Tulare Lake

2023 Annual Water Supply and Demand Assessment Summary Report

California Department of Water Resources (DWR) | September 30th, 2023

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

This annual 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.

2023 Drinking Water Needs Assessment

California State Water Resources Control Board (SWRCB) | April 30th, 2023

The Needs Assessment is comprised of four core components: the Failing Water System List (Failing list), the Risk Assessment, the Cost Assessment, and the Affordability A

The Needs Assessment is comprised of four core components: the Failing Water System List (Failing list), the Risk Assessment, the Cost Assessment, and the Affordability Assessment. Public input that the State Water Board received via workshops held in 2022 and February 2023 helped improve the 2023 Needs Assessment.

2023 Water Resilience Portfolio Progress Report

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

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 Bayesian approach to infer nitrogen loading rates from crop and land-use types surrounding private wells in the Central Valley, California

European Geosciences Union (EGU) | May 7th, 2018

This study is focused on nitrogen loading from a wide variety of crop and land-use types in the Central Valley, California, USA, an intensively farmed region with highÂ

This study is focused on nitrogen loading from a wide variety of crop and land-use types in the Central Valley, California, USA, an intensively farmed region with high agricultural crop diversity. Nitrogen loading rates for several crop types have been measured based on field-scale experiments, and recent research has calculated nitrogen loading rates for crops throughout the Central Valley based on a mass balance approach. However, research is lacking to infer nitrogen loading rates for the broad diversity of crop and land use types directly from groundwater nitrate measurements. Relating groundwater nitrate measurements to specific crops must account for the uncertainty about and multiplicity in contributing crops (and other land uses) to individual well measurements, and for the variability of nitrogen loading within farms and from farm to farm for the same crop type. In this study, we developed a Bayesian regression model that allowed us to estimate land-use-specific groundwater nitrogen loading rate probability distributions for 15 crop and land-use groups based on a database of recent nitrate measurements from 2149 private wells in the Central Valley. The water and natural, rice, and alfalfa and pasture groups had the lowest median estimated nitrogen loading rates, each with a median estimate below 5 kg N ha -1 yr -1. Confined animal feeding operations (dairies) and citrus and subtropical crops had the greatest median estimated nitrogen loading rates at approximately 269 and 65 kg N ha -1 yr-1, respectively. In general, our probability-based estimates compare favorably with previous direct measurements and with mass-balance-based estimates of nitrogen loading. Nitrogen mass-balance-based estimates are larger than our groundwater nitrate derived estimates for manured and non-manured forage, nuts, cotton, tree fruit, and rice crops. These discrepancies are thought to be due to groundwater age mixing, dilution from infiltrating river water, or denitrification between the time when nitrogen leaves the root zone (point of reference for mass-balance derived loading) and the time and location of groundwater measurement.

A conceptual model for floodplains in the Sacramento-San Joaquin Delta

University of California, Davis (UC Davis) | October 3rd, 2012

Floodplains are among the most biologically productive and diverse ecosystems on Earth and they provide significant benefits to society such as attenuation of floodwaters

Floodplains are among the most biologically productive and diverse ecosystems on Earth and they provide significant benefits to society such as attenuation of floodwaters, groundwater recharge, filtration of nutrients and sediments, carbon sequestration, fisheries productivity and recreation.  However, floodplains are also among the most converted and threatened ecosystems.  Floodplain habitats in the Sacramento-San Joaquin Delta, and throughout California’s Central Valley, have been greatly reduced from their historic extent and key processes that create and maintain floodplains, such as flood flows and meander migration, have been greatly altered.  These widespread alterations to habitats and processes have lead to declines in many species’ populations in California’s Central Valley and Delta, creating challenges for both environmental and water management. To address these challenges numerous entities and programs are now focused on restoring floodplains and other Delta habitats.  This paper provides a conceptual model for floodplains that characterizes the key features and identifies the critical processes, drivers, and linkages that allow floodplains to produce a variety of functional outputs of management importance.  These outputs include: (1) the floodplain habitat mosaic, including riparian vegetation and its associated wildlife; (2) spawning and rearing habitat for native fish; and (3) food-web productivity that can support native fish on the floodplain as well as be exported to downstream ecosystems.  The model emphasizes that the production of these outputs from floodplains requires vertical and lateral hydrological connectivity across a broad range of flow conditions.  For example, long-duration flooding in the spring promotes native fish spawning and food-web productivity that benefits native species.

A Conjunctive Use Model for the Tule Groundwater Sub-Basin Area in the Southern-Eastern San Joaquin Valley, California

U.S. Bureau of Reclamation (USBR) | February 4th, 2003

The Tule groundwater sub-basin is an agriculturally-intensive area located in the eastern-central part of the southern San Joaquin Valley, California. Urban and a

The Tule groundwater sub-basin is an agriculturally-intensive area located in the eastern-central part of the southern San Joaquin Valley, California. Urban and agricultural stakeholders in the Tule sub-basin depend on a combination of imported surface water and pumped groundwater to meet their water demands. The water service districts there receive surface water deliveries from the Friant Unit of the Central Valley Project (CVP) (United States Bureau of Reclamation), the State Water Project (SWP) (California Department of Water Resources), the Kings River (United States Army Corps of Engineers), or the Success Reservoir (United States Army Corps of Engineers). All of these surface water sources develop their supplies from run-off and snow melt in the foothills and watersheds of the Sierra Nevada mountain range. The state of California is prone to recurring droughts, some lasting several years. During drought periods, irrigated agriculture depends more heavily on groundwater pumping as surface water supplies are generally less available. To buffer the effects of drought, districts in the Tule sub-basin have cooperatively managed their surface water and groundwater resources conjunctively. During a normal to wet year, excess available surface water supplies (e.g. releases for flood control) are used by some districts to recharge their groundwater reservoirs. However, a prolonged multi-year drought invariably leads to an increased dependence on groundwater pumping and overdraft of the groundwater sub-basin storage. In addition to climate variability, changes in future surface water supplies may also occur due to the passage of the Central Valley Project Improvement Act of 1992, which mandates that 400,000 acre-feet per year of CVP water be released from the Friant Unit into the San Joaquin River for restoration purposes. To better understand the impacts of irrigated agriculture, fluctuating surface water supplies, and groundwater pumping practices on water levels and groundwater storage in the Tule sub-basin area, we developed a GIS-based con- junctive use model to study them. The study area is 541,580 acres in size and contains the entire Tule groundwater sub-basin and parts of the Kaweah and Tulare Lake groundwater sub-basins. The incorporated land in the study area is divided into 26 water service districts: 21 irrigation, water, or public utility districts; 2 major cities; 2 private contractors; and 1 water company. These districts are either completely or partially located within the study area. The study area is further delineated into 9,114 individual land units from a 1985 land use survey of Tulare County. Agriculture is the largest land use, compris- ing 72% of the study area. Native and urban land use comprise 22% and 4% of the study area, respectively. Semi-agricultural and special conditions (i.e. fallow) land use each comprise 1%. Twelve crops account for 95% of the area under agricultural production. Cotton, grain & grass hay, citrus, vineyards, and alfalfa individually represent 20.3, 18.6, 13.6, 13, and 10.3% of the total productive acreage, respectively. Surface water supplies are distributed to the districts and ultimately to the individual land units by a surface water supply system. The surface water supply system in the model is divided into two parts: 1) an inter-district surface water channel network, and 2) an intra-district surface water distribution system. The inter-district channel network consists of the explicitly modeled source and diversion channels which import surface water into the study area and deliver it to individual districts. The intra-district distribution system con- sists of the implicitly modeled district channels (e.g. laterals, ditches, canals, farm turnouts) which deliver surface water to individual land units within each district. The conjunctive use model consists of three loosely-coupled sub-models: 1) a surface water supply (SWS) model, 2) an unsaturated zone water budget (UZWB) model, and 3) a groundwater flow model. The base period of the study covers the fiscal water years of 1970-99. The purpose of the SWS model is to calculate the surface water balance for the source and diversion channels in the inter-district channel network. For each modeled surface water channel, the SWS model computes surface water deliveries from it to each district and conveyance losses from it due to evaporation and channel seepage. The primary model outputs are monthly surface water deliveries to each district and monthly seepage rates from modeled channels. The surface water deliveries became input for the UZWB model. The channel seepage became input for the groundwater flow model as localized aquifer recharge. The allocation of surface water within each district, via the implicitly modeled intra-district surface water distribution system, is estimated by the UZWB model. The total imported surface water for 1970-99 from the CVP and the Success Reservoir are 13,329,262 and 4,653,501 acre-feet (af), respectively. The SWP and the Kings River imported the lesser amounts of 88,625 and 7,332 af, respec- tively. Annual CVP diversions varied from 125,970 af in 1977 to 679,298 af in 1993 with a 30-year annual average of 444,309 af. The Tule River and Pioneer Ditch both receive regulated releases from Success Reservoir. Tule River annual imports varied from 11,034 af in 1977 to 607,154 af in 1983 while the Pioneer Ditch varied from 3,445 af in 1973 to 5,874 af in 1990. The total natural runoff from the Deer Creek and White River from 1970-99 were 703,444 and 219,098 af, respectively. Deer Creek runoff varied from 4,082 af in 1992 to 103,716 af in 1983 while the White River runoff varied from 422 af in 1977 to 37,985 af in 1998. From 1970-99, a total of 15 million af of surface water was applied by the service districts in the study area. The applied surface water varied from a low of 135,482 af in 1977 to a high of 708,293 af in 1996. The Lower Tule River Ir- rigation District and the Delano-Earlimart Irrigation District together account for 59% of the total applied surface water while occupying approximately 40% of the incorporated area in the study area. Over the 30-year base period, an estimated total of 3.5 million af of seepage conveyance loss occurred in all sur- face water channels. Seepage in the Tule River, Deer Creek, and White River accounted for 85% of the total seepage. Total annual seepage varied from a low of 8,128 af in 1977 to 467,084 af in 1983. The UZWB model then calculates the monthly water storage changes in the soil root zone and deep vadose zone of each land unit, where the land unit is the UZWB model scale of resolution. It also models the intra-district surface water distribution system by estimating the monthly allocation of surface water to individual land units within each district. The main model outputs were the recharge to the unconfined aquifer from surface applied water and precipitation, and the groundwater pumping demand from the unconfined and confined aquifers. The recharge and groundwater pumping rates became input for the groundwater flow model. The total annual agricultural and urban consumptive use ranged from 865,800 af in 1970 to 1,246,700 af in 1999. The estimated total pumping ranged from 148,100 af in 1978 to 570,000 af in 1990. As expected, pumping was heaviest during the droughts of 1975-77 and 1987-92, and lightest during the wet years of 1973, 1978, 1982-83, 1995, and 1998. Precipitation totals varied from 177,800 af in 1990 to 974,400 af in 1998. Diffuse recharge from surface applied water ranged from 64,800 af in 1992 to 350,100 af in 1983. The net aquifer recharge for the entire study area was computed by ag- gregating the aquifer recharge and groundwater pumping of each land unit to this scale and adding the contribution to aquifer recharge from channel seepage. The monthly net recharge was then summed to produce a cumulative annual net recharge from 1970 to each fiscal water year from 1971-99. The water balance computed for the entire study area neglects horizontal groundwater inflows and outflows through its vertical boundaries. Groundwater fluxes undoubtedly exist along these boundaries. However, net fluxes are likely small in comparison to the total changes in storage due to vertical stresses applied to the entire study area (e.g. groundwater pumping, evapotranspiration, applied surface water, channel seepage). Horizontal groundwater flow on the inter-land unit and inter-district scales is expected to be more significant. For computing a total water balance, however, we made the simplifying assumption that the study area behaves as a relatively closed system where the net horizontal groundwater inflows through its vertical boundaries are small. Invoking this assumption, we then use the cumulative net recharge as an estimate of the cumulative groundwater storage change in the aquifer system. Ideally, verification of these estimates is performed by comparing them with an objective measure of the study area aquifer storage changes. However, changes in groundwater storage are not directly observable and must always be estimated using non-direct measures. As such, an objec- tive measure for verification does not exist. As an alternative, we compare the water balance model results with those produced by the water-table fluctuation (WTF) method. The trends in cumulative annual groundwater storage changes computed from the water balance and the WTF method from 1970-99 were quite similar. The minimum and maximum differences between them were 2,450 af (1980) and 752,387 af (1991), respectively. From 1970, the maximum amount of ground- water accumulation occurred in the spring of 1987 with the WTF method and the water balance estimating positive storage changes of 1,146,286 and 898,128 af, respectively. The maximum groundwater overdraft occurred in 1993 with the WTF method and the water balance estimating negative storage changes of 1,610,210 and 1,218,566 af, respectively. The 1987 and 1993 fiscal water years marked the beginning and ending of a major 6-year drought in California, respectively. Finally, the groundwater flow model calculates the changes in water levels in the aquifer system subject to transient groundwater recharge and pump- ing stresses. A post-processing routine calculates the cumulative groundwater storage changes over each district and the entire study area for each stress pe- riod. An automated calibration of the groundwater flow model was performed to refine the conceptual model of the hydrogeology and to estimate the spa- tial distributions of the aquifer system horizontal hydraulic conductivity. The calibration period of the groundwater flow model is 1970-85 and the validation period is 1986-99. Three different conceptual models of the aquifer system horizontal hydraulic conductivity, Kh, structure were evaluated in the calibration process: 1) Khas an exponential function of the specific yield, Sy, distribution, 2) Kh as a linear function of the saturated hydraulic conductivity of the soil survey map- ping units, and 3) division of the model domain into square zones of uniform size. The models were calibrated against both spatially distributed hydraulic head targets and cumulative groundwater storage change targets for seven of the largest districts. The discretization of the model domain into uniform square zones provided the most robust Kh structure and produced the most reason- able estimates of hydraulic head and district groundwater storage changes from the three conceptual models over the 1971-85 calibration period. The calibrated model was then used to compute the annual net inter-district groundwater fluxes between adjacent districts. In general, groundwater flux directions were con- sistent with the large-scale hydraulic gradients. Annual inter-district net fluxes between adjacent districts ranged from negligibly small ( < 100 af) to as much as 80,000 af (e.g. net flux from Lower Tule River ID to Pixley ID). Net inter-district fluxes were generally a function of the local transmissivity, the length of the shared border between adjacent districts, and the differences in their surface water supplies.

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 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.