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Coupling Xinanjiang model and SWAT to simulate agricultural non-point source pollution in Songtao watershed of Hainan, China

机译:耦合新安江模型与SWAT模型模拟海南松桃小流域农业面源污染

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Agriculture has been identified as the major contributor of non-point source pollution of Hainan water resources. In this study, we coupled the Xinanjiang model and SWAT to a new model, EcoHAT, to assess the non-point source pollution in Hainan. The study site is located around the Songtao reservoir, Hainan Island, China, which is regarding as the most important water source system for Hainan. EcoHAT, including algorithms for the hydrological cycle, nutrient cycle, and plant growth cycle, simulated the non-point source pollution for the watershed in calculated grid cell units based on remote sensing data. Remote sensing data were used to interpret the spatial land surface information and derive the model parameters. Besides the remote sensing data, other essential databases such as the meteorological databases, soil chemical and physical databases, and plant nutrients databases were also used in this study. The EcoHAT model was calibrated and validated with 5 years of monitored water quantity and quality data in the Songtao reservoir watershed. The study results indicated that the EcoHAT model has simulated the hydrologic pollutant adequately. After the calibration and validation, the parameters were applied to simulate the nutrient and sediment transport in the Songtao watershed during 2003-2007. In the end, the effects of several specific scenarios of changes in the land covers or management practices on the local watershed nutrients transport were also simulated. The results revealed that: (1) the model has predicted the runoff volume within a range of acceptable accuracy which was reflected by a large coefficient of determination; (2) regression analysis between the observed and simulated values resulted in high values of coefficient of determination (R~2) during the calibration and validation period. The high values of Nash-Sutcliffe simulation efficiency were achieved with a close agreement between the observed and simulated pollutants concentrations in the runoff. It also indicated that the model simulated the NO_3-N, NH_4~+-N, and P concentrations in the runoff for the Songtao watershed with considerable accuracy; (3) the sediment loads, TN, and TP, experienced temporal and spatial variations, with strong correlations existing between the parameters and the land use as well as the precipitation; (4) the scenario analysis showed that with 40% fertilization reduction, 7.51% and 7.76% reduction on TN and TP loads respectively could be reached. Besides, the conservation measures are more effective in the study area to reduce the sediment loads than in other areas.
机译:农业已被确定为海南水资源面源污染的主要贡献者。在这项研究中,我们将新安江模型和SWAT模型与新模型EcoHAT进行了耦合,以评估海南面源污染。研究地点位于中国海南岛松涛水库附近,被认为是海南最重要的水源系统。 EcoHAT包括水文循环,养分循环和植物生长循环的算法,基于遥感数据,以计算的网格单元为单位,模拟了流域的面源污染。遥感数据被用来解释空间地表信息并推导出模型参数。除遥感数据外,本研究还使用了其他重要数据库,例如气象数据库,土壤化学和物理数据库以及植物养分数据库。通过对松桃水库流域的5年监测水量和水质数据进行校准和验证,对EcoHAT模型进行了验证。研究结果表明,EcoHAT模型已经充分模拟了水文污染物。经过校准和验证后,使用这些参数模拟松桃流域2003-2007年的养分和沉积物迁移。最后,还模拟了土地覆盖或管理实践的几种特定情景对当地流域养分迁移的影响。结果表明:(1)该模型预测了径流量在可接受的精度范围内,这反映在较大的确定系数上; (2)观测值和模拟值之间的回归分析导致在校准和验证期间确定系数(R〜2)较高。 Nash-Sutcliffe模拟效率的高值是在径流中观察到的和模拟的污染物浓度之间达成一致的前提下实现的。还表明该模型模拟了松桃流域径流中NO_3-N,NH_4〜+ -N和P的浓度,具有相当高的准确性。 (3)沉积物负荷TN和TP经历了时空变化,参数与土地利用和降水之间存在很强的相关性; (4)情景分析表明,施肥减少40%时,TN和TP负荷分别减少7.51%和7.76%。此外,保护区在研究区比其他地区更有效地减少了泥沙负荷。

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