A document by Daniel White. Click on the document to view its contents.

The Multi-Radar Multi-Sensor (MRMS) product incorporates radar, climate model, and gage data at a high spatiotemporal resolution for the contiguous United States. MRMS is subject to various sources of measurement error, especially in complex terrain. The goal of this study is to provide a framework for understanding the uncertainty of MRMS in mountainous areas with limited observations. We evaluate 8-hour time series samples of MRMS 15-minute intensity through a comparison to 204 gages located in the mountains of Colorado. This analysis shows that the MRMS surface precipitation rate product tends to overestimate rainfall with a median normalized root mean squared error (RMSE) of 42\% of the maximum MRMS 15-minute intensity. For each time series sample, various features related to the physical characteristics influencing MRMS performance are calculated from the topography, surrounding storms, and rainfall observed at the gage location. A gradient-boosting regressor is trained on these features and is optimized with quantile loss, using the RMSE as a target, to model nonlinear patterns in the features that relate to a range of error. This model was used to predict a range of error throughout the mountains of Colorado during warm months, spanning 6 years, resulting in a spatiotemporally varying error model of MRMS for sub-hourly precipitation rates. Mapping of this dataset by aggregating normalized RMSE over time reveals that areas further from radar sites in higher elevation terrain show consistently greater error. However, the model predicts larger performance variability in these regions compared to alternative error assessments.

Stream channel incision and deposition are common after wildfire, and these geomorphic changes may impact runoff mechanisms and the composition of pre-event and event water in runoff. To investigate this, we monitored discharge and electrical conductivity at 6 nested sites within a 15.5 km 2 watershed in the northern Colorado Front Range that had recently burned, experienced large flooding, and well-documented and significant channel erosion and deposition. Over the study period, the watershed experienced seven precipitation events. For each hydrograph, we separate baseflow from runoff using a new method to characterize and account for the strong diurnal signal in the baseflow. Electrical conductivity is used as a tracer in a two-component end-member mixing analysis to separate the event hydrographs into event and pre-event water. Correlation coefficients were computed between key variables of the hydrologic response (such as runoff ratio, volumes of event and pre-event water) to storm and basin characteristics (including stream channel erosion/deposition, fraction of high/moderate burn severity, precipitation intensity, and antecedent precipitation). The strength and significance of correlations was found to vary seasonally. In the early season, event and pre-event volumes did not vary significantly with basin or storm characteristics. In the late season, antecedent precipitation correlated with a decrease in event runoff (R 2 = 0.34) and total runoff (R 2 = 0.40), increased precipitation intensity correlated with an increase in event runoff (R 2 = 0.48), and local erosion correlated with an increase in pre-event runoff (R 2 = 0.60) and total runoff (R 2 = 0.53). These findings indicate that seasonality and post-fire stream channel erosion influence the makeup of runoff response, most likely through their impact on the gradient of the near-stream groundwater table.

Mixed bedrock-alluvial rivers can exhibit partial alluvial cover, which may play an important role in controlling bedrock erosion rates and landscape evolution. However, numerical morphodynamic models generally are unable to predict the pattern of alluviation in these channels. Hence we present a new two-dimensional depth-averaged morphodynamic model that can be applied to both fully alluvial and mixed bedrock-alluvial channels, and we use the model to gain insight into the mechanisms responsible for the development of sediment patches and patterns of bedrock alluviation. The model computes hydrodynamics, sediment transport, and bed evolution, using a roughness partitioning that accounts for differential roughness of sediment and bedrock, roughness due to sediment transport, and form drag. The model successfully replicates observations of bar development and migration from a fully alluvial flume experiment, and it models persistent sediment patches observed in a mixed bedrock-alluvial flume experiment. Numerical experiments in which the form drag, sediment transport roughness, and ripple factor correction were neglected did not successfully reproduce the observed persistent sediment cover in the mixed bedrock-alluvial case, suggesting that accounting for these different roughness components is critical to successfully model sediment dynamics in bedrock channels.

Understanding the development and spatial distribution of alluvial patches in mixed bedrock-alluvial rivers is necessary to predict the mechanisms of the interactions between sediment transport, alluvial cover, and bedrock erosion. This study aims to analyze patterns of bedrock alluviation using a 2D morphodynamic model, and to use the model results to better understand the mechanisms responsible for alluvial patterns observed experimentally. A series of simulations are conducted to explore how alluvial patterns in mixed bedrock-alluvial channels form and evolve for different channel slopes and antecedent sediment layer thicknesses. In initially bare bedrock low-slope channels, the model predicts a linear relationship between sediment cover and sediment supply because areas of subcritical flow enable sediment deposition, while in steep-slope channels the flow remains fully supercritical and the model predicts so-called runaway alluviation. For channels initially covered with sediment, the model predicts a slope-dependent sediment supply threshold above which a linear relationship between bedrock exposure and sediment supply develops, and below which the bedrock becomes fully exposed. For a given sediment supply, the fraction of bedrock exposure and average alluvial thickness converge toward the equilibrium value regardless of the initial cover thickness so long as it exceeds a minimum threshold. Steep channels are able to maintain a continuous strip of sediment under sub-capacity sediment supply conditions by achieving a balance between increased form drag as bedforms develop and reduced surface roughness as the portion of alluvial cover decreases. In lower-slope channels, alluvial patches are distributed sporadically in regions of the subcritical flow.

Meandering gravel-bed rivers tend to exhibit bed surface sorting patterns with coarse particles located in pools and fine particles on bar tops. The mechanism by which these patterns emerge has been explored in sand-bed reaches; however, for gravel-bed meandering channels it remains poorly understood. Here we present results from a flume experiment in which bed morphology, velocity, sediment sorting patterns, and bed load transport were intensively documented. The experimental channel is 1.35 meters wide, 15.2 meters long, and its centerline follows a sine-generated curve with a crossing angle of 20 degrees. Water and sediment input were held constant throughout the experiment and measurements were collected under quasi-equilibrium conditions. Boundary shear stress calculated from near-bed velocity measurements indicates that in a channel with mild sinuosity, deposition of fine particles on bars is a result of divergent shear stress at the inside bend of the channel, downstream of the apex. Boundary shear stress in the upstream half of the pool was below critical for coarse particles (>8 mm), leading to an armored pool. Inward directed selective transport was responsible for winnowing of fine particles in the pool. Fine and coarse sediment followed similar trajectories through the meander bend, which contrasts earlier studies of sand-bedded meanders where the loci of fine and coarse particles cross paths. This suggests a different sorting mechanism for gravel bends. This experiment shows that a complex interaction of quasi-equilibrium bed topography, selective sediment transport, and secondary currents are responsible for the sorting patterns seen in gravel-bed, meandering channels.

High-resolution topographic data are used in geomorphic and hydrologic research for many purposes, including topographic change detection, development of computational meshes for hydraulic models, characterizing channel and hillslope geometry, measuring vegetation structure and density. These data can be collected in a variety of ways, ranging from manual surveying with a Total Station or GPS system, airborne LiDAR, terrestrial laser scanning (TLS), and Structure-from-Motion (SfM) photogrammetry using images collected from drones or pole-mounted cameras. These methods can be very time consuming to collect, and the equipment they require can be very costly. With the release of the 2020 iPad Pro and iPhone 12 Pro, Apple added a LiDAR sensor to their devices, enabling them to be used as hand-held 3D scanners. This new technology has the potential to enable very rapid collection of high-resolution topographic data at low cost. Here, we investigate how well iPad-based LiDAR characterizes topography and topographic change in hillslope and fluvial environments. A 2020 iPad Pro using two apps (3D Scanner and Polycam) was used to collect topographic data over areas ranging from about 100 – 600 m2. These same areas were scanned with a Topcon GLS-2000 TLS system, and aerial imagery were collected with a UAV and processed with Agisoft Metashape to create SfM point clouds. Ground-based targets visible in the datasets were surveyed with an RTK-GNSS system and used to register and scale the datasets. The datasets were aligned using the ICP algorithm in CloudCompare, and cross-sections and topographic differences were extracted from each dataset and compared. Our analysis indicates that transects collected with the iPad LiDAR have mean absolute differences with TLS and SfM data within 3 cm, making these data comparable to other high-resolution topographic data collection methods.