Backwater Confluences of the Ohio River: Organic and Inorganic Fingerprints Explain Sediment Dynamics in Wetlands and Marinas

In the Ohio River (OR), backwater confluence sedimentation dynamics are understudied, however, these river features are expected to be influential on the system’s ecological and economic function when integrated along the river’s length. In the following paper, we test the efficacy of organic and inorganic tracers for sediment fingerprinting in backwater confluences; we use fingerprinting results to evidence sediment dynamics controlling deposition patterns in confluences used for wetland and marina functions; and we quantify the spatial extent of tributary drainages with wetland and marina features in OR confluences. Both organic and inorganic tracers statistically differentiate sediment from stream and river end‐members. Carbon and nitrogen stable isotopes produce greater uncertainty in fingerprinting results than inorganic elemental tracers. Uncertainty analysis of the nonconservative tracer term in the organic matter fingerprinting application estimates an apparent enrichment of the carbon stable isotopes during instream residence, and the nonconservativeness is quantified with a statistical approach unique to the fingerprinting literature. Wetland and marina features in OR confluences impact 42% and 11% of tributary drainage areas, respectively. Sediment dynamics show wetland and marina confluences experience deposition from river backwaters with longitudinally linear and nonlinear patterns, respectively, from sediment sources.     

Adaptation of Climate Model Projections of Streamflow to Account for Upstream Anthropogenic Impairments

A statistical procedure is developed to adjust natural streamflows simulated by dynamical models in downstream reaches, to account for anthropogenic impairments to flow that are not considered in the model. The resulting normalized downstream flows are appropriate for use in assessments of future anthropogenically impaired flows in downstream reaches. The normalization is applied to assess the potential effects of climate change on future water availability on the Rio Grande at a gage just above the major storage reservoir on the river. Model‐simulated streamflow values were normalized using a statistical parameterization based on two constants that relate observed and simulated flows over a 50‐year historical baseline period (1964–2013). The first normalization constant is a ratio of the means, and the second constant is the ratio of interannual standard deviations between annual gaged and simulated flows. This procedure forces the gaged and simulated flows to have the same mean and variance over the baseline period. The normalization constants can be kept fixed for future flows, which effectively assumes that upstream water management does not change in the future, or projected management changes can be parameterized by adjusting the constants. At the gage considered in this study, the effect of the normalization is to reduce simulated historical flow values by an average of 72% over an ensemble of simulations, indicative of the large fraction of natural flow diverted from the river upstream from the gage. A weak tendency for declining flow emerges upon averaging over a large ensemble, with tremendous variability among the simulations. By the end of the 21st Century the higher‐emission scenarios show more pronounced declines in streamflow.     

Mitigating Impact of Devils Lake Flooding on the Sheyenne River Sulfate Concentration

Devils Lake is a terminal lake located in northeast North Dakota. Because of its glacial origin and accumulated salts from evaporation, the lake has a high concentration of sulfate compared to the surrounding water bodies. From 1993 to 2011, Devils Lake water levels rose by ~10 m, which flooded surrounding communities and increased the chance of an overspill to the Sheyenne River. To control the flooding, the State of North Dakota constructed two outlets to pump the lake water to the river. However, the pumped water has raised concerns about of water quality degradation and potential flooding risk of the Sheyenne River. To investigate these perceived impacts, a Soil and Water Assessment Tool (SWAT) model was developed for the Sheyenne River and it was linked to a coupled SWAT and CE‐QUAL‐W2 model that was developed for Devils Lake in a previous study. While the current outlet schedule has attempted to maintain the total river discharge within the confines of a two‐year flood (36 m³/s), our simulation from 2012 to 2018 revealed that the diversion increased the Sheyenne River sulfate concentration from an average of 125 to >750 mg/L. Furthermore, a conceptual optimization model was developed with a goal of better preserving the water quality of the Sheyenne River while effectively mitigating the flooding of Devils Lake. The optimal solution provides a “win–win” outlet management that maintains the efficiency of the outlets while reducing the Sheyenne River sulfate concentration to ≤600 mg/L.     

Spatial and Seasonal Response of Municipal Water Use to Weather across the Contiguous U.S.

Weather variability has the potential to influence municipal water use, particularly in dry regions such as the western United States (U.S.). Outdoor water use can account for more than half of annual household water use and may be particularly responsive to weather, but little is known about how the expected magnitude of these responses varies across the U.S. This nationwide study identified the response of municipal water use to monthly weather (i.e., temperature, precipitation, evapotranspiration [ET]) using monthly water deliveries for 229 cities in the contiguous U.S. Using city‐specific multiple regression and region‐specific models with city fixed effects, we investigated what portion of the variability in municipal water use was explained by weather across cities, and also estimated responses to weather across seasons and climate regions. Our findings indicated municipal water use was generally well‐explained by weather, with median adjusted R² ranging from 63% to 95% across climate regions. Weather was more predictive of water use in dry climates compared to wet, and temperature had more explanatory power than precipitation or ET. In response to a 1°C increase in monthly maximum temperature, municipal water use was shown to increase by 3.2% and 3.9% in dry cities in winter and summer, respectively, with smaller changes in wet cities. Quantifying these responses allows urban water managers to plan for weather‐driven variability in water use.     

Use of Predictive Weather Uncertainties in an Irrigation Scheduling Tool Part II: An Application of Metrics and Adjoints

We apply predictive weather metrics and land model sensitivities to improve the Colorado State University Water Irrigation Scheduler for Efficient Application (WISE). WISE is an irrigation decision aid that integrates environmental and user information for optimizing water use. Rainfall forecasts and verification performance metrics are used to estimate predictive rainfall probabilities that are used as input data within the irrigation decision aid. These input data errors are also used within a land model sensitivity study to diagnose important prognostic water movement behaviors for irrigation tool development purposes simultaneously performing the analysis in space and time. Thus, important questions such as “how long can a crop water application be delayed while maintaining crop yield production?” are addressed by evaluating crop growth stage interactions as a function of soil depth (i.e., space), rainfall events (i.e., time), and their probabilistic uncertainties. Editor’s note : This paper is part of the featured series on Optimizing Ogallala Aquifer Water Use to Sustain Food Systems. See the February 2019 issue for the introduction and background to the series.     

Comparison and Evaluation of Gridded Precipitation Datasets in a Kansas Agricultural Watershed Using SWAT

Gridded precipitation datasets are becoming a convenient substitute for gauge measurements in hydrological modeling; however, these data have not been fully evaluated across a range of conditions. We compared four gridded datasets (Daily Surface Weather and Climatological Summaries [DAYMET], North American Land Data Assimilation System [NLDAS], Global Land Data Assimilation System [GLDAS], and Parameter‐elevation Regressions on Independent Slopes Model [PRISM]) as precipitation data sources and evaluated how they affected hydrologic model performance when compared with a gauged dataset, Global Historical Climatology Network‐Daily (GHCN‐D). Analyses were performed for the Delaware Watershed at Perry Lake in eastern Kansas. Precipitation indices for DAYMET and PRISM precipitation closely matched GHCN‐D, whereas NLDAS and GLDAS showed weaker correlations. We also used these precipitation data as input to the Soil and Water Assessment Tool (SWAT) model that confirmed similar trends in streamflow simulation. For stations with complete data, GHCN‐D based SWAT‐simulated streamflow variability better than gridded precipitation data. During low flow periods we found PRISM performed better, whereas both DAYMET and NLDAS performed better in high flow years. Our results demonstrate that combining gridded precipitation sources with gauge‐based measurements can improve hydrologic model performance, especially for extreme events.     

Practitioner Insights into Weed Management on California’s Rangelands and Natural Areas

Working rangelands and natural areas span diverse ecosystems and face both ecological and economic threats from weed invasion. Restoration practitioners and land managers hold a voluminous cache of place-based weed management experience and knowledge that has largely been untapped by the research community. We surveyed 260 California rangeland managers and restoration practitioners to investigate invasive and weedy species of concern, land management goals, perceived effectiveness of existing practices (i.e., prescribed fire, grazing, herbicide use, and seeding), and barriers to practice implementation. Respondents identified 196 problematic plants, with yellow starthistle (Centaurea solstitialis L.) and medusahead (Elymus caput-medusae L.) most commonly listed. Reported adoption and effectiveness of weed management practices varied regionally, but the most highly rated practice in general was herbicide use; however, respondents identified considerable challenges including nontarget effects, cost, and public perception. Livestock forage production was the most commonly reported management goals (64% of respondents), and 25% of respondents were interested in additional information on using grazing to manage invasive and weedy species; however, 19% of respondents who had used grazing for weed management did not perceive it to be an effective tool. Across management practices, we also found common barriers to implementation, including operational barriers (e.g., permitting, water availability), potential adverse impacts, actual effectiveness, and public perception. Land manager and practitioner identified commonalities of primary weeds, management goals, perceived practice effectiveness, and implementation barriers across diverse bioregions highlight major needs that could be immediately addressed through management–science partnerships across the state’s expansive rangelands and natural areas.     

Using vertebrate environmental DNA from seawater in biomonitoring of marine habitats

Conservation and management of marine biodiversity depends on biomonitoring of marine habitats, but current approaches are resource-intensive and require different approaches for different organisms. Environmental DNA (eDNA) extracted from water samples is an efficient and versatile approach to detecting aquatic animals. In the ocean, eDNA composition reflects local fauna at fine spatial scales, but little is known about the effectiveness of eDNA-based monitoring of marine communities at larger scales. We investigated the potential of eDNA to characterize and distinguish marine communities at large spatial scales by comparing vertebrate species composition among marine habitats in Qatar, the Arabian Gulf (also known as the Persian Gulf), based on eDNA metabarcoding of seawater samples. We conducted species accumulation analyses to estimate how much of the vertebrate diversity we detected. We obtained eDNA sequences from a diverse assemblage of marine vertebrates, spanning 191 taxa in 73 families. These included rare and endangered species and covered 36% of the bony fish genera previously recorded in the Gulf. Sites of similar habitat type were also similar in eDNA composition. The species accumulation analyses showed that the number of sample replicates was insufficient for some sampling sites but suggested that a few hundred eDNA samples could potentially capture >90% of the marine vertebrate diversity in the study area. Our results confirm that seawater samples contain habitat-characteristic molecular signatures and that eDNA monitoring can efficiently cover vertebrate diversity at scales relevant to national and regional conservation and management.     

Decline and diversity in Swedish seas: Environmental narratives in marine history,science and policy

Ambio - Before the mid-twentieth century, there was no comprehensive narrative about empirical conditions in Swedish seas. Around 1970, this view changed profoundly. In line with growing research...     

Improving dialogue among researchers,local and indigenous peoples and decision-makers to address issues of climate change in the North

Ambio - The Circumpolar North has been changing rapidly within the last decades, and the socioeconomic systems of the Eurasian Arctic and Siberia in particular have displayed the most dramatic...