San Joaquin River

A holistic stochastic model for precipitation events

Nature Portfolio (Springer Nature) | February 7th, 2025

In the western United States, much of the annual precipitation falls during relatively few storm events. When precipitation is measured as daily (or hourly, etc.) accumul

In the western United States, much of the annual precipitation falls during relatively few storm events. When precipitation is measured as daily (or hourly, etc.) accumulations, these events appear as sequences of various durations. We describe a trivariate probability distribution and assert that it provides a natural description for the durations, magnitudes, and maximum intensities of such events. We show that this distribution, in its most straightforward application, indeed describes precipitation events composed of daily observations at most long-term weather stations across the western United States, allowing for a flexible assessment of specific event probabilities and return periods with respect to their three defining components. This characterization opens the door to further understanding of risk associated with out-of-sample meteorological events.

A hybrid machine learning model to predict and visualize nitrate concentration throughout the Central Valley aquifer, California, USA

Science of the Total Environment (Elsevier) | June 9th, 2017

Intense demand for water in the Central Valley of California and related increases in groundwater nitrate concentration threaten the sustainability of the groundwater res

Intense demand for water in the Central Valley of California and related increases in groundwater nitrate concentration threaten the sustainability of the groundwater resource. To assess contamination risk in the region, we developed a hybrid, non-linear, machine learning model within a statistical learning framework to predict nitrate contamination of groundwater to depths of approximately 500m below ground surface. A database of 145 predictor variables representing well characteristics, historical and current field and landscape-scale nitrogen mass balances, historical and current land use, oxidation/reduction conditions, groundwater flow, climate, soil characteristics, depth to groundwater, and groundwater age were assigned to over 6000 private supply and public supply wells measured previously for nitrate and located throughout the study area. The boosted regression tree (BRT) method was used to screen and rank variables to predict nitrate concentration at the depths of domestic and public well supplies. The novel approach included as predictor variables outputs from existing physically based models of the Central Valley. The top five most important predictor variables included two oxidation/reduction variables (probability of manganese concentration to exceed 50 ppb and probability of dissolved oxygen concentration to be below 0.5 ppm), field-scale adjusted unsaturated zone nitrogen input for the 1975 time period, average difference between precipitation and evapotranspiration during the years 1971–2000, and 1992 total landscape nitrogen input. Twenty-five variables were selected for the final model for log-transformed nitrate. In general, increasing probability of anoxic conditions and increasing precipitation relative to potential evapotranspiration had a corresponding decrease in nitrate concentration predictions. Conversely, increasing 1975 unsaturated zone nitrogen leaching flux and 1992 total landscape nitrogen input had an increasing relative impact on nitrate predictions. Three-dimensional visualization indicates that nitrate predictions depend on the probability of anoxic conditions and other factors, and that nitrate predictions generally decreased with increasing groundwater age.

A Machine Learning Approach to Predict Groundwater Levels in California Reveals Ecosystems at Risk

Frontiers in Earth Science (Frontiers) | December 17th, 2021

Groundwater dependent ecosystems (GDEs) are increasingly threatened worldwide, but the shallow groundwater resources that they are reliant upon are seldom monitored. In t

Groundwater dependent ecosystems (GDEs) are increasingly threatened worldwide, but the shallow groundwater resources that they are reliant upon are seldom monitored. In this study, we used satellite-based remote sensing to predict groundwater levels under groundwater dependent ecosystems across California, USA. Depth to groundwater was modelled for a 35-years period (1985–2019) within all groundwater dependent ecosystems across the state (n = 95,135). Our model was developed within Google Earth Engine using Landsat satellite imagery, climate data, and field-based groundwater data [n = 627 shallow (< 30 m) monitoring wells] as predictors in a Random Forest model. Our findings show that 1) 44% of groundwater dependent ecosystems have experienced a significant long-term (1985–2019) decline in groundwater levels compared to 28% with a significant increase; 2) groundwater level declines have intensified during the most recent two decades, with 39% of groundwater dependent ecosystems experiencing declines in the 2003–2019 period compared to 27% in the 1985–2002 period; and 3) groundwater declines are most prevalent within GDEs existing in areas of the state where sustainable groundwater management is absent. Our results indicate that declining shallow groundwater levels may be adversely impacting California’s groundwater dependent ecosystems. Particularly where groundwater levels have fallen beneath plant roots or streams thereby affecting key life processes, such as forest recruitment/succession, or hydrological processes, such as streamflow that affects aquatic habitat. In the absence of groundwater monitoring well data, our model and findings can be used to help state and local water agencies fill in data gaps of shallow groundwater conditions, evaluate potential effects on GDEs, and improve sustainable groundwater management policy in California.

A Machine Learning Tool for Design of Behavioral Fish Barriers in the Sacramento-San Joaquin River Delta

U.S. Geological Survey (USGS) | January 16th, 2024

Survival of out-migrating juvenile salmonids (Oncorhynchus spp.) through the Sacramento-San Joaquin River Delta averages less than 33 percent, depending on water flow thr

Survival of out-migrating juvenile salmonids (Oncorhynchus spp.) through the Sacramento-San Joaquin River Delta averages less than 33 percent, depending on water flow through the delta, and is partially governed by the distribution of fish among three Sacramento River distributaries: Sutter, Steamboat, and Georgiana sloughs. Behavioral altering structures in the junctions of the distributaries can effectively increase entrainment into favorable routes, thereby increasing through-delta (Verona to Chips Island, California) survival. The effectiveness of these structures, hence forth called “behavioral barriers,” are dependent on shape, length, location, barrier type, and water velocity, which is governed by Sacramento River discharge (hereinafter referred to as “flow”).

A Marsh Transformed: Historical Ecology and Landscape Change in Suisun Marsh

San Francisco Estuary Institute (SFEI) | December 30th, 2024

A Marsh Transformed: Historical Ecology and Landscape Change in Suisun Marsh is a report for managers to inform landscape-scale, collaborative adaptation planning in the

A Marsh Transformed: Historical Ecology and Landscape Change in Suisun Marsh is a report for managers to inform landscape-scale, collaborative adaptation planning in the Suisun Marsh. Using historical documents such as archival maps, photographs, and narrative accounts, SFEI pieced together a complete picture of landscape conditions in Suisun Marsh in the early 1800s, before European-American colonizers began to transform the land. A Marsh Transformed provides the historical and present-day context that can form the foundation of science-based conservation goals for Suisun. SFEI has undertaken similar efforts in San Francisco Bay and the Delta that led to collaborative regional goals for those ecosystems. SFEI’s aim is for the next stage of work in Suisun to use the content of A Marsh Transformed to run a collaborative process with resource managers, landowners, marsh visitors, and Tribes to crystallize goals for Suisun.

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 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 Path Forward for California’s Freshwater Ecosystems

Public Policy Institute of California (PPIC) | December 2nd, 2019

Californians rely on freshwater ecosystems for many things: water supply, hydropower, recreation, fisheries, flood risk reduction, biodiversity, and more. These ecosystem

Californians rely on freshwater ecosystems for many things: water supply, hydropower, recreation, fisheries, flood risk reduction, biodiversity, and more. These ecosystems—and the social, economic and environmental benefits they provide—are part of the state’s natural infrastructure. But they are changing in undesirable ways in response to water and land use, pollution, introduction of non-native species, and a changing climate. Declines in native biodiversity are the most direct measure of these changes, with numerous species now protected by state and federal endangered species acts (ESAs) and many times more likely to need protection in future.  For the past 40 years, the ESAs have played a prominent role in managing the state’s freshwater ecosystems. While this approach has prevented extinctions, it also places an emphasis on reducing harm to listed species, rather than improving overall ecosystem condition necessary to recover their populations. And these laws are not forward-looking enough to help species adapt to changing climate and reduce future species listings. This approach also fuels controversy and litigation due to perceptions about trade-offs between species protection and economic uses of land and water.  To maintain the benefits that Californians derive from their freshwater ecosystems—and arrest the decline of native biodiversity—a new approach is needed. We recommend that the state adopt the principles and practices of ecosystem-based management. This involves the simultaneous management of water, land, and organisms to achieve a desired ecosystem condition that benefits both native biodiversity and human well-being. The goal of ecosystem-based planning is to develop a shared vision for the ecosystem, agreement upon a common set of facts, and a unified plan to achieve it. We are not proposing major reforms to state or federal endangered species acts. Rather, we recommend a shift in the way these acts are implemented.  Ecosystem-based management relies on robust governance frameworks that are transparent, collaborative, and supported by science and secure funding. Many programs are starting to adopt its principles and practices, but much more needs to be done. Actions needed to achieve ecosystem-based objectives include setting aside water budgets for the environment and using this water to improve ecosystem condition and create multiple benefits. Binding comprehensive agreements between regulatory agencies, stakeholders, and water users—developed as part of sustainable watershed management plans—should guide implementation. These plans can be used to align agency actions and permitting and can be adopted by the State Water Board as water quality control plans.  California needs to change course in how it manages freshwater ecosystems to protect the many beneficial uses they provide. Ecosystem-based management offers a more comprehensive, flexible, and adaptive approach, and one that is compatible with existing laws. We believe this approach is better able to improve ecosystem outcomes that benefit both people and nature and respond to today’s challenges while preparing for an uncertain future. 

A pesticide and iPSC dopaminergic neuron screen identifies and classifies Parkinson-relevant pesticides

Nature Portfolio (Springer Nature) | May 16th, 2023

Parkinson’s disease (PD) is a complex neurodegenerative disease with etiology rooted in genetic vulnerability and environmental factors. Here we combine quantitative ep

Parkinson’s disease (PD) is a complex neurodegenerative disease with etiology rooted in genetic vulnerability and environmental factors. Here we combine quantitative epidemiologic study of pesticide exposures and PD with toxicity screening in dopaminergic neurons derived from PD patient induced pluripotent stem cells (iPSCs) to identify Parkinson’s-relevant pesticides. Agricultural records enable investigation of 288 specific pesticides and PD risk in a comprehensive, pesticide-wide association study. We associate long-term exposure to 53 pesticides with PD and identify co-exposure profiles. We then employ a live-cell imaging screening paradigm exposing dopaminergic neurons to 39 PD-associated pesticides. We find that 10 pesticides are directly toxic to these neurons. Further, we analyze pesticides typically used in combinations in cotton farming, demonstrating that co-exposures result in greater toxicity than any single pesticide. We find trifluralin is a driver of toxicity to dopaminergic neurons and leads to mitochondrial dysfunction. Our paradigm may prove useful to mechanistically dissect pesticide exposures implicated in PD risk and guide agricultural policy.

A Preliminary Study of the Effects of Water Circulation in the San Francisco Bay Estuary

U.S. Geological Survey (USGS) | July 15th, 1970