Critical Source Areas Research Articles

Manipulation of fertiliser regimes in phosphorus enriched soils can reduce phosphorus loss to leachate through an increase in pasture and microbial biomass production

Phytoextraction of phosphorus (P) has been proposed as a strategy to reduce the potential for P loss from P enriched soils. Compared to pastures that receive adequate P fertilisers to maintain soil P concentrations, applying half maintenance rates, or no P, slowly decreases soil P concentration and P losses to water. We hypothesise that the quantity of P lost in leachate can be further decreased by the addition of nitrogen (N) fertiliser to stimulate plant-P uptake. A 451 day lysimeter trial investigated subsurface P losses from three New Zealand soil types (USDA soil taxonomy: Udand, Dystrudept and Vitrand) under three N fertiliser rates, zero, 150 and 300kgNha−1yr−1 and two rates of P fertiliser, zero and half maintenance application with regular cutting and removal of pasture. For two of the soil types (Dystrudept and Vitrand), N application increased pasture production and decreased the load of dissolved reactive P (DRP) leached by 53–76% and the load of total dissolved P (TDP) by 39–53% compared to when no N was applied. Furthermore, for these soils, compared to the no P and no N treatment, applying P at half the rate designed to maintain soil P concentration, decreased the load of DRP and TDP in leachate by a 62–68% and 54–59% due to immobilisation of P within the microbial biomass. A high sorption capacity, leading to slow but sustained release of P to the soil solution, was seen as the probable reason for the lack of treatment effect in the third soil (Udand). This study highlights the potential for manipulating fertiliser regimes and implementing a cut and carry system on critical source areas of P loss within a farm as a strategy to reduce P loss from P enriched pastoral soils without impacting on productivity.

Accommodating environmental thresholds and extreme events in hydrological models: A Bayesian approach

Extreme events appear to play an important role in pollutant export and the overall functioning of watershed systems. Because they are expected to increase in frequency as urbanization and recent climate change trends continue, the development of techniques that can effectively accommodate the behavior of watersheds during extreme events is one of the challenges of the contemporary modeling practice. In this regard, we present a Bayesian framework which postulates that the watershed response to precipitation occurs in distinct states. Precipitation depth above a certain threshold triggers an extreme state, which is characterized by a qualitatively different response of the watershed to precipitation. Our calibration framework allows us to identify these extreme states and to characterize the different watershed behavior by allowing parameter values to vary between states. We applied this framework to SWAT model implementations in two creeks in the Hamilton Harbour watershed of Redhill Creek, an urban catchment, and Grindstone Creek, an agricultural one. We found that our framework is able to coherently identify watershed states and state-specific parameters, with extreme states being characterized by a higher propensity for runoff generation. Our framework resulted in better model fit above the precipitation threshold, although there were not consistent improvements of model fit overall. We demonstrate that accommodating threshold-type of behavior may improve the use of models in locating critical source areas of non-point source pollution.

Application of analytical hierarchy process for effective selection of agricultural best management practices

In this study an analytical hierarchy process (AHP) was used for ranking best management practices (BMPs) in the Saginaw River Watershed based on environmental, economic and social factors. Three spatial targeting methods were used for placement of BMPs on critical source areas (CSAs). The environment factors include sediment, total nitrogen, and total phosphorus reductions at the subbasin level and the watershed outlet. Economic factors were based on total BMP cost, including installation, maintenance, and opportunity costs. Social factors were divided into three favorability rankings (most favorable, moderately favorable, and least favorable) based on area allocated to each BMP. Equal weights (1/3) were considered for the three main factors while calculating the BMP rank by AHP. In this study three scenarios were compared. A comprehensive approach in which environmental, economic, and social aspects are simultaneously considered (Scenario 1) versus more traditional approaches in which both environmental and economic aspects were considered (Scenario 2) or only environmental aspects (sediment, TN, and TP) were considered (Scenario 3). In Scenario 1, only stripcropping (moderately favorable) was selected on all CSAs at the subbasin level, whereas stripcropping (49–69% of CSAs) and residue management (most favorable, 31–51% of CSAs) were selected by AHP based on the watershed outlet and three spatial targeting methods. In Scenario 2, native grass was eliminated by moderately preferable BMPs (stripcropping) both at the subbasin and watershed outlet levels due the lower BMP implementations cost compared to native grass. Finally, in Scenario 3, at subbasin level, the least socially preferable BMP (native grass) was selected in 100% of CSAs due to greater pollution reduction capacity compared to other BMPs. At watershed level, nearly 50% the CSAs selected stripcropping, and the remaining 50% of CSAs selected native grass and residue management equally.

Simulating the impact of watershed management for surface water quality protection: A case study on reducing inorganic nitrogen load at a watershed scale

The management and control of non-point source pollutants (NPSPs) has been an increasing concern throughout the world. Controlling NPSP is critical to achieve the goals for improving surface water quality. This study used the Soil and Water Assessment Tool (SWAT) model to compare the effects of different kinds of watershed management measures on the transport of sediments and nutrients (ammonium nitrogen and nitrate nitrogen) in one of the main tributaries of the Xiangjiang River, the Zhengshui River. Our results confirmed that agricultural areas were the dominant sources of nutrients and sediment in the watershed and that a filter strip can significantly mitigate surface water pollution by intercepting and absorbing nutrients. We hypothesized five management plans to improve water quality for achieving the standard set by the National Twelfth Five-Year Plan (NTFYP). These plans were assessed according to their effects, economic cost efficiency and social impacts. The results showed that all five plans can successfully meet the goal of the NTFYP to reduce pollutant loads at the outlet of the watershed by more than 10%; and the results showed that plan that focused on critical source areas (CSAs) did not show its superiority as previous studies had theoretically suggested when the economic and social impacts were considered. Meanwhile, plans which considered economic factors and cost effectiveness would be more feasible and suitable as their cost efficiency were 10% and 30% higher than that of CSA plans for NO3− and NH4+, respectively, and their social impacts were much lower than other plans. Moreover, regression analysis further revealed the main characteristics of subbasin controlling the treatments efficiency of the inorganic nitrogen. Our study highlights the significance of combining theory with actual practice and would facilitate the selection and implement of more effective and reasonable measures to improve water quality.

Contrasting the spatial management of nitrogen and phosphorus for improved water quality: Modelling studies in New Zealand and France

Critical source areas (CSAs) define areas of a farm or catchment that emit the majority of water quality contaminants but account for a minority of the area at a field, farm or catchment scale. Using process based modelling we tested the hypothesis that the definition and management of CSAs would decrease losses of phosphorus (P) in two New Zealand catchments and nitrogen (N) in a French catchment. In the New Zealand catchment, CSAs of P loss were isolated to small areas within fields commensurate with surface flow pathways, while in the French catchment, CSAs for N loss were influenced by factors (inputs and land use) relevant at a field (or multiple field) scale. Scenarios were tested that examined the management of CSAs versus whole field management for P, and decreasing N loss within CSAs by increasing the proportion of grassland fields and changing their location relative to cropland. The results showed that N losses decreased by up to 25% as more grassland was incorporated into the catchment, especially in wet areas near valley bottoms due to a longer growth period and better utilisation/storage of N than cropland. For P, focusing mitigation on CSAs decreased catchment losses to a similar degree as mitigations applied across the whole catchment, but was on average 6–7 times more cost-effective. Therefore, the definition and management of CSAs at an appropriate scale is recommended to improve water quality and minimise the impact on farm profitability.

Quantifying the spatial variability of soil physical and chemical properties in relation to mitigation of diffuse water pollution

Understanding spatial variability of soil properties in response to land-use impacts is essential for evaluating the effectiveness of measures taken to address diffuse water pollution from agriculture. However, despite the growing emphasis on integrated catchment-scale implementation of land-use mitigation measures, the baseline landscape-scale evaluation of the spatial variability of key soil nutrients remains scarce. This study employs a high resolution geostatistical approach to characterise the spatial variability of parameters, including soil bulk density (BD), total soil carbon (TC), nitrogen (TN), phosphorus (TP), inorganic phosphorus (IP), organic phosphorus (OP), stable nitrogen isotope ratio (δ15N), C:N ratio, carbon storage and nitrogen storage in two study catchments with contrasting land uses (agricultural and semi-natural) that are subject to targeted management interventions to reduce flood risk and improve water quality. We found a stronger degree of spatial dependence of all soil properties in the agricultural than the semi-natural catchment, except for bulk density and δ15N. Furthermore, bulk density, TP, IP, OP, C:N ratio, δ15N and carbon storage showed a longer range or spatial auto-correlation in the agricultural catchment. The central tendency (median and mean) of all soil properties was also significantly different between the two catchments, with the exception of IP and δ15N. The spatial correlations between the soil properties pointed to the mechanisms that were responsible for the observed differences, whilst the krigged surfaces of soil variables identified most likely critical source areas for targeted land management interventions to improve water quality. Arable and intensive grasslands were identified as ‘high-impact’ land uses, associated with negative alteration of soil properties and increased diffuse water pollution, whilst moorland was a ‘low impact’ land use associated with improved water quality. A comparison with the national soil survey dataset shows that whilst it can be relied upon for the broad characterisation of carbon and TP stocks in the two study catchments, it underestimates the spatial variability of key soil properties in certain soil types and land uses. As the restoration of soil spatial heterogeneity may take several decades, a high resolution geostatistical approach should be included in the future design of catchment-scale monitoring schemes to inform catchment management strategies and elucidate the time frame over which landscape scale improvements in soil properties and corresponding ecosystem services can be achieved.

Prediction of dissolved reactive phosphorus losses from small agricultural catchments: calibration and validation of a parsimonious model

Abstract. Eutrophication of surface waters due to diffuse phosphorus (P) losses continues to be a severe water quality problem worldwide, causing the loss of ecosystem functions of the respective water bodies. Phosphorus in runoff often originates from a small fraction of a catchment only. Targeting mitigation measures to these critical source areas (CSAs) is expected to be most efficient and cost-effective, but requires suitable tools. Here we investigated the capability of the parsimonious Rainfall-Runoff-Phosphorus (RRP) model to identify CSAs in grassland-dominated catchments based on readily available soil and topographic data. After simultaneous calibration on runoff data from four small hilly catchments on the Swiss Plateau, the model was validated on a different catchment in the same region without further calibration. The RRP model adequately simulated the discharge and dissolved reactive P (DRP) export from the validation catchment. Sensitivity analysis showed that the model predictions were robust with respect to the classification of soils into "poorly drained" and "well drained", based on the available soil map. Comparing spatial hydrological model predictions with field data from the validation catchment provided further evidence that the assumptions underlying the model are valid and that the model adequately accounts for the dominant P export processes in the target region. Thus, the parsimonious RRP model is a valuable tool that can be used to determine CSAs. Despite the considerable predictive uncertainty regarding the spatial extent of CSAs, the RRP can provide guidance for the implementation of mitigation measures. The model helps to identify those parts of a catchment where high DRP losses are expected or can be excluded with high confidence. Legacy P was predicted to be the dominant source for DRP losses and thus, in combination with hydrologic active areas, a high risk for water quality.

Identifying critical source areas of nonpoint source pollution with SWAT and GWLF

Identification of critical source areas (CSAs) (areas contributing most of the pollutants in a watershed) is important for cost-effective implementation of best management practices. Identification of such areas is often done through watershed modeling. Various watershed models are available for this purpose, however it is not clear if the choice (and complexity) of a model would lead to differences in locations of CSAs. The objective of this study was to use two models of different complexity for identifying CSAs. The relatively complex Soil and Water Assessment Tool (SWAT) and the simpler Generalized Watershed Loading Function (GWLF) were used to identify CSAs of sediment and nutrients in the Saugahatchee Creek watershed in east central Alabama. Models were calibrated and validated for streamflow, sediment, total nitrogen (TN) and total phosphorus (TP) at a monthly time scale. While both models performed well for streamflow, SWAT performed slightly better than GWLF for sediment, TN and TP. Sub-watersheds dominated by urban land use were among those producing the highest amount of sediment, TN and TP loads, and thus identified as CSAs. Sub-watersheds with some amount of agricultural crops were also identified as CSAs of TP and TN. A few hay/pasture dominated sub-watersheds were identified as CSAs of TN. The identified land use source areas were also supported by field collected water quality data. A combined index was used to identify the sub-watersheds (CSAs) that need to be targeted for overall reduction of sediment, TN and TP. While many CSAs identified by SWAT and GWLF were the same, some CSAs were different. Therefore, this study concludes that model choice will affect the location of some CSAs.

Evaluation of a surface hydrological connectivity index in agricultural catchments

Identification of surface hydrological connectivity at scales where critical source areas (CSAs) can be managed is fundamental to achieving effective management of phosphorus loss in agricultural c...

Predicting critical source areas of sediment in headwater catchments

Mitigation of diffuse nutrient and sediment delivery to streams requires successful identification and management of critical source areas within catchments. Approaches to predicting high risk areas for sediment loss have typically relied on structural drivers of connectivity and risk, with little consideration given to process driven water quality responses. To assess the applicability of structural metrics to predict critical source areas, geochemical tracing of land use sources was conducted in three headwater agricultural catchments in Co. Down and Co. Louth, Ireland, within a Monte Carlo framework. Outputs were applied to the inverse optimisation of a connectivity model, based on LiDAR DEM data, to assess the efficacy of land use risk weightings to predict sediment source contributions over the 18 month study period in the Louth Upper, Louth Lower and Down catchments. Results of the study indicated sediment proportions over the study period varied from 6 to 10%, 84 to 87%, 4%, and 2 to 3% for the Down Catchment, 79 to 85%, 9 to 17%, 1 to 3% and 2 to 3% in the Louth Upper and 2 to 3%, 79 to 85%, 10 to 17% and 2 to 3% in the Louth Lower for arable, channel bank, grassland, and woodland sources, respectively. Optimised land use risk weightings for each sampling period showed that at the larger catchment scale, no variation in median land use weightings were required to predict land use contributions. However, for the two smaller study catchments, variation in median risk weightings was considerable, which may indicate the importance of functional connectivity processes at this spatial scale. In all instances, arable land consistently generated the highest risk of sediment loss across all catchments and sampling times. This study documents some of the first data on sediment provenance in Ireland and indicates the need for cautious consideration of land use as a tool to predict critical source areas at the headwater scale

Evaluation of a surface hydrological connectivity index in agricultural catchments

Identification of surface hydrological connectivity at scales where critical source areas (CSAs) can be managed is fundamental to achieving effective management of phosphorus loss in agricultural catchments. This study investigated the potential for the ‘Network Index’ (NI) to predict surface connectivity at CSA-management scales in contrasting agricultural catchments (ca. 1200 ha) using a 5 m DEM. One catchment had mostly permeable soils and a low surface ditch density. The other catchment had mostly impermeable soils and a high surface ditch density. The importance of surface ditch data for accurately modelling the location and magnitude of surface connectivity was also evaluated. Modelled surface connectivity at the subcatchment scale (ca. 130 ha) was validated using observed channel (ditch and stream) densities. Modelled surface connectivity at the field scale (ca. 2 ha) was evaluated using four indicators of observed field connectivity. At the subcatchment scale, modelled surface connectivity matched observations well (R2 = 0.52) despite the soil type variability across catchments. However, some errors in subcatchment boundary delineation (total of 133 ha in one catchment) occurred which could distort the extent of predicted CSAs. At the field scale, the NI had potential for broadly discerning the most connected from the least connected fields which is valuable for identifying where CSA-based management should be targeted. Detailed ditch information was required to accurately delineate subcatchment boundaries; however, it was not needed for predicting subcatchment connectivity.

Quantification of Phosphorus Transport from a Karstic Agricultural Watershed to Emerging Spring Water

The degree to which waters in a given watershed will be affected by nutrient export can be defined as that watershed's nutrient vulnerability. This study applied concepts of specific phosphorus (P) vulnerability to develop intrinsic groundwater vulnerability risk assessments in a 32 km(2) karst watershed (spring zone of contribution) in a relatively intensive agricultural landscape. To explain why emergent spring water was below an ecological impairment threshold, concepts of P attenuation potential were investigated along the nutrient transfer continuum based on soil P buffering, depth to bedrock, and retention within the aquifer. Surface karst features, such as enclosed depressions, were reclassified based on P attenuation potential in soil at the base. New techniques of high temporal resolution monitoring of P loads in the emergent spring made it possible to estimate P transfer pathways and retention within the aquifer and indicated small-medium fissure flows to be the dominant pathway, delivering 52-90% of P loads during storm events. Annual total P delivery to the main emerging spring was 92.7 and 138.4 kg total P (and 52.4 and 91.3 kg as total reactive P) for two monitored years, respectively. A revised groundwater vulnerability assessment was used to produce a specific P vulnerability map that used the soil and hydrogeological P buffering potential of the watershed as key assumptions in moderating P export to the emergent spring. Using this map and soil P data, the definition of critical source areas in karst landscapes was demonstrated.

Spatial Model Assessment of P Transport from Soils to Waterways in an Eastern Mediterranean Watershed

P index is a management tool commonly used to identify critical source areas (CSAs) in agro-catchments. We tested the applicability of several P-index models adjusted to Eastern Mediterranean conditions. On the basis of model structure and data requirements, we selected the Arkansas model and two models with the RUSLE equation and runoff curve number (RCN). Concurrently, we developed a GIS-based Hermon-P model which was designed to simulate rainfall–runoff events representing the major nutrient-transport mechanism in Eastern Mediterranean. The P index values computed by the Arkansas and RUSLE models exhibited low correlation (r2 < 0.32) with the measured soluble reactive (SRP) and total P (TP), while the RCN model result correlations were somewhat higher (r2 = 0.53 for SRP and 0.45 for TP). High correlations between the calculated and measured P during rainfall–runoff events were only achieved with the Hermon model (r2 = 0.77 to 0.9). These high coefficients resulted from avoiding subjective categorization of the continuous variables and using the measured site-specific erosional predictors instead. On one occasion, during the first significant runoff event of the year, the Hermon model failed to predict total P in the stream water (r2 = 0.14) because of considerable resuspension of stream sediments. Most of the P-index models are based on the perceptual transfer-continuum framework that was developed for temperate agro-catchments; this framework does not consider P resuspension along streams during rainfall–runoff events. Hence, a new set of equations should be added to the P index to account for potential resuspension in Eastern Mediterranean streams at the beginning of the hydrological year.

Modeling Methods for Identifying Critical Source Areas of Bacteria: Recent Developments and Future Perspectives

Identification of critical source areas of bacteria in a watershed is essential to environmental management and restoration. As a result of the nonpoint and distributed nature of bacterial pollution in watersheds, it is often difficult to identify specific source areas of bacteria for remediation because bacteria collected from different sampling sites might display similar fingerprints. Over the past decade, extensive efforts have been made to identify microbial pollution sources, especially in watersheds. The primary objective of this study was to identify effective methods that can be applied to tracking critical source areas of bacteria in a watershed by a review of recent developments in several modeling methods. Comparisons of the models and their applications revealed that comprehensive watershed-scale source area tracking primarily involves two steps-geographical tracking and mathematical tracking. In terms of geographical tracking, bacterial source locations must be identified to prepare structural best management practices or low impact development for site treatments. For mathematical tracking, the quantity (strength) or release history of bacterial sources must be computed to develop total maximum daily loads (TMDLs) for bacterial load reduction and water quality restoration. Mathematically, source tracking is essentially an inverse modeling issue under uncertainty, requiring inverse modeling combined with a geostatistical method or an optimization algorithm. Consequently, combining biological methods, mathematical models, and sensor technologies (including remote sensing and in-situ sensing) provides an effective approach to identifying critical source locations of bacteria at the watershed-scale.

Effects of critical source area on sediment yield and streamflow

AbstractThe spatial discretization scale determines the degree to which the spatial heterogeneity of the surface condition and the stream network can be represented. Discretization scale can have a significant impact on watershed model results, with respect to both hydrology and water quality. The impact of critical source area (CSA) on simulated water yield for each cell can be assessed for streamflow and run‐off using the Annualized Agricultural Non‐Point Source (AnnAGNPS) model. The total number of cells simulated for the six watersheds ranged from 8 to 352 for various CSA combinations. Increasing the number of subwatersheds increased water and sediment yield in many watersheds. Increasing the number of cells influences the depiction of land use and soil type accordingly and therefore influences run‐off. The applied CSAs were also compared with topographic parameters such as average cell slope, average channel slope and length to width ratio of each watershed.

Modeling impacts of sediment delivery ratio and land management on adsorbed non-point source nitrogen and phosphorus load in a mountainous basin of the Three Gorges reservoir area, China

Agricultural nonpoint source (NPS) pollution at the Three Gorges reservoir area in China has been increasingly recognized as a threat to aquatic environment in recent years due to the serious eutrophication problem. Adsorbed NPS pollution is one of the major forms of NPS pollution in mountainous regions, the essential of the adsorbed NPS pollution is soil loss. Thus, simple, highly sensitive and continuous methods are required to simulate and quantify sediments yield at watershed scales. It is imperative to construct an integrated model to estimate the sediment yield and adsorbed NPS pollution load. According to the characteristics of climate, hydrology, topography, geology, geomorphology and land use types in Three Gorges reservoir area, a GIS-based dynamic-integrated-distributed model of annual adsorbed NPS load was presented in view of impacts of the rainfall intensity, sediment delivery ratio (SDR) and land management, where the temporally dynamic-continuous model of annual sediment yield was established by modifying the revised Universal Soil Loss Equation (RUSLE), and the spatially integrated-distributed model of annual adsorbed NPS load was then developed via the correlation between sediment yield and adsorbed NPS load. Furthermore, a case study of the Jialing River basin in China was applied to validate the integrated model, the dynamic-distributed coupling among GIS technology, sediment yield model, and adsorbed NPS load model was achieved successfully. The simulation results demonstrate the following: (1) runoff and sediment are influenced greatly by rainfall intensity, SDR and vegetation cover; rainfall and land management show high sensitivities to the integrated model; the average annual adsorbed TN and TP pollution loads from 2006 to 2010 decreased by 76 and 74 % compared with the previous treatment (1990), respectively. (2) Spatio-temporal variations of adsorbed NPS nitrogen and phosphorus load are mainly related to different land use types and the background level of nutriments in topsoil; different land use types have different contribution rates; the largest contribution rates of adsorbed total nitrogen (TN, 58.9 %) and total phosphorus (TP, 53 %) loads are both from the dryland cropland. (3) The identification of critical source areas can help to implement the prevention and control measures aiming at the reduction of water environmental pollution. These results will provide useful and valuable information for decision makers and planners to take sustainable land use management and soil conservation measures for the control of sediment pollution in the Three Gorges reservoir area. The application of this model in the catchment shows that the integrated model may be used as a major tool to assess sediment yield risks and adsorbed NPS pollution load at mountainous watersheds.

A phosphorus index that combines critical source areas and transport pathways using a travel time approach

Summary Spatially distributed nonpoint source (NPS) pollution indices are used to identify areas in a watershed where potential pollutant loading coincides with runoff generating areas. However, most such indices either ignore the degree of hydrologic connectivity to the stream network or they estimate it based simply on the distance of the pollution generating area from an open channel. We propose an NPS pollution index based on runoff travel times from saturated variable source areas (VSAs) to the natural stream network as a means for including hydrologic connectivity between source areas and streams. Although this method could be generalized to any pollutant transported by storm runoff, here we focus on phosphorus and refer to the index as the travel-time phosphorus index (TTPI). The TTPI was applied to a 38 km2 agricultural watershed in central New York and shown to yield realistic, spatially explicit predictions of critical phosphorus loading areas and routing pathways. One interesting finding is the potential role of man-made drainage networks (e.g., road- or agricultural-ditches) in NPS pollution and the possibilities of targeting water quality protection practices around or within these networks. Because the technique is GIS-based, relatively simple to apply, uses readily available geospatial data, and the theoretical underpinnings are transparent, it can provide a useful screening tool for water resource managers charged with the identification and remediation of critical NPS pollution source areas.

Integrated watershed- and farm-scale modeling framework for targeting critical source areas while maintaining farm economic viability

Quantitative risk assessments of pollution and data related to the effectiveness of mitigating best management practices (BMPs) are important aspects of nonpoint source pollution control efforts, particularly those driven by specific water quality objectives and by measurable improvement goals, such as the total maximum daily load (TMDL) requirements. Targeting critical source areas (CSAs) that generate disproportionately high pollutant loads within a watershed is a crucial step in successfully controlling nonpoint source pollution. The importance of watershed simulation models in assisting with the quantitative assessments of CSAs of pollution (relative to their magnitudes and extents) and of the effectiveness of associated BMPs has been well recognized. However, due to the distinct disconnect between the hydrological scale in which these models conduct their evaluation and the farm scale at which feasible BMPs are actually selected and implemented, and due to the difficulty and uncertainty involved in transferring watershed model data to farm fields, there are limited practical applications of these tools in the current nonpoint source pollution control efforts by conservation specialists for delineating CSAs and planning targeting measures. There are also limited approaches developed that can assess impacts of CSA-targeted BMPs on farm productivity and profitability together with the assessment of water quality improvements expected from applying these measures. This study developed a modeling framework that integrates farm economics and environmental aspects (such as identification and mitigation of CSAs) through joint use of watershed- and farm-scale models in a closed feedback loop. The integration of models in a closed feedback loop provides a way for environmental changes to be evaluated with regard to the impact on the practical aspects of farm management and economics, adjusted or reformulated as necessary, and revaluated with respect to effectiveness of environmental mitigation at the farm- and watershed-levels. This paper also outlines steps needed to extract important CSA-related information from a watershed model to help inform targeting decisions at the farm scale. The modeling framework is demonstrated with two unique case studies in the northeastern United States (New York and Vermont), with supporting data from numerous published, location-specific studies at both the watershed and farm scales. Using the integrated modeling framework, it can be possible to compare the costs (in terms of changes required in farm system components or financial compensations for retiring crop lands) and benefits (in terms of measurable water quality improvement goals) of implementing targeted BMPs. This multi-scale modeling approach can be used in the multi-objective task of mitigating CSAs of pollution to meet water quality goals while maintaining farm-level economic viability.

Watershed-scale evaluation for land-based nonpoint source nutrients management in the Bohai Sea Bay, China

A watershed-scale approach was used to evaluate the land-based nonpoint source nutrients in the Bohai Sea Bay, which plays a vital ecological and economic role on regional scale in China. This study firstly defined the watershed boundary of the Bohai Sea Bay with an area of approximately 676,000 sq km. Loads and source apportionment of land-based nonpoint source nutrients from the 13 coastal cities, seven major watersheds, and five sub-sea areas were quantified. The land-based nonpoint source total nitrogen (TN) and total phosphorus (TP) loads from the 13 coastal cities contributed only 16.9% and 12.5% of the total land-based nonpoint source TN and TP loads entering the Bohai Sea. Land-sea integration was highlighted for managing land-based nonpoint source TN and TP loads entering the Bohai Sea. Soil losses, livestock and poultry breeding should be regulated for land-based nonpoint source TN load management while soil losses, livestock and fertilizer use should be controlled for land-based nonpoint source TP load management. Several critical source areas for land-based nonpoint source TN and TP loads were identified where management measures to reduce nutrient should be directed. The findings of this study would be helpful in gaining insights into water quality issues derived from land-based nonpoint source pollution in the BSBW from a holistic perspective, thereby providing scientific support for land-based management in the Bohai Sea.

Identification of critical source areas for erosion and phosphorus losses in small agricultural catchment in central Sweden

Identification of main contributing sources and critical source areas is a precondition for a cost-effective abatement strategy. This task is even more complicated in small ungauged catchments where inadequate amount of input, calibration, and validation data exists and thereby limit the application of process-based, parameter-heavy models. In this study, we present methodology to assess spatial variations in erosion and P losses, and based on source-apportionment and erosion modeling, to develop a base for source- and site-specific abatement countermeasures. Water quality monitoring program was developed to cover spatial variability in erosion and phosphorus (P) losses. Conducted soil survey detected high variations in soil P content within the catchment and strong correlation between soil P test and easily soluble P. A simple water balance model, calibrated for the comparable catchment with existing water flow measurements, was applied to determine water discharge, providing necessary input data for calculations of loads and source-apportionment. Source-apportionment modeling based on results from water discharge modeling, water quality monitoring program and soil survey identified P losses from arable fields as main source of P to two small lakes situated in the catchment, contributing roughly a half of total P load to the lakes. Inclusion of an assumed but previously undocumented point source considerably improved the performance of source-apportionment model. Existence of such a point-source was afterwards confirmed by results from detailed water quality monitoring program. Erosion modeling based on high-resolution digital elevation data identified areas most prone to erosion, and model results were compared to farmers' observations and analyses of 137Cs. Use of a spectrum of models and assessment tools formed a broad support for further steps in abatement efforts, and created concrete discussion base for implementation of detailed site-specific countermeasures.