Spatial and temporal scales of variability of cyanobacteria harmful algal blooms from NOAA GLERL airborne hyperspectral imagery

NOAA GLERL has routinely flown a hyperspectral imager to detect cyanobacteria harmful algal blooms (cyanoHABs) over the Great Lakes since 2015. Three consecutive years of hyperspectral imagery over the Great Lakes warn drinking water intake managers of the presence of cyanoHABs. Western basin imagery of Lake Erie contributes to a weekly report to the Ohio Environmental Protection Agency using the cyanobacteria index (CI) as an indicator of the presence of cyanoHABs. The CI is also used for the weekly NOAA NCCOS cyanoHAB Lake Erie bulletin applied to satellite data. To date, there has not been a sensor comparison to look at the variability between the satellite and hyperspectral imagery on a pixel-by-pixel basis, as well as a time scale comparison between measurements from buoys and shipboard surveys. The spatial scale is a measure of size of a cyanobacteria bloom on a scale of meters to kilometers. The change in the spatial scale or spatial variability has been quantified from satellite and airborne imagery using a decorrelation scale analysis to find the point at which the values are not changing or are not correlated with each other. The decorrelation scales were also applied to the buoy and shipboard survey data to look at temporal scales or changes in time on hourly to daytime scales for blue-green algae, chlorophyll and temperature. These scales are valuable for ecosystem modelers and for those initiating sampling efforts to optimize sampling plans and to infer a potential mechanism in an observational study from a synoptic viewpoint.     

Fungal community dynamics associated with harmful cyanobacterial blooms in two Great Lakes

Harmful algal blooms (HABs) impose major costs on aquatic ecosystems worldwide, including the Laurentian Great Lakes. Microbial consumers, including fungi, can have important interactions with bloom-forming algae and cyanobacteria, although relatively few studies have investigated the relationship between fungi and HABs. We examined changes in the aquatic fungal community coincident with the occurrence of large cyanobacterial blooms in two areas of the Great Lakes (western Lake Erie and Saginaw Bay, Lake Huron). We collected water samples over the course of bloom development, peak, and decline from 3 sites in western Lake Erie on 11 dates and 2 sites in Saginaw Bay on 4 dates. Single molecule sequencing (PacBio RS II) with two molecular markers (the internal transcribed spacer (ITS) of the rRNA locus using fungal-specific primers and the 18S rRNA with primers targeting early-diverging lineages of fungi) was used to estimate fungal community composition. Results indicate a diverse fungal community within the lakes, including several major fungal phyla. The Chytridiomycota were particularly well-represented (54.8% and 45.4% of ITS and 18S sequences, respectively), and we also found representation from both Cryptomycota and Aphelidiomycota, which are putatively obligate intracellular parasites. Further, we found associations between the fungal community (alpha diversity; community composition) and measures of bloom magnitude (chlorophyll a, phycocyanin, and microcystin concentrations) in western Lake Erie. Our results suggest potentially important spatial and temporal heterogeneity in the fungal community that motivates further research on functional importance of fungi in the Great Lakes and consequences for HABs and freshwater ecosystems more broadly.     

Spatial and temporal variations in algal blooms in the coastal waters of the western South China Sea

Algal blooms occur frequently in the coastal waters of the western South China Sea (SCS). This paper reports spatial and temporal variations of algal bloom events in these waters from 1993 to 2007. Twenty-five algal bloom events occurred in summer in the coastal waters of South and Central Vietnam where they were associated with wind-induced, coastal, nutrient upwelling and river discharges; a further eight events occurred in the coastal waters of North Vietnam. A greater number of algal bloom events were observed in 1999 and 2002, and were accompanied by several previously unobserved species for the study period. These events may be related to the El Niño events of 1998 and 2002. Furthermore, the bloom-causative species Trichodesmium erythraeum (Cyanophyta) entirely dominated the phytoplankton community of algal blooms during 1993–1999 whereas the species Phaeocystis globosa (Haptophyta) dominated blooms after 2002. This study establishes a basis for further long-term research of algal bloom event variations, and provides a compiled scientific reference that may be used for later prediction of Harmful algal blooms (HABs).     

Algal bloom transport in Lake Erie using remote sensing and hydrodynamic modelling: Sensitivity to buoyancy velocity and initial vertical distribution

In this study, we simulate three-dimensional transport of algal blooms in Lake Erie using a combination of remote sensing and hydrodynamic modelling. The remote sensing algorithms use data from the Sentinel-3 OLCI satellite sensor to derive chlorophyll-a concentration from cyanobacteria blooms in Lake Erie. The derived chlorophyll-a concentration initializes an algal bloom transport model driven by the lake component of the Water Cycle Prediction System for the Great Lakes, a system of coupled atmosphere-lake-hydrological models operated out of Environment and Climate Change Canada. The bloom is modelled as Microcystis aeruginosa, a buoyant species that is often dominant in harmful algal blooms in western Lake Erie. Short-term (a few days) predictions of algal bloom transport from July 27 to October 8, 2017 are modelled in both Eulerian and Lagrangian frameworks. The Eulerian framework is used to evaluate the sensitivity of model results to the initial vertical distribution of the bloom. In this work, the Lagrangian framework is limited to two-dimensional surface confined particles. We use several error metrics to evaluate model predictions. We find that results are sensitive to the buoyancy velocity for cases where the bloom was initially distributed over a large portion of the water column. An initial vertical distribution selected from modelled chlorophyll-a half depth shows the highest accuracy for the entire range of buoyancy velocities tested. We also find that the Pierce skill score is difficult to interpret, particularly in cases where bloom intensity is greatly overpredicted by the model.     

Dolichospermum blooms in Lake Superior: DNA-based approach provides insight to the past,present and future of blooms

Cyanobacterial blooms are increasing in frequency, duration, and severity globally in freshwater ecosystems. The Laurentian Great Lakes are prone to toxin-producing cyanobacterial blooms and have experienced annually recurring blooms. Because of its oligotrophic nature, Lake Superior has been relatively free of bloom occurrences. However, in recent years, Dolichospermum blooms have occurred with increasing frequency, especially in the western arm. During a Dolichospermum bloom in 2018, opportunistic samples were collected from the offshore bloom and investigated with shotgun metagenomics. We identified a near-complete Dolichospermum genome that is highly similar to genomes from cultures recovered in Lakes Erie and Ontario. The genomes from the Laurentian Great Lakes are typified by their putative ability to produce a suite of secondary metabolites like anabaenopeptin, but not toxins like microcystin. Additionally, we recovered a Dolichospermum lemmermannii 16S rRNA gene from the bloom and using datasets collected from the epilimnion and sediments in Lake Superior show this organism is ubiquitous and that several strains may exist. While there is much to learn about Lake Superior cyanobacterial bloom development and triggers, understanding this organism is endemic to the region, what its genome is capable of and that specific strains may have provenance within the lake provides a distinct ecological basis for understanding and working towards a predictive framework for future blooms.     

An analysis of satellite-derived chlorophyll and algal bloom indices on Lake Winnipeg

Lake Winnipeg has experienced dramatic increases in nutrient loading and phytoplankton biomass over the last few decades, accompanied by a marked shift in community composition towards the dominance of cyanobacteria. Comprehensive lake-wide observations of algal blooms are critical to assessing the lake's health status, its response to nutrient management practices, and an improved understanding of the processes driving blooms. We present an analysis of the spatial and temporal variability of algal blooms on Lake Winnipeg using satellite-derived chlorophyll and indices for algal bloom intensity, spatial extent, severity, and duration over the period of ESA's MERIS mission (2002–2011). Imagery documented extensive blooms covering as much as 93% of the lake surface. Bloom conditions were analysed in the context of in-lake and watershed processes to gain further insight on the drivers of bloom events. Day to day bloom variability was driven primarily by intermittent wind mixing events, with quiescent periods leading to the formation of dense surface blooms. Seasonal bloom distribution was consistent with light limitation in the south basin and lake circulation transporting bloom material towards the north-east shore. Inter-annual variability in average bloom severity was related to both total phosphorus (TP) loadings and summer lake surface temperatures. Results provide a valuable historical time series of bloom conditions to which ongoing observations from Sentinel-3's OLCI sensor can be added for longer term monitoring and change detection.     

Mussel-derived stimulation of benthic filamentous algae: The importance of nutrients and spatial scale

The reoccurrence of benthic filamentous algal (FA) blooms in the Great Lakes, without associated increases in phosphorus loading, has stimulated renewed interest in determining the causes of Great Lakes benthic algal blooms. We investigated the potential roles of invasive mussels and nutrient limitation with experimental substrata within inner Saginaw Bay. FA abundance on live mussel substrata was typically significantly greater than that on inert (empty shell or rock) substrata. Nutrient addition (from an artificial source) significantly increased FA abundance on inert substrata. These results suggest that: 1) mussel nutrient excretion could be a primary stimulatory mechanism; 2) mussel-mediated stimulation may be even stronger in other, more oligotrophic, Great Lakes nearshore zones; and 3) increased nutrient loading to inner Saginaw Bay may exacerbate existing FA blooms. FA abundance on inert substrata was not affected, even in close proximity to mussels, indicating that the observed stimulatory effect of mussel-derived P on live mussels attenuated at very small spatial scales, on the order of centimeters or less.     

Status,causes and controls of cyanobacterial blooms in Lake Erie

The Laurentian Great Lakes are among the most prominent sources of fresh water in the world. Lake Erie's infamous cyanobacterial blooms have, however, threatened the health of this valuable freshwater resource for decades. Toxic blooms dominated by the cyanobacterium Microcystis aeruginosa have most recently been one of primary ecological concerns for the lake. These toxic blooms impact the availability of potable water, as well as public health and revenues from the tourism and fishery industries. The socioeconomic effects of these blooms have spurred research efforts to pinpoint factors that drive bloom events. Despite decades of research and mitigation efforts, these blooms have expanded both in size and duration in recent years. However, through continued joint efforts between the Canadian and United States governments, scientists, and environmental managers, identification of the factors that drive bloom events is within reach. This review provides a summary of historical and contemporary research efforts in the realm of Lake Erie's harmful cyanobacterial blooms, both in terms of experimental and management achievements and insufficiencies, as well as future directions on the horizon for the lake's research community.     

Cyanotoxin transport from surface water to groundwater: Simulation scenarios for Lake Erie

Harmful algal bloom (HAB) and cyanotoxin studies in the Great Lakes region have been typically focused on surface-water issues, with few investigating or reporting on groundwater. This study aims to theoretically explore whether groundwater can be contaminated by microcystins from HABs in surface water due to surface-water and groundwater interaction. Specifically, a 3-D MODFLOW/MT3DMS model was developed to simulate pumping-induced reverse groundwater flow and solute transport from Lake Erie to the aquifer underneath the South Bass Island in Ohio. Our simulation results based on typical, base case settings showed that after microcystins were detected and released from the lake, it would take about two, three, and 13 months for the water in a well on the island to reach the EPA advisory levels of microcystin for detection (0.1 µg/l), infants and children (0.3 μg/l), and school-age children to adults (1.6 μg/l), respectively. Furthermore, our scenario analyses showed that, as expected, higher pumping rate and higher lakebed leakance would accelerate the microcystin transport to the well. However, higher hydraulic conductivity would increase the time to reach the EPA levels due to mixing and dilution effects. The 3-D modeling scheme developed in this study was suitable to simulate the complex surface-water and groundwater interaction and transport processes occurring in the Great Lakes. This theoretical study provides useful insight for managing coastal groundwater aquifers and resources under threat from HABs in the Great Lakes. Future improvements to the model would include incorporating reactions and fractures and obtaining water-quality data for model calibration.     

The impact of phytoplankton community composition on optical properties and satellite observations of the 2017 western Lake Erie algal bloom

Since the early 2000s Lake Erie has seen a dramatic increase in phytoplankton biomass, manifested in particular by the rise in the severity of cyanobacteria blooms and the prevalence of potentially toxic taxa such as Microcystis. Satellite remote sensing has provided a unique capacity for the synoptic detection of these blooms, enabling spatial and temporal trends in their extent and severity to be documented. Algorithms for satellite detection of Lake Erie algal blooms often rely on a single consistent relationship between algal or cyanobacterial biomass and spectral indices such as the Maximum Chlorophyll Index (MCI) or Cyanobacteria Index (CI). Blooms, however, are known to vary significantly in community composition over space and time. A suite of phytoplankton and optical property measurements during the western Lake Erie algal bloom of 2017 showed highly diverse bloom composition with variable absorption and backscatter properties. Elevated backscattering coefficients were observed in the Maumee Bay, likely due to phytoplankton cell morphology and buoyancy regulating gas vacuoles, compared with typically Planktothrix dominated blooms in Sandusky Bay. MCI and CI calibrated to historical chlorophyll observations and applied to Sentinel 3's OLCI sensor accurately captured the 2017 bloom in Maumee Bay but underestimated the Sandusky Bay bloom by nearly 80%. The phycoerythrin-rich picocyanobacteria Aphanothece and Synechococcus were found in abundance throughout the western and central basins, resulting in substantial biomass underestimations using blue to green ratio-based algorithms. Potential misrepresentation of bloom severity resulting from phytoplankton optical properties should be considered in assessments of bloom conditions on Lake Erie.     

Real time HABs mapping using NASA Glenn hyperspectral imager

The hyperspectral imaging system (HSI) developed by the NASA Glenn Research Center was used from 2015 to 2017 to collect high spatial resolution data over Lake Erie and the Ohio River. Paired with a vicarious correction approach implemented by the Michigan Tech Research Institute, radiance data collected by the HSI system can be converted to high quality reflectance data which can be used to generate near-real time (within 24 h) products for the monitoring of harmful algal blooms using existing algorithms. The vicarious correction method relies on imaging a spectrally constant target to normalize HSI data for atmospheric and instrument calibration signals. A large asphalt parking lot near the Western Basin of Lake Erie was spectrally characterized and was determined to be a suitable correction target. Due to the HSI deployment aboard an aircraft, it is able to provide unique insights into water quality conditions not offered by space-based solutions. Aircraft can operate under cloud cover and flight paths can be chosen and changed on-demand, allowing for far more flexibility than space-based platforms. The HSI is also able to collect data at a high spatial resolution (~1 m), allowing for the monitoring of small water bodies, the ability to detect small patches of surface scum, and the capability to monitor the proximity of blooms to targets of interest such as water intakes. With this new rapid turnaround time, airborne data can serve as a complementary monitoring tool to existing satellite platforms, targeting critical areas and responding to bloom events on-demand.     

Comparison of satellite reflectance algorithms for estimating turbidity and cyanobacterial concentrations in productive freshwaters using hyperspectral aircraft imagery and dense coincident surface observations

We analyzed 37 satellite reflectance algorithms and 321 variants for five satellites for estimating turbidity in a freshwater inland lake in Ohio using coincident real hyperspectral aircraft imagery converted to relative reflectance and dense coincident surface observations. This study is part of an effort to develop simple proxies for turbidity and algal blooms and to evaluate their performance and portability between satellite imagers for regional operational turbidity and algal bloom monitoring. Turbidity algorithms were then applied to synthetic satellite images and compared to in situ measurements of turbidity, chlorophyll-a (Chl-a), total suspended solids (TSS) and phycocyanin as an indicator of cyanobacterial/blue green algal (BGA) abundance. Several turbidity algorithms worked well with real Compact Airborne Spectrographic Imager (CASI) and synthetic WorldView-2, Sentinel-2 and Sentinel-3/MERIS/OLCI imagery. A simple red band algorithm for MODIS imagery and a new fluorescence line height algorithm for Landsat-8 imagery had limited performance with regard to turbidity estimation. Blue-Green Algae/Phycocyanin (BGA/PC) and Chl-a algorithms were the most widely applicable algorithms for turbidity estimation because strong co-variance of turbidity, TSS, Chl-a, and BGA made them mutual proxies in this experiment.     

Evaluating sediments as an ecosystem service in western Lake Erie via quantification of nutrient cycling pathways and selected gene abundances

Lake Erie experiences annual summer cyanobacterial harmful algal blooms (HABs), comprised mostly of non-nitrogen-fixing Microcystis, due to excess nitrogen (N) and phosphorus (P) inputs (eutrophication). Lake Erie's watershed is mostly agricultural, and fertilizers, manure, and drainage practices contribute to high nutrient loads. This study aimed to clarify the role of western Lake Erie sediments in either exacerbating or mitigating conditions that fuel HABs via recycling and/or removal, respectively, of excess N and reactive P. Sediment-water interface N and orthophosphate (ortho-P) dynamics and functional gene analyses of key N transformations were evaluated during a dry, low HAB year (2016) and a wet, high HAB year (2017). On average, western basin sediments were a net N sink and thus perform a valuable ecosystem service via N removal. However, sediments were a source of ortho-P and chemically reduced N. Western basin sediments can theoretically remove 29% of average annual watershed total N loading. Denitrification rates were lower during the high (2017) versus low bloom year (2016), suggesting that high external N loading and large HABs inhibit the capacity of sediments to perform that ecosystem service. Despite being a net N sink on average, western basin sediments released ammonium and urea, chemically reduced N forms that are energetically conducive to non-N-fixing, toxin-producing cyanobacterial HABs, especially during the critical period of low external loading and high biomass. These results support other recent work highlighting the urgent need to include N cycling and internal load dynamics in ecosystem models and mitigation efforts for eutrophic systems.     

Prediction of chlorophyll a concentration using HJ-1 satellite imagery for Xiangxi Bay in Three Gorges Reservoir简

Since the impoundment of the Three Gorges Reservoir in 2003, algal blooms have frequently been observed in it. The chlorophyll a concentration is an important parameter for evaluating algal blooms. In this study, the chlorophyll a concentration in Xiangxi Bay, in the Three Gorges Reservoir, was predicted using HJ-1 satellite imagery. Several models were established based on a correlation analysis between in situ measurements of the chlorophyll a concentration and the values obtained from satellite images of the study area from January 2010 to December 2011. Chlorophyll a concentrations in Xiangxi Bay were predicted based on the established models. The results show that the maximum correlation is between the reflectance of the band combination of B4/（B2＋B3） and in situ measurements of chlorophyll a concentration. The root mean square errors of the predicted values using the linear and quadratic models are 18.49 mg/m3 and 18.52 mg/m3, respectively, and the average relative errors are 37.79% and 36.79%, respectively. The results provide a reference for water bloom prediction in typical tributaries of the Three Gorges Reservoir and contribute to large-scale remote sensing monitoring and water quality management.     

Mapping of coastal surface chlorophyll-a concentration by multispectral reflectance measurement from unmanned aerial vehicles

In subtropical coastal waters, the explosive growth of phytoplankton under favorable conditions can lead to water discolouration and massive fish kills. Manual field sampling and laboratory analysis of chlorophyll-a concentration (Chl-a) as an indicator to algal biomass, is resources intensive and time consuming, delaying responses to disastrous harmful algal blooms. Cloudy weather often precludes the use of satellite images for water quality and algal bloom monitoring. This study aims at developing an estimator algorithm for quantitative mapping of surface Chl-a for coastal waters, based on surface reflectance measurement from an Unmanned Aerial Vehicle (UAV) with a five-band multispectral camera. The surface reflectance is obtained from calibrated multispectral images which are radiometric-corrected against incoming solar radiation. It is found that Chl-a has an inverse correlation with the Normalized Green-Red Difference Index (NGRDI). A regression estimator model for Chl-a from NGRDI is developed, showing excellent performance for fish farms in coastal waters with different characteristics. The technology is demonstrated for mapping the spatial and temporal variation of Chl-a during an algal bloom, offering a useful complement to traditional field monitoring for fisheries management and emergency response.     

Validation of 2015 Lake Erie MODIS image spectral decomposition using visible derivative spectroscopy and field campaign data

Timely identification of color-producing agents (CPAs) in Lake Erie is a challenging, but vital aspect of monitoring harmful algal blooms (HABs). In particular, HABs that include large amounts of cyanobacteria (CyanoHABs) can be toxic to humans, posing a threat to drinking water, in addition to recreational and economic use of Lake Erie. The optical signal of Lake Erie is complex (Becker et al., 2009; Moore et al., 2017), typically comprised of phytoplankton, cyanobacteria, colored dissolved organic matter (CDOM), detritus, and terrigenous inorganic particles, varying in composition both spatially and temporally. The Kent State University (KSU) spectral decomposition method effectively partitions CPAs using a varimax-rotated, principal component analysis (VPCA) of visible reflectance spectra measured using lab, field or satellite instruments (Ali et al., 2013; Ortiz et al., 2017, 2013). We analyze 2015 imagery acquired by the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor and field samples collected during the early 2015 cyanoHAB season. We identified four primary CPA spectral signatures, and the spatial distribution of each identified CPA, in the reflectance spectra datasets of both the MODIS and lab-measured water samples. The KSU spectral decomposition method results in mixtures of specific pigments, pigment degradation products, and minerals that describe the optically complex water. We found very good agreement between the KSU VPCA spectral decomposition results and in situ measurements, indicating that this method may be a powerful tool for rapid CyanoHAB monitoring and assessment in large lakes using instruments that provide moderate resolution imagery (0.3 to 1 km²).     

三峡水库大宁河春季水华藻类分布及影响因子

以三峡水库支流大宁河2010年3月中旬的水华调查数据为依据,分析浮游藻类分布规律,并探讨其影响因子。结果表明,在容易暴发水华的库湾开阔地带之外,狭窄的峡谷地带亦会暴发严重水华,并且持续时间更长;河口区域的浮游藻类则表现出比其他区域更好的群落稳定性和生物均匀性。在此次水华的前、中期,浮游藻类的群落稳定性随生物量增大而降低;后期则随其增大而增加。通过对样品的定量分析,共鉴定浮游藻类7门24属,主要为绿藻和甲藻,第一优势种为拟多甲藻,占到总藻类的38%,其次为小球藻和衣藻。观察到藻类群体有垂直迁移现象,可能因藻类有趋光性所致。拟多甲藻水华的暴发会降低水体氮磷比,而随着水华的消退,水体氮磷比会较迅速地恢复到一个较高水平。在水华前期,总磷与浮游藻类群落稳定性呈现高度负相关,在中、后期其相关性减弱。     

Requirements for Autonomous Underwater Vehicles (AUVs) for scientific data collection in the Laurentian Great Lakes: A questionnaire survey

Using mobile environmental monitoring can aid in gathering ecological data to meet fish community goals in the Great Lakes. One such approach is the use of large Autonomous Underwater Vehicles (AUVs) to gather data, or the potential use of AUV swarms, where multiple small AUVs work together with each having different data-gathering capabilities. To understand data needs that could be collected by mobile sensor networks to inform decision making, we surveyed Great Lakes professionals involved directly and indirectly in such decision making. Basic data that respondents chose as most important to collect were water temperature, dissolved oxygen, chlorophyll a, turbidity, and blue-green “algae”, which seems to align with variables affecting fish directly or indirectly (through identification of harmful algal blooms). Specialized data chosen as most important were mapping of habitat characteristics, sonar of groupings of fish, and images/video. The time of year to collect all data was chosen as all seasons by the majority of respondents, the frequency most chosen was once a season for mapping of habitat characteristics, once a week for sonar detection of groupings of fish, and once per day for images/video and water temperature. Results were very similar when respondents were asked where data should be collected in the Great Lakes (i.e., tributaries, nearshore areas, etc.) except respondents indicated that images/video should be collected most in fish spawning habitats. Understanding data important to inform decisions of resource professionals will help guide the design of mobile and stationary sensor networks in the Great Lakes.     

Reporting on the status,trends, and drivers of algal blooms on Lake of the Woods using satellite-derived bloom indices (2002–2021)

Despite significant declines in external phosphorus loads, Lake of the Woods continues to experience severe recurring cyanobacterial harmful algal blooms (cHABs) covering as much as 80% of the lake surface area. Satellite-derived bloom indices were used to assess the status, trends, and drivers of cHAB conditions for the period 2002 to 2021 in support of developing ecosystem objectives and response indicators for the lake. Areas of greatest potential concern, with the most prolonged bloom occurrences, were in the southeast of the lake. Significant decreases in bloom indices suggest the lake may now be responding to historical nutrient reductions. The greatest rates of decrease were within the main water flow paths, with little change in the more isolated embayments, suggesting flushing plays a key role in regulating regional bloom severity. Significant inter-annual variability in bloom phenology was observed, with blooms peaking later in recent years, which may be in response to climate-induced changes in the lake and watershed. The absence of a direct relationship between external phosphorus loads and annual bloom severity reflects the complexity of the lake’s response to eutrophication and the potential roles of other drivers including climate and a strong legacy effect of sedimentary nutrients. A case study of the 2017 bloom season captures the compounding interaction of meteorological variability and seasonal nutrient delivery in regulating the bloom response. Results highlight the need for greater understanding of seasonal and regional variability of bloom drivers to aid in forecasting the lake’s recovery under both nutrient management and climate change scenarios.