通过建立监测点评估溪流水质,探索大型混合土地利用流域的 PCSWMM

Mohd Sohib Ansari, Suresh Sharma, Felicia P. Armstrong, Mark Delisio, Sahar Ehsani
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引用次数: 0

摘要

在流域内进行广泛的水文和水质建模,可以从长期的流量和营养数据集中获益,从而对模型进行适当的校准和验证。然而,由于缺乏当地的水质数据,通常采用较为简单的水质建模技术。本研究在两个不同地点设立了监测点,以收集水力数据,用于模型的水力校准和验证。此外,还在八个监测点收集了水质样本,并在实验室分析了各种参数,以进行校准。其中包括总悬浮固体 (TSS)、可溶性磷、五天生化需氧量 (BOD5) 和溶解氧 (DO)。个人电脑雨水管理模型 (PCSWMM) 7.6 软件使用事件平均浓度 (EMC) 模拟所有污染物负荷。该模型在美国地质调查局的两个测量站进行了河水流量校准,其性能令人满意,纳什-苏克里夫效率 (NSE) 值为 0.51 至 0.54,决定系数 (R2) 为 0.71 至 0.72。该模型还借助历史流量数据进行了验证,其 NSE 值介于 0.5 至 0.79 之间,R2 值介于 0.6 至 0.95 之间。水力校准也显示了可接受的结果,NSE 值和 R2 值都比较合理。然后,将监测站记录的水质数据与模拟的水质模型结果进行比较。通过使用散点图进行目测,模型合理地模拟了水质。我们的分析表明,上游支流(尤其是来自农业区的支流)比下游支流排放了更多的污染物。总之,这项研究表明,通常用于城市流域建模的 PCSWMM 也可用于较大型混合土地利用流域的建模,并具有合理的准确性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Exploring PCSWMM for Large Mixed Land Use Watershed by Establishing Monitoring Sites to Evaluate Stream Water Quality
Extensive hydrologic and water quality modeling within a watershed benefits from long-term flow and nutrient data sets for appropriate model calibration and validation. However, due to a lack of local water quality data, simpler water quality modeling techniques are generally adopted. In this study, the monitoring sites were established at two different locations to collect hydraulic data for the hydraulic calibration and validation of the model. In addition, water quality samples were collected at eight monitoring sites and analyzed in the lab for various parameters for calibration. This includes total suspended solids (TSS), soluble phosphorus, five-day biochemical oxygen demand (BOD5), and dissolved oxygen (DO). The Personal Computer Storm Water Management Model (PCSWMM) 7.6 software was used to simulate all the pollutant loads using event mean concentrations (EMCs). The performance of the model for streamflow calibration at the two USGS gauging stations was satisfactory, with Nash–Sutcliffe Efficiency (NSE) values ranging from 0.51 to 0.54 and coefficients of determination (R2) ranging from 0.71 to 0.72. The model was also validated with the help of historical flow data with NSE values ranging from 0.5 to 0.79, and R2 values ranging from 0.6 to 0.95. The hydraulic calibration also showed acceptable results with reasonable NSE and R2 values. The water quality data recorded at the monitoring stations were then compared with the simulated water quality modeling results. The model reasonably simulated the water quality, which was evaluated through visual inspection using a scatter plot. Our analysis showed that the upstream tributaries, particularly from agricultural areas, were contributing more pollutants than the downstream tributaries. Overall, this study demonstrates that the PCSWMM, which was typically used for modeling urban watersheds, could also be used for modeling larger mixed land use watersheds with reasonable accuracy.
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