Total Phosphorus Loads Research Articles

Spatiotemporal variation characteristics of sediment nutrient load from the soil erosion of the Yangtze River Basin of China from 1901 to 2010

Over the past 70 years, the Yangtze River Basin-the third longest river basin in the world-has been under the influence of augmented human activities. Regarding environmental protection and policy design, the total nitrogen and phosphorus (TN and TP, respectively) loads from soil erosion are objectively and quantitatively critical parameters while assessing the spatio-temporal soil erosion changes over the 1901–2010 period. First, soil erosion in our study area was calculated using the Revised Universal Soil Loss Equation (RUSLE) model. Second, the TN and TP loads from soil erosion were assessed using the constructed Nutrient Loss Empirical Model (NLEM). Third, we conducted spatial autocorrelation analysis, Mann-Kendall (M–K) trend tests, and wavelet analysis of the soil erosion, and related TN and TP loads. At Datong Station over the 1901–2010 period, the average annual TN and TP loads from soil erosion were 1.77 and 0.56 million tonnes, respectively. Moran’s I values of the average annual soil erosion, and related TN and TP loads from soil erosion indicates the existence of positive correlations, while clustering was the prevailing spatial distribution pattern. High-high (H-H) cluster areas were mainly evident in high-altitude region of the western and southern Yangtze River Basin, conversely, low-low (L-L) cluster areas were scattered primarily in regions with high population density and intense human activities. Soil erosion increased rapidly around 2001; hence, 2001 was a changing point. According to the M–K test, the time intersection of soil erosion, and related TN and TP loads at Datong Station was around 1990; hence, 1990 may have been a changing point, likely due to the operation of Gezhouba Dam during 1981-1986. This Dam trapped large amounts of sediments. If the sediment load data of control hydrological station of 12 sub-basins is available, SDR, sediment load, AN and AP calculation could be more accurate and interpretable.

Multi-parameter approaches for improved ensemble prediction accuracy in hydrology and water quality modeling

Ensemble approaches can be a quick way to improve hydrological prediction accuracy by considering multiple plausible modeling representations of a system. Previous studies showed that the use of multiple types of models could increase prediction accuracy, but model selection subjectivity and parameter equifinality issues remain unsolved. This study proposes to consider multiple parameter sets identified in the model calibration process and information on their performance for improved accuracy of ensemble hydrological prediction. In this study, we investigate whether multi-parameter ensemble (MP) compared to multi-model ensemble (MM) approaches can improve the accuracy of hydrology and water quality prediction. A variety of ensemble methods, including simple averaging, median filtering, weighted averaging, Bayesian model averaging (BMA), and their variants, were tested in watershed modeling implemented to predict daily water discharge and total phosphorus (TP) loads of a study watershed. The parameter spaces of two watershed loading models, Soil and Water Assessment Tool (SWAT) and Hydrological Simulation Program-FORTRAN (HSPF), were sampled using a multi-objective heuristic optimization algorithm, namely A Multi-Algorithm Genetically Adaptive Multiobjective (AMALGAM). The modeling experiment showed that the MP approach more effectively improved modeling accuracy statistics compared to the MM of both models as well as single-model and single-parameter approaches. The ensemble approaches did not always improve modeling accuracy, and their efficiency depended on ways to determine weights for ensemble members. The BMA method outperformed the other weighting schemes tested in this study, and its performance was responsive to the number and diversity (or variance) of ensemble member candidates and the computational cost to create the candidates. The findings demonstrate how the information of parameter sets sampled in model calibration and their performance statistics could help improve hydrological prediction accuracy.

Stochastic export coefficient model to predict annual variation in phosphorus loading from diffuse runoff

A stochastic export coefficient (EC) model is needed to predict variation in annual diffuse or nonpoint source nutrient loads and develop realistic river basin management plans. This study aimed to develop such a model by using observed discharge occurrence probability to: a) select EC values from a probabilistic database covering all US river basins; and b) adjust the delivery ratio value, transforming edge of field loads into basin outlet loads via riverine storage. The model was tested in three river basins from Colorado, New York, and Ohio, with sizes ranging from 742 to 3450 km2, where observed loads of total phosphorus had been recorded for 27 to 38 years; the first three years of data were used to calibrate a single parameter, the base delivery ratio in the new model. The stochastic EC model was compared to the standard EC model, i.e., deterministic and temporally static. The stochastic EC model outperformed the standard EC model, with on average 74% lower root mean square error (RMSE) and had better coefficients of determination (R2), and statistically significant temporal correlation with observed loads (p < 0.01). The stochastic EC model has flexibility with observed discharge inputs, and based on statistical tests its predictions were equally accurate using discharge data from any representative tributary in a multi-tributary basin, and were only slightly less accurate when using annual in place of daily discharge time series. The stochastic EC model is within the i-Tree Buffer toolkit to assist river basin nutrient management plans by automating simulations, bounding estimates with uncertainty, providing default parameters, and allowing inputs of observed or predicted discharge time series. Research-impact statementThis study presents a Stochastic Export Coefficient Model within the i-Tree Buffer model to assist with nutrient management plans for river basins. This research is the first to utilize the high spatial resolution US federal dataset of export coefficients to generate variation in annual loading and match time series observations.

Modeling future land cover and water quality change in Minneapolis, MN, USA to support drinking water source protection decisions

AbstractContinued alteration of the nitrogen cycle exposes receiving waters to elevated nitrogen concentrations and forces drinking water treatment services to plan for such increases in the future. We developed four 2011–2050 land cover change scenarios and modeled the impact of projected land cover change on influent water quality to support long‐term planning for the Minneapolis Water Treatment Distribution Service (MWTDS) using Soil Water and Assessment Tool. Projected land cover changes based on relatively unconstrained economic growth led to substantial increases in total nitrogen (TN) loads and modest increases in total phosphorus (TP) loads in spring. Changes in sediment, TN, and TP under two “constrained” growth scenarios were near zero or declined modestly. Longitudinal analysis suggested that the extant vegetation along the Mississippi River corridor upstream of the MWTDS may be a sediment (and phosphorus) trap. Autoregressive analysis of current (2008–2017) chemical treatment application rates (mass per water volume processed) and extant (2001–2011) land cover change revealed that statistically significant increases in chemical treatment rates were temporally congruent with urbanization and conversion of pasture to cropland. Using the current trend in chemical treatment application rates and their inferred relationship to extant land cover change as a bellwether, the unconstrained growth scenarios suggest that future land cover may present challenges to the production of potable water for MWTDS.

Prioritizing Irrigation‐System Upgrades to Maximize Improvements to Agricultural Drain‐Network Water Quality: A Graph‐Theoretic Approach

AbstractWe develop and demonstrate an approach to optimally prioritize fields for irrigation‐system upgrades to improve agricultural‐drain discharge water quality. The approach accounts for the attenuation of pollutants transported along complex drain networks from fields to drain‐network outlets. The approach produces a Pareto‐optimal cost curve of irrigation‐system upgrade combinations that compromise between maximizing the reduction of drain‐outlet loads and minimizing upgrade implementation costs. The approach utilizes a graph‐theoretic data structure to organize drain‐network data, route drain‐network flow paths, and formulate objectives and constraints for multi‐objective optimization. Due to the linearity of the objective functions and constraints, Pareto‐optimal solutions are obtained along the cost curve by prioritizing fields by decreasing load‐reduction/cost ratio (pollutant load‐reduction per cost). Multi‐objective optimization will identify additional Pareto‐optimal solutions along the cost curve providing additional compromises. The approach allows for a computationally efficient prioritization, providing a level of complexity that is able to incorporate commonly available data. The cost curve is highly sensitive to the pollutant attenuation coefficient, with higher attenuation coefficients resulting in low sensitivity of load reduction to cost. While the focus of this work is on irrigation‐system upgrades, there are other conservation actions and/or land‐use change scenarios that this method could also prioritize. Analyses of this nature could prove valuable in improving the allocation of limited conservation funding and/or developing cost‐effective water quality trading programs. We demonstrate the approach on a collection of drain networks around Grand View, Idaho, USA with the objective to reduce total phosphorus loads to the Snake River.

Analysis of Seasonal Water Characteristics and Water Quality Responses to the Land Use/Land Cover Pattern: A Case Study in Tianjin, China

As the second largest city in northern China, Tianjin has a unique geographical and social status. Following its rapid economic development, Tianjin is experiencing high levels of surface water pollution. The land use/land cover (LULC) pattern has a considerable impact on hydrological cycling and pollutant transmission, and thus on regional water quality. A full understanding of the water quality response to the LULC pattern is critical for water resource management and improvement of the natural environment in Tianjin. In this study, surface water monitoring station data and LULC data from 2021 to 2022 were used to investigate the surface water quality in Tianjin. A cluster analysis was conducted to compare water quality among monitoring stations, a factor analysis was conducted to identify potential pollution sources, and an entropy weight calculation was used to analyze the impact of the land use pattern on water quality. The mean total nitrogen (TN) concentration exceeded the class Ⅴ water quality standard throughout the year, and the correlation coefficient of the relationship between dissolved oxygen (DO) and pH exceeded 0.5 throughout the year, with other water quality parameters showing seasonal changes. On the basis of their good water quality, the water quality monitoring stations near large water source areas were distinguished from those near areas with other LULC patterns via the cluster analysis. The factor analysis results indicated that the surface water in Tianjin suffered from nutrient and organic pollution, with high loadings of ammonia nitrogen (NH3N), TN, and total phosphorus (TP). Water pollution was more serious in areas near built-up land, especially in the central urban area. The entropy weight calculation results revealed that water, built-up land, and cultivated/built-up land had the greatest impact on NH3N, while cultivated land had the greatest impact on electrical conductivity (EC). This study discusses the seasonal changes of surface water and impact of land use/land cover pattern on water quality at a macro scale, and highlighted the need to improve surface water quality in Tianjin. The results provide guidance for the sustainable utilization and management of local water resources.

Vulnerability of Estuarine Systems in the Contiguous United States to Water Quality Change Under Future Climate and Land‐Use

AbstractChanges in climate and land‐use and land‐cover (LULC) are expected to influence surface water runoff and nutrient characteristics of estuarine watersheds, but the extent to which estuaries are vulnerable to altered nutrient loading under future conditions is poorly understood. The present work aims to address this gap through the development of a new vulnerability assessment framework that accounts for (a) estuarine exposure to projected changes in total nitrogen (TN) and total phosphorus (TP) loads as a function of LULC and climate change under several scenarios, (b) sensitivity, and (c) adaptive capacity. The framework was applied to 112 estuaries and their contributing watersheds across the contiguous U.S., specifically to look at regional variability in estuarine vulnerability to nutrient loading. Study findings revealed that the largest increases in estuarine nutrient loads are expected in the North and South Atlantic regions and eastern Gulf of Mexico, while the lowest increases are expected in the North and South Pacific regions and the western Gulf of Mexico. However, the North Atlantic and the South Pacific had the highest adaptive capacity, which could potentially counteract the effects of LULC and climate change on nutrient loads. Strong variation in predicted estuarine nutrient loads was observed as a function of climate model projections, while projected LULC changes were more consistently associated with elevated loads. Our findings illustrate the benefits of integrating natural and socio‐ecological factors to identify opportunities to develop adaptation plans and policies to mitigate ecological degradation in vitally important estuaries.

Effects of land use on water quality at different spatial scales in the middle reaches of Huaihe River

The impact of land use on water quality is spatially scale-dependent. Exploring the relationship between land use pattern and river water quality provides an important basis for water quality safety and land planning. This study explores the effect of land use patterns on surface water quality using land use data and water quality data of 16 sampling points in the middle reach of the Huai River. It investigates the water quality status of the middle reach of the Huai River and the relationship between land use patterns and water quality indices at 100, 500, and 1000 m and watershed scales utilising Spearman correlation, redundancy analysis (RDA), and multiple linear regression analysis. Multiple linear regression analysis showed that the water pollutants in the middle reach of the Huai River are mainly total nitrogen (TN) and total phosphorus (TP). Redundancy analysis (RDA) indicated that the impact of land use patterns on water quality is significant on watershed scales. Spearman correlation indicated that water quality and land use showed significant correlation differences, the building land was positively correlated with TP, TN and NH3-N, the cultivated land was positively correlated with TN, and the grassland was negatively correlated with TP and NH3-N. This studies have revealed that building land acts as a 'source’ of pollutants, grassland purifies the load of TP and NH3-N, and forest land has the effect of interception and deposition of pollutants. The results can provide important information for land use planning and water quality protection measures at multiple spatial scales.

Reconstruction of Sparse Stream Flow and Concentration Time‐Series Through Compressed Sensing

AbstractMonitoring water quality at high frequency is challenging and costly. Compressed sensing (CS) offers an approach to reconstruct high‐frequency water quality data from limited measurements, given that water quality signals are commonly “sparse” in the frequency domain. In this study, we investigated the sparsity of stream flow and concentration time‐series and tested reconstruction with CS. All stream signals were sparse using 15‐min discrete time‐series transformed to the Fourier domain. Stream temperature, conductance, dissolved oxygen, and nitrate plus nitrite (NOx‐N) concentration were sparser than discharge, turbidity, and total phosphorus (TP) concentration. CS effectively reconstructed these signals with only 5%–10% of measurements needed. Stream NOx‐N and TP loads were well estimated with errors of −6.6% ± 3.8% and −9.0% ± 2.9% with effective sampling frequencies of 10 and 0.4 days, respectively. For broader applications in environmental geosciences and engineering domains, CS can be integrated with dimensionality reduction and optimization techniques for more efficient sampling schemes.

Geochemical distribution and forms of phosphorus in the surface sediment of Netravathi-Gurupur estuary, southwestern coast of India

Estuaries are the most productive transition ecosystem and phosphorus (P) plays an important role in these ecosystems. Therefore, in the present study, sequential extraction method was used to determine the abundance of five sediment P fractions (calcium (Ca-P), Iron (FeP), aluminum (AlP), exchangeable (Ex-P) and organic (OrgP) bound P) in Netravathi-Gurupur estuary, India. Total phosphorus (TP) content varied from 435-810 mg/kg (non-monsoon) and 258–699 mg/kg (monsoon). Inorganic P was dominant part. Different P fractions followed similar order (Fe-P > Ca-P > Al-P > Org-P > Ex-P) with respect to seasons. FeP was dominant fraction, indicating probable anthropogenic stress. Sediment may act as source of P as bioavailable P constituted 40–69.2 % of TP. Molar ratio of OC to Org-P in sediment indicated terrestrial sources of organic matter. However, the estimated phosphorus pollution index were lower than one except a few cases indicating less ecological risk with respect to sedimentary TP load.

Agricultural productivity and water quality tradeoffs of winter cover crops at a landscape scale through the lens of remote sensing

A large body of scientific research studying the impacts of cover crops on crop-water-nutrient dynamics has been based on short-term and/or controlled experimental trials without much consideration of the spatial and temporal heterogeneity that typically exists in the working landscape. As such, those findings are difficult to reproduce on working farms. This study is focused on quantifying the long-term impacts of cover crops on both cash crop (corn and soybean) yields and water quality across the Maumee River watershed (MRW) using publicly accessible historic satellite data, from 2009 to 2019, that leverage spatial and temporal variabilities in the agricultural landscape. Corn and soybean yield maps at 30-m resolution were prepared using regression models that integrated satellite images, weather, and county-scale crop yield data. These yield maps were overlaid with 30-m resolution cover crop maps, which were created for the same period using a machine learning classification model using satellite data and ground-truth observations, to quantify yield differences between corn and soybean fields with and without cover crops. The annual variability in winter cover crop area was compared with publicly accessible data on spring nitrate (NO3–N), total phosphorus (TP), and soluble reactive phosphorus (SRP) loads and concentrations, that were collected at multiple water quality monitoring stations within the MRW. During the study period, cover crops, typically showed minimal to negative yield impacts on cash crops, with yield reductions of up to 1.35 ton/ha for corn and 0.24 ton/ha for soybean. However, crop yield variabilities were found to decline with a longer cover crop history. The winter cover crop area within the MRW was found to have water quality impact, with a strong negative correlation with the spring NO3–N concentration (correlation coefficient (R) = −0.70) and loads (R = −0.65) at the MRW outlet. There was a mixed relationship between the cover crop area and P load and concentration, with a positive correlation (R = 0.17) with spring TP concentration and a negative one with spring TP load (R = −0.29), spring SRP concentration (R = −0.30), and spring SRP load (R = −0.40). Similar relationships were also observed across sub-watersheds within the MRW, suggesting that cover crops can be more effective in reducing NO3–N losses compared to P. These findings highlight the importance of using satellite-based remote sensing to improve the understanding of cover crops and their impacts on crop productivity and water quality at a broader scale.

Addressing data challenges in riverine nutrient load modeling of an intensively managed agro‐industrial watershed

AbstractData limitations often challenge the reliability of water quality models, especially in intensively managed watersheds. While numerous studies report successful hydrological model setup and calibration, few have addressed in detail the data challenges for multisite and multivariable model calibration to an intensively managed watershed. In this study, we address some of these challenges based on our reflective experience calibrating the Soil and Water Assessment Tool (SWAT) to the Upper Sangamon River Watershed in central Illinois based on daily flow, annual crop yield, and monthly sediment, nitrate, and total phosphorus loads. We highlight some challenges in SWAT calibration processes due to data errors and inconsistencies, and insufficient precipitation and water quality observations. Following, we demonstrate the merits of additional weather and water quality observations that could help reduce input uncertainties, and we provide suggestions for selecting appropriate observations for the model calibration. After dealing with the data issues, we show that the SWAT model could be calibrated with acceptable results for the case study watershed.

Calibration and Validation of RZWQM2-P Model to Simulate Phosphorus Loss in a Clay Loam Soil in Michigan

Highlights RZWQM2-P was tested and validated for clay loam soil using daily discharge and load data. The model performed satisfactorily in predicting hydrology and TP load, but DRP prediction was unsatisfactory. Inability of the model to simulate P loss in subsurface drainage discharge after fertilization event was one of the reasons for the unsatisfactory model performance. Abstract. Phosphorus (P) loss and transport through subsurface drainage systems is a primary focus for addressing harmful algal blooms in freshwater systems. The recent development of the phosphorus (P) routine of the Root Zone Water Quality Model (RZWQM2-P) has the potential to enhance our understanding of the fate and transport of P from subsurface-drained fields to surface water. However, there is a need to test the model under different fertilization, soil, climate, and cropping conditions. The objective of this study was to test the model's performance with daily drainage discharge, dissolved reactive phosphorus (DRP), and total phosphorus (TP) load collected from a subsurface-drained field with clay loam soil. We calibrated RZWQM2-P using two years of measured data. Subsequently, we validated RZWQM2-P using a year and nine months of measured data. We used the Nash-Sutcliffe model efficiency (NSE) and percentage bias (PBIAS) statistics for the RZWQM2-P model evaluation. The results showed that the model performance was “good” (daily NSE = 0.66 and PBIAS = -7.16) in predicting hydrology for the calibration period. For the validation period, the hydrology prediction of the model was “very good” (daily NSE = 0.76), but it had a “satisfactory” underestimation bias (PBIAS = 23.57). The model’s performance was “unsatisfactory” in simulating DRP for both calibration (daily NSE = 0.31 and PBIAS = -61.50) and validation (daily NSE = 0.32 and PBIAS = 43.68) periods. The P model showed “satisfactory” performance in predicting TP load for both calibration (daily NSE = 0.46 and PBIAS = -32.41) and validation (daily NSE = 0.39 and PBIAS = 42.90) periods, although both periods showed “unsatisfactory” percent bias. The underperformance may have been due to the model’s inability to partition fertilizer P into different P pools under high water tables or ponding conditions when using daily data. In conclusion, the RZWQM2-P model performed well for drainage discharge with daily data, but further investigation is needed to improve the P component of the model. Keywords: Field-scale modeling, Nutrient load, Phosphorus modeling, Subsurface drainage, Tile drainage, Water Quality.

Twenty years of conservation effects assessment in the St. Joseph River watershed, Indiana

Lake Erie has a long and storied history with water quality impairment and conservation. Following the passing of the Clean Water Act in the 1970s, total phosphorus (P) loading to the lake substantially decreased through permitting of point sources and through conservation efforts to decrease sediment loss from agricultural fields. While total P losses to Lake Erie have remained relatively stable since the 1990s, dissolved P has increased and resulted in increases in the extent and severity of algal blooms over the past two decades (Smith et al. 2015b). Both agricultural industry and environmental quality are vital to local and regional economies. To achieve a balance between these important resources, there is a critical need to better understand the effect of agricultural practices on crop production and water quality in the national priority Lake Erie watershed.

Evaluation of Biogeochemical Changes in Channelized and Restored Portions of a Subtropical Floodplain

Floodplains are critically important ecosystems that provide a whole suite of ecosystem services, including nutrient and carbon sequestration, flood mitigation, water storage, and critical wildlife habitat. However, human modification of rivers and floodplains through channelization, artificial levee construction, reductions in the active floodplain area, and water management can significantly reduce the ecosystem function of river–floodplain systems. In this study, we evaluated the changes in the nutrient loading of the Kissimmee River floodplain during the restoration of the river–floodplain system. In addition to time-series loading analysis, we also evaluated soil nutrient concentrations across the lower portion of the Kissimmee River floodplain. During the 44-year nutrient loading time-series, the floodplain remained a nutrient exporter with changes in nutrient loading generally corresponding to both water quality (i.e., point source reductions) and hydrologic restoration activities in the watershed and Kissimmee River floodplain. During the study period, inputs of total phosphorus and total nitrogen loads from upstream either significantly increased or remained the same. In addition to external sources of nutrients, internal sources of nutrients from floodplain soils can also contribute to the total nutrient export from the system. These internal sources could be organic via the decomposition of organic matter or geologic from the original excavation of the canal and/or restoration backfilling. Soil nutrient concentrations vary between vegetative communities and landscape position and could be a significant source of phosphorus to the downstream system, which is plagued by eutrophic conditions. Therefore, as floodplain function in the Kissimmee River continues to be restored and managed, additional effort may be needed to address nutrient inputs and internal legacy nutrients.

Predicting phosphorus and nitrate loads by using SWAT model in Vamanapuram River Basin, Kerala, India

Evaluations of probable environmental impacts of point and diffuse source pollution at regional sizes are essential to achieve sustainable development of natural resources such as land and water. This research focused on how nitrate and phosphorus load varied over time and space in the Vamanapuram River Basin (VRB). Phosphorus and nitrate loads have been evaluated in the VRB using the semi-distributed Soil and Water Assessment Tool (SWAT) hydrological model. SWAT Calibration and Uncertainty Programs (SWAT-CUP) have simulated the developed model using the Sequential Uncertainty Fitting, version 2(SUFI-2). The developed model was simulated for 2001 to 2008, and it was split into two-phase calibration and validation phases. Model performance was evaluated by the percentage of bias (PBAIS) and Nash-Sutcliffe efficiency coefficient (NSE). The simulated performance of nitrate was indicated as NSE = 0.22-0.59 and PBIAS = 51.86-65.88. The simulated performance of phosphorus showed NSE = 0.06-0.33 and PBIAS = 15.14-33.97. Total Phosphorus load was most sensitive to the organic Phosphorus enrichment ratio (ERORGP) and CH_N2 for streamflow simulation. This study concluded that the South-western region was a high potential for nutrient loads. This study will explain the nutrient load and guidelines for land management practice in the study area.

Monitoring the effects of urban and forested land uses on runoff quality: Implications for improved stormwater management

Urban stormwater is a substantial source of non-point source pollution. Despite considerable monitoring efforts, little is known about stormwater quality in certain geographic regions. These spatial gaps induce uncertainty when extrapolating data and reduce model calibration capabilities, thereby limiting pollutant load reduction strategies. In this study, stormwater quality was monitored from 15 watersheds to characterize pollutant event mean concentrations (EMCs) and loads as a function of urban and forested (i.e., surrogates for pre-development) land use and land covers (LULCs) and rainfall patterns from a geographic region where these data are sparse. Residential and heavy industrial, heavy industrial, and industrial and commercial LULCs, respectively, were the primary generators of nutrients, total suspended solids (TSS), and heavy metals. Increased rainfall intensities (average and peak) significantly increased the EMCs of all particulate bound pollutants. Pollutant loads increased with rainfall depth and, in general, did not follow the same LULC trends as EMCs, suggesting loads were influenced substantially by watershed hydrologic responses. Mean annual urban loads of total phosphorus, total nitrogen, TSS, and zinc (Zn) ranged from 0.4 (low density residential [LDR]) to 1.5 (heavy industrial), 3.2 (single family residential [SFR]) to 11.5 (heavy industrial), 122.6 (SFR) to 1219.9 (heavy industrial), and 0.1 (LDR) to 0.7 (commercial) kg/ha/yr, respectively. Annual urban loads of TSS were 3.5 to 34 and − 1.5 to 6.8-fold greater than annual loads from forested and agricultural watersheds, respectively. Mean annual loads of heavy metals from urban LULCs were substantially greater than loads produced by forested and agricultural watersheds (e.g., 8.6 to 92 and 6.8 to 73-fold greater, respectively, for Zn), while loads of nutrients were generally similar between urban and agricultural watersheds. Findings herein suggest non-point source pollution will continue to threaten surface water quality as land is developed; results can help guide the development of cost-efficient stormwater management strategies.

A reconnaissance survey of channel bank particulate phosphorus concentrations, controls and estimated contributions to riverine loads across England.

Channel banks can contribute a significant proportion of fine-grained (<63 μm) sediment to rivers, thereby also contributing to riverine total particulate phosphorus loads. Improving water quality through better agricultural practices alone can be difficult since the contributions from non-agricultural sources, including channel banks, can generate a 'spatial mismatch' between the efficacy of best management applied on farms and the likelihood of meeting environmental objectives. Our study undertook a reconnaissance survey (n=76 sites each with 3 profiles sampled) to determine the total phosphorus (TP) concentrations of channel banks across England and to determine if TP content can be predicted using readily accessible secondary data. TP concentrations in adjacent field topsoils, local soil soil type/texture and geological parent material were examined as potential predictors of bank TP. Carbon and nitrogen content were also analysed to explore the impacts of organic matter content on measured TP concentrations. The results suggest that channel bank TP concentrations are primarily controlled by parent material rather than P additions to adjacent topsoils through fertilizer and organic matter inputs, but significant local variability in concentrations prevents the prediction of bank TP content using mapped soil type or geology. A median TP concentration of 873 mg kg-1 was calculated for the middle section of the sampled channel bank profiles, with a 25th percentile of 675 mg kg-1, and 75th percentile of 1159 mg kg-1. Using these concentrations and, in comparison with previously published estimates, the estimated number of inland WFD waterbodies in England for which channel bank erosion contributes >20% of the riverine total PP load increased from 15 to 25 (corresponding range of 17-35 using the 25th and 75th percentiles of measured TP concentrations). Collectively, these 25 waterbodies account for 0.2% of the total inland WFD waterbody area comprising England.

Modelling streamflow and phosphorus fluxes in the Lake of the Woods watershed

This study provides an evaluation of streamflow and the spatial and temporal variability of phosphorus (P) fluxes for the transboundary Lake of the Woods (LoW) watershed using the Canadian version of the Soil and Water Assessment Tool (CanSWAT). The model calibration and validation generally indicate good performance for the simulated flow, especially for the Rainy River, the main tributary to LoW, while the sediment and nutrient calibration performance was satisfactory. Model results indicated Rainy River is the primary source of total phosphorus (TP), contributing about 88% of the external non-point source (NPS) and point source P loads to LoW, with the majority being NPS. Simulated TP loads varied seasonally with over approximately 60% occurring during the spring period and varied spatially across the LoW watershed. TP yields tended to be lower upstream of Rainy Lake in the Precambrian Shield (a.k.a., Canadian Shield) and higher downstream of Rainy Lake in the Glacial Lake Agassiz lakebed, particularly in the Lower Rainy and Little Fork sub-watersheds. Point sources along the Rainy River constituted the largest anthropogenic TP source. Tributary P loads estimated by the model were also used in a simple lake mass-balance model that suggested 32–46% of TP load to LoW was retained within the lake.

Spatial optimization of ecological ditches for non-point source pollutants under urban growth scenarios.

Non-point source (NPS) pollution is regarded as the major threat to water quality worldwide, and ecological ditches (EDs) are considered an important and widely used method to collect and move NPS pollutants from fields to downstream water bodies. However, few studies have been conducted to optimize the spatial locations of EDs, particularly when the watershed experiences urbanization and rapid land-use changes. As land-use patterns change the spatial distribution of NPS loads, this study used a cellular automata-Markov method to simulate future land-use changes in a typical agricultural watershed. Three scenarios are included as follows: historical trend, rapid urbanization, and ecological protection scenarios. The spatial distributions of particulate phosphorus loads were simulated using the revised universal soil loss equation and sediment transport distribution model. The results suggested that the total particulate phosphorus (TP) load in the Zhuxi watershed decreased by 10,555.2kg from 2000 to 2020, primarily because the quality and quantity of forests in Zhuxi County improved over the last 20years. The TP load in Zhuxi watershed would be 2588.49, 2639.15, and 2553.32kg in 2040 in historical trend, rapid urbanization, and ecological protection scenarios, respectively, compared with 2308.1kg in 2020. This indicated that urban expansion increases the TP load, and the faster the expansion rate, the more the TP load. Consequently, the optimal locations of EDs were determined based on the intercepted loads and the period during which they existed during land-use changes. The results suggested that rapid urbanization would consequently reduce the space available for building EDs and also increase the cost of building EDs to control the NPS pollution in the watershed.