Non-Point Source Pollution Modelling with GIS » książka
Non-Point Source Pollution Modelling with GIS
ISBN-13: 9783844318210 / Angielski / Miękka / 2011 / 156 str.
Over the last few decades, non-point source (NPS) pollution has grown into a global environmental issue. The Annualized Agricultural Non-Point Source pollutant model (AnnAGNPS) is a watershed modelling tool to predict runoff, peak flow, sediment loading and nutrient loadings. The present study has been carried out in Seberang Perai Selatan to develop regional models for rainfall erosivity (EI30) values, to evaluate the performance of AnnAGNPS under Malaysian condition and to introduce the model as a long-term watershed monitoring system in the country. Three different modelling procedures were applied for the estimation of monthly rainfall erosivity values. While storm rainfall (P) and duration (D) data were used in the first approach, the second approach used monthly rainfall for days with rainfall 10 mm (rain10) and monthly number of days with rainfall 10 (days10). The third approach however used the Fournier index as the independent variable. Using data from additional sixteen stations and the Fournier index based regional model, EI30 values were computed for each month.
Over the last few decades, non-point source (NPS) pollution has grown into a global environmental issue. The Annualized Agricultural Non-Point Source pollutant model (AnnAGNPS) is a watershed modelling tool to predict runoff, peak flow, sediment loading and nutrient loadings. The present study has been carried out in Seberang Perai Selatan to develop regional models for rainfall erosivity (EI30) values, to evaluate the performance of AnnAGNPS under Malaysian condition and to introduce the model as a long-term watershed monitoring system in the country. Three different modelling procedures were applied for the estimation of monthly rainfall erosivity values. While storm rainfall (P) and duration (D) data were used in the first approach, the second approach used monthly rainfall for days with rainfall ≥ 10 mm (rain10) and monthly number of days with rainfall ≥ 10 (days10). The third approach however used the Fournier index as the independent variable. Using data from additional sixteen stations and the Fournier index based regional model, EI30 values were computed for each month.
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