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Denitrification in woodchip bioreactors (WBRs) treating agricultural drainage and runoff is frequently carbon-limited due to the recalcitrance of carbon (C) in lignocellulosic woodchip biomass. Recent research has shown that redox fluctuations, achieved through periodic draining and re-flooding of WBRs, can increase nitrate removal rates by enhancing the release of labile C during oxic periods. While dying–rewetting (DRW) cycles appear to hold great promise for improving the performance of denitrifying WBRs, redox fluctuations in nitrogen-rich environments are commonly associated with enhanced emissions of the greenhouse gas nitrous oxide (N2O) due to inhibition of N2O reduction in microaerophilic conditions. Here, we evaluate the effects of oxic–anoxic cycling associated with DRW on the quantity and quality of C mobilized from woodchips, nitrate removal rates, and N2O accumulation in a complementary set of flow-through and batch laboratory bioreactors at 20 °C. Redox fluctuations significantly increased nitrate removal rates from 4.8–7.2 g N m−3 d−1 in a continuously saturated (CS) reactor to 9.8–11.2 g N m−3 d−1 24 h after a reactor is drained and re-saturated. Results support the theory that DRW conditions lead to faster NO3− removal rates by increasing mobilization of labile organic C from woodchips, with lower aromaticity in the dissolved C pool of oxic–anoxic reactors highlighting the importance of lignin breakdown to overall carbon release. There was no evidence for greater N2O accumulation, measured as N2O product yields, in the DRW reactors compared to continuously saturated reactors. We propose that greater organic C availability for N2O reducers following oxic periods outweighs the effect of microaerophilic inhibition of N2O reduction in controlling N2O dynamics. Implications of these findings for optimizing DRW cycling to enhance nitrate removal rates in denitrifying WBRs are discussed.

This study focuses on water quality and quantity impacts from natural resource development on watersheds originating on Crow tribal lands in southeastern Montana. My field research analysis will focus on the surface water quality in three adjacent watersheds. This study will determine impacts to water quality from reclaimed coal mine spoils surface runoff and produced water discharge from coal bed methane wells within the watersheds. A secondary research objective is to determine a baseline assessment of surface water in watersheds prior to proposed mine development, particularly on tribally owned and allotted tracts. Historical data from state agencies will also be compared to data collected within watersheds on tribal lands. Water quality impacts from mining development may be more pronounced than that of coal bed methane as the reclaimed mining sites have demonstrated lasting impacts on the nearby surface water quality in the study area. Historical and current samples have demonstrated elevated contaminant levels downstream of a mine site in a tributary to the primary watershed. A sample from a pond in another reclaimed mine site contained the most elevated sodium adsorption ratio levels of all surface water samples. Coal bed methane development impacts may have been transient in the primary watershed surface water based on sample results. Historical oil and gas development appears to be impacting surface water quality within the southernmost watershed. Analysis has shown the increasing degradation of water quality in watersheds downstream and across the state boundary of Montana into Wyoming where natural resource development has occurred.

Proposed hydropower dams at more than 350 sites throughout the Amazon require strategic evaluation of trade-offs between the numerous ecosystem services provided by Earth’s largest and most biodiverse river basin. These services are spatially variable, hence collective impacts of newly built dams depend strongly on their configuration. We use multiobjective optimization to identify portfolios of sites that simultaneously minimize impacts on river flow, river connectivity, sediment transport, fish diversity, and greenhouse gas emissions while achieving energy production goals. We find that uncoordinated, dam-by-dam hydropower expansion has resulted in forgone ecosystem service benefits. Minimizing further damage from hydropower development requires considering diverse environmental impacts across the entire basin, as well as cooperation among Amazonian nations. Our findings offer a transferable model for the evaluation of hydropower expansion in transboundary basins.

Microplastics in aquatic environments, and specifically their effects on the health of organisms, are of growing concern worldwide. Of particular concern are microplastics in a similar size range to zooplankton, as they have been found in the digestive tracks of organisms, such as fish, who typically seek zooplankton as a food source. It is unclear, however, to what degree, if any, fish select for or against microplastic particles when feeding. It is also unclear whether ingestion of microplastics affects fish condition. To answer these questions, the estimated physical condition and degree of selective feeding on microplastics were determined for juvenile fish collected from the Hudson River. Considering only particles 0.335–5.0 mm, microplastics made up 12% of fish diets but 21% of particles found in the surrounding water column. Relying on Jacob's Modified Electivity (JME) to quantify selectivity in feeding, our results reveal selective feeding on zooplankton and avoidance of microplastics. There was no correlation between condition and degree of selectivity toward any particular food types, including microplastics. Future work needs to consider fish with different feeding strategies and potential bioaccumulation of microplastics in the food web. Fish selectivity of ingestion in regards to microplastics should additionally be tested on wider spatial and temporal scales.

2017 American Geophysical Union Horton Research grant; IGERT Cross-Scale Biogeochemistry and Climate Small grant

Microplastics, particles less than 5 mm in size, are an emerging contaminant in waterways worldwide. Most microplastic studies focus on spatial trends in concentration, but in systems as dynamic as rivers, to draw conclusions from existing spatial studies, we must first examine how microplastic concentrations may change with time and flow conditions. In this study, we investigate how microplastic concentrations change over a 24-h period and between seasonally high and low flows. We do this in two streams, controlling for wastewater treatment strategy: one stream in a watershed where waste is treated with septic systems and the other receiving wastewater treatment plant effluent. We hypothesized that a stream with wastewater treatment plant effluent would exhibit higher and more variable microplastic concentrations than a stream in a watershed with septic systems. Results indicate, however, that there is no significant difference between the two streams despite their differing treatment strategies. Additionally, no significant variation in concentrations was measured over two 24-h sampling campaigns. There was, however, significantly higher concentrations measured in summer low flow conditions relative to spring high flow conditions across both sampled streams (p value

Current generation general circulation models (GCMs) simulate synoptic-scale climate state variables such as geopotential heights, specific humidity, and integrated vapor transport (IVT) more reliably than mesoscale precipitation. Statistical downscaling methods that condition precipitation on GCM-based, synoptic-scale climate features have shown promise in the reproduction of local precipitation. However, current approaches to climate-state-informed downscaling impose some limitations on the skill of precipitation reproduction, including hard clustering of climate modes into a discrete set of states, utilization of numerical clustering methodologies poorly suited to nonnormal data, and a tendency to focus on relationships to a limited set of large-scale climate modes. This study presents a methodology based on emerging machine learning techniques to develop global approximators of regional precipitation and discharge extremes given a suite of synoptic-scale climate state variables. Archetypal analysis is first used to define regional modes of winter and summer extreme precipitation and discharge across the eastern contiguous United States. A 2D convolution neural network (NN) is then used to predict the co-occurrence of the archetypes using 300- and 700-hPa geopotential heights, 300- and 700-hPa specific humidity, and IVT. Results suggest that 300-hPa geopotential height, 700-hPa specific humidity, and IVT yield the most reliable predictions, although with some important differences by season and region. Finally, we demonstrate that the trained activations of NN convolutional layers can be used to infer the causal pathways between synoptic-scale climate features and regional extremes.

Dams are known to trap pollutants such as metals and PCBs in the sediment that accumulates within their reservoirs. As more attention is paid to microplastics, an emerging contaminant in waterways worldwide, and how they move along rivers, whether microplastic particles also accumulate behind dams is an important question for informing estimates of global river inputs to oceans. In this study, we measured microplastic concentrations above, below, and within the reservoirs of six dams near Ithaca, NY USA. Samples were processed following the wet peroxide oxidation method and visual counting, followed by Raman Spectroscopy validation. We found that microplastic concentrations in sediment within reservoirs was significantly higher than in sediment above the dams (p = 0.005), and in water samples, concentrations within reservoirs was significantly lower (p = 0.02). Plastic fibers were the dominant plastic type, but in within-reservoir sediment samples, less abundant plastic types such as plastic fragments were found in higher proportions. These results show that the sediment collecting behind dams is one sink for microplastics in river systems at long timescales, indicating that accounting for dams may be important when modeling global riverine microplastic transport.

In recent years, many cities have experienced serious urban flood and non-point pollution issues due to hydrological process changes in rapidly urbanizing areas. Understanding the relationship between impervious surface and direct runoff is important for urban planning to protect the urban hydrological system. In this study, we used a mixed spectral decomposition method to interpret the long-term series of impervious surface of Shenyang, China. Direct runoff was evaluated by an improved SCS-CN (Soil Conservation Service curve number) model, and the relative influences of five underlying surface factors on the direct runoff of each period were analyzed by boosted regression trees. The overall impervious area was significantly increased in both the study area and built-up area from 1984 to 2015. The impervious ratio showed a decreasing trend in the built-up area and increasing trend in the whole study area. The runoff coefficient of the built-up area showed a significantly decreasing trend. The runoff ratio of the built-up area to the whole study area was increased dramatically, reaching 0.26 by 2015. NDVI (normalized distribution vegetation index), vegetation, and impervious surface were the most important urban surface conditions in the study area for direct runoff generation. The relative influence of impervious surface showed a rapidly increasing trend and then gradually decreased from 2000.

Microplastic pollution is measured with a variety of sampling methods. Field experiments indicate that commonly used sampling methods, including net, pump and grab samples, do not always result in equivalent measured concentration. We investigate the comparability of these methods through a meta-analysis of over one hundred surface water microplastic studies. We find systematic relationships between measured concentration and sampled volume, method of collection, mesh size used for filtration, and water body sampled. Most significantly, a strong log-linear relationship exists between sample volume and measured concentration, with small-volume grab samples measuring up to 10^4 particles/L higher concentrations than larger volume net samples, even when sampled concurrently. Potential biases explored included filtration size (±10^2 particles/L), net volume overestimation (±10^1 particles/L), fiber loss through net mesh (unknown magnitude), and intersample variability (±10^1 particles/L). Contamination is the one potential bias with an effect large enough (±10^3 particles/L) to explain the observed differences. Based on these results, we caution the practice of comparing concentrations across multiple studies or combining multiple study results to identify regional patterns. Additionally, we reiterate previous recommendations emphasizing the importance of contamination reduction strategies, namely that blank samples be collected, tested, and reported as a matter of course for such studies.

Litter was collected from 12 roadside ditches in the Finger lakes Region of New York State over two sampling periods: pre-COVID-19 pandemic and during COVID-19 pandemic. Litter pieces were washed in DI water, oven dried, massed and plastic-type visually determined. Macroplastic data was analysed to assess the impact of land use, traffic, and COVID-19 variables on macroplastic accumulation on a piece, mass, and plastic-type basis. These data are all litter pieces collected, including both plastics categorized as 1 through 7 in the RIC resin classification codes as well as non-plastic litter. These data have wide-ranging reuse potential, as terrestrial microplastic accumulation is not well documented. These data could be compared with other litter accumulation across regions, specifically to assess total environmental macroplastic loading and enable contaminant mitigation strategies. These data also have direct application to modelling and transport of macroplastics into surface water bodies as a result of road ditch sampling locations. Macroplastic accumulation data across varying land uses, traffic, and COVID-19 conditions has been published [1].

Despite ongoing management efforts, phosphorus (P) loading from agricultural landscapes continues to impair water quality. Wastewater treatment research has enhanced our knowledge of microbial mechanisms influencing P cycling, especially regarding microbes known as polyphosphate accumulating organisms (PAOs) that store P as polyphosphate (polyP) under oxic conditions and release P under anoxic conditions. However, there is limited application of PAO research to reduce agricultural P loading and improve water quality. Herein, we conducted a meta-analysis to identify articles in Web of Science on polyP and its use by PAOs across five disciplines (i.e., wastewater treatment, terrestrial, freshwater, marine, and agriculture). We also summarized research that provides preliminary support for PAO-mediated P cycling in natural habitats. Terrestrial, freshwater, marine, and agriculture disciplines had fewer polyP and PAO articles compared to wastewater treatment, with agriculture consistently having the least. Most meta-analysis articles did not overlap disciplines. We found preliminary support for PAOs in natural habitats and identified several knowledge gaps and research opportunities. There is an urgent need for interdisciplinary research linking PAOs, polyP, and oxygen availability with existing knowledge of P forms and cycling mechanisms in natural and agricultural environments to improve agricultural P management strategies and achieve water quality goals.

These data were from semi-structured interviews with dairy farmers. The content of the interviews focused on antibiotic transport and usage on dairy farms. Twenty-seven interviews were conducted in Central New York in 2019. Interviews were recorded and subsequently transcribed for qualitative thematic analysis. Qualitative coding analysis was preformed using ATLAS.ti and content filtered to ensure farmer anonymity. The dataset includes direct quotations from dairy farmers paired with farm and farmer characteristics. Quotations are subdivided thematically into the themes of disease prevention, antibiotic usage, non-antibiotic treatments, antibiotic transport, and environmental residue presence impacts, as structured in Georgakakos et al. [1] . Farm characteristics include management practice, farm size, and farm generation. Farm size was determined by number of lactating cows: small (0-50), medium-small (51-100), medium (101-500), medium-large (501-1000), and large (>1000). Farmer characteristics were farmer age categorized by birth year: Baby Boomer (1946-1964), Gen X (1965-1980), and Millennial (1981-1996). This dataset is particularly promising for longitudinal studies, incorporation of human behaviour into contaminant load models, or for recoding and analysis for themes other than those discussed by Georgakakos et al. [1] .

This study coupled a landscape-scale metagenomic survey of denitrification gene abundance in soils with in situ denitrification measurements to show how environmental factors shape distinct denitrification communities that exhibit varying denitrification activity. Across a hydrologic gradient, the distribution of total denitrification genes (nap/nar + nirK/nirS + cNor/qNor + nosZ) inferred from metagenomic read abundance exhibited no consistent patterns. However, when genes were considered independently, nirS, cNor and nosZ read abundance was positively associated with areas of higher soil moisture, higher nitrate and higher annual denitrification rates, whereas nirK and qNor read abundance was negatively associated with these factors. These results suggest that environmental conditions, in particular soil moisture and nitrate, select for distinct denitrification communities that are characterized by differential abundance of genes encoding apparently functionally redundant proteins. In contrast, taxonomic analysis did not identify notable variability in denitrifying community composition across sites. While the capacity to denitrify was ubiquitous across sites, denitrification genes with higher energetic costs, such as nirS and cNor, appear to confer a selective advantage in microbial communities experiencing more frequent soil saturation and greater nitrate inputs. This study suggests metagenomics can help identify denitrification hotspots that could be protected or enhanced to treat non-point source nitrogen pollution.

Headwater streams are critical components of drainage systems, directly connecting terrestrial and downstream aquatic ecosystems. The amount of water in a stream can alter hydrologic connectivity between the stream and surrounding landscape and is ultimately an important driver of what constituents headwater streams transport. There is a shortage of studies that explore concentration–discharge (C‐Q) relationships in headwater systems, especially forested watersheds, where the hydrological and ecological processes that control the processing and export of solutes can be directly investigated. We sought to identify the temporal dynamics and spatial patterns of stream chemistry at three points along a forested headwater stream in Northern Michigan and utilize C‐Q relationships to explore transport dynamics and potential sources of solutes in the stream. Along the stream, surface flow was seasonal in the main stem, and perennial flow was spatially discontinuous for all but the lowest reaches. Spring snowmelt was the dominant hydrological event in the year with peak flows an order of magnitude larger at the mouth and upper reaches than annual mean discharge. All three C‐Q shapes (positive, negative, and flat) were observed at all locations along the stream, with a higher proportion of the analytes showing significant relationships at the mouth than at the mid or upper flumes. At the mouth, positive (flushing) C‐Q shapes were observed for dissolved organic carbon and total suspended solids, whereas negative (dilution) C‐Q shapes were observed for most cations (Na+, Mg2+, Ca2+) and biologically cycled anions (NO3−, PO43−, SO42−). Most analytes displayed significant C‐Q relationships at the mouth, indicating that discharge is a significant driving factor controlling stream chemistry. However, the importance of discharge appeared to decrease moving upstream to the headwaters where more localized or temporally dynamic factors may become more important controls on stream solute patterns.

Denitrifying bioreactors can be an effective management strategy to address excess nitrate from tile-drained fields. However, there has been minimal research on how bioreactors function during storm events, which can account for large portions of annual nitrate load. Storm events cause reduced hydraulic retention time and potentially temperature and flow path fluctuations that may disturb denitrifying microbial communities within the bioreactor. In this study, nitrate concentration and flow rate were monitored during baseflow and storm-induced flow in four denitrifying bioreactors treating agricultural tile drainage to compare removal rate (RR) and removal efficiency (RE) between flow conditions. Removal rate was higher in stormflow than baseflow for all the bioreactors, though this was only significant for two bioreactors. However, RR was lower on average following events compared with pre-storm rates. Lag time, the period between a parcel of water entering the bioreactor and exiting, ranged between 2 and 24 h for the observed storms. Calculations of RR and RE that account for lag time were significantly lower than standard calculations. The model of RR that included a variable of flow variation (coefficient of variation in flow over a 24-hour period) was most effective at describing observed data. This, along with differences in RR between baseflow and stormflow, suggests that bioreactor function in storms should be considered separately from baseflow. Decreased RR following storms also suggests that bioreactors may require additional design considerations to reduce disturbance, especially for fields where much of the annual nitrate runoff load is concentrated in storm-induced events.

The effect of long-term versus short-term water content on soil organic carbon (SOC) mineralizability was evaluated in a six-week incubation trial. Soils were sampled from field sites in upstate New York used for rain-fed bioenergy crop production: nitrogen (N)- fertilized reed canarygrass, switchgrass, switchgrass + N, as well as a broadleaf-grass fallow. Within each cropping system, natural moisture gradients due to topography and subsoil structure allowed us to sample across regions with high (0.5 g g−1), mid (0.4 g g−1) and low (0.3 g g−1) water content. Moisture of the laboratory incubations was adjusted mimicking the three average field moisture levels in a full factorial design. Increasing laboratory moisture in the incubations increased cumulative carbon mineralization per unit soil (C mineralization) and cumulative C mineralization per unit SOC (C mineralizability) (main effect p 0.05) and root biomass marginally influenced (p = 0.05) C mineralizability after correcting for soil texture variations. Additionally, after correcting for soil texture variations and biomass inputs, C mineralizability significantly decreased with higher field moisture (p = 0.02), indicating possible stabilization mechanisms through mineral interactions of SOC under high water content. Bulk contents of pedogenic iron and aluminum determined by oxalate extraction did not clearly explain differences in mineralizability. However, exchangeable calcium and magnesium contents were significantly (p

Failure of aging, undersized, and poorly maintained culverts is a problem throughout the United States. A lack of data on culvert location and condition complicates decision-making around t...

Recent developments in bottom-up vulnerability-based decision analysis frameworks present promising opportunities for flood practitioners to simplify complex decisions regarding risk mitigation and climate adaptation. This family of methodologies relies on strong social networks among flood practitioners and the public to support careful definition of stakeholder-relevant thresholds and vulnerabilities to hazards. In parallel, flood researchers are directly considering distinct atmospheric mechanisms that induce flooding to readily incorporate information on future climate projections. We perform a case study of flood professionals actively engaged in flood risk mitigation within Tompkins County, New York, USA, a community dealing with moderate flooding, to gage how much variance exists among professionals from the perspective of establishing a bottom-up flood mitigation study from an atmospheric perspective. Results of this case study indicate disagreement among flooding professionals as to which socioeconomic losses constitute a flood, disagreement on anticipated community needs, weak understanding of climate–weather–flood linkages, and some disagreement on community perceptions of climate adaptation. In aggregate, the knowledge base of the Tompkins County flood practitioners provides a well-defined picture of community vulnerability and perceptions. Our research supports the growing evidence that collaborative interdisciplinary flood mitigation work could reduce risk, and potentially better support the implementation of emerging bottom-up decision analysis frameworks for flood mitigation and climate adaptation.

Onsite septic systems use soil microbial communities to treat wastewater, in the process creating potent greenhouse gases (GHGs): methane (CH4) and nitrous oxide (N2O). Subsurface soil dispersal systems of septic tank overflow, known as leach fields, are an important part of wastewater treatment and have the potential to contribute significantly to GHG cycling. This study aimed to characterize soil microbial communities associated with leach field systems and quantify the abundance and distribution of microbial populations involved in CH4 and N2O cycling. Functional genes were used to target populations producing and consuming GHGs, specifically methyl coenzyme M reductase (mcrA) and particulate methane monooxygenase (pmoA) for CH4 and nitric oxide reductase (cnorB) and nitrous oxide reductase (nosZ) for N2O. All biomarker genes were found in all soil samples regardless of treatment (leach field, sand filter, or control) or depth (surface or subsurface). In general, biomarker genes were more abundant in surface soils than subsurface soils suggesting the majority of GHG cycling is occurring in near-surface soils. Ratios of production to consumption gene abundances showed a positive relationship with CH4 emissions (mcrA:pmoA, p 0.05). Of the three measured soil parameters (volumetric water content (VWC), temperature, and conductivity), only VWC was significantly correlated to a biomarker gene, mcrA (p = 0.0398) but not pmoA or either of the N2O cycling genes (p > 0.05 for cnorB and nosZ). 16S rRNA amplicon library sequencing results revealed soil VWC, CH4 flux and N2O flux together explained 64% of the microbial community diversity between samples. Sequencing of mcrA and pmoA amplicon libraries revealed treatment had little effect on diversity of CH4 cycling organisms. Overall, these results suggest GHG cycling occurs in all soils regardless of whether or not they are associated with a leach field system.

Study region: Finger Lakes Region, New York, USA. Study focus: The relationship between stream temperature and daily respiration and reaeration rates was investigated from May 2015 – May 2016 for two sites, one in the headwaters and the other near the mouth of a net heterotrophic stream. Daily respiration and reaeration rates were calculated by applying the night time slope method to open-channel diel oxygen concentrations during baseflow conditions. New hydrological insights for the region: Reference values for activation energy (Ea) are presented that could be applicable to lower order streams in temperate climates receiving significant inputs from surface and subsurface flows. There was a sizeable difference in temperature sensitivity between upstream and downstream sites. Ecosystem respiration (ER) exhibited a strong Arrhenius temperature dependence with daily respiration rates of 0.5–9.5 g O2 m−2 d−1 (Ea = 0.61 eV) in the headwaters and 1.8–30.3 g O2 m−2 d−1 (Ea = 0.67 eV) at the downstream location where ER in the headwaters was less sensitive to temperature. Ea for both sites fell within the expected theoretical range for heterotrophic respiration in aquatic ecosystems. Temperature reaeration rate coefficients ranged from 1.011 to 1.020 at the headwaters and from 1.005 to 1.009 downstream suggesting that daily reaeration rates at the headwaters were more sensitive to temperature than the downstream location where reaeration was likely controlled by stream discharge and driven by turbulence. Keywords: Stream metabolism, Dissolved oxygen, Arrhenius kinetics, Gas transfer coefficient, Aquatic respiration, Baseflow, Lotic ecosystems, Nighttime slope method

Escherichia coli (E. coli) level in streams is a public health indicator. Therefore, being able to explain why E. coli levels are sometimes high and sometimes low is important. Using citizen science data from Fall Creek in central NY we found that complementarily using principal component analysis (PCA) and partial least squares (PLS) regression provided insights into the drivers of E. coli and a mechanism for predicting E. coli levels, respectively. We found that stormwater, temperature/season and shallow subsurface flow are the three dominant processes driving the fate and transport of E. coli. PLS regression modeling provided very good predictions under stormwater conditions (R2 = 0.85 for log (E. coli concentration) and R2 = 0.90 for log (E. coli loading)); predictions under baseflow conditions were less robust. But, in our case, both E. coli concentration and E. coli loading were significantly higher under stormwater condition, so it is probably more important to predict high-flow E. coli hazards than low-flow conditions. Besides previously reported good indicators of in-stream E. coli level, nitrate-/nitrite-nitrogen and soluble reactive phosphorus were also found to be good indicators of in-stream E. coli levels. These findings suggest management practices to reduce E. coli concentrations and loads in-streams and, eventually, reduce the risk of waterborne disease outbreak.

The microbial community and function along with nitrate/nitrite (NOx) removal rates, and nitrogen (N) partitioning into "uptake", "denitrification", and "remaining" via isotope tracers, were studied in soil bioretention mesocolumns (8 unique plant species). Total denitrification gene reads per million (rpm) were positively correlated with % denitrified ( r = 0.69) but negatively correlated with total NOx removal following simulated rain events ( r = -0.79). This is likely due to plant-microbe interactions. Plant species with greater root volume, plant and microbial assimilation %, and NOx removal % had lower denitrification genes and rates. This implies that although microorganisms have access to N, advantageous functions, like denitrification, may not increase. At the conclusion of the 1.5-year experiment, the microbial community was strongly influenced by plant species within the Top zone dominated by plant roots, and the presence or absence of a saturated zone influenced the microbial community within the Bottom zone. Leptospermum continentale was an outlier from the other plants and had much lower denitrification gene rpm (average 228) compared to the other species (range: 277 to 413). The antimicrobial properties and large root volume of Leptospermum continentale likely caused this denitrification gene depression.

Intensive agriculture is an emerging theme of green roof technology. Production of affordable, nutritious vegetables for local consumption and stormwater management are among the ecosystem services emphasized in studies of rooftop farming. However, intensive vegetable production requires irrigation, which can reduce stormwater retention, and there is little empirical data from full-scale operational rooftop farms. In this paper, we report the hydrologic performance of the Brooklyn Grange Navy Yard Farm, a 0.61-ha rooftop farm atop an 11-story building in New York City USA. We monitored soil water content, precipitation, irrigation, drainage, and crop yields to provide water balance and water use efficiency. We found cumulative discharge exceeded precipitation by 11% during the entire study period, hence the farm was a net source of water in the urban hydrologic cycle. Depending on crop types, water use efficiency at the Grange was lower than in-ground agriculture in dry regions with high irrigation demands. For the Brooklyn Grange to integrate stormwater management and water-efficient crop production, it will be important to increase soil water storage, to reduce irrigation demands and drainage loss of water while maintaining average soil moisture levels in the range of readily available water between 10 and 100 kPa of soil water tension.

Macroplastics are a ubiquitous and growing environmental contaminant with impacts in both marine and terrestrial systems. Marine sampling has dominated research in this field, despite the terrestrial origins of most plastic debris. Due to the high surface water connectivity facilitated by roadside ditches, these landscape features provide a unique sampling location linking terrestrial and surface water systems. We collected and analyzed macroplastic accumulation by number of pieces, mass, and polymer type in roadside ditches across four land uses, before and during the COVID-19 pandemic in the Finger Lakes Region of New York State. Commercial land use plastic accumulation rate was highest, while forested land use accumulation rates were lowest on a piece basis. Pre-COVID-19 piece accumulation rates were significantly higher than COVID-19 piece accumulation rates across all land uses. Mass accumulation rates followed similar patterns observed in piece accumulation, but the patterns were not always statistically significant. Plastic type 4 (i.e. thin plastic films), especially plastic bags and wrappers, was the most frequently collected type of macroplastic by piece across all land uses within the 1-7 Resin Identification Codes. By mass, the data were distributed less consistently across land uses. Cigarette filters, containing the polymer cellulose acetate, were the most frequently found roadside plastic, but are not within the 1-7 classification system. Our results suggest that policies in place limiting plastic bag usage could substantially reduce roadside plastics but other plastics, such as food wrappers and other single use plastic films, which comprised a large proportion of the plastic debris collected, should also be regulated to further decrease macroplastic pollution.

There is a chronic disconnection among purely probabilistic flood frequency analysis of flood hazards, flood risks, and hydrological flood mechanisms, which hamper our ability to assess future flood impacts. We present a vulnerability-based approach to estimating riverine flood risk that accommodates a more direct linkage between decision-relevant metrics of risk and the dominant mechanisms that cause riverine flooding. We adapt the conventional peaks-over-threshold (POT) framework to be used with extreme precipitation from different climate processes and rainfall-runoff-based model output. We quantify the probability that at least one adverse hydrologic threshold, potentially defined by stakeholders, will be exceeded within the next N years. This approach allows us to consider flood risk as the summation of risk from separate atmospheric mechanisms, and supports a more direct mapping between hazards and societal outcomes. We perform this analysis within a bottom-up framework to consider the relevance and consequences of information, with varying levels of credibility, on changes to atmospheric patterns driving extreme precipitation events. We demonstrate our proposed approach using a case study for Fall Creek in Ithaca, NY, USA, where we estimate the risk of stakeholder-defined flood metrics from three dominant mechanisms: summer convection, tropical cyclones, and spring rain and snowmelt. Using downscaled climate projections, we determine how flood risk associated with a subset of mechanisms may change in the future, and the resultant shift to annual flood risk. The flood risk approach we propose can provide powerful new insights into future flood threats.

The advent of real-time, short-term farm management tools is motivated by the need to protect water quality above and beyond the general guidance offered by existing nutrient management plans. Advances in high-performance computing and hydrologic or climate modeling have enabled rapid dissemination of real-time information that can assist landowners and conservation personnel with short-term management planning. This paper reviews short-term decision support tools for agriculture that are under various stages of development and implementation in the United States: (i) Wisconsin's Runoff Risk Advisory Forecast (RRAF) System, (ii) New York's Hydrologically Sensitive Area Prediction Tool, (iii) Virginia's Saturated Area Forecast Model, (iv) Pennsylvania's Fertilizer Forecaster, (v) Washington's Application Risk Management (ARM) System, and (vi) Missouri's Design Storm Notification System. Although these decision support tools differ in their underlying model structure, the resolution at which they are applied, and the hydroclimates to which they are relevant, all provide forecasts (range 24-120 h) of runoff risk or soil moisture saturation derived from National Weather Service Forecast models. Although this review highlights the need for further development of robust and well-supported short-term nutrient management tools, their potential for adoption and ultimate utility requires an understanding of the appropriate context of application, the strategic and operational needs of managers, access to weather forecasts, scales of application (e.g., regional vs. field level), data requirements, and outreach communication structure. USDA-NRCS Conservation Innovation Grants This review was supported, in part, by USDA-NRCS Conservation Innovation Grants to improve P indexing and modeling and to improve short-term forecasting tools for nutrient management planning.

Bartlett et al. [2016] performed a re-interpretation and modification of the space-time lumped USDA NRCS (formerly SCS) Curve Number (CN) method to extend its applicability to forested watersheds. We believe that the well documented limitations of the CN method severely constrains the applicability of the modifications proposed by Bartlett et al. [2016]. This forward-looking comment urges the research communities in hydrologic science and engineering to consider the CN method as a stepping stone that has outlived its usefulness in research. The CN method fills a narrow niche in certain settings as a parsimonious method having utility as an empirical equation to estimate runoff from a given amount of rainfall, which originated as a static functional form that fits rainfall-runoff data sets. Sixty five years of use and multiple reinterpretations have not resulted in improved hydrological predictability using the method. We suggest that the research community should move forward by (1) identifying appropriate dynamic hydrological model formulations for different hydro-geographic settings, (2) specifying needed model capabilities for solving different classes of problems (e.g. flooding, erosion/sedimentation, nutrient transport, water management, etc.) in different hydro-geographic settings, and (3) expanding data collection and research programs to help ameliorate the so-called “over-parameterization” problem in contemporary modeling. Many decades of advances in geo-spatial data and processing, computation, and understanding are being squandered on continued focus on the static CN regression method. It is time to truly “move beyond” the Curve Number method.

The environmental benefits of organic farming compared to conventional agriculture are well documented, but relatively few studies have assessed their differences in emissions of nitrous oxide (N 2 O), a potent greenhouse gas (GHG). The objective of the study was to assess the interactive impact of management and landscape positions on soil characteristics and N 2 O emissions. A field-scale experiment was conducted in two adjacent grain farms in upstate New York that have both undergone the same management for 20 years. In the conventional field (CNV), inorganic fertilizer was the only nitrogen (N) source, but in the organic fields (ORG), a legume cover crop, red clover ( Trifolium pratense) , was frost-seeded into a winter grain (spelt, Triticum spelta ), and then incorporated in spring as a N source for the subsequent maize plants ( Zea mays ). Measurements of soil properties and N 2 O emissions were conducted at shoulder and toeslope positions on both CNV and ORG fields in 2012. Based on Principal Component Analysis, landscape position, management regime, and rotation phases explained 67% of the variation in the soil properties; these three major sources of variation in soil properties (principal components) were correlated significantly with seasonal average N 2 O emissions. Comparable N 2 O emissions were found from the clover-maize (ORG Cl-M) phase in the ORG field and the bare fallow-maize phase in the CNV field. The spelt-clover phase in the ORG field had the lowest N 2 O emissions due to low N availability. In the CNV field, seasonal average N 2 O emissions were driven mainly by the elevated gas fluxes after fertilizer application. High soil moisture and inorganic N pools towards the end of the growing season probably resulted in increased denitrification rates. The impact of landscape position on N 2 O emissions was mainly found in the CNV field, probably because greater moisture and pH drove greater rates of complete denitrification at toeslope positions. In the ORG Cl-M phase, the seasonal average N 2 O emissions were dominated by the emission peaks that immediately followed incorporation of clover. Greater clover biomass at shoulder slope positions resulted in greater N 2 O peaks there, but the position effect was not statistically significant. Our study suggests that ecosystem state factors, such as landscape characteristics, interacted with management practices to impact soil properties, crop growth, and microbial communities and, therefore, had interactive effects on N dynamics, including N 2 O emissions.

P availability often limits primary production in freshwater ecosystems and excessive P inputs promote accelerated eutrophication. Microbial mechanisms may control O2-dependent uptake/release of P in stream sediments and biofilms, but specific organisms responsible for these cycles have not been identified. Polyphosphate accumulating organisms (PAOs) are purposely enriched in treatment plants to remove P from wastewater. PAOs release P under anaerobic conditions and take it up under aerobic conditions. We hypothesized that alternating aerobic/anaerobic conditions promote patterns of P uptake/release similar to those attributed to PAOs in wastewater treatment. We collected intact, native stream biofilms and subjected them to laboratory treatments to impose conditions similar to what may occur because of diel oxygenic and respiratory cycles: 1) continuous sparging with air and 2) alternate sparging with air or anaerobic gas (20∶80% by volume CO2∶N2). We monitored PO43−, Ca, Mg, total Mn, K, Fe2+, an...

Naturally occurring stable water isotope tracers provide useful information for hydrologic model development and calibration. Existing models include varied approaches concerning unsaturated zone percolation mixing (preferential versus matrix flow) and evapotranspiration (ET) partitioning. We assess the impact of unsaturated zone simplifying assumptions when simulating the Shale Hills Watershed (SHW), a small (7.9 ha), temperate, forested watershed near Petersburg, Pennsylvania, USA with a relatively simple model. We found that different model structures/assumptions and parameterizations of unsaturated zone percolation had substantial impacts on the agreement between simulated and observed unsaturated-zone water isotopic signatures. We show that unsaturated zone percolation mixing primarily affects the unsaturated zone δ18O and δ2H during winter and spring and that percolation was best represented as a combination of both preferential and matrix flow. We evaluate the importance and implications related to the partitioning of ET into evaporation (E) and transpiration (T) and demonstrated that incorporation of a plant growth model for ET partitioning substantially improved reproduction of observed hydrologic isotopic patterns of the unsaturated zone during the spring season. We show that unsaturated zone percolation mixing and ET partitioning approaches do not substantially influence stream δ18O and δ2H and conclude that observed streamflow isotopic data is not always a strong predictor of model performance with respect to intra-watershed processes.