Stream power

About: Stream power is a research topic. Over the lifetime, 1135 publications have been published within this topic receiving 51324 citations.

An evaluation of the geomorphically effective event for fluvial processes over long periods

Abstract: [1] Fluvial processes erode landscapes in response to a wide range of discharges. The importance of a given discharge to the erosion of a basin can be calculated by multiplying the discharge's frequency of occurrence and the erosion rate produced by the discharge. The discharge that contributes the most geomorphic work is called the geomorphically effective event (GEE). In this paper, the behavior of the GEE is examined when a generic stream power model with a threshold is used to describe either the detachment or transport of sediment by flowing water. The results suggest that the return period of the GEE depends primarily on the threshold value when the exponent on discharge is less than 2. Otherwise, it depends primarily on the exponent. The GEE usually cannot be substituted for the probability density function of discharge because it produces a different long-term erosion rate. Furthermore, the return period of the GEE can vary spatially in a basin. For example, the return period can be different between locations where the fluvial process is dominant and subdominant if the threshold is nonzero. For a detachment-limited model the return period of the GEE is different upstream and downstream of knickpoints, and for a transport-limited model the return period is different along channel profiles even at steady state. Spatial variation in streamflow generation also produces spatial variations in the return period of the GEE.

Remote Sensing Analysis of Crustal Deformation using River Networks

Abstract: In this paper we show that a quantitative analysis of remote sensing data allows the localization of active deformation and the quantification of strain intensity and kinematics. As tectonics controls topography, which in turn constrains river patterns, the procedure is based on the calculation of nonlinear morphometric parameters describing the disequilibrium of rivers. Thus the meandricity and the dendricity of rivers are estimated by fractal analysis. Structural control on river paths is quantified by correlation and covariance analyses. Terrain uplifts are quantified by stream power approach. These parameters are a valuable addition to existing methods especially when in situ measurements are scarce.

Fluvial landscape evolution controlled by the sediment deposition coefficient: Estimation from experimental and natural landscapes

Abstract: The evolution of a fluvial landscape is a balance between tectonic uplift, fluvial erosion, and sediment deposition. The erosion term can be expressed according to the stream power model, stating that fluvial incision is proportional to powers of river slope and discharge. The deposition term can be expressed as proportional to the sediment flux divided by a transport length. This length can be defined as the water flux times a scaling factor ζ. This factor exerts a major control on the river dynamics, on the spacing between sedimentary bedforms, or on the overall landscape erosional behavior. Yet, this factor is difficult to measure either in the lab or in the field. Here, we propose a new formulation for the deposition term based on a dimensionless coefficient, G, which can be estimated at the scale of a landscape from the slopes of rivers at the transition between a catchment and its fan. We estimate this deposition coefficient from 29 experimental catchment–alluvial fan systems and 68 natural examples. Based on our data set, we support the idea of Davy and Lague (2009) that G is a relevant parameter to characterize the erosional and transport mode of a fluvial landscape, which can be field calibrated, with a continuum from detachment-limited (G = 0) to transport-limited behavior (G >0.4 from the studied examples).

Measures of Flow Intensity as Predictors of Bed Load

Abstract: The relative merits of different measures of flow intensity as predictors of bed‐load transport rates are assessed. Tractive stress, when based on depth and slope, is a poor predictor, especially in narrow channels. Correct application requires elimination of wall and bedform roughness, leaving only that stress acting on the bed grains. When this is done, excellent correspondence with bed‐load rates is found in the experiments of Gilbert, Meyer‐Peter, and G. P. Williams, analyzed here. Vertically averaged velocity requires a correction for depth and particle size; then, it is directly comparable with grain tractive stress. Stream power has limitations as a predictor because it is the product of vertically averaged velocity and bed tractive stress, and hence subject to the problems that those variables individually experience. Unit stream power is a good predictor of concentration in some circumstances. It performs poorly, however, in the data set of Williams, and reasons for this are given.