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Depositional styles and adjustment of channel cross sections in a seasonal deltaic system: a large scale physical experiment

Luo You, Zhang Hongbin, Zhu Senlin

Acta Geophysica

2021

Vol. 69, no. 6

2443--2452

Small scale experiments provide limited mechanistic insight on the evolution of the cross-sections of the channel in a deltaic system. Here, we report the results of a large-scale tank experiment on the deltaic processes in a new river course into the Qinglan Lake. The depositional body occurred from upstream to downstream in the new river course in the depositional processes. Three depositional styles have been observed in the delta building: levee, stable-bar, and wandering-bar. Single thread and braided channels have been formed with deposits of levee style and stable-bar style. Wandering-bar style, which is an autogenic process, refers to the switching in the location of the main silting zones at different spatial–temporal scales and is frequently accompanied by avulsion, river braiding, and mainstream migration. The elevation of levees and bars increased to the bankfull elevation in the blocked river reach in the first 30~40 years and impacted the main channel flow. Comparing to the blocked river reach, the evolution of the bankfull elevation and geomorphic coefficient B0.5/H (B is width, H is water depth) of the cross-sections in the new reach indicates that the evolution pathways of the cross-sections could be divided into two stages: convergent stage and autogenic feedback stage. The convergent stage refers to a positive feedback loop, while the autogenic feedback stage is dominated by autogenic process. The present study shows the diversity of landforms, complex feedbacks and internal thresholds of a seasonal deltaic system, and the results provide another view on hydraulic geometry.

River/stream water temperature forecasting using artifcial intelligence models: a systematic review

Zhu Senlin, Piotrowski Adam P.

Acta Geophysica

2020

Vol. 68, no. 5

1433--1442

Water temperature is one of the most important indicators of aquatic system, and accurate forecasting of water temperature is crucial for rivers. It is a complex process to accurately predict stream water temperature as it is impacted by a lot of factors (e.g., meteorological, hydrological, and morphological parameters). In recent years, with the development of computational capacity and artifcial intelligence (AI), AI models have been gradually applied for river water temperature (RWT) forecasting. The current survey aims to provide a systematic review of the AI applications for modeling RWT. The review is to show the progression of advances in AI models. The pros and cons of the established AI models are discussed in detail. Overall, this research will provide references for hydrologists and water resources engineers and planners to better forecast RWT, which will beneft river ecosystem management.