The soil cover of the Selenga delta area in the Baikal region

Gyninova, A. B.; Korsunov, V. M.

doi:10.1134/s1064229306030021

Cited by 12 publications

(8 citation statements)

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“…As channels at a few transects are eroding into Quaternary-aged fluvial terraces, bank elevations may not be built by modern floodplain depositional processes (highlighted by light blue circles in Figure 1c; Dong et al, 2019). Bankfull stages at these transects are modified by 0.5 m, as it has been documented that stage at effective discharge conditions is ∼0.5-1 m below these terraces (Gyninova & Korsunov, 2006). There are two survey transects that did not have time series stage data due to instrumentation failure (marked by red circles in Figures 1c and 4c and 4d).…”

Section: Building Both Rating-curve Models and Flood Hydrographsmentioning

confidence: 90%

Predicting Water and Sediment Partitioning in a Delta Channel Network Under Varying Discharge Conditions

Dong

Nittrouer

McElroy

et al. 2020

Water Resources Research

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Channel bifurcations control the distribution of water and sediment in deltas, and the routing of these materials facilitates land building in coastal regions. Yet few practical methods exist to provide accurate predictions of flow partitioning at multiple bifurcations within a distributary channel network. Herein, multiple nodal relations that predict flow partitioning at individual bifurcations, utilizing various hydraulic and channel planform parameters, are tested against field data collected from the Selenga River delta, Russia. The data set includes 2.5 months of time-continuous, synoptic measurements of water and sediment discharge partitioning covering a flood hydrograph. Results show that width, sinuosity, and bifurcation angle are the best remotely sensed, while cross-sectional area and flow depth are the best field measured nodal relation variables to predict flow partitioning. These nodal relations are incorporated into a graph model, thus developing a generalized framework that predicts partitioning of water discharge and total, suspended, and bedload sediment discharge in deltas. Results from the model tested well against field data produced for the Wax Lake, Selenga, and Lena River deltas. When solely using remotely sensed variables, the generalized framework is especially suitable for modeling applications in large-scale delta systems, where data and field accessibility are limited.

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Section: Building Both Rating-curve Models and Flood Hydrographsmentioning

confidence: 90%

Predicting Water and Sediment Partitioning in a Delta Channel Network Under Varying Discharge Conditions

Dong

Nittrouer

McElroy

et al. 2020

Water Resources Research

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“…The inter-distributary bays are filled with sandy deposits frequently redistributed by waves. The delta consists of sandy lobe islands separated by numerous elongated and bifurcated channels [ 81 ]. Numerous distributary channels with natural levees separate extensive marshes, and lakes, channel cutoffs, and oxbows are abundant on the lobate islands.…”

Section: Methodsmentioning

confidence: 99%

Decision-Tree, Rule-Based, and Random Forest Classification of High-Resolution Multispectral Imagery for Wetland Mapping and Inventory

et al. 2018

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Efforts are increasingly being made to classify the world’s wetland resources, an important ecosystem and habitat that is diminishing in abundance. There are multiple remote sensing classification methods, including a suite of nonparametric classifiers such as decision-tree (DT), rule-based (RB), and random forest (RF). High-resolution satellite imagery can provide more specificity to the classified end product, and ancillary data layers such as the Normalized Difference Vegetation Index, and hydrogeomorphic layers such as distance-to-a-stream can be coupled to improve overall accuracy (OA) in wetland studies. In this paper, we contrast three nonparametric machine-learning algorithms (DT, RB, and RF) using a large field-based dataset (n = 228) from the Selenga River Delta of Lake Baikal, Russia. We also explore the use of ancillary data layers selected to improve OA, with a goal of providing end users with a recommended classifier to use and the most parsimonious suite of input parameters for classifying wetland-dominated landscapes. Though all classifiers appeared suitable, the RF classification outperformed both the DT and RB methods, achieving OA >81%. Including a texture metric (homogeneity) substantially improved the classification OA. However, including vegetation/soil/water metrics (based on WorldView-2 band combinations), hydrogeomorphic data layers, and elevation data layers to increase the descriptive content of the input parameters surprisingly did not markedly improve the OA. We conclude that, in most cases, RF should be the classifier of choice. The potential exception to this recommendation is under the circumstance where the end user requires narrative rules to best manage his or her resource. Though not useful in this study, continuously increasing satellite imagery resolution and band availability suggests the inclusion of ancillary contextual data layers such as soil metrics or elevation data, the granularity of which may define its utility in subsequent wetland classifications.

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“…Covering $600 km 2 , the modern Selenga River delta is one of the largest lacustrine deltas in the world. It contains Water Resources Research 10.1029/2017WR021985 three active lobes with multiple distributary channels that receive varying amounts of water and sediment (Coleman, 1998;Gyninova & Korsunov, 2006;Il'icheva, 2008;Il'icheva et al, 2014;Scholz et al, 1998). A previous study has classified the orders of the distributary channels using topological method of Hack (1957) and Dong et al, (2016).…”

Section: Selenga River Deltamentioning

confidence: 99%

Roles of Bank Material in Setting Bankfull Hydraulic Geometry as Informed by the Selenga River Delta, Russia

Dong

Nittrouer

Czapiga

et al. 2019

Water Resources Research

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A semi‐empirical bankfull Shields number relation as a function of slope, bed, and bank sediment grain size is obtained based on a field data set that includes the delta of the Selenga River, Russia, and other rivers from around the globe. The new Shields number relation is used in conjunction with continuity, flow resistance, and sediment transport equations to deduce predictive relations for bankfull width, depth, and slope of sand‐bed rivers. In addition, hydraulic geometry relations are obtained specifically for the Selenga River delta. Key results of this study are as follows: (1) bankfull width is strongly dependent on water discharge and is directly related to bank sediment size; (2) bankfull shear velocity is weakly dependent on bed sediment size and is inversely related to bank sediment size; (3) sand‐bed deltas with multiple distributary channels maintain smaller bankfull Shields numbers than is typical of alluvial rivers. This analysis is the first of its kind to include bank sediment size into a predictive bankfull Shields number relation to obtain relations for bankfull hydraulic geometry. The relations presented here can be utilized in morphodynamic models that explore how fluvial and deltaic systems respond to a range of imposed conditions, such as variable base level, sediment, and water supply.

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The soil cover of the Selenga delta area in the Baikal region

Cited by 12 publications

References 0 publications

Predicting Water and Sediment Partitioning in a Delta Channel Network Under Varying Discharge Conditions

Predicting Water and Sediment Partitioning in a Delta Channel Network Under Varying Discharge Conditions

Decision-Tree, Rule-Based, and Random Forest Classification of High-Resolution Multispectral Imagery for Wetland Mapping and Inventory

Roles of Bank Material in Setting Bankfull Hydraulic Geometry as Informed by the Selenga River Delta, Russia

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