Gauging ungauged catchments – Active learning for the timing of point discharge observations in combination with continuous water level measurements
Gauging ungauged catchments – Active learning for the timing of point discharge observations in combination with continuous water level measurements
复制标题
测量未测量的集水区 – 结合连续水位测量，主动学习点流量观测的时间
DOI：
10.1016/j.jhydrol.2021.126448
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发表时间：
2021
影响因子：
6.4
通讯作者：
J. Seibert
中科院分区：
文献类型：
--
作者：
S. Pool;J. Seibert
水文模型传统上用于无资料流域的预测，尽管模型参数化的相关挑战。短期测量活动可能是获得支持这些集水区模型校准所需的一些基本信息的一种方式。本研究通过以下方式探索了此类实地活动的潜力：i）测试连续水位时间序列和点流量观测值的相对值，以进行模型校准; ii）评估使用专家知识和主动学习收集的点流量观测值，以指导何时测量径流。这项研究是基于美国本土100个测量集水区，我们假装只有有限的水文观测，即，在一个水文年内进行的不同假设实地考察的连续日水位和10个日点流量观测。水位数据被用作一个单一的信息来源，以及除了点流量观测，校准HBV模型。通过不断添加来自10个现场测量之一的新观测，迭代地对点放电观测进行校准。我们的研究结果表明，点流量观测中包含的信息是特别有价值的限制年度水平衡和径流响应的事件规模，提高预测的基础上，仅根据水位高达50%后，10个现场观测。与此相反，水位是有价值的，以增加模拟的每日径流动态的准确性。信息放电采样日期相似时，选择与积极学习或专业知识，通常聚集在高流量的季节。
Hydrological models have traditionally been used for the prediction in ungauged basins despite the related challenge of model parameterization. Short measurement campaigns could be a way to obtain some basic information that is needed to support model calibration in these catchments. This study explores the potential of such field campaigns by i) testing the relative value of continuous water-level time series and point discharge observations for model calibration, and by ii) evaluating the value of point discharge observations collected using expert knowledge and active learning to guide when to measure streamflow. The study was based on 100 gauged catchments across the contiguous United States for which we pretended to have only limited hydrological observations, i.e., continuous daily water levels and ten daily point discharge observations from different hypothetical field trips conducted within one hydrological year. Water level data were used as a single source of information, as well as in addition to point discharge observations, for calibrating the HBV model. Calibration against point discharge observations was conducted iteratively by continually adding new observations from one of the ten field measurements. Our results suggested that the information contained in point discharge observations was especially valuable for constraining the annual water balance and streamflow response at the event scale, improving predictions based solely on water levels by up to 50% after ten field observations. In contrast, water levels were valuable to increase the accuracy of simulated daily streamflow dynamics. Informative discharge sampling dates were similar when selected with either active learning or expert knowledge and typically clustered during seasons with high streamflow.
连续采样与基于事件的采样：需要多少样本才能获得厄瓜多尔帕拉莫地区的一般水文信息？
影响因子：
3.2
作者：
Correa;Windhorst;Crespo;Célleri;Breuer
通讯作者：
Breuer