Geology topics

Research about Central Valley

Source-linked reports with geographic coverage including Central Valley.

At least 37 records · Page 2Linked to original sources

USGS2020

Phytoremediation of slightly brackish, polycyclic aromatic hydrocarbon‐contaminated groundwater from 250 ft below land surface: A pilot‐scale study using salt‐tolerant, endophyte‐enhanced hybrid poplar trees at a Superfund site in the Central Valley of California, April‒November 2019

Slightly brackish groundwater contaminated by polycyclic aromatic hydrocarbons (PAHs) at a Superfund site in the Central Valley of California was pumped from 250 feet below land surface to a water storage tank using solar power and then gravity‐fed into 18, 330‐gallon intermediate bulk containers (totes) as follows: (1) Five totes contained planting medium with three salt‐tolerant hybrid poplar trees per tote ( n = 15); (2) Seven totes contained planting medium with three salt‐tolerant hybrid poplar trees per tote and inoculated with the naturally occurring, PAH‐degrading endophyte Pseudomonas putida PD1 ( n = 21); (3) Three totes contained planting medium only ( n = 0); (4) One tote contained groundwater with three PD1‐inoculated trees ( n = 3) and one tote contained groundwater with three regular trees ( n = 3); and (5) One tote contained groundwater only ( n = 0). All trees grew well during the 7‐month growing season in spite of the area's hot, dry air temperature, little precipitation, tote‐influent chloride concentrations of 290 mg/L, and tote‐influent naphthalene concentrations that ranged from 650 to 5100 mg/L. PD1‐inoculated trees initially had 56% larger tree area (tree height × tree width) than regular trees and up to 69% larger tree area by the end of the growing season, indicating some conferred phytoprotection to the PAH contamination. All trees had similar trunk caliper (diameter) and leaf chlorophyll content by the end of the growing season. Total naphthalene removal ranged from 88% to 100% across all totes. The lowest naphthalene removal of 88% was observed in a tote that contained only planting medium and indicates substantial adsorption of naphthalene onto the high organic content of the planting medium. Contaminant removal due to uptake by the hybrid poplar trees was confirmed by the detection of naphthalene in in vivo passive samplers placed in tree trunks. Benzene, toluene, ethylbenzene, total xylenes, 2‐methylnaphthalene, 1,2,4‐trimethylbenzene, and isopropylbenzene were also detected. These results from the pilot‐scale study indicate that a full‐scale application of using salt‐tolerant hybrid poplar trees at this site could effectively decrease naphthalene concentrations in groundwater pumped from the deep aquifer. These initial results provide hope for similar application at other contaminated sites characterized by groundwater at considerable depths, especially at Superfund sites where costly pump‐and‐treat systems have been used long term to treat low levels of groundwater contamination.

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USGS2020

Quantifying drought’s influence on moist soil seed vegetation in California’s Central Valley through time-series remote sensing

Californias Central Valley, USA is a critical component of the Pacific Flyway despite loss of more than 90% of its wetlands. Moist soil seed (MSS) wetland plants are now produced by mimicking seasonal flooding in managed wetlands to provide an essential food resource for waterfowl. Managers need MSS plant area and productivity estimates to support waterfowl conservation, yet this remains unknown at the landscape scale. Also the effects of recent drought on MSS plants have not been quantified. We generated Landsat-derived estimates of extents and productivity (seed yield or its proxy, the green chlorophyll index) of major MSS plants including watergrass (Echinochloa crusgalli) and smartweed (Polygonum spp.) (WGSW), and swamp timothy (Crypsis schoenoides) (ST) in all Central Valley managed wetlands from 20072017. We tested the effects of water year, land ownership and region on plant area and productivity with a multifactor nested analysis of variance. For the San Joaquin Valley we explored the association between water year and water supply, and we developed metrics to support management decisions. MSS plant area maps were based on a support vector machine classification of Landsat phenology metrics (2017 map overall accuracy: 89%). ST productivity maps were created with a linear regression model of seed yield (n=68, R2 = 0.53, normalized RMSE = 10.5%). The Central Valley-wide estimated area for ST in 2017 was 32,369 ha 2,524 ha (95% C.I.), and 13,012 ha 1,384 ha for WGSW. Mean ST seed yield ranged from 577 kg/ha in the Delta Basin to 365 kg/ha in the San Joaquin Basin. WGSW area and ST seed yield decreased while ST area increased in critical drought years compared to normal water years (Scheffes test, p<0.05). Greatest ST area increases occurred in the Sacramento Valley (~75%). Voluntary water deliveries increased in normal water years, and ST seed yield increased with water supply. Z-scores of ST seed yield can be used to evaluate wetland performance and aid resource allocation decisions. Updated maps will support habitat monitoring, conservation planning and water management in future years, which are likely to face greater uncertainty in water availability with climate change.

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USGS2020

Quantifying drought’s influence on moist soil seed vegetation in California’s Central Valley through remote sensing

Across the Central Valley of California, millions of wintering waterfowl rely on moist soil seed (MSS) plants that grow in managed seasonal wetlands as a critical source of food. Estimates of MSS plant production are used to set waterfowl habitat targets yet this information is not well known. We created the first Central Valley-wide time series maps of MSS plant distributions and productivity. We found that MSS plant seed yield declined in critical drought years, which corresponded with reduced water delivery to managed wetlands. Our results provide improved food resource estimates and information to help managers prioritize actions as water supply becomes more uncertain with climate change.

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USGS2020

Linking nest microhabitat selection to nest survival within declining pheasant populations in the Central Valley of California

Context: The ring-necked pheasant ( Phasianus colchicus ) has experienced considerable population declines in recent decades, especially in agricultural environments of the Central Valley of California. Although large-scale changes in land cover have been reported as an important driver of population dynamics, the effects of microhabitat conditions on specific demographic rates (e.g. nesting) are largely unknown. Aims: Our goal was to identify the key microhabitat factors that contribute to wild pheasant fitness by linking individual-level selection of each microhabitat characteristic to the survival of their nests within the California Central Valley. Methods: We radio- or GPS-marked 190 female ring-necked pheasants within five study areas and measured nest-site characteristics and nest fates during 2013–2017. Specifically, we modeled microhabitat selection using vegetation covariates measured at nest sites and random sites and then modeled nest survival as a function of selecting each microhabitat characteristic. Key results: Female pheasants tended to select nest sites with greater proportions of herbaceous cover and avoided areas with greater proportions of bare-ground. Specifically, perennial grass cover was the most explanatory factor with regard to nest survival, but selection for increasing visual obstruction alone was not shown to have a significant effect on survival. Further, we found strong evidence that pheasants selecting sites with greater perennial grass height were more likely to have successful nests. Conclusions: Although pheasants will select many types of vegetation available as cover, our models provided evidence that perennial grasses are more beneficial than other cover types to pheasants selecting nesting sites. Implications: Focusing management actions on promoting perennial grass cover and increased heights at the microsite level, in lieu of other vegetative modifications, may provide improved quality of habitat for nesting pheasants and, perhaps, result in increased productivity. This is especially important if cover is limited during specific times of the nesting period. Understanding how microhabitat selection influences fitness can help land managers develop strategies to increase the sustainability of hunted populations of this popular game-bird species.

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USGS2019

Delineation of spatial extent, depth, thickness, and potential volume of aquifers used for domestic and public water-supply in the Central Valley, California

Identification of the groundwater resources used for drinking-water supplies is essential for the design of strategies to manage those resources. In this study, the spatial extent, depths, thicknesses, and volumes of groundwater aquifers used for domestic and public drinking-water supply were estimated from locations and well-construction data from 11,725 domestic-supply wells and 2,376 public-supply wells in the Central Valley, California. The data were compiled as part of the U.S. Geological Survey National Water Quality Assessment Project and California State Water Resources Control Board Groundwater Ambient Monitoring and Assessment Program Priority Basin Project. The spatial distributions of the depth to top and bottom of well screens were interpolated using Empirical Bayesian Kriging across buffer areas surrounding domestic- and public-supply wells. These surfaces provide a measure of the likely maximum horizontal and vertical extent of the aquifer volume accessed for drinking water in the Central Valley during the past century. Well depth generally increased from north to south, and over time from 1905 to 2010. Well-construction depths were generally more consistent in the Sacramento Valley than in the San Joaquin Valley. The total potential aquifer volume accessed for public supply was calculated to be greater than domestic-supply access, even though the estimated spatial extent of domestic-supply wells was 1.5 times larger than the spatial extent of public-supply wells. Public-supply wells commonly have screen lengths greater than 51 meters, whereas domestic-supply wells typically have shorter screen lengths (overall median of 6 meters). Consequently, the accessed thickness and volume of the aquifer is on average 1.8 and 1.4 times greater for public-supply wells than domestic-supply wells, respectively. Results are presented as maps of areal extent, depth, and thickness of zones in the Central Valley aquifer system used for domestic and public drinking-water supplies.

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USGS2019

Integrating stream gage data and Landsat imagery to complete time-series of surface water extents in Central Valley, California

Accurate monitoring of surface water location and extent is critical for the management of diverse water resource phenomena. The multi-decadal archive of Landsat satellite imagery is punctuated by missing data due to cloud cover during acquisition times, hindering the assembly of a continuous time series of inundation dynamics. This study investigated whether streamflow volume measurements could be integrated with satellite data to fill gaps in monthly surface water chronologies for the Central Valley region of California, USA, from 1984 to 2015. We aggregated measurements of maximum monthly water extent within each of the study area’s 50 8-digit hydrologic unit code [HUC] watersheds from two Landsat-derived datasets: the European Commission’s Joint Research Centre (JRC) Monthly Water History and the U.S. Geological Survey Dynamic Surface Water Extent (DSWE). We calculated Spearman rank correlation coefficients between water extent values in each HUC and streamflow discharge data. Linear regression fits of the water extent/streamflow data pairs with the highest correlations served as the basis for interpolation of missing imagery surface water values on a HUC-wise basis. Results show strong (ρ > 0.7) maximum correlations in 11 (22.4%) and 25 (51.0%) HUCs for the DSWE and JRC time series, respectively, when comparisons were restricted to imagery and gages co-located in each HUC. Strong maximum correlations occurred in 39 (79.6%; DSWE) and 42 (85.7%; JRC) HUCs when imagery was paired with discharge data from any study area gage, providing a solid basis for reconstruction of water extent values. We generated continuous time series of 30+ years in 35 HUCs, demonstrating that this technique can provide quantitative estimates of historical surface water extents and elucidate flooding or drought events over the period of data collection. Results of a non-parametric trend analysis of the long-term time series on an annual, seasonal, and monthly basis varied among HUCs, though most trends indicate an increase in surface water over the past 30 years.

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USGS2019

Human-induced and natural carbon storage in floodplains of the Central Valley of California

Active floodplains can putatively store large amounts of organic carbon (SOC) in subsoils originating from catchment erosion processes with subsequent floodplain deposition. Our study focussed on the assessment of SOC pools associated with alluvial floodplain soils that are affected by human-induced changes in floodplain deposition and in situ SOC mineralisation due to land use change and drainage. We evaluated depth-dependent SOC contents based on 23 soil cores down to 3 m and 10 drillings down to 7 m in a floodplain area of the lower Cosumnes River. An estimate of 266 Mg C ha −1 or about 59% of the entire SOC stored within the 7 m profiles was found in the upper 2 m. Most profiles (n = 25) contained discrete buried A horizons at depths of approximately 0.8 m. These profiles had up to 130% higher SOC stocks. The mean δ 13 C of all deep soil profiles clearly indicated that arable land use has already altered the stable isotopic signature in the first meter of the profile. Radiocarbon dating showed that the 14 C age in the buried horizon was younger than in overlaying soils indicating a substantial sedimentation phase for the overlaying soils. An additional analysis of total mercury contents in the soil profiles indicated that this sedimentation was associated with upstream hydraulic gold mining after the 1850s. In summary, deep alluvial soils in floodplains store large amounts of SOC not yet accounted for in global carbon models. Historic data give evidence that large amounts of sediment were transported into the floodplains of most rivers of the Central Valley and deposited over organically rich topsoil, which promoted the stabilization of SOC, and needs to be considered to improve our understanding of the human-induced interference with C cycling.

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USGS2018

Maps showing predicted probabilities for selected dissolved oxygen and dissolved manganese threshold events in depth zones used by the domestic and public drinking water supply wells, Central Valley, California

The purpose of the prediction grids for selected redox constituents—dissolved oxygen and dissolved manganese—are intended to provide an understanding of groundwater-quality conditions at the domestic and public-supply drinking water depths. The chemical quality of groundwater and the fate of many contaminants is influenced by redox processes in all aquifers, and understanding the redox conditions horizontally and vertically is critical in evaluating groundwater quality. The redox condition of groundwater—whether oxic (oxygen present) or anoxic (oxygen absent)—strongly influences the oxidation state of a chemical in groundwater. The anoxic dissolved oxygen thresholds of <0.5 milligram per liter (mg/L), <1.0 mg/L, and <2.0 mg/L were selected to apply broadly to regional groundwater-quality investigations. Although the presence of dissolved manganese in groundwater indicates strongly reducing (anoxic) groundwater conditions, it is also considered a “nuisance” constituent in drinking water, making drinking water undesirable with respect to taste, staining, or scaling. Three dissolved manganese thresholds, <50 micrograms per liter (µg/L), <150 µg/L, and <300 µg/L, were selected to create predicted probabilities of exceedances in depth zones used by domestic and public-supply water wells. The 50 µg/L event threshold represents the secondary maximum contaminant level (SMCL) benchmark for manganese (U.S. Environmental Protection Agency, 2017; California Division of Drinking Water, 2014), whereas the 300 µg/L event threshold represents the U.S. Geological Survey (USGS) health-based screening level (HBSL) benchmark, used to put measured concentrations of drinking-water contaminants into a human-health context (Toccalino and others, 2014). The 150 µg/L event threshold represents one-half the USGS HBSL. The resultant dissolved oxygen and dissolved manganese prediction grids may be of interest to water-resource managers, water-quality researchers, and groundwater modelers concerned with the occurrence of natural and anthropogenic contaminants related to anoxic conditions. Prediction grids for selected redox constituents and thresholds were created by the USGS National Water-Quality Assessment (NAWQA) modeling and mapping team.

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USGS2018

Waterbird habitat in California's Central Valley basins under climate, urbanization, and water management scenarios

California's Central Valley provides critical, but threatened habitat and food resources for migrating and wintering waterfowl, shorebirds, and other waterbirds. The Central Valley is comprised of nine basins that were defined by the Central Valley Joint Venture (CVJV) to assist in conservation planning. Basins vary in composition and extent of habitats, which primarily include croplands and wetlands that rely on water supplies shared with other competing human and environmental uses. Changes in climate, urban development, and water supply management are uncertain and could reduce future availability of water supplies supporting waterbird habitats and limit effectiveness of wetland restoration planned by the CVJV to support wintering waterbirds. We modeled 17 plausible scenarios including combinations of three climate projections, three urbanization rates, and five water supply management options to promote agricultural and urban water uses, with and without wetland restoration. Our research examines the reduction in quantity and quality of habitats during the fall migration-wintering period by basin under each scenario, and the efficacy of planned wetland restoration to compensate reductions in flooded areas of wetland habitats. Scenario combinations of projected climate, urbanization, and water supply management options reduced availability of flooded cropland and wetland habitats during fall-winter and degraded the quality of seasonal wetlands (i.e., summer-irrigation for improved forage production), though the extent and frequency of impacts varied by basin. Planned wetland restoration may substantially compensate for scenario-related effects on wetland habitats in each basin. However, results indicate that Colusa, Butte, Sutter, San Joaquin, and Tulare Basins may require additional conservation to support summer-irrigation of seasonal wetlands and winter-flooding of cropland habitats. Still further conservation may be required to provide sufficient areas of flooded seasonal and semi-permanent wetlands in San Joaquin and Tulare Basins during fall-winter. The main objective of this research is to provide decision-support for achieving waterbird conservation goals in the valley and to inform CVJV's regional conservation planning.

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USGS2018

Spatial and temporal variability in growth of giant gartersnakes: Plasticity, precipitation, and prey

The growth rate of reptiles is plastic and often varies among individuals, populations, and years in response to environmental conditions. For an imperiled species, the growth rate of individual animals is an important component of demographic models, and changes in individual growth rates might precede changes in abundance. We analyzed a long-term dataset on the growth of Giant Gartersnakes ( Thamnophis gigas ) to characterize spatial and temporal variability and evaluate potential environmental predictors of growth. We collected data on the growth in snout–vent length (SVL) of Giant Gartersnakes over 22 yr (1995–2016) from eight sites distributed throughout the Sacramento Valley of California, USA. The von Bertalanffy growth curves indicated male Giant Gartersnakes grew faster toward shorter, asymptotic SVL than did females. Nearly equal variability in growth was attributable to differences among years and among sites. From 2003–2016 we collected data on precipitation, temperature, and the abundance of fish and anuran prey at each site and used these variables as predictors in growth models of Giant Gartersnakes. Snake growth was positively related to the amount of precipitation that fell during the prior water year and the abundance of anurans at a site. Fish and frog abundance interacted to affect snake growth: at low abundances of one prey type, the other positively affected growth, but the slope of this relationship decreased as alternative prey abundance increased. Our results highlight the plasticity of growth in this threatened snake species, point to potential environmental drivers of growth, and provide valuable data for demographic modeling efforts.

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USGS2018

Serologic surveillance of wild and pen-reared ring-necked pheasants (Phasianus colchicus) as a method of understanding disease reservoirs

We investigated exposure to infectious diseases in wild ( n =33) and pen-reared ( n =12) Ring-necked Pheasants ( Phasianus colchicus ) in the Central Valley of California during 2014 and 2015. Serologic tests were positive for antibodies against hemorrhagic enteritis (HE), infectious bursal disease (IBD), and Newcastle disease (ND) viruses in both wild and pen-reared pheasants.

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USGS2017

Fallow-land Algorithm based on Neighborhood and TemporalAnomalies (FANTA) to map planted versus fallowed croplands usingMODIS data to assist in drought studies leading to water and foodsecurity assessments

An important metric to monitor for optimizing water use in agricultural areas is the amount of cropland left fallowed, or unplanted. Fallowed croplands are difficult to model because they have many expressions; for example, they can be managed and remain free of vegetation or be abandoned and become weedy if the climate for that season permits. We used 250 m, 8-day composite Moderate Resolution Imaging Spectroradiometer normalized difference vegetation index data to develop an algorithm that can routinely map cropland status (planted or fallowed) with over 75% user’s and producer’s accuracies. The Fallow-land Algorithm based on Neighborhood and Temporal Anomalies (FANTA) compares the current greenness of a cultivated pixel to its historical greenness and to the greenness of all cultivated pixels within a defined spatial neighborhood, and is therefore transportable across space and through time. This article introduces FANTA and applies it to California from 2001 to 2015 as a case study for use in data-poor places and for use in historical modeling. Timely and accurate knowledge of the extent of fallowing can provide decision makers with insights and knowledge to mitigate the impacts of drought and provide a scientific basis for effective management response. This study is part of the WaterSMART (Sustain and Manage America’s Resources for Tomorrow) project, an interdisciplinary and collaborative research effort focused on improving water conservation and optimizing water use.

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USGS2017

Predicted pH at the domestic and public supply drinking water depths, Central Valley, California

This scientific investigations map is a product of the U.S. Geological Survey (USGS) National Water-Quality Assessment (NAWQA) project modeling and mapping team. The prediction grids depicted in this map are of continuous pH and are intended to provide an understanding of groundwater-quality conditions at the domestic and public supply drinking water zones in the groundwater of the Central Valley of California. The chemical quality of groundwater and the fate of many contaminants is often influenced by pH in all aquifers. These grids are of interest to water-resource managers, water-quality researchers, and groundwater modelers concerned with the occurrence of natural and anthropogenic contaminants related to pH. In this work, the median well depth categorized as domestic supply was 30 meters below land surface, and the median well depth categorized as public supply is 100 meters below land surface. Prediction grids were created using prediction modeling methods, specifically boosted regression trees (BRT) with a Gaussian error distribution within a statistical learning framework within the computing framework of R ( http://www.r-project.org/ ). The statistical learning framework seeks to maximize the predictive performance of machine learning methods through model tuning by cross validation. The response variable was measured pH from 1,337 wells and was compiled from two sources: USGS National Water Information System (NWIS) database (all data are publicly available from the USGS: http://waterdata.usgs.gov/ca/nwis/nwis ) and the California State Water Resources Control Board Division of Drinking Water (SWRCB-DDW) database (water quality data are publicly available from the SWRCB: http://www.waterboards.ca.gov/gama/geotracker_gama.shtml ). Only wells with measured pH and well depth data were selected, and for wells with multiple records, only the most recent sample in the period 1993–2014 was used. A total of 1,003 wells (training dataset) were used to train the BRT model, and 334 wells (hold-out dataset) were used to validate the prediction model. The training r-squared was 0.70, and the root-mean-square error (RMSE) in standard pH units was 0.26. The hold-out r-squared was 0.43, and RMSE in standard pH units was 0.37. Predictor variables consisting of more than 60 variables from 7 sources were assembled to develop a model that incorporates regional-scale soil properties, soil chemistry, land use, aquifer textures, and aquifer hydrology. Previously developed Central Valley model outputs of textures (Central Valley Textural Model, CVTM; Faunt and others, 2010) and MODFLOW-simulated vertical water fluxes and predicted depth to water table (Central Valley Hydrologic Model, CVHM; Faunt, 2009) were used to represent aquifer textures and groundwater hydraulics, respectively. In this work, wells were attributed to predictor variable values in ArcGIS using a 500-meter buffer. Faunt, C.C., ed., 2009, Groundwater availability in the Central Valley aquifer, California: U.S. Geological Survey Professional Paper 1776, 225 p., accessed at https://pubs.usgs.gov/pp/1766/ . Faunt, C.C., Belitz, K., and Hanson, R.T., 2010, Development of a three-dimensional model of sedimentary texture in valley-fill deposits of Central Valley, California, USA: Hydrogeology Journal, v. 18, no. 3, p. 625–649, https://doi.org/10.1007/s10040-009-0539-7 .

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USGS2017

Body mass, wing length, and condition of wintering ducks relative to hematozoa infection

Waterfowl managers lack information regarding factors that may be reducing the positive response of waterfowl body condition to habitat improvements. Protozoan blood parasites (i.e., hematozoa) are commonly found in birds and have been related to reduced body mass, wing length, and body condition. We studied relationships between 12 measures of hematozoa infection and body mass, wing length, and body mass divided by wing length (i.e., body condition index [BCI]) of the five most common duck species (northern pintail [ Anas acuta ], mallard [ A. platyrhynchos ], green-winged teal [ A. crecca ], American wigeon [ A. Americana ], northern shoveler [ A. clypeata ]) wintering in the Central Valley of California during October 2006-January 2007. After accounting for variation due to species, age-sex cohort, Central Valley region, and month; wing length, body mass, and BCI were found to be negatively related to infection by Leucocytozoon and by "any hematozoa" but not related to infection by only Plasmodium or Haemoproteus , or coinfections of greater than one genera or parasite haplotype (albeit, few ducks had Plasmodium or Haemoproteus infection or coinfections). Evidence of a negative relationship with infection was stronger for body mass and BCI than for wing length and indicated that the relationships varied among species, age-sex cohorts, regions, and months. Compared to uninfected ducks, hematozoa-infected duck body mass, wing length, and BCI was -1.63% (85% CI = -2.79%- -0.47%), -0.12% (-0.41%- +0.17%), and -1.38% (-2.49%- -0.26%), respectively. Although, seemingly small, the -1.63% difference in body mass represents a large percentage (e.g., 38% for northern pintail) of the observed increase in wintering duck body mass associated with Central Valley habitat improvements. Because infection prevalence and relationship to body condition might change over time due to climate or other factors, tracking hematozoa infection prevalence might be important to inform and accurately assess the effect of conservation programs designed to improve waterfowl body condition.

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USGS2017

Projected impacts of climate, urbanization, water management, and wetland restoration on waterbird habitat in California’s Central Valley

The Central Valley of California is one of the most important regions for wintering waterbirds in North America despite extensive anthropogenic landscape modification and decline of historical wetlands there. Like many other mediterranean-climate ecosystems across the globe, the Central Valley has been subject to a burgeoning human population and expansion and intensification of agricultural and urban development that have impacted wildlife habitats. Future effects of urban development, changes in water supply management, and precipitation and air temperature related to global climate change on area of waterbird habitat in the Central Valley are uncertain, yet potentially substantial. Therefore, we modeled area of waterbird habitats for 17 climate, urbanization, water supply management, and wetland restoration scenarios for years 2006–2099 using a water resources and scenario modeling framework. Planned wetland restoration largely compensated for adverse effects of climate, urbanization, and water supply management changes on habitat areas through 2065, but fell short thereafter for all except one scenario. Projected habitat reductions due to climate models were more frequent and greater than under the recent historical climate and their magnitude increased through time. After 2065, area of waterbird habitat in all scenarios that included severe warmer, drier climate was projected to be >15% less than in the “existing” landscape most years. The greatest reduction in waterbird habitat occurred in scenarios that combined warmer, drier climate and plausible water supply management options affecting priority and delivery of water available for waterbird habitats. This scenario modeling addresses the complexity and uncertainties in the Central Valley landscape, use and management of related water supplies, and climate to inform waterbird habitat conservation and other resource management planning. Results indicate that increased wetland restoration and additional conservation and climate change adaptation strategies may be warranted to maintain habitat adequate to support waterbirds in the Central Valley.

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USGS2017

Prediction and visualization of redox conditions in the groundwater of Central Valley, California

Regional-scale, three-dimensional continuous probability models, were constructed for aspects of redox conditions in the groundwater system of the Central Valley, California. These models yield grids depicting the probability that groundwater in a particular location will have dissolved oxygen (DO) concentrations less than selected threshold values representing anoxic groundwater conditions, or will have dissolved manganese (Mn) concentrations greater than selected threshold values representing secondary drinking water-quality contaminant levels (SMCL) and health-based screening levels (HBSL). The probability models were constrained by the alluvial boundary of the Central Valley to a depth of approximately 300 m. Probability distribution grids can be extracted from the 3-D models at any desired depth, and are of interest to water-resource managers, water-quality researchers, and groundwater modelers concerned with the occurrence of natural and anthropogenic contaminants related to anoxic conditions. Models were constructed using a Boosted Regression Trees (BRT) machine learning technique that produces many trees as part of an additive model and has the ability to handle many variables, automatically incorporate interactions, and is resistant to collinearity. Machine learning methods for statistical prediction are becoming increasing popular in that they do not require assumptions associated with traditional hypothesis testing. Models were constructed using measured dissolved oxygen and manganese concentrations sampled from 2767 wells within the alluvial boundary of the Central Valley, and over 60 explanatory variables representing regional-scale soil properties, soil chemistry, land use, aquifer textures, and aquifer hydrologic properties. Models were trained on a USGS dataset of 932 wells, and evaluated on an independent hold-out dataset of 1835 wells from the California Division of Drinking Water. We used cross-validation to assess the predictive performance of models of varying complexity, as a basis for selecting final models. Trained models were applied to cross-validation testing data and a separate hold-out dataset to evaluate model predictive performance by emphasizing three model metrics of fit: Kappa; accuracy; and the area under the receiver operator characteristic curve (ROC). The final trained models were used for mapping predictions at discrete depths to a depth of 304.8 m. Trained DO and Mn models had accuracies of 86–100%, Kappa values of 0.69–0.99, and ROC values of 0.92–1.0. Model accuracies for cross-validation testing datasets were 82–95% and ROC values were 0.87–0.91, indicating good predictive performance. Kappas for the cross-validation testing dataset were 0.30–0.69, indicating fair to substantial agreement between testing observations and model predictions. Hold-out data were available for the manganese model only and indicated accuracies of 89–97%, ROC values of 0.73–0.75, and Kappa values of 0.06–0.30. The predictive performance of both the DO and Mn models was reasonable, considering all three of these fit metrics and the low percentages of low-DO and high-Mn events in the data.

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USGS2017

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

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 500 m 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.

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USGS2017

Modeling waterfowl habitat selection in the Central Valley of California to better understand the spatial relationship between commercial poultry and waterfowl

Wildlife researchers frequently study resource and habitat selection of wildlife to understand their potential habitat requirements and to conserve their populations. Understanding wildlife spatial-temporal distributions related to habitat have other applications such as to model interfaces between wildlife and domestic food animals in order to mitigate disease transmission to food animals. The highly pathogenic avian influenza (HPAI) virus represents a significant risk to the poultry industry. The Central Valley of California offers a unique geographical confluence of commercial poultry and wild waterfowl, which are thought to be a key reservoir of avian influenza (AI). Therefore, understanding spatio-temporal distributions of waterfowl could improve our understanding of potential risk of HPAI exposure from a commercial poultry perspective. Using existing radio-telemetry data on waterfowl (U.S. Geological Survey) in combination with habitat and vegetation data based on Geographic Information Systems (GIS), we are developing GIS-based statistical models that predict the probability of waterfowl presence (Habitat Suitability Mapping). Near-real-time application can be developed using recent habitat data derived from Landsat imagery (acquired by satellites and publicly available through the U.S. Geological Survey) to predict temporally- and spatially-varying distributions of waterfowl in the Central Valley. These results could be used to provide decision support for the poultry industry in addressing potential risk of HPAI exposure related to waterfowl proximity.

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