In agricultural catchments where groundwater (GW) base flow discharge contributes substantially towards stream flow, the information linking GW inflow/outflow with contaminant import/export is scarce. However, this in...In agricultural catchments where groundwater (GW) base flow discharge contributes substantially towards stream flow, the information linking GW inflow/outflow with contaminant import/export is scarce. However, this information is essential to address aquatic ecosystem health hazard/risk associated with nitrate export and subsequent loading in sensitive surface water bodies (SWB). The objectives of this study were to assess the temporal dynamics of (i) rain water inflow/outflow behaviour in three agricultural catchments in the humid tropics of far-northeast Queensland of Australia, (ii) solute import via inflow and subsequent export in outflow, and (iii) the association between GW inflow/outflow and solute import/export. Approximately 71% of the average seasonal rainfall percolated (inflow) into the porous basaltic regolith of the Johnstone River Catchment (JRC) compared with 44% into the alluvial regolith in the Mulgrave River Catchment (MRC) and 29% into the metamorphic regolith in the Tully River Catchment (TRC), respectively. The outflows from the basaltic, alluvial, and metamorphic regoliths were 56%, 36%, and 55% of the inflows, respectively. The cumulative nitrate import per season was 25 k/ha in the JRC compared with 11 kg/ha in MRC and 34 kg/ha in TRC. The corresponding exports were 24 kg/ha, 8 kg/ha 26 kg/ha in JRC, MRC, and TRC, respectively. The total dissolved solute (TDS) exports were 82%, 77%, 75%, of the corresponding imports in JRC, MRC, and TRC, respectively. Simple correlations indicated that nitrate export was positively correlated with the outflow in each one of the regolith and similar trends were observed between inflow and import. The import/export mass balance for nitrate shows that 73% to 96% of the imports were exported during the same rainy season, suggesting the potential for nitrate associated ecosystem health hazard/risk in sensitive SWB receiving the outflows.展开更多
Monitoring groundwater quality by cost-effective techniques is important as the aquifers are vulnerable to contamination due to point sources and non point sources. This paper presents Artificial neural Network (ANN) ...Monitoring groundwater quality by cost-effective techniques is important as the aquifers are vulnerable to contamination due to point sources and non point sources. This paper presents Artificial neural Network (ANN) Models that might be used to predict water parameters from a few known parameters. The sample data from 112 hand pumps and hand operated tube well water samples used for drinking purposes by the local population was used. The ANN model features a back propagation algorithm and neuron members were determined for optimization of the model architecture by trial and error method. The model simulations show that the optimum network of 4-50-50-6 has mean error of –0.023% on complete data was utilized. This demonstrated that the developed model has high accuracy for predicting. Thus it has been established that the two hidden layers neural network has more efficiency than asymptotic regression in the present. This model can be used for analysis and prediction of subsurface water quality prediction.展开更多
文摘In agricultural catchments where groundwater (GW) base flow discharge contributes substantially towards stream flow, the information linking GW inflow/outflow with contaminant import/export is scarce. However, this information is essential to address aquatic ecosystem health hazard/risk associated with nitrate export and subsequent loading in sensitive surface water bodies (SWB). The objectives of this study were to assess the temporal dynamics of (i) rain water inflow/outflow behaviour in three agricultural catchments in the humid tropics of far-northeast Queensland of Australia, (ii) solute import via inflow and subsequent export in outflow, and (iii) the association between GW inflow/outflow and solute import/export. Approximately 71% of the average seasonal rainfall percolated (inflow) into the porous basaltic regolith of the Johnstone River Catchment (JRC) compared with 44% into the alluvial regolith in the Mulgrave River Catchment (MRC) and 29% into the metamorphic regolith in the Tully River Catchment (TRC), respectively. The outflows from the basaltic, alluvial, and metamorphic regoliths were 56%, 36%, and 55% of the inflows, respectively. The cumulative nitrate import per season was 25 k/ha in the JRC compared with 11 kg/ha in MRC and 34 kg/ha in TRC. The corresponding exports were 24 kg/ha, 8 kg/ha 26 kg/ha in JRC, MRC, and TRC, respectively. The total dissolved solute (TDS) exports were 82%, 77%, 75%, of the corresponding imports in JRC, MRC, and TRC, respectively. Simple correlations indicated that nitrate export was positively correlated with the outflow in each one of the regolith and similar trends were observed between inflow and import. The import/export mass balance for nitrate shows that 73% to 96% of the imports were exported during the same rainy season, suggesting the potential for nitrate associated ecosystem health hazard/risk in sensitive SWB receiving the outflows.
文摘Monitoring groundwater quality by cost-effective techniques is important as the aquifers are vulnerable to contamination due to point sources and non point sources. This paper presents Artificial neural Network (ANN) Models that might be used to predict water parameters from a few known parameters. The sample data from 112 hand pumps and hand operated tube well water samples used for drinking purposes by the local population was used. The ANN model features a back propagation algorithm and neuron members were determined for optimization of the model architecture by trial and error method. The model simulations show that the optimum network of 4-50-50-6 has mean error of –0.023% on complete data was utilized. This demonstrated that the developed model has high accuracy for predicting. Thus it has been established that the two hidden layers neural network has more efficiency than asymptotic regression in the present. This model can be used for analysis and prediction of subsurface water quality prediction.
