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首页> 外文期刊>Journal of Hydrology >Identifying critical source areas for water quality: 1. Mapping and validating transport areas in three headwater catchments in Otago, New Zealand
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Identifying critical source areas for water quality: 1. Mapping and validating transport areas in three headwater catchments in Otago, New Zealand

机译:确定水质的关键来源地区:1.绘制和验证新西兰奥塔哥的三个上游水源流域的运输区域

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Validity of five empirical to process-based, hydrological models described by Srinivasan and McDowell (2007) in mapping transport areas was tested in the Invermay and Glenomaru headwater catchments in Otago, New Zealand. These transport areas together with contaminant source areas form critical source areas (CSAs), where the majority of contaminant loss occurs and therefore represent areas where mitigation potential would be most efficient. Rainfall and 15-min instantaneous surface flows at the catchment outlets and the shallow water table (<1 m from surface) dynamics within 5-40 m of perennial streams were recorded. In the Glenomaru deer sub-catchment, subsurface flow from a tile drain and surface flow in an ephemeral stream were also measured. In the Invermay catchment, surface soil moisture was recorded periodically during stormflow and baseflow periods to map the expansion and contraction of surface saturation areas. Analysis of spatial and time-series data from August 2006 to February 2008 indicated that during dry seasons (below-average rainfall periods), the majority of stormflow came from direct precipitation, wet areas (areas at or above saturation like deer wallows) adjacent to the stream and semi-pervious areas such as animal tracks. During wet periods (above-average rainfall), flow from these areas accounted for 10-70% of total stormflows. Water table data indicated that saturated areas with the water table at the surface rarely extended >10 m from the stream during storm events. There appeared to be an active subsurface (shallow) flow system transferring flows from land to streams. However, during many rainfall events, semi-pervious areas like fence lines, animal tracks and gateways were connected to the stream via infiltration-excess surface runoff, as measured by surface runoff samplers. This may be a significant for contaminant transfer given the amount of time spent by animals on these areas and deposition of contaminants (e.g., in dung) and is explored in a companion paper. Of the approaches used to delineate transport areas during storms, generally the empirically-based curve number model and Phosphorus Index over-predicted stormflow areas. The drainage density approach predicted stormflow areas well, but under-predicted flow volumes by 55-82%. The physically-based topographic index (TI) model and a model combining TI model and surface runoff from semi-pervious areas predicted flow volumes within 6% of that observed, but the extent of predicted transport areas greatly differed from those observed. Among the five approaches, the process-based approaches were identified to be more applicable and expandable for future prediction of CSAs for contaminant loss. In contrast, the other approaches offered little flexibility in representing the dynamic hydrology of these catchments. However, improved input data (elevation, soil properties) may improve the applicability these models.
机译:Srinivasan和McDowell(2007)所描述的五个以过程为基础的基于过程的经验水文模型在测绘运输区域中的有效性在新西兰奥塔哥的Invermay和Glenomaru水源流域进行了测试。这些运输区域与污染物源区域一起形成了关键源区域(CSA),在该区域中大部分污染物流失发生,因此代表了缓解潜力最有效的区域。记录了集水区出口处的降雨和15分钟的瞬时地表流量,以及多年生水流5-40 m内的浅水位(距地表<1 m)动态。在格伦诺马鲁小鹿汇水区,还测量了瓷砖排水口的地下流量和短暂流中的地面流量。在因弗梅河流域,在暴雨和基流时期定期记录地表土壤水分,以绘制地表饱和区域的膨胀和收缩图。对2006年8月至2008年2月的空间和时间序列数据的分析表明,在干旱季节(低于平均降雨量的时期),大部分风暴流来自直接降雨,临近地区的湿润地区(饱和度或更高的区域,如鹿谷)。溪流和半透水区域,例如动物足迹。在雨季(高于平均降雨量),来自这些地区的流量占总暴雨流量的10-70%。地下水位数据表明,在暴风雨期间,水位在表层的饱和区域很少从河流中伸出> 10 m。似乎有一个活跃的地下(浅)流系统将水流从陆地转移到溪流。但是,在许多降雨事件中,半渗透区(如围栏线,动物足迹和通道)通过入渗过量的地表径流连接到河流,这是由地表径流采样器测量的。鉴于动物在这些区域上花费的时间和污染物的沉积(例如在粪便中),这对于污染物的转移可能是重要的,并在随附的文件中进行了探讨。在划定暴风雨期间的运输区域的方法中,通常基于经验的曲线数模型和磷指数高估了暴风雨区域。排水密度法很好地预测了暴雨面积,但低估了55-82%的流量。基于物理的地形指数(TI)模型以及结合了TI模型和来自半透水区域的地表径流的模型预测的流量在所观察到的范围之内,但预测的运输范围与所观察到的范围有很大差异。在这五种方法中,基于过程的方法被确定为对污染物损失的CSA的未来预测更适用和可扩展。相反,其他方法在表示这些流域的动态水文学方面几乎没有灵活性。但是,改进的输入数据(高程,土壤性质)可能会提高这些模型的适用性。

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