从示踪结果和机器学习模型看,快速流动的水文通道的响应时间控制着低梯度流域的沉积滞后现象

IF 5.9 1区 地球科学 Q1 ENGINEERING, CIVIL
Arlex Marin-Ramirez , David Tyler Mahoney , Brenden Riddle , Leonie Bettel , James F. Fox
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引用次数: 0

摘要

水文对泥沙输运时间和泥沙滞后模式的控制仍然是水文学的一个开放性研究领域,尤其是对于有大量河内泥沙沉积的低坡度流域。泥沙滞后描述了水文峰值与泥沙峰值之间的不匹配,有助于阐明流域内泥沙输运的机制。通常情况下,滞后的控制因素是沉积物源与流域内监测点的距离。然而,这一假设虽然应用广泛,却很少得到验证。我们在美国肯塔基州中部蓝草地区的一个低梯度系统中研究了泥沙滞后的控制因素。安装在流域出口处的浊度和电导率传感器提供了量化沉积滞后的数据,并利用基于示踪剂的方法分离了水文流动路径(即通过描述输送到流域出口的水源)。我们估算了预测性水文参数,包括水文路径、事件量级和先决条件,并根据水文相似性进行了分组。之后,我们使用一种量身定制的集合特征选择方法,结合三种机器学习算法--随机森林、K-近邻和梯度提升树,确定了预测沉积滞后所需的参数。对两年内发生的 68 次暴雨事件的分析结果表明,顺时针方向的事件占总沉积物量的 85%,而顺时针方向的事件仅占 53%。在所考虑的 39 个水文参数中,有 3 个参数可以预测滞后指数(HI)(r = 0.8,RMSE = 0.12)。滞后指数最重要的预测因素反映了事件降雨量以及水文图中新水(即暴雨事件期间降水产生的水)和旧水(即流域内先前储存的水)的相对比例。进一步的分析表明,新水时间(随降雨量变化而变化)与沉积物时间密切相关,这表明滞后模式的变化受控于快速流水路径响应时间的变化。这意味着,在该流域,控制泥沙滞后的是水文路径,而不是泥沙是否接近流域出口。这些结果对于更好地理解控制流域尺度沉积物迁移的机制具有重要意义。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Response time of fast flowing hydrologic pathways controls sediment hysteresis in a low-gradient watershed, as evidenced from tracer results and machine learning models
Hydrologic controls on the timing of sediment transport and sediment hysteresis patterns remain an open area of investigation in hydrology, especially for low-gradient watersheds with substantial instream sediment deposition. Sediment hysteresis, which describes the mismatch between hydrograph peak and sedigraph peak, aids with elucidation of the mechanisms of sediment transport in watersheds. Most frequently, the controls of hysteresis are attributed to the proximity of sediment sources to monitoring locations in a watershed. However, this assumption, while widely applied, is infrequently verified. We investigated the controls of sediment hysteresis in a low gradient system located in the Bluegrass Region of central Kentucky, USA. Turbidity and conductivity sensors installed at the basin outlet provided data to quantify sediment hysteresis and separate hydrologic flow pathways (i.e., by describing the source of water delivered to the watershed’s outlet) using a tracer-based approach. Predictive hydrologic parameters, including hydrologic pathways, event magnitude, and antecedent conditions, were estimated and grouped based on hydrologic similitude. Thereafter, we identified parameters required to predict sediment hysteresis using a tailored ensemble feature selection approach coupled with three machine learning algorithms—Random Forest, K-Nearest Neighbors, and Gradient Boosted Trees. Results from the analysis of 68 storm events occurring over a two-year period showed that clockwise events accounted for 85 % of the total sediment yield despite comprising only 53 % of the events. The hysteresis index (HI) can be predicted (r = 0.8, RMSE = 0.12) using three, out of the thirty-nine hydrologic parameters considered. The most important predictors of HI reflect the volume of event rainfall and the relative proportions of new water (i.e., water derived from precipitation during the storm event) and old water (i.e., water previously stored in the watershed) comprising the hydrograph. Further analyses reveal that new water timing—which changes with the rainfall volume—and sediment timing are closely linked, suggesting that variations in the hysteresis patterns are controlled by changes in the response time of fast flowing water pathways. This implies that hydrologic pathways, as opposed to sediment proximity to the watershed outlet, control sediment hysteresis in this watershed. These results have important implications for better understanding the mechanisms controlling sediment transport at the watershed scale.
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来源期刊
Journal of Hydrology
Journal of Hydrology 地学-地球科学综合
CiteScore
11.00
自引率
12.50%
发文量
1309
审稿时长
7.5 months
期刊介绍: The Journal of Hydrology publishes original research papers and comprehensive reviews in all the subfields of the hydrological sciences including water based management and policy issues that impact on economics and society. These comprise, but are not limited to the physical, chemical, biogeochemical, stochastic and systems aspects of surface and groundwater hydrology, hydrometeorology and hydrogeology. Relevant topics incorporating the insights and methodologies of disciplines such as climatology, water resource systems, hydraulics, agrohydrology, geomorphology, soil science, instrumentation and remote sensing, civil and environmental engineering are included. Social science perspectives on hydrological problems such as resource and ecological economics, environmental sociology, psychology and behavioural science, management and policy analysis are also invited. Multi-and interdisciplinary analyses of hydrological problems are within scope. The science published in the Journal of Hydrology is relevant to catchment scales rather than exclusively to a local scale or site.
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