Stochastic hydrological process models have two conceptual advantages over deterministic models. First, even though water flow in a well-defined environment is governed by deterministic differential equations, a hydrological system, at the level we can observe it, does not behave deterministically. Reasons for this behavior are unobserved spatial heterogeneity and fluctuations of input, unobserved influence factors, heterogeneity and variability in soil and aquifer properties, and an imprecisely known initial state. A stochastic model provides thus a more realistic description of the system than a deterministic model. Second, hydrological models simplify real processes. The resulting structural deficits can better be accounted for by stochastic than by deterministic models because they, even for given parameters and input, allow for a probability distribution of different system evolutions rather than a single trajectory. On the other hand, stochastic process models are more susceptible to identifiability problems and Bayesian inference is computationally much more demanding. In this paper, we review the use of stochastic, time-dependent parameters to make deterministic models stochastic, discuss options for the numerical implementation of Bayesian inference, and investigate the potential and challenges of this approach with a case study. We demonstrate how model deficits can be identified and reduced, and how the suggested approach leads to a more realistic description of the uncertainty of internal and output variables of the model compared to a deterministic model. In addition, multiple stochastic parameters with different correlation times could explain the variability in the time scale of output error fluctuations identified in an earlier study.

This dataset conforms to the Tier 3 Content Model for Geologic Reservoirs Version 1.0. It contains the known hydrocarbon reservoirs within the study area of the Geothermal Play Fairway Analysis for the Appalachian Basin (GPFA-AB) as part of Phase 1, Natural Reservoirs Quality Analysis. The final values for Reservoir Productivity Index (RPI) and uncertainty (in terms of coefficient of variation, CV) are included. RPI is in units of liters per MegaPascal-second (L/MPa-s), quantified using permeability, thickness of formation, and depth. A higher RPI is more optimal. Coefficient of Variation (CV) is the ratio of the standard deviation to the mean RPI for each reservoir. A lower CV is more optimal. Details on these metrics can be found in the Reservoirs_Methodology_Memo.pdf uploaded to the Geothermal Data Repository Node of the NGDS in October of 2015.

The GitHub site has the dataset used to create the manuscript figures, which examined which simulation parameters influenced the spread of contamination within a water distribution system. The dataset includes the values for the metrics of interest, which were extent of contamination, population impacted, extent of contamination in the unknown zone, and the population impacted in the unknown zone. These values changed according to the simulation parameter combination set that was used in modeling the affects of a contamination incident within a water distribution system. These parameters included demand, valve closure, contaminant reaction coefficient, injection start time, injection duration, and injection location (as listed in Table 1 of the paper). Two different water distribution system models were used in the paper, the KL network and the N6 network. In addition, some of the simulation model input files that were used to create the data are also provided on the GitHub site, but not all of the input files are provided since there were approximately 25 million simulations. A README file is provided on the GitHub site for more explanation of the files provided. This dataset is associated with the following publication: Hart, D., J.S. Rodriguez, J. Burkhardt, B. Borchers, C. Laird, R. Murray, K. Klise, and T. Haxton. Quantifying hydraulic and water quality uncertainty to inform sampling of drinking water distribution systems. JOURNAL OF WATER RESOURCES PLANNING AND MANAGEMENT. American Society of Civil Engineers (ASCE), Reston, VA, USA, 145(1): ., (2019).

Matlab scripts and functions and data used to build Poly3D models and create permeability potential GIS layers for 1) Mount St. Helens seismic zone, 2) Wind River Valley, and 3) Mount Baker geothermal prospect areas located in Washington state.

This file contains file geodatabases of the Mount St. Helens seismic zone (MSHSZ), Wind River valley (WRV) and Mount Baker (MB) geothermal play-fairway sites in the Washington Cascades. The geodatabases include input data (feature classes) and output rasters (generated from modeling and interpolation) from the geothermal play-fairway in Washington State, USA. These data were gathered and modeled to provide an estimate of the heat and permeability potential within the play-fairways based on: mapped volcanic vents, hot springs and fumaroles, geothermometry, intrusive rocks, temperature-gradient wells, slip tendency, dilation tendency, displacement, displacement gradient, max coulomb shear stress, sigma 3, maximum shear strain rate, and dilational strain rate at 200m and 3 km depth. In addition this file contains layer files for each of the output rasters. For details on the areas of interest please see the 'Phase 1 Technical Report' in the download package. This submission also includes a file with the geothermal favorability of the Washington Cascade Range based off of an earlier statewide assessment. Additionally, within this file there are the maximum shear and dilational strain rate rasters for all of Washington State.