Abstract:Despite decades of effort toward reducing nitrogen and phosphorus flux to Chesapeake Bay, water-quality and ecological responses in surface waters have been mixed. Recent research, however, provides useful insight into multiple factors complicating the understanding of nutrient trends in bay tributaries, which we review in this paper, as we approach a 2025 total maximum daily load (TMDL) management deadline. Improvements in water quality in many streams are attributable to management actions that reduced point… Show more
“…Our logistic regression model suggests that when using the continuous version of agriculture and when put into context with other landscape variables, the likelihood of a degraded stream condition increased 2.0% for every unit increase in agriculture in upstream watersheds. This, combined with our contingency table analysis where we found no evidence of a flow-alteration effect on stream condition in the agriculture-focused dataset, suggests that other stressors associated with agriculture (e.g., sediment (Noe et al 2020 ) and nutrients (Ator et al 2020 )) may be stronger factors affecting macroinvertebrates. Our logistic regression model also supports the importance of other variables as, in addition to the intensity of hydrologic alteration and urban development, we found several natural landscape variables (bioregion, drainage area, elevation, depth to the water table, calcium oxide lithology, % grasslands, and % wetland cover) and additional anthropogenic landscape variables (total freshwater withdrawals, % barren land cover) significant.…”
Section: Discussionsupporting
confidence: 51%
“…5 ) may be more responsive to management practices because of fewer aspects of the hydrograph requiring remediating. We acknowledge that flow is one of many possible stressors and the most effective use of our results will be when they are put into context with other leading stressors for the Chesapeake Bay watershed (e.g., sediment (Noe et al 2020 ) and nutrients (Ator et al 2020 )). Fig.…”
Regionally scaled assessments of hydrologic alteration for small streams and its effects on freshwater taxa are often inhibited by a low number of stream gages. To overcome this limitation, we paired modeled estimates of hydrologic alteration to a benthic macroinvertebrate index of biotic integrity data for 4522 stream reaches across the Chesapeake Bay watershed. Using separate random-forest models, we predicted flow status (inflated, diminished, or indeterminant) for 12 published hydrologic metrics (HMs) that characterize the main components of flow regimes. We used these models to predict each HM status for each stream reach in the watershed, and linked predictions to macroinvertebrate condition samples collected from streams with drainage areas less than 200âkm2. Flow alteration was calculated as the number of HMs with inflated or diminished status and ranged from 0 (no HM inflated or diminished) to 12 (all 12 HMs inflated or diminished). When focused solely on the stream condition and flow-alteration relationship, degraded macroinvertebrate condition was, depending on the number of HMs used, 3.8â4.7 times more likely in a flow-altered site; this likelihood was over twofold higher in the urban-focused dataset (8.7â10.8), and was never significant in the agriculture-focused dataset. Logistic regression analysis using the entire dataset showed for every unit increase in flow-alteration intensity, the odds of a degraded condition increased 3.7%. Our results provide an indication of whether altered streamflow is a possible driver of degraded biological conditions, information that could help managers prioritize management actions and lead to more effective restoration efforts.
“…Our logistic regression model suggests that when using the continuous version of agriculture and when put into context with other landscape variables, the likelihood of a degraded stream condition increased 2.0% for every unit increase in agriculture in upstream watersheds. This, combined with our contingency table analysis where we found no evidence of a flow-alteration effect on stream condition in the agriculture-focused dataset, suggests that other stressors associated with agriculture (e.g., sediment (Noe et al 2020 ) and nutrients (Ator et al 2020 )) may be stronger factors affecting macroinvertebrates. Our logistic regression model also supports the importance of other variables as, in addition to the intensity of hydrologic alteration and urban development, we found several natural landscape variables (bioregion, drainage area, elevation, depth to the water table, calcium oxide lithology, % grasslands, and % wetland cover) and additional anthropogenic landscape variables (total freshwater withdrawals, % barren land cover) significant.…”
Section: Discussionsupporting
confidence: 51%
“…5 ) may be more responsive to management practices because of fewer aspects of the hydrograph requiring remediating. We acknowledge that flow is one of many possible stressors and the most effective use of our results will be when they are put into context with other leading stressors for the Chesapeake Bay watershed (e.g., sediment (Noe et al 2020 ) and nutrients (Ator et al 2020 )). Fig.…”
Regionally scaled assessments of hydrologic alteration for small streams and its effects on freshwater taxa are often inhibited by a low number of stream gages. To overcome this limitation, we paired modeled estimates of hydrologic alteration to a benthic macroinvertebrate index of biotic integrity data for 4522 stream reaches across the Chesapeake Bay watershed. Using separate random-forest models, we predicted flow status (inflated, diminished, or indeterminant) for 12 published hydrologic metrics (HMs) that characterize the main components of flow regimes. We used these models to predict each HM status for each stream reach in the watershed, and linked predictions to macroinvertebrate condition samples collected from streams with drainage areas less than 200âkm2. Flow alteration was calculated as the number of HMs with inflated or diminished status and ranged from 0 (no HM inflated or diminished) to 12 (all 12 HMs inflated or diminished). When focused solely on the stream condition and flow-alteration relationship, degraded macroinvertebrate condition was, depending on the number of HMs used, 3.8â4.7 times more likely in a flow-altered site; this likelihood was over twofold higher in the urban-focused dataset (8.7â10.8), and was never significant in the agriculture-focused dataset. Logistic regression analysis using the entire dataset showed for every unit increase in flow-alteration intensity, the odds of a degraded condition increased 3.7%. Our results provide an indication of whether altered streamflow is a possible driver of degraded biological conditions, information that could help managers prioritize management actions and lead to more effective restoration efforts.
“…To meet the TMDL load reduction targets, state and local governments are responsible for developing Watershed Implementation Plans (WIPs) that describe needed management practices. Coincident with these efforts, which have also included point source decreases (Ator et al 2020) and reductions in atmospheric nitrogen deposition (Eshleman et al 2013;Da et al 2018), water clarity and dissolved oxygen (DO) concentrations have improved some (Zhang et al 2018) and submerged aquatic vegetation has expanded in some regions (Gurbisz and Kemp 2014;Lefcheck et al 2018). However, progress has been slow (Boesch 2006) and currently less than half of the Bay area meets all water quality goals (Zhang et al 2018).…”
Ecological forecasts are quantitative tools that can guide ecosystem management. The co-emergence of extensive environmental monitoring and quantitative frameworks allows for widespread development and continued improvement of ecological forecasting systems. We use a relatively simple estuarine hypoxia model to demonstrate advances in addressing some of the most critical challenges and opportunities of contemporary ecological forecasting, including predictive accuracy, uncertainty characterization, and management relevance. We explore the impacts of different combinations of forecast metrics, drivers, and driver time windows on predictive performance. We also incorporate multiple sets of state-variable observations from different sources and separately quantify model prediction error and measurement uncertainty through a flexible Bayesian hierarchical framework. Results illustrate the benefits of 1) adopting forecast metrics and drivers that strike an optimal balance between predictability and relevance to management, 2) incorporating multiple data sources in the calibration dataset to separate and propagate different sources of uncertainty, and 3) using the model in scenario mode to probabilistically evaluate the effects of alternative management decisions on future ecosystem state. In the Chesapeake Bay, the subject of this case study, we find that average summer or total annual hypoxia metrics are more predictable than monthly metrics and that measurement error represents an important source of uncertainty. Application of the model in scenario mode suggests that absent watershed management actions over the past decades, long-term average hypoxia would have increased by 7% compared to 1985. Conversely, the model projects that if management goals currently in place to restore the Bay are met, long-term average hypoxia would eventually decrease by 32% with respect to the mid-1980s.
“…causing some estimated reductions to be overstated, according to a study published in May 2020 by US Geological Survey hydrologist Scott Ator and colleagues, which sent ripples of alarm through the scientific community (11,12). By using a model based on actual monitoring data, the researchers showed that nitrogen pollution from agriculture barely declined, if at all, between the early 1990s and early 2010s, compared with the double-digit percentage decrease predicted by the management model.…”
On a warm October day, six researchers ventured out to a reef in Baines Creek, a tributary of Virginia's Elizabeth River. Back in 2014, the Army Corps of Engineers had dumped tons of fossil oyster shells into the water in the hopes of attracting more of one of the key inhabitants of the Chesapeake Bay-oysters. Now the researchers, wearing masks and socially distancing as they wade through the water at low tide, are measuring the progress.What they are finding is just astounding, says Romuald Lipcius, professor of fisheries science at the Virginia Institute of Marine Science (VIMS) at Gloucester Point, and also the School of Marine Science at William & Mary University. The team counts hundreds of oysters growing on each square meter of reef. Some oysters had reached the ripe old age of five, once considered unachievable because of diseases that had been introduced into the Bay; oysters' fecundity increases exponentially with size and age. "All these oysters peeking above the water is a beautiful sight," says William & Mary professor of geology Rowan Lockwood, who studies fossil oysters. "It is a glimpse of what the Bay was like 6,000 years ago." And maybe, what it could be like again.But that ideal future is still a long shot. A couple of weeks after Lipcius plumbed the depths of Baines Creek, Matt Pluta piloted his small boat through a dense fog on the Choptank River, which flows into the Bay from Delaware and Maryland's Eastern Shore. Director of Riverkeeper Programs at ShoreRivers, based in Easton, MD, Pluta is making his last sampling run of 2020, working alone because of the pandemic. Over the past few years, the levels of nutrient pollution in the Numerous tributaries feed into the Chesapeake Bay, such as the Severn River near Annapolis, MD. They enhance the beauty of the Bay's watershed-they also shuttle pollutants that imperil the Bay's ecosystems.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citationsâcitations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
