Spatial and temporal variability of stream phosphorus in a New Zealand high‐country agricultural catchment

Caruso, Brian S.

doi:10.1080/00288233.2000.9513424

Cited by 12 publications

(6 citation statements)

References 28 publications

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“…Basefl ow, although it does not generally contain high P concentrations, can act as a constant background source of P , and contribute substantially to cumulative P loads (Hooda et al, 1999;Caruso, 2000;Maguire and Sims, 2002;Hively et al, 2006). A lumped export coeffi cient approach is used to model basefl ow P loss:…”

Section: Dissolved Phosphorus Loss Associated With Basefl Owmentioning

confidence: 99%

“…Baseflow, although it does not generally contain high P concentrations, can act as a constant background source of P (McDowell et al, 2001), and contribute substantially to cumulative P loads (Hooda et al, 1999; Caruso, 2000; Maguire and Sims, 2002; Hively et al, 2006). A lumped export coefficient approach is used to model baseflow P loss:

\begin{matrix} L_{normalB, normalt} = μ_{normalB} B_{normalt} \end{matrix}

where L B,t is the DP load (kg d −1 ) in baseflow, B t is the VSLF predicted baseflow volume at the watershed outlet (m 3 d −1 ), and μ B is the baseflow DP export coefficient adjusted for temperature with an Arrhenius equation similarly to soils (Eq.…”

Section: Model Descriptionmentioning

confidence: 99%

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Combined Monitoring and Modeling Indicate the Most Effective Agricultural Best Management Practices

2008

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Although water quality problems associated with agricultural nonpoint source (NPS) pollution have prompted the rapid and widespread adoption of a variety of so called "best management practices" (BMPs), there have been few realistic eff orts to assess their combined eff ectiveness in reducing NPS pollution. Th is study used the Variable Source Loading Function (VSLF) model, a distributed watershed model, to simulate phosphorus (P) loading from an upstate New York dairy farm before and after the implementation of a suite of BMPs. With minimal calibration, the model calculates the dissolved P (DP) losses from impervious surfaces (e.g., barnyards), the plant/soil complex, fi eld-applied manure, and loads associated with basefl ow conditions. Th e simulated DP loads agreed well with measured loads for both the pre-BMP and post-BMP periods. More importantly, results showed that BMPs reduced DP loads by 35%, which is over half of the expected reduction if all manure was removed from the watershed, i.e., ~50% reduction. Th e model results indicate that had no BMPs been installed DP loads would be ~37% greater than observed at the watershed outlet. Th e most eff ective BMPs were those that disassociated pollutant loading areas from areas prone to generating runoff , i.e., hydrologically sensitive areas. By contrast, attempts to reduce P content in manure were somewhat less eff ective. Th is study demonstrates that a combination of distributed, mechanistic modeling and long-term monitoring provides better insights into the eff ectiveness of water quality protection eff orts than either individually.

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Section: Dissolved Phosphorus Loss Associated With Basefl Owmentioning

confidence: 99%

\begin{matrix} L_{normalB, normalt} = μ_{normalB} B_{normalt} \end{matrix}

Section: Model Descriptionmentioning

confidence: 99%

Combined Monitoring and Modeling Indicate the Most Effective Agricultural Best Management Practices

2008

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“…Elliott and Sorrell (2002) provide N and P losses for 'low-intensity pasture'. Notably, Caruso's (2000) reported P loss is much lower than Elliott and Sorrell's (2002).…”

Section: Pastoral Land Coverscontrasting

confidence: 57%

Improving and parameterising nitrogen and phosphorus modelling for application of LUCI in New Zealand

Trodahl¹

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<p>Over the last 50 years freshwater and marine environments have become severely impaired due to contamination from pathogens, heavy metals, sediment, industrial chemicals and nutrients (MEA 2005b). In many countries, including New Zealand, increased nitrogen (N) and phosphorus (P) loading to terrestrial and freshwater environments from diffuse nutrient sources are of particular concern (MEA 2005a; PCE 2015b; Steffen et al. 2015) and many governments now mandate control of diffuse nutrient loss to water. Water quality models are invaluable tools that can assist with decision making around this widespread issue through exploration of the current situation and future scenarios. Many water quality models exist, functioning at a variety of temporal and spatial scales and varying in detail and complexity. However, few, if any, simultaneously represent sub-field to catchment scale processes and outcomes, both of which are required to fully address water quality issues associated with diffuse nutrient sources. Those that do, likely require extensive time and expertise to operate. Water quality models embedded in the Land Utilisation and Capability Indicator (LUCI), an ecosystem service decision support framework, offer the opportunity to overcome these limitations. Being highly spatially explicit, yet straightforward to use, they can inform and assist individual land owners, catchment managers and other stakeholders with planning, decision making and management of water quality at sub-field to landscape scale. To model diffuse nutrient losses LUCI, like many catchment scale water quality models, requires some form of estimated nutrient loss, or export coefficient, from land units within the catchment of interest. To be representative export coefficients must consider climate, soil, topography, and land cover and management variables. A number of methods of export coefficient derivation exist, although generally they consider only very limited geo-climatic, land cover and land management variables. The principal aim of this study is development of algorithms capable of calculating New Zealand site specific N and P export coefficients from detailed geo-climatic, land cover and land management variables, for application in LUCI water quality models. Algorithms for pastoral land cover are developed from a large dataset comprising real pastoral farm input and output data from nutrient budgeting model OVERSEER. Algorithms are extended to land covers other than pasture, albeit in a limited manner. This is achieved through rescaling of the pastoral algorithms to account for relative differences in literature reported N and P losses from pasture and a variety of other New Zealand land covers. Application of the developed algorithms in LUCI water quality models results in positioning of export coefficients at the DEM grid square scale (≤ 15 m x 15 m for New Zealand). In addition, intra-basin configuration is considered in LUCI, at the same grid square scale, as water and nutrient flows are cascaded through the catchment. Application of the export coefficient calculating algorithms are applied to two contrasting New Zealand catchments. Tuapaka catchment, an 85ha agricultural foothill catchment in Manawatu, North Island, and Lake Rotorua catchment, a 502 km2 volcanic, mixed land cover catchment in Bay of Plenty, North Island. This research is supported by Ravensdown, a farmer owned co-operative, which plans to use LUCI extensively to advise and assist farmers with water quality issues. The ability to model mitigation strategies in LUCI is an important capability. Therefore, this research also includes a review of five particularly important on-farm mitigation strategies, which will later be used by the wider LUCI development team to assist with better parameterisation and improved performance of mitigation options in LUCI. Application of the developed algorithms at farm to catchment scale in LUCI results in considerably more nuanced, detailed maps and data showing N and P sources and pathways, compared to LUCI’s previously used ‘one export coefficient per land cover’ approach. Although results indicate absolute nutrient loss values are not always ‘correct’ compared to either OVERSEER predictions or in-stream water quality measurements, these differences appear comparable to those seen with similar water quality models. In addition, the issue of representativeness of OVERSEER predictions and in-stream water quality measurements exists. Nevertheless improvement to absolute predictions is always an aim. This research indicates further improvements to LUCI water quality predictions could result from refinement of both pastoral and other land cover algorithms, and from improved representation of attenuation processes in LUCI, including groundwater representation. However, lack of measured on-land and in-stream N and P loss data is a major challenge to both algorithm refinement and to evaluation of results. In addition, more detailed spatial data would provide more nuanced results from algorithm application. Although the algorithm application context in this research is LUCI water quality models applied in New Zealand, this does not preclude application of the developed algorithms in other export coefficient based, catchment scale water quality models. Using spatial data pertaining to climate, soil, topographic and land management variables, land units of combined variables can be identified and the algorithms applied, resulting in explicitly positioned export coefficients that can be fed into the catchment scale water quality model of interest. Therefore, developments made here potentially represent a wider contribution to catchment scale modelling using export coefficients.</p>

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“…Base flow, although it does not contain high P concentrations, can act as a constant background source of P [ McDowell et al , 2001], and contribute substantially to cumulative P loads [ Maguire and Sims , 2002]. For example Caruso [2000] found base flow P exports to be 47% of the P lost from an agricultural watershed, while Hively et al [2005] found base flow P exports to be 30% of the total in a dairy farm dominated watershed. Additionally, soils exhibiting macropore flow can contribute significant P loads to the subsoil, or directly to groundwater [ Hooda et al , 1999], thus increasing the base flow P contribution, especially in tile drained watersheds.…”

Section: Dissolved Phosphorus Model Developmentmentioning

confidence: 99%

Identifying dissolved phosphorus source areas and predicting transport from an urban watershed using distributed hydrologic modeling

Easton

Gerard-Marchant

Walter

et al. 2007

Water Resources Research

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[1] A reduction in surface water quality in urban watersheds due to nonpoint source phosphorus (P) loading has prompted municipalities to consider management practices to reduce P loss from landscapes. However, locating P source areas can be time consuming and expensive. Use of distributed models allows delineation of P source areas and focused management strategies. Using the spatially distributed soil moisture distribution and routing model, we adapt and validate a dissolved P (DP) loading model for application to an urban watershed, in Ithaca, New York, to identify P source areas. The model calculates DP loss separately for base flow, impervious surfaces, plant-soil complex, and fertilized areas. The load at the outlet is the sum of P loss from the four components distributed throughout the watershed. Both stream and distributed DP loss were well predicted as indicated by comparison with measured data. The model predicted the largest contribution from plant-soil complexes (36%). Impervious surfaces contributed 10% of the total load but as much as 17% in the winter. More important, the impervious surfaces increased DP losses from the adjacent areas due to runoff from the impervious surfaces saturating the soil, thus increasing runoff losses. Fertilizer contributed substantially following application but decreased rapidly thereafter, a result of conversion from soluble to insoluble P. However, fertilization increased soil P levels, and thus DP losses were higher as a whole (19%). Results demonstrate that correctly predicting the coincidence of P and runoff source areas can be a powerful tool to identify and mitigate contamination of surface waters.Citation: Easton, Z. M., P. Gérard-Marchant, M. T. Walter, A. M. Petrovic, and T. S. Steenhuis (2007), Identifying dissolved phosphorus source areas and predicting transport from an urban watershed using distributed hydrologic modeling, Water Resour. Res.,

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Spatial and temporal variability of stream phosphorus in a New Zealand high‐country agricultural catchment

Cited by 12 publications

References 28 publications

Combined Monitoring and Modeling Indicate the Most Effective Agricultural Best Management Practices

Combined Monitoring and Modeling Indicate the Most Effective Agricultural Best Management Practices

Improving and parameterising nitrogen and phosphorus modelling for application of LUCI in New Zealand

Identifying dissolved phosphorus source areas and predicting transport from an urban watershed using distributed hydrologic modeling

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