This study compares several statistical rating curve techniques to estimate turbidity, a proxy for suspended sediment concentration, in fluvial systems based on available discharge data. Seven models were tested, including variants of quadratic rating curves, quantile regression, local regression, dynamic linear models (DLMs), and Box-Jenkins models. Two comparisons were conducted in a case study of the Esopus Creek watershed in New York, a major water source for the New York City Water Supply System (NYCWSS). First, the models were tested in their ability to forecast turbidity at 1–7 day lead times assuming perfect forecasts of discharge using two daily datasets of varying record lengths and resolution. Second, the models were used to gap-fill turbidity data based on available discharge data, and the resulting continuous turbidity time series were used to assess optimal reservoir operations in the NYCWSS to manage water quality. Results suggest that DLMs coupled with additional time series modeling on the residuals produce the most robust forecasts across lead times for both high and low turbidity values. During average conditions, differences between rating curves have little impact on inferred reservoir operations due to the buffering effect of storage. But during extreme events, rating curve differences lead to large differences in inferred operations, suggesting that rating curve choice can play an important role in assessing the risk of reservoir-based water quality management.

中文翻译：

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浊度-流量等级曲线的模型间比较及其对水库运营管理的影响

本研究比较了几种统计评级曲线技术，以根据可用的排放数据估算河流系统中的浊度，这是悬浮泥沙浓度的代表。测试了七个模型，包括二次评级曲线的变体、分位数回归、局部回归、动态线性模型 (DLM) 和 Box-Jenkins 模型。在纽约市 Esopus Creek 流域的案例研究中进行了两次比较，该流域是纽约市供水系统 (NYCWSS) 的主要水源。首先，假设使用两个不同记录长度和分辨率的每日数据集进行完美的排放预测，模型在 1-7 天的提前期预测浊度的能力进行了测试。其次，这些模型用于根据可用的排放数据填补浊度数据，并且由此产生的连续浊度时间序列用于评估 NYCWSS 的最佳水库操作以管理水质。结果表明，DLM 与残差的附加时间序列建模相结合，可以对高浊度值和低浊度值在整个交付周期内产生最可靠的预测。在平均条件下，由于存储的缓冲作用，评级曲线之间的差异对推断的储层操作影响很小。但在极端事件中，评级曲线的差异导致推断操作的巨大差异，表明评级曲线的选择可以在评估基于水库的水质管理风险中发挥重要作用。结果表明，DLM 与残差的附加时间序列建模相结合，可以对高浊度值和低浊度值在整个交付周期内产生最可靠的预测。在平均条件下，由于存储的缓冲作用，评级曲线之间的差异对推断的储层操作影响很小。但在极端事件中，评级曲线的差异导致推断操作的巨大差异，表明评级曲线的选择可以在评估基于水库的水质管理风险中发挥重要作用。结果表明，DLM 与残差的附加时间序列建模相结合，可以对高浊度值和低浊度值在整个交付周期内产生最可靠的预测。在平均条件下，由于存储的缓冲作用，评级曲线之间的差异对推断的储层操作影响很小。但在极端事件中，评级曲线的差异导致推断操作的巨大差异，表明评级曲线的选择可以在评估基于水库的水质管理风险中发挥重要作用。