An official website of the United States government
Abstract 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,https://doi.org.10.1002/2023JF007327).
more »
« less
Orographic Controls on Subdaily Rainfall Statistics and Flood Frequency in the Colorado Front Range, USA
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.
more »
« less
- PAR ID:
- 10448280
- Publisher / Repository:
- DOI PREFIX: 10.1029
- Date Published:
- Journal Name:
- Geophysical Research Letters
- Volume:
- 47
- Issue:
- 4
- ISSN:
- 0094-8276
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
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.more » « less
-
Abstract Mountain landscapes have dynamic climates that, together with tectonic processes, influence their topographic evolution. Spatial and temporal variations in rainfall are ubiquitous in these settings as orographic precipitation patterns evolve with climate change and topography. Despite important implications such changes have for river incision, their influence is understudied. Here, we investigate how changes in rainfall pattern should affect both the steady state form and transient evolution of river profiles at the catchment scale using the stream power model. We find that spatially varied rainfall patterns can complicate steady state relationships between mean rainfall, channel steepness and fluvial relief, depending on where rainfall is concentrated in catchments, and lead to unexpected transient behavior if they are neglected. Specifically, changes in rainfall pattern cause multi‐stage transient responses that differ from responses to uniform changes in rainfall. Disparate responses by rivers that experience different rainfall conditions, particularly trunk and tributary rivers, are also an important factor in understanding catchment‐wide responses to climate change. Accounting for such disparities in sampling strategies and topographic analyses may, therefore, be vital for detecting and quantifying climate's role in landscape evolution. Lastly, we show how explicitly accounting for rainfall patterns in channel steepness indices, and thus spatial variations in erosional efficiency, may advance understanding of landscape sensitivity to climate. These results have important implications for detecting transient responses to changes in rainfall pattern (and more broadly climate), interpretation of morphometrics in steady state and transient landscapes, and quantifying the sensitivity of landscapes and erosion rates to climate.more » « less
-
Abstract Bedrock river width is an essential geometric parameter relevant to understanding flood hazards and gauging station rating curves, and is critical to stream power incision models and many other landscape evolution models. Obtaining bedrock river width measurements, however, typically requires extensive field campaigns that take place in rugged and steep topography where river access is often physically challenging. Although prior work has turned to measuring channel width from satellite imagery, these data present a snapshot in time, are typically limited to rivers ≥ 10–30 m wide due to the image resolution, and are physically restricted to areas devoid of vegetation. For these reasons, we are generally data limited, and the factors impacting bedrock channel width remain poorly understood. Due to these limitations, researchers often turn to assumptions of width‐scaling relationships with drainage area or discharge to estimate bedrock channel width. Here we present a new method of obtaining bedrock channel width at a desired river discharge through the incorporation of a high‐resolution bare‐earth digital elevation model (DEM) using MATLAB Topotoolbox and the HEC‐RAS river analysis system. We validate this method by comparing modeled results to US Geological Survey (USGS) field measurements at existing gauging stations, as well as field channel measurements. We show that this method can capture general characteristics of discharge rating curves and predict field‐measured channel widths within uncertainty. As high‐resolution DEMs become more available across the United States through the USGS three‐dimensional elevation program (3DEP), the future utility of this method is notable. Through developing and validating a streamlined, open‐source, and freely available workflow of channel width extraction, we hope this method can be applied to future research to improve the quantity of channel width measurements and improve our understanding of bedrock channels.more » « less
-
Abstract We present 17 new 10Be erosion rates from southern Peru sampled across an extreme orographic rainfall gradient. Using a rainfall-weighted variant of the normalized channel steepness index, ksnQ, we show that channel steepness values, and thus topography, are adjusted to spatially varying rainfall. Rocks with similar physical characteristics define distinct relationships between ksnQ and erosion rate (E), suggesting ksnQ is also resolving lithologic variations in erodibility. However, substantial uncertainty exists in parameters describing these relationships. By combining our new data with 38 published erosion rates from Peru and Bolivia, we collapse the range of compatible parameter values and resolve robust, nonlinear ksnQ–E relationships suggestive of important influences of erosional thresholds, rock properties, sediment characteristics, and temporal runoff variability. In contrast, neither climatic nor lithologic effects are clear using the traditional channel steepness metric, ksn. Our results highlight that accounting for spatial rainfall variations is essential for disentangling the multiple influences of climate, lithology, and tectonics common in mountain landscapes, which is a necessary first step toward greater understanding of how these landscapes evolve.more » « less
