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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 469 records · Page 26
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Simulation of hydropower at subcontinental to global scales: a state-of-the-art review

Abstract Hydroelectric power is playing a new and often expanded role in the world’s major power grids, offering low carbon generating capacity in industrializing, dam-building economies while providing reserve and flexibility to co-manage fledgling wind and solar resources in high income countries. Driven by river flows, conventional hydropower is exposed to the vagaries of weather and climate, motivating drought and climate change hydropower impact studies at large spatial scales. Here we review methods of climate-driven hydropower simulation at large spatial scales, specifically multi-basin regions to global. We identify four types of approach based on complexity of tools and richness of data applied to the problem. Since the earliest attempts to model climate-driven hydropower at continental scale almost two decades ago, the field has transitioned from one of scientific curiosity to practical application, with studies increasingly motivated by the need to inform power grid expansion planning and operation. As the hydrological and water management models used in large-scale hydropower studies become more sophisticated, new opportunities will emerge to study the impacts of changing hydropower on power system reliability and performance at large power grid scale. To grasp these opportunities, the water resources community must continue to enhance data and models for representing river flows and anthropogenic water use and management at subcontinental to global scales.

13 HYDRO ENERGY↗
DOE OSTI2020

Integrated Model of Models for Global Flood Alerting

A dramatic increase in frequency of minor to major flooding since 2000 has caused significant economic losses across the world. To mitigate and recover from these losses, actions have been taken to build resilient communities and infrastructures, specifically, by providing situational awareness in near real-time about flood impacts to enhance response and recovery efforts. Several hydrologic and hydraulic flood models are available at various spatial and temporal resolutions to forecast flood events at regional to global scale. Given the global coverage of two operational flood models – GloFAS (Global Flood Awareness System) and GFMS (Global Flood Monitoring System), the purpose of this project is to implement a Model of Models (MoM) approach to integrate the outputs from these two models to classify flood severity at watershed level worldwide, and send alerts based on severity similar to the USGS PAGER (used for severity alerting and impact analysis for earthquakes) to flood impacted communities. The alerts containing flood impacts and severity information will be disseminated through the DisasterAWARE platform, operated by the Pacific Disaster Center (PDC), that provides global multi-hazard alerting and Situational Awareness information to the emergency management community and public. The current version of the MoM approach was implemented for a case study flood event that occurred during January and February of 2020 in South and Central Africa. The findings of the case study event reveal that the approach is effective in identifying potential flood impact areas and the spatio-temporal variation of flood severity, flood depth and extent at watershed level, which will be used to assess infrastructure and societal impacts using earth-observation data and for alerting.

Kar, Bandana↗
DOE OSTI2021

Investigation of Scale-Dependent Groundwater/Surface-water Exchange in Rivers by Gradient Self-Potential Logging: Numerical Modeling and Field Experiments

Exchanges of groundwater and surface-water are fundamental to a wide range of water-supply and water-quality management issues but challenging to map beyond the reach scale. Waterborne gradient self-potential (SP) measurements are directly sensitive to water flow through riverbed sediments and can be used to infer exchange locations, direction (gain versus loss), scale, and relative changes, but to date applications to river corridor hydrology are limited. Numerical modeling and field experiments were therefore performed herein, each emphasizing waterborne gradient SP logging for identifying and locating focused vertical groundwater discharge (surface-water gain) and recharge (surface-water loss) in a river. Two and three-dimensional numerical models were constructed to simulate the polarities, appearances, and peak amplitudes of streaming-potential and electric-field anomalies on a riverbed and in the surface-water that were attributable to steady-state vertical fluxes of groundwater through high-permeability conduits in the riverbed. Effects of varied hydraulic length-scale of exchange and surface-water depth were tested through numerical modeling. Modeling results aided in data acquisition and interpretation for three repeated field experiments performed along a 1.5–2.0 km reach of the Quashnet River in Cape Cod, Massachusetts, where focused, meter-scale groundwater discharges occur at discrete locations within otherwise ubiquitous and more diffuse groundwater upwelling conditions. Strong gradient SP anomalies were repeatedly measured in the Quashnet River at previously confirmed locations of focused groundwater discharge, showing the efficacy of waterborne gradient SP logging in identifying and characterizing groundwater/surface water exchange dynamics at multiple river network scales.

Engineering↗
DOE OSTI2026

Unprecedented Shifts in Hydrology Are Emerging Across California's Critical Basins: An Evaluation From 0.5 to 3.5°C

With advances in climate models and downscaling techniques, stakeholders anticipate high-resolution analysis to inform regional to local changes in water management. Here, we produce hydrologic projections from an ensemble of Earth System Models (ESMs) that were selected and downscaled to support California's 5th Climate Assessment. An ensemble of 19 ESMs was downscaled to a 3-km resolution across California using a statistical-dynamical downscaling approach and subsequently run through two calibrated hydrology models. Although California has been extensively studied in the context of climate change, we provide the first evaluation of the warming thresholds at which hydroclimate metrics demonstrate statistically significant shifts. We show that present-day to near-term warming levels in Klamath and Northern Sierra Nevada basins, which serve as a critical source of water for California, show statistically significant decreases in snowfall and peak snowpack and associated decreases in summer snowmelt and runoff. More generally, shifts in these hydroclimate metrics occur for intermediate elevation basins ranging from 1,315 to 1,455 m (4,314.3–4,773.6 ft), while the warming level of emergence is delayed for lower and higher elevation basins. We also find that several basins already demonstrate significant increases in 5- to 100-year runoff intensities, primarily due to the increasing influence of precipitation falling as rain. Hydroclimate metrics with trends that demonstrate near-term warming levels of emergence are reflected in reanalysis-based observations, suggesting California is entering a fundamentally different hydroclimate regime. While this will likely stress California's water management, the research provided can support when to implement adaptation efforts.

Bass, B. [Univ. of California, Los Angeles, CA (Un↗
DOE OSTI2020

The Global‐Mean Precipitation Response to CO 2 ‐Induced Warming in CMIP6 Models

Abstract We examine the response of globally averaged precipitation to global warming—the hydrologic sensitivity (HS)—in the Coupled Model Intercomparison Project phase 6 (CMIP6) multi‐model ensemble. Multi‐model mean HS is 2.5% K −1 (ranging from 2.1–3.1% K −1 across models), a modest decrease compared to CMIP5 (where it was 2.6% K −1 ). This new set of simulations is used as an out‐of‐sample test for observational constraints on HS proposed based on CMIP5. The constraint based on clear‐sky shortwave absorption sensitivity to water vapor has weakened, and it is argued that a proposed constraint based on surface low cloud longwave radiative effects does not apply to HS. Finally, while a previously proposed mechanism connecting HS and climate sensitivity via low clouds is present in the CMIP6 ensemble, it is not an important factor for variations in HS. This explains why HS is uncorrelated with climate sensitivity across the CMIP5 and CMIP6 ensembles.

Pendergrass, A. G.↗
DOE OSTI2025

Effectiveness of nature-based solutions to reduce flooding in Quad Cities Metro Area (QCMA) using SWMM-HEC based flood model

Nature-based solutions (NbS) have gained significant attention as strategies for addressing urban environmental challenges, particularly since the establishment of the UN Sustainable Development Goals (SDGs) for 2030. However, the current research on NbS for urban flood management lacks comprehensive methodological approaches for identifying suitable areas and evaluating their effectiveness across different urban settings. Here, this study attempts to fill this gap by proposing a methodological framework integrating multi-criteria analysis with a SWMM-HEC-based hydrologic and hydraulic (HH) model to assess the suitability of NbS for the Quad Cities Metro Area (QCMA), consisting of Davenport, Bettendorf, Moline, and Rock Island. Eight NbS options-green roofs, rain gardens, infiltration trenches, permeable pavements, vegetative swales, dry detention basins, retention ponds, and rain barrels/cisterns - were considered based on volumetric efficiency and runoff reduction efficiency. The study reveals that implementing the proposed NbS could have substantially reduced flood depths in key historical flood events by 21% in 1993, 15% in 2008, 16% in 2011, 23% in 2014, 40% in 2019, and 10% in 2023. The findings highlight a critical trade-off between peak runoff and NbS implementation: while NbS effectively reduce flood impacts, they also enhance volumetric efficiency by approximately 43%. In high-density areas of the QCMA, flood depth reductions of around 20% suggest that NbS are a viable solution for dense urban environments with limited space. This shows the potential for integrating NbS into existing infrastructure, offering a promising approach for cities facing increasing flooding risks. The proposed methodology provides a practical framework for incorporating NbS into urban stormwater management, addressing gaps in optimizing NbS performance, and offering a pathway to scale their application in other urban areas with various environmental and social contexts.

CMIP6↗
DOE OSTI2024

Impacts of Topography‐Driven Water Redistribution on Terrestrial Water Storage Change in California Through Ecosystem Responses

Abstract Lateral subsurface flow plays an essential role in sustaining the terrestrial ecosystem, but it is not explicitly represented in most Earth System Models. In this study, we implemented an explicit lateral saturated flow model into the E3SM land model (ELM). The model explicitly describes lateral flow in the saturated zone by representing, for each model grid, an idealized hillslope consisting of five hydrologically connected soil columns. We conducted three model experiments driven by 0.125° atmospheric forcing data during 1980–2015 over California using models of the default ELM, a modified version of ELM to enhance infiltration, and the model with the lateral saturated flow model. The simulated runoff, evapotranspiration, and terrestrial water storage anomaly (TWSA) from the three simulations were evaluated against available observations, and the model explicitly representing lateral flow performs best. The new model produces greater gridcell‐averaged evapotranspiration especially over the mountainous regions with moderate relief and seasonally dry climates. Most importantly, it improves the modeled seasonal variations, interannual variabilities, and the recent decadal decline of TWSA. Many of these improvements can be attributed to the enhanced ecosystem resilience to droughts as demonstrated by transpiration increases caused by lateral flow. Model sensitivity experiments suggest that subsurface runoff is most sensitive to the ratio between horizontal and vertical saturated hydraulic conductivity, followed by hillslope planforms (convergent, divergent, and uniform), number of columns, and lower boundary conditions. Future work should effectively characterize hillslopes in global models and explore the long‐term influences of lateral water movement on modeled biogeochemical cycle.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Fracture Sustainability in Enhanced Geothermal Systems: Experimental and Modeling Constraints

Enhanced geothermal systems (EGS) offer the potential for a much larger energy source than conventional hydrothermal systems. Hot, low-permeability rocks are prevalent at depth around the world, but the challenge of extracting thermal energy depends on the ability to create and sustain open fracture networks. Laboratory experiments were conducted using a suite of selected rock cores (granite, metasediment, rhyolite ash-flow tuff, and silicified rhyolitic tuff) at relevant pressures (uniaxial loading up to 20.7 MPa and fluid pressures up to 10.3 MPa) and temperatures (150–250 °C) to evaluate the potential impacts of circulating fluids through fractured rock by monitoring changes in fracture aperture, mineralogy, permeability, and fluid chemistry. Because a fluid in disequilibrium with the rocks (deionized water) was used for these experiments, there was net dissolution of the rock sample: this increased with increasing temperature and experiment duration. Thermal-hydrological-mechanical-chemical (THMC) modeling simulations were performed for the rhyolite ash-flow tuff experiment to test the ability to predict the observed changes. These simulations were performed in two steps: a thermal-hydrological-mechanical (THM) simulation to evaluate the effects of compression of the fracture, and a thermal-hydrological-chemical (THC) simulation to evaluate the effects of hydrothermal reactions on the fracture mineralogy, porosity, and permeability. Furthermore, these experiments and simulations point out how differences in rock mineralogy, fluid chemistry, and geomechanical properties influence how long asperity-propped fracture apertures may be sustained. Such core-scale experiments and simulations can be used to predict EGS reservoir behavior on the field scale.

15 GEOTHERMAL ENERGY↗
DOE OSTI2022

Forest regeneration within Earth system models: current process representations and ways forward

Earth system models must predict forest responses to global change in order to simulate future global climate, hydrology, and ecosystem dynamics. These models are increasingly adopting vegetation demographic approaches that explicitly represent tree growth, mortality, and recruitment, enabling advances in the projection of forest vulnerability and resilience, as well as evaluation with field data. To date, simulation of regeneration processes has received far less attention than simulation of processes that affect growth and mortality, in spite of their critical role maintaining forest structure, facilitating turnover in forest composition over space and time, enabling recovery from disturbance, and regulating climate-driven range shifts. Here, our critical review of regeneration process representations within current Earth system vegetation demographic models reveals the need to improve parameter values and algorithms for reproductive allocation, dispersal, seed survival and germination, environmental filtering in the seedling layer, and tree regeneration strategies adapted to wind, fire, and anthropogenic disturbance regimes. These improvements require synthesis of existing data, specific field data-collection protocols, and novel model algorithms compatible with global-scale simulations. Vegetation demographic models offer the opportunity to more fully integrate ecological understanding into Earth system prediction; regeneration processes need to be a critical part of the effort.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Foundational Dataset for Developing Large-Sample Stream Temperature Models in the Conterminous United States

This dataset provides inputs, evaluation results, and trained weights from a large-sample Long Short-Term Memory (LSTM) model designed to predict daily stream temperatures across unregulated river reaches in the conterminous United States (CONUS). It includes dynamic meteorological and hydrologic forcings, static physiographic attributes, and model outputs from cross-validation experiments spanning 300 basins. It supports reproducible modeling, direct application for new basins, and provides data suitable for integration with reservoir and river simulations under current and future climates. It contains two .zip files described below · RQ-AI_runs.zip: Model outputs from 10-fold cross-validation experiments, including observed and predicted daily stream temperatures, along with test performance metrics for water years 2017–2019. Two versions are included: 1. Model trained and validated using subbasin-area weighted dynamic features. 2. Model trained and validated using whole-basin area weighted dynamic features. · RQ-AI_inputs.zip: Collection of all formatted dynamic and static predictor datasets (meteorological, hydrologic, and physiographic features) used in model training and analysis. Detailed instructions and data structure is held at the following GitLab repository: https://code.ornl.gov/tempwise/training.

Gomez-Velez, Jesus [Oak Ridge National Laboratory ↗
DOE OSTI2024

Using an Isotope Enabled Mass Balance to Evaluate Existing Land Surface Models

Abstract Land surface models (LSMs) play a crucial role in elucidating water and carbon cycles by simulating processes such as plant transpiration and evaporation from bare soil, yet calibration often relies on comparing LSM outputs of landscape total evapotranspiration ( ET ) and discharge with measured bulk fluxes. Discrepancies in partitioning into component fluxes predicted by various LSMs have been noted, prompting the need for improved evaluation methods. Stable water isotopes serve as effective tracers of component hydrologic fluxes, but data and model integration challenges have hindered their widespread application. Leveraging National Ecological Observation Network measurements of water isotope ratios at 16 US sites over 3 years combined with LSM‐modeled fluxes, we employed an isotope‐enabled mass balance framework to simulate ET isotope values ( δET ) within three operational LSMs (Mosaic, Noah, and VIC) to evaluate their partitioning. Models simulating δET values consistent with observations were deemed more reflective of water cycling in these ecosystems. Mosaic exhibited the best overall performance (Kling‐Gupta Efficiency of 0.28). For both Mosaic and Noah there were robust correlations between bare soil evaporation fraction and error (negative) as well as transpiration fraction and error (positive). We found the point at which errors are smallest ( x ‐intercept of the multi‐site regression) is at a higher transpiration fraction than is currently specified in the models. Which means that transpiration fraction is underestimated on average. Stable isotope tracers offer an additional tool for model evaluation and identifying areas for improvement, potentially enhancing LSM simulations and our understanding of land‐surface hydrologic processes.

58 GEOSCIENCES↗
DOE OSTI2021

From calibration to parameter learning: Harnessing the scaling effects of big data in geoscientific modeling

Abstract The behaviors and skills of models in many geosciences (e.g., hydrology and ecosystem sciences) strongly depend on spatially-varying parameters that need calibration. A well-calibrated model can reasonably propagate information from observations to unobserved variables via model physics, but traditional calibration is highly inefficient and results in non-unique solutions. Here we propose a novel differentiable parameter learning (dPL) framework that efficiently learns a global mapping between inputs (and optionally responses) and parameters. Crucially, dPL exhibits beneficial scaling curves not previously demonstrated to geoscientists: as training data increases, dPL achieves better performance, more physical coherence, and better generalizability (across space and uncalibrated variables), all with orders-of-magnitude lower computational cost. We demonstrate examples that learned from soil moisture and streamflow, where dPL drastically outperformed existing evolutionary and regionalization methods, or required only ~12.5% of the training data to achieve similar performance. The generic scheme promotes the integration of deep learning and process-based models, without mandating reimplementation.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Can Simple Machine Learning Tools Extend and Improve Temperature-Based Methods to Infer Streambed Flux?

Temperature-based methods have been developed to infer 1D vertical exchange flux between a stream and the subsurface. Current analyses rely on fitting physically based analytical and numerical models to temperature time series measured at multiple depths to infer daily average flux. These methods have seen wide use in hydrologic science despite strong simplifying assumptions including a lack of consideration of model structural error or the impacts of multidimensional flow or the impacts of transient streambed hydraulic properties. We performed a “perfect-model experiment” investigation to examine whether regression trees, with and without gradient boosting, can extract sufficient information from model-generated subsurface temperature time series, with and without added measurement error, to infer the corresponding exchange flux time series at the streambed surface. Using model-generated, synthetic data allowed us to assess the basic limitations to the use of machine learning; further examination of real data is only warranted if the method can be shown to perform well under these ideal conditions. We also examined whether the inherent feature importance analyses of tree-based machine learning methods can be used to optimize monitoring networks for exchange flux inference.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

The relative influences of hydrologic information and dams’ hydropower scheduling decisions on electricity price forecasts

Price dynamics in wholesale electricity markets are driven by supply and demand. In markets with hydroelectric dams, the timing and amount of hydropower offered can influence prices in similar ways to wind and solar power. Unlike variable renewable energy, however, the supply of hydropower in wholesale markets is a function of both water availability and operational decisions at dams. Dam operators maximize revenues in wholesale markets by aligning generation with the periods of highest expected prices, and these scheduling decisions may in turn influence prices. Here, we examine the relative importance of two types of information in predicting forward electricity prices: a) water availability at dams, in the form of short-to-medium-range hydrological forecasts; and b) hourly scheduling decisions at dams. Using softly coupled hydrologic, hydropower scheduling, and power systems models spanning the U.S. Western Interconnection, we quantify the importance of hydrologic forecast accuracy in correctly predicting wholesale electricity prices and compare this with the influence of dam operators’ own hourly scheduling decisions on realized market prices. We find that aligning hydropower generation schedules with the periods of high forecasted prices causes larger, inadvertent price forecast errors than imperfect hydrologic forecasts. This suggests that knowledge of how water is managed by dam operators within the week is more important than weekly inflow forecast errors when predicting forward electricity prices. Our findings have implications for optimal hydropower scheduling by region. Specifically, accounting for price effects is critical in markets dominated by hydropower capacity.

Electricity markets↗
DOE OSTI2023

Model Inputs, Outputs, and Scripts associated with: “Combined effects of stream hydrology and land use on basin-scale hyporheic zone denitrification in the Columbia River Basin”

This data package is associated with the publication “Combined effects of stream hydrology and land use on basin‐scale hyporheic zone denitrification in the Columbia River Basin”, published in Water Resource Research (Son et al.2022) available at https://doi.org/10.1029/2021WR031131. This data package includes the key model inputs/outputs of the river corridor model for the Columbia River Basin (CRB) and the model source codes used in the manuscript. The model is a carbon-nitrogen-coupled river corridor model (RCM), and the model is used to quantify hyporheic zone (HZ) denitrification at the NHDPLUS stream reach scales. The RCM used in this study combines empirical substrate models derived from observations and three microbially driven reactions, including two-step denitrification and aerobic respiration, are considered within the HZ. The key input data of the model are exchange flux, residence time, and stream solute (dissolved organic carbon (DOC), dissolved oxygen (DO), and nitrate concentrations). These inputs are constant over time and represent long-term averaged values. This study uses the RCM to explore the spatial patterns of HZ denitrification across reaches with different sizes and land use in the CRB. Our main objective is to use the RCM as a virtual reality model, and the machine-learning models as surrogates that encapsulate the complexities of the physics-based model while identifying the importance of different variables that are not evident in the model conceptualization. We do not include a direct comparison of the modeled HZ denitrification and measurements; however, the RCM can capture the overall spatial patterns of the HZ denitrification because the model inputs and its reaction networks are based on well-established theory and a physical-based model. The combination of the model-based predictions and a machine-learning approach (e.g., random forest) is used to improve our understanding of what variables of the model are associated with spatial patterns of the modeled denitrification across reaches with different sizes and land uses, and to develop a proxy model using measurable variables to reproduce the simulated patterns.This dataset contains five folders: (1) model_inputs, (2) model_outputs, (3) Rscripts, (4) figures, and (5) model_codes. It also contains a readme, file level metadata (FLMD), and data dictionary (dd). Please see the FLMD for a list of all the files contained in this data package and descriptions for each. The model_inputs folder contains the model inputs used to drive the model simulations. The model_outputs folder contains key model output files from the river corridor model. The Rscripts folder contains the Rscripts for pre- and post- processing model results. The figures folder contains the raw figures associated with the manuscript. The model_codes folder includes key model source codes/input files. All files are .jpg, .jpeg, .out, .e, .od, .dat, .sub, .F90, .0, .R, .sbx, .cpg, .sbn, .shx, .shp, .dbf, .prj, .tfw, .tif, .xml, .pdf, or .csv.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Applying a Multisector Scenario Framework to Evaluate Past and Future Public Surface Water Supply Infrastructure Strategies in Texas

Datasets supporting the index model and scenario analysis used in evaluating surface water supply strategies across different water system types in Texas. These data underpin the scenario development and application of five key indicators: Water Availability Index (WAI), Water Quality Index (WQI), Energy Requirement Index (ERI), Water Treatment Cost (WTC), and Water Infrastructure Cost (WIC). The datasets are organized by system type—stream reaches (flowlines), waterbodies, and reservoirs—and include both raw and standardized index values. The integrated datasets also provide scenario classifications (original and adjusted) based on infrastructure and planning priorities, enabling comparison across Shared Socioeconomic Pathways (SSPs). Additional strategy-level data are included to support evaluation of state-level new reservoir projects in relation to cost and availability tradeoffs. Please refer to the README file provided in Files for more details. Descriptions of the datasets are provided below. Dataset(s) Descriptions Folder: Index_model_database.zip Subfolder: Stream_reach.zip Fl_wf.csv, Fl_wq.csv, Fl_er.csv, Fl_wf_wtcUV.csv, Fl_wf_wtcnoUV.csv, Fl_allfac_wic1.csv, Fl_allfac_wic2.csvDatasets for computing WAI, WQI, ERI, WTC, and WIC for surface water systems classified as stream reaches (flowlines). Subfolder: Waterbody.zip Wb_wf.csv, Wb_wq.csv, Wb_er.csv, Wb_wf_wtcUV.csv, Wb_wf_wtcnoUV.csv, Wb_allfac_wic1.csv, Wb_allfac_wic2.csvEquivalent index model datasets for waterbodies, reflecting hydrologic and infrastructure attributes specific to impounded natural systems. Subfolder: Reservoir.zip Rs_wf.csv, Rs_wq.csv, Rs_er.csv, Rs_wf_wtcUV.csv, Rs_wf_wtcnoUV.csv, Rs_allfac_wic1.csv, Rs_allfac_wic2.csvIndex model datasets specific to regulated reservoir systems, incorporating both resource indicators and cost parameters. Folder: Integrated data.zip combined_merged_data.csv, combined_merged_data_scenario.csvDatasets integrating index model indicators (both raw and scaled) with scenario classifications, including adjustments reflecting SSP-aligned transitions and planning shifts. Folder: Additional data.zip wai_supplystrat_wic_merged.csvCurated dataset capturing proposed major reservoir-based municipal water supply strategies in Texas. Integrates site-level planning data with estimated capital infrastructure costs and water availability scores for comparative assessment.

geospatial↗
DOE OSTI2022

Process Interactions Can Change Process Ranking in a Coupled Complex System Under Process Model and Parametric Uncertainty

For a complex hydrologic system with multiple processes and process interactions, global sensitivity analysis is often used to identify important or influential parameters for model development and improvement. The identification is complicated by process model uncertainty, when a system process can be represented by multiple process models. This study develops a new total-effect process sensitivity index to identify influential processes under model uncertainty. This is done by extending Sobol's total-effect parameter sensitivity index for one system model to total-effect process sensitivity index for multiple system models to account for uncertainty in process models and model parameters. The total-effect process sensitivity index includes not only the first-order process sensitivity index for measuring the importance of individual processes but also higher-order indices that account for process interactions. The total-effect process sensitivity index can identify an influential process that itself and its interactions with other processes influence a model output. Here, the total-effect process sensitivity index is applied to two numerical examples: (a) Sobol's G*-functions with analytical solutions of first-order and total-effect process sensitivity indices, and (b) groundwater flow models with interactions between recharge, geology, and snowmelt processes. The second evaluation shows that, due to second-order and higher-order process interactions, the first-order and total-effect process sensitivity indices give different process ranking. It is thus necessary to estimate both first-order and total-effect process sensitivity indices to appreciate the difference between the first-order impact of a process alone and the overall total-effect impact of the process itself and its interactions with other processes on a model output.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

The impact of forest-controlled snow variability on late-season streamflow varies by climatic region and forest structure

Previous studies have documented how forests influence snow at fine spatial scales, but none have documented the influence that existing forest-snow variability has on streamflow. Here to test how much forest-controlled snow variability influences streamflow, a tiling parameterization based on classifications from high-resolution (1–3 m) vegetation maps was incorporated into the Distributed Hydrology and Soil Vegetation Model (DHSVM). Within each grid cell (90–150 m), the tiling parameterization simulated forest-snow variability with four independently evolving snowpacks. Each tile had unique radiation conditions to represent conditions underneath the canopy, in exposed areas, and along north- and south-facing forest edges. This tiled parameterization was used to test where and when detailed forest-snow modelling should be considered further and where and when the impacts are too small to be worth the effort. To test this, tiled model simulations of streamflow were compared to non-tiled model simulations in the Sierra Nevada, CA, the Jemez Mountains, NM, and the Eastern Cascades, WA. In Tuolumne, CA, the tiled model simulated little difference in grid cell average SWE, and late-season streamflow decreased by only 3%–4% compared to the non-tiled model. In Jemez, NM, the tiled model decreased late-season streamflow by 18% due to increased sublimation. In Chiwawa, WA, the tiled model increased late-season streamflow by 15% due to high shortwave radiation attenuation and less longwave radiation enhancement from the forest. Furthermore, within the Chiwawa, a substantial silvicultural practice was synthetically implemented to increase the north-facing edge's fractional area. This silvicultural experiment, which used the same fractional forest area in all simulations increased late-season streamflow by 35% compared to tiled model simulations that did not represent forest edges. In conclusion, representing forest-SWE variability had an effect on late-season streamflow in some watersheds but not in others based on the fractional area of the forest edges, forest characteristics, and climate conditions.

54 ENVIRONMENTAL SCIENCES↗