Influence of Spatial Rainfall Gradients on River Longitudinal Profiles and the Topographic Expression of Spatially and Temporally Variable Climates in Mountain Landscapes

Abstract

Introduction MotivationAdvances in tectonic geomorphology require quantitative understanding about relationships among climate, tectonics, and erosion. In temperate mountain landscapes, studies of bedrock rivers provide important insights into interactions between these processes (e.g.,

Isolating climatic, tectonic, and lithologic controls on mountain landscape evolution

“…Then, we apply the χ-transform ( 34 ) to stream networks to linearize profiles in χ-elevation plotsnormalχ=∫xbx(A0A(x))θrefdxzfalse(xfalse)=zfalse(xbfalse)+ksn⋅normalχwhere x is the distance upstream; x b is the outlet position; A 0 is a reference drainage area, here set equal to 1 km 2 ; and z is the elevation. The above equations can similarly be cast in terms of discharge ( Q ) rather than drainage area to arrive at χ Q and k snQ ( 11 , 40 )normalχQ=∫xbx(Q0Q(x))θrefdxzfalse(xfalse)=zfalse(xbfalse)+ksnQ⋅normalχQ…”

“…k snQ is a generalized version of similar metrics [e.g., ( 9 , 10 )] that uses the product of drainage area ( A ) and average upstream rainfall (P¯) estimated from mean annual rainfall (MAP) as an improved discharge proxy ( 1 ). This allows k snQ to account for spatial rainfall variability ( 11 ). Although the stochastic natures of storms and floods are not captured, MAP resolves spatial patterns well and scales quasi-linearly with larger geomorphically relevant discharges ( 12 ).…”

“…We can evaluate the effect of rainfall on collinearity by modifying χ to scale with Q (i.e., P¯ A ), which we define as χ Q (see Materials and Methods) ( 11 , 34 , 40 ). The slope of river profiles on χ Q - z plots is k snQ .…”

“…Here, we present a detailed, large-scale analysis of topography as represented in river profiles and 10 Be erosion rates from the northcentral Andes of Peru and Bolivia (Fig. 1), testing the hypothesis that k snQ is a better predictor of erosion rates than k sn (1,11). In doing so, we highlight assumptions implicit to each metric and quantitatively evaluate the implications of carrying forward these different sets of assumptions on understanding the roles of climate, tectonics, and lithology on landscape evolution.…”

Drivers of landscape evolution in eastern Tibet

“…Many factors may be responsible for the changes in concavity, such as temporal or spatial variability in climate (Chen et al, 2019;Han et al, 2014;Leonard and Whipple, 2021;Whipple andTucker, 1999), rock strength (VanLaningham et al, 2006) or uplift rate (Hurst et al, 2019;VanLaningham et al, 2006;Wickert and Schildgen, 2019). There is some topographic evidence (Hurst et al, 2019;Wickert and Schildgen, 2019), as well as theory, that suggests the concavity index decreases with increasing uplift rates (Hurst et al, 2019;Wickert and Schildgen, 2019).…”