000167875 001__ 167875
000167875 005__ 20260121151420.0
000167875 0247_ $$2doi$$a10.1126/sciadv.1701683
000167875 0248_ $$2sideral$$a143909
000167875 037__ $$aART-2017-143909
000167875 041__ $$aeng
000167875 100__ $$0(orcid)0000-0002-3800-5304$$aTejedor, Alejandro
000167875 245__ $$aScale-dependent erosional patterns in steady-state and transient-state landscapes
000167875 260__ $$c2017
000167875 5060_ $$aAccess copy available to the general public$$fUnrestricted
000167875 5203_ $$aLandscape topography is the expression of the dynamic equilibrium between external forcings (for example, climate and tectonics) and the underlying lithology. The magnitude and spatial arrangement of erosional and depositional fluxes dictate the evolution of landforms during both statistical steady state (SS) and transient state (TS) of major landscape reorganization. For SS landscapes, the common expectation is that any point of the landscape has an equal chance to erode below or above the landscape median erosion rate. We show that this is not the case. Afforded by a unique experimental landscape that provided a detailed space-time recording of erosional fluxes and by defining the so-called E50-area curve, we reveal for the first time that there exists a hierarchical pattern of erosion. Specifically, hillslopes and fluvial channels erode more rapidly than the landscape median erosion rate, whereas intervening parts of the landscape in terms of upstream contributing areas (colluvial regime) erode more slowly. We explain this apparent paradox by documenting the dynamic nature of SS landscapes—landscape locations may transition from being a hillslope to being a valley and then to being a fluvial channel due to ridge migration, channel piracy, and small-scale landscape dynamics through time. Under TS conditions caused by increased precipitation, we show that the E50-area curve drastically changes shape during landscape reorganization. Scale-dependent erosional patterns, as observed in this study, suggest benchmarks in evaluating numerical models and interpreting the variability of sampled erosional rates in field landscapes.
000167875 540__ $$9info:eu-repo/semantics/openAccess$$aby-nc$$uhttps://creativecommons.org/licenses/by-nc/4.0/deed.es
000167875 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/publishedVersion
000167875 700__ $$aSingh, Arvind
000167875 700__ $$aZaliapin, Ilya
000167875 700__ $$aDensmore, Alexander L.
000167875 700__ $$aFoufoula-Georgiou, Efi
000167875 773__ $$g3, 9 (2017), e1701683 [7 pp.]$$pSci. adv.$$tScience advances$$x2375-2548
000167875 8564_ $$s1200377$$uhttps://zaguan.unizar.es/record/167875/files/texto_completo.pdf$$yVersión publicada
000167875 8564_ $$s3665262$$uhttps://zaguan.unizar.es/record/167875/files/texto_completo.jpg?subformat=icon$$xicon$$yVersión publicada
000167875 909CO $$ooai:zaguan.unizar.es:167875$$particulos$$pdriver
000167875 951__ $$a2026-01-21-14:55:24
000167875 980__ $$aARTICLE