Numerical simulation of the effects of groundwater withdrawal and injection of high-salinity water on salinity and groundwater discharge, Kaloko-Honokōhau National Historical Park, Hawaiʻi

Delwyn S. Oki

doi:10.3133/sir20215004

Home/Archive/USGS

USGS · sir20215004

Numerical simulation of the effects of groundwater withdrawal and injection of high-salinity water on salinity and groundwater discharge, Kaloko-Honokōhau National Historical Park, Hawaiʻi

Delwyn S. Oki

View original source ↗Read source document ↗Export metadata ↓

Abstract

Kaloko-Honokōhau National Historical Park (KAHO) is located on the west coast of the island of Hawaiʻi and contains water resources exposed in fishponds, anchialine pools, and marine waters that are cultural resources and that provide habitat for threatened, endangered, and other culturally important native species. KAHO’s water resources are sustained by and dependent on groundwater discharge. In 1978, the year of KAHO authorization, the lands immediately surrounding KAHO were undeveloped and zoned for conservation purposes; at present, most surrounding lands are either developed or zoned for industrial, commercial, or residential use. Urbanization of the North Kona District has increased the need for additional drinking and nonpotable (irrigation) water. Because KAHO’s water resources may be affected by existing and proposed groundwater withdrawals and injections of high-salinity water in the surrounding area, the U.S. Geological Survey, in cooperation with the National Park Service, undertook this study to refine the understanding of how groundwater withdrawals and injection of high-salinity water may affect KAHO’s water resources. Changes in KAHO water resources, in terms of changes in salinity and groundwater discharge, were modeled for selected scenarios of groundwater withdrawal and high-salinity water injection in the aquifer. The numerical model was developed using the model code SUTRA, which accounts for density-dependent flow and salinity transport, and included the coastal-confined groundwater system beneath the coastal freshwater-lens system. Model results indicate that withdrawal of additional groundwater from the coastal freshwater-lens system will affect the salinity of KAHO’s anchialine pools, which provide habitat for the endangered orangeblack Hawaiian damselfly ( Megalagrion xanthomelas ). The magnitude of the effect is dependent on the amount and location of the withdrawal. Greater withdrawal rates cause greater increases in salinity in KAHO, other factors being equal. For a given withdrawal rate, the greatest increase in salinity in KAHO is associated with wells withdrawing groundwater in an area inland of KAHO and the least increase in salinity is associated with wells near the coast. Model results also indicate that withdrawal of additional groundwater from the coastal freshwater-lens system will affect the groundwater discharge, in terms of the freshwater component (water with zero salinity) of the discharge, through KAHO. Greater withdrawal rates cause greater reductions in freshwater discharge through KAHO. For a given withdrawal rate, the greatest reduction in freshwater discharge through KAHO is associated with wells near the north boundary of KAHO and the least reduction is associated with wells near the coast to the north and south of KAHO. Injection of high-salinity water that is denser than ocean water can affect the salinity of damselfly habitat in KAHO, with the magnitude of the effect dependent on the location of the injection. Model results indicate that salinity may either increase or decrease in the anchialine pools that provide damselfly habitat in KAHO, depending on the site of injection. Injection inland of KAHO and at sites immediately north and south of KAHO causes a simulated decrease in salinity at the damselfly habitat, whereas injection farther north and south of KAHO causes an increase in salinity. Injection of high-salinity water also causes a reduction in freshwater discharge through KAHO, with the greatest reduction associated with distant injection wells to the north and south of KAHO and the least reduction associated with wells located near and immediately inland from KAHO. The numerical groundwater models developed for this study have a number of limitations. Lack of understanding of the subsurface geology constrains the ability to accurately model the groundwater-flow system. The models developed for this study are nonunique, cannot account for local-scale heterogeneities in the aquifer, and contain uncertainties related to recharge, boundary conditions, assigned parameter values in the model, and representations of the different hydrogeological features. Confidence in model results can be improved by addressing these and other limitations. In spite of these limitations, the three-dimensional numerical model developed for this study provides a useful conceptual understanding of the potential effects of additional withdrawals and injections on groundwater resources in KAHO. Further evaluation of the ecologic effects of the simulated changes in groundwater quality and quantity in KAHO is needed but is beyond the scope of this study.

Explore related subjects

Scientific Investigations Report Report

Source-reported bounding extent: 19.66829132832601° to 19.69350614042769° latitude; -156.03761672973633° to -156.01186752319336° longitude. This indicates report coverage, not an exact sampling location. View area on OpenStreetMap.

Keep this discovery

Explore connections, maps & timelines

BibTeX RIS

Delwyn S. Oki. 2021-03-15. Numerical simulation of the effects of groundwater withdrawal and injection of high-salinity water on salinity and groundwater discharge, Kaloko-Honokōhau National Historical Park, Hawaiʻi. https://doi.org/10.3133/sir20215004

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related USGS reports

USGS2026

Karst hydrologic processes in carbonate fensters of the western Great Smoky Mountains, Tennessee, 2017–18

The geology of the Great Smoky Mountains in Tennessee and North Carolina is dominated by metamorphic strata. Along the western extent of the range, however, several carbonate fensters expose the Ordovician Jonesboro Limestone along the western edge of the Great Smoky Fault. The fensters (Cades Cove, Tuckaleechee Cove, and Wear Cove) create opportunities for allogenic recharge at the contact between the surrounding, relatively insoluble, strata and the underlying soluble formations, resulting in the development of karst systems. Karst environments have a high degree of connectivity between surface streams and groundwater systems; thus, karst aquifers are inherently vulnerable to contamination from surface activities. Karst environments also provide a specific environment for sensitive biota. Protection of the unique resources of karst environments requires an understanding of the behavior of karst aquifers, knowledge of karst recharge areas, and the mapping of groundwater flow paths. A study began in 2017 to characterize the hydrologic behavior of the karst systems in Great Smoky Mountains National Park through hydrologic monitoring, dye tracing, and seepage investigations. Hydrologic monitoring equipment was installed at several karst hydrogeologic features: two in-cave sites, two major springs, Abrams Creek in Cades Cove, and a karst depression wetland. Dye tracing was conducted to determine resurgences for the cave systems, to delineate spring recharge areas, and to estimate karst groundwater velocities. Seepage investigations along Abrams Creek helped determine if streams were sinking into the Jonesboro Limestone, either at the contact with insoluble strata or as streams flowed across the carbonate fenster. Study results indicate considerable differences in groundwater behavior between the conduit-dominated flow systems in Tuckaleechee Cove compared to the lower-gradient, colluvium-covered karst systems in Cades Cove. Study results also indicate that contaminants in the area of Bull sink and Whiteoak Sink could be rapidly transported through karst groundwater towards springs in Tuckaleechee Cove. Karst resources in Cades Cove are most likely to be affected by local events or activities taking place within the cove itself or in upper Abrams Creek upstream of the Great Smoky Fault. This study provides information on the vulnerability and uniqueness of the karst systems in the western Great Smoky Mountains, which are characterized by complex geology, high-gradient subterranean streams, deep caves, and carbonate fensters.

Tennessee↗

USGS2026

Analysis of groundwater geochemistry in the Lansing, Michigan, area

Groundwater resources in the Lansing, Michigan, area are protected by a wellhead protection plan, but previous studies have indicated that groundwater in this area may be susceptible to surface contamination. The U.S. Geological Survey, in cooperation with the Lansing Board of Water & Light, analyzed geochemical and aquifer characteristics to assess the potential for contamination of groundwater resources. Recharge age categories were determined from tritium concentrations for some wells, and by using ternary diagrams and principal component analysis for other wells. These age classifications, combined with hydrogeologic and geochemical data, were analyzed by using statistical methods to identify trends that may indicate areas of increased groundwater susceptibility. Wells with modern recharge tended to have higher sodium, chloride, and chloride:bromide ratios than did wells with mixed recharge, suggesting greater surface connectivity and potential influence from anthropogenic sources such as road salt. In contrast, wells in the mixed age category exhibited more stable geochemistry and lower constituent concentrations. Classification and regression tree models identified aquifer geometry—particularly the thickness of the uppermost bedrock unit and presence of confining layers—as key predictors of recharge age and geochemical indicators. The trends identified from these analyses could help to reveal locations in the Lansing area that are among the most susceptible to contamination.

Michigan↗

USGS2026

Evaluating the number of realizations (train-test splits) necessary to ensure robust estimates of conventional hydrothermal resource favorability using Monte Carlo cross-validation, the Great Basin, Western USA

Supervised machine learning approaches used to produce favorability maps in resource assessments commonly partition an input dataset into training and testing subsets (that is, through train-test splits). However, if a single split is not representative of underlying resource conditions, the resulting favorability map may be biased, particularly with sparse or heterogeneous datasets. To mitigate the potential for unrepresentative favorability maps, recent works by U.S. Geological Survey researchers have used Monte Carlo cross-validation to implement a large number ( n) of randomized train-test splits to produce n realizations. The realizations are then aggregated using cell-wise median predictions to produce the resulting final favorability map, recognizing that the median will remove extreme values that might have been estimated from a comparatively few unrepresentative realizations. Theoretically, the median prediction at every cell converges to a single estimate as n approaches infinity, and the question addressed herein is: How many realizations are necessary to get a robust estimate (that is, an estimate unaffected by additional realizations)? Therefore, convergence metrics are described to identify the number of train-test splits and realizations needed to make a reliable map using the data and supervised machine learning workflow for conventional hydrothermal resource assessment of the Great Basin of the Western United States. For this study, robust prediction occurs with n approximately equal to 600 realizations, and smaller ensembles (for example, n is approximately equal to 200 or even 100) may be suitable during preliminary or exploratory analyses. Although the resulting threshold numbers will vary depending on the data and modeling approach used, the convergence metrics proposed and applied herein are applicable to other supervised machine learning studies that use Monte Carlo cross-validation.

California, Idaho, Nevada, Oregon, Utah↗