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Predicting sea-level rise vulnerability of terrestrial habitat and wildlife of the Northwestern Hawaiian Islands

If current climate change trends continue, rising sea levels may inundate low-lying islands across the globe, placing island biodiversity at risk. Recent models predict a rise of approximately one meter (1 m) in global sea level by 2100, with larger increases possible in areas of the Pacific Ocean. Pacific Islands are unique ecosystems home to many endangered endemic plant and animal species. The Northwestern Hawaiian Islands (NWHI), which extend 1,930 kilometers (km) beyond the main Hawaiian Islands, are a World Heritage Site and part of the Papahanaumokuakea Marine National Monument. These NWHI support the largest tropical seabird rookery in the world, providing breeding habitat for 21 species of seabirds, 4 endemic land bird species and essential foraging, breeding, or haul-out habitat for other resident and migratory wildlife. In recent years, concern has grown about the increasing vulnerability of the NWHI and their wildlife populations to changing climatic patterns, particularly the uncertainty associated with potential impacts from global sea-level rise (SLR) and storms. In response to the need by managers to adapt future resource protection strategies to climate change variability and dynamic island ecosystems, we have synthesized and down scaled analyses for this important region. This report describes a 2-year study of a remote northwestern Pacific atoll ecosystem and identifies wildlife and habitat vulnerable to rising sea levels and changing climate conditions. A lack of high-resolution topographic data for low-lying islands of the NWHI had previously precluded an extensive quantitative model of the potential impacts of SLR on wildlife habitat. The first chapter (chapter 1) describes the vegetation and topography of 20 islands of Papahanaumokuakea Marine National Monument, the distribution and status of wildlife populations, and the predicted impacts for a range of SLR scenarios. Furthermore, this chapter explores the potential effects of SLR on wildlife breeding habitats for each island. The subsequent chapter (chapter 2) details a study of the Laysan Island ecosystem, describing a quantitative model that incorporates SLR, storm wave, and rising groundwater inundation. Wildlife, storm, and oceanographic data allowed for an assessment of the phenological and spatial vulnerability of Laysan Island's breeding bird species to SLR and storms. Using remote sensing and geospatial techniques, we estimated topography, classified vegetation, modeled SLR, and evaluated a range of climate change scenarios. On the basis of high-resolution airborne data collected during 2010-11 (root-mean-squared error = 0.05-0.18 m), we estimated the maximum elevation of 20 individual islands extending from Kure Atoll to French Frigate Shoals (range: 1.8-39.7 m) and computed the mean elevation (1.7 m, standard deviation 1.1 m) across all low-lying islands. We also analyzed general climate models to describe rainfall and temperature scenarios expected to influence adaptation of some plants and animals for this region. Outcomes for the NWHI predicted an increase in temperature of 1.8-2.6 degrees Celsius (°C) and an annual decrease in precipitation of 24.7-76.3 millimeters (mm) across the NWHI by 2100. Our models of passive SLR (excluding wave-driven effects, erosion, and accretion) showed that approximately 4 percent of the total land area in the NWHI will be lost with scenarios of +1.0 m of SLR and 26 percent will be lost with +2.0 m of SLR. Some atolls are especially vulnerable to SLR. For example, at Pearl and Hermes Atoll our analysis indicated substantial habitat losses with 43 percent of the land area inundated at +1.0 m SLR and 92 percent inundated at +2.0 m SLR. Across the NWHI, seven islands will be completely submerged with +2.0 m SLR. The limited global ranges of some tropical nesting birds make them particularly vulnerable to climate change impacts in the NWHI. Climate change scenarios and potential SLR impacts presented here emphasize the need for early climate change adaptation and mitigation planning, especially for species with limited breeding distributions and/or ranges restricted primarily to the low-lying NWHI: Cyperus pennatiformis var. bryanii , Black-footed Albatross ( Phoebastria nigripes ), Laysan Albatross ( P. immutabilis ), Bonin Petrel ( Pterodroma hypoleuca ), Gray-backed Tern ( Onychoprion lunatus ), Laysan Teal ( Anas laysanensis ), Laysan Finch ( Telespiza cantans ), and Hawaiian monk seal ( Monachus schauinslandi ). Furthermore, SLR scenarios that include the effects of wave dynamics and groundwater rise may indicate amplified vulnerability to climate change driven habitat loss on low-lying islands. In chapter 2, we incorporated the combined effects of SLR, dynamic wave-driven inundation, and rising groundwater in a quantitative study specifically for the Laysan Island ecosystem. This is the first hydrodynamic model to simulate the combined impacts of SLR and wave-driven inundation in the NWHI. We developed a high-resolution digital elevation model (mean vertical accuracy of 0.32 m) for the island. Then using recent satellite imagery, geospatial models, and historical oceanographic, storm, and biological data we estimated potential inundation extent, habitat loss, and wildlife population impacts for a range of potential SLR scenarios (0.00, +0.50, +1.00, +1.50, and +2.00 m) that may occur over the next century. Additionally, we estimated the carrying capacity of Laysan Island for five species based on the available population monitoring data and described how potential losses in nesting habitat could influence population dynamics for Black-footed Albatross, Laysan Albatross, Red-footed Booby (Sula sula), Laysan Teal, and Laysan Finch. For some other seabird populations (Masked Booby, S. dactylatra ; Brown Booby, S. leucogaster ; Great Frigatebird, Fregata minor ; and Sooty Tern, Onychoprion fuscata ), we used recent colony distribution data, land cover maps, and nesting behavior to estimate potential losses of nesting habitat from SLR and wave-driven inundation. We observed far greater potential impacts of SLR to wildlife with the dynamic wave-driven modeling approach than with the passive modeling approach. Depending on SLR scenario and coastal orientation, during storms under a +2.00 m SLR scenario, the wave-driven inundation model predicted three times more inundation than the passive model (17.2 percent of total terrestrial area versus 4.6 percent, respectively). Large-wave events generally added 1 m of water height to passive inundation surfaces, therefore our dynamic models (during storm events) forecasted comparable inundation extents earlier than passive models. Although wave-driven water levels were highest in the northwest quadrant of Laysan Island, the greatest extent of inundation occurred in the southeast where coastal dunes less than 3 m above mean sea level provide little protection from wave-driven inundation. When wave-driven inundation was included in the SLR model for Laysan Island greater nesting habitat loss and potential impacts on wildlife population dynamics were predicted. The consequences of habitat loss due to SLR may be worse for species with colonies in the wave-exposed coastal zones (for example, Black-footed Albatross) and for populations already near the island's carrying capacity (for example, Laysan Teal). Species whose peak incubation and chick-rearing periods coincide with seasonally high wave heights also will be increasingly vulnerable, especially those species nesting on the ground in areas vulnerable to inundation, such as Gray-backed Tern and Black-footed Albatross. Other species that have space for population growth, or are not restricted to a narrow range of habitat types on Laysan (for instance, Sooty Terns), may be less sensitive to habitat loss from SLR over the next century. Our assessments of inundation risk, habitat loss, and wildlife species vulnerability synthesize current knowledge about individual islands and contribute to a broader understanding of the impacts of inundation from SLR and storm-induced waves. Yet, most NWHI and their bird populations lack monitoring data to evaluate adaptations to and impacts of climate change. Exceptions include some data sets from long-term monitoring of wildlife populations, tides, or weather at French Frigate Shoals, Laysan Island, and Midway Atoll. These data sets are potentially valuable baselines, which could be informative for adaptive learning (integrating management and science) to predict, adapt, and mitigate the effects of climate change on NWHI wildlife in the future. This study provides the first quantitative vulnerability assessment for all of the low-lying NWHI, and results identify biological communities, locales, and resident endangered species of Papahanaumokuakea Marine National Monument expected to be at risk from SLR. This report is also intended as a reference for managers and conservation planners, a tool to identify and potentially reduce uncertainty, and a starting place for developing climate change monitoring priorities and future scientific studies.

Hawai'i↗

Mineral-deposit model for lithium-cesium-tantalum pegmatites

Lithium-cesium-tantalum (LCT) pegmatites comprise a compositionally defined subset of granitic pegmatites. The major minerals are quartz, potassium feldspar, albite, and muscovite; typical accessory minerals include biotite, garnet, tourmaline, and apatite. The principal lithium ore minerals are spodumene, petalite, and lepidolite; cesium mostly comes from pollucite; and tantalum mostly comes from columbite-tantalite. Tin ore as cassiterite and beryllium ore as beryl also occur in LCT pegmatites, as do a number of gemstones and high-value museum specimens of rare minerals. Individual crystals in LCT pegmatites can be enormous: the largest spodumene was 14 meters long, the largest beryl was 18 meters long, and the largest potassium feldspar was 49 meters long. Lithium-cesium-tantalum pegmatites account for about one-fourth of the world’s lithium production, most of the tantalum production, and all of the cesium production. Giant deposits include Tanco in Canada, Greenbushes in Australia, and Bikita in Zimbabwe. The largest lithium pegmatite in the United States, at King’s Mountain, North Carolina, is no longer being mined although large reserves of lithium remain. Depending on size and attitude of the pegmatite, a variety of mining techniques are used, including artisanal surface mining, open-pit surface mining, small underground workings, and large underground operations using room-and-pillar design. In favorable circumstances, what would otherwise be gangue minerals (quartz, potassium feldspar, albite, and muscovite) can be mined along with lithium and (or) tantalum as coproducts. Most LCT pegmatites are hosted in metamorphosed supracrustal rocks in the upper greenschist to lower amphibolite facies. Lithium-cesium-tantalum pegmatite intrusions generally are emplaced late during orogeny, with emplacement being controlled by pre-existing structures. Typically, they crop out near evolved, peraluminous granites and leucogranites from which they are inferred to be derived by fractional crystallization. In cases where a parental granite pluton is not exposed, one is inferred to lie at depth. Lithium-cesium-tantalum LCT pegmatite melts are enriched in fluxing components including H2O, F, P, and B, which depress the solidus temperature, lower the density, and increase rates of ionic diffusion. This, in turn, enables pegmatites to form thin dikes and massive crystals despite having a felsic composition and temperatures that are significantly lower than ordinary granitic melts. Lithium-cesium-tantalum pegmatites crystallized at remarkably low temperatures (about 350–550 °C) in a remarkably short time (days to years). Lithium-cesium-tantalum pegmatites form in orogenic hinterlands as products of plate convergence. Most formed during collisional orogeny (for example, Kings Mountain district, North Carolina). Specific causes of LCT pegmatite-related magmatism could include: ordinary arc processes; over thickening of continental crust during collision or subduction; slab breakoff during or after collision; slab delamination before, during, or after collision; and late collisional extensional collapse and consequent decompression melting. Lithium-cesium-tantalum pegmatite deposits are present in all continents including Antarctica and in rocks spanning 3 billion years of Earth history. The global age distribution of LCT pegmatites is similar to those of common pegmatites, orogenic granites, and detrital zircons. Peak times of LCT pegmatite genesis at about 2640, 1800, 960, 485, and 310 Ma (million years before present) correspond to times of collisional orogeny and supercontinent assembly. Between these pulses were long intervals when few or no LCT pegmatites formed. These minima overlap with supercontinent tenures at ca. 2450–2225, 1625–1000, 875–725, and 250–200 Ma. Exploration and assessment for LCT pegmatites are guided by a number of observations. In frontier areas where exploration has been minimal at best, the key first-order criteria are an orogenic hinterland setting, appropriate regional metamorphic grades, and the presence of evolved granites and common granitic pegmatites. New LCT pegmatites are most likely to be found near known deposits. Pegmatites tend to show a regional mineralogical and geochemical zoning pattern with respect to the inferred parental granite, with the greatest enrichment in the more distal pegmatites. Mineral-chemical trends in common pegmatites that can point toward an evolved LCT pegmatite include: increasing rubidium in potassium feldspar, increasing lithium in white mica, increasing manganese in garnet, and increasing tantalum and manganese in columbite-tantalite. Most LCT pegmatite bodies show a distinctive internal zonation featuring four zones: border, wall, intermediate (where lithium, cesium, and tantalum are generally concentrated), and core. This zonation is expressed both in cross section and map view; thus, what may appear to be a common pegmatite may instead be the edge of a mineralized body. Neither lithium-cesium-tantalum pegmatites nor their parental granites are likely to cause serious environmental concerns. Soils and country rock surrounding a LCT pegmatite, as well as waste from mining operations, may be enriched in characteristic elements relative to global average soil and bedrock values. These elements may include lithium, cesium, tantalum, beryllium, boron, fluorine, phosphorus, manganese, gallium, rubidium, niobium, tin, and hafnium. Among this suite of elements, however, the only ones that might present a concern for environmental health are beryllium and fluorine, which are included in the U.S. Environmental Protection Agency drinking-water regulations with maximum contaminant levels of 4 micrograms per liter and 4 milligrams per liter, respectively.

Scientific Investigations Report↗

Description of input and examples for PHREEQC version 3: A computer program for speciation, batch-reaction, one-dimensional transport, and inverse geochemical calculations

PHREEQC version 3 is a computer program written in the C and C++ programming languages that is designed to perform a wide variety of aqueous geochemical calculations. PHREEQC implements several types of aqueous models: two ion-association aqueous models (the Lawrence Livermore National Laboratory model and WATEQ4F), a Pitzer specific-ion-interaction aqueous model, and the SIT (Specific ion Interaction Theory) aqueous model. Using any of these aqueous models, PHREEQC has capabilities for (1) speciation and saturation-index calculations; (2) batch-reaction and one-dimensional (1D) transport calculations with reversible and irreversible reactions, which include aqueous, mineral, gas, solid-solution, surface-complexation, and ion-exchange equilibria, and specified mole transfers of reactants, kinetically controlled reactions, mixing of solutions, and pressure and temperature changes; and (3) inverse modeling, which finds sets of mineral and gas mole transfers that account for differences in composition between waters within specified compositional uncertainty limits. Many new modeling features were added to PHREEQC version 3 relative to version 2. The Pitzer aqueous model ( pitzer.dat database, with keyword PITZER ) can be used for high-salinity waters that are beyond the range of application for the Debye-Hückel theory. The Peng-Robinson equation of state has been implemented for calculating the solubility of gases at high pressure. Specific volumes of aqueous species are calculated as a function of the dielectric properties of water and the ionic strength of the solution, which allows calculation of pressure effects on chemical reactions and the density of a solution. The specific conductance and the density of a solution are calculated and printed in the output file. In addition to Runge-Kutta integration, a stiff ordinary differential equation solver (CVODE) has been included for kinetic calculations with multiple rates that occur at widely different time scales. Surface complexation can be calculated with the CD-MUSIC (Charge Distribution MUltiSIte Complexation) triple-layer model in addition to the diffuse-layer model. The composition of the electrical double layer of a surface can be estimated by using the Donnan approach, which is more robust and faster than the alternative Borkovec-Westall integration. Multicomponent diffusion, diffusion in the electrostatic double layer on a surface, and transport of colloids with simultaneous surface complexation have been added to the transport module. A series of keyword data blocks has been added for isotope calculations— ISOTOPES, CALCULATE_VALUES, ISOTOPE_ALPHAS, ISOTOPE_RATIOS, and NAMED_EXPRESSIONS . Solution isotopic data can be input in conventional units (for example, permil, percent modern carbon, or tritium units) and the numbers are converted to moles of isotope by PHREEQC. The isotopes are treated as individual components (they must be defined as individual master species) so that each isotope has its own set of aqueous species, gases, and solids. The isotope-related keywords allow calculating equilibrium fractionation of isotopes among the species and phases of a system. The calculated isotopic compositions are printed in easily readable conventional units. New keywords and options facilitate the setup of input files and the interpretation of the results. Keyword data blocks can be copied (keyword COPY ) and deleted (keyword DELETE ). Keyword data items can be altered by using the keyword data blocks with the _MODIFY extension and a simulation can be run with all reactants of a given index number (keyword RUN_CELLS ). The definition of the complete chemical state of all reactants of PHREEQC can be saved in a file in a raw data format ( DUMP and _RAW keywords). The file can be read as part of another input file with the INCLUDE$ keyword. These keywords facilitate the use of IPhreeqc, which is a module implementing all PHREEQC version 3 capabilities; the module is designed to be used in other programs that need to implement geochemical calculations; for example, transport codes. Charting capabilities have been added to some versions of PHREEQC. Charting capabilities have been added to Windows distributions of PHREEQC version 3. (Charting on Linux requires installation of Wine.) The keyword data block USER_GRAPH allows selection of data for plotting and manipulation of chart appearance. Almost any results from geochemical simulations (for example, concentrations, activities, or saturation indices) can be retrieved by using Basic language functions and specified as data for plotting in USER_GRAPH . Results of transport simulations can be plotted against distance or time. Data can be added to a chart from tab-separated-values files. All input for PHREEQC version 3 is defined in keyword data blocks, each of which may have a series of identifiers for specific types of data. This report provides a complete description of each keyword data block and its associated identifiers. Input files for 22 examples that demonstrate most of the capabilities of PHREEQC version 3 are described and the results of the example simulations are presented and discussed.

Techniques and Methods↗

Estimating peak-flow frequency statistics for selected gaged and ungaged sites in naturally flowing streams and rivers in Idaho

The U.S. Geological Survey, in cooperation with the Idaho Transportation Department, updated regional regression equations to estimate peak-flow statistics at ungaged sites on Idaho streams using recent streamflow (flow) data and new statistical techniques. Peak-flow statistics with 80-, 67-, 50-, 43-, 20-, 10-, 4-, 2-, 1-, 0.5-, and 0.2-percent annual exceedance probabilities (1.25-, 1.50-, 2.00-, 2.33-, 5.00-, 10.0-, 25.0-, 50.0-, 100-, 200-, and 500-year recurrence intervals, respectively) were estimated for 192 streamgages in Idaho and bordering States with at least 10 years of annual peak-flow record through water year 2013. The streamgages were selected from drainage basins with little or no flow diversion or regulation. The peak-flow statistics were estimated by fitting a log-Pearson type III distribution to records of annual peak flows and applying two additional statistical methods: (1) the Expected Moments Algorithm to help describe uncertainty in annual peak flows and to better represent missing and historical record; and (2) the generalized Multiple Grubbs Beck Test to screen out potentially influential low outliers and to better fit the upper end of the peak-flow distribution. Additionally, a new regional skew was estimated for the Pacific Northwest and used to weight at-station skew at most streamgages. The streamgages were grouped into six regions (numbered 1_2, 3, 4, 5, 6_8, and 7, to maintain consistency in region numbering with a previous study), and the estimated peak-flow statistics were related to basin and climatic characteristics to develop regional regression equations using a generalized least squares procedure. Four out of 24 evaluated basin and climatic characteristics were selected for use in the final regional peak-flow regression equations. Overall, the standard error of prediction for the regional peak-flow regression equations ranged from 22 to 132 percent. Among all regions, regression model fit was best for region 4 in west-central Idaho (average standard error of prediction=46.4 percent; pseudo-R 2 >92 percent) and region 5 in central Idaho (average standard error of prediction=30.3 percent; pseudo-R 2 >95 percent). Regression model fit was poor for region 7 in southern Idaho (average standard error of prediction=103 percent; pseudo-R 2 <78 percent) compared to other regions because few streamgages in region 7 met the criteria for inclusion in the study, and the region’s semi-arid climate and associated variability in precipitation patterns causes substantial variability in peak flows. A drainage area ratio-adjustment method, using ratio exponents estimated using generalized least-squares regression, was presented as an alternative to the regional regression equations if peak-flow estimates are desired at an ungaged site that is close to a streamgage selected for inclusion in this study. The alternative drainage area ratio-adjustment method is appropriate for use when the drainage area ratio between the ungaged and gaged sites is between 0.5 and 1.5. The updated regional peak-flow regression equations had lower total error (standard error of prediction) than all regression equations presented in a 1982 study and in four of six regions presented in 2002 and 2003 studies in Idaho. A more extensive streamgage screening process used in the current study resulted in fewer streamgages used in the current study than in the 1982, 2002, and 2003 studies. Fewer streamgages used and the selection of different explanatory variables were likely causes of increased error in some regions compared to previous studies, but overall, regional peak‑flow regression model fit was generally improved for Idaho. The revised statistical procedures and increased streamgage screening applied in the current study most likely resulted in a more accurate representation of natural peak-flow conditions. The updated, regional peak-flow regression equations will be integrated in the U.S. Geological Survey StreamStats program to allow users to estimate basin and climatic characteristics and peak-flow statistics at ungaged locations of interest. StreamStats estimates peak-flow statistics with quantifiable certainty only when used at sites with basin and climatic characteristics within the range of input variables used to develop the regional regression equations. Both the regional regression equations and StreamStats should be used to estimate peak-flow statistics only in naturally flowing, relatively unregulated streams without substantial local influences to flow, such as large seeps, springs, or other groundwater-surface water interactions that are not widespread or characteristic of the respective region.

Idaho↗

Borehole geophysical investigation of a formerly used defense site, Machiasport, Maine, 2003-2006

The U.S. Geological Survey, in cooperation with the U.S. Army Corps of Engineers, collected borehole geophysical logs in 18 boreholes and interpreted the data along with logs from 19 additional boreholes as part of an ongoing, collaborative investigation at three environmental restoration sites in Machiasport, Maine. These sites, located on hilltops overlooking the seacoast, formerly were used for military defense. At each of the sites, chlorinated solvents, used as part of defense-site operations, have contaminated the fractured-rock aquifer. Borehole geophysical techniques and hydraulic methods were used to characterize bedrock lithology, fractures, and hydraulic properties. In addition, each geophysical method was evaluated for effectiveness for site characterization and for potential application for further aquifer characterization and (or) evaluation of remediation efforts. Results of borehole geophysical logging indicate the subsurface is highly fractured, metavolcanic, intrusive, metasedimentary bedrock. Selected geophysical logs were cross-plotted to assess correlations between rock properties. These plots included combinations of gamma, acoustic reflectivity, electromagnetic induction conductivity, normal resistivity, and single-point resistance. The combined use of acoustic televiewer (ATV) imaging and natural gamma logs proved to be effective for delineating rock types. Each of the rock units in the study area could be mapped in the boreholes, on the basis of the gamma and ATV reflectivity signatures. The gamma and mean ATV reflectivity data were used along with the other geophysical logs for an integrated interpretation, yielding a determination of quartz monzonite, rhyolite, metasedimentary units, or diabase/gabbro rock types. The interpretation of rock types on the basis of the geophysical logs compared well to drilling logs and geologic mapping. These results may be helpful for refining the geologic framework at depth. A stereoplot of all fractures intersecting the boreholes indicates numerous fractures, a high proportion of steeply dipping fractures, and considerable variation in fracture orientation. Low-dip-angle fractures associated with unloading and exfoliation are also present, especially at a depth of less than 100 feet below the top of casing. These sub-horizontal fractures help to connect the steeply dipping fractures, making this a highly connected fracture network. The high variability in the fracture orientations also increases the connectivity of the fracture network. A preliminary comparison of all fracture data from all the boreholes suggests fracturing decreases with depth. Because all the boreholes were not drilled to the same depth, however, there is a clear sampling bias. Hence, the deepest boreholes are analyzed separately for fracture density. For the deepest boreholes in the study, the intensity of fracturing does not decline significantly with depth. It is possible the fractures observed in these boreholes become progressively tighter or closed with depth, but this is difficult to verify with the borehole methods used in this investigation. The fact that there are more sealed fractures at depth (observed in optical televiewer logs in some of the boreholes) may indicate less opening of the sealed fractures, less water moving through the rock, and less weathering of the fracture infilling minerals. Although the fracture orientation remained fairly constant with depth, differences in the fracture patterns for the three restoration sites indicate the orientation of fractures varies across the study area. The fractures in boreholes on Miller Mountain predominantly strike northwest-southeast, and to a lesser degree they strike northeast. The fractures on or near the summit of Howard Mountain strike predominantly east-west and dip north and south, and the fractures near the Transmitter Site strike northeast-southwest and dip northwest and southeast. The fracture populations for the boreholes on or near the summit of Howard Mountain show more variation than at the other two sites. This variation may be related to the proximity of the fault, which is northeast of the summit of Howard Mountain. In a side-by-side comparison of stereoplots from selected boreholes, there was no clear correspondence between fracture orientation and proximity to the fault. There is, however, a difference in the total populations of fractures for the boreholes on or near the summit of Howard Mountain and the boreholes near the Transmitter Site. Further to the southwest and further away from the fault, the fractures at the Transmitter Site predominantly strike northeast-southwest and northwest-southeast.Heat-pulse flowmeter (HPFM) logging was used to identify transmissive fractures and to estimate the hydraulic properties along the boreholes. Ambient downflow was measured in 13 boreholes and ambient upflow was measured in 9 boreholes. In nine other bedrock boreholes, the HPFM did not detect measurable vertical flow. The observed direction of vertical flow in the boreholes generally was consistent with the conceptual flow model of downward movement in recharge locations and upward flow in discharge locations or at breaks in the slope of land surface. Under low-rate pumping or injection rates [0.25 to 1 gallon per minute (gal/min)], one to three inflow zones were identified in each borehole. Two limitations of HPFM methods are (1) the HPFM can only identify zones within 1.5 to 2 orders of magnitude of the most transmissive zone in each borehole, and (2) the HPFM cannot detect flow rates less than 0.010 + or - 0.005 gal/min, which corresponds to a transmissivity of about 1 foot squared per day (ft2/d). Consequently, the HPFM is considered an effective tool for identifying the most transmissive fractures in a borehole, down to its detection level. Transmissivities below that cut-off must be measured with another method, such as packer testing or fluid-replacement logging. Where sufficient water-level and flowmeter data were available, HPFM results were numerically modeled. For each borehole model, the fracture location and measured flow rates were specified, and the head and transmissivity of each fracture zone were adjusted until a model fit was achieved with the interpreted ambient and stressed flow profiles. The transmissivities calculated by this method are similar to the results of an open-hole slug test; with the added information from the flowmeter, however, the head and transmissivity of discrete zones also can be determined. The discrete-interval transmissivities ranged from 0.16 to 330 ft2/d. The flowmeter-derived open-hole transmissivity, which is the combined total of each of the transmissive zones, ranged from 1 to 511 ft2/d. The whole-well open-hole transmissivity values determined with HPFM methods were compared to the results of open-hole hydraulic tests. Despite the fact that the flowmeter-derived transmissivities consistently were lower than the estimates derived from open-hole hydraulic tests alone, the correlation was very strong (with a coefficient of determination, R2, of 0.9866), indicating the HPFM method provides a reasonable estimate of transmissivities for the most transmissive fractures in the borehole. Geologic framework, fracture characterization, and estimates of hydraulic properties were interpreted together to characterize the fracture network. The data and interpretation presented in this report should provide information useful for site investigators as the conceptual site groundwater flow model is refined. Collectively, the results and the conceptual site model are important for evaluating remediation options and planning or implementing the design of a well field and borehole completions that will be adequate for monitoring flow, remediation efforts, groundwater levels, and (or) water quality. Similar kinds of borehole geophysical logging (specifically the borehole imaging, gamma, fluid logs, and HPFM) should be conducted in any newly installed boreholes and integrated with interpretations of any nearby boreholes. If boreholes are installed close to existing or other new boreholes, cross-hole flowmeter surveys may be appropriate and may help characterize the aquifer properties and connections between the boreholes.

Maine↗