Search for: All records

Due to the nature of nitrogen cycling, policies designed to address water quality concerns have the potential to provide benefits beyond the targeted water quality improvements. For example, actions to protect water quality by reducing nitrate leaching from agriculture also reduce emissions of nitrous oxide, a potent greenhouse gas. These positive effects, which are incidental to the regulation's intended target, are termed “co‐benefits.” To quantify the co‐benefits associated with reduced nitrate leaching, we integrate an economic model of farmer decision making with a model of terrestrial nitrogen cycling for the watershed surrounding Lake Mendota, Wisconsin, USA. Our modeling approach provides a framework that links air and water pollutants in an agri‐environmental system and offers a direction for future studies. Our model results highlight the finding that the co‐benefits from nitrous oxide abatement are substantial, and their inclusion increases the benefit–cost ratio of water quality policies. Consideration of these co‐benefits has the potential to reverse the conclusions of benefit–cost analysis in the assessment of current water quality policies.

 

The LAGOS-US LAKE DEPTH v1.0 module (hereafter, called DEPTH) contains in situ measurements of lake depth for a subset of all lakes (n = 17,675) in the conterminous U.S. > 1 ha (3.7% of 479,950) that are in the LAGOS-US LOCUS v1.0 data module (Smith et al. 2021). All 17,675 lakes in DEPTH have a maximum depth value and 6,137 lakes have a mean depth. DEPTH includes approximately 65 data sources obtained from community, government, and university monitoring programs, as well as academic reports and commercial websites. DEPTH includes lake identifiers, lake location, lake area, lake depth (both maximum and mean depth when available), source information, and data flags. The unique lake identifier (lagoslakeid) for all lakes is the same one used in LAGOS-US LOCUS v1.0. 

Lake water residence time and depth are known to be strong predictors of phosphorus (P) retention. However, there is substantial variation in P retention among lakes with the same depth and residence time. One potential explanatory factor for this variation is differences in freshwater connectivity of lakes (i.e., the type and amount of freshwater connections to a lake), which can influence watershed P trapping or the particulate load fraction of P delivered to lakes via stream connections. To examine the relationship between P retention and connectivity, we quantified several different measures of connectivity including those that reflect downstream transport of material (sediment, water, and nutrients) within lake‐stream networks (lake‐stream‐based metrics) as well as those that reflect transport of material from hillslope and riparian areas adjacent to watershed stream networks (stream‐based metrics). Because it is not always clear what spatial extent is appropriate for determining functional differences in connectivity among lakes, we compared connectivity metrics at two important spatial extents: the lake subwatershed extent and the lake watershed extent. We found that variation in P retention among lakes was more strongly associated with connectivity metrics measured at the broader lake watershed extent rather than metrics measured at the finer lake subwatershed extent. Our results suggest that both connectivity between lakes and streams as well as connectivity of lakes and their terrestrial watersheds influence P retention.

 

Agricultural land use is typically associated with high stream nutrient concentrations and increased nutrient loading to lakes. For lakes, evidence for these associations mostly comes from studies on individual lakes or watersheds that relate concentrations of nitrogen (N) or phosphorus (P) to aggregate measures of agricultural land use, such as the proportion of land used for agriculture in a lake’s watershed. However, at macroscales (i.e., in hundreds to thousands of lakes across large spatial extents), there is high variability around such relationships and it is unclear whether considering more granular (or detailed) agricultural data, such as fertilizer application, planting of specific crops, or the extent of near‐stream cropping, would improve prediction and inform understanding of lake nutrient drivers. Furthermore, it is unclear whether lake N and P would have different relationships to such measures and whether these relationships would vary by region, since regional variation has been observed in prior studies using aggregate measures of agriculture. To address these knowledge gaps, we examined relationships between granular measures of agricultural activity and lake total phosphorus (TP) and total nitrogen (TN) concentrations in 928 lakes and their watersheds in the Northeastern and Midwest U.S. using a Bayesian hierarchical modeling approach. We found that both lake TN and TP concentrations were related to these measures of agriculture, especially near‐stream agriculture. The relationships between measures of agriculture and lake TN concentrations were more regionally variable than those for TP. Conversely, TP concentrations were more strongly related to lake‐specific measures like depth and watershed hydrology relative to TN. Our finding that lake TN and TP concentrations have different relationships with granular measures of agricultural activity has implications for the design of effective and efficient policy approaches to maintain and improve water quality.

 

Wildfires are becoming larger and more frequent across much of the United States due to anthropogenic climate change. No studies, however, have assessed fire prevalence in lake watersheds at broad spatial and temporal scales, and thus it is unknown whether wildfires threaten lakes and reservoirs (hereafter, lakes) of the United States. We show that fire activity has increased in lake watersheds across the continental United States from 1984 to 2015, particularly since 2005. Lakes have experienced the greatest fire activity in the western United States, Southern Great Plains, and Florida. Despite over 30 years of increasing fire exposure, fire effects on fresh waters have not been well studied; previous research has generally focused on streams, and most of the limited lake‐fire research has been conducted in boreal landscapes. We therefore propose a conceptual model of how fire may influence the physical, chemical, and biological properties of lake ecosystems by synthesizing the best available science from terrestrial, aquatic, fire, and landscape ecology. This model also highlights emerging research priorities and provides a starting point to help land and lake managers anticipate potential effects of fire on ecosystem services provided by fresh waters and their watersheds.

 

Aquatic scientists require robust, accurate information about nutrient concentrations and indicators of algal biomass in unsampled lakes in order to understand and predict the effects of global climate and land‐use change. Historically, lake and landscape characteristics have been used as predictor variables in regression models to generate nutrient predictions, but often with significant uncertainty. An alternative approach to improve predictions is to leverage the observed relationship between water clarity and nutrients, which is possible because water clarity is more commonly measured than lake nutrients. We used a joint‐nutrient model that conditioned predictions of total phosphorus, nitrogen, and chlorophyll aon observed water clarity. Our results demonstrated substantial reductions (8–27%; median = 23%) in prediction error when conditioning on water clarity. These models will provide new opportunities for predicting nutrient concentrations of unsampled lakes across broad spatial scales with reduced uncertainty.

 

Although ecosystems respond to global change at regional to continental scales (i.e., macroscales), model predictions of ecosystem responses often rely on data from targeted monitoring of a small proportion of sampled ecosystems within a particular geographic area. In this study, we examined how the sampling strategy used to collect data for such models influences predictive performance. We subsampled a large and spatially extensive data set to investigate how macroscale sampling strategy affects prediction of ecosystem characteristics in 6,784 lakes across a 1.8‐million‐km²area. We estimated model predictive performance for different subsets of the data set to mimic three common sampling strategies for collecting observations of ecosystem characteristics: random sampling design, stratified random sampling design, and targeted sampling. We found that sampling strategy influenced model predictive performance such that (1) stratified random sampling designs did not improve predictive performance compared to simple random sampling designs and (2) although one of the scenarios that mimicked targeted (non‐random) sampling had the poorest performing predictive models, the other targeted sampling scenarios resulted in models with similar predictive performance to that of the random sampling scenarios. Our results suggest that although potential biases in data sets from some forms of targeted sampling may limit predictive performance, compiling existing spatially extensive data sets can result in models with good predictive performance that may inform a wide range of science questions and policy goals related to global change.