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 1,007 US Geological Survey gages across the continental United States (CONUS). We find that the geometric mean (GM) SSC spans over five orders of magnitude with spatial correlations across CONUS. Here we utilize publicly available geospatial datasets (topography, soils, and climate) to explore potential drivers of the continental pattern of GM SSC. We reduced 345 candidate geospatial variables (attributes and their summary statistics) to eight variables. We find that these geospatial variables provide few robust explanatory univariate trends across the observed variation in GM SSC. A five-fold cross-validated multiple nonlinear regression reveals that the geospatial pattern in GM SSC is most strongly associated with climate, topographic, and soil properties; however, the substantial variability in GM SSC prevents confident CONUS-wide predictions. Exploration of the outlier values reveals that dams, arid and actively meandering river dynamics are poorly captured within existing geospatial datasets. Understanding SSC dependence on watershed properties remains an important pathway for linking watershed processes and management strategies to fine-grained transport dynamics given uncertain future hydroclimatic and environmental change.

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.