Estimates Of Nutrient Loads Research Articles

Analysis of stream water quality and estimation of nutrient load with the aid of Quick Bird remote sensing imagery

Human activities have created high nutrient surpluses in agricultural lands due to the increasing rate of chemical fertilizer application and the increase in livestock production. To analyse the nutrient characteristics and estimate the nutrient load in streams, we conducted extensive field survey and water quality experiments from 2007 to 2008 in Koise River, a major river of the Lake Kasumigaura watershed, Japan. Water quality indicators of total nitrogen (TN), total phosphorus (TP) and total organic carbon (TOC) were investigated. The nutrient loads of TN, TP and TOC, as well as dissolved total nitrogen, dissolved inorganic nitrogen, dissolved organic nitrogen, particle organic nitrogen, dissolved total phosphorus, dissolved organic carbon and particle organic carbon were also estimated for the Koise River. Seasonal variation of the nutrient concentration from 2007 to 2008 was analysed considering the river discharge variation and agricultural activities. The results showed that the irrigation water from Lake Kasumigaura has the potential ability to decrease the TN concentration and increase the TOC concentration in the Koise River. Significant correlation coefficients between nutrient load and river discharge were found. The monthly pollution loads from different sources were then evaluated based on land cover classification generated from high-resolution Quick Bird remote sensing imagery. This study presents a useful interpretation of water quality data sets with a view to obtaining better information about water quality for more effective management of water resources in river basins. Editor Z.W. Kundzewicz Citation He, B., Oki, K., Wang, Y., Oki, T., Yamashiki, Y., Takara, K., Miura, S., Imai, A., Komatsu, K. and Kawasaki, N., 2012. Analysis of stream water quality and estimation of nutrient load with the aid of Quick Bird remote sensing imagery. Hydrological Sciences Journal, 57 (5), 850–860.

Introducing sampling frequency estimation using variograms in water research with the example of nutrient loads in the Kis-Balaton Water Protection System (W Hungary)

In most of the world along with urbanization came the deterioration of surface waters. The case of Hungary's Lake Balaton (the largest shallow freshwater lake in Central Europe) and its catchment area was no exception. In the second part of the twentieth century it became clear that measures had to be taken to prevent further water quality deterioration. In order to do so, the Kis-Balaton Water Protection System (KBWPS) was established. To measure its effectiveness in filtering Lake Balaton's water input, total phosphorus and total nitrogen loads are regularly estimated. In this study the practice of temporal sampling frequency estimation is described using data series from the KBWPS in order to suggest a sampling frequency that is suitable for estimating annual nutrient loads and still produces a dataset from which environmental processes can be followed. In this case, temporal sampling frequency estimation was conducted using variogram analysis, before which trend removal was performed on four sampling sites and two daily sampled parameters for the years 1993–2007. Although the calculations were only carried out on two parameters they are applicable to other environmental parameters as well. Nutrient load estimation is used as a verification tool. The result of the variogram analysis pointed towards a three-day sampling frequency for the whole KBWPS in the case of total phosphorus and total nitrogen (if the purpose of the monitoring is nutrient estimation). This reduction in sampling frequency would make an approximately 50% cost reduction possible. The study gives an example of sampling frequency estimation which is a key question in every aspect of environmental science, so that hopefully it will be useful for scientists dealing with water protection issues.

Estimates of sediment and nutrient loads in 10 major catchments draining to the Great Barrier Reef during 2006–2009

The Great Barrier Reef (GBR) catchment area has been monitored simultaneously for sediment and nutrient exports from 10 priority catchments discharging into the GBR lagoon between 2006 and 2009. This allows GBR catchment-wide exports to be estimated and spatially compared within a discrete time-frame. Elevated levels of sediment and nutrient exports were recorded in all monitored catchments as compared to pre-European estimates, but vary around previous estimates of mean annual loads. During the period of monitoring, the Burdekin and Fitzroy catchments contributed the highest sediment and nutrient exports, however when loads were normalised for area, these catchments produced the lowest unit yields. In contrast, the highest yields were produced in the wetter and proportionately more intensively cultivated Johnstone, O’Connell, and Pioneer catchments particularly for dissolved nitrogens. This assessment offers the necessary scientific foundation for future monitoring, assessment, and management of sediment and nutrient loads entering the GBR.

Integration of monthly water balance modeling and nutrient load estimation in an agricultural catchment

Non-point sources pollution has become a serious environmental problem in the aquatic systems throughout the world. The Xitiaoxi catchment is located in the southwest of Taihu Basin, contributing large amounts of runoff and associated nutrients to Taihu Lake. Thus, identifying critical non-point sources pollution in this catchment is urgent and essential to control water pollution, improve the water quality, and reduce the pollutants drained into water bodies. The present study integrated a monthly water balance model with the export coefficient model for total nitrogen and total phosphorus loads estimation in the Xitiaoxi catchment in southeastern China. The simulated monthly runoff are in good agreement with the observed streamflow at both Hengtangcun and Fanjiancun stations with Nash–Sutcliffe coefficients higher than 0.80. The predictions showed reasonable ranges from 1687 to 2046 t/y (2002–2005) for total nitrogen loads, and from 106 to 157 t/y for total phosphorus loads (1999–2007), respectively, which are consistent with the observed values at Hengtangcun. Overall, the monthly export coefficient model coupling monthly water balance simulation to export coefficient model presented both the seasonal dynamics and magnitude for streamflow and nutrients loads, which generally match well with the observations. These findings demonstrate that the proposed model can provide encouraging results and can be used as an efficient tool to identify the pollution sources for planning and management of large-scale agricultural catchment.

Estimation of shallow groundwater discharge and nutrient load into a river

Pollution of rivers with excess nutrients due to groundwater discharge, storm water runoff, surface loading, and atmospheric deposition is an increasing environmental concern worldwide. While the storm water runoff and surface loading of nutrients into many rivers have been explored in great detailed, the groundwater discharge of nutrients into the rivers has not yet been thoroughly quantified. This study ascertained the shallow groundwater discharges and nutrient loads into the Lower St. Johns River (LSJR), FL, USA. The groundwater discharges were obtained using Darcy's law along with field measured hydrological parameters, whereas the groundwater nutrient loads were calculated based on the groundwater discharges and the field measured nutrient concentrations. The average rate of groundwater discharge per unit cross-section area over the four selected sites along the LSJR was about 1.2 × 10 −2 m 3 m −2 d −1. The average loads of groundwater nutrients into the adjacent LSJR were 10.6 and 5.6 mg m −2 d −1, respectively, for nitrate- and nitrite-nitrogen (NO x -N) and total phosphorus (TP). In general, seasonal variations of the groundwater levels were larger than the river stages, whereas site variations of groundwater nutrient concentrations were larger than seasonal variations of groundwater nutrient concentrations. Results from this study are useful for estimation of groundwater contamination and river eutrophication.

Determining storm sampling requirements for improving precision of annual load estimates of nutrients from a small forested watershed

This study sought to determine the lowest number of storm events required for adequate estimation of annual nutrient loads from a forested watershed using the regression equation between cumulative load (∑L) and cumulative stream discharge (∑Q). Hydrological surveys were conducted for 4 years, and stream water was sampled sequentially at 15-60-min intervals during 24 h in 20 events, as well as weekly in a small forested watershed. The bootstrap sampling technique was used to determine the regression (∑L-∑Q) equations of dissolved nitrogen (DN) and phosphorus (DP), particulate nitrogen (PN) and phosphorus (PP), dissolved inorganic nitrogen (DIN), and suspended solid (SS) for each dataset of ∑L and ∑Q. For dissolved nutrients (DN, DP, DIN), the coefficient of variance (CV) in 100 replicates of 4-year average annual load estimates was below 20% with datasets composed of five storm events. For particulate nutrients (PN, PP, SS), the CV exceeded 20%, even with datasets composed of more than ten storm events. The differences in the number of storm events required for precise load estimates between dissolved and particulate nutrients were attributed to the goodness of fit of the ∑L-∑Q equations. Bootstrap simulation based on flow-stratified sampling resulted in fewer storm events than the simulation based on random sampling and showed that only three storm events were required to give a CV below 20% for dissolved nutrients. These results indicate that a sampling design considering discharge levels reduces the frequency of laborious chemical analyses of water samples required throughout the year.

Estimation of monthly diffuse nutrient loads for a watershed in Turkey

Diffuse pollution has a complex nature depending on various land-use activities like agriculture, livestock breeding and forestry. In this study, an alternative methodology is applied for decreasing the spatial uncertainty of diffuse nutrient load estimations. It is applied in the Melen Watershed, Turkey, which is an important watershed from where additional water will be supplied for the megacity Istanbul via interbasin water transfer. Monthly diffuse nutrient loads were estimated for each subwatershed by utilizing remote sensing (RS) and geographical information systems (GIS). Estimated nutrient loads were considerably different in various months due to the temporally irregular fertilizer application and meteorological conditions. Temporal differences were also predicted in the spatial distribution of the nutrient loads. The methodology is applicable in watershed protection studies, especially where necessary etudes should be conducted in the short term and with limited data. Such efforts in identifying and determining the diffuse loads are important for sustainable management of the watershed.

Application of the Soil and Water Assessment Tool for six watersheds of Lake Erie: Model parameterization and calibration

The Soil and Water Assessment Tool (SWAT), a physically-based watershed-scale model, holds promise as a means to predict tributary sediment and nutrient loads to the Laurentian Great Lakes. In the present study, model performance is compared across six watersheds draining into Lake Erie to determine the applicability of SWAT to watersheds of differing characteristics. After initial model parameterization, the Huron, Raisin, Maumee, Sandusky, Cuyahoga, and Grand SWAT models were calibrated (1998–2001) and confirmed, or validated (2002–2005), individually for stream water discharge, sediment loads, and nutrient loads (total P, soluble reactive P, total N, and nitrate) based on available datasets. SWAT effectively predicted hydrology and sediments across a range of watershed characteristics. SWAT estimation of nutrient loads was weaker although still satisfactory at least two-thirds of the time across all nutrient parameters and watersheds. SWAT model performance was most satisfactory in agricultural and forested watersheds, and was less so in urbanized settings. Model performance was influenced by the availability of observational data with high sampling frequency and long duration for calibration and confirmation evaluation. In some instances, it appeared that parameter adjustments that improved calibration of hydrology negatively affected subsequent sediment and nutrient calibration, suggesting trade-offs in calibrating for hydrologic vs. water quality model performance. Despite these considerations, SWAT accurately predicted average stream discharge, sediment loads, and nutrient loads for the Raisin, Maumee, Sandusky, and Grand watersheds such that future use of these SWAT models for various scenario testing is reasonable and warranted.

Estimation of total phosphorus loads for a large, flashy river of a highly developed watershed—seasonal and hysteresis effects

Estimation of nutrient loads is important for the assessment of the links between phosphorus loading and biotic impairment. The challenge for many monitoring programs is to make the best use of available information (flow and concentration data) for load estimations. This study evaluates several total phosphorus loading estimation procedures using long-term (1990–2009) data from a large, highly developed watershed that flows into Lake Simcoe. The procedures involved the grouping of long-term data into 7-year moving windows, stratifying flow and concentration data, and calculating annual load for the central year of each window. A procedure involving seasonal stratification and then further stratification of the winter/spring strata by hydrograph limb performed better than stratification by flow. The former stratification distinguished more homogenous groups based on concentration versus flow relationships (significant slopes and higher r 2) and had better sampling of flow ranges. Annual loads calculated using the Beale Ratio load estimation method ranged from 3920 to 20,642 kg (1993–2005) and were greater and more consistent than loads calculated using the method historically used to calculate loads to Lake Simcoe (the Midpoint method). It is recommended, for this river, and rivers of other regions with similar characteristics (urban/agricultural watersheds affected by seasonality and hysteresis) that sampling aims at capturing the full range of flows of the hydrograph in all seasons. Data stratified by season, hydrograph or flow should be investigated further to identify combinations of these factors; then more understanding of the concentration/flow dynamics and more accurate loads can be acquired for a system.

SWAT-Based Streamflow and Embayment Modeling of Karst-Affected Chapel Branch Watershed, South Carolina

SWAT is a GIS-based basin-scale model widely used for the characterization of hydrology and water quality of large, complex watersheds; however, SWAT has not been fully tested in watersheds with karst geomorphology and downstream reservoir-like embayment. In this study, SWAT was applied to test its ability to predict monthly streamflow dynamics for a 1,555 ha karst watershed, Chapel Branch Creek, which drains to a large embayment and is comprised of highly diverse land uses. SWAT was able to accurately simulate the monthly streamflow at a cave spring (CS) outlet draining mostly agricultural and forested lands and a golf course plus an unknown groundwater discharging area, only after adding known monthly subsurface inputs as a point source at that location. Monthly streamflows at two other locations, both with multiple land uses, were overpredicted when lower lake levels were prevalent as a result of surface water flow to groundwater (losing streams). The model underpredicted the flows during rising lake levels, likely due to high conductivity and also a deep percolation coefficient representing flow lost to shallow and deep groundwater. At the main watershed outlet, a wide section performing as a reservoir embayment (R-E), the model was able to more accurately simulate the measured mean monthly outflows. The R-E storage was estimated by using a daily water balance approach with upstream inflows, rainfall, and PET as inputs and using parameters obtained by bathymetric survey, LiDAR, and downstream lake level data. Results demonstrated the substantial influence of the karst features in the water balance, with conduit and diffuse flow as an explanation for the missing upstream flows appearing via subsurface conveyance to the downstream cave spring, thus providing a more accurate simulation at the embayment outlet. Results also highlighted the influences of downstream lake levels and karst voids/conduits on the watershed hydrologic balance. Simulation performance of hydrology could be improved with more accurate DEMs obtained from LiDAR for karst feature identification and related modification of SWAT parameters. This SWAT modeling effort may have implications on nutrient and sediment loading estimates for TMDL development and implementation in karst watersheds with large downstream embayments that have significant changes in water level due to adjoining lakes.

Advances in Watershed Management: Modeling, Monitoring, and Assessment

This article introduces a special collection of nine articles that address a wide range of topics all related to improving the application of watershed management planning. The articles are grouped into two broadly defined categories: modeling applications, and monitoring and assessment. The modeling application articles focus on one of two widely used watershed-scale water quality modeling packages: HSPF or SWAT. The HSPF article assesses the model's robustness when applied to watersheds across a range of topographic settings and climatic conditions. In the SWAT-related articles, researchers used the model to inform watershed management efforts in a variety of ways, including subwatershed prioritization in the context of achieving broader watershed management goals, examining the utility of applying SWAT in a watershed receiving groundwater inputs from outside the topographic watershed boundaries, and estimating the uncertainty and risk associated with meeting TMDL target loads. The monitoring and assessment articles cover such diverse topics as an examination of how best management practice effectiveness is assessed, examination of estimated nutrient loads to a reservoir where a nutrient TMDL has been developed, examination of the sources of fecal indicator bacteria in an urban watershed, and detailed accounting of issues related to flow measurements in small watersheds. The articles in this collection contribute to the body of literature that seeks to inform and advance sound watershed management planning and execution.

Evaluation of runoff, peak flow and sediment yield for events simulated by the AnnAGNPS model in a belgian agricultural watershed

ABSTRACTThe AnnAGNPS model, widely utilized as a practical tool for addressing erosion problems and land use planning, was implemented in a small agricultural watershed located in central Belgium, to assess its prediction capacity of runoff, peak flow and sediment yield in humid temperate conditions. Model performance was evaluated at the event scale by using a database reporting hydrological, geomorphologic and land use data collected during a 2‐year period. Seventeen events were modelled and compared with the corresponding observations at the watershed outlet.The model performed well in predicting the largest runoff volumes, as shown by the high values achieved for the coefficients of efficiency (E = 0·89) and determination (r2 = 0·92). However, some events resulted in zero runoff simulation. The prediction capability for peak flow and sediment yield was poor (E = 0·35 and 0·16, respectively). This inaccuracy can have several causes: the internal model deficiencies may be due to the incomplete representation of watershed complex processes, while external problems may be related to the conditions within the modelled watershed and the quality of recorded data.On the whole the AnnAGNPS model may be considered as being suitable to simulate the significant runoff events in the experimental watershed. However, the model may be seen as better suited for comparative assessments of alternative management and policy scenarios and for gross estimation of nutrient loads rather than the precise prediction of a single event, consequently helping in the prediction of land degradation problems in the experimented conditions. Copyright © 2010 John Wiley & Sons, Ltd.

Rating curve estimation of nutrient loads in Iowa rivers

Accurate estimation of nutrient loads in rivers and streams is critical for many applications including determination of sources of nutrient loads in watersheds, evaluating long-term trends in loads, and estimating loading to downstream waterbodies. Since in many cases nutrient concentrations are measured on a weekly or monthly frequency, there is a need to estimate concentration and loads during periods when no data is available. The objectives of this study were to: (i) document the performance of a multiple regression model to predict loads of nitrate and total phosphorus (TP) in Iowa rivers and streams; (ii) determine whether there is any systematic bias in the load prediction estimates for nitrate and TP; and (iii) evaluate streamflow and concentration factors that could affect the load prediction efficiency. A commonly cited rating curve regression is utilized to estimate riverine nitrate and TP loads for rivers in Iowa with watershed areas ranging from 17.4 to over 34,600 km2. Forty-nine nitrate and 44 TP datasets each comprising 5–22 years of approximately weekly to monthly concentrations were examined. Three nitrate data sets had sample collection frequencies averaging about three samples per week. The accuracy and precision of annual and long term riverine load prediction was assessed by direct comparison of rating curve load predictions with observed daily loads. Significant positive bias of annual and long term nitrate loads was detected. Long term rating curve nitrate load predictions exceeded observed loads by 25% or more at 33% of the 49 measurement sites. No bias was found for TP load prediction although 15% of the 44 cases either underestimated or overestimate observed long-term loads by more than 25%. The rating curve was found to poorly characterize nitrate and phosphorus variation in some rivers.

Integration of numerical modeling and Bayesian analysis for setting water quality criteria in Hamilton Harbour, Ontario, Canada

The credibility of the scientific methodology of mathematical models and their adequacy to form the basis of public policy decisions has frequently been challenged. We believe that the development of novel methods for rigorously assessing model uncertainty should be a top priority of the modeling community. In this regard, we introduce the Bayesian calibration of process-based models as a methodological advancement that warrants consideration in aquatic ecosystem research. This modeling framework combines the advantageous features of both mechanistic and statistical approaches, i.e., mechanistic understanding that remains within the bounds of data-based parameter estimation. Other advantages of the Bayesian approach are the ability to sequentially update “beliefs” as new knowledge is available, the consistency with the scientific process of progressive learning and the policy practice of adaptive management. In this study, the Bayesian calibration framework is used to guide the water quality criteria setting process in Hamilton Harbour, Ontario, Canada. First, we present the results of the Bayesian calibration exercise and examine the ability of the model to adequately reproduce the average observed patterns along with the major cause–effect relationships underlying the Harbour water quality conditions. We then address the following critical questions regarding the future response of the system: How possible is it to meet the objective of delisting the study system as an Area of Concern, if the nutrient loading reductions proposed by the Hamilton Harbour Remedial Action Plan are actually implemented? What additional remedial actions are needed to increase the likelihood of meeting the water quality targets? In this regard, the present modeling study undertakes an estimation of the critical nutrient loads in the Harbour based on acceptable exceedance frequencies and confidence of compliance levels with different water quality criteria, e.g., chlorophyll a, total phosphorus (TP). Our analysis suggests that the water quality goals for TP (17 μg L−1) and chlorophyll a concentrations (5–10 μg L−1) will likely be met, if the recommendation for phosphorus loading at the level of 142 kg day−1 is achieved. We also provide evidence that the anticipated structural shifts of the zooplankton community will determine the restoration rate of the Harbour. Finally, we pinpoint two critical aspects of the system dynamics that invite further investigation and will likely modulate the stability of the new trophic state, i.e., the coupling between the benthic and pelagic habitat and the relative importance of the allochthonous organic matter in sustaining the secondary production in the system.

On-line measurements provide more accurate estimates of nutrient loading: a case of the Yläneenjoki river basin, southwest Finland

Automatic on-line measurement stations for water quality components and water level were equipped with dataloggers and GSM transmitters; the stations were installed at two sites in the Yläneenjoki river basin, SW Finland. Measurements during five seasons in 2006-2007 were conducted to find out whether the produced data would provide more accurate estimates of material and nutrient transport than traditional water sampling. Sensor-based monitoring estimates for transport of total suspended solids (TSS) were clearly higher (difference -6-61%), total phosphorus also higher, and that of nitrate (NO(3)-N) somewhat lower (difference (-51%-4%), as compared with manual sampling based estimates. The winter season studied here was mild i.e. winter-type which is becoming more common in Finland with the changing climate. Sensor-based monitoring proved its benefits particularly in such conditions.

Eutrophication risk assessment in Hamilton Harbour: System analysis and evaluation of nutrient loading scenarios

Environmental modeling has been an indispensable tool of the Hamilton Harbour restoration efforts, where a variety of data-oriented and process-based models have been used for linking management actions with potential ecosystem responses. In this study, our objective is to develop a biogeochemical model that can effectively describe the interplay among the different ecological mechanisms modulating the eutrophication problems in Hamilton Harbour, Ontario, Canada. First, we provide the rationale for the model structure adopted, the simplifications included, and the formulations used during the development phase of the model. We then present the results of a calibration exercise and examine the ability of the model to sufficiently reproduce the average observed patterns along with the major cause–effect relationships underlying the Harbour water quality conditions. The present modeling study also undertakes an estimation of the critical nutrient loads in the Harbour based on acceptable probabilities of compliance with different water quality criteria (e.g., chlorophyll a, total phosphorus). Our model suggests that the water quality goals for TP (17 μg L − 1) and chlorophyll a concentrations (5–10 μg L − 1) will likely be met, if the Hamilton Harbour RAP phosphorus loading target at the level of 142 kg day − 1 is achieved. We also provide evidence that the anticipated structural shifts of the zooplankton community will determine the restoration rate as well as the stability of the new trophic state in the Harbour.

Using NEXRAD and Rain Gauge Precipitation Data for Hydrologic Calibration of SWAT in a Northeastern Watershed

The value of watershed-scale, hydrologic and water quality models to ecosystem management is increasingly evident as more programs adopt these tools to evaluate the effectiveness of different management scenarios and their impact on the environment. Quality of precipitation data is critical for appropriate application of watershed models. In small watersheds, where no dense rain gauge network is available, modelers are faced with a dilemma to choose between different data sets. In this study, we used the German Branch (GB) watershed (~50 km2), which is included in the USDA Conservation Effects Assessment Project (CEAP), to examine the implications of using surface rain gauge and next-generation radar (NEXRAD) precipitation data sets on the performance of the Soil and Water Assessment Tool (SWAT). The GB watershed is located in the Coastal Plain of Maryland on the eastern shore of Chesapeake Bay. Stream flow estimation results using surface rain gauge data seem to indicate the importance of using rain gauges within the same direction as the storm pattern with respect to the watershed. In the absence of a spatially representative network of rain gauges within the watershed, NEXRAD data produced good estimates of stream flow at the outlet of the watershed. Three NEXRAD datasets, including (1) non-corrected (NC), (2) bias-corrected (BC), and (3) inverse distance weighted (IDW) corrected NEXRAD data, were produced. Nash-Sutcliffe efficiency coefficients for daily stream flow simulation using these three NEXRAD data ranged from 0.46 to 0.58 during calibration and from 0.68 to 0.76 during validation. Overall, correcting NEXRAD with rain gauge data is promising to produce better hydrologic modeling results. Given the multiple precipitation datasets and corresponding simulations, we explored the combination of the multiple simulations using Bayesian model averaging. The results show that this Bayesian scheme can produce better deterministic prediction than any single simulation and can provide reasonable uncertainty estimation. The optimal water balance obtained in this study is an essential precursor to acquiring realistic estimates of sediment and nutrient loads in future GB modeling efforts. The results presented in this study are expected to provide insights into selecting precipitation data for watershed modeling in small Coastal Plain catchments.

The importance of decadal-scale climate variability to wind-driven modulation of hypoxia in Chesapeake Bay

AbstractMillions of dollars are spent annually to reduce nutrient loading to Chesapeake Bay, with a fundamental goal of reducing the extent and severity of low dissolved oxygen (hypoxia) during the summertime months^1^. Yet despite recent reductions in nutrient loading, large volumes of the Bay continue to be impacted by hypoxia and anoxia during the summer months^2-3^. One obstacle to assessing efforts to improve water quality in the Bay and other estuarine systems is a complete understanding of the physical processes that modulate dissolved oxygen and the long-term variability of these processes. Here I analyze a 58-year data set of estimated hypoxic volume in the Bay^2^ and demonstrate the importance that wind direction plays in controlling the extent and severity of summertime hypoxia. This analysis indicates that wind direction explains a greater percentage of the observed inter-annual variation in hypoxic volume than estimates of nutrient loading. The implication is that physical processes play a dominant role in modulating hypoxia and that much of the increased hypoxia observed since the early 1980s can be attributed to changes in wind forcing that are the result of decadal-scale climate variability. These findings emphasize the importance of understanding the physical processes that modulate dissolved oxygen in coastal and estuarine systems and highlight the potential impact that climate change may have on water quality in Chesapeake Bay and other estuarine systems.

Large scale nutrient modelling using globally available datasets: A test for the Rhine basin

Summary Nutrient discharge to coastal waters from rivers draining populated areas can cause vast algal blooms. Changing conditions in the drainage basin, like land use change, or climate induced changes in hydrology, may alter riverine nitrogen (N) and phosphorus (P) fluxes and further increase the pressure on coastal water quality. Several large scale models have been employed to quantify riverine nutrient fluxes on a yearly to decadal timescale. Seasonal variation of these fluxes, governed by internal nutrient transformations and attenuation, is often larger than the inter-annual variation and may contain crucial information on nutrient transfer through river basins and should therefore not be overlooked. In the last decade the increasing availability of global datasets at fine resolutions has enabled the modelling of multiple basins using a coherent dataset. Furthermore, the use of global datasets will aid to global change impact assessment. We developed a new model, RiNUX, to adequately simulate present and future river nutrient loads in large river basins. The RiNUX model captures the intra-annual variation at the basin scale in order to provide more accurate estimates of future nutrient loads in response to global change. With an incorporated dynamic sediment flux model, the particulate nutrient loads can be assessed. It is concluded that the RiNUX model provides a powerful, spatial and temporal explicit tool to estimate intra-annual variations in riverine nutrient loads in large river basins. The model was calibrated using the detailed RHIN dataset and its overall efficiency was tested using a coarser dataset GLOB for the Rhine basin. Using the RHIN dataset seasonal variable nutrient load at the river outlet can be satisfactorily modelled for both total N (E = 0.50) and total P (E = 0.47). The largest prediction errors occur in estimating high TN loads. When using the GLOB dataset, the model efficiency is lower for TN (E = 0.12), due to overestimated nutrient emissions. For TP, the model efficiency is only slightly lower (E = 0.36) in comparison to the RHIN dataset. Despite the lower model efficiencies for the GLOB dataset, we conclude that this dataset provided reasonably good estimates of seasonal nutrient loads in the Rhine basin and is considered promising for application to other, less documented, large river basins.

Flood water quality and marine sediment and nutrient loads from the Tully and Murray catchments in north Queensland, Australia

Current estimates of sediment and nutrient loads from the Tully–Murray floodplain to the Great Barrier Reef lagoon are updated by taking explicit account of flood events. New estimates of flood discharge that include over-bank flows are combined with direct measurements of sediment and nutrient concentrations in flood waters to calculate the loads of sediment and nutrient delivered to the ocean during 13 floods that occurred between 2006 and 2008. Although absolute concentrations of sediment and nutrient were quite low, the large volume of water discharged during floods means that they make a large contribution (30–50%) to the marine load. By not accounting for flood flows correctly, previous estimates of the annual average discharge are 15% too low and annual loads of nitrogen and phosphorus are 47% and 32% too low respectively. However, because sediments may be source-limited, accounting for flood flows simply dilutes their concentration and the resulting annual average load is similar to that previously estimated. Flood waters also carry more dissolved organic nitrogen than dissolved inorganic nitrogen and this is the opposite of their concentrations in river water. Consequently, dissolved organic nitrogen loads to the ocean may be around twice those previously estimated from riverine data.