Browsing Teagasc funded research by Funder "Teagasc"
Now showing items 1-4 of 4
A comparison of SWAT, HSPF and SHETRAN/GOPC for modelling phosphorus export from three catchments in IrelandRecent extensive water quality surveys in Ireland revealed that diffuse phosphorus (P) pollution originating from agricultural land and transported by runoff and subsurface flows is the primary cause of the deterioration of surface water quality. P transport from land to water can be described by mathematical models that vary in modelling approach, complexity and scale (plot, field and catchment). Here, three mathematical models (SWAT, HSPF and SHETRAN/GOPC) of diffuse P pollution have been tested in three Irish catchments to explore their suitability in Irish conditions for future use in implementing the European Water Framework Directive. After calibrating the models, their daily flows and total phosphorus (TP) exports are compared and assessed. The HSPF model was the best at simulating the mean daily discharge while SWAT gave the best calibration results for daily TP loads. Annual TP exports for the three models and for two empirical models were compared with measured data. No single model is consistently better in estimating the annual TP export for all three catchments.
Comparison of physically based catchment models for estimating Phosphorus lossesAs part of a large EPA-funded research project, coordinated by TEAGASC, the Centre for Water Resources Research at UCD reviewed the available distributed physically based catchment models with a potential for use in estimating phosphorous losses for use in implementing the Water Framework Directive. Three models, representative of different levels of approach and complexity, were chosen and were implemented for a number of Irish catchments. This paper reports on (i) the lessons and experience gained in implementing these models, (ii) compares the performances of the individual models and (iii) assesses their sensitivities to the main parameters and to spatial scales.
Developing an independent, generic, phosphorus modelling component for use with grid-oriented, physically-based distributed catchment modelsGrid-oriented, physically based catchment models calculate fields of various hydrological variables relevant to phosphorous detachment and transport. These include (i) for surface transport: overland flow depth and flow in the coordinate directions, sediment load, and sediment concentration and (ii) for subsurface transport: soil moisture and hydraulic head at various depths in the soil. These variables can be considered as decoupled from any chemical phosphorous model since phosphorous concentrations, either as dissolved or particulate, do not influence the model calculations of the hydrological fields. Thus the phosphorous concentration calculations can be carried out independently from and after the hydrological calculations. This makes it possible to produce a separate phosphorous modelling component which takes as input the hydrological fields produced by the catchment model and which calculates, at each step the phosphorous concentrations in the flows. This paper summarise the equations and structure of Grid Oriented Phosphorous Component (GOPC) developed for simulating the phosphorus concentrations and loads using the outputs of a fully distributed physical based hydrological model. Also the GOPC performance is illustrated by am example of an experimental catchment (created by the author) subjected to some ideal conditions.
Semi-supervised linear discriminant analysisFisher's linear discriminant analysis is one of the most commonly used and studied classification methods in chemometrics. The method finds a projection of multivariate data into a lower dimensional space so that the groups in the data are well separated. The resulting projected values are subsequently used to classify unlabeled observations into the groups. A semi-supervised version of Fisher's linear discriminant analysis is developed, so that the unlabeled observations are also used in the model fitting procedure. This approach is advantageous when few labeled and many unlabeled observations are available. The semi-supervised linear discriminant analysis method is demonstrated on a number of data sets where it is shown to yield better separation of the groups and improved classification over Fisher's linear discriminant analysis.