Exploring the effects of uncertainty in hydrologic modeling and reconciling spatiotemporal data gaps at the continental scale
type: CreativeWork
description: Spatiotemporally continuous estimates of the hydrologic cycle are often generated through hydrologic modeling, reanalysis, or remote sensing methods, and commonly applied as a supplement to, or a substitute for, in-situ measurements when observational data are sparse or unavailable. Increased access over the past decade to distributed cloud systems and high-performance clusters have greatly increased the computational processing power available to the hydrologic community. In turn, the number of these modeled products estimating various components of the hydrologic cycle over continental and global extents have grown year by year. Despite this, significant gaps in our knowledge of terrestrial hydrology remain. Models typically host high levels of uncertainty in many parts of the conterminous United States (CONUS). Classic model validation studies are insufficient in describing model skill over such large extents due to limitations in data availability. Further, models are increasingly more expensive to operate and analyze due to higher levels of complexity in terms of internal equations, parameter requirements, differences in meteorological forcings, and calibration methodologies. Hydrologic and reanalysis models, capable of retrospective and forecast prediction, are spatially constrained to moderate or coarse resolutions. Remote sensing models, capable of fine resolution estimates of individual hydrologic components, are temporally constrained by availability of satellite observations. This dissertation first seeks to better quantify the uncertainty in modeled estimates of precipitation (P), actual evapotranspiration (ET), runoff (R), snow water equivalent (SWE), and soil moisture (SM) from 87 unique datasets generated by 47 hydrologic models, reanalysis datasets, and remote sensing products across the conterminous United States (CONUS). Uncertainty between hydrologic component estimates was shown to be high in the western CONUS, with median uncertainty (measured a…
abstract: Spatiotemporally continuous estimates of the hydrologic cycle are often generated through hydrologic modeling, reanalysis, or remote sensing methods, and commonly applied as a supplement to, or a substitute for, in-situ measurements when observational data are sparse or unavailable. Increased access over the past decade to distributed cloud systems and high-performance clusters have greatly increased the computational processing power available to the hydrologic community. In turn, the number of these modeled products estimating various components of the hydrologic cycle over continental and global extents have grown year by year. Despite this, significant gaps in our knowledge of terrestrial hydrology remain. Models typically host high levels of uncertainty in many parts of the conterminous United States (CONUS). Classic model validation studies are insufficient in describing model skill over such large extents due to limitations in data availability. Further, models are increasingly more expensive to operate and analyze due to higher levels of complexity in terms of internal equations, parameter requirements, differences in meteorological forcings, and calibration methodologies. Hydrologic and reanalysis models, capable of retrospective and forecast prediction, are spatially constrained to moderate or coarse resolutions. Remote sensing models, capable of fine resolution estimates of individual hydrologic components, are temporally constrained by availability of satellite observations. This dissertation first seeks to better quantify the uncertainty in modeled estimates of precipitation (P), actual evapotranspiration (ET), runoff (R), snow water equivalent (SWE), and soil moisture (SM) from 87 unique datasets generated by 47 hydrologic models, reanalysis datasets, and remote sensing products across the conterminous United States (CONUS). Uncertainty between hydrologic component estimates was shown to be high in the western CONUS, with median uncertainty (measured a…
author: Saxe, Samuel
language: eng
language: English
keywords: modeling, statistics, evaluation, surface water, remote sensing
identifier: T 9117
date published: 2021
date modified: 2026-07-24T01:41:37Z
provider: Colorado School of Mines
publisher: Networked Digital Library of Theses and Dissertations
type: WebPage
source organization: Colorado School of Mines. Arthur Lakes Library

PropertyValue
property ID: Handle
value: https://hdl.handle.net/11124/176453