Critical Source Areas Research Articles

Analysis of the efficacy and cost-effectiveness of best management practices for controlling sediment yield: A case study of the Joumine watershed, Tunisia

Soil erosion can be reduced through the strategic selection and placement of best management practices (BMPs) in critical source areas (CSAs). In the present study, the Soil Water Assessment Tool (SWAT) model was used to identify CSAs and investigate the effectiveness of different BMPs in reducing sediment yield in the Joumine watershed, an agricultural river catchment located in northern Tunisia. A cost-benefit analysis (CBA) was used to evaluate the cost-effectiveness of different BMP scenarios. The objective of the present study was to determine the most cost-effective management scenario for controlling sediment yield. The model performance for the simulation of streamflow and sediment yield at the outlet of the Joumine watershed was good and satisfactory, respectively. The model indicated that most of the sediment was originated from the cultivated upland area. About 34% of the catchment area consisted of CSAs that were affected by high to very high soil erosion risk (sediment yield >10t/ha/year). Contour ridges were found to be the most effective individual BMP in terms of sediment yield reduction. At the watershed level, implementing contour ridges in the CSAs reduced sediment yield by 59%. Combinations of BMP scenarios were more cost-effective than the contour ridges alone. Combining buffer strips (5-m width) with other BMPs depending on land slope (> 20% slope: conversion to olive orchards; 10–20% slope: contour ridges; 5–10% slope: grass strip cropping) was the most effective approach in terms of sediment yield reduction and economic benefits. This approach reduced sediment yield by 61.84% with a benefit/cost ratio of 1.61. Compared with the cost of dredging, BMPs were more cost-effective for reducing sediment loads to the Joumine reservoir, located downstream of the catchment. Our findings may contribute to ensure the sustainability of future conservation programs in Tunisian regions.

Evaluation of Phosphorus Site Assessment Tools: Lessons from the USA.

Critical source area identification through phosphorus (P) site assessment is a fundamental part of modern nutrient management planning in the United States, yet there has been only sparse testing of the many versions of the P Index that now exist. Each P site assessment tool was developed to be applicable across a range of field conditions found in a given geographic area, making evaluation extremely difficult. In general, evaluation with in-field monitoring data has been limited, focusing primarily on corroborating manure and fertilizer "source" factors. Thus, a multiregional effort (Chesapeake Bay, Heartland, and Southern States) was undertaken to evaluate P Indices using a combination of limited field data, as well as output from simulation models (i.e., Agricultural Policy Environmental eXtender, Annual P Loss Estimator, Soil and Water Assessment Tool [SWAT], and Texas Best Management Practice Evaluation Tool [TBET]) to compare against P Index ratings. These comparisons show promise for advancing the weighting and formulation of qualitative P Index components but require careful vetting of the simulation models. Differences among regional conclusions highlight model strengths and weaknesses. For example, the Southern States region found that, although models could simulate the effects of nutrient management on P runoff, they often more accurately predicted hydrology than total P loads. Furthermore, SWAT and TBET overpredicted particulate P and underpredicted dissolved P, resulting in correct total P predictions but for the wrong reasons. Experience in the United States supports expanded regional approaches to P site assessment, assuming closely coordinated efforts that engage science, policy, and implementation communities, but limited scientific validity exists for uniform national P site assessment tools at the present time.

The pollution source assessment tool for Sydney's drinking water catchments

WaterNSW funds a range of interventions to minimise pollution within the drinking water catchments of Sydney. To assist with the process of targeting this expenditure, WaterNSW has developed a spatial analysis tool, known as the Pollution Source Assessment Tool (PSAT), which uses a hybrid of multi-criteria analysis and load-based methods to highlight high pollution potential sites and critical source areas for pathogens, nutrients and suspended sediment. The design of the PSAT enables results to be summarised by any set of spatial units and with any combination of pollution sources. The most recent PSAT outputs have been used at multiple scales; to guide identification of priorities across the whole of the drinking water catchments of Sydney and also to target programmes on individual properties, sites and urban areas.

Development of a soil moisture‐based distributed hydrologic model for determining hydrologically based critical source areas

AbstractA simple grid cell‐based distributed hydrologic model was developed to provide spatial information on hydrologic components for determining hydrologically based critical source areas. The model represents the critical process (soil moisture variation) to run‐off generation accounting for both local and global water balance. In this way, it simulates both infiltration excess run‐off and saturation excess run‐off. The model was tested by multisite and multivariable evaluation on the 50‐km2 Little River Experimental Watershed I in Georgia, U.S. and 2 smaller nested subwatersheds. Water balance, hydrograph, and soil moisture were simulated and compared to observed data. For streamflow calibration, the daily Nash‐Sutcliffe coefficient was 0.78 at the watershed outlet and 0.56 and 0.75 at the 2 nested subwatersheds. For the validation period, the Nash‐Sutcliffe coefficients were 0.79 at the watershed outlet and 0.85 and 0.83 at the 2 subwatersheds. The per cent bias was less than 15% for all sites. For soil moisture, the model also predicted the rising and declining trends at 4 of the 5 measurement sites. The spatial distribution of surface run‐off simulated by the model was mainly controlled by local characteristics (precipitation, soil properties, and land cover) on dry days and by global watershed characteristics (relative position within the watershed and hydrologic connectivity) on wet days when saturation excess run‐off was simulated. The spatial details of run‐off generation and travel time along flow paths provided by the model are helpful for watershed managers to further identify critical source areas of non‐point source pollution and develop best management practices.

Evaluating the risk of phosphorus loss with a distributed watershed model featuring zero-order mobilization and first-order delivery

Many semi-distributed models that simulate pollutants' losses from watersheds do not handle well detailed spatially distributed and temporal data with which to identify accurate and cost-effective strategies for controlling pollutants issuing from non-point sources. Such models commonly overlook the flow pathways of pollutants across the landscape. This work aims at closing such knowledge gap by developing a Spatially and Temporally Distributed Empirical model for Phosphorus Management (STEM-P) that simulates the daily phosphorus loss from source areas to receiving waters on a spatially-distributed grid-cell basis. STEM-P bypasses the use of complex mechanistic algorithms by representing the phosphorus mobilization and delivery processes with zero-order mobilization and first-order delivery, respectively. STEM-P was applied to a 217km2 watershed with mixed forest and agricultural land uses situated in southwestern China. The STEM-P simulation of phosphorus concentration at the watershed outlet approximated the observed data closely: the percent bias (Pbias) was −7.1%, with a Nash-Sutcliffe coefficient (ENS) of 0.80 on a monthly scale for the calibration period. The Pbias was 18.1%, with a monthly ENS equal to 0.72 for validation. The simulation results showed that 76% of the phosphorus load was transported with surface runoff, 25.2% of which came from 3.4% of the watershed area (classified as standard A critical source areas), and 55.3% of which originated from 17.1% of the watershed area (classified as standard B critical source areas). The standard A critical source areas were composed of 51% residences, 27% orchards, 18% dry fields, and 4% paddy fields. The standard B critical source areas were mainly paddy fields (81%). The calculated spatial and temporal patterns of phosphorus loss and recorded flow pathways identified with the STEM-P simulations revealed the field-scale critical source areas and guides the design and placement of effective practices for non-point source pollution control and water quality conservation.

Grazing strategies for reducing contaminant losses to water from forage crop fields grazed by cattle during winter

ABSTRACTA paired catchment study quantified fluxes of Nitrogen (N), Phosphorus (P), sediment and Escherichia coli (E. coli) in flow from fields where cattle were wintered on swede (Brassica napus) and kale (Brassica oleracea) crops. The effectiveness of a strategic grazing approach that protected critical source areas was evaluated to determine if these fluxes could be reduced. Reduced soil infiltration caused by cattle treading was a contributing factor to overland flow events that mostly occurred in the six to eight weeks following crop grazing. Estimated annual fluxes of contaminants in overland flow ranged up to 27, 3.8 and 3740 kg ha−1 for N, P and sediment, respectively, and up to 4.3 × 1010 MPN ha−1 for E. coli. Strategic grazing significantly reduced overland flow volumes compared to standard grazing practice, reducing contaminant fluxes to levels similar to those observed when the catchments were returned to pasture and lightly grazed by sheep.

Modified control strategies for critical source area of nitrogen (CSAN) in a typical freeze-thaw watershed

The management of critical source areas of diffuse nitrogen (CSANs) remains challenging in freeze-thaw areas due to the different N loss characteristics in different hydrological conditions and seasons. To address these challenges, a modified strategy was proposed in this study using the Soil and Water Assessment Tool (SWAT) to simulate diffuse N loads in the study catchments. Specifically, the spatial and temporal variations of CSANs caused by differences in precipitation and seasons were considered. In addition, the selection of best management practices (BMPs) was selected according to BMP performance and their seasonal characteristics in diffuse N control. The diffuse N load formed during freeze-thaw seasons accounts for approximately 50% of the annual diffuse N load. The diffuse N load discharged to rivers was higher in wet conditions than dry conditions by 127.4% and 181.5% during freeze-thaw seasons and growing seasons, respectively. The spatial distribution of CSANs was more sensitive to differences between freeze-thaw and growing seasons. Among BMPs, buffer strips (BS), no tillage (NT) and reducing N fertilizer applications (RNFA) all showed differences in their diffuse N removal efficiency under different hydrological conditions and seasons, while reforestation operations were not affected by these factors. The benefit of reforestation operations was lower in flatter areas. When areas with slopes greater than 2 degrees were reforested, the average N removal efficiency of the 1st CSAN could be as high as 82.4%. In the 2nd CSAN, the average N removal efficiency of BS was relatively constant across freeze-thaw seasons. Across growing seasons, the N removal efficiency of BS in wet years was 8%-10% higher than in dry conditions due to the lower percentage of lateral flow. The average N removal efficiency of NT was higher during freeze-thaw seasons and lower during growing seasons with average values of 9.3% and 6.1%, respectively. The N control efficiency of RNFA 10% and RNFA 20% (a 10% or 20% reduction in fertilizer application, respectively) was highest during dry growing seasons with average N control efficiencies of 9.6% and 17.8%, respectively. This study is expected to improve diffuse pollution control in cold areas and to improve the understanding of how N removal efficiency of BMPs responds to variations in hydrological conditions.

High‐resolution mapping of soil phosphorus concentration in agricultural landscapes with readily available or detailed survey data

SummaryHigh‐resolution mapping of soil phosphorus (P) concentration is necessary to identify critical source areas reliably where a large risk of transport coincides with a large potential source of P in agricultural landscapes. However, dense soil P data are not usually available to produce such maps and to obtain them is expensive. In this study, we modelled and mapped soil extractable P (ExtP) and total P (TP) concentrations in an intensively farmed 12‐km2 catchment in Brittany (NW France) with two different datasets to test the suitability of readily available regional or national databases for high‐resolution mapping. We used a machine learning tool (Cubist) to develop rule‐based predictive models from a calibration dataset. Covariates included pedological, geological, agricultural, terrain and geophysical‐related attributes obtained specifically in the study area (SURVEY) or derived from readily available regional or national databases (DATABASE). Even though better predictions were obtained with the SURVEY data (RMSE = 0.018 g kg−1 for ExtP and RMSE = 0.219 g kg−1 for TP), the DATABASE data produced acceptable predictions (RMSE = 0.024 g kg−1 for ExtP and RMSE = 0.253 g kg−1 for TP). The machine learning tool helped to identify key covariates that would improve the prediction of soil P when detailed data are not available. Readily available data about crop rotations could increase the accuracy of existing ExtP maps. These maps, combined with additional soil analysis for extractable Al, would improve the mapping of TP and the identification of areas with a large potential source of P.Highlights Modelling and mapping of soil phosphorus with the machine learning algorithm Cubist. Comparison of regional or national databases and detailed survey data for prediction. Models with regional and national data performed well, but some areas with large concentrations of P were not identified. Information about crop rotation and soil extractable Al improved model performance.

Use of spatially distributed time-integrated sediment sampling networks and distributed fine sediment modelling to inform catchment management

Under the EU Water Framework Directive, suspended sediment is omitted from environmental quality standards and compliance targets. This omission is partly explained by difficulties in assessing the complex dose-response of ecological communities. But equally, it is hindered by a lack of spatially distributed estimates of suspended sediment variability across catchments. In this paper, we demonstrate the inability of traditional, discrete sampling campaigns for assessing exposure to fine sediment. Sampling frequencies based on Environmental Quality Standard protocols, whilst reflecting typical manual sampling constraints, are unable to determine the magnitude of sediment exposure with an acceptable level of precision. Deviations from actual concentrations range between −35 and +20% based on the interquartile range of simulations. As an alternative, we assess the value of low-cost, suspended sediment sampling networks for quantifying suspended sediment transfer (SST). In this study of the 362 km2 upland Esk catchment we observe that spatial patterns of sediment flux are consistent over the two year monitoring period across a network of 17 monitoring sites. This enables the key contributing sub-catchments of Butter Beck (SST: 1141 t km2 yr−1) and Glaisdale Beck (SST: 841 t km2 yr−1) to be identified. The time-integrated samplers offer a feasible alternative to traditional infrequent and discrete sampling approaches for assessing spatio-temporal changes in contamination. In conjunction with a spatially distributed diffuse pollution model (SCIMAP), time-integrated sediment sampling is an effective means of identifying critical sediment source areas in the catchment, which can better inform sediment management strategies for pollution prevention and control.

Effectiveness of Best Management Practices with Changing Climate in a Maryland Watershed

Abstract. The potential impacts of climate change on BMP effectiveness were investigated using SWAT simulations for an agricultural watershed that drains into the Chesapeake Bay in the U.S. Northeast climate region. Critical source areas (CSAs) for sediments, nitrogen, and phosphorus, identified for current and future climate (SRES scenarios A1B and A2), were classified by density to support BMP prioritization schemes. BMPs were designed for these CSAs and tested against current and future climate using SWAT simulations to evaluate their robustness. A second set of BMPs was designed by optimization for all agricultural and urban lands in the study watershed and was similarly tested for robustness. In both cases, the design goal was for the watershed’s water quality response to meet the bay TMDLs once BMPs were implemented. Results indicated that density 2 and 3 CSAs (hotspots exporting excess amounts of 2 or 3 constituents) may be good prioritization targets, but reaching the bay TMDLs would still require targeting all CSAs. BMPs designed for CSAs under current climate were effective to reach bay TMDLs under current climate but not under scenarios A1B and A2. BMPs designed for CSAs under scenario A2 were effective to reach the bay TMDLs under all climates, except for nitrogen under A2. Similarly, BMPs optimized for agricultural and urban lands, when designed for current climate, were effective in meeting TMDLs for current climate only. Optimizing these BMPs for future climate produced a design that met TMDLs under both current and future climates, except for nitrogen with future climate. However, in this case, the nitrogen TMDL was exceeded by a smaller amount than in the CSA design. It was concluded that, in the U.S. Northeast, BMPs designed to remediate water quality problems under current climate will be insufficient to maintain water quality with climate change. Increased annual rainfall and storm intensity will increase the proportion of watershed area needing BMPs, and current hotspots will generate excess amounts of new constituents that will require re-design of existing BMPs. Community-based participatory strategies will likely be required to foster BMP adoption and sustain water quality gains in the Chesapeake Bay region. Keywords: BMPs, Best management practices, Climate change, NPS pollution, SWAT model, Water quality, Watershed hydrology.

Impact of Macropores and Gravel Outcrops on Phosphorus Leaching at the Plot Scale in Silt Loam Soils

Abstract. In response to increased nutrient loads in surface waters, scientists and engineers need to identify critical nutrient source areas and transport mechanisms within a catchment to protect beneficial uses of aquatic systems in a cost-effective manner. It was hypothesized that hydrologic heterogeneities (e.g., macropores and gravel outcrops) in the vadose zone play an integral role in affecting flow and solute transport between the soil surface and shallow alluvial aquifers. The objective of this research was to characterize phosphorus (P) leaching through silt loam soils to alluvial gravel aquifers in the floodplains of the Ozark ecoregion at the plot scale. Solute injection experiments used plots (1 m × 1 m, 3 m × 3 m, and 10 m × 10 m) that maintained a constant head for up to 52 h. Solutes in the injection water included P (highly sorptive), Rhodamine WT (slightly sorptive), and chloride (conservative). Electrical resistivity imaging identified zones of preferential flow. Fluid samples from observation wells indicated nonuniform subsurface flow and transport. The surface soil type, ranging from silt loam to clean gravel outcrops, had a significant impact on P leaching capacity, with gravel outcrops resulting in high infiltration rates and rapid solute detection in wells (e.g., 4 min). Even in silt loam soils without gravel outcrops, macropore flow resulted in rapid transport of P. Maximum transport velocity for soluble reactive P in one silt loam plot was 810 cm h-1, compared with a mean pore water velocity in the range of 25 to 130 cm h-1. Soluble reactive P concentrations in observation wells reached up to 0.54 mg L-1 in silt loam plots and 1.3 mg L-1 in gravel outcrop plots, demonstrating that a highly sorbing solute can be mobile. Keywords: Gravel outcrop, Leaching, Macropores, Ozark ecoregion, Phosphorus.

Simulation of land use impacts on sediment and nutrient transfer in coastal areas of Western Cape, South Africa

AbstractA major challenge for water resource management in Western Cape, South Africa, is the reduction of the growing sediment and nutrient loads in coastal areas, which belong to the areas most affected by land use change. We used the WebGIS based software STOFFBILANZ to simulate runoff, soil loss, sediment, phosphorus, and nitrogen input in the surface water and groundwater of study area (ca. 6,450 km²). The simulated runoff shows a large regional variability caused by the heterogeneous distribution of rainfall. For the reference catchment Klein River simulated total daily runoff fit the observed values of the reference year 2012. The calculation of potential input of sediment, phosphorus, and nitrogen into waters is based on aggregated or generalized information on climate data, land use types, crop and fruit types, yields, mineral fertilizers, farm manure, nitrogen fixing by leguminous plants, atmospheric nitrogen deposition, and soil denitrification. Critical source areas for potential sediment input, particulate P input and diffuse N input are mainly agricultural areas. Additionally, point sources of high relevance for N and P are found in urban areas. Based on the potential input of sediment and nutrients the impacts of current land use change on water resources were estimated. We used the web-based information system WebLand for the simulation aiming at the provision of stakeholders with information for decision making in water resource management.

Climate Change Impact on Critical Source Area Identification in a Maryland Watershed

The potential impacts of climate change on critical source areas (CSAs) of surface runoff, sediments, nitrogen, and phosphorus were evaluated in an agricultural watershed of the Chesapeake Bay drainage basin, in the U.S. Northeast climate region. The SWAT model was calibrated for the study watershed and used to establish its baseline response and constituent CSAs under current climate (years 1990 to 2004). The calibrated model was then subjected to weather time series downscaled from the CMIP3 GFDL CM2.1 Atmosphere-Ocean Global Circulation Model (AOGCM) for IPCC SRES scenarios B1 (low emissions), A1B (medium emissions), and A2 (high emissions) to predict the watershed‘s response to climate change and identify how constituent CSAs may change under future climate (years 2046 to 2064 and 2081 to 2100). The utility of targeting best management practices (BMPs) to CSAs was assessed by computing advantage ratios that relate the fraction of watershed-generated constituents that emanate from CSAs to the fraction of watershed area occupied by these CSAs. Results indicated that, under current conditions, CSAs occupying 11% to 21% of the watershed area contribute 31% to 45% of constituents, corresponding to advantage ratios of 1.5:1 for runoff control and approximately 3:1 for other constituents. Under climate change scenario B1, constituent yields were predicted to increase by factors of 1.5 to 1.8 at the watershed outlet, from an increase in annual rainfall of 25% predicted by the AOGCM, over current conditions. Under scenarios A1B and A2, constituent yields were predicted to increase by factors of 1.8 to 2.3 over current conditions, from an increase of 30% in annual rainfall. The area of runoff CSAs was predicted to more than triple with climate change, leading to negligible advantage of targeting runoff control BMPs to CSAs under future climate. The areas of sediment, nitrogen, and phosphorus CSAs were predicted to increase by factors of 2 to 3 with climate change, causing BMP-targeting advantage ratios to decrease from approximately 3:1 (baseline) to 2:1 (future). While advantage ratios for suspended and dissolved constituents remain favorable, even under future climate, the much larger area predicted to be covered by CSAs (2 to 3 times current values) suggests that stakeholder involvement and community-oriented participatory approaches will be increasingly important for achieving Chesapeake Bay TMDLs with climate change.

Potential use of ionic species for identifying source land-uses of stormwater runoff

Identifying critical land-uses or source areas is important to prioritize resources for cost-effective stormwater management. This study investigated the use of information on ionic composition as a fingerprint to identify the source land-use of stormwater runoff. We used 12 ionic species in stormwater runoff monitored for a total of 20 storm events at five sites with different land-use compositions during the 2012-2014 wet seasons. A stepwise forward discriminant function analysis (DFA) with the jack-knifed cross validation approach was used to select ionic species that better discriminate the land-use of its source. Of the 12 ionic species, 9 species (K+, Mg2+, Na+, NH4+, Br-, Cl-, F-, NO2-, and SO42-) were selected for better performance of the DFA. The DFA successfully differentiated stormwater samples from urban, rural, and construction sites using concentrations of the ionic species (70%, 95%, and 91% of correct classification, respectively). Over 80% of the new data cases were correctly classified by the trained DFA model. When applied to data cases from a mixed land-use catchment and downstream, the DFA model showed the greater impact of urban areas and rural areas respectively in the earlier and later parts of a storm event.

A sub-field scale critical source area index for legacy phosphorus management using high resolution data

Diffuse phosphorus (P) mitigation in agricultural catchments should be targeted at critical source areas (CSAs) that consider source and transport factors. However, development of CSA identification needs to consider the mobilisation potential of legacy soil P sources at the field scale, and the control of (micro)topography on runoff generation and hydrological connectivity at the sub-field scale. To address these limitations, a ‘next generation’ sub-field scale CSA index is presented, which predicts the risk of dissolved P losses in runoff from legacy soil P. The GIS-based CSA Index integrates two factors; mobile soil P concentrations (water extractable P; WEP) and a hydrologically sensitive area (HSA) index. The HSA Index identifies runoff-generating-areas using high resolution LiDAR Digital Elevation Models (DEMs), a soil topographic index (STI) and information on flow sinks and effects on hydrological connectivity. The CSA Index was developed using four intensively monitored agricultural catchments (7.5–11km2) in Ireland with contrasting agri-environmental conditions. Field scale soil WEP concentrations were estimated using catchment and land use specific relationships with Morgan P concentrations. In-stream total reactive P (TRP) concentrations and discharge were measured sub-hourly at catchment outlet bankside analysers and gauging stations during winter closed periods for fertiliser spreading in 2009–14, and hydrograph/loadograph separation methods were used to estimate TRP loads and proportions from quickflow (surface runoff). A strong relationship between TRP concentrations in quickflow and soil WEP concentrations (r2=0.73) was used to predict dissolved P concentrations in runoff at the field scale, which were then multiplied by the HSA Index to generate sub-field scale CSA Index maps. Evaluation of the tool showed a very strong relationship between the total CSA Index value within the HSA and the total TRP load in quickflow (r2=0.86). Using a CSA Index threshold value of ≥0.5, the CSA approach identified 1.1–5.6% of catchment areas at highest risk of legacy soil P transfers, compared with 4.0–26.5% of catchment areas based on an existing approach that uses above agronomic optimum soil P status. The tool could be used to aid cost-effective targeting of sub-field scale mitigation measures and best management practices at delivery points of CSA pathways to reduce dissolved P losses from legacy P stores and support sustainable agricultural production.

Defining optimal DEM resolutions and point densities for modelling hydrologically sensitive areas in agricultural catchments dominated by microtopography

Defining critical source areas (CSAs) of diffuse pollution in agricultural catchments depends upon the accurate delineation of hydrologically sensitive areas (HSAs) at highest risk of generating surface runoff pathways. In topographically complex landscapes, this delineation is constrained by digital elevation model (DEM) resolution and the influence of microtopographic features. To address this, optimal DEM resolutions and point densities for spatially modelling HSAs were investigated, for onward use in delineating CSAs. The surface runoff framework was modelled using the Topographic Wetness Index (TWI) and maps were derived from 0.25m LiDAR DEMs (40 bare-earth points m−2), resampled 1m and 2m LiDAR DEMs, and a radar generated 5m DEM. Furthermore, the resampled 1m and 2m LiDAR DEMs were regenerated with reduced bare-earth point densities (5, 2, 1, 0.5, 0.25 and 0.125 points m−2) to analyse effects on elevation accuracy and important microtopographic features. Results were compared to surface runoff field observations in two 10km2 agricultural catchments for evaluation. Analysis showed that the accuracy of modelled HSAs using different thresholds (5%, 10% and 15% of the catchment area with the highest TWI values) was much higher using LiDAR data compared to the 5m DEM (70–100% and 10–84%, respectively). This was attributed to the DEM capturing microtopographic features such as hedgerow banks, roads, tramlines and open agricultural drains, which acted as topographic barriers or channels that diverted runoff away from the hillslope scale flow direction. Furthermore, the identification of ‘breakthrough’ and ‘delivery’ points along runoff pathways where runoff and mobilised pollutants could be potentially transported between fields or delivered to the drainage channel network was much higher using LiDAR data compared to the 5m DEM (75–100% and 0–100%, respectively). Optimal DEM resolutions of 1–2m were identified for modelling HSAs, which balanced the need for microtopographic detail as well as surface generalisations required to model the natural hillslope scale movement of flow. Little loss of vertical accuracy was observed in 1–2m LiDAR DEMs with reduced bare-earth point densities of 2–5 points m−2, even at hedgerows. Further improvements in HSA models could be achieved if soil hydrological properties and the effects of flow sinks (filtered out in TWI models) on hydrological connectivity are also considered.

An Integrated Approach for Targeting Critical Source Areas to Control Nonpoint Source Pollution in Watersheds

This study presents an integrated approach for targeting critical source areas (CSAs) to control nonpoint source pollution in watersheds. CSAs are the intersections between hydrologically sensitive areas (HSAs) and high pollution producing areas of watersheds. HSAs are the areas with high hydrological sensitivity and potential for generating runoff. They were based on a soil topographic index in consistence of a saturation excess runoff process. High pollution producing areas are the areas that have a high potential for generating pollutants. Such areas were based on simulated pollution loads to streams by the Soil and Water Assessment Tool. The integrated approach is applied to the Neshanic River watershed, a suburban watershed with mixed land uses in New Jersey in the U.S. Results show that several land uses result in water pollution: agricultural land causes sediment, nitrogen and phosphorus pollution; wetlands cause sediment and phosphorus pollution; and urban lands cause nitrogen and phosphorus pollution. The primary CSAs are agricultural lands for all three pollutants, urban lands for nitrogen and phosphorus, and wetlands for sediment and phosphorus. Some pollution producing areas were not classified into CSAs because they are not located in HSAs and the pollutants generated in those areas are less likely to be transported by runoff into streams. The integrated approach identifies CSAs at a very fine scale, which is useful for targeting the implementation of best management practices for water quality improvement, and can be applied broadly in different watersheds to improve the economic efficiency of controlling nonpoint source pollution.

Detecting and analyzing soil phosphorus loss associated with critical source areas using a remote sensing approach

The detection of critical source areas (CSAs) is a key step in managing soil phosphorus (P) loss and preventing the long-term eutrophication of water bodies at regional scale. Most related studies, however, focus on a local scale, which prevents a clear understanding of the spatial distribution of CSAs for soil P loss at regional scale. Moreover, the continual, long-term variation in CSAs was scarcely reported. It is impossible to identify the factors driving the variation in CSAs, or to collect land surface information essential for CSAs detection, by merely using the conventional methodologies at regional scale. This study proposes a new regional-scale approach, based on three satellite sensors (ASTER, TM/ETM and MODIS), that were implemented successfully to detect CSAs at regional scale over 15years (2000–2014). The approach incorporated five factors (precipitation, slope, soil erosion, land use, soil total phosphorus) that drive soil P loss from CSAs. Results show that the average area of critical phosphorus source areas (CPSAs) was 15,056km2 over the 15-year period, and it occupied 13.8% of the total area, with a range varying from 1.2% to 23.0%, in a representative, intensive agricultural area of China. In contrast to previous studies, we found that the locations of CSAs with P loss are spatially variable, and are more dispersed in their distribution over the long term. We also found that precipitation acts as a key driving factor in the variation of CSAs at regional scale. The regional-scale method can provide scientific guidance for managing soil phosphorus loss and preventing the long-term eutrophication of water bodies at regional scale, and shows great potential for exploring factors that drive the variation in CSAs at global scale.

Delineating floodplain and upland areas for hydrologic models: a comparison of methods

AbstractA spatially distributed representation of basin hydrology and transport processes in hydrologic models facilitates the identification of critical source areas and the placement of management and conservation measures. Floodplains are critical landscape features that differ from neighbouring uplands in terms of their hydrological processes and functions. Accordingly, an important step in watershed modelling is the representation of floodplain and upland areas within a watershed. The aim of this study is (1) to evaluate four floodplain–upland delineation methods that use readily available topographic data (topographic wetness index, slope position, uniform flood stage, and variable flood stage) with regard to their suitability for hydrological models and (2) to introduce an evaluation scheme for the delineated landscape units. The methods are tested in three U.S. watersheds ranging in size from 334 to 629 km2 with different climatic, hydrological, and geomorphological characteristics. Evaluation of the landscape delineation methods includes visual comparisons, error matrices (i.e. cross‐tabulations of delineated vs reference data), and geometric accuracy metrics. Reference data were obtained from the Soil Survey Geographic (SSURGO) database and Federal Emergency Management Agency (FEMA) flood maps. Results suggest that the slope position and the variable flood stage method work very well in all three watersheds. Overall percentages of floodplain and upland areas allocated correctly were obtained by comparing delineated and reference data. Values range from 83 to 93% for the slope position and from 80 to 95% for the variable flood stage method. Future studies will incorporate these two floodplain–upland delineation methods into the subwatershed‐based hydrologic model Soil and Water Assessment Tool (SWAT) to improve the representation of hydrological processes within floodplain and upland areas. Copyright © 2016 John Wiley & Sons, Ltd.

Identification of critical source areas for nonpoint source pollution in the Danjiangkou Reservoir Basin, China

ABSTRACTZhuang Y, Zhang L, Du Y, Yang W, Wang L, Cai X. 2016. Identification of critical source areas for nonpoint source pollution in the Danjiangkou Reservoir Basin, China. Lake Reserve Manage. 32:341–352.Danjiangkou Reservoir is an important water source for the middle route of the South-to-North Water Diversion Project in China. Water quality is crucial for this project, and the identification of critical source areas (CSAs) plays a significant role in nonpoint source (NPS) pollution control. The load–area curve based on the Export Empirical Model was used to quantitatively classify CSAs of NPS pollution in the Danjiangkou Reservoir Basin as CSAs, sub-CSAs, and non-CSAs. About 70% of loads were from 30% of the basin. In CSAs, 45.4% of total nitrogen (TN) loads and 48.0% of total phosphorus (TP) loads were from 14.5% and 13.3% of the basin, respectively; in sub-CSAs, 19.7% of TN loads and 18.3% of TP loads were from 14.5% and 13.3% of the basin, respectively. CSAs of both TN and TP loads were mainly from areas near the reservoir, which are subject to severe soil erosion and large runoff discharge. In the Danjiangkou Reservoir Basin, we recommend a CSAs→sub-CSAs→non-CSAs hierarchical control in the NPS pollution management plan specifying that sloping farmland in Xichuan and Yunxian counties should be preferentially treated. The load–area curve offers a new way of identifying CSAs of NPS pollution and helps to establish effective control measures for NPS pollution.