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Model calibration criteria for estimating ecological flow characteristics

机译:用于估算生态流量特征的模型校准标准

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摘要

Quantification of streamflow characteristics in ungauged catchments remains a challenge. Hydrological modeling is often used to derive flow time series and to calculate streamflow characteristics for subsequent applications that may differ from those envisioned by the modelers. While the estimation of model parameters for ungauged catchments is a challenging research task in itself, it is important to evaluate whether simulated time series preserve critical aspects of the streamflow hydrograph. To address this question, seven calibration objective functions were evaluated for their ability to preserve ecologically relevant streamflow characteristics of the average annual hydrograph using a runoff model, HBV-light, at 27 catchments in the southeastern United States. Calibration trials were repeated 100 times to reduce parameter uncertainty effects on the results, and 12 ecological flow characteristics were computed for comparison. Our results showed that the most suitable calibration strategy varied according to streamflow characteristic. Combined objective functions generally gave the best results, though a clear underprediction bias was observed. The occurrence of low prediction errors for certain combinations of objective function and flow characteristic suggests that (1) incorporating multiple ecological flow characteristics into a single objective function would increase model accuracy, potentially benefitting decision-making processes; and (2) there may be a need to have different objective functions available to address specific applications of the predicted time series.
机译:对未灌水的流域的水流特征进行量化仍然是一个挑战。水文建模通常用于导出流量时间序列并计算后续应用的流量特征,这些应用可能与建模者所设想的不同。虽然估算未吞水集水区的模型参数本身是一项艰巨的研究任务,但评估模拟时间序列是否保留了流量水文图的关键方面很重要。为了解决这个问题,在美国东南部的27个流域,使用径流模型HBV-light评估了七个标定目标函数的能力,以保持生态年平均水文流量相关特征。重复进行了100次校准试验,以减少参数不确定性对结果的影响,并计算了12种生态流量特征进行比较。我们的结果表明,最合适的校准策略根据流量特性而变化。尽管观察到明显的预测不足偏差,但组合的目标函数通常可提供最佳结果。对于目标函数和流量特征的某些组合,较低的预测误差的发生表明:(1)将多个生态流量特征合并到单个目标函数中将提高模型的准确性,从而可能有利于决策过程; (2)可能需要使用不同的目标函数来处理预测时间序列的特定应用。

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