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Mountain topography alters the phase, amount, and spatial distribution of precipitation. Past efforts focused on how orographic precipitation can alter spatial patterns in mean runoff, with less emphasis on how time‐varying runoff statistics may also vary with topography. Given the importance of the magnitude and frequency of runoff events to fluvial erosion, we evaluated whether orographic patterns in mean runoff and daily runoff variability can be constrained using the global WaterGAP3 water model data. Model runoff data are validated against observational data in the contiguous United States, showing agreement with mean runoff in all settings and daily runoff variability in settings where rainfall‐runoff predominates. In snowmelt‐influenced settings, runoff variability is overestimated by the water model data. Cognizant of these limitations, we use the water model data to develop relationships between mean runoff and daily runoff variability and how these are mediated by snowmelt fraction in mountain topography globally. A global analysis of topographic controls on hydroclimatic variables using a random forest model was ambiguous. Instead, relationships between topography and runoff parameters are better assessed at the mountain range scale. Rulesets linking topography to mean runoff and snowmelt fraction are developed for three mid‐latitude mountain landscapes—British Columbia, European Alps, and Greater Caucasus. Increasing topographic elevation and relief together leads to higher mean runoff and lower runoff variability due to the increasing contribution of snowmelt. The three sets of empirical relationships developed here serve as the basis for a suite of numerical experiments in our companion manuscript (Part 2, Forte & Rossi, 2024a).
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Stochastic in Space and Time: 2. Effects of Simulating Orographic Gradients in Daily Runoff Variability on Bedrock River Incision
Abstract The extent to which climate and tectonics can be coupled rests on the degree to which topography and erosion rates scales linearly. The stream power incision model (SPIM) is commonly used to interpret such relationships, but is limited in probing mechanisms. A promising modification to stream power models are stochastic‐threshold incision models (STIM) which incorporate both variability in discharge and a threshold to erosion. In this family of models, the form of the topography erosion rate relationship is largely controlled by runoff variability. Applications of STIM typically assume temporally variable, but spatially uniform and synchronous runoff generating events, an assumption that is likely broken in regions with complicated orography. To address this limitation, we develop a new 1D STIM model, which we refer to as spatial‐STIM. This modified version of STIM allows for stochasticity in both time and space and is driven by empirical relationships between topography and runoff statistics. Coupling between mean runoff and runoff variability via topography in spatial‐STIM generates highly nonlinear relationships between steady‐state topography and erosion rates. We find that whether the daily statistics of runoff are spatially linked or unlinked, which sets whether there is spatial synchronicity in the recurrence interval of runoff generating events, is a fundamental control on landscape evolution. Many empirical topography—erosion rate data sets are based on data that span across the endmember scenarios of linked versus unlinked behavior. It is thus questionable whether singular SPIM relationships fit to those data can be meaningfully related to their associated hydroclimatic conditions.
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- PAR ID:
- 10508914
- Publisher / Repository:
- AGU
- Date Published:
- Journal Name:
- Journal of Geophysical Research: Earth Surface
- Volume:
- 129
- Issue:
- 3
- ISSN:
- 2169-9003
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript
- Journal Article:
- https://doi.org/10.1029/2023JF007327
