Measurement of the nutrient concentrations in the stream is usually done on weekly, biweekly or monthly basis due to limited resources. There is need to estimate concentration and loads during the period when no data ...Measurement of the nutrient concentrations in the stream is usually done on weekly, biweekly or monthly basis due to limited resources. There is need to estimate concentration and loads during the period when no data is available. The objectives of this study were to test the performance of a suite of regression models in predicting continuous water quality loading data and to determine systematic biases in the prediction. This study used the LOADEST model which includes several predefined regression models that specify the model form and complexity. Water quality data primarily nitrogen and phosphorus from five monitoring stations in the Neuse River Basin in North Carolina, USA were used in the development and analyses of rating curves. We found that LOADEST performed generally well in predicting loads and observation trends with general tendency/bias towards overestimation. Estimated Total Nitrogen (TN) varied from observation (“true” load) by -1% to 9%, but for the Total Phosphorus (TP) it ranged from -2% to 27%. Statistical evaluation using R2, Nash-Sutcliff Efficiency (NSE) and Partial Load Factor (PLF) showed a strong correlation in prediction.展开更多
Ascertaining the relationship between "source-sink" landscape and non-point source(NPS) pollution is crucial for reducing NPS pollution, however, it is not easy to realize this target on cell unit scale. To ...Ascertaining the relationship between "source-sink" landscape and non-point source(NPS) pollution is crucial for reducing NPS pollution, however, it is not easy to realize this target on cell unit scale. To reveal the relationships between "sourcesink" landscape and NPS pollution based on cell units of a small catchment in the Three Gorges Reservoir Region(TGRR), the runoff and nutrient yields were simulated first by rainfall events on a cell unit scale based on the Annualized AGricultural Non-Point Source Pollution Model(AnnAGNPS). Landscape structure and pattern were quantified with "sourcesink" landscape indicators based on cell units including landscape area indices and locationweighted landscape indices. The results showed that:the study case of small Wangjiagou catchment highlighted a good prediction capability of runoff and nutrient export by the AnnAGNPS model. Throughout the catchment, the spatial distribution trends of four location-weighted landscape indices were similar to the trends of simulated total nitrogen(TN) and total phosphorus(TP), which highlighted the importance of spatial arrangement of "source" and "sink" landscape types in a catchment when estimating pollutant loads. Results by Pearson correlation analysis indicated that the location-weighted landscape index provided a more comprehensive account of multiple factors, and can better reflect NPS-related nutrient loss than other landscape indices applied in single-factor analysis. This study provides new findings for applying the "source-sink" landscape indices based on cell units in small catchments to explain the effect of "source-sink" landscape on nutrient export based on cell unit, and helps improve the understanding of the correlation between "source-sink" landscape and NPS pollution.展开更多
In China, some areas with intensive agricultural use are facing serious environmental problems caused by non-point source pollution(NPSP) as a consequence of soil erosion(SE). Until now, simultaneous monitoring of NPS...In China, some areas with intensive agricultural use are facing serious environmental problems caused by non-point source pollution(NPSP) as a consequence of soil erosion(SE). Until now, simultaneous monitoring of NPSP and SE is difficult due to the intertwined effects of crop type, topography and management in these areas. In this study, we developed a new integrated method to simultaneously monitor SE and NPSP in an intensive agricultural area(about 6 000 km2) of Nanjing in eastern China, based on meteorological data,a geographic information system database and soil and water samples, and identified the main factors contributing to NPSP and SE by calculating the NPSP and SE loads in different sub-areas. The levels of soil total nitrogen(TN), total phosphorus(TP), available nitrogen(AN) and available phosphorus(AP) could be used to assess and predict the extent of NPSP and SE status in the study area.The most SE and NPSP loads occurred between April to August. The most seriously affected area in terms of SE and NPSP was the Jiangning District, implying that the effective management of SE and NPSP in this area should be considered as a priority. The sub-regions with higher vegetation coverage contributed to less SE and NPSP, confirming the conclusions of previous studies, namely that vegetation is an effective factor controlling SE and NPSP. Our quantitative method has both high precision and reliability for the simultaneous monitoring of SE and NPSP occurring in intensive agricultural areas.展开更多
文摘Measurement of the nutrient concentrations in the stream is usually done on weekly, biweekly or monthly basis due to limited resources. There is need to estimate concentration and loads during the period when no data is available. The objectives of this study were to test the performance of a suite of regression models in predicting continuous water quality loading data and to determine systematic biases in the prediction. This study used the LOADEST model which includes several predefined regression models that specify the model form and complexity. Water quality data primarily nitrogen and phosphorus from five monitoring stations in the Neuse River Basin in North Carolina, USA were used in the development and analyses of rating curves. We found that LOADEST performed generally well in predicting loads and observation trends with general tendency/bias towards overestimation. Estimated Total Nitrogen (TN) varied from observation (“true” load) by -1% to 9%, but for the Total Phosphorus (TP) it ranged from -2% to 27%. Statistical evaluation using R2, Nash-Sutcliff Efficiency (NSE) and Partial Load Factor (PLF) showed a strong correlation in prediction.
基金supported by the National Natural Science Foundation of China (Nos. 41671291)
文摘Ascertaining the relationship between "source-sink" landscape and non-point source(NPS) pollution is crucial for reducing NPS pollution, however, it is not easy to realize this target on cell unit scale. To reveal the relationships between "sourcesink" landscape and NPS pollution based on cell units of a small catchment in the Three Gorges Reservoir Region(TGRR), the runoff and nutrient yields were simulated first by rainfall events on a cell unit scale based on the Annualized AGricultural Non-Point Source Pollution Model(AnnAGNPS). Landscape structure and pattern were quantified with "sourcesink" landscape indicators based on cell units including landscape area indices and locationweighted landscape indices. The results showed that:the study case of small Wangjiagou catchment highlighted a good prediction capability of runoff and nutrient export by the AnnAGNPS model. Throughout the catchment, the spatial distribution trends of four location-weighted landscape indices were similar to the trends of simulated total nitrogen(TN) and total phosphorus(TP), which highlighted the importance of spatial arrangement of "source" and "sink" landscape types in a catchment when estimating pollutant loads. Results by Pearson correlation analysis indicated that the location-weighted landscape index provided a more comprehensive account of multiple factors, and can better reflect NPS-related nutrient loss than other landscape indices applied in single-factor analysis. This study provides new findings for applying the "source-sink" landscape indices based on cell units in small catchments to explain the effect of "source-sink" landscape on nutrient export based on cell unit, and helps improve the understanding of the correlation between "source-sink" landscape and NPS pollution.
基金Supported by the State Key Laboratory of Soil and Sustainable Agriculture,Institute of Soil Science,Chinese Academy of Sciences(No.0812201210)the National Natural Science Foundation of China(No.41301307)the Knowledge Innovation Program of Chinese Academy of Sciences(No.ISSASIP1114)
文摘In China, some areas with intensive agricultural use are facing serious environmental problems caused by non-point source pollution(NPSP) as a consequence of soil erosion(SE). Until now, simultaneous monitoring of NPSP and SE is difficult due to the intertwined effects of crop type, topography and management in these areas. In this study, we developed a new integrated method to simultaneously monitor SE and NPSP in an intensive agricultural area(about 6 000 km2) of Nanjing in eastern China, based on meteorological data,a geographic information system database and soil and water samples, and identified the main factors contributing to NPSP and SE by calculating the NPSP and SE loads in different sub-areas. The levels of soil total nitrogen(TN), total phosphorus(TP), available nitrogen(AN) and available phosphorus(AP) could be used to assess and predict the extent of NPSP and SE status in the study area.The most SE and NPSP loads occurred between April to August. The most seriously affected area in terms of SE and NPSP was the Jiangning District, implying that the effective management of SE and NPSP in this area should be considered as a priority. The sub-regions with higher vegetation coverage contributed to less SE and NPSP, confirming the conclusions of previous studies, namely that vegetation is an effective factor controlling SE and NPSP. Our quantitative method has both high precision and reliability for the simultaneous monitoring of SE and NPSP occurring in intensive agricultural areas.
