Time series features for supporting hydrometeorological explorations and predictions in ungauged locations using large datasets
利用大数据时间序列特征探索水文气象并预测无测站地点AI 解读 ↓
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利用大数据时间序列特征探索水文气象并预测无测站地点
- 研究问题
- 温度和降水的通用时间序列特征，能否与传统流域属性结合，用于预测无测站流域的径流特征并揭示可区域化的差异？
- 方法与思路
- 作者从五百一十一个流域的日温度、降水及径流序列提取二十八种特征，与地形、土地覆盖、土壤和地质属性合并进行回归分析。
- 材料陈述的贡献
- 摘要称部分气象时间序列特征及传统属性具有预测价值，径流的谱熵、季节性强度与若干自相关特征比其他特征更易区域化。
- 阅读关注点
- 应核对特征计算、区域化目标和回归设定，检查跨流域验证方式及信息泄漏，并比较新增特征相对传统属性的贡献。
- 阅读边界
- 这是 arXiv 预印本，同行评审及期刊接收未经核实；摘要未提供预测误差或外推边界，空间区域化结果不能直接等同于时间预测能力。
原文摘要
Regression-based frameworks for streamflow regionalization are built around catchment attributes that traditionally originate from catchment hydrology, flood frequency analysis and their interplay. In this work, we deviated from this traditional path by formulating and extensively investigating the first regression-based streamflow regionalization frameworks that largely emerge from general-purpose time series features for data science and, more precisely, from a large variety of such features. We focused on 28 features that included (partial) autocorrelation, entropy, temporal variation, seasonality, trend, lumpiness, stability, nonlinearity, linearity, spikiness, curvature and others. We estimated these features for daily temperature, precipitation and streamflow time series from 511 catchments, and then merged them within regionalization contexts with traditional topographic, land cover, soil and geologic attributes. Precipitation and temperature features (e.g., the spectral entropy, seasonality strength and lag-1 autocorrelation of the precipitation time series, and the stability and trend strength of the temperature time series) were found to be useful predictors of many streamflow features. The same applies to traditional attributes, such as the catchment mean elevation. Relationships between predictor and dependent variables were also revealed, while the spectral entropy, the seasonality strength and several autocorrelation features of the streamflow time series were found to be more regionalizable than others.