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Calibration of hydrological models using flow-duration curves

机译:使用持续时间曲线校准水文模型

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摘要

The degree of belief we have in predictions from hydrologic models will normally depend on how well they can reproduce observations. Calibrations with traditional performance measures, such as the Nash-Sutcliffe model efficiency, are challenged by problems including: (1) uncertain discharge data, (2) variable sensitivity of different performance measures to different flow magnitudes, (3) influence of unknown input/output errors and (4) inability to evaluate model performance when observation time periods for discharge and model input data do not overlap. This paper explores a calibration method using flow-duration curves (FDCs) to address these problems. The method focuses on reproducing the observed discharge frequency distribution rather than the exact hydrograph. It consists of applying limits of acceptability for selected evaluation points (EPs) on the observed uncertain FDC in the extended GLUE approach. Two ways of selecting the EPs were tested – based on equal intervals of discharge and of volume of water. The method was tested and compared to a calibration using the traditional model efficiency for the daily four-parameter WASMOD model in the Paso La Ceiba catchment in Honduras and for Dynamic TOPMODEL evaluated at an hourly time scale for the Brue catchment in Great Britain. The volume method of selecting EPs gave the best results in both catchments with better calibrated slow flow, recession and evaporation than the other criteria. Observed and simulated time series of uncertain discharges agreed better for this method both in calibration and prediction in both catchments. An advantage with the method is that the rejection criterion is based on an estimation of the uncertainty in discharge data and that the EPs of the FDC can be chosen to reflect the aims of the modelling application, e.g. using more/less EPs at high/low flows. While the method appears less sensitive to epistemic input/output errors than previous use of limits of acceptability applied directly to the time series of discharge, it still requires a reasonable representation of the distribution of inputs. Additional constraints might therefore be required in catchments subject to snow and where peak-flow timing at sub-daily time scales is of high importance. The results suggest that the calibration method can be useful when observation time periods for discharge and model input data do not overlap. The method could also be suitable for calibration to regional FDCs while taking uncertainties in the hydrological model and data into account.
机译:我们对水文模型预测的信心程度通常取决于它们能否很好地再现观测结果。使用传统性能指标(例如Nash-Sutcliffe模型效率)进行标定面临以下问题:(1)排放数据不确定,(2)不同性能指标对不同流量大小的可变灵敏度,(3)未知输入/输出错误和(4)当放电观察时间与模型输入数据的观察时间不重叠时,无法评估模型性能。本文探讨了使用流量持续时间曲线(FDC)的校准方法来解决这些问题。该方法着重于再现观察到的放电频率分布,而不是精确的水位图。它包括在扩展的GLUE方法中对观察到的不确定FDC施加选定评估点(EP)的可接受限度。测试了两种选择EP的方法-基于相等的排放间隔和水量。测试该方法并将其与使用传统模型效率的标定值进行比较,以比较洪都拉斯Paso La Ceiba流域的每日四参数WASMOD模型和英国每小时Brue流域按小时尺度评估的Dynamic TOPMODEL。与其他标准相比,选择EP的体积法在两个流域均获得了最佳结果,并且标定的慢流量,后退和蒸发量得到了更好的校准。在两个流域的标定和预测中,观测和模拟的不确定排放的时间序列对于此方法的一致性更好。该方法的优点在于,拒绝标准基于放电数据中不确定性的估计,并且可以选择FDC的EP以反映建模应用程序的目的,例如:在高/低流量下使用更多/更少的EP。尽管该方法对认知输入/输出错误的敏感性比以前对放电时间序列直接应用的可接受性限制的敏感性低,但仍需要合理表示输入分布。因此,在受雪覆盖的集水区以及在次日尺度上的高峰流量时间具有高度重要性的集水区可能需要附加约束。结果表明,当放电的观测时间段和模型输入数据不重叠时,校准方法会很有用。在考虑水文模型和数据的不确定性的同时,该方法也可能适用于校准区域FDC。

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