-of-atmosphere reflectance) showed that the best Chla maps and optimal time series (up to 100 mg m−3) were produced using a coupled SVR-iCOR system.

Microbial communities are an important component of freshwater biodiversity that is threatened by anthropogenic impacts. Wastewater discharges pose a particular concern by being major sources of anthropogenic contaminants and microorganisms that may influence the composition of natural microbial communities. Nevertheless, the effects of wastewater treatment plant (WWTP) effluents on microbial communities remain largely unexplored. In this study, the effects of wastewater discharges on microbial communities from five different WWTPs in Southern Saskatchewan were investigated using rRNA gene metabarcoding. In parallel, nutrient levels and the presence of environmentally relevant organic pollutants were analyzed. Higher nutrient loads and pollutant concentrations resulted in significant changes in microbial community composition. The greatest changes were observed in Wascana Creek (Regina), which was found to be heavily polluted by wastewater discharges. Several taxa occurred in greater relative abundance in the wastewater-influenced stream segments, indicating anthropogenic pollution and eutrophication, especially taxa belonging to Proteobacteria, Bacteroidota, and Chlorophyta. Strong decreases were measured within the taxa Ciliphora, Diatomea, Dinoflagellata, Nematozoa, Ochrophyta, Protalveolata, and Rotifera. Across all sample types, a significant decline in sulfur bacteria was measured, implying changes in functional biodiversity. In addition, downstream of the Regina WWTP, an increase in cyanotoxins was detected which was correlated with a significant change in cyanobacterial community composition. Overall, these data suggest a causal relationship between anthropogenic pollution and changes in microbial communities, possibly reflecting an impairment of ecosystem health.

Flow management has the potential to significantly affect ecosystem condition. Shallow lakes in arid regions are especially susceptible to flow management changes, which can have important implications for the formation of cyanobacterial blooms. Here, we reveal water quality shifts associated with changing source water inflow management. Using in situ monitoring data, we studied a seven-year time span during which inflows to a shallow, eutrophic drinking water reservoir transitioned from primarily natural landscape runoff (2014–2015) to managed flows from a larger upstream reservoir (Lake Diefenbaker; 2016–2020) and identified significant changes in cyanobacteria (as phycocyanin) using generalized additive models to classify cyanobacterial bloom formation. We then connected changes in water source with shifts in chemistry and the occurrence of cyanobacterial blooms using principal components analysis. Phycocyanin was greater in years with managed reservoir inflow from a mesotrophic upstream reservoir (2016–2020), but dissolved organic matter (DOM) and specific conductivity, important determinants of drinking water quality, were greatest in years when landscape runoff dominated lake water source (2014–2015). Most notably, despite changing rapidly, it took multiple years for lake water to return to a consistent and reduced level of DOM after managed inflows from the upstream reservoir were resumed, an observation that underscores how resilience may be hindered by weak resistance to change and slow recovery. Environmental flows for water quality are rarely defined, yet we show that trade-offs exist between poor water quality via elevated conductivity and DOM and higher bloom risk, depending on water source. Our work highlights the importance of source water quality, not just quantity, to water security, and our findings have important implications for water managers who must protect ecosystem services while adapting to projected hydroclimatic change.

Abstract In the Canadian prairies, eutrophication is a widespread issue, with agriculture representing a major anthropogenic nutrient source in many watersheds. However, efforts to mitigate agricultural nutrient export are challenged by the lack of coordinated monitoring programs and the unique hydrological characteristics of the prairies, notably, the dominance of snowmelt in both water flows and nutrient loads, variable runoff, variable contributing area and the issues of understanding how scale affects nutrient concentrations and prevalence of dissolved nutrient transport (over total nutrients). Efforts are being made to integrate these characteristics in process-based water quality models, but the models are often complex and are not yet ready for use by watershed managers for prioritizing implementation of beneficial management practices (BMPs). In this study, a screening and scoping approach based on nutrient export coefficient modeling was used to prioritize BMPs for the 55,700 km2 Qu’Appelle Watershed, Saskatchewan. By integrating land use information, in-stream monitoring data, stakeholder input and nutrient export coefficient modeling, the study assessed potential efficiencies of six BMPs involving fertilizer, manure, grazing, crop and wetland management in nutrient load reductions for nine tributaries of the watershed. Uncertainty around the effectiveness of the BMPs was assessed. Field-level export coefficients were adjusted with nutrient delivery ratios for estimating watershed-level exports. Of the BMPs examined, in general, wetland restoration had the greatest potential to reduce both nitrogen and phosphorus loads in most tributaries, followed by fertilizer management. The importance of wetland restoration was supported by positive, significant, linear correlations between nutrient delivery ratios and drainage intensity in the tributaries (nitrogen: R 2 = 0.67; phosphorus: R 2 = 0.82). Notably, the relative ranking of BMP efficiencies varied with tributaries, as a result of differing landscape characteristics, land uses and nutrient inputs. In conclusion, the approach developed here acknowledges uncertainty, but provides a means to guide management decisions within the context of an adaptive management approach, where BMP implementation is partnered with monitoring and assessment to revise ongoing plans and ensures selected practices are meeting goals for nutrient abatement.

Cyanobacterial blooms present challenges for water treatment, especially in regions like the Canadian prairies where poor water quality intensifies water treatment issues. Buoyant cyanobacteria that resist sedimentation present a challenge as water treatment operators attempt to balance pre-treatment and toxic disinfection by-products. Here, we used microscopy to identify and describe the succession of cyanobacterial species in Buffalo Pound Lake, a key drinking water supply. We used indicator species analysis to identify temporal grouping structures throughout two sampling seasons from May to October 2018 and 2019. Our findings highlight two key cyanobacterial bloom phases - a mid-summer diazotrophic bloom of Dolichospermum spp. and an autumn Planktothrix agardhii bloom. Dolichospermum crassa and Woronichinia compacta served as indicators of the mid-summer and autumn bloom phases, respectively. Different cyanobacterial metabolites were associated with the distinct bloom phases in both years: toxic microcystins were associated with the mid-summer Dolichospermum bloom and some newly monitored cyanopeptides (anabaenopeptin A and B) with the autumn Planktothrix bloom. Despite forming a significant proportion of the autumn phytoplankton biomass (>60%), the Planktothrix bloom had previously not been detected by sensor or laboratory-derived chlorophyll-a. Our results demonstrate the power of targeted taxonomic identification of key species as a tool for managers of bloom-prone systems. Moreover, we describe an autumn Planktothrix agardhii bloom that has the potential to disrupt water treatment due to its evasion of detection. Our findings highlight the importance of identifying this autumn bloom given the expectation that warmer temperatures and a longer ice-free season will become the norm.

The neurotoxic alkaloid β-N-methyl-amino-l-alanine (BMAA) and related isomers, including N-(2-aminoethyl glycine) (AEG), β-amino-N-methyl alanine (BAMA), and 2,4-diaminobutyric acid (DAB), have been reported previously in cyanobacterial samples. However, there are conflicting reports regarding their occurrence in surface waters. In this study, we evaluated the impact of amending lake water samples with trichloroacetic acid (0.1 M TCA) on the detection of BMAA isomers, compared with pre-existing protocols. A sensitive instrumental method was enlisted for the survey, with limits of detection in the range of 5-10 ng L-1. Higher detection rates and significantly greater levels (paired Wilcoxon's signed-rank tests, p < 0.001) of BMAA isomers were observed in TCA-amended samples (method B) compared to samples without TCA (method A). The overall range of B/A ratios was 0.67-8.25 for AEG (up to +725%) and 0.69-15.5 for DAB (up to +1450%), with absolute concentration increases in TCA-amended samples of up to +15,000 ng L-1 for AEG and +650 ng L-1 for DAB. We also documented the trends in the occurrence of BMAA isomers for a large breadth of field-collected lakes from Brazil, Canada, France, Mexico, and the United Kingdom. Data gathered during this overarching campaign (overall, n = 390 within 45 lake sampling sites) indicated frequent detections of AEG and DAB isomers, with detection rates of 30% and 43% and maximum levels of 19,000 ng L-1 and 1100 ng L-1, respectively. In contrast, BAMA was found in less than 8% of the water samples, and BMAA was not found in any sample. These results support the analyses of free-living cyanobacteria, wherein BMAA was often reported at concentrations of 2-4 orders of magnitude lower than AEG and DAB. Seasonal measurements conducted at two bloom-impacted lakes indicated limited correlations of BMAA isomers with total microcystins or chlorophyll-a, which deserves further investigation.

Instrumented buoys are used to monitor water quality, yet there remains a need to evaluate whether in vivo fluorometric measures of chlorophyll a (Chl a) produce accurate estimates of phytoplankton abundance. Here, 6 years (2014–2019) of in vitro measurements of Chl a by spectrophotometry were compared with coeval estimates from buoy-based fluorescence measurements in eutrophic Buffalo Pound Lake, Saskatchewan, Canada. Analysis revealed that fluorometric and in vitro estimates of Chl a differed both in terms of absolute concentration and patterns of relative change through time. Three models were developed to improve agreement between metrics of Chl a concentration, including two based on Chl a and phycocyanin (PC) fluorescence and one based on multiple linear regressions with measured environmental conditions. All models were examined in terms of two performance metrics; accuracy (lowest error) and reliability (% fit within confidence intervals). The model based on PC fluorescence was most accurate (error = 35%), whereas that using environmental factors was most reliable (89% within 3σ of mean). Models were also evaluated on their ability to produce spatial maps of Chl a using remotely sensed imagery. Here, newly developed models significantly improved system performance with a 30% decrease in Chl a errors and a twofold increase in the range of reconstructed Chl a values. Superiority of the PC model likely reflected high cyanobacterial abundance, as well as the excitation–emission wavelength configuration of fluorometers. Our findings suggest that a PC fluorometer, used alone or in combination with environmental measurements, performs better than a single-excitation-band Chl a fluorometer in estimating Chl a content in highly eutrophic waters.

Cyanobacterial blooms present challenges for water treatment, especially in regions like the Canadian prairies where poor water quality intensifies water treatment issues. Buoyant cyanobacteria that resist sedimentation present a challenge as water treatment operators attempt to balance pre-treatment and toxic disinfection by-products. Here, we used microscopy to identify and describe the succession of cyanobacterial species in Buffalo Pound Lake, a key drinking water supply. We used indicator species analysis to identify temporal grouping structures throughout two sampling seasons from May to October 2018 and 2019. Our findings highlight two key cyanobacterial bloom phases - a mid-summer diazotrophic bloom of Dolichospermum spp. and an autumn Planktothrix agardhii bloom. Dolichospermum crassa and Woronichinia compacta served as indicators of the mid-summer and autumn bloom phases, respectively. Different cyanobacterial metabolites were associated with the distinct bloom phases in both years: toxic microcystins were associated with the mid-summer Dolichospermum bloom and some newly monitored cyanopeptides (anabaenopeptin A and B) with the autumn Planktothrix bloom. Despite forming a significant proportion of the autumn phytoplankton biomass (>60%), the Planktothrix bloom had previously not been detected by sensor or laboratory-derived chlorophyll-a. Our results demonstrate the power of targeted taxonomic identification of key species as a tool for managers of bloom-prone systems. Moreover, we describe an autumn Planktothrix agardhii bloom that has the potential to disrupt water treatment due to its evasion of detection. Our findings highlight the importance of identifying this autumn bloom given the expectation that warmer temperatures and a longer ice-free season will become the norm.

Phosphorus (P) runoff from agricultural land plays a critical role in downstream water quality. This article summarizes P and sediment runoff data for both snowmelt and rainfall runoff from 30 arable fields in the Canadian provinces of Saskatchewan, Manitoba and Ontario. The data were collected from 216 site-years of field experiments, with climates ranging from semi-arid to humid and a wide range of field management practices. In the article, mean annual and seasonal (in terms of snowmelt and rain) precipitation inputs, runoff depths, and P and sediment concentrations and loads are presented, along with ranges of yearly values. In addition, information of field management and soil characteristics (e.g. soil type and soil Olsen P) is also presented for each field. The data have potential to be reused for national and international cross-region comparisons of P and sediment losses, constructing and validating decision-support models and tools for assessing and managing P losses in both snowmelt and rainfall runoff, and informing beneficial management practices to improve agricultural water quality. Interpretation of the data is found in “Phosphorus runoff from Canadian agricultural land: A cross-region synthesis of edge-of-field results” [1] .

Algal blooms fueled by phosphorus (P) enrichment are threatening surface water quality around the world. Although P loss from arable land is a critical contributor to P loads in many agricultural watersheds , there has been a lack of understanding of P loss patterns and drivers across regions. Here, we synthesized edge-of-field P and sediment runoff data for 30 arable fields in the Canadian provinces of Saskatchewan, Manitoba and Ontario (a total of 216 site-years) to elucidate spatial and temporal differences in runoff and P mobilization in snowmelt and rainfall runoff, and discuss climatic, soil and management drivers for these patterns. Across all regions, precipitation inputs were positively correlated with runoff amounts and consequently P loads. Runoff and P losses were dominated by snowmelt across all sites, however, regional differences in runoff amounts, and P concentrations, loads and speciation were apparent. Proportions of total P in the dissolved form were greater in the prairie region (55–94% in Manitoba) than in the Great Lakes region (26–35% in Ontario). In Manitoba, dissolved P concentrations in both snowmelt and rainfall runoff were strongly positively correlated to soil Olsen P concentrations in the 0–5 cm soil depth; however, this relationship was not found for Ontario fields, where tile drainage dominated hydrologic losses. Although precipitation amounts and runoff volumes were greater in Ontario than Manitoba, some of the greatest P loads were observed from Manitoba fields, driven by management practices. This synthesis highlights the differences across the Canadian agricultural regions in P runoff patterns and drivers, and suggests the need of co-ordinated and standardized monitoring programs to better understand regional differences and inform management. Phosphorus runoff patterns vary with climatic regions across Canada. †The dissolved P was measured as total dissolved P in MB and dissolved reactive P in SK and ON. ‡Total P was not measured in SK. • Phosphorus runoff patterns and drivers vary with climatic regions across Canada. • Co-ordinated and standardized monitoring programs are key to clarify regional differences. • Snowmelt dominates runoff volume and phosphorus loss across Canada. • The predominant form of P in runoff differs between the Prairie region and the Great Lakes region. • Reducing phosphorus sources is important for mitigating phosphorus runoff.

Extensive wetland drainage has occurred across the Canadian Prairies, and drainage activities are ongoing in many areas (Dahl 1990; Watmough and Schmoll 2007; Bartzen et al. 2010; Dahl 2014; Prairi...

Chlorophyll-a concentration (chla) is a useful indicator of harmful algal blooms in early warning systems that use remote sensing data as input. However, its retrieval is challenging in small waterbodies due to the lack of high spatial-resolution water-color sensors and the substantial optical interference of other water constituents. Here, we demonstrate the potential of support vector machines and Sentinel-2 images to retrieve chla in a shallow eutrophic lake (Buffalo Pound Lake, Saskatchewan, Canada). Following validation against in-situ chla measurements over three open water seasons (2017–2019), our proposed method based on Support Vector Regression (SVR) outperforms the most common semi-empirical models, i.e., locally-tuned indices (normalized difference chlorophyll index, 2band, and 3band), as well as the state-of-the-art global empirical model (Mixture Density Network). The superiority of SVR is shown in terms of overall and stratified accuracy, as well as spatial validity. We argue that for small waterbodies where numerous matched pairs of in-situ chla and reflectance are not available, SVR might retrieve chla more accurately than locally-tuned indices and global empirical models.

Molot LA, Schiff SL, Venkiteswaran JJ, Baulch HM, Higgins SN, Zastepa A, Verschoor MJ, Walters D. 2021. Low sediment redox promotes cyanobacteria blooms across a trophic range: implications for man...

Abstract Two small, oligotrophic lakes at the IISD-Experimental Lakes Area in northwestern Ontario, Canada were fertilized weekly with only phosphorus (P) in the summer and early fall of 2019. The P fertilization rates were high enough (13.3 µ g l −1 added weekly) to produce dense, month-long blooms of N 2 -fixing Dolichospermum species in both lakes within 9–12 weeks after fertilization began, turning them visibly green without the addition of nitrogen. P-only fertilization increased average seasonal chlorophyll a concentrations and cyanobacteria biomass well above the pre-fertilization levels of 2017 and 2018. Nitrogen (N) content in the epilimnion of thermally stratified Lake 304 and the water column of shallow Lake 303 doubled and P storage in the water column temporarily increased during the blooms. These whole-lake fertilization experiments demonstrate that large cyanobacteria blooms can develop rapidly under high P loading without anthropogenic N inputs, suggesting that aggressive N control programs are unlikely to prevent bloom formation and that P controls should remain the cornerstone for cyanobacteria management.

• Application of popular catchment nutrient models is problematic in cold regions. • New nutrient modules have been developed for the Cold Regions Hydrological Model. • The model was applied to a sub-basin of the increasingly eutrophic Lake Winnipeg, Canada. • Simulated SWE, discharge, NO3, NH4, SRP and partP were compared against observations. • Typical ∼9 day-freshet accounted for 16–31% of the total annual nutrient load. Excess nutrients in aquatic ecosystems is a major water quality problem globally. Worsening eutrophication issues are notable in cold temperate areas, with pervasive problems in many agriculturally dominated catchments. Predicting nutrient export to rivers and lakes is particularly difficult in cold agricultural environments because of challenges in modelling snow, soil, frozen ground, climate, and anthropogenic controls. Previous research has shown that the use of many popular small basin nutrient models can be problematic in cold regions due to poor representation of cold region hydrology. In this study, the Cold Regions Hydrological Modelling Platform (CRHM), a modular modelling system, which has been widely deployed across Canada and cold regions worldwide, was used to address this problem. CRHM was extended to simulate biogeochemical and transport processes for nitrogen and phosphorus through a complex of new process-based modules that represent physicochemical processes in snow, soil and freshwater. Agricultural practices such as tillage and fertilizer application, which strongly impact the availability and release of soil nutrients, can be explicitly represented in the model. A test case in an agricultural basin draining towards Lake Winnipeg shows that the model can capture the extreme hydrology and nutrient load variability of small agricultural basins at hourly time steps. It was demonstrated that fine temporal resolutions are an essential modelling requisite to capture strong concentration changes in agricultural tributaries in cold agricultural environments. Within these ephemeral and intermittent streams, on average, 30%, 31%, 20%, and 16% of the total annual load of nitrate (NO 3 ), ammonium (NH 4 ), soluble reactive phosphorus (SRP), and particulate phosphorous (partP)NO 3 , NH 4 , SRP and partP occurred during the episodic snowmelt freshet ( ∼ 9 days, accounting for 21% of the annual flow), but shows extreme temporal variation. The new nutrient modules are critical tools for predicting nutrient export from small agricultural drainage basins in cold climates via better representation of key hydrological processes, and a temporal resolution more suited to capture dynamics of ephemeral and intermittent streams.

Globally, harmful algal blooms (HABs) are on the rise, as is evidence of their toxicity. The impacts associated with blooms, however, vary across Nation states, as do the strategies and protocols to assess, monitor, and manage their occurrence. In Canada, water quality guidelines are standardized nationally, but the management strategies for HABs are not. Here, we explore current strategies to understand how to better communicate risks associated with HABs to the public. Our team conducted an environmental scan on provincial and territorial government agency protocols around HABs. Results suggest that there are variations in the monitoring, managing, and communicating of risk to the public: British Columbia, Manitoba, New Brunswick, and Quebec have well-established inter-agency protocols, and most provinces report following federal guidelines for water quality. Notably, 3 northern territories have no HABs monitoring or management protocols in place. More populous provinces use a variety of information venues (websites, social media, on site postings, and radio) to communicate risks associated with HABs, whereas others’ communications are limited. To induce more collaboration on HABs monitoring and management and reduce the associated risks, creating a coherent system with consistent messaging and inter-agency communication is suggested.

Here, we explore how people entangled in natural resource conflicts employ and discuss data. We draw on ethnographic research with two cases of conflict: salmon fisheries in Alaska, USA, and agricultural water management in Saskatchewan, Canada. Both cases illustrate how data, through the scientization of environmental governance, can become a means of empowerment and disempowerment: empowering those with access and influence over data and disempowering those without such access. In both locales, people find it necessary to perform their expertise, justify the veracity of their data, and discount the data held by others if they wish to achieve or maintain standing. We call this “datamentality” and draw lessons from these cases for how we can structure environmental governance such that it benefits from robust data and science while meeting the needs of individuals, avoiding or managing power struggles, and protecting the rights of all involved.

The increasing prevalence of cyanobacteria-dominated harmful algal blooms is strongly associated with nutrient loading and changing climatic patterns. Changes to precipitation frequency and intensity, as predicted by current climate models, are likely to affect bloom development and composition through changes in nutrient fluxes and water column mixing. However, few studies have directly documented the effects of extreme precipitation events on cyanobacterial composition, biomass, and toxin production. We tracked changes in a eutrophic reservoir following an extreme precipitation event, describing an atypically early toxin-producing cyanobacterial bloom and successional progression of the phytoplankton community, toxins, and geochemistry. An increase in bioavailable phosphorus by more than 27-fold in surface waters preceded notable increases in Aphanizomenon flos-aquae throughout the reservoir approximately 2 weeks postevent and ∼5 weeks before blooms typically occur. Anabaenopeptin-A and three microcystin congeners (microcystin-LR, -YR, and -RR) were detected at varying levels across sites during the bloom period, which lasted between 3 and 5 weeks. These findings suggest extreme rainfall can trigger early cyanobacterial bloom initiation, effectively elongating the bloom season period of potential toxicity. However, effects will vary depending on factors including the timing of rainfall and reservoir physical structure.

Agricultural drainage is a complicated and often conflict-ridden natural resource management issue, impacting contested ecosystem services related to the retention of wetlands as well as the productivity of farmland. This research identifies opportunities to transform the conflict over agricultural drainage in Saskatchewan, Canada, towards collaboration. We report on ethnographic research informed by a conservation conflict-transformation framework to evaluate the nature of the conflict and whether drivers of the conflict operate principally at the level of disputes over discrete ecosystem services or if they reach deeper into local social circumstances and build on larger unresolved conflict(s) among groups in the region. In addition to the conflict-transformation framework, we apply the Social–Ecological Systems Framework to elicit details regarding the substantive, relational, and material dimensions of this conflict. Our research suggests that processes for governing natural resources, such as those in place for governing drainage in Saskatchewan, need to have mechanisms to facilitate relationship building and shared understandings, need to be adaptable to people’s changing needs and concerns, and should focus on inclusivity and empowerment of actors to address conflict.

In cold agricultural regions, seasonal snowmelt over frozen soils provides the primary source of runoff and transports large nutrient loads downstream. The postglacial landscape of the Canadian Prairies and Northern Plains of the United States creates challenges for hydrological and water quality modeling. Here, the application of conventional hydrological models is problematic because of cold regions hydrological and chemical processes, the lack of fluvially eroded drainage systems, large noncontributing areas to streamflow and level topography. A new hydrodynamic model was developed to diagnose overland flow from snowmelt in this situation. The model was used to calculate the effect of variable contributing areas on (1) hydrological connectivity and the development of (2) tipping points in streamflow generation and (3) predominant chemical transport pathways. The agricultural Steppler Basin in Manitoba, Canada, was used to evaluate the model and diagnose snowmelt runoff. Relationships were established between contributing area and (1) snowmelt runoff intensity, (2) seasonal snowmelt volumes and duration, and (3) inundated, active and connected areas. Variations in the contributing area depended on terrain and snowmelt characteristics including wind redistribution of snow. Predictors of hydrological response and the size of the contributing area were developed which can be used in larger scale hydrological models of similar regions

Abstract The hydrology of cold regions has been studied for decades with substantial progress in process understanding and prediction. Simultaneously, work on nutrient yields from agricultural land in cold regions has shown much slower progress. Advancement of nutrient modelling is constrained by well-documented issues of spatial heterogeneity, climate dependency, data limitations and over-parameterization of models, as well as challenges specific to cold regions due to the complex (and often unknown) behaviour of hydro-biogeochemical processes at temperatures close to and below freezing where a phase change occurs. This review is a critical discussion of these issues by taking a close look at the conceptual models and methods behind used catchment nutrient models. The impact of differences in model structure and the methods used for the prediction of hydrological processes, erosion and biogeochemical cycles are examined. The appropriateness of scale, scope, and complexity of models are discussed to propose future research directions.

Abstract Freshwater ecosystems, particularly those in agricultural areas, remain at risk of eutrophication due to anthropogenic inputs of nutrients. While community-based monitoring has helped improve awareness and spur action to mitigate nutrient loads, monitoring is challenging due to the reliance on expensive laboratory technology, poor data management, time lags between measurement and availability of results, and risk of sample degradation during transport or storage. In this study, an easy-to-use smartphone-based application (The Nutrient App) was developed to estimate NO 3 and PO 4 concentrations through the image-processing of on-site qualitative colorimetric-based results obtained via cheap commercially-available instantaneous test kits. The app was tested in rivers, wetlands, and lakes across Canada and relative errors between 30% (filtered samples) and 70% (unfiltered samples) were obtained for both NO 3 and PO 4 . The app can be used to identify sources and hotspots of contamination, which can empower communities to take immediate remedial action to reduce nutrient pollution.

Chlorophyll-A concentration is one of the most commonly measured water quality parameters. It is an indicator of algal biomass and provides insight into stressors such as eutrophication and bloom risk. It is also a widely used metric in terrestrial ecosystems as an indicator of photosynthetic activity and nutrient limitation. Laboratory-based methods for measuring chlorophyll-A require expensive instrumentation. In this paper, we proposed a smart, low-cost, and portable smart sensor system to measure the concentration of chlorophyll-A in an extracted solution using two consumer-grade spectral sensors that read the reflectance at 12 discrete wavelengths in visible and near-infrared spectra. The system was tuned for an optimal distance from the sensors to the solution and an enclosure was printed to maintain the distance, as well as to avoid natural light interference. Extracted chlorophyll solutions of 51 different concentrations were prepared, and at least 100 readings per sample were taken using our smart sensor system. The ground truth values of the samples were measured in the laboratory using Thermo Nano 2000C. After cleaning the anomalous data, different machine learning models were trained to determine the significant wavelengths that contribute most towards chlorophyll-A measurement. Finally, a decision tree model with 5 important features was chosen based on the lowest Root Mean Square and Mean Absolute Error when it was tested on the validation set. Our final model resulted in a mean error of ±0.9μg/L when applied on our test set. The total cost was around $150.

Abstract Cyanobacterial blooms are causing increasing issues across the globe. Bloom forecasting can facilitate adaptation to blooms. Most bloom forecasting models depend on weekly or fortnightly sampling, but these sparse measurements can miss important dynamics. Here we develop forecasting models from five years of high frequency summer monitoring in a shallow lake (which serves as an important regional water supply). A suite of models were calibrated to predict cyanobacterial fluorescence (a biomass proxy) using measurements of: cyanobacterial fluorescence, water temperature, light, and wind speed. High temporal autocorrelation contributed to relatively strong predictive power over 1, 4 and 7 day intervals. Higher order derivatives of water temperature helped improve forecasting accuracy. While traditional monitoring and modelling have supported forecasting on longer timescales, we show high frequency monitoring combined with telemetry allows forecasting over timescales of 1 day to 1 week, supporting early warning, enhanced monitoring, and adaptation of water treatment processes.

Abstract The increasing prevalence of cyanobacteria-dominated harmful algal blooms is strongly associated with nutrient loading and changing climatic patterns. Changes to precipitation frequency and intensity, as predicted by current climate models, are likely to affect bloom development and composition through changes in nutrient fluxes and water column mixing. However, few studies have directly documented the effects of extreme precipitation events on cyanobacterial composition, biomass, and toxin production. We tracked changes in a eutrophic reservoir following an extreme precipitation event, describing an atypically early toxin-producing cyanobacterial bloom, successional progression of the phytoplankton community, toxins, and geochemistry. An increase in bioavailable phosphorus by more than 27-fold in surface waters preceded notable increases in Aphanizomenon flos-aquae throughout the reservoir approximately 2 weeks post flood and ~5 weeks before blooms typically occur. Anabaenopeptin-A and three microcystin congeners (microcystin-LR, -YR, and -RR) were detected at varying levels across sites during the bloom period, which lasted between 3 and 5 weeks. Synthesis and applications: These findings suggest extreme rainfall can trigger early cyanobacterial bloom initiation, effectively elongating the bloom season period of potential toxicity. However, effects will vary depending on factors including the timing of rainfall and reservoir physical structure. In contrast to the effects of early season extreme rainfall, a mid-summer runoff event appeared to help mitigate the bloom in some areas of the reservoir by increasing flushing.

The Northern Great Plains is a key region to global food production. It is also a region of water stress that includes poor water quality associated with high concentrations of nutrients. Agricultural nitrogen and phosphorus loads to surface waters need to be reduced, yet the unique characteristics of this environment create challenges. The biophysical reality of the Northern Great Plains is one where snowmelt is the major period of nutrient transport, and where nutrients are exported predominantly in dissolved form. This limits the efficacy of many beneficial management practices (BMPs) commonly used in other regions and necessitates place-based solutions. We discuss soil and water management BMPs through a regional lens—first understanding key aspects of hydrology and hydrochemistry affecting BMP efficacy, then discussing the merits of different BMPs for nutrient control. We recommend continued efforts to “keep water on the land” via wetlands and reservoirs. Adoption and expansion of reduced tillage and perennial forage may have contributed to current nutrient problems, but both practices have other environmental and agronomic benefits. The expansion of tile and surface drainage in the Northern Great Plains raises urgent questions about effects on nutrient export and options to mitigate drainage effects. Riparian vegetation is unlikely to significantly aid in nutrient retention, but when viewed against an alternative of extending cultivation and fertilization to the waters’ edge, the continued support of buffer strip management and refinement of best practices (e.g., harvesting vegetation) is merited. While the hydrology of the Northern Great Plains creates many challenges for mitigating nutrient losses, it also creates unique opportunities. For example, relocating winter bale-grazing to areas with low hydrologic connectivity should reduce loadings. Managing nutrient applications must be at the center of efforts to mitigate eutrophication. In this region, ensuring nutrients are not applied during hydrologically sensitive periods such as late autumn, on snow, or when soils are frozen will yield benefits. Working to ensure nutrient inputs are balanced with crop demands is crucial in all landscapes. Ultimately, a targeted approach to BMP implementation is required, and this must consider the agronomic and economic context but also the biophysical reality.

Cold agricultural regions are important sites of global food production. This has contributed to widespread water quality degradation influenced by processes and hydrologic pathways that differ from warm region analogues. In cold regions, snowmelt is often a dominant period of nutrient loss. Freeze-thaw processes contribute to nutrient mobilization. Frozen ground can limit infiltration and interaction with soils, and minimal nutrient uptake during the nongrowing season may govern nutrient export from agricultural catchments. This paper reviews agronomic, biogeochemical, and hydrological characteristics of cold agricultural regions and synthesizes findings of 23 studies that are published in this special section, which provide new insights into nutrient cycling and hydrochemical processes, model developments, and the efficacy of different potentially beneficial management practices (BMPs) across varied cold regions. Growing evidence suggests the need to redefine optimum soil phosphorus levels and input regimes in cold regions to allow achievement of water quality targets while still supporting strong agricultural productivity. Practices should be considered through a regional and site-specific lens, due to potential interactions between climate, hydrology, vegetation, and soils, which influence the efficacy of nutrient, crop, water, and riparian buffer management. This leads to differing suitability of BMPs across varied cold agricultural regions. We propose a systematic approach (""), to achieve water quality objectives in variable and changing climates, which combines nutrient transport process onceptualization, nderstanding BMP functions, redicting effects of variability and change, onsideration of producer input and agronomic and environmental tradeoffs, practice daptation, nowledge mobilization, and valuation of water quality improvement.

Managing P export from agricultural land is critical to address freshwater eutrophication. However, soil P management, and options to draw down soil P have received little attention in snowmelt-dominated regions because of limited interaction between soil and snowmelt. Here, we assessed the impacts of soil P drawdown (reducing fertilizer P inputs combined with harvest removal) on soil Olsen P dynamics, runoff P concentrations, and crop yields from 1997 to 2014 in paired fields in Manitoba, Canada. We observed that Olsen P concentrations in the 0- to 5-cm soil layer were negatively correlated with the cumulative P depletion and declined rapidly at the onset of the drawdown practice (3.1 to 5.4 mg kg yr during 2007-2010). In both snowmelt runoff and rainfall runoff, concentrations of total dissolved P (TDP) were positively correlated with the concentrations of soil Olsen P. Soil P drawdown to low to moderate fertility levels significantly decreased mean annual flow-weighted TDP concentrations in snowmelt runoff from 0.60 to 0.30 mg L in the field with high initial soil P and from 1.17 to 0.42 mg L in the field with very high initial soil P. Declines in TDP concentration in rainfall runoff were greater. Critically, yields of wheat ( spp.) and canola ( L.) were not affected by soil P depletion. In conclusion, we demonstrate that relatively rapid reductions in P loads are achievable at the field scale via managing P inputs and soil P pools, highlighting a management opportunity that can maintain food security while improving water security in cold regions.

The use of cover crops and crop residues is a common strategy to mitigate sediment and nutrient losses from land to water. In cold climates, elevated dissolved P losses can occur associated with freeze-thaw of plant materials. Here, we review the impacts of cover crops and crop residues on dissolved P and total P loss in cold climates across ∼41 studies, exploring linkages between water-extractable P (WEP) in plant materials and P loss in surface runoff and subsurface drainage. Water-extractable P concentrations are influenced by plant type and freezing regimes. For example, WEP was greater in brassica cover crops than in non-brassicas, and increased with repeated freeze-thaw cycles. However, total P losses in surface runoff and subsurface drainage from cropped fields under cold climates were much lower than plant WEP, owing to retention of 45 to >99% of released P by soil. In cold climatic regions, cover crops and crop residues generally prevented soil erosion and loss of particle-bound P during nongrowing seasons in erodible landscapes but tended to elevate dissolved P loss in nonerodible soils. Their impact on total P loss was inconsistent across studies and complicated by soil, climate, and management factors. More research is needed to understand interactions between these factors and plant type that influence P loss, and to improve the assessment of crop contributions to P loss in field settings in cold climates. Further, tradeoffs between P loss and the control of sediment loss and N leaching by plants should be acknowledged.

There is great interest in modelling the export of nitrogen (N) and phosphorus (P) from agricultural fields because of ongoing challenges of eutrophication. However, the use of existing hydrochemistry models can be problematic in cold regions because models frequently employ incomplete or conceptually incorrect representations of the dominant cold regions hydrological processes and are overparameterized, often with insufficient data for validation. Here, a process‐based N model, WINTRA, which is coupled to a physically based cold regions hydrological model, was expanded to simulate P and account for overwinter soil nutrient biochemical cycling. An inverse modelling approach, using this model with consideration of parameter equifinality, was applied to an intensively monitored agricultural basin in Manitoba, Canada, to help identify the main climate, soil, and anthropogenic controls on nutrient export. Consistent with observations, the model results suggest that snow water equivalent, melt rate, snow cover depletion rate, and contributing area for run‐off generation determine the opportunity time and surface area for run‐off–soil interaction. These physical controls have not been addressed in existing models. Results also show that the time lag between the start of snowmelt and the arrival of peak nutrient concentration in run‐off increased with decreasing antecedent soil moisture content, highlighting potential implications of frozen soils on run‐off processes and hydrochemistry. The simulations showed TDP concentration peaks generally arriving earlier than NO₃ but also decreasing faster afterwards, which suggests a significant contribution of plant residue Total dissolved Phosphorus (TDP) to early snowmelt run‐off. Antecedent fall tillage and fertilizer application increased TDP concentrations in spring snowmelt run‐off but did not consistently affect NO₃ run‐off. In this case, the antecedent soil moisture content seemed to have had a dominant effect on overwinter soil N biogeochemical processes such as mineralization, which are often ignored in models. This work demonstrates both the need for better representation of cold regions processes in hydrochemical models and the model improvements that are possible if these are included.

Water quality is increasingly at risk due to nutrient pollution entering river systems from cities, industrial zones and agricultural areas. Agricultural activities are typically the largest non-point source of water pollution. The dynamics of agricultural impacts on water quality are complex and stem from the decisions and activities of multiple stakeholders, often with diverse business plans, values, and attitudes towards practices that can improve water quality. This study proposes a framework to understand and incorporate stakeholders' viewpoints into water quality modeling and management. The framework was applied to the Qu'Appelle River Basin, Saskatchewan, Canada. Q-methodology was used to understand viewpoints of stakeholders, namely agricultural producers (annual croppers, cattle producers, mixed farmers) and cottage owners, regarding a range of agricultural Beneficial Management Practices (BMPs) that can improve water quality, and to identify their preferred BMPs. A System Dynamics (SD) approach was employed to develop a transparent and user-friendly water quality model, SD-Qu'Appelle, to simulate nutrient loads in the region before and after implementation of stakeholder identified BMPs. The SD-Qu'Appelle was used in real-time engagement of stakeholders in model simulations to demonstrate and explore the potential effects of different BMPs in mitigating water pollution. Stakeholder perspectives were explored to understand the functionality and value of the SD-Qu'Appelle, preferred policies and potential barriers to BMP implementation on their land. Results show that although there are differences between viewpoints of stakeholders, they identified wetland restoration/retention, flow and erosion control, and relocation of corrals near creeks to sites more distant from waterways as the most effective BMPs for improving water quality. Economics was identified as a primary factor that causes agricultural producers to either accept or refuse the implementation of BMPs. Agricultural producers believe that incentives rather than regulations are the best policies for increasing the adoption of BMPs. Overall, stakeholders indicated the SD-Qu'Appelle had considerable value for water quality management and provided a set of recommendations to improve the model.

As more hydroelectric dams regulate rivers to meet growing energy demands, there is ongoing concern about downstream effects, including impacts on downstream benthic macroinvertebrate (BMI) communities. Hydropeaking is a common hydroelectric practice where short‐term variation in power production leads to large and often rapid fluctuations in discharge and water level. There are key knowledge gaps on the ecosystem impacts of hydropeaking in large rivers, the seasonality of these impacts, and whether dams can be managed to lessen impacts. We assessed how patterns of hydropeaking affect abundance, taxonomic richness, and relative tolerance of BMIs in the Saskatchewan River (Saskatchewan, Canada). Reaches immediately (<2 km) downstream of the dam generally had high densities of BMIs and comparable taxonomic diversity relative to upstream locations but were characterized by lower ratios of sensitive (e.g., Ephemeroptera, Plecoptera, and Trichoptera) to tolerant (e.g., Chironomidae) taxa. The magnitude of effect varied with seasonal changes in discharge. Understanding the effects of river regulation on BMI biodiversity and river health has implications for mitigating the impacts of hydropeaking dams on downstream ecosystems. Although we demonstrated that a hydropeaking dam may contribute to a significantly different downstream BMI assemblage, we emphasize that seasonality is a key consideration. The greatest differences between upstream and downstream locations occurred in spring, suggesting standard methods of late summer and fall sampling may underestimate ecosystem‐scale impacts.

Storage and removal of nutrients by wetlands and riparian areas is an important process in understanding catchment nutrient fluxes and in helping to mitigate current issues of eutrophication in man...

Complex interactions between water, society, the economy, and the environment necessitate attention to how water issues are framed, and the limitations of a water-centric framework for analyzing or solving problems. We explore this complexity through an example of an existing complex, or wicked, policy problem - the case of agricultural wetland drainage in the Canadian Prairies. Agricultural wetland drainage expands the amount of productive agricultural land, increasing agricultural efficiency and productivity. Drainage is also one of the primary drivers of the loss of Canada’s wetlands and is a hotly contentious issue between actors with divergent views and values in the Canadian Prairies. Using the nuances of drainage as an exemplar, we discuss how fragmented framings of water foster perspectives and solutions that fail to consider the full range of aspects and interactions, and contribute to the enduring conflicts that accompany drainage debates in many regions. First, we discuss agricultural wetland drainage as practiced in the province of Saskatchewan, where significant regulatory and governance changes are in progress. Next, we discuss the challenges of policy and governance fragmentation, both specific to water and to the surrounding system. Finally, we note potential alternative framings that, while specific to prairie water governance, provide guidance for how other complex social-ecological challenges might be approached.

‘I would walk to the end of the street and out over the prairie with the clickety grasshoppers bunging in arcs ahead of me and I could hear the hum and twang of the wind in the great prairie harp o...

Modeling nutrient transport during snowmelt in cold regions remains a major scientific challenge. A key limitation of existing nutrient models for application in cold regions is the inadequate representation of snowmelt, including hydrological and biogeochemical processes. This brief period can account for more than 80% of the total annual surface runoff in the Canadian Prairies and Northern Canada and processes such as atmospheric deposition, over-winter redistribution of snow, ion exclusion from snow crystals, frozen soils, and snowcovered area depletion during melt influence the distribution and release of snow and soil nutrients, thus affecting the timing and magnitude of snowmelt runoff nutrient concentrations.Research in cold regions suggests that nitrate (NO3) runoff at the field scale can be divided into five phases during snowmelt. In the first phase, water and ions originating from ion-rich snow layers travel and diffuse through the snowpack. This process causes ion concentrations in runoff to gradually increase. The second phase occurs when this snow ion meltwater front has reached the bottom of the snowpack and forms runoff to the edge-of-the-field (EOF). During the third and fourth phases, the main source of NO3 transitions from the snowpack to the soil. Finally, the fifth and last phase occurs when the snow has completely melted, and the thawing soil becomes the main source of NO3 to the stream.In this research, a process-based model was developed to simulate hourly export based on this five-phase approach. Results from an application in the Red River Basin of southern Manitoba, Canada shows that the model can adequately capture the dynamics and rapid changes of NO3 concentrations during this period at relevant temporal resolutions. This is a significant achievement to advance the current nutrient modeling paradigm in cold climates, which is generally limited to satisfactory results at monthly or annual resolutions. The approach can inform catchment-scale nutrient models to improve simulation of this critical snowmelt period.Nutrient exports Winter Snow Nitrate Agriculture Nutrient model