South Lahontan

2023 Water Resilience Portfolio Progress Report

California Natural Resources Agency (CNRA) | October 31st, 2023

2024 Annual Water Supply and Demand Assessment Summary Report

California Department of Water Resources (DWR) | October 1st, 2024

This report summarizes the Department of Water Resources’ (DWR’s) review of Urban Water Suppliers’ Annual Water Shortage Assessment Reports (Annual Shortage Reports

This report summarizes the Department of Water Resources’ (DWR’s) review of Urban Water Suppliers’ Annual Water Shortage Assessment Reports (Annual Shortage Reports) for the State Water Resources Control Board (State Water Board). As directed by the California Water Code (CWC) §10644(c)(1)(B), this summary report includes water shortage information at the supplier level, as well as regional and statewide analyses of water supply conditions.

21st century California drought risk linked to model fidelity of the El Niño teleconnection

Nature Portfolio (Springer Nature) | September 3rd, 2018

Greenhouse gas-induced climate change is expected to lead to negative hydrological impacts for southwestern North America, including California (CA). This includes a decr

Greenhouse gas-induced climate change is expected to lead to negative hydrological impacts for southwestern North America, including California (CA). This includes a decrease in the amount and frequency of precipitation, reductions in Sierra snow pack, and an increase in evapotranspiration, all of which imply a decline in surface water availability, and an increase in drought and stress on water resources. However, a recent study showed the importance of tropical Pacific sea surface temperature (SST) warming and an El Niño Southern Oscillation (ENSO)-like teleconnection in driving an increase in CA precipitation through the 21st century, particularly during winter (DJF). Here, we extend this prior work and show wetter (drier) CA conditions, based on several drought metrics, are associated with an El Niño (La Niña)-like SST pattern. Models that better simulate the observed ENSO-CA precipitation teleconnection also better simulate the ENSO-CA drought relationships, and yield negligible change in the risk of 21st century CA drought, primarily due to wetting during winter. Seasonally, however, CA drought risk is projected to increase during the non-winter months, particularly in the models that poorly simulate the observed teleconnection. Thus, future projections of CA drought are dependent on model fidelity of the El Niño teleconnection. As opposed to focusing on adapting to less water, models that better simulate the teleconnection imply adaptation measures focused on smoothing seasonal differences for affected agricultural, terrestrial, and aquatic systems, as well as effectively capturing enhanced winter runoff.

A brief geologic and hydrologic reconnaissance of the Furnace Creek Wash area, Death Valley National Monument, California

U.S. Geological Survey (USGS) | July 1st, 1964

A comparison of the taxonomic and trait structure of macroinvertebrate communities between the riffles and pools of montane headwater streams

Hydrobiologia (Springer) | May 30th, 2018

Macroinvertebrate community taxonomic and trait structure showed consistent differences between riffles and pools across 12 headwater streams in the Sierra Nevada (Cali

Macroinvertebrate community taxonomic and trait structure showed consistent differences between riffles and pools across 12 headwater streams in the Sierra Nevada (California) even as flows varied from wet to dry years and between seasons. Densities of Ephemeroptera, Plecoptera, Trichoptera, Elmidae, Orthocladiinae and Diamesinae midges, and mites were greater in riffles, whereas Tanypodinae, Chironominae, Sialis, and Pisidium were more abundant in pools. Pools had higher densities but estimated biomass was greater in riffles. Collector-gatherer and micropredator abundances were greater in pools whereas grazers, collector-filterers, and macropredators were more abundant in riffles. Stonefly shredders were most abundant in riffles but some caddis shredders were more abundant in pools. Trait state patterns were related to food resource and physical habitat differences between riffles and pools. Of the distinct pool–riffle differences we found among taxa, only about half conformed to expectations from the literature. Pool and riffle assemblages were most dissimilar at intermediate discharge and converged at low and high flows when one or the other habitat dominated. Bioassessment sampling will need to account for these flow-related differences. Benthic invertebrate communities in these mountain streams clearly differ between pools and riffles, but the relative extent of habitats and biological similarity shift with flow regime.

A geohydrologic reconnaissance of the Saratoga Spring area, Death Valley National Monument, California

U.S. Geological Survey (USGS) | March 1st, 1966

A global poleward shift of atmospheric rivers

American Association for the Advancement of Science (AAAS) | October 11th, 2024

A Guide for Using the Transient Ground-Water Flow Model of the Death Valley Regional Ground-Water Flow System, Nevada and California

U.S. Geological Survey (USGS) | November 4th, 2006

This report is a guide for executing numerical simulations with the transient ground-water flow model of the Death Valley regional ground-water flow system, Nevada and Ca

This report is a guide for executing numerical simulations with the transient ground-water flow model of the Death Valley regional ground-water flow system, Nevada and California using the U.S. Geological Survey modular finite-difference ground-water flow model, MODFLOW-2000. Model inputs, including observations of hydraulic head, discharge, and boundary flows, are summarized. Modification of the DVRFS transient ground-water model is discussed for two common uses of the Death Valley regional ground-water flow system model: predictive pumping scenarios that extend beyond the end of the model simulation period (1998), and model simulations with only steady-state conditions.

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 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.