Berkeley Lab

New study holds implications for future water supply in the Colorado River Basin

Monsoon rain in the East River, Colorado. Cover Image of Geophysical Research Letters, Volume 47, Issue 23. Image credit: Xavier Fane.

An SFA study led by Rosemary Carroll (Desert Research Institute) found that where rain falls within a Colorado River headwater basin strongly effects whether that rain makes it to the stream.

Co-authored by SFA collaborator David Gochis (NCAR) and SFA Field deputy Ken Williams (LBNL), the study suggests that in a warmer future, summer rains are likely to produce less streamflow, adding to water challenges caused by decreasing snowpack. Read more »

Stunning Visuals Tell a Fluid Story of Water in the Upper Gunnison River Basin

Jeremy Snyder is a photographer and science communicator interested in sharing science in ways that allow people to see and understand the world in new ways. At the time of writing, Jeremy has accepted a position with the communications team at Berkeley Lab’s Molecular Foundry. (image and caption credit: Jeremy Snyder)

As part of a DOE Science Undergraduate Laboratory Internship (SULI), Jeremy Snyder authored “Rocky Mountain Water: The stories of Natural, Impacted, and Managed water in the Upper Gunnison River Basin”. Using the ArcGIS StoryMaps platform and stunning visuals, the story focuses on the Colorado Upper Gunnison River Basin—home to the Watershed Function SFA’s study site, the East River Watershed. See the full story here »

For more of Jeremy’s work, see his personal website.

This work was supported in part by the U.S. Department of Energy, Office of Science, Office of Workforce Development for Teachers and Scientists (WDTS) under the Science Undergraduate Laboratory Internship (SULI) program, under the supervision of Ken Williams, Watershed Function SFA Field Deputy.

Watershed Function at AGU 2020

Researchers from the Watershed Function SFA are thoroughly engaged in this year’s 2020 AGU fall meeting, which will take place virtually December 1 – 17.

17 Talks (2 invited)
19 posters (1 invited)
Chairing 4 special sessions
Leading 1 workshop

A complete listing of the SFA’s 2020 AGU activities is available here.

According to AGU, “AGU20 Fall Meeting will be one of the world’s largest virtual scientific conferences, with exciting programming and events. This will be our most diverse, engaging and dynamic Fall Meeting to date.”

East River-Based Surface Atmosphere Integrated Field Laboratory (SAIL) Campaign Sets in Motion

This August 23, 2020, picture from the Rocky Mountain Biological Laboratory (RMBL) in Gothic, Colorado, looks west toward Gothic Mountain, which rises up over more than 1 kilometer over the East River, and shows the area’s complex terrain. Caption credit: DOE-ARM. Photo credit: Dan Feldman, Lawrence Berkeley National Laboratory

Earlier this year, the Department of Energy Atmospheric Radiation Measurement (DOE-ARM) program announced that their next mobile facility deployment would be coming to East River, CO, complementing research conducted by the Watershed Function SFA and its extended network of collaborators.

Most recently, ARM published a progress update provided by PI Dan Feldman (Berkeley Lab). Read more »

Differential Concentration-Discharge (C-Q) analysis: A new approach to identifying contaminant hot spots along stream segments

A comparison of traditional C-Q patterns for nitrate across individual stations and differential C-Q approach across the upstream and downstream reaches encompassing those stations. The C-Q patterns show a consistent L-shaped pattern for nitrate across all three stations of the East River Catchment. In comparison, differential C-Q shows gains in nitrate in the upstream reach and losses in the downstream reach during high gains in discharge.

An easy-to-use C-Q approach has been developed that can account for gains, losses and/or fractional solute turnover over each stream segment. This new approach is found to yield a better accounting of the specific sources, hillslope contributions and critical stream segments that can adversely impact river water quality than traditional approaches.

The differential C- Q analysis is a valuable tool for assessing differences across stream reaches, comparing accumulation and mobilization of harmful chemicals within and across reaches, and monitoring solute behavior in the face of hydrologic and climatic perturbations. This approach can therefore aid watershed and land managers in identifying the stream segments that are essential to monitor and for designing pollution prevention/intervention strategies.


Concentration-discharge (C-Q) relationships are often used to describe how water moves through streams and the chemicals that are transported with it. These relationships are typically examined at individual sampling stations, which do not provide sufficient information about accumulation or mobilization of harmful chemicals, pesticides or other solutes. In this study, we present a new differential C-Q approach that can capture the increase, decrease, and/or the fractional solute turnover over each stream segment. To evaluate and compare this differential approach with traditionally-used approaches, water quality data collected at the East River CO watershed was used. The traditional C-Q patterns showed a consistent L-shaped pattern for nitrate across three stations of the East River watershed. In comparison, differential C-Q approach showed gains in nitrate in the upstream reach and losses in the downstream reach during high gains in discharge. In contrast to nitrate, gains in phosphate, organic carbon, molybdenum and several other solutes were observed in the downstream reach due to its low-relief, meandering terrain. In this manner, the new C-Q approach clearly indicated when and where small increases in nutrients like phosphorus and nitrate can be particularly concerning given the potential for algal growth and eutrophication. Overall, the differential C-Q approach holds potential for aiding water quality managers in the identification of critical stream reaches that assimilate harmful chemicals.


B. Arora, M. Burrus, M. Newcomer, C. I. Steefel, R. W. H. Carroll, D. Dwivedi, W. Dong, K. H. Williams, and S. S. Hubbard (2020), Differential CQ Analysis: A New Approach to Inferring Lateral Transport and Hydrologic Transients within Multiple Reaches of a Mountainous Headwater Catchment. Front. Water 2: 24, DOI: 10.3389/frwa .2020.00024

Machine learning-based zonation for understanding snow, plant, and soil moisture dynamics within a mountain ecosystem

ML-identified zones associated with co-varied dynamics of snow, plant and soil moisture, and microtopographic features

In the headwater catchments of the Rocky Mountain region, plant dynamics are largely influenced by snow accumulation and melting as well as water availability. The key properties – snow coverage, soil moisture and plant productivity – are highly heterogeneous in mountainous terrains. This study identifies the spatiotemporal patterns in co-varied snow, plant and soil moisture dynamics associated with microtopography based on high-resolution satellite imagery and unsupervised machine learning.

The results of this study show that unsupervised leaning methods can reduce the dimensionality of time-lapse images effectively, and identify spatial regions – a group of pixels – that have similar snow-plant dynamics (based on Normalized Difference Vegetation Index) as well as their association with key topographic features as well as soil moisture. This cluster-based analysis can tractably analyze high-resolution time-lapse images to examine plant-soil-snow interactions, guide sampling and sensor placements, and identify areas likely vulnerable to ecological change in the future.


In the headwater catchments of the Rocky Mountain region, plant productivity and its dynamics are largely influenced by water availability. Understanding and quantifying the interactions between snow, plants, and soil moisture has been challenging, since these interactions are highly heterogeneous in mountainous terrain, particularly as they are influenced by microtopography within a hillslope. This study investigates the relationships among topography, snowmelt, soil moisture, and plant dynamics in the East River watershed, Crested Butte, Colorado, based on a time series of 3-meter resolution PlanetScope Normalized Difference Vegetation Index (NDVI) images. To make use of a large volume of high-resolution time-lapse images, this study uses unsupervised machine learning methods to identify the spatial zones that have characteristic NDVI time series, and to reduce the dimensionality of time lapse images into spatial zones. Results show that the identified zones are associated with snow-plant dynamics and microtopographic features. In addition, soil moisture probe and sensor data confirm that each zone has a unique soil moisture distribution. This cluster-based analysis can tractably analyze high-resolution time-lapse images to examine plant-soil-snow interactions, guide sampling and sensor placements, and identify areas likely vulnerable to ecological change in the future.


Devadoss, J., N. Falco, B. Dafflon, Y. Wu, M. Franklin, A. Hermes, E.-L. S. Hinckley, and H. Wainwright (2020), Remote Sensing-Informed Zonation for Understanding Snow, Plant and Soil Moisture Dynamics within a Mountain Ecosystem, Remote Sensing, 12(17), 2733, DOI: 10.3390/rs12172733.

Varadharajan presents at Virtual Aquatic Data/Open Science Summit

SFA Data Management co-lead Charuleka Varadharajan presented at a Virtual Summit “Incorporating Data Science and Open Science Techniques in Aquatic Research” on July 23, 2020. The summit was intended to discuss how aquatic researchers work with big data, develop new modeling frameworks, develop tools and software for the larger community, and apply their work for natural resource management and monitoring purposes.

Dr. Varadharajan’s talk focused on how the SFA and her related DOE Early Career Project deal with management and integration of complex data, including the SFA’s end-end field-data framework and workflow. The talk also highlights the SFA’s co-design approach that enables the 3-way exchange between observations, models and analytics to address science questions or hypotheses about watershed behavior. The presentation is an example of SFA researchers’ leadership in the broader data science and environmental cyberinfrastructure community.

Systematic integration of emerging technologies into watershed network and collaboration strategies hold potential to significantly advance predictive understanding of watershed hydrobiogeochemical behavior

Systematic integration of emerging technologies into watershed network and collaboration strategies hold potential to significantly advance predictive understanding of watershed hydrobiogeochemical behavior.

This invited commentary documented how the emerging technologies listed above are starting to advance key elements important for predicting watershed hydrobiogeochemical behavior, including watershed characterization, data and informatics, and modeling. The commentary also described and recommended a systematic community development of co-design strategies, whereby the emerging technologies could seamlessly weave together characterization, data and modeling capabilities across scales, enabling two-way, near-real time feedback between observation and modeling systems.

While society depends on watersheds for clean water, energy, agricultural productivity and other benefits, state-of-the-art scientific tools are not yet regularly used to underpin resource management. Recent advances in emerging technologies – together with instrumented watershed observatories, open-science principles and new modes of collaboration – offer significant potential to transform our ability to address complex scientific questions, develop generalizable insights, and propel accurate yet tractable approaches to predict watershed hydrobiogeochemical behavior. As resource managers struggle to make increasingly difficult decisions in the coming decades, we hope that the concepts described in this commentary will mobilize the scientific enterprise toward the systematic developments needed to provide actionable information over space and time scales useful for such decisions.


Several emerging technologies are now starting to reveal their promise for greatly enhancing the predictive understanding of watershed hydro-biogeochemical behavior, including machine learning and artificial intelligence, exascale computing, 5G wireless communications, and cloud data storage and compute capacity. We describe a co-design strategy to unify diverse characterization, data and simulation capabilities, allowing near real-time, autonomous communication and feedback between modelling and field observation systems. Paired with watershed observatory networks, open science principles, and radical collaboration strategies, the co-design strategies are expected to enable rapid progress on challenging scientific questions, such as: how do different types of watersheds respond to different stressors, such as climate change, droughts, floods, wildfire, and land-use? How will multiple stressors impact sustainability of municipal, industry, food, and energy systems that rely on water? Can generalizable metrics of resilience be identified and tracked? What is the minimum but sufficient amount of information needed to predict watershed behavior at temporal and spatial scales critical for underpinning resource management decisions? While systematic incorporation of emerging technologies and adoption of new modes of collaboration will require substantial coordination, resources and commitment to overcome technical, social, and organizational barriers, we are encouraged by the many recent efforts focused on advancing collaborations and tools across watershed communities, observatories, and government agencies.


Hubbard, S.S., C. Varadharajan, Y. Wu, H. Wainwright and D. Dwivedi, Emerging technologies and radical collaboration to advance predictive understanding of watershed hydrobiogeochemistry, Hydrological Processes, 35, DOI: 10.1002/hyp.13807

Watershed Function SFA Hosts its first Virtual Mini-retreat

The Watershed Function SFA hosted its first-ever virtual mini-retreat. Using Zoom, 90 team members, collaborators, and invited guests engaged in two half-day sessions of live and recorded presentations, virtual breakouts, and discussions by video and text.

A subset of the 25+ pre-recorded “pop-up” presentations featuring Watershed SFA research updates are featured below. For some meeting snapshots, see here.


Effects of variation in changing climate and snowmelt date on Evapotranspiration in the East River Watershed, Colorado

Shale bedrock variability across the watershed scale

Baseflow Age Distributions in a Headwater Mountain Stream

Snowmelt Microbial Ecology Across Chemical, Molecular, and Spatial Scales

Rates of bedrock weathering and N fluxes including N2O emission

Quantifying dynamic surface-subsurface interactions using RTM along a riparian corridor

Hysteresis patterns of watershed nitrogen retention and loss


Simulating groundwater-streamflow connections in the Upper Colorado River Basin

A series of hypothetical numerical experiments with and without lateral groundwater flow is performed to isolate the role of lateral groundwater flow which are often over-simplified in traditional hydrologic model. Results show that that peak flows increase up to 57% when lateral groundwater flow processes are included.

The Upper Colorado River Basin (UCRB) is subject to climate extremes that pose a challenge to simulate and predict. Open questions remain about the correct model physical parameterization for accurate simulation of large, headwaters systems with a mix of terrain. Results show that including lateral groundwater flow component not only improving simulation accuracy but also quantifying the role of subsurface hydrological processes.

Stream flow time series for two representative stations in the Upper Colorado River Basin. Blue lines are observed daily flow, red and green lines are simulated flows from lateral and no lateral simulations respectively.


The Upper Colorado River Basin (UCRB) is subject to climate extremes that pose a challenge to simulate and predict. Open questions remain about the correct model physical parameterization for accurate simulation of large, headwaters systems with a mix of terrain. This study focusses on the role of lateral groundwater flow on total annual and peak streamflow in the UCRB using a physical hydrology model, ParFlow. Results for the simulated water year of 1983 (the high snowmelt year that almost destroyed the Glen Canyon Dam), suggest an increase in peak flow of up to 57% when lateral groundwater flow processed are included. This is an unexpected result for flood conditions, which are generally assumed to be independent of groundwater. Including lateral groundwater flow produces 25% to 50% more the larger downstream rivers in the domain and 20% to 40% less flow in headwater streams. These changes are mainly driven by hydraulic gradients in the subsurface which are often over-simplified in traditional hydrology models and can only be captured when lateral flow is considered. Lastly, the authors find that the impact of including lateral groundwater flow on the simulated flows exceeds the impact of an order of magnitude change in hydraulic conductivity. While the results focus on the UCRB, the authors feel that these simulations have relevance to other headwater systems worldwide.


Tran, H., J. Zhang, J.-M. Cohard, L. E. Condon, and R. M. Maxwell (2020), Simulating Groundwater-Streamflow Connections in the Upper Colorado River Basin, Groundwater, 58(3), 392-405, DOI: 10.1111/gwat.13000.