Current Projects

Note: Projects listed here include activities directly funded through Mountain Hydrology LLC as well as academic projects undertaken by Eli Boardman through the University of Nevada, Reno (UNR). Academic projects through UNR demonstrate auxiliary experience of Mountain Hydrology’s lead scientist; these projects have titles in red.

Wyoming Snow Water Supply Forecasting

Wind River Range, Wyoming (Green River and Wind River)

Cooperative Agreement with the Bureau of Reclamation, prime award (total value $1.3M) to Mountain Hydrology LLC with Eli Boardman P.I.

Includes 3 years of airborne lidar surveys (ASO), field surveys, DHSVM-WSF forecasting, and decision support tool development

Close collaboration with the Wind River Water Resources Control Board on the Wind River Indian Reservation

DHSVM Model Development and Application

Actively developing a fork of the Distributed Hydrology Soil Vegetation Model (DHSVM), current versions DHSVM X1 and X2

New functionality: surface-groundwater interactions, stabilized water table flow routing, stabilized albedo, pattern-based snowfall distribution, and more

Proprietary high-dimensional calibration pipeline enables accurate and rapid model deployment, with typical daily NSE of 0.8-0.9 and < 10% yearly error

Glaciers, Snow Drifts, and Streamflow Resilience

Leverage repeat lidar in the Wind River Range, Wyoming (including ASO in-kind contribution to glacier surveys) to map snow and ice storage changes

Quantified contribution of glacier/snowfield net mass loss to summer streamflow across 5 watersheds

Late-summer streamflow will likely remain above-average in deglaciating watersheds due to topographically mediated patterns of snow drifting

Forest Restoration and Water Resources*

*UNR project, Eli Boardman dissertation chapter

Tahoe-Central Sierra region, California and Nevada

Applied DHSVM using outputs from LANDIS-II forest ecosystem model to predict hydrological effects of landscape-scale forest restoration

Restoring the historical disturbance return interval can provide a valuable drought hedge over the coming decades

Differentiable Snow Transport Modeling

First application of differentiable modeling to alpine snow transport

Developed novel neural network application where all parameters have direct physical interpretations

Major alpine snow drifts require long-distance transport, on the order of 1-2 km

Introduced novel “snowshed” maps based on interbasin snow transport (i.e., snow blowing across topographic divides)

Surface-Groundwater Interaction Modeling

Developed fully coupled surface-groundwater interaction scheme for DHSVM

Enables dynamic stream network expansion/contraction, stream network disconnection, losing streams, groundwater recharge, riparian ET, and more

Simulation results closely match field observations of stream network expansion

Subsurface Mediation of Emergent Wet Meadows*

*UNR project, Eli Boardman dissertation chapter

Yosemite National Park, California

Organized and lead week-long geophysics field expedition, resulting in measurements of fracture-zone groundwater exfiltration

Synthesized geophysics data with hyperspectral remote sensing to quantify the potential role of subsurface structure in subsidizing wet meadows after fire

Hydrological Calibration with Land Surface Disturbance*

*UNR project, Eli Boardman dissertation chapter

San Joaquin River, California

Applied multi-objective Bayesian optimization to DHSVM before and after a megafire (2020 Creek Fire)

Exploring importance of land surface representation on hydrological model accuracy after major forest disturbances

Past Projects

Process-Based Snow Data Assimilation

Patented novel method for assimilation of distributed SWE data into physical hydrological models

Process-based assimilation preserves the water mass balance, leveraging the physical model structure to correct errors in snow, groundwater, and streamflow

Demonstrated large reduction in model error compared to open-loop modeling (no assimilation) or direct insertion (industry standard)

Statistical Forecast Uncertainty Attribution*

*UNR project, Eli Boardman dissertation chapter

Tuolumne and Merced rivers, California

Developed Bayesian statistical framework to isolate and attribute sources of seasonal water supply forecast uncertainty

Quantified large improvement in statistical forecast skill using ASO data

Quantified increased forecast uncertainty during snow drought

A Nevada Limited Liability Corporation

Current Projects

Wyoming Snow Water Supply Forecasting

Wind River Range, Wyoming (Green River and Wind River)

Cooperative Agreement with the Bureau of Reclamation, prime award (total value $1.3M) to Mountain Hydrology LLC with Eli Boardman P.I.

Includes 3 years of airborne lidar surveys (ASO), field surveys, DHSVM-WSF forecasting, and decision support tool development

Close collaboration with the Wind River Water Resources Control Board on the Wind River Indian Reservation

DHSVM Model Development and Application

Actively developing a fork of the Distributed Hydrology Soil Vegetation Model (DHSVM), publicly available from GitHub [link]

New functionality: surface-groundwater interactions, stabilized water table flow routing, stabilized albedo, pattern-based snowfall distribution, and more

Experimental implementation of 3-dimensional snow wind transport scheme

Proprietary high-dimensional calibration pipeline enables accurate and rapid model deployment, with typical daily NSE of 0.8-0.9 and < 10% yearly error

Glaciers, Snow Drifts, and Streamflow Resilience

Leverage repeat lidar in the Wind River Range, Wyoming (including ASO in-kind contribution to glacier surveys) to map snow and ice storage changes

Quantified contribution of glacier/snowfield net mass loss to summer streamflow across 5 watersheds

Late-summer streamflow will likely remain above-average in deglaciating watersheds due to topographically mediated patterns of snow drifting

Forest Restoration and Water Resources*

*UNR project, Eli Boardman dissertation chapter

Tahoe-Central Sierra region, California and Nevada

Applied DHSVM using outputs from LANDIS-II forest ecosystem model to predict hydrological effects of landscape-scale forest restoration

Restoring the historical disturbance return interval can provide a valuable drought hedge over the coming decades

Differentiable Snow Transport Modeling

First application of differentiable modeling to alpine snow transport

Developed novel neural network application where all parameters have direct physical interpretations

Major alpine snow drifts require long-distance transport, on the order of 1-2 km

Introduced novel “snowshed” maps based on interbasin snow transport (i.e., snow blowing across topographic divides)

Surface-Groundwater Interaction Modeling

Developed fully coupled surface-groundwater interaction scheme for DHSVM

Enables dynamic stream network expansion/contraction, stream network disconnection, losing streams, groundwater recharge, riparian ET, and more

Simulation results closely match field observations of stream network expansion

Subsurface Mediation of Emergent Wet Meadows*

*UNR project, Eli Boardman dissertation chapter

Yosemite National Park, California

Organized and lead week-long geophysics field expedition, resulting in measurements of fracture-zone groundwater exfiltration

Synthesized geophysics data with hyperspectral remote sensing to quantify the potential role of subsurface structure in subsidizing wet meadows after fire

Hydrological Calibration with Land Surface Disturbance*

*UNR project, Eli Boardman dissertation chapter

San Joaquin River, California

Applied multi-objective Bayesian optimization to DHSVM before and after a megafire (2020 Creek Fire)

Exploring importance of land surface representation on hydrological model accuracy after major forest disturbances

Past Projects

Process-Based Snow Data Assimilation

Patented novel method for assimilation of distributed SWE data into physical hydrological models

Process-based assimilation preserves the water mass balance, leveraging the physical model structure to correct errors in snow, groundwater, and streamflow

Demonstrated large reduction in model error compared to open-loop modeling (no assimilation) or direct insertion (industry standard)

Statistical Forecast Uncertainty Attribution*

*UNR project, Eli Boardman dissertation chapter

Tuolumne and Merced rivers, California

Developed Bayesian statistical framework to isolate and attribute sources of seasonal water supply forecast uncertainty

Quantified large improvement in statistical forecast skill using ASO data

Quantified increased forecast uncertainty during snow drought

A Nevada Limited Liability Corporation