Predicting Nitrogen Loading With Land-Cover Composition: How Can Watershed Size Affect Model Performance?

Watershed-wide land-cover proportions can be used to predict the in-stream non–point source pollutant loadings through regression modeling. However, the model performance can vary greatly across different study sites and among various watersheds. Existing literature has shown that this type of regression modeling tends to perform better for large watersheds than for small ones, and that such a performance variation has been largely linked with different interwatershed landscape heterogeneity levels. The purpose of this study is to further examine the previously mentioned empirical observation based on a set of watersheds in the northern part of Georgia (USA) to explore the underlying causes of the variation in model performance. Through the combined use of the neutral landscape modeling approach and a spatially explicit nutrient loading model, we tested whether the regression model performance variation over the watershed groups ranging in size is due to the different watershed landscape heterogeneity levels. We adopted three neutral landscape modeling criteria that were tied with different similarity levels in watershed landscape properties and used the nutrient loading model to estimate the nitrogen loads for these neutral watersheds. Then we compared the regression model performance for the real and neutral landscape scenarios, respectively. We found that watershed size can affect the regression model performance both directly and indirectly. Along with the indirect effect through interwatershed heterogeneity, watershed size can directly affect the model performance over the watersheds varying in size. We also found that the regression model performance can be more significantly affected by other physiographic properties shaping nitrogen delivery effectiveness than the watershed land-cover heterogeneity. This study contrasts with many existing studies because it goes beyond hypothesis formulation based on empirical observations and into hypothesis testing to explore the fundamental mechanism.     

LAND USE AND STREAMWATER NITRATE-N DYNAMICS IN AN URBAN-RURAL FRINGE WATERSHED1

ABSTRACT: Agricultural and residential activities are key non-point sources of nitrogen pollution in urban-rural fringe areas. A GIS-based watershed approach was used to compare land use indicators (septic system and animal unit densities), to streamwater nitrate-N in the Salmon River near Vancouver, B.C., Canada. The density of septic systems was used as an indicator of residential development while animal unit density was used as an indicator of the intensity of agricultural activity. Nitrate-nitrogen (nitrate-N) concentrations as high as 7.1 mg·L^?1 were found in the mid-portion of the watershed during the summer months, when streamflow is low and groundwater comprises a large proportion of water in the stream. The major aquifer supplying water to the midsection of the watershed is contaminated with nitrate-N. A comparison of the relationships between septic system and animal unit density and nitrate-N in the upstream to downstream direction provided evidence that both residential and agricultural activities contribute to elevated nitrate-N in the Salmon River mainstem. In contrast, only septic system density corresponded to the pattern of streamwater nitrate-N in Coghlan Creek, the main tributary to the Salmon River.     

Reducing Fertilizer‐Nitrogen Losses from Rowcrop Landscapes: Insights and Implications from a Spatially Explicit Watershed Model

We present conceptual and quantitative models that predict changes in fertilizer‐derived nitrogen delivery from rowcrop landscapes caused by agricultural conservation efforts implemented to reduce nutrient inputs and transport and increase nutrient retention in the landscape. To evaluate the relative importance of changes in the sources, transport, and sinks of fertilizer‐derived nitrogen across a region, we use the spatially explicit SPAtially Referenced Regression On Watershed attributes watershed model to map the distribution, at the small watershed scale within the Upper Mississippi‐Ohio River Basin (UMORB), of: (1) fertilizer inputs; (2) nutrient attenuation during delivery of those inputs to the UMORB outlet; and (3) nitrogen export from the UMORB outlet. Comparing these spatial distributions suggests that the amount of fertilizer input and degree of nutrient attenuation are both important in determining the extent of nitrogen export. From a management perspective, this means that agricultural conservation efforts to reduce nitrogen export would benefit by: (1) expanding their focus to include activities that restore and enhance nutrient processing in these highly altered landscapes; and (2) targeting specific types of best management practices to watersheds where they will be most valuable. Doing so successfully may result in a shift in current approaches to conservation planning, outreach, and funding.     

Spatiotemporal variability of wet atmospheric nitrogen deposition to the Neuse River Estuary, North Carolina

Excessive nitrogen (N) loading to N-sensitive waters such as the Neuse River estuary (North Carolina) has been shown to promote changes in microbial and algal community composition and function (harmful algal blooms), hypoxia and anoxia, and fish kills. Previous studies have estimated that wet atmospheric deposition of nitrogen (WAD-N), as deposition of dissolved inorganic nitrogen (DIN: NO3-, NH3/NH4+) and dissolved organic nitrogen, may contribute at least 15% of the total externally supplied or "new" N flux to the coastal waters of North Carolina. In a 3-yr study from June 1996 to June 1999, we calculated the weekly wet deposition of inorganic and organic N at eleven sites on a northwest-southeast transect in the watershed. The annual mean total (wet DIN + wet organics) WAD-N flux for the Neuse River watershed was calculated to be 956 mg N/m2/yr (15026 Mg N/yr). Seasonally, the spring (March-May) and summer (June-August) months contain the highest total weekly N deposition; this pattern appears to be driven by N concentration in precipitation. There is also spatial variability in WAD-N deposition; in general, the upper portion of the watershed receives the lowest annual deposition and the middle portion of the watershed receives the highest deposition. Based on a range of watershed N retention and in-stream riverine processing values, we estimate that this flux contributes approximately 24% of the total "new" N flux to the estuary.     

Representing the Connectivity of Upland Areas to Floodplains and Streams in SWAT+

In recent years, watershed modelers have put increasing emphasis on capturing the interaction of landscape hydrologic processes instead of focusing on streamflow at the watershed outlet alone. Understanding the hydrologic connectivity between landscape elements is important to explain the hydrologic response of a watershed to rainfall events. The Soil and Water Assessment Tool+ (SWAT+) is a new version of SWAT with improved runoff routing capabilities. Subbasins may be divided into landscape units (LSUs), e.g., upland areas and floodplains, and flow can be routed between these LSUs. We ran three scenarios representing different extents of connectivity between uplands, floodplains, and streams. In the first and second scenarios, the ratio of channelized flow from the upland to the stream and sheet flow from the upland to the floodplain was 70/30 and 30/70, respectively, for all upland/floodplain pairs. In the third scenario, the ratio was calculated for each upland/floodplain pair based on the upland/floodplain area ratio. Results indicate differences in streamflow were small, but the relative importance of flow components and upland areas and floodplains as sources of surface runoff changed. Also, the soil moisture in the floodplains was impacted. The third scenario was found to provide more realistic results than the other two. A realistic representation of connectivity in watershed models has important implications for the identification of pollution sources and sinks.     

Wet/Dry Mapping: Using Citizen Scientists to Monitor the Extent of Perennial Surface Flow in Dryland Regions

Wet/dry mapping provides a low-cost, comprehensive snapshot for monitoring flow conditions in rivers with interrupted perennial (spatially intermittent) surface flow. When used in conjunction with more traditional point-specific stream flow or groundwater measurements, it provides a better understanding of hydrologic systems at the broad landscape or watershed scale. Through use of trained volunteers, we mapped reaches with surface water during the driest time of year to track annual variation in length and location of perennial flow. Data from 12 years of wet/dry mapping on the San Pedro River in Arizona, USA, showed 62 reaches with surface flow in every year, totaling 32% of the river length through the San Pedro Riparian National Conservation Area. They also show areas with high year-to-year variation in flow length, which indicate changes in local groundwater conditions and may provide early warning of ecological changes. Data and maps from this project have been useful for a wide variety of conservation, management, and research efforts.     

大气干湿沉降对城市景观水体水质影响的评价

选择上海城市化地区的绿地、水体、道路、工地及空中5个类型的环境区域,定期收集大气干湿沉降。测定其大气沉降通量及CODCr、TN、TP的沉降量,并计算分析了其对城市景观水体水质的影响。研究结果表明：在仅受大气干湿沉降影响的条件下,水质处于地表水V类中值、水深为0.5m、1.0m、1.5m和2.0m的城市景观水体,经过28d、54d、83d和214d即可转变为劣V类水体。通过实例分析,提出了应对大气干湿沉降影响的水质保持措施。研究成果为城市景观水体的水质保育提供了评价依据与借鉴。     

A GIS-based technique for linking landscape characteristics to non-point source nitrogen export potential: implications for contributing areas management

A Nitrate-N Leaching Index (NLI) is calculated and the results indicate that nitrogen loss in the study area occurs through both leaching and surface runoff. A non-linear regression model of trapping efficiency was combined with a first order decay model to examine the impact of soil characteristics, slope, vegetative cover, land use and distance to streams on the spatial pattern of non-point source nitrogen inputs to streams. The model evaluates the statistical significance of each landscape factor and provides an easy interpretation of the landscape delivery ratio of nitrogen based on a pixel-based characterisation of the watershed. The model was validated by comparing the distributions of the observed and estimated monthly nitrogen concentrations. The exploratory GIS-based method presented here can improve the understanding of the impact of landscape characteristics on nitrate-nitrogen contributing areas and therefore assist watershed management efforts.     

Integrated Modular Modeling of Water and Nutrients From Point and Nonpoint Sources in the Patuxent River Watershed1

Abstract: We present a simple modular landscape simulation model that is based on a watershed modeling framework in which different sets of processes occurring in a watershed can be simulated separately with different models. The model consists of three loosely coupled submodels: a rainfall‐runoff model (TOPMODEL) for runoff generation in a subwatershed, a nutrient model for estimation of nutrients from nonpoint sources in a subwatershed, and a stream network model for integration of point and nonpoint sources in the routing process. The model performance was evaluated using monitoring data in the watershed of the Patuxent River, a tributary to the Chesapeake Bay in Maryland, from July 1997 through August 1999. Despite its simplicity, the landscape model predictions of streamflow, and sediment and nutrient loads were as good as or better than those of the Hydrological Simulation Program‐Fortran model, one of the most widely used comprehensive watershed models. The landscape model was applied to predict discharges of water, sediment, silicate, organic carbon, nitrate, ammonium, organic nitrogen, total nitrogen, organic phosphorus, phosphate, and total phosphorus from the Patuxent watershed to its estuary. The predicted annual water discharge to the estuary was very close to the measured annual total in terms of percent errors for both years of the study period (≤2%). The model predictions for loads of nutrients were also good (20‐30%) or very good (<20%) with exceptions of sediment (40%), phosphate (36%), and organic carbon (53%) for Year 1.     

Modeling atmospheric nitrogen deposition and transport in the Chesapeake Bay watershed

Atmospheric deposition of nitrate nitrogen and ammonium nitrogen has been identified as a major factor in the decline of water quality in the Chesapeake Bay. Reports have indicated that atmospheric deposition may account for 25 to 80% of the total nitrogen load entering the bay. However, uncertainties exist regarding the accuracy of the atmospheric deposition inputs, nitrogen retention coefficients, and in-stream nutrient uptake rates used in these studies. This project was designed to reassess the potential inputs of atmospheric nitrogen deposition to the bay through the use of a high-resolution wet deposition model, improved wet and dry deposition and nutrient retention estimates, existing soils and land use data, and geographic information systems software. Model results indicate that the methods used in previous studies may overestimate the contribution of atmospheric nitrate and ammonium deposition to the Chesapeake Bay watershed (CBW). Wet and dry atmospheric nitrate and ammonium nitrogen deposition estimates to the CBW ranged from 52.7 to 141.9 and 41.9 to 60.1 million kg/yr, respectively, between 1984 and 1996. Dry and total atmospheric deposition loads to the watershed are substantially less than previous estimates. Estimates of the percent contribution of atmospherically deposited nitrogen to the Chesapeake Bay represent between 20 and 32% of the total nitrate and ammonium nitrogen load to the watershed from all nitrogen sources. While these estimates are lower than many other published estimates, regression analysis of model parameters, nitrogen retention coefficients, output, and measured in-stream nitrogen loads indicate that the calculated nitrogen loads may still be too high.     

Modeling sediment and nitrogen export from a rural watershed in eastern Canada using the soil and water assessment tool

Watershed simulation models can be used to assess agricultural nonpoint-source pollution and for environmental planning and improvement projects. However, before application of any process-based watershed model, the model performance and reliability must be tested with measured data. The Soil and Water Assessment Tool version 2005 (SWAT2005) was used to model sediment and nitrogen loads from the Thomas Brook Watershed, which drains a 7.84 km rural landscape in the Annapolis Valley of Nova Scotia, Canada. The Thomas Brook SWAT model was comprised of 28 subbasins and 265 hydrologic response units, most of them containing agricultural land use, which is the main nonpoint nitrogen source in the watershed. Crop rotation schedules were incorporated into the model using field data collected within Agriculture and Agri-Food Canada's Watershed Evaluation of Beneficial Management Practices program. Model calibration (2004-2006) and validation (2007-2008) were performed on a monthly basis using continuous stream flow, sediment, and nitrogen export measurements. Model performance was evaluated using the coefficient of determination, Nash-Sutcliff efficiency (NSE), and percent bias (PBIAS) statistics. Study results show that the model performance was satisfactory (NSE > 0.4; > 0.5) for stream flow, sediment, nitrate-nitrogen, and total nitrogen simulations. Annual corn, barley, and wheat yields were also simulated well, with PBIAS values ranging from 0.3 to 7.2%. This evaluation of SWAT demonstrated that the model has the potential to be used as a decision support tool for agricultural watershed management in Nova Scotia.     

Assessing the Effectiveness of Winter Cover Crops for Controlling Agricultural Nutrient Losses

A nutrient loss reduction strategy is necessary to guide the efforts of improving water quality downstream of an agricultural watershed. In this study, the effectiveness of two winter cover crops, namely cereal rye and annual ryegrass, is explored as a loss reduction strategy in a watershed that ultimately drains into a water supply reservoir. Using a coupled optimization-watershed model, optimal placements of the cover crops were identified that would result in the tradeoffs between nitrate-N losses reduction and adoption levels. Analysis of the 10%, 25%, 50%, and 75% adoption levels extracted from the optimal tradeoffs showed that the cover crop placements would provide annual nitrate-N loss reductions of 3.0%–3.7%, 7.8%–8.8%, 15%–17.5%, and 20.9%–24.3%, respectively. In addition, for the same adoption levels (i.e., 10%–75%), sediment (1.8%–17.7%), and total phosphorus losses (0.8%–8.6%) could be achieved. Results also indicate that implementing each cover crop on all croplands of the watershed could cause annual water yield reduction of at least 4.8%, with greater than 28% in the months of October and November. This could potentially be detrimental to the storage volume of the downstream reservoir, especially in drought years, if cover crops are adopted in most of the reservoir's drainage area. Evaluating water yield impacts, particularly in periods of low flows, is thus critical if cover crops are to be considered as best management practices in water supply watersheds.     

An assessment of landscape characteristics affecting estuarine nitrogen loading in an urban watershed

Exploring the quantitative association between landscape characteristics and the ecological conditions of receiving waters has recently become an emerging area for eco-environmental research. While the landscape-water relationship research has largely targeted on inland aquatic systems, there has been an increasing need to develop methods and techniques that can better work with coastal and estuarine ecosystems. In this paper, we present a geospatial approach to examine the quantitative relationship between landscape characteristics and estuarine nitrogen loading in an urban watershed. The case study site is in the Pensacola estuarine drainage area, home of the city of Pensacola, Florida, USA, where vigorous urban sprawling has prompted growing concerns on the estuarine ecological health. Central to this research is a remote sensor image that has been used to extract land use/cover information and derive landscape metrics. Several significant landscape metrics are selected and spatially linked with the nitrogen loading data for the Pensacola bay area. Landscape metrics and nitrogen loading are summarized by equal overland flow-length rings, and their association is examined by using multivariate statistical analysis. And a stepwise model-building protocol is used for regression designs to help identify significant variables that can explain much of the variance in the nitrogen loading dataset. It is found that using landscape composition or spatial configuration alone can explain most of the nitrogen loading variability. Of all the regression models using metrics derived from a single land use/cover class as the independent variables, the one from the low density urban gives the highest adjusted R-square score, suggesting the impact of the watershed-wide urban sprawl upon this sensitive estuarine ecosystem. Measures towards the reduction of non-point source pollution from urban development are necessary in the area to protect the Pensacola bay ecosystem and its ecosystem services.     

MODELING TRANSIENT pH DEPRESSIONS IN COASTAL STREAMS OF BRITISH COLUMBIA USING NEURAL NETWORKS1

ABSTRACT: Transient events in water chemistry in small coastal watersheds, particularly pH depressions, are largely driven by inputs of precipitation. While the response of each watershed depends upon both the nature of the precipitation event and the season of the year, how the response changes over time can provide insight into landscape changes. Neural network models for an urban watershed and a rural‐suburban watershed were developed in an attempt to detect changes in system response resulting from changes in the landscape. Separate models for describing pH depressions for wet season and dry season conditions were developed for a seven year period at each watershed. The neural network models allowed separation of the effects of precipitation variations and changes in watershed response. The ability to detect trends in pH depression magnitudes was improved by analyzing neural network residuals rather than the raw data. Examination of sensitivity plots of the models indicated how the neural networks were affected by different inputs. There were large differences in effects between seasons in the rural‐suburban watershed whereas effects in the urban watershed were consistent between seasons. During the study period, the urban watershed showed no change in pH depression response, while the rural‐suburban watershed showed a significant increase in the magnitude of pH depressions, likely the result of increased urbanization.     

Application of SPARROW Modeling to Understanding Contaminant Fate and Transport from Uplands to Streams

Understanding spatial variability in contaminant fate and transport is critical to efficient regional water‐quality restoration. An approach to capitalize on previously calibrated spatially referenced regression (SPARROW) models to improve the understanding of contaminant fate and transport was developed and applied to the case of nitrogen in the 166,000 km² Chesapeake Bay watershed. A continuous function of four hydrogeologic, soil, and other landscape properties significant (α = 0.10) to nitrogen transport from uplands to streams was evaluated and compared among each of the more than 80,000 individual catchments (mean area, 2.1 km²) in the watershed. Budgets (including inputs, losses or net change in storage in uplands and stream corridors, and delivery to tidal waters) were also estimated for nitrogen applied to these catchments from selected upland sources. Most (81%) of such inputs are removed, retained, or otherwise processed in uplands rather than transported to surface waters. Combining SPARROW results with previous budget estimates suggests 55% of this processing is attributable to denitrification, 23% to crop or timber harvest, and 6% to volatilization. Remaining upland inputs represent a net annual increase in landscape storage in soils or biomass exceeding 10 kg per hectare in some areas. Such insights are important for planning watershed restoration and for improving future watershed models.     

Denitrification enzyme activity of fringe salt marshes in New England (USA)

Coastal salt marshes are a buffer between the uplands and adjacent coastal waters in New England (USA). With increasing N loads from developed watersheds, salt marshes could play an important role in the water quality maintenance of coastal waters. In this study we examined seasonal relationships between denitrification enzyme activity (DEA) in salt marshes of Narragansett Bay, Rhode Island, and watershed N loadings, land use, and terrestrial hydric soils. In a manipulative experiment, the effect of nutrient enrichment on DEA was examined in a saltmeadow cordgrass [Spartina patens (Aiton) Muhl.] marsh. In the high marsh, DEA significantly (p < 0.05) increased with watershed N loadings and decreased with the percent of hydric soils in a 200-m terrestrial buffer. In the low marsh, we found no significant relationships between DEA and watershed N loadings, residential land development, or terrestrial hydric soils. In the manipulation experiment, we measured increased DEA in N-amended treatments, but no effect in the P-amended treatments. The positive relationships between N loading and high marsh DEA support the hypothesis that salt marshes may be important buffers between the terrestrial landscape and estuaries, preventing the movement of land-derived N into coastal waters. The negative relationships between marsh DEA and the percent of hydric soils in the adjacent watershed illustrate the importance of natural buffers within the terrestrial landscape. Denitrification enzyme activity appears to be a useful index for comparing relative N exposure and the potential denitrification activity of coastal salt marshes.     

Nitrogen Sources and Sinks Within the Middle Rio Grande,New Mexico1

Abstract: Relationships between discharge, land use, and nitrogen sources and sinks were developed using 5 years of synoptic sampling along a 300 km reach of the Rio Grande in central New Mexico. Average river discharge was higher during 2001 and 2005 “wet years” (15 m³/s) than during the drought years of 2002‐04 “dry years” (8.9 m³/s), but there were no differences in nitrogen loading from wastewater treatment plants (WWTPs) which were the largest and most consistent source of nitrogen to the river (1,330 kg/day). Average total dissolved nitrogen (TDN) concentrations remained elevated for 180 km downstream of the Albuquerque WWTP averaging 1.2 mg/l in wet years and 0.52 mg/l in dry years. Possible explanations for the constant elevated TDN concentrations downstream of the major point source include reduced nitrogen retention capacity, minimal contact with riparian or channel vegetation, large suspended sediment loads, and low algal biomass. Somewhat surprisingly, agricultural return flows had lower average nitrogen concentrations than river water originally diverted to agriculture in both wet (0.81 mg/l) and dry years (0.19 mg/l), indicating that the agricultural system is a sink for nitrogen. Lower average nitrogen concentrations in the river during the dry years can be explained by the input of agricultural returns which comprise the majority of river flow in dry years.     

LANDSCAPE ATTRIBUTES AS CONTROLS ON GROITHD WATER NITRATE REMOVAL CAPACITY OF RIPARIAN ZONES1

ABSTRACT: Inherent site factors can generate substantial variation in the ground water nitrate removal capacity of riparian zones. This paper examines research in the glaciated Northeast to relate variability in ground water nitrate removal to site attributes depicted in readily available spatial databases, such as SSUIRGO. Linking site‐specific studies of riparian ground water nitrate removal to spatial data can help target high‐value riparian locations for restoration or protection and improve the modeling of watershed nitrogen flux. Site attributes, such as hydric soil status (soil wetness) and geomorphology, affect the interaction of nitrate‐enriched ground water with portions of the soil ecosystem possessing elevated biogeochemical transformation rates (i.e., biologically active zones). At our riparian sites, high ground water nitrate‐N removal rates were restricted to hydric soils. Geomorphology provided insights into ground water flowpaths. Riparian sites located on outwash and organic/alluvial deposits have high potential for nitrate‐enriched ground water to interact with biologically active zones. In till deposits, ground water nitrate removal capacity may be limited by the high occurrence of surface seeps that markedly reduce the time available for biological transformations to occur within the riparian zone. To fully realize the value of riparian zones for nitrate retention, landscape controls of riparian nitrate removal in different climatic and physiographic regions must be determined and translated into available spatial databases.     

INTEGRATED MODELING FOR WATERSHED MANAGEMENT: MULTIPLE OBJECTIVES AND SPATIAL EFFECTS1

ABSTRACT: This paper presents an optimization framework for prioritizing sites for wetlands restoration on a watershed or landscape scale. The framework is designed for analyzing the potential environmental impacts of alternative management strategies while accounting for economic constraints, thereby aiding decision makers in explicitly considering multiple management objectives. The modeling strategy consists of two phases. First, relationships between the configuration of land use types in a watershed and valued ecosystem services are specified mathematically. Second, those functions are incorporated into a spatial optimization model that allows comparisons of the expected environmental impacts and economic costs of management strategies that change the configuration of land use in the watershed. By way of a stylized example, this paper develops the general structure of the framework, presents simulation results based on two production functions for ecosystem services, and discusses the potential utility of the methodology for watershed management.     

Relating landscape characteristics to non-point source pollution in mine waste-located watersheds using geospatial techniques

Landscape characteristics of a watershed are important variables that influence surface water quality. Understanding the relationship between these variables and surface water quality is critical in predicting pollution potential and developing watershed management practices to eliminate or reduce pollution risk. To understand the impacts of landscape characteristics on water quality in mine waste-located watersheds, we conducted a case study in the Tri-State Mining District which is located in the conjunction of three states (Missouri, Kansas and Oklahoma). Severe heavy metal pollution exists in that area resulting from historical mining activities. We characterized land use/land cover over the last three decades by classifying historical multi-temporal Landsat imagery. Landscape metrics such as proportion, edge density and contagion were calculated based on the classified imagery. In-stream water quality data over three decades were collected, including lead, zinc, iron, cadmium, aluminum and conductivity which were used as key water quality indicators. Statistical analyses were performed to quantify the relationship between landscape metrics and surface water quality. Results showed that landscape characteristics in mine waste-located watersheds could account for as much as 77% of the variation of water quality indicators. A single landscape metric alone, such as proportion of mine waste area, could be used to predict surface water quality; but its predicting power is limited, usually accounting for less than 60% of the variance of water quality indicators.