ABSTRACT:

High concentrations of suspended sediment (SSC) in a river can represent a critical water quality concern, reduce the storage capacity of reservoirs, and impact aquatic habitat. Within a river, SSC can be conceptualized as a function of reach-scale hydraulics translating discharge into shear stress and watershed processes that determine the types and quantities of sediment supplied to the river. To explore watershed controls on sediment supply, we utilized SSC data from over 1000 US Geological Survey gages spread across the continental United States (CONUS). We find that the geometric mean SSC spans over five orders of magnitude with clustered high and low values throughout the CONUS indicating a dependence on regional watershed properties. Here we utilize publicly available geospatial datasets (topography, soils, land use, and climate) to explore the potential dependence of mean SSC for over 100 variables. We find that catchment-wide and point-scale geospatial variables provide few explanatory univariate trends for the observed mean SSC patterns. We utilized principal components analysis to reduce the dimensionality of the exploration to a limited subset of variables. Extreme variability within mean SSC and data limitations prevents a complete prediction of SSC from geospatial data, however multiple nonlinear regression reveals that the geospatial pattern in mean SSC is primarily a function of climate (aridity), vegetation, and soil properties. Understanding SSC dependence on watershed properties represents an important step for linking watershed processes and fine-grained transport dynamics and how changes in climate and the environment may further affect sediment volumes and watershed management.

ABSTRACT:

My final project in my Spring 2023 Remote Sensing class. This project is an exploration into land classification using google earth engine via python and hydroshare's jupyter notebook. This project identifies a region in the South Metro Denver, CO, pulls in NLCD and landsat8 images from multiple years to identify changes to land cover classification over time.

ABSTRACT:

This code retrieves stream info from nwis using the dataretrieval tool in python. You can input site, parameters, and dates at the top. This code pulls daily measurements, annual_stats, and daily_stats.
We calculate 30-year normals, as well as plot annual average flows, annual min, max, and mean flows, and percentile flows. This resource only pulls from the USGS ftp site and doesn't have or require any local storage.