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.

DE-AC22-93BC14964

A collection of shapefiles created and compiled by the National Oceanic and Atmospheric Administration. The data is intended to help people understand and predict weather patterns - in particular to plan for potentially dangerous weather conditions such as storms and droughts.

A collection of shapefiles created and compiled by the National Oceanic and Atmospheric Administration. The data is intended to help people understand and predict weather patterns - in particular to plan for potentially dangerous weather conditions such as storms and droughts. The Storm Prediction Center holds shapefiles and KML links for active watches, fire weather outlooks, convective outlooks, and storm reports.

The Channel Migration Potential (CHAMP) layer contains stream networks of Western Washington (and much of Western Oregon) with associated data and information important for assessing channel migration activity. It also features information on channel characteristics such as stream flow and physical dimensions. This data layer’s main feature is a classification of channel migration potential based on channel confinement and erosion potential. The layer was derived from existing statewide geospatial datasets and classified according to channel migration measurements by the High Resolution Change Detection (HRCD) project for the Puget Sound Region (Washington Department of Fish and Wildlife, 2014). While the layer identifies the potential for channel migration, it does not predict channel migration rates. Thus, this data layer should be used to screen and prioritize stream reaches for further channel migration evaluation. The tool helps plan and prioritize floodplain management actions such as Channel Migration Zone mapping, erosion risk reduction, and floodplain restoration. The background, use, and development of the CHAMP layer are fully described in Ecology Publication 15-06-003 (full report citation and URL below). That report also describes visual assessment techniques that should be used along with the CHAMP layer to assess channel migration potential. Legg, N.T. and Olson, P.L., 2015, Screening Tools for Identifying Migrating Stream Channels in Western Washington: Geospatial Data Layers and Visual Assessments: Washington State Department of Ecology Publication 15-06-003, 40 p. https://fortress.wa.gov/ecy/publications/SummaryPages/1506003.htmlThe tool developers would like to thank the following people for their contribution to this work: • Brian D. Collins (University of Washington) • Jerry Franklin (Washington Department of Ecology) • Christina Kellum (Washington Department of Ecology) • Matt Muller (Washington Department of Fish and Wildlife) • Hugh Shipman (Washington Department of Ecology) • Terry Swanson (Washington Department of Ecology) This project has been funded wholly or in part by the United States Environmental Protection Agency under Puget Sound Ecosystem Restoration and Protection Cooperative Agreement Grant PC-00J27601 with Washington Department of Ecology. The contents of this document do not necessarily reflect the views and policies of the Environmental Protection Agency, nor does mention of trade names or commercial products constitute endorsement or recommendation for use.Generally, this data layer should be used to screen and prioritize stream reaches for further channel migration evaluation. The data resolution does not allow one to predict channel migration. The classification identifies stream segments for further examination, and those that likely require limited attention or analysis. The potential uncertainty involved in the classification approach is a reason for the visual assessment techniques (described below in Ecology Publication 15-06-003) being described along with the CHAMP data layer.

The National Centers for Environmental Prediction (NCEP) Miscellaneous Precipitation Dataset is one of several precipitation datasets provided in the Global Energy and Water Cycle Experiment (GEWEX) Continental-Scale International Project (GCIP) Near-Surface Observation Data Set (NESOB) 1997. This dataset contains all hourly precipitation data from the National Centers for Environmental Prediction (NCEP) stations. Stations that reported at standard or incremental times are also included in the various NESOB 1997 precipitation composite datasets. The miscellaneous precipitation dataset contains data from stations in the NESOB 1997 domain (94.5 W to 100.5W longitude and 34N to 39N latitude) and time period (01 April 1997 through 31 March 1998). These data were not quality controlled by the University Corporation for Atmospheric Research/Joint Office for Science Support (UCAR/JOSS). The National Centers for Environmental Prediction (NCEP) Miscellaneous Precipitation Dataset contains eight parameters and uses code tables from the Standard Hydrometeorological Exchange Format (SHEF). The eight parameters repeat once for each time period, where the time period is nominally hourly. The Physical Element code field should always contain a PP indicating that the precipitation data is reported as incremental values. Missing values are not reported. Each precipitation value has an associated observation date and time which are UTC times. The algorithms used to form the NCEP Precipitation data are not currently available.

Geothermal power plants typically show decreasing heat and power production rates over time. Mitigation strategies include optimizing the management of existing wells - increasing or decreasing the fluid flow rates across the wells - and drilling new wells at appropriate locations. The latter is expensive, time-consuming, and subject to many engineering constraints, but the former is a viable mechanism for periodic adjustment of the available fluid allocations. Data and supporting literature from a study describing a new approach combining reservoir modeling and machine learning to produce models that enable strategies for the mitigation of decreased heat and power production rates over time for geothermal power plants. The computational approach used enables translation of sets of potential flow rates for the active wells into reservoir-wide estimates of produced energy and discovery of optimal flow allocations among the studied sets. In our computational experiments, we utilize collections of simulations for a specific reservoir (which capture subsurface characterization and realize history matching) along with machine learning models that predict temperature and pressure timeseries for production wells. We evaluate this approach using an "open-source" reservoir we have constructed that captures many of the characteristics of Brady Hot Springs, a commercially operational geothermal field in Nevada, USA. Selected results from a reservoir model of Brady Hot Springs itself are presented to show successful application to an existing system. In both cases, energy predictions prove to be highly accurate: all observed prediction errors do not exceed 3.68% for temperatures and 4.75% for pressures. In a cumulative energy estimation, we observe prediction errors that are less than 4.04%. A typical reservoir simulation for Brady Hot Springs completes in approximately 4 hours, whereas our machine learning models yield accurate 20-year predictions for temperatures, pressures, and produced energy in 0.9 seconds. This paper aims to demonstrate how the models and techniques from our study can be applied to achieve rapid exploration of controlled parameters and optimization of other geothermal reservoirs. Includes a synthetic, yet realistic, model of a geothermal reservoir, referred to as open-source reservoir (OSR). OSR is a 10-well (4 injection wells and 6 production wells) system that resembles Brady Hot Springs (a commercially operational geothermal field in Nevada, USA) at a high level but has a number of sufficiently modified characteristics (which renders any possible similarity between specific characteristics like temperatures and pressures as purely random). We study OSR through CMG simulations with a wide range of flow allocation scenarios. Includes a dataset with 101 simulated scenarios that cover the period of time between 2020 and 2040 and a link to the published paper about this project, where we focus on the Machine Learning work for predicting OSR's energy production based on the simulation data, as well as a link to the GitHub repository where we have published the code we have developed (please refer to the repository's readme file to see instructions on how to run the code). Additional links are included to associated work led by the USGS to identify geologic factors associated with well productivity in geothermal fields. Below are the high-level steps for applying the same modeling + ML process to other geothermal reservoirs: 1. Develop a geologic model of the geothermal field. The location of faults, upflow zones, aquifers, etc. need to be accounted for as accurately as possible 2. The geologic model needs to be converted to a reservoir model that can be used in a reservoir simulator, such as, for instance, CMG STARS, TETRAD, or FALCON 3. Using native state modeling, the initial temperature and pressure distributions are evaluated, and they become the initial conditions for dynamic reservoir simulations 4. Using history matching with tracers and available production data, the model should be tuned to represent the subsurface reservoir as accurately as possible 5. A large number of simulations is run using the history-matched reservoir model. Each simulation assumes a different wellbore flow rate allocation across the injection and production wells, where the individual selected flow rates do not violate the practical constraints for the corresponding wells. 6. ML models are trained using the simulation data. The code in our GitHub repository demonstrates how these models can be trained and evaluated. 7. The trained ML models can be used to evaluate a large set of candidate flow allocations with the goal of selecting the most optimal allocations, i.e., producing the largest amounts of thermal energy over the modeled period of time. The referenced paper provides more details about this optimization process

The Water Erosion Prediction Project (WEPP) model is a process-based, distributed parameter, continuous simulation, erosion prediction model for use on personal computers running Windows 95/98/NT/2000/XP/Vista/Windows7. The current model version (v2012.8) available for download is applicable to hillslope erosion processes (sheet and rill erosion), as well as simulation of the hydrologic and erosion processes on small watersheds. Included in the download package is the WEPP model (version 2012.8), WEPP Windows interface (August 2012), CLIGEN climate generators (versions 4.3 and 5.3), documentation and example data. The objective of the Water Erosion Prediction Project is to develop new generation prediction technology for use by the USDA-Natural Resources Conservation Service, USDA-Forest Service, USDI-Bureau of Land Management, and others involved in soil and water conservation and environmental planning and assessment. This improved erosion prediction technology is based on modern hydrologic and erosion science, is process-oriented, and is computer-implemented. This document is a detailed description of the WEPP erosion model as developed for application to small watersheds and hillslope profiles within those watersheds. The WEPP erosion model is a continuous simulation computer program which predicts soil loss and sediment deposition from overland flow on hillslopes, soil loss and sediment deposition from concentrated flow in small channels, and sediment deposition in impoundments. In addition to the erosion components, it also includes a climate component which uses a stochastic generator to provide daily weather information, a hydrology component which is based on a modified Green-Ampt infiltration equation and solutions of the kinematic wave equations, a daily water balance component, a plant growth and residue decomposition component, and an irrigation component. The WEPP model computes spatial and temporal distributions of soil loss and deposition, and provides explicit estimates of when and where in a watershed or on a hillslope that erosion is occurring so that conservation measures can be selected to most effectively control soil loss and sediment yield.