Intraseasonal‐to‐Interannual Analysis of Discharge and Suspended Sediment Concentration Time‐Series of the Upper Changjiang (Yangtze River)

Abstract

 Distinct time-scales of discharge and SSC are identified and related qualitatively to seasonal, annual, and short (1.1-5 year) and long-term (5.5-20 year) variability  Similar dominant time-scales of variability for discharge and SSC are identified in the Upper Changjiang for a 50-years study period  Bi-modal seasonal climatic pattern influence short-term time-scales whereas high magnitude flow events control intra-annual time-scales Accepted ArticleThis article has been accepted for publication and undergone full peer review but has not been through the copyediting, typesetting, pagination and proofreading process, which may lead to differences between this version and the Version of Record. Please cite this article as

Influence of the Active Layer Thickness of Permafrost in Eastern Siberia on the River Discharge of Nutrients into the Arctic Ocean

“…It is important to note that some of the nutrients may not reach the Arctic Ocean and pass into bottom sediments, combining with particles of suspended solids and thus forming river sediments [9]. However, our results confirm the hypothesis that the export of nutrients to the Arctic Ocean largely depends on ALT variations on catchments of the cryolithozone, which controls the dominant flow paths of water.…”

“…It should also be noted that the processes of permafrost degradation in the catchments of the permafrost zone have a great potential to influence not only the concentration and transport of soluble substances by rivers, but also the transport of suspended solids, which may also contain nutrients [9]. These processes can also affect changes in river runoff [10] and local vegetation [11].…”

Suspended sediment concentration estimation in the Sacramento‐San Joaquin Delta of California using long short‐term memory networks

“…Statistical approaches can help to identify and extract patterns in the input time series. For instance, Juez et al (2021) utilized the wavelet transformation to capture process variability at multiple timescales for a long record of flow discharge and suspended sediment fluxes. This temporal information extracted can be used to better train the machine learning models.…”

“…Although versatile in its application, Fourier spectral analysis can only be used on statistically stationary data (Davoodi et al, 2009). Hence, as an alternative, researchers have utilized wavelet transformation, which is capable of processing non‐stationary time series (Juez et al, 2021). In fact, temporal information obtained from the Fourier spectral analysis or wavelet transformation can be used to inform machine‐learning models.…”

Six Decades of Hindsight Into Yesa Reservoir (Central Spanish Pyrenees): River Flow Dwindles as Vegetation Cover Increases and Mediterranean Atmospheric Dynamics Take Control

“…It thus provides a complete time‐scale representation of localized and transient phenomena occurring at different time‐scales (Torrence & Compo, 1998). In this research, we make use of the Morlet wavelet, which was successfully used in the past to analyze precipitation and discharge time‐series (Carey et al., 2013; Juez & Nadal‐Romero, 2021; Juez et al., 2021; Pérez‐Ciria et al., 2019). This wavelet type is characterized as: $$${\varphi }_{0}(\eta )={\pi }^{-1/4}{e}^{i{\omega }_{0}\eta }{e}^{-{\eta }^{2}/2}$$$ where $${\varphi }_{0}(\eta )$$ is the wavelet function, $$\eta $$, is a dimensionless time parameter, i is the imaginary unit and $${\omega }_{0}$$ is the dimensionless angular frequency taken as 6 as it provides a good match between time and frequency localization.…”