Groundwater nitrate contamination imposes a safety concern. Mitigating groundwater nitrate levels requires a quantitative assessment of source contributions, which is also challenging to estimate considering the data uncertainty and the agriculture environmental complexity. The Pineland Sand Region in north central Minnesota, United States, features sandy soils and has a mixed land-use landscape of agriculture and natural background, with part of the area located within the White Earth Nation Reservation region. We analyzed the land-use patterns and identified nitrate contaminant sources in nature, wetlands, row crops, pasture, and urban areas. To estimate the nitrate source apportionment and its uncertainty in this study area, we first characterized these sources using calcium, sulfate, chloride, and sodium, and then applied a Bayesian mass-balance mixing model using a Monte Carlo Markov Chain approach in 164 groundwater wells and further obtained the probability distributions of the fractional contributions from those specific sources. The results show that nature and wetlands source fractions dominate sites with negligible agriculture impacts, while row crop and pasture sources are estimated with higher contaminant contributions in sites subject to more intensive agriculture management. We also discussed a few specific sites’ contaminant source fractions and suggest a future source characterization scheme to advance groundwater nitrate understanding in the study area.

Urban lake drainage systems are heavily impacted by terrain, soil characteristics, and precipitation, which influence water infiltration and groundwater movement. This study focused on the drainage catena around Lake Nokomis in Minneapolis, Minnesota, where local residents have experienced wet basements and yards. The primary goal was to identify the factors contributing to these water-related problems, particularly soil permeability and how it responds to precipitation. By conducting soil borings, using pressure transducers, and measuring saturated hydraulic conductivity (Kfs), the study compared upland and lowland areas. Findings indicated that upland soils, primarily composed of sandy fill, had much higher infiltration rates, with Kfs values ranging from 72.4 cm/hr to 149 cm/hr. In contrast, lowland areas characterized by lacustrine and organic soils exhibited significantly lower Kfs values, ranging from 1 cm/hr to 14.8 cm/hr. Between 2022 and 2024, wet and dry seasons occurred, yet recorded more than 127.5 cm of rain and snow water equivalent, further contributing to groundwater rise and surface water presence in low-lying regions. The study concluded that increased precipitation, coupled with specific hydrogeologic conditions, was the main factor causing elevated groundwater levels and surface saturation in these areas. To address these challenges, Minnesota's water management authorities are encouraged to implement strategies that consider the increasing magnitude and intensity of precipitation events due to climate change. Incorporating hydrogeologic assessments into urban planning is recommended to better manage water infiltration, reduce flood risks, and strengthen the resilience of drainage systems to changing climate patterns.

The risk of nitrate contamination became a reality for Fairmont in Minnesota, when water rich in NO3-N exceeded the drinking water standard of 10 mg/L. This was unexpected because this city draws its municipal water from a chain of lakes that are fed primarily by shallow groundwater under row-crop land use. Spring soil thaw drives cold water into a subsurface pipe where almost no NO3-N reduction occurs. This paper focuses on NO3-N reduction before the water enters the lakes and no other nitrogen management practices in the watershed. A novel denitrifying bioreactor was constructed behind a sediment forebay, which then flowed into a chamber covered by a greenhouse before entering a woodchip bioreactor. In 2022 and 2023, water depth, dissolved oxygen, and temperature were measured at several locations in the bioreactor, and continuous NO3-N was measured at the entry and exit of the bioreactor. The results showed better performance at a low water depth with lower dissolved oxygen and higher water temperature. The greenhouse raised the inlet temperature in 2022 but did not in 2023. The forebay and the greenhouse may have impeded the denitrification process due to the high dissolved oxygen concentrations in the influent and the stratification of dissolved oxygen caused by algae in the bioreactor.

The volume of a lake is a crucial component in understanding environmental and hydrologic processes. The State of Minnesota (USA) has tens of thousands of lakes, but only a small fraction has readily available bathymetric information. In this paper we develop and test methods for predicting water volume in the lake-rich region of Central Minnesota. We used three different published regression models for predicting lake volume using available data. The first model utilized lake surface area as the sole independent variable. The second model utilized lake surface area but also included an additional independent variable, the average change in land surface area in a designated buffer area surrounding a lake. The third model also utilized lake surface area but assumed the land surface to be a self-affine surface, thus allowing the surface area-lake volume relationship to be governed by a scale defined by the Hurst coefficient. These models all utilized bathymetric data available for 816 lakes across the region of study. The models explained over 80% of the variation in lake volumes. The sum difference between the total predicted lake volume and known volumes were <2%. We applied these models to predicting lake volumes using available independent variables for over 40,000 lakes within the study region. The total lake volumes for the methods ranged from 1,180,000- and 1,200,000-hectare meters. We also investigated machine learning models for estimating the individual lake volumes and found they achieved comparable and slightly better predictive performance than from the three regression analysis methods. A 15-year time series of satellite data for the study region was used to develop a time series of lake surface areas and those were used, with the first regression model, to calculate individual lake volumes and temporal variation in the total lake volume of the study region. The time series of lake volumes quantified the effect on water volume of a dry period that occurred from 2011 to 2012. These models are important both for estimating lake volume, but also provide critical information for scaling up different ecosystem processes that are sensitive to lake bathymetry.

Very little outdoor recreation and tourism research uses scientifically-grounded climate change projections or weather data to predict future recreation demand using standard contingent behavior methods. The demand studies that have presented visitors with projected changes to climate and weather are limited to predicting visitation demand in a single season at a single destination. This research note reports a replication of a winter tourism demand model for the summer tourism season at the same nature-based tourism destination. A comparison of model findings between the two seasons allows us to determine if, and how, summer and winter tourism demand to a specific destination will be affected by climate change. While winter demand is driven by multiple dimensions of place meanings, summer travel is motivated solely by how the destination shapes individuals' identities. This replication also considers an additional weather variable - daily high temperature on the day visitors completed the survey - to better understand the relationship between in situ weather conditions and recreationists' intended travel behaviors. Management implications: North Shore visitors' future travel behavior, contingent upon warmer temperatures and altered environmental conditions, was not significantly different than past travel behavior. The projected conditions presented in the scenarios might not have been severe enough that respondents believed they would substantially impact recreational opportunities on the North Shore. The maximum daily high temperature on the day a respondent was surveyed was not significantly related to contingent travel behaviors. Recreation resource managers and those in the tourism industry are not likely to see substantial shifts in tourism demand to the region over the next 20 years.

Nature-based tourism is one of the most economically important industries in the state of Maine, USA. Climate change impacts are projected to affect important tourism assets in Maine, which could result in behavioral shifts related to destination selection, seasonal visitation, and activity participation. Risk perceptions can be important predictors in visitor travel decisions. Recent tourism studies have focused on the effects of climate impacts on risk perceptions, but few have examined the social-psychological drivers of climate change risk perceptions. Drawing on social-psychological theories, we address this gap by understanding visitor climate change risk perceptions in Maine. We surveyed visitors to Acadia National Park in the summer of 2018 to assess the impact of socio-demographics, cognition, experience, and socio-cultural factors on visitor climate change risk perceptions. We used two-stage cluster probability sampling and intercepted 1317 visitors on site; 480 participants completed the online follow-up survey. Using hierarchical regression, we explained 45.5% of the variance in visitors’ climate change risk perceptions at a nature-based tourism destination. Visitors identifying as female, having higher levels of belief in climate change, more first-hand experience with climate impacts, and a higher altruistic values orientation amplified risk perceptions. Understanding determinants of climate change risk perceptions within an outdoor recreation setting has implications for offering high quality visitor experiences while maintaining the integrity of the natural resource base upon which visitation relies.Risk perceptions can be important predictors of visitor travel behavior. Climate change is expected to impact nature-based tourism visitor experiences and travel decisions, natural and cultural resources that serve as attractions, and visitor safety in protected areas. Understanding the drivers of climate change risk perceptions are important for managing visitors and their recreation behaviors while providing satisfactory tourism experiences. If park managers and other tourism stakeholders want to convey information about climate change with the goal of influencing perceptions and behaviors, we suggest that they focus on visitors’ past experiences with climate change impacts and appeals to altruistic values, rather than solely providing climate change facts. Understanding how to motivate visitor compliance with park policies and visitor resource use guidelines will be critical in maintaining positive visitor experience in nature-based tourism settings and protecting the integrity of natural and cultural resources from changes in visitation, such as increased tourist numbers.

Many communities located in natural resource rich landscapes have transitioned to tourism-based economies. This transition might not be sustainable, as climate and environmental change have unknown effects on the visitation patterns of outdoor recreationists and tourists. We address this uncertainty by estimating shifts in the demand for outdoor recreation destinations along Minnesota's North Shore region of Lake Superior under a range of projected climatic and environmental conditions. We also employ a finite-mixture modeling approach to capture the preference heterogeneity across North Shore visitors. Our findings indicate projected climate and environmental changes are not likely to significantly affect visitation patterns in the next 20years. However, utilizing a finite-mixture modeling approach enabled us to identify distinct types of visitors with divergent visitation behaviors under altered climate and environmental conditions. Our findings suggest that the demand for outdoor recreation along the North Shore will be relatively stable in the near future, however different types of visitors will respond to warming winter conditions in divergent ways. Shifting visitation patterns under climate and environmental change may have more drastic alterations to the economic well-being of the region under a longer planning horizon.

Eurasian watermilfoil (Myriophyllum spicatum L.) is a submersed macrophyte, exotic to North America where it is a major nuisance. One potential biological control agent is the native weevil Euhrychiopsis lecontei (Dietz) (Curculionidae). To determine the effects of known densities of the weevil on Eurasian watermilfoil in controlled conditions, we stocked weevils into outdoor 0.38 m3 tanks containing watermilfoil. Watermilfoil stems (20 cm long; 150 m−2 were planted in each tank and given 3 weeks to root and grow. Plants developed extensive roots and grew 10–15 cm prior to stocking. Weevils were then applied at four stocking densities (0, 6, 12 or 24 adults) to 16 tanks arrayed in a Latin square design. Plant length was measured and weevils were counted weekly in the tanks. Sediment nutrients, plant mass and nutrient content, and weevil densities were determined from a sample of plants prior to stocking and 3 and 4 weeks after stocking. After 4 weeks, all plant material was removed, biomass was determined and weevils were counted. Weevils survived in the stocked tanks and eggs appeared soon after introduction. There was an average of over 200 weevils (25 adults) in each of the stocked tanks at 4 weeks. Weevil stocking density resulted in a significant decline in watermilfoil biomass (P < 0.005) with biomass in the tanks stocked with 24 weevils reaching only 40% of the control. Root biomass also declined with weevil density (P < 0.005) and biomass in the high density tanks reached only 55% of the control. However, detached sunken and floating watermilfoil biomass increased with density of weevils and no change in total above ground biomass (sum of standing, floating and sunken watermilfoil) was found, indicating that effects were caused by plant damage rather than direct consumption. Periodic estimates of plant height, mass and root mass showed that effects on plants resulted from reductions in stems and roots from peak levels at Weeks 2 or 3 and were thus not simply a suppression of growth. Percent sugars and total nonstructural carbohydrates declined with stocking density in both roots and shoots. The total stock (g per plant) of sugars, starch and total nonstructural carbohydrates was reduced in the roots. Weevil densities of ≈ 300 m−2 can have rapid and substantial effects on Eurasian watermilfoil both above and below ground. Herbivory by weevils may have long term effects via disruption of plant carbohydrate stores that are essential for overwinter survival and subsequent regrowth.