A classification approach to reconstruct local daily drying dynamics at headwater streams
A classification approach to reconstruct local daily drying dynamics at headwater streams
复制标题
重建源头当地日常干燥动态的分类方法
DOI：
10.1002/hyp.13445
复制
发表时间：
2019
影响因子：
3.2
通讯作者：
E. Sauquet
中科院分区：
文献类型：
--
作者：
A. Beaufort;J. Carreau;E. Sauquet
水源流(HSS)一般具有天然的水流间歇性。最近，人们对这些断断续续的河流和短暂溪流的兴趣显著增加，特别是在评估干旱对水生生态系统的影响方面。这项工作的两个目标是(A)确定气流间歇动力学的主要驱动因素和(B)重建局部每日干燥动力学。离散流动状态--“流动”与“干燥”--被建模为协变量的函数，其中包括气候、水文、地下水和盆地描述符的信息。三种使用协变量估计水流状态的分类器在法国的四个对比地区进行了测试：(A)正则化的线性分类器(套索最小绝对收缩和选择算子)和两个非线性非参数分类器，(B)单隐层前馈人工神经网络(ANN)分类器，和(C)随机森林(RF)分类器。将这三个分类器与基准分类器(BC)进行比较，基准分类器基于观测值(不使用协变量)简单地估计每个月的主要流动状态。通过交叉验证进行的2012-2016年期间的绩效评估表明，基于协变量的三种流态分类器的表现优于BC分类器。这证明了协变量的预测能力。人工神经网络是预测每日干燥动态的全局最好的分类器，而RF和LASSO都倾向于低估干燥状态的比例。根据每个分类器的相关性对协变量进行排序。由离散观测网络提供的每月干燥状态的比例对于ANN、LASSO和RF这三个分类器具有重要意义。这可能反映了场地的间歇性流动的倾向。人工神经网络对气候和水文协变量的重视程度更高，其非线性具有更大的自由度。
Headwater streams (HSs) are generally naturally prone to flow intermittence. These intermittent rivers and ephemeral streams have recently seen a marked increase in interest, especially to assess the impact of drying on aquatic ecosystems. The two objectives of this work are (a) to identify the main drivers of flow intermittence dynamics in HS and (b) to reconstruct local daily drying dynamics. Discrete flow states—“flowing” versus “drying”—are modelled as functions of covariates that include information on climate, hydrology, groundwater, and basin descriptors. Three classifiers to estimate flow states using covariates are tested on four contrasted regions in France: (a) a linear classifier with regularization (LASSO for least absolute shrinkage and selection operator) and two non‐linear non‐parametric classifiers, (b) a one‐hidden‐layer feedforward artificial neural network (ANN) classifier, and (c) a random forest (RF) classifier. The three classifiers are compared with a benchmark classifier (BC) that simply estimates dominant flow state for each month based on observations (without using covariates). The performance assessment over the period 2012–2016 carried out by cross‐validation shows that the three classifiers for flow state based on covariates outperformed the BC. This demonstrates the predictive power of the covariates. ANN is the classifier that globally achieves the best performance to predict the daily drying dynamics whereas both RF and LASSO tend to underestimate the proportion of drying states. The covariates are ranked in terms of relevance for each classifier. The monthly proportion of drying states provided by the discrete observation network has a major importance for the three classifiers ANN, LASSO, and RF. This may reflect the proclivity of a site to flow intermittence. ANN gives higher importance to climatic and hydrological covariates and its non‐linearity allows a greater degree of freedom.