What sets river width?

One of the simplest questions in riverine science remains unanswered: “What determines the width of rivers?” While myriad environmental and geological factors have been proposed to control alluvial river size, no accepted theory exists to explain this fundamental characteristic of river systems. We combine analysis of a global dataset with a field study to support a simple hypothesis: River geometry adjusts to the threshold fluid entrainment stress of the most resistant material lining the channel. In addition, we demonstrate how changes in bank strength dictate planform morphology by exerting strong control on channel width. Our findings greatly extend the applicability of threshold channel theory, which was originally developed to explain straight gravel-bedded rivers with uniform grain size and stable banks. The parsimonious threshold-limiting channel model describes the average hydraulic state of natural rivers across a wide range of conditions and may find use in river management, stratigraphy, and planetary science.

Predicting river channel pattern based on stream power, bed material and bank strength

Rivers exhibit a wide variety of channel patterns, and predicting changes in channel pattern is important in order to foresee river responses to climate change and river restoration. Many discriminators have been developed to define approximate boundary conditions for different channel patterns, based on channel-pattern-controlling parameters such as discharge and valley gradient. However, presently available discriminators have two main shortcomings. First, they perform poorly for rivers with cohesive, relatively erosion-resistant banks. For this subset, discriminators tend to indicate an actively meandering channel pattern, whereas the river morphology and dynamics show that many of these rivers should be classified as laterally stable. Second, channel pattern discriminators are often used to predict channel patterns, which is only valid when parameters are used that are independent of actual channel pattern. This condition is often not met, as many discriminators use the channel slope or width–depth ratio of the channel as input. To resolve both shortcomings, we first propose an additional class of rivers with scroll bars and tortuous channel patterns, which have an inhibited mobility due to their self-formed cohesive deposits. Second, we compare frequently used empirical and mechanistic channel pattern discriminators, taking into account the success in predicting channel pattern and the independence of causal factors used. Thirdly, we present a novel channel pattern discriminator and predictor that includes the effect of a cohesive floodplain, using the average silt-plus-clay fraction of the river banks as proxy. We show that this new predictor outperforms previously used empirical and mechanistic approaches, and successfully predicts channel pattern for 87% of the rivers from a dataset of 70. This new predictor is widely applicable, as it is relatively simple and based on easily obtainable, and mostly independent, parameters.

Coupled Model of Bank Erosion and Meander Evolution for Cohesive Riverbanks

In this paper, a physics-based model that couples a bank erosion model with a meander evolution model is developed and evaluated. The physics-based bank erosion model considers the cantilever failure mechanism with slump blocks and decomposition effects. Moreover, bank accretion was considered using critical values of time required for landing, shear stresses and water depths. Two cases were tested. The first case consists of a hypothetical small-scale channel with cohesive riverbanks. Cross sections in the straight and curved part of the channel were compared to evaluate the curvature effect. Furthermore, the effect of the bank strength in the plan shape of the channel was tested in this case. The results show that the curvature increases the erosion rate in the outer bank and changes the cross-sectional profile by narrowing and widening the channel width. The plan shape of the channel changed as the bank strength was increased. In the second case, the model is compared with the River meander migration software (RVR meander) and the advantages and limitations of the model are discussed in terms of meander migration plan form and bank erosion processes. The results showed that the presented model is capable of simulating asymmetric bends.

Development of Chute Cutoff in the Lower Course of River Mayo-Inne, Yola South, Nigeria

The study investigates the development of chute cutoff in the lower course of River Mayo-Inne, Yola South LGA, Adamawa State, Nigeria. The study employed the integrated approach of Remote Sensing, Geographic Information System, Field Survey, Laboratory Analysis, Oral Interview and Personal Observation in examining the influences of some relevant channel planform parameters (Sinuosity Index, Cutoff Ratio and Braiding Index), land use/land cover, channel bank materials, water stage and channel depth on the development of the chute cutoff over a period of Twenty five years (1990-2015). Results revealed the drastic reduction of Sinuosity Index from 1.57 in 1990 to 1.46 in 2015, changing the channel from meandering to the straight pattern. The analysis of changes in cut-off ratio unveiled the development of chute cutoff in bend II, which ultimately separated the river flow, forming a weak braided channel with a braiding index of 0.43. These developments were attributed to incessant flooding in the study area and floodplain characteristics such as floodplain elevation, bank strength and changes in vegetal cover.

Effect of riparian vegetation roots on development of meander bends in Tarim River, Northwest China

Meandering channel with distorted bends develops along the Tarim River, the longest dryland river in China. The river bank and bed is majorly composed of coarse silt and fine sand and almost none clay content, making the bank strength very low. The development of meander bend in such environment hence is somehow irrational. We preliminarily investigate the effects of riparian vegetation root on bend development in this region through examining the root cohesion and its enhancement on bank stability. In-situ measurements and sampling of roots from local typical riparian vegetation (i.e., populous, Tamarix, and Phragmites Australis) were conducted to obtain root parameters like diameter and root area ratio. Local bend curvature corresponding to sampling sites are also obtained. BSTEM model is used to quantitatively estimate the effects of different root conditions on improving channel bank strength. Four vegetation root scenarios were modelled, i.e., tree (populous), shrub (Tamarix), grass (Phragmites Australis) and no root. The results show that root supplies effective cohesive reinforcement for the channel bank and enhances the bank safety factors (Fs). Riparian vegetation should be a necessary condition for development of meandering channel in Tarim River.

PENGARUH KINERJA KEUANGAN DAN INDIKATOR KESULITAN FINANSIL TERHADAP HARGA SAHAM BANK STUDI KASUS BANK BCA

<p>The main problem of bank is maintaining 3 financial health indicators, namely on aspects of liquidity, profitability, and solvency. These three bank performance parameters are part of the CAMEL surveillance system, without a single M (management) that can only be taken into account by the Bank Supervisory Team from Bank Indonesia for each bank. The purpose of research to determine the level of financial and financial performance of banks and the level of difficulty of banks that have gone public in Indonesia to the stock price of banks. This study was conducted to determine the impact of four groups of financial indicators on stocks, especially size of rentability, liquidity, solvency, and financial size. The various combinations of these 4 groups of indicators yield 45 independent variables that are estimated to affect the price and the number of 13 variables excluded, automatically by SPSS, in the estimation process. Of the 32 free variable, only 9 independent variables significantly affect stock price variables. The 9 independent variables are working capital (p5), cash ratio (q1), bank strength level (r3), sales (r9), operational (r8), financial burden indicator (s5), credit in rupiah (x2), investment non-credit (x4) and ROI (x5b).</p><p> </p>