A hydrological simulation dataset of the Upper Colorado River Basin from 1983 to 2019

Nature Portfolio (Springer Nature) | January 20th, 2022

The Upper Colorado River Basin (UCRB) is one of the most over-allocated basins in the world. It provides water for 40 million people in Colorado and downstream states of

The Upper Colorado River Basin (UCRB) is one of the most over-allocated basins in the world. It provides water for 40 million people in Colorado and downstream states of Arizona, California, Nevada, and Utah. However, the UCRB water capacity is decreasing as a combined result of climate change and anthropogenic activities. Understanding the dynamic of the UCRB water cycle is crucial for long-term and short-term water resources management.

A low-to-no snow future and its impacts on water resources in the western United States

Nature Portfolio (Springer Nature) | October 26th, 2021

Anthropogenic climate change is decreasing seasonal snowpacks globally, with potentially catastrophic consequences on water resources, given the long-held reliance on sno

Anthropogenic climate change is decreasing seasonal snowpacks globally, with potentially catastrophic consequences on water resources, given the long-held reliance on snowpack in water management. In this Review, we examine the changes and trickle-down impacts of snow loss in the western United States (WUS). Across the WUS, snow water equivalent declines of ~25% are expected by 2050, with losses comparable with contemporary historical trends. There is less consensus on the time horizon of snow disappearance, but model projections combined with a new low-to-no snow definition suggest ~35–60 years before low-to-no snow becomes persistent if greenhouse gas emissions continue unabated. Diminished and more ephemeral snowpacks that melt earlier will alter groundwater and streamflow dynamics. The direction of these changes are difficult to constrain given competing factors such as higher evapotranspiration, altered vegetation composition and changes in wildfire behaviour in a warmer world. These changes undermine conventional WUS water management practices, but through proactive implementation of soft and hard adaptation strategies, there is potential to build resilience to extreme, episodic and, eventually, persistent low-to-no snow conditions. Federal investments offer a timely opportunity to address these vulnerabilities, but they require a concerted portfolio of activities that cross historically siloed physical and disciplinary boundaries.

A multi-city COVID-19 forecasting model utilizing wastewater-based epidemiology

Science of the Total Environment (Elsevier) | January 15th, 2025

A multi-scale approach for identification of potential pesticide use sites impacting vernal pool critical habitat in California

Science of the Total Environment (Elsevier) | October 17th, 2022

Spatially explicit ecological risk assessment (ERA) requires estimating the overlap between chemical and receptor distribution to evaluate the potential impacts of exposu

Spatially explicit ecological risk assessment (ERA) requires estimating the overlap between chemical and receptor distribution to evaluate the potential impacts of exposure on nontarget organisms. Pesticide use estimation at field level is prone to error due to inconsistencies between ground-reporting and geospatial data coverage; attempts to rectify these inconsistencies have been limited in approach and rarely scaled to multiple crop types. We built upon a previously developed Bayesian approach to combine multiple crop types for a probabilistic determination of field-crop assignments and to examine co-occurrence of critical vernal pool habitats and bifenthrin application within a 5-county area in California (Madera, Merced, Sacramento, San Joaquin, and Stanislaus counties). We incorporated a multi-scale repeated sampling approach with an area constraint to improve the delineation of field boundaries and better capture variability in crop assignments and rotation schemes. After comparing the accuracy of the spatial probabilistic approach to USDA Census of Agriculture crop acreage data, we found our approach allows more specificity in the combination of crop types represented by the potential application area and improves acreage estimates when compared to traditional deterministic approaches. In addition, our multi-scale sampling scheme improved estimates of bifenthrin acreage variability for co-occurrence analysis and allowed for estimates of crop rotations that were previously uncaptured. Our approach could be leveraged for more realistic, spatially resolved exposure and effects models both in and outside of California.

A multiscale model for El Niño complexity

Nature Portfolio (Springer Nature) | March 11th, 2022

El Niño-Southern Oscillation (ENSO) exhibits diverse characteristics in spatial pattern, peak intensity, and temporal evolution. Here we develop a three-region multiscal

El Niño-Southern Oscillation (ENSO) exhibits diverse characteristics in spatial pattern, peak intensity, and temporal evolution. Here we develop a three-region multiscale stochastic model to show that the observed ENSO complexity can be explained by combining intraseasonal, interannual, and decadal processes. The model starts with a deterministic three-region system for the interannual variabilities. Then two stochastic processes of the intraseasonal and decadal variation are incorporated. The model can reproduce not only the general properties of the observed ENSO events, but also the complexity in patterns (e.g., Central Pacific vs. Eastern Pacific events), intensity (e.g., 10–20 year reoccurrence of extreme El Niños), and temporal evolution (e.g., more multi-year La Niñas than multi-year El Niños). While conventional conceptual models were typically used to understand the dynamics behind the common properties of ENSO, this model offers a powerful tool to understand and predict ENSO complexity that challenges our understanding of the twenty-first century ENSO.

A New Technique for Modeling Land Subsidence Facilitates Better Groundwater Management

Stanford University | December 1st, 2019

Land subsidence – the sudden sinking or gradual settling of Earth’s surface – can occur naturally or be triggered by human activity. One of the most comm

Land subsidence – the sudden sinking or gradual settling of Earth’s surface – can occur naturally or be triggered by human activity. One of the most common human-affected factors is groundwater pumping, a practice that has been steadily increasing due to prolonged periods of drought. Groundwater pumping is so prevalent in some areas that it is severely compromising the quantity and quality of the groundwater as well as the physical structure of the land and aquifer capacity beneath it. Large-scale initiatives for putting water back into the ground, or groundwater recharge, are being implemented in some regions. However, groundwater resource managers still need better information to determine where the land is at greatest risk of subsidence as well as where recharge efforts would be the most effective. Integrating two reliable data gathering sources – helicopter-deployed systems (airborne electromagnetic or AEM technology) that measure electromagnetic fields and satellite-deployed systems (interferometric synthetic aperture radar or InSAR) to measure deformations – offers groundwater managers an improved method for more accurately modeling changes in the land surface related to the pumping and recharge of groundwater. Additionally, the ability to more precisely detect subsidence may offer an early warning system for declining groundwater levels.

A Rapid Assessment Method to Identify Potential Groundwater Flooding Hotspots as Sea Levels Rise in Coastal Cities

Water, Multidisciplinary Digital Publishing Institute (MDPI) | October 25th, 2019

Sea level rise (SLR) will cause shallow unconfined coastal aquifers to rise. Rising groundwater can emerge as surface flooding and impact buried infrastructure, soil beh

Sea level rise (SLR) will cause shallow unconfined coastal aquifers to rise. Rising groundwater can emerge as surface flooding and impact buried infrastructure, soil behavior, human health, and nearshore ecosystems. Higher groundwater can also reduce infiltration rates for stormwater, adding to surface flooding problems. Levees and seawalls may not prevent these impacts. Pumping may accelerate land subsidence rates, thereby exacerbating flooding problems associated with SLR. Public agencies at all jurisdiction levels will need information regarding where groundwater impacts are likely to occur for development and infrastructure planning, as extreme precipitation events combine with SLR to drive more frequent flooding. We used empirical depth-to-water data and a digital elevation model of the San Francisco Bay Area to construct an interpolated surface of estimated minimum depth-to-water for 489 square kilometers along the San Francisco Bay shoreline. This rapid assessment approach identified key locations where more rigorous data collection and dynamic modeling is needed to identify risks and prevent impacts to health, buildings, and infrastructure, and develop adaptation strategies for SLR.

A real-time groundwater management model using data assimilation

American Geophysical Union (AGU) | June 30th, 2011

We apply the proposed management model to the Aquifer Storage and Recovery (ASR) Project of the Las Posas Groundwater Basin in southern California. The adapted nudging al

We apply the proposed management model to the Aquifer Storage and Recovery (ASR) Project of the Las Posas Groundwater Basin in southern California. The adapted nudging algorithm is used to estimate the missing pumping rates of private wells. We then use the estimated pumping rates in a real-time groundwater management model to improve management decisions of the ASR well fields.

A shift from drought to extreme rainfall drives a stable landslide to catastrophic failure

Nature Portfolio (Springer Nature) | February 7th, 2019

The addition of water on or below the earth’s surface generates changes in stress that can trigger both stable and unstable sliding of landslides and faults.

The addition of water on or below the earth’s surface generates changes in stress that can trigger both stable and unstable sliding of landslides and faults. While these sliding behaviours are well-described by commonly used mechanical models developed from laboratory testing (e.g., critical-state soilmechanics and rate-and-state friction), less is known about the field-scale environmental conditions orkinematic behaviours that occur during the transition from stable to unstable sliding. Here we use radarinterferometry (InSAR) and a simple 1D hydrological model to characterize 8 years of stable sliding of the Mud Creek landslide, California, UsA, prior to its rapid acceleration and catastrophic failure on May 20, 2017. Our results suggest a large increase in pore-fluid pressure occurred during a shift from historic drought to record rainfall that triggered a large increase in velocity and drove slip localization, overcoming the stabilizing mechanisms that had previously inhibited landslide acceleration. Given thepredicted increase in precipitation extremes with a warming climate, we expect it to become more common for landslides to transition from stable to unstable motion, and therefore a better assessmentof this destabilization process is required to prevent loss of life and infrastructure.

A Simulation-Optimization Model for Water-Resources Management, Santa Barbara, California

U.S. Geological Survey (USGS) | September 1st, 1998

A simulation-optimization model has been developed for the optimal management of the city of Santa Barbara's water resources during a drought. The model, which links grou

A simulation-optimization model has been developed for the optimal management of the city of Santa Barbara's water resources during a drought. The model, which links ground-water simulation with linear programming, has a planning horizon of 5 years. The objective is to minimize the cost of water supply subject to: water demand constraints, hydraulic head constraints to control seawater intrusion, and water capacity constraints. The decision variables are monthly water deliveries from surface water and ground water. The state variables are hydraulic heads. The drought of 1947–51 is the city's worst drought on record, and simulated surface-water supplies for this period were used as a basis for testing optimal management of current water resources under drought conditions. The simulation-optimization model was applied using three reservoir operation rules. In addition, the model's sensitivity to demand, carryover [the storage of water in one year for use in a later year(s)], head constraints, and capacity constraints was tested.