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Abstract Generalizable relationships for how subdaily rainfall statistics imprint into runoff statistics are lacking. We use the Colorado Front Range, known for destructive rainfall‐triggered floods and landslides, to assess whether orographic patterns in runoff generation are a direct consequence of rainstorm climatology. Climatological analysis relies on a dense network of tipping‐bucket rain gauges and gridded precipitation frequency estimates from the National Oceanic and Atmospheric Administration to evaluate relationships among subdaily rainfall statistics, topography, and flood frequency throughout the South Platte River basin. We find that event‐scale rainfall statistics only weakly depend on elevation, suggesting that orographic gradients in runoff “extremes” are not simply a consequence of rainfall patterns. In contrast, bedrock exposure strongly varies with elevation in a way that plausibly explains enhanced runoff generation at lower elevations via reduced water storage capacity. These findings are suggestive of feedbacks between bedrock river evolution and hillslope hydrology not typically included in models of landscape evolution.
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This content will become publicly available on June 19, 2026
A non-stationary trans-Gaussian model for daily rainfall over complex topography
Abstract. The orographic effects that influence rainfall fields in mountainous regions depend on elevation and the exposure of the topography to prevailing winds. Transitions between wet and dry areas can occur within a few kilometers, creating strong horizontal gradients of various rainfall statistics such as the frequency of occurrence, the distribution of intensity and the structure of spatial correlation. Most statistical models of daily rainfall assume spatial stationarity (i.e., the spatial homogeneity of rainfall statistics) and are therefore not well suited for studying the highly non-homogeneous characteristics of orographic rainfall. To overcome this limitation, we design a non-stationary trans-Gaussian geostatistical model for the analysis of daily rainfall fields over complex topography. The modeling framework presented in this paper infers rainfall statistics from sparse rain gauge observations, simulates realistic rainfall fields after calibration and stochastically interpolates rain gauge observations to create rainfall maps. The performance of the model is assessed with data from the Island of Hawai‘i where extreme spatial gradients in rainfall are observed. The results presented in this paper demonstrate that a non-stationary trans-Gaussian model can skillfully reproduce orographic rainfall statistics as well as their variations in space.
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- Award ID(s):
- 2117975
- PAR ID:
- 10657044
- Publisher / Repository:
- EGUsphere
- Date Published:
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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