Grantee Research Project Results
2011 Progress Report: Impact of Climate Change and Variability on the Nation's Water Quality andEcosystem State
EPA Grant Number: R834187Title: Impact of Climate Change and Variability on the Nation's Water Quality andEcosystem State
Investigators: Vörösmarty, Charles J. , Clements, William , Poff, N. LeRoy , Gettel, Gretchen M. , Fekete, Balazs , Green, Mark , Wollheim, Wil
Current Investigators: Vörösmarty, Charles J. , Clements, William , Poff, N. LeRoy , Wollheim, Wil , Fekete, Balazs , Green, Mark , Gettel, Gretchen M.
Institution: University of New Hampshire , City College of the City University of New York , Colorado State University
Current Institution: City College of the City University of New York , Colorado State University , University of New Hampshire
EPA Project Officer: Packard, Benjamin H
Project Period: October 1, 2009 through September 30, 2012 (Extended to September 30, 2014)
Project Period Covered by this Report: October 1, 2010 through September 30,2011
Project Amount: $799,554
RFA: Consequences of Global Change for Water Quality (2008) RFA Text | Recipients Lists
Research Category: Ecological Indicators/Assessment/Restoration , Climate Change , Watersheds , Aquatic Ecosystems , Water
Objective:
The primary aim of this project is to develop a national-scale, multi-constituent biogeochemistry model by using new and existing modules to compare regional sensitivities of water quality and aquatic ecosystem habitat to climate change and
variability forced by regional, downscaled GCM ensemble outputs (North American Regional Climate Change Assessment Program; NARCCAP). The objectives of this study are three-fold:
- To expand an existing modeling framework to integrate state-of-the-art regional climate projections (NARCCAP) with hydrology and aquatic process models, in order to assess how strongly projected climate change and variability propagate through the Nation's waterways and thus alter the emergence, distribution, severity, and timing of water quality problems.
- To evaluate the extent to which selected attributes of aquatic ecosystem state are made vulnerable to projected regional climate change across the conterminous United States.
- To present to agency planners a blueprint for systematic monitoring of the Nation's vulnerable and impaired waterways.
Progress Summary:
Existing Framework Development and Application
The primary improvement in FrAMES was accomplished by integrating FrAMES and our data archive. The Global Hydrological Archive and Analysis System (GHAAS) that the CCNY and UNH teams maintained for over a decade provides a series of unique GIS capabilities to manage and manipulate time series (grid, or point) along with the gridded networks (Vörösmarty et al. 2000). The GHAAS infrastructure combined with FrAMES now allows for the facilitated configuration of module integration in modeling exercises using different input forcing data.
The clear separation of process modules and data flow and model execution management in FrAMES allowed us to parallelize our framework to utilize symmetric multiprocessor (SMP) computers using light weight process and shared memory. The parallel version of FrAMES can make model simulations 2 - 3 times faster on 8 core machines than running on single CPU. FrAMES also was submitted to the Community Surface Dynamics Modeling System (CSDMS) (an NSF-funded national center), where it is available to registered CSDMS user. The CSDMS computing facilities also mirror the full data archive along with the GHAAS tools supporting FrA
During the period 2010-2011, this modeling framework was continuously refined as part of the core project activity. Because the model infrastructure functions were already in place (by end of year 1) much of the refinement was essentially debugging and minor code improvements. The biggest change for those who participated in developing new modules within FrAMES was our recent move from its original Subversion version control to Git, which was driven by our need to better communicate code changes between the UNH and the CCNY modeling teams. In particular, as new students joined both the CCNY and UNH teams and who needed access to FrAMES and to actively modify the various modules implemented within the framework, it was critical to move away from the centralized version control provided by Subversion to a more modern decentralized solution as embodied via Git. Git allows developers to maintain discrete records of their own branches and exchange changes. While Git does not limit the number of repositories indivduals can maintain, we choose GitHub as a "meeting ground" for exchanging changes made to FrAMES and to its modules.
Hydrological and Other Physical Variables
Improvements to River Network and Corresponding Elevation
We applied the regridding algorithm from Fekete et al. (2001)to aggregate the ~500 m (15 arc second on longitude × latitude) HydroSHEDS gridded river network (Lehner, et al., 2008) to coarser resolutions (3 and 6 arc minutes). We merged HydroSHEDS with our existing 6-minute gridded river network to expand its coverage beyond the spatial limitations of the HydroSHEDS domain. Additionally, we obtained a new high-resolution digital elevation data set with full global extent derived from stereographic processing of ASTER images from NASA’s Aqua and Terra satellites. While this dataset is less accurate than SRTM, its full coverage allowed us to complete our network at high latitudes where HydroSHEDS is lacking. Furthermore, we applied a network defragmentation routine, which eliminates sporadic basin fragments (as a result of DEM errors) by identifying endorheic (inland mouth-of-river endpoint) basins and searching for potential "pour points" (digital exits) through presumed endorheic watershed boundaries within a given elevation threshold. We applied DEM corrections based on the gridded networks at aggregated resolutions, which established a consistency between the DEM and the river network by lowering the elevation (essentially cutting valleys) along all potential river courses from headwater to true river mouth.
During Year 2 of the project, we can report the full completion of our regridding of HydroSHEDS (a 15 arc second gridded network) to various coarser resolutions (3, 5, 6, 15 and 30’ on longitude latitude grid) with full consistency, applying our
regridding algorithm (Fekete, et al., 2001), which aggregates the ~500 m (15 arc second on longitude × latitude) HydroSHEDS gridded river network (Lehner, et al., 2008) to coarser resolutions. This year, we also carried out a comprehensive
comparison between HydroSHEDS and the National Hydrography Dataset3 (NHD) from USGS. This comparison not only confirmed the accuracy of the original HydroSHEDS source data (at 500 m), but allowed us to establish the
linkage between basins derived from HydroSHEDS to their NHD equivalent in Hydrological Unit Codes (HUC). The HUC outlets identified on HydroSHEDS enables us to transfer the established linkage to coarser resolution networks that
we developed last year. We worked with HUC8, but this year we will consider redoing our analysis at HUC12, which will offer the evaluation of smaller basins. The ability to work in gridded network space and map it back to HUC basins will allow us to present our results in NHD context that is a preferred means of reporting to federal agencies (EPA and USGS).
The Vörösmarty lab is in the process of delineating watershed boundaries for each EPA/NAQWA biological site using the now-validated HydroSHEDS digital elevation model to link to the basins associated with the 3,378 ecological test sites identified by the Colorado State University partner team. These ecological sites span a wide range of river types ranging from wadeable small rivers to medium sized rivers, tens of meters in width. The co-registration requires further refinement because some of the captured sites erroneously represent either too large or too small river basins. The correction of these errors will require a largely manual process to identify the river sections that has the closest upstream catchment area to the reported values in the ecological sites catalog.
As part of our network development, we assigned parameterized riverbed geometry to every grid cell at each gridded network resolution (starting from the HydroSHEDS 15” to 3,5,6,15”) using established empirical relationships relating mean annual river discharge to mean river width and depth. We assumed that the river channel takes the shape of a power function that was found consistent with empirical formulas relating river discharge to key flow properties (mean depth, width, average flow velocity) (Dingman, 2007). We will use the ecological sites to refine the empirical relationships and replace our a priori riverbed geometry parameterization with more robust estimates.
Wetlands Database
We developed a wetlands data set for the U.S. that is applicable to continental scale modeling. We obtained the entire National Wetlands Inventory digital archive from Tom Dahl at the U.S. Fish and Wildlife Service. This represents a very high resolution (1:24000) digital data set that covers 58% of the conterminous United States and Alaska. We also extracted wetland data depicted by the National Land Cover Data (NLCD) set. We aggregated the data to 6-minute spatial resolution, to provide a wetland layer of percent of grid cell as wetlands.
Reservoir Operations
Dams and reservoirs are a key characteristic of the modern hydrologic system, with a particular impact on altering the natural streamflow, thermal characteristics, and biogeochemical fluxes of rivers. Depending on dam characteristics (i.e., capacity, height, etc.), watershed characteristics (i.e., upstream catchment area, land use, average precipitation, etc.) and the purpose of building a dam (i.e., for irrigation, hydro electricity, etc.), each reservoir has a specific optimum operating rule. While this is critical for human water security, from an earth sciences perspective, it means that literally 84,000 dams in the National Inventory of Dams potentially follows 84,000 different sets of rules for storing and releasing water which must somehow be accounted for in our modeling. In reality, there is no comprehensive observational dataset depicting these operating rules that is available. Thus, we will simulate these rules. Our perspective is not to find the optimum operating rule per se but to find composite behaviors that are consistent with the nominal use of each reservoir and their impacts on observed streamgage behaviors. We just began an exploration into the use of Artificial Neural Networks (ANN) in this context. We see as an important advantage of ANN its ability to detect complex nonlinear relations between input and output data, which makes it a valuable tool for time series prediction and fitness approximation. High quality, measured parameters are available throughout the United States (i.e., USGS gauges); thus, we are able to use measured data to train and test our Artificial Neural Networks.
For our purposes, output is monthly discharge from a reservoir or cluster of reservoirs aligned along mainstems and tributaries. For input variables, we are using the prior N (= 2 or 3 initially) months of discharge by dam, past N = 2 or 3 months of inflow to the reservoir (or precipitation upstream). Other possible parameters are upstream catchment area, reservoir maximum capacity, reservoir normal capacity and reservoir height. We are exploring whether it is best to
create lumped ANN parameterizations or sub-sets based on reported reservoir purpose (i.e. irrigation, hydro electricity, etc.). It is also possible to divide the United States into a small set of climate and/or precipitation categories (wet, moderate, dry), which may reduce simulation error.
Climate Change and Ensemble Variability
Near Real-time Climate Data Archive
A core element of our data archive is a series of climate products from different sources. We currently are in the process of incorporating the North American Climate Change Assessment Project (NARCCAP) downscaled, regional, ensemble climate change simulations which will be used as boundary conditions for FrAMES. Access to the data has been granted and we have downloaded the requisite data sets. The precipitation data sets at different spatial resolutions in conjunction with the regridded river networks discussed previously allowed us to carry out a series of tests assessing the impact of resolution on the riverine flow routing. Our test confirmed the intuitive findings of Fekete et al. (2001), namely
that flow routing at daily or sub-daily temporal resolution requires high resolution river networks, but due to the averaging nature of the horizontal water transport processes the same temporal resolution for computing the vertical water balances is not needed.
During 2010-2011, we continued to maintain our near real-time climate forcing data archive from a variety of sources such as the NCEP Reanalysis products (Kalnay, et al., 1996; Kistler, et al., 2001), precipitation from the Global Precipitation
Climate Center, Offenbach, Germany, which provides the basis for a series of water balance model derived products that the CUNY team regularly updates and reports on to the community (Fekete and MacDonald, 2011; Fekete, et al.,
2004, 2010). Our near real-time data archive enables us to ultimately operationalize our water quality assessments, poising us to produce a key byproduct of this effort, but admittedly beyond the scope of the current project.
This past year, we also joined the NARCCAP user group, which not only enables direct access to a series of downscaled global circulation model outputs for the United States, but allows us to directly follow the most recent and salient updates and directions of the NARCCAP development. In addition to tests of the NARCCAP archives, we joined the Inter-sectoral Impact Model Intercomparison Project (ISI-MIP) that is testing a series of Global Circulation Model outputs in the context of various land-surface, terrestrial ecosystem and hydrological model (Schiermeier, 2012). Our contribution to this project will be numerical experiments of anticipated impacts of climate change on water quality that will be build on the results of this EPA-STAR effort.
Land-to-Aquatic Loadings
Development of National Wastewater Treatment Plant Geospatial Database
The spatial distribution of wastewater treatment facilities in the US in 1984 and 2004 was mapped according to data acquired from the EPA Clean Water Need Surveys (CWNS) database (Rychtecka, et al., in prep) for the publicly funded
treatment plants nationwide. Beside the core characteristics of the wastewater treatment plants (location, treatment levels, capacity, number of people served, etc.) the CWNS database contains information on the water quality and discharge rates of the effluent flow, from which loads are computed.
We consolidated the differences between the 1984 and 2004, and analyzed the improvement of the wastewater treatment in terms of total nitrogen (TN) release to freshwater. The majority of the treatment plants release their outflow to surface waters. Assigning removal efficiency to all treatment plants allowed us to estimate the TN loading in 1984 and 2004. While significant regional differences exist our most important finding was that despite of the cumulative public and
private sector capital expenditure of $202.5 billion (indexed to constant 2004 dollars), the nation-wide TN loading only decreased slightly (5.6%). While the removal efficiency increased from 67% to 78% (due to plant upgrades, most in 2004 at secondary or advanced treatment levels, and with the addition of plants), the influent flow increased (by 26%), largely offseting the added treatment capacities and improved TN removal efficiency.
Aquatic Processing
The current aquatic ecosystem model takes advantage of hydrologic functionality provided by a new version of our hydrology model--WBMplus--to route material through continental river systems. WBM plus incorporates water withdrawals
(from surface diversions and groundwater) plus reservoir operation modules to treat direct human impacts on streamflow (Fekete, et al., 2010; Wisser, et al., 2008, 2010a, 2010b). We have developed submodels in FrAMES to route water temperature, nitrogen and carbon.
Water Temperature Component
The water temperature model predicts average daily water temperatures based on mixing of terrestrial runoff and re-equilibration during discharge routing. Mean annual temperatures correspond well with observations across all latitudes. Longitudinal profiles along large river transects are consistent with the expectation that re-equilibration with ambient air temperature is a dominant influence on water temperatures. The water temperature component offers the potential to understand climate change impacts on aquatic biogeochemistry.
During Year 2 of this grant, simulations were conducted for the continental USA at the 6-minute resolution (latitude, longitude) for the years 1960 through 2000. Comparison of observed and predicted average daily discharges and
temperatures at 290 selected USGS gauges indicated good fits at some gauges (i.e., Passaic River) and poor fits at others (i.e., Yellowstone River).
We then tested the model’s capability to simulate thermoelectric power plants and the impact they have on downstream water temperatures with a case study simulation in the northeastern US at the 3-minute resolution (latitude, longitude). Power plant data was acquired and assembled from the U.S. Energy Information Administration (EIA) database. Estimates were made regarding water usage and effluent temperature based on power plant type and energy output. Average summer water temperatures increased by as much as 9 degrees as a result of thermoelectric power plants, and impacts were more widespread during drought conditions. The next step will be to model these impacts at the national domain (6’ L/L resolution) and calculate the total length of rivers impacted under various flow levels.
Several model improvements currently are underway. First, despite the relatively robust water temperature results the predicted river discharge is being modified. To improve performance we are calibrating the model using observation data at 36 gauge stations listed in the USGS Hydrologic Benchmark Network (HBN) using the Generalized Likelihood Uncertainty Estimate (GLUE) (Beven and Binley, 1992). The HBN is a collection of USGS gauge stations across the United States that provides long-term streamflow measurements in areas where anthropogenic impacts are minimal. We will also be updating our climate drivers with higher resolution MERRA data sets from NASA’s Global Modeling and Assimilation Office (GMAO) for the calibration period of 1979 through 2011.
Second, new sub-routines including irrigation, impervious surfaces, and reservoirs are being developed so their signature on discharge and water temperatures will be reflected in the model. The irrigation function will withdraw river water and redistribute it across crop surfaces whereas imperious surfaces will create flashier flows in urban areas. Both irrigation and impervious surfaces will extend the exposure of water to atmospheric heat forcings. Reservoirs will adjust the timing of discharge in the river network and will provide a long-term mixture of upstream water temperatures. These functions will be tested and optimized with historical observations of discharge and water temperatures.
Finally, future IPCC climate scenario data will be used as input for the model. The model will be used to test the relative impact of climate change and direct human engineering (i.e. reservoirs, irrigation, and impervious surfaces) have on river discharge and water temperatures. These relative impacts will be evaluated across various flow conditions ranging from drought to high flow periods.
Denitrification/Respiration Component
For nitrogen, we are using simple removal parameters that are based on concentration and water temperature (Mulholland, 2008; Wollheim, et al., 2008a; Wollheim, et al., 2008b). The empirical studies indicate that denitrification is a non-linear function of concentration (Mulholland, 2008). On average, each order of magnitude increase in loading to a watershed results in a 24% decline in the proportion of aquatic loading that is removed. However, the construction of reservoirs can offset some, though not all of this decline. Our analysis indicates that efficiency loss contributes 25% of the increase in nutrient exports to the coastal zone in the contemporary era due to anthropogenic activities (Wollheim, et al., in prep).
Using these model results we constructed maps of the proportion of local DIN loadings to aquatic systems that are exported to the coastal ocean. These maps indicate regions where aquatic control has weakened considerably, but also indicates regions where aquatic regulation of exports remains high, despite increased loading. Such maps may be useful for identifying where continued or intensified agricultural activity may be possible with minimal impact to the coastal ocean. The model has also been used to estimate N2O emissions from aquatic systems globally. These findings suggest that freshwater systems account for 10% of global anthropogenic N2O emissions (Beaulieu, et al., 2011).
DOC Fluxes
This modeling component seeks to provide predictions of discharge, water temperature, and DOC daily time series by accounting for spatial and temporal variability in loading from land and transformations in river systems. We are using the Framework for Aquatic Modeling of the Earth System (FrAMES) as the modeling environment, which is optimized for horizontal transfer of water and materials through river systems and developed by the study team (Wisser, et al., 2010a,b; Wollheim et al. 2008a,b; Wollheim, in prep). The discharge component in FrAMES is based on recent updates to the UNH Water Balance Model, WBMplus (Vorosmarty, et al. 1998; Wisser, et al., 2010a,b).
This past year we have developed a river light model (based on Julian et al., 2008) that predicts the spatial and seasonal variability of light reaching the water surface and attenuation through depth. Together, these factors will be used to drive both microbial and photodegradation of colored DOC. Water temperature predictions are needed in order to model the fate of aquatic DOC, because transformation rates are temperature dependent. The light model takes into account variation in solar radiation, canopy height, reflectance, and attenuation through the water column, which is a function of DOC and turbidity.
We continue to develop our DOC model within FrAMES to predict DOC fluxes throughout the continental U.S. In the model, we apply either empirically based land use specific loading relationships of DOC as a function of runoff (e.g.,
Raymond and Saiers 2010), or incorporate model predicted DOC loadings from terrestrial ecosystem models. We have run the model in both modes, including using predictions of monthly DOC loading from the Terrestrial Ecosystem Model,
developed by Jerry Melillo and Dave Kicklighter at the Marine Biological Laboratory. TEM predicts DOC runoff from a variety of land uses as a function of runoff and terrestrial ecosystem processes, which accounts for variability due to seasonality, precipitation, and land use. However, the role of wetlands, an important source, is not currently included in these approaches. In the coming year, we expect to modify the loading approaches to account for wetlands using empirical relationships derived from the USGS database.
For both loading approaches, we predict spatially and temporally varying DOC inputs from land to water throughout drainage basins. These inputs are then routed downstream and undergo transformation in the aquatic system, allowing prediction of fluxes throughout river systems. Preliminary results using this approach are promising but discrepancies remain, likely because wetlands and aquatic DOC production are currently not accounted for. Enhancements this coming year will account for differential loading from wetlands, and more realistic parameterization of aquatic transformation, including photodegradation.
Stream/River Habitat Assessment
We have combined 1639 sites from the EPA Wadeable Stream Assessment and 1536 sites from the USGS National Water-Quality Assessment Program (NAQWA) that contain macroinvertebrate benthos samples. The EPA data were collected in 2000-2005 and NAQWA data collected in 1993-2004. The benthos samples consist of abundance and richness values for each taxon collected and identified. We are using this biological data to derive multiple community-based indices and metrics that reflect stream condition. These metrics include taxonomic metrics (e.g., EPT, richness), tolerance metrics (e.g., % tolerant taxa, IBIs), and trait-based metrics (e.g., functional feeding group, thermal tolerance), the latter being derived from the trait database in Poff, et al. (2006). These various metrics are being used to predict community responses to flow and stream temperature metrics derived from different climactic and disturbance scenarios.
We are using the daily discharge and temperature data generated by the hydrology sub-team to derive flow and temperature metrics that we hypothesizeto be predictive of the taxonomic and functional characteristics of stream macroinvertebrate communities. We are deriving flow metrics such as mean daily or monthly flows to r eflect the availability of fresh water to the stream ecosystem. Other metrics reflect the stability and extreme conditions in the stream system, such as the coefficient of variation in daily flows, 7-day maxima, 7-day minima, and count and duration of high and low flow pulses. Extreme flow conditions can have a strong influence on stream fauna. For example, a preliminary analysis of 256 EPA sites reveals that streams with more unstable flow regimes (reflected in CV of flow) have more species with fast development times, greater generations per year, very short life spans, high dispersal, higher propensity to drift, and small size, all traits of organisms adapted to extreme conditions in streams. The Poff lab will also derive metrics from temperature data, particularly annual mean temperature, mean temperature for each month, and minimum and maximum temperatures. Air temperature is strongly correlated with stream temperature, which in turn has very strong influence on stream fauna. We expect to see a greater proportion of thermally tolerant species in communities in streams with warmer temperatures.
Nationally, there are 464 USGS stream gauges that are located within the same stream network of a biological sampling site, within 8 linear km of the sampling site, and have both flow and stream temperature data. These sites will be used to verify predicted stream temperature and discharge from the aquatic ecosystem models.
Future Activities:
Planned Activities for Reporting Period 10/1/2011 – 9/30/2012:
Data
- Finalize contemporary time series for baseline runs
- Stage and incorporate NARCCAP ensemble outputs
- Apply contemporary water engineering statistics as needed
- Continue staging relevant cal/val data (USGS, EMAP, NAWQA)
- Configure scenarios, as needed, of future land use, engineering, pollutant load
FrAMES Modules
- Continue development of enhanced carbon dynamics model (land-to-aquatic)
- Construct habitat/ecosystem state model and indicators
- Run, validate, analyze future scenarios
Product Distribution and Outreach
- Establish dedicated project website
- Populate website with key input/output data sets
- Prepare/execute mid-project consultation with management experts
- Continue preparation/publication of scientific papers.
References:
Beaulieu J, Hamilton S, Wollheim W, Hall R, Mulholland PJ, Ashkenas LR, Cooper LW, Dahm CN, Dodds W, Grimm NB, Johnson SL, McDowell WH, Poole GC, Valett HM, Arango CP, Bernot MJ, Burgin AJ, Crenshaw CL, Helton AM, Johnson LT, O'Brien JM, Potter JD, Sheibley RW, Sobota DJ, Thomas SM. Nitrous oxide emission from denitrification in stream and river networks. Proceedings of the National Academy of Sciences 2011;108:214-219.
Beven KJ, Binley AM. The future of distributed models: model calibration and uncertainty prediction. Hydrological Processes 1992;6:279-298.
Dingman SL. Analytical derivation of at-a-station hydraulic geometry relations. Journal of Hydrology 2007;334:17-27.
Fekete BM, Lammers RB, MacDonald AM, Bowling LC, Lawford R. River discharge [state of the climate in 2009]. Bulletin of the American Meteorological Society 2010;91:S35.
Fekete BM, MacDonald AM. River discharge [state of the climate in 2010]. Bulletin of the American Meteorological Society 2011;92:S46-S48.
Fekete BM, Vörösmarty CJ, Lammers RB. Scaling gridded river networks for macro-scale hydrology: development and analysis and control of error. Water Resources Research 2001;37:1955-1968.
Fekete BM, Vörösmarty CJ, Roads J, Willmott C. Uncertainties in precipitation and their impacts on runoff estimates. Journal of Climatology 2004;17:294-303.
Fekete BM, Wisser D, Kroeze C, Mayorga E, Bouwman L, Wollheim WM, Vörösmarty C. Millennium Ecosystem Assessment scenario drivers (1970-2050): climate and hydrological alterations. Global Biochemical Cycles 2010;24(4):GB0A12, doi: 10.1029/2009GB003593.
Julian JP, Doyle MW, Stanley EH. Empirical modeling of light availability in rivers. Journal of Geophysical Research–Biogeosciences 2008;113:G03022, doi:10.1029/2007JG000601.
Kalnay E, et al. The NCEP/NCAR 40-year reanalysis project. Bulletin of the American Meteorological Society 1996;77:437-471, doi:10.1175/1520-0477(1996)077.
Kistler, R. et al. The NCEP/NCAR 50-year reanalysis: monthly means CD-ROM and documentation. Bulletin of the American Meteorological Society 2001;82:247-267, doi:10.1175/1520-0477(2001)082.
Lehner B, Verdin K, Jarvis A. New global hydrography derived from spaceborne elevation data. AGU EOS Transactions 2008;89:93-94.
Mulholland PJ, Helton AM, Poole GC, Hall RO, Hamilton SK, Peterson BJ, Tank JL, Ashkenas RL, Cooper LW, Dahm CN, Dodds WK, Findlay SEG, Gregory SV, Grimm NB, Johnson SL, McDowell WH, Meyer JL, Valett HM, Webster JR, Arango CP, Beaulieu JJ, Bernot MJ, Burgin AJ, Crenshaw CL, Johnson LT, Niederlehner BR, O'Brien JM, Potter JD, Sheibley RW, Sobota DJ, Thomas SM. Stream denitrification across biomes and its response to anthropogenic nitrate loading. Nature 2008;452:202-205.
Poff NL, Olden JD, Vieira NKM, Finn DS, Simmons MP, Kondratieff BC. Functional trait niches of North American lotic insects: traits-based ecological applications in light of phylogenetic relationships. Journal of the North American Benthological Society 2006;25:730-755.
Raymond PA, Saiers JE. Event controlled DOC export from forested watersheds. Biogeochemistry 2010;100(1):197-209, doi: 10.1007/s10533-010-9416-7.
Rychtecka M, Vörösmarty CJ, Green P, Fekete BM, Neale A. Spatio-temporal impact of wastewater point sources on nitrogen pollution in U.S. watersheds. In preparation, 2011.
Schiermeier Q. Models hone picture of climate impacts.Nature 2012;482:286, doi:10.1038/482286a.Vörösmarty CJ, Federer CA, Schloss AL. Potential evaporation functions compared on US watersheds: possible implications for global-scale water balance and terrestrial ecosystem modeling. Journal of Hydrology 1998;207:147-169.
Vörösmarty CJ, Fekete BM, Meybeck M, Lammers RB. Global system of rivers: its role in organizing continental land mass and defining land-to-ocean linkages. Global Biochemical Cycles 2000;14:599-621.
Wisser D, Fekete BM, Vörösmarty CJ, Schumann AH. Reconstructing 20th century global hydrography: a contribution to the Global Terrestrial Network-Hydrology (GTN-H). Hydrology and Earth System Sciences 2010;14:1-24.
Wisser D, Frolking S, Douglas E, Fekete BM, Schuman AH, Vörösmarty CJ. The significance of local water resources captured in small reservoirs for crop production-a global-scale analysis. Journal of Hydrology 2010;384:264-275.
Wisser D, Frolking S, Douglas EM, Fekete BM, Vörösmarty CJ, Schumann AH. Global irrigation water demand: variability and uncertainties arising from agricultural and climate data sets. Geophysical Research Letters 2008;35(24):L24408, doi:10.1029/2008GL035296.
Wollheim WM, Peterson BJ, Vörösmarty CJ, Hopkinson CS, Thomas SA. Dynamics of N removal over annual time scales in a suburban river network. Biogeosciences 2008;G03038, doi:10.1029/2007JG000660.
Wollheim WM, Peterson BJ, Vorosmarty CJ. Decline in aquatic ecosystem service efficiency magnifies global flux of anthropogenic nitrogen to coastal zones (in preparation).
Wollheim WM, Vörösmarty CJ, Bouwman AF, Green PA, Harrison J, Linder E, Peterson BJ, Seitzinger S, Syvitski JPM. Global N removal by freshwater aquatic systems: a spatially distributed, within-basin approach. Global Biochemical Cycles 2008;GB2026, doi:10.1029/2007GB002963.
Journal Articles on this Report : 5 Displayed | Download in RIS Format
Other project views: | All 69 publications | 13 publications in selected types | All 12 journal articles |
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Alexander RB, Bohlke JK, Boyer EW, David MB, Harvey JW, Mulholland PJ, Seitzinger SP, Tobias CR, Tonitto C, Wollheim WM. Dynamic modeling of nitrogen losses in river networks unravels the coupled effects of hydrological and biogeochemical processes. Biogeochemistry 2009;93(1-2):91-116. |
R834187 (2010) R834187 (2011) R834187 (2012) R834187 (2013) R834187 (Final) R833261 (2008) R833261 (2009) R833261 (2010) R833261 (Final) |
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Beaulieu JJ, Tank JL, Hamilton SK, Wollheim WM, Hall Jr. RO, Mulholland PJ, Peterson BJ, Ashkenas LR, Cooper LW, Dahm CN, Dodds WK, Grimm NB, Johnson SL, McDowell WH, Poole GC, Valett HM, Arango CP, Bernot MJ, Burgin AJ, Crenshaw CL, Helton AM, Johnson LT, O'Brien JM, Potter JD, Sheibley RW, Sobota DJ, Thomas SM. Nitrous oxide emission from denitrification in stream and river networks. Proceedings of the National Academy of Sciences of the United States of America 2011;108(1):214-219. |
R834187 (2010) R834187 (2011) R834187 (2012) R834187 (2013) R834187 (Final) R833261 (2010) R833261 (Final) |
Exit Exit Exit |
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Green MB, Wollheim WM, Basu NB, Gettel G, Rao PS, Morse N, Stewart R. Effective denitrification scales predictably with water residence time across diverse systems. Nature Precedings 2009;3520.1. |
R834187 (2010) R834187 (2011) R834187 (2012) R834187 (2013) R834187 (Final) R833261 (2009) R833261 (2010) R833261 (Final) |
Exit Exit Exit |
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Harrison JA, Maranger RJ, Alexander RB, Giblin AE, Jacinthe P-A, Mayorga E, Seitzinger SP, Sobota DJ, Wollheim WM. The regional and global significance of nitrogen removal in lakes and reservoirs. Biogeochemistry 2009;93(1-2):143-157. |
R834187 (2010) R834187 (2011) R834187 (2012) R834187 (2013) R834187 (Final) R833261 (2008) R833261 (2009) R833261 (2010) R833261 (Final) |
Exit |
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Helton AM, Poole GC, Meyer JL, Wollheim WM, Peterson BJ, Mulholland PJ, Bernhardt ES, Stanford JA, Arango C, Ashkenas LR, Cooper LW, Dodds WK, Gregory SV, Hall Jr. RO, Hamilton SK, Johnson SL, McDowell WH, Potter JD, Tank JL, Thomas SM, Valett HM, Webster JR, Zeglin L. Thinking outside the channel: modeling nitrogen cycling in networked river ecosystems. Frontiers in Ecology and the Environment 2011;9(4):229-238. |
R834187 (2010) R834187 (2011) R834187 (2012) R834187 (2013) R834187 (Final) R833261 (2010) R833261 (Final) |
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Supplemental Keywords:
regionalization, climate change/variability, hydrology, aquatic habitat, indicators, water quality, risk assessment, climate models;, RFA, Air, climate change, environmental monitoring, water resources, watershed, modelingProgress and Final Reports:
Original AbstractThe perspectives, information and conclusions conveyed in research project abstracts, progress reports, final reports, journal abstracts and journal publications convey the viewpoints of the principal investigator and may not represent the views and policies of ORD and EPA. Conclusions drawn by the principal investigators have not been reviewed by the Agency.