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A New Perspective on Mount St. Helens - Dramatic Landform Change and Associated Hazards at the Most Active Volcano in the Cascade Range

Mount St. Helens has erupted more frequently than any other volcano in the Cascade Range during the past 4,000 years. The volcano has exhibited a variety of eruption styles?explosive eruptions of pumice and ash, slow but continuous extrusions of viscous lava, and eruptions of fluid lava. Evidence of the volcano?s older eruptions is recorded in the rocks that build and the deposits that flank the mountain. Eruptions at Mount St. Helens over the past three decades serve as reminders of the powerful geologic forces that are reshaping the landscape of the Pacific Northwest. On May 18, 1980, a massive landslide and catastrophic explosive eruption tore away 2.7 cubic kilometers of the mountain and opened a gaping, north-facing crater. Lahars flowed more than 120 kilometers downstream, destroying bridges, roads, and buildings. Ash from the eruption fell as far away as western South Dakota. Reconstruction of the volcano began almost immediately. Between 1980 and 1986, 80 million cubic meters of viscous lava extruded episodically onto the crater floor, sometimes accompanied by minor explosions and small lahars. A lava dome grew to a height of 267 meters, taller than the highest buildings in the nearby city of Portland, Oregon. Crater Glacier formed in the deeply shaded niche between the 1980-86 lava dome and the south crater wall. Its tongues of ice flowed around the east and west sides of the dome. Between 1989 and 1991, multiple explosions of steam and ash rocked the volcano, possibly a result of infiltrating rainfall being heated in the still-hot interior of the dome and underlying crater floor. In September 2004, rising magma caused earthquake swarms and deformation of the crater floor and glacier, which indicated that Mount St. Helens might erupt again soon. On October 1, 2004, a steam and ash explosion signaled the beginning of a new phase of eruptive activity at the volcano. On October 11, hot rock reached the surface and began building a new lava dome immediately south of the 1980-86 lava dome. The erupting lava cleaved Crater Glacier in half and bulldozed it aside, causing thickening, crevassing, and rapid northward advance of the glacier?s east and west arms. Intermittent steam and ash explosions, some generating plumes that rose up to 11 kilometers, preceded and accompanied extrusion of the new lava dome, but ceased by early 2005. As the new dome grew, a series of large fins or spines of hot lava rose, some more than 100 meters high, and then crumbled producing sometimes spectacular rock falls. The largest of these rock falls generated dust or steam plumes that rose high above the crater rim. By February 2006, the new dome had grown to a volume similar to that of the 1980-86 lava dome; and by July 2007, the new dome had grown to a volume of 93 million cubic meters, exceeding the volume of the 1980-86 lava dome. The height of the new dome also exceeded that of the 1980-86 lava dome, and at its highest point (before collapse in 2005) reached to within 2 meters of the lowest point on the south crater rim. At this height, the new dome was taller than the Empire State Building in New York City. The new lava dome initially grew very quickly, at rates of 2 to 3 cubic meters (one small dump truck load) per second. If it had continued to grow at these rates for about 100 years, it would have replaced the volume of rock removed from the volcano during the May 18, 1980, eruption. However, the lava extrusion rate slowed throughout the eruption, and, by July 2007, it was oozing at a rate of 0.1 cubic meters per second. At that rate, it would take over 700 years to replace the volume of rock lost in 1980. Lava dome extrusion has continued into early 2008.

Scientific Investigations Map↗

Aeromagnetic study of the midcontinent gravity high of central United States

A composite map of detailed aeromagnetic surveys over the midcontinent gravity high provides coverage of the 600-mi-long buried belt of mafic rocks of the Keweenawan Series from their outcrop localities in Minnesota and Wisconsin through Iowa and Nebraska. A map of the subsurface extent of the mafic rocks, based on the intricate magnetic patterns, shows that the rocks form a long, semicontinuous block, averaging 40 mi wide and consisting mainly of a sequence of layered flows. This sequence is probably fault-bounded and has been tilted up along the margins, where the linearity of the anomalies indicates steeper dips. The associated clastic rocks, indicated by a smoother magnetic pattern, occur in basins along both sides of the mafic belt and in grabens and a series of axial basins on the upper surface of the block. The well-defined outliers of flows marginal to the main block and the truncation of some of the outermost flow units along a diagonal boundary striking at an angle to them suggest that the present boundaries of the block are postdepositional structural features. The basins and the edges of the block appear to have controlled later, largely vertical movement in the overlying Paleozoic and younger sedimentary cover. Calculated models based on coincident magnetic and detailed gravity profiles along typical cross sections of the midcontinent gravity high show that the block of mafic rocks is steep-sided and as much as several miles thick. The free-air gravity anomaly, which consists of a large positive maximum flanked by minima, averages very close to zero, indicating that this major crustal feature is regionally compensated, although locally each of its components shows a large departure from equilibrium. Remanent magnetization is a primary factor in the interpretation of the magnetic data. Magnetic property studies of Keweenawan mafic rocks in the Lake Superior region show that remanent magnetization may be five times the magnetization induced by the present Earth's field and differs from it radically in direction. This magnetization was acquired before the flows were tilted into their present positions. A computed magnetic profile shows that a trough of flows with such a magnetization and inward-dipping limbs can account for the observed persistent lows along the western edge of the block, the relatively low magnetic values along the axis of the block, and the large positive anomaly along the eastern side of the block. Flows as much as 1 mi thick near the base of the sequence have a remanent magnetization with a nearly opposite polarity. This reverse polarity has been measured on both sides of Lake Superior and is probably also present farther south, particularly in Iowa where the outer units of the block in an area north of Des Moines give rise to a prominent magnetic low. The axis of this long belt of Keweenawan mafic rocks cuts discordantly through the prevailing east-west-trending fabric of the older Precambrian terrane from southern Kansas to Lake Superior. This belt has several major left-lateral offsets, one of which produces a complete hiatus in the vicinity of the 40th parallel where an east-west transcontinental rift or fracture zone has been proposed. The axial basins of clastic rocks are outlined by linear magnetic anomalies and show a concordant relation to the structure of the mafic flows. These basins are oriented at an angle to the main axis, suggesting that the entire feature originated as a major rift composed of a series of short, linear, en echelon segments with offsets similar to the transform faults characterizing the present mid-ocean rift system. This midcontinent rift may well have been part of a Keweenawan global rift system with initial offsets consisting of transform faults along pre-existing fractures, but apparently it never fully developed laterally into an ocean basin, and the upwelling mafic material was localized along a relatively narrow belt.

Geological Society of America Bulletin↗

Archive of single beam and swath bathymetry data collected nearshore of the Gulf Islands National Seashore, Mississippi, from West Ship Island, Mississippi, to Dauphin Island, Alabama: Methods and data report for USGS Cruises 08CCT01 and 08CCT02, July 2008, and 09CCT03 and 09CCT04, June 2009

During the summers of 2008 and 2009 the USGS conducted bathymetric surveys from West Ship Island, Miss., to Dauphin Island, Ala., as part of the Northern Gulf of Mexico (NGOM) Ecosystem Change and Hazard Susceptibility project. The survey area extended from the shoreline out to approximately 2 kilometers and included the adjacent passes (fig. 1). The bathymetry was primarily used to create a topo-bathymetric map and provide a base-level assessment of the seafloor following the 2005 hurricane season. Additionally, these data will be used in conjunction with other geophysical data (chirp and side scan sonar) to construct a comprehensive geological framework of the Mississippi Barrier Island Complex. The culmination of the geophysical surveys will provide baseline bathymetry necessary for scientists to define and interpret seafloor habitat for this area and for scientists to predict future geomorpholocial changes of the islands with respect to climate change, storm impact, and sea-level rise. Furthermore, these data provide information for feasibility of barrier island restoration, particularly in Camille Cut, and for the preservation of historical Fort Massachusetts. For more information refer to http://ngom.usgs.gov/gomsc/mscip/index.html. Since bathymetric surveys have often been conducted for navigational purposes, soundings have traditionally been referenced to a water level datum using tide gages and tide models. Bathymetric measurements referenced to a Global Positioning System (GPS) is a more accurate way of representing water depth and has been implemented in the acquisition and processing procedures for these datasets. Previous single-beam bathymetric studies performed at the USGS Center for Coastal and Marine Science have successfully referenced bathymetric measurements to GPS (DeWitt and others, 2007; Hansen 2008 and 2009). The 2008-2009 bathymetry surveys were conducted as a test to (1) develop acquisition and processing technology utilizing both single beam and swath bathymetry survey methods together, (2) reference both types of measurements to GPS rather than water level, and (3) compare the differences between methods in acquisition and processing. Results of the survey are explained in greater detail within this report. To acquire suitable coverage of the study area in a limited time frame, the seafloor-elevation survey was conducted using three techniques: single-beam bathymetry, interferometric swath bathymetry, and a walking kinematic survey of the island shorelines. All three techniques utilized GPS measurements. Implementation of these techniques was executed concurrently yet independently aboard two research vessels: the RV Survey Cat , a 26-foot (ft) shallow-draft Glacier Bay Coastal Runner, and the 50-ft RV G.K. Gilbert . A portable push buggy with a rigid antenna mount served as the platform for the kinematic shoreline survey. Data from each survey technique was post-processed and edited independently with proper inclusion of the differentially processed external navigation files. The x,y,z components from each method were then combined and the two survey years (2008 and 2009) were merged into one dataset. The 2008 bathymetry data were processed at the USGS Center for Coastal and Marine Science in St. Petersburg, Fla., and the 2009 bathymetry data were processed at the USGS Coastal and Marine Science Center located in Woods Hole, Mass. This report serves as an archive of the processed single beam and interferometric swath bathymetry, outlines the methodology, and reports the results. Data products herein include gridded and interpolated digital depth surfaces, and x,y,z data products for both single beam and interferometric swath bathymetry. Additional files include trackline maps, navigation files, geographic information system (GIS) files, Field Activity Collection System (FACS) logs, and formal Federal Geographic Data Committee (FGDC) metadata. Scanned images of the handwritten FACS logs and digital FACS logs are also provided as PDF files. Refer to the Acronyms page for description of acronyms and abbreviations used in this report or hold the cursor over an acronym for a pop-up explanation. The USGS St. Petersburg Coastal and Marine Science Center assigns a unique identifier to each cruise or field activity. For example, 08CCT01 indicates that the data were collected in 2008 for the Coastal Change and Transport (CCT) study and the data were collected during the first (01) field activity for that project in that calendar year. Refer to http://walrus.wr.usgs.gov/infobank/programs/html/definition/activity.html for a detailed description of the method used to assign the field activity ID. See the digital FACS equipment log for details about the acquisition equipment used. Raw datasets are stored digitally at the USGS St. Petersburg Coastal and Marine Science Center and processed systematically using Novatel's GrafNav version 7.6, SANDS version 3.7, SEA SWATH plus version 3.06.04.03, CARIS HIPS AND SIPS version 3.6, and ESRI ArcGIS version 9.3.1. For more information on processing refer to the Equipment and Processing page. Chirp seismic data were also collected during these surveys and are archived separately.

Mississippi;Alabama↗

Archive of post-Hurricane Charley coastal oblique aerial photographs collected during U.S. Geological Survey field activity 04CCH01 from Marco Island to Fort DeSoto, Florida, August 15, 2004

On August 15, 2004, the U.S. Geological Survey (USGS) conducted an oblique aerial photographic survey off the southwest coast of Florida, from Marco Island to Fort DeSoto, aboard a Navajo Chieftain airplane, tail number N2KK, at an altitude of 500 ft and approximately 1000 ft offshore. These photographs were used to document coastal changes such as beach erosion and overwash caused by Hurricane Charley. They will also be used as baseline data for future coastal change. The oblique photography also served as qualitative ground truthing for the Experimental Advanced Airborne Research Lidar (EAARL) coastal topography and bathymetry data collected on August 16, 2004 (Bonisteel and others, 2009). This report serves as an archive of photographs collected during the August 15, 2004, post-Hurricane Charley coastal oblique aerial survey along with associated flight path maps, KML files, navigation files, digital Field Activity Collection System (FACS) logs, and Federal Geographic Data Committee (FGDC) metadata. Refer to the Acronyms page for expansions of all acronyms and abbreviations used in this report. The USGS St. Petersburg Coastal and Marine Science Center (SPCMSC) assigns a unique identifier to each cruise or field activity. For example, 04CCH01 tells us the data were collected in 2004 for the Coastal Change Hazards (CCH) study and the data were collected during the first field activity for that project in that calendar year. Refer to http://walrus.wr.usgs.gov/infobank/programs/html/definition/activity.html for a detailed description of the method used to assign the ID number. Two separate records of flight navigation were collected during the survey. The first was a continuous ASCII text file from the PLGR that recorded only latitudes, longitudes, and altitudes every 30 sec for the entire flight. No time values were recorded by the PLGR. The second navigation record was recorded by a Trimble Centurion GPS and converted to subtitles on the video, using a Compix Titler unit. The video was shot continuously during the survey. The video subtitles recorded day, month, year, latitude, longitude, and time in hours, minutes, and seconds. In order to produce a digital record of the navigation values that included latitude, longitude, and time, each was manually extracted from the video every 5 min, and these values were matched to the latitude and longitude in the PLGR file. Next, the time was interpolated between these 5-min fixes using Excel to produce time values for each navigation fix recorded in the PLGR file. The location of each photograph taken was determined in the following manner. A Nikon MF-14 data back marks the time each photograph was acquired on the lower right corner of the image in day, hour, and minute format (in UTC). These values were entered from the photographs into an Excel spreadsheet. It is assumed for the purposes of locating the images that the photographs were taken at a constant rate during any given minute of flight. To assign the time value in seconds to each photograph, the number of photographs taken during each minute was evenly distributed across that minute. For example, if 15 photographs were taken during minute 19:00:00, we assume that a picture was taken every 4 sec. The photographs were assigned the time values 19:00:00, 19:00:04, 19:00:08, and so on. The video time navigation file was then merged with the new interpolated photograph file based on time to produce the point of which each photograph was collected. As a result, the positions assigned to each photograph are an estimate of the aircraft position, not the location of the landmark photographed. The photographs provided here are JPEG scanned images of the analog slides. The metadata values for photo creation time, GPS latitude, GPS longitude, GPS position, keywords, credit, artist, caption, copyright, and contact were added to each photograph's EXIF header using EXIFtools (see the Software page). Photographs can be opened directly with any JPEG-compatible image viewer by clicking on a thumbnail on the contact sheet, or, when viewing the Google Earth KML file, by clicking on the marker and then clicking on either the thumbnail or the link below the thumbnail. The KML files were created using the photographic navigation files and the CreateKML JavaScript (see the Software page). To view the survey maps and navigation files, and for more information about these items, see the Navigation page. Figure 1 displays the acquisition geometry. The tables provide detailed information about the assigned location, name, data, and time the photograph was taken along with links to the photo and corresponding 5-min contact sheet. Refer to table 1 and table 2 for details of the northern and southern county photographs, respectively.

Florida↗

Natural-color and color-infrared image mosaics of the Colorado River corridor in Arizona derived from the May 2009 airborne image collection

The Grand Canyon Monitoring and Research Center (GCMRC) of the U.S. Geological Survey (USGS) periodically collects airborne image data for the Colorado River corridor within Arizona (fig. 1) to allow scientists to study the impacts of Glen Canyon Dam water release on the corridor’s natural and cultural resources. These data are collected from just above Glen Canyon Dam (in Lake Powell) down to the entrance of Lake Mead, for a total distance of 450 kilometers (km) and within a 500-meter (m) swath centered on the river’s mainstem and its seven main tributaries (fig. 1). The most recent airborne data collection in 2009 acquired image data in four wavelength bands (blue, green, red, and near infrared) at a spatial resolution of 20 centimeters (cm). The image collection used the latest model of the Leica ADS40 airborne digital sensor (the SH52), which uses a single optic for all four bands and collects and stores band radiance in 12-bits. Davis (2012) reported on the performance of the SH52 sensor and on the processing steps required to produce the nearly flawless four-band image mosaic (sectioned into map tiles) for the river corridor. The final image mosaic has a total of only 3 km of surface defects in addition to some areas of cloud shadow because of persistent inclement weather during data collection. The 2009 four-band image mosaic is perhaps the best image dataset that exists for the entire Arizona part of the Colorado River. Some analyses of these image mosaics do not require the full 12-bit dynamic range or all four bands of the calibrated image database, in which atmospheric scattering (or haze) had not been removed from the four bands. To provide scientists and the general public with image products that are more useful for visual interpretation, the 12-bit image data were converted to 8-bit natural-color and color-infrared images, which also removed atmospheric scattering within each wavelength-band image. The conversion required an evaluation of the histograms of each band’s digital-number population within each map tile throughout the corridor and the determination of the digital numbers corresponding to the lower and upper one percent of the picture-element population within each map tile. Visual examination of the image tiles that were given a 1-percent stretch (whereby the lower 1- percent 12-bit digital number is assigned an 8-bit value of zero and the upper 1-percent 12-bit digital number is assigned an 8-bit value of 255) indicated that this stretch sufficiently removed atmospheric scattering, which provided improved image clarity and true natural colors for all surface materials. The lower and upper 1-percent, 12-bit digital numbers for each wavelength-band image in the image tiles exhibit erratic variations along the river corridor; the variations exhibited similar trends in both the lower and upper 1-percent digital numbers for all four wavelength-band images (figs. 2–5). The erratic variations are attributed to (1) daily variations in atmospheric water-vapor content due to monsoonal storms, (2) variations in channel water color due to variable sediment input from tributaries, and (3) variations in the amount of topographic shadows within each image tile, in which reflectance is dominated by atmospheric scattering. To make the surface colors of the stretched, 8-bit images consistent among adjacent image tiles, it was necessary to average both the lower and upper 1-percent digital values for each wavelength-band image over 20 river miles to subdue the erratic variations. The average lower and upper 1-percent digital numbers for each image tile (figs. 2–5) were used to convert the 12-bit image values to 8-bit values and the resulting 8-bit four-band images were stored as natural-color (red, green, and blue wavelength bands) and color-infrared (near-infrared, red, and green wavelength bands) images in embedded geotiff format, which can be read and used by most geographic information system (GIS) and image-processing software. The tiff world files (tfw) are provided, even though they are generally not needed for most software to read an embedded geotiff image. All image data are projected in the State Plane (SP) map projection using the central Arizona zone (202) and the North American Datum of 1983 (NAD83). The map-tile scheme used to segment the corridor image mosaic followed the standard USGS quarter-quadrangle (QQ) map borders, but the high resolution (20 cm) of the images required further quarter segmentation (QQQ) of the standard QQ tiles, where the image mosaic covered a large fraction of a QQ map tile (segmentation shown in (figure 6), where QQ_1 to QQ_4 shows the number convention used to designate a quarter of a QQ tile). To minimize the size of each image tile, each image or map tile was subset to only include that part of the tile that had image data. In addition, some QQQ image tiles within a QQ tile were combined when adjacent QQQ map tiles were small. Thus, some image tiles consist of combinations of QQQ map tiles, some consist of an entire QQ map tile, and some consist of two adjoining QQ map tiles. The final image tiles number 143, which is a large number of files to list on the Internet for both the natural-color and color-infrared images. Thus, the image tiles were placed in seven file folders based on the one-half-degree geographic boundaries within the study area (fig. 7). The map tiles in each file folder were compressed to minimize folder size for more efficient downloading. The file folders are sequentially referred to as zone 1 through zone 7, proceeding down river (fig. 7). The QQ designations of the image tiles contained within each folder or zone are shown on the index map for each respective zone (figs. 8–14).

Arizona↗

Streams, springs, and volcanic lakes for volcano monitoring

Introduction Volcanic unrest can trigger appreciable change to surface waters such as streams, springs, and volcanic lakes. Magma degassing produces gases and soluble salts that are absorbed into groundwater that feeds streams and lakes. As magma ascends, the amount of heat and degassing will increase, and so will any related geochemical and thermal signal. Subsurface magma movement can cause pressurization that alters hydrostatic head and may induce groundwater discharge. Fluid-pressure changes have been linked to distal volcano-tectonic earthquakes (White and McCausland, 2016; Coulon and others, 2017) and phreatic eruptions (for example, Yamaoka and others, 2016). Clearly, changes in groundwater and surface waters are both indicators of unrest and clues to how and where magma is rising toward the surface. Where possible, it is prudent to incorporate real-time hydrologic data into multiparameter monitoring of restless volcanoes. Hydrologic dynamics can also be tracked by changes in groundwater levels that are commonly measured in shallow boreholes (see of Flinders, A.F., Lowenstern, J.B., Coombs, M.L., and Poland, M.P., eds., Recommended capabilities and instrumentation for volcano monitoring in the United States: U.S. Geological Survey Scientific Investigations Report 2024–5062–K, 5 p., https://doi.org/10.3133/sir20245062k. ">chapter K , this volume, on boreholes; Hurwitz and Lowenstern, 2024). Although inferred to be common, relatively few volcano-hydrology anomalies are well documented, and many are essentially anecdotal (Newhall and others, 2001), reflecting the fact that high-resolution time series remain rare. Extreme examples include the 2008 eruption of Nevado del Huila, Colombia, where relatively minor phreatomagmatic eruptions were accompanied by expulsion of as much as 300 million cubic meters of groundwater from fissures high on the volcano (Worni and others, 2011), generating large lahars. Substantial decreases in flow rate from springs about 8 kilometers from the summit of Mayon Volcano, Philippines, have been noted before most eruptions in the 20th century (Newhall and others, 2001). Stream monitoring at Redoubt Volcano in 2009 allowed Werner and others (2012) to recognize that groundwater was unable to absorb (or scrub) the high flux of volcanic gas and that a high CO 2 /SO 2 precursor signal had been evident for 5 months prior to the eruption. A key to better interpreting hydrologic anomalies—or even identifying them—is therefore obtaining adequate baseline data. Most hydrologic monitoring at U.S. volcanoes has been accomplished by intermittent sampling surveys with annual or less frequent sampling (for example, https://hotspringchem.wr.usgs.gov/index.php ). More frequent sampling, however, generally is needed to establish reliable baselines. A recent hydrologic and hydrothermal monitoring experiment at 25 sites and 10 of the 12 level 4 (very high threat) volcanoes in the U.S. portion of the Cascade Range demonstrated that there is sufficient temporal variability in hydrothermal fluxes, even during quiescent periods, that one-time measurements will commonly have limited interpretive value (Crankshaw and others, 2018). Thus, surveys are best augmented with data from streamgages (for example, Evans and others, 2004; Bergfeld and others, 2008). Streamflow (water discharge) data allow measured temperature and specific conductance to be converted to heat and solute mass fluxes, which could be insightful parameters for detecting anomalous activity (McCleskey and others, 2012). At the Yellowstone Caldera, long-term monitoring of river solutes has allowed calculation of the chloride flux, a proxy for heat discharge (Hurwitz and others, 2007; McCleskey and others, 2016) from the subsurface magma. This is readily accomplished because data from streamgages are continuously recorded and archived by the U.S. Geological Survey (USGS) National Water Information System (NWIS) (USGS, 2024). Similar studies on stratovolcanoes or shield volcanoes would be scientifically useful, and yet are logistically challenging, requiring streamgages on numerous radial drainages complemented by either frequent manual sampling or numerous deployments of equipment to measure water temperature and specific conductance as a proxy for water chemistry. Another challenge is that some volcanic areas, especially shield volcanoes, are characterized by near-surface porous rocks and soils, such that surface streams are rare and replaced by distant, dilute large-volume springs with only a trace of any original volcanically sourced water (Manga, 2001; Hurwitz and others, 2021). Volcanic lakes are worthy of special attention for monitoring efforts, as their temperature and composition can provide evidence of increased flux of volatile-rich fluids from below. Quantifying changes in volatile and heat release from magma can be simpler in lakes than for volcanoes with radial drainages and no major lakes. Moreover, volcanic lakes pose a range of hazards themselves, including phreatomagmatic eruptions, debris flows, flank collapse, tsunamis, and toxic gas release (Mastin and Witter, 2000; Delmelle and others, 2015; Manville, 2015; Rouwet and others, 2015)—hazards that have historically been responsible for substantial loss of life at many volcanoes worldwide (Manville, 2015). Catastrophic CO 2 release at Lake Nyos, Cameroon, in 1986 suffocated about 1,750 people and about 3,500 livestock and was probably triggered by a large landslide into the gas-saturated lake (Kling and others, 1987; Evans and others, 1993). Gas-charged springs in Soda Bay within Clear Lake (California) have caused almost a dozen deaths to bathers in the past hundred years (ABC News, 2000). A 2005 example of lake overturn and abundant gas release was documented at Mount Chiginagak in Alaska (Schaefer and others, 2008) but did not result in any human casualties. Although thermally stratified lakes, which promote trapping of exsolved magmatic gas, tend to develop in tropical regions, the phenomenon can also arise where salinity creates meromixis (a condition in which a lake does not mix completely), as occurs in Mono Lake, California (Jellison and Melack, 1993; Jellison and others, 1998). If magma erupts or flows into a lake, the interaction between hot magma and cold water can be explosive (Mastin and others, 2004; Zimanowski and others, 2015) and substantially expand the area affected by the eruption. Another hazard is the breaching of crater rims by landslides triggered by volcanic and (or) seismic activity. Under some circumstances, substantial volumes of water can be displaced, leading to large floods and lahars. Late Holocene lake flooding from Aniakchak Crater in the Alaska Peninsula (Waythomas, 2022) and from Paulina Lake in Newberry Crater, Oregon (Chitwood and Jensen, 2000), caused by the failure of outlet sills, testify to the substantial hazards at lake-filled calderas. Several volcanic systems in the United States host lakes known to receive heat and gas from underlying magma. These lakes vary widely in area, depth, and chemical composition. Lakes are present at level 4 volcanoes, including Crater Lake and Newberry Volcano in Oregon; Yellowstone Caldera in Wyoming; Long Valley Caldera, Clear Lake volcanic field, Medicine Lake, and Salton Buttes in California; and Aniakchak Crater, Mount Katmai, Fisher Caldera, Mount Okmok, and Kaguyak Crater, among others, in Alaska. A water lake was present in Halemaʻumaʻu, the crater of Kīlauea, Hawai‘i (fig. F1), from October 2019 to December 2020. Level 3 volcanoes with lakes include Mono Lake volcanic field (Calif.), Mount Bachelor (Ore.), Ukinrek Maars and Mount Chiginagak (Alaska), and Soda Lake (Nevada). In addition, there are lakes at many levels 1 and 2 volcanoes. In the United States, there are no strongly acidic lakes that receive abundant input of magmatic gas, such as those found at Mount Ruapehu (New Zealand), Ijen and Kelud (Indonesia), and Poás (Costa Rica). Nevertheless, many contain fluids that provide clues to magmatic processes below. Since publication of a previous report on recommended instrumentation for volcano monitoring (Moran and others, 2008), continuous hydrologic monitoring has become increasingly feasible. However, changes in water pressure, temperature, and chemistry remain, in general, poorly studied phenomena at volcanoes (Sparks, 2003; National Academies of Sciences, Engineering, and Medicine, 2017). Recent efforts by the USGS have included the temporary study of Cascade Range volcanoes, which included frequent (15 minute to hourly) temporal sampling of temperature, depth, and conductivity (Crankshaw and others, 2018; Ingebritsen and Evans, 2019). At Yellowstone Caldera, many streamgages have now added thermistors and specific conductance sensors, allowing estimation of time-dependent chloride flux as a proxy for variations in subsurface heat flux (McCleskey and others, 2012, 2016). Efforts to better understand lakes have also accelerated, with bathymetric mapping and sampling carried out at several locations in the United States. Especially thorough work was done at Yellowstone Lake thanks to the Hydrothermal Dynamics of Yellowstone Lake (HD-YLAKE, https://hdylake.org ) project, funded primarily by the National Science Foundation. In addition to geophysical surveys and recovery of cores and other samples, HD-YLAKE investigations included remotely operated vehicle (ROV) investigations of hydrothermal vents on the lake floor (fig. F2). Data collected by the ROV provided a better understanding of the thermal and chemical influx from lake-bottom hydrothermal systems (Sohn and others, 2017). In this chapter, we focus on detecting changes in the chemistry, temperature, discharge, or water levels of streams, springs, and lakes that can be caused by seismicity, volumetric strains, or increases in gas flux associated with ascending magma. There is unavoidable overlap with other chapters of this report. Samples of water and gas can also be obtained in boreholes ( of Flinders, A.F., Lowenstern, J.B., Coombs, M.L., and Poland, M.P., eds., Recommended capabilities and instrumentation for volcano monitoring in the United States: U.S. Geological Survey Scientific Investigations Report 2024–5062–K, 5 p., https://doi.org/10.3133/sir20245062k. ">chapter K , this volume; Hurwitz and Lowenstern, 2024), both shallow and deep. Gas monitoring ( of Flinders, A.F., Lowenstern, J.B., Coombs, M.L., and Poland, M.P., eds., Recommended capabilities and instrumentation for volcano monitoring in the United States: U.S. Geological Survey Scientific Investigations Report 2024–5062–E, 11 p., https://doi.org/10.3133/sir20245062e.">chapter E , this volume; Lewicki and others, 2024) relies in part on samples from springs and wells, particularly where measurable gas plumes are absent. Water acts as a trigger and lubricant for landslides and sediment-rich floods, and so hydrology has obvious relevance for lahar monitoring, as discussed in of Flinders, A.F., Lowenstern, J.B., Coombs, M.L., and Poland, M.P., eds., Recommended capabilities and instrumentation for volcano monitoring in the United States: U.S. Geological Survey Scientific Investigations Report 2024–5062–H, 6 p., https://doi.org/10.3133/sir20245062h. ">chapter H (this volume; Thelen and others, 2024). Shared situational awareness among scientists engaged in geophysical, gas, and hydrologic monitoring will improve overall understanding of the volcanic hazard.

Scientific Investigations Report↗

Global cropland-extent product at 30-m resolution (GCEP30) derived from Landsat satellite time-series data for the year 2015 using multiple machine-learning algorithms on Google Earth Engine cloud

Executive Summary Global food and water security analysis and management require precise and accurate global cropland-extent maps. Existing maps have limitations, in that they are (1) mapped using coarse-resolution remote-sensing data, resulting in the lack of precise mapping location of croplands and their accuracies; (2) derived by collecting and collating national statistical data that are often subjective, leading to substantial uncertainties in cropland-area estimates, as well as their locations; and (3) extracted from one or more classes of a land use–land cover product in which cropland classes are not the focus of mapping, leading to their mixing with other classes and creating significant errors of omission and commission. These limitations can be overcome by producing high-resolution cropland-extent maps using satellite-sensor data, such as Landsat 30-m resolution or higher. The most fundamental cropland product is the high-resolution cropland-extent map because all higher level cropland products, such as crop-watering method (that is, whether crops are irrigated or rainfed), crop types, cropping intensities, cropland fallows, crop productivity, and crop-water productivity, are dependent on a precise and accurate cropland-extent product. Given these realities, the overarching goal of this study was to produce a Landsat satellite-derived global cropland-extent product at 30-m resolution. The work, which involved a paradigm shift in how global cropland-extent maps are produced, involved the following five key steps: (1) petabyte-scale computing that involved multiyear, 8- to 16-day, time-series Landsat 30-m resolution data for the global land surface; (2) composition of analysis-ready data (ARD) cubes; (3) creation of a large global-reference data hub for machine learning; (4) use of multiple machine-learning algorithms (MLAs) by writing software and computing in the cloud; and (5) Google Earth Engine (GEE) cloud computing. The five key steps involved nine distinct phases. First, the world was segmented into 74 agroecological zones (AEZs). Second, Landsat 8- to 16-day data were used to time-composite 10-band (blue, green, red, near-infrared, short-wave infrared band 1, short-wave infrared band 2, thermal infrared, enhanced vegetation index, normalized difference water index, and normalized difference vegetation index) Landsat 30-m resolution data cubes for every 2- to 4-month time period during 3- to 4-year periods (stated as nominal-year 2015 or, simply, 2015), along with two additional 30-m resolution bands (Shuttle Radar Topography Mission elevation, and slope) in each of the 74 AEZs. Third, more than 100,000 reference-training data samples were collected using ground data (some of which were collected using a mobile application), as well as submeter- to 5-m-resolution, very high-resolution imagery sourced from other reliable sources. Fourth, reference-training data were used to create a knowledge base for separating cropland from noncropland. Fifth, MLAs such as the pixel-based supervised random forest and support-vector machines were written on the GEE using Python and JavaScript. Sixth, object-based recursive hierarchical segmentation algorithm was used, in addition to MLAs, to overcome uncertainties. Seventh, MLAs used the knowledge base to classify and separate cropland from noncropland. Eighth, accuracy assessment was conducted by generating error matrices for each of the 74 AEZs using 19,171 independent validation-data samples. Ninth, cropland areas were computed for all countries of the world and compared with United Nation’s (UN’s) Food and Agricultural Organization (FAO) and other national statistics. The outcome was a Landsat-derived global cropland-extent product at 30-m resolution (GCEP30), which has an overall accuracy of 91.7 percent. For the cropland class, producer’s accuracy was 83.4 percent, and user’s accuracy was 78.3 percent. GCEP30 calculated (using direct pixel count) the global net-cropland area (GNCA) for the year 2015 as 1.873 billion hectares (~12.6 percent of the Earth’s terrestrial area). The continental cropland distribution as a percentage of GNCA was Asia, 33 percent; Europe, 25.5 percent; Africa, 16.7 percent; North America, 14.4 percent; South America, 8.1 percent; and Australia and Oceania, 2.4 percent. The worldwide cropland areas in GCEP30 for 2015 were higher by 236 to 299 million hectares (Mha) compared to national statistics reported elsewhere for the same year (for example, in Food and Agriculture Organization’s corporate statistical database [FAOSTAT] and in the monthly irrigated and rainfed crop areas [MIRCA] database). The global cropland area reported for 2015 increased by 344 Mha (22.5 percent), compared to the year 2000. During the same period (2000–2015), the world’s population increased by 20 percent. Whereas some of these areal increases are real increases in cropland areas, others are due to the types of data, methods, and approaches used. Using the highest known resolution (compared to previous coarse-resolution global products) enabled this study to capture fragmented croplands. Coarse-resolution data compute areas on the basis of subpixels, which, for a large proportion of certain land use–land cover classes, will show only a certain percentage of the total pixel area as actual area. Subpixel areas can lead to substantial uncertainties in area computation, as determining the exact fraction of cropland areas within a coarse-resolution pixel is resource intensive and subject to errors. Other innovations in GCEP30 include reference-data hubs, machine learning, and cloud computing. Cropland areas in 214 countries, territories, departments, and regions were calculated for the year 2015 using GCEP30, on the basis of UN’s global administrative unit layers (GAUL) boundaries. The 10 leading countries in terms of cropland area (as a percentage of the GNCA) were India (9.6 percent), United States (8.95 percent), China (8.82 percent), Russia (8.32 percent), Brazil (3.42 percent), Ukraine (2.32 percent), Canada (2.29 percent), Argentina (2.05 percent), Indonesia (2 percent), and Nigeria (1.91 percent). Together, these 10 countries occupy 50 percent of the global cropland, and they have 52 percent of the global population. Their combined cropland area increased by 2 percent between 2000 and 2015, compared to the substantial increase in population of 517 million (15.5 percent). Together, India, United States, China, and Russia encompass 36 percent of the total area. In the United States and Canada, from 2000 to 2015, cropland decreased by about 2 percent, whereas their populations increased by 14 and 13 percent, respectively. The additional food requirements in these 10 countries, which are caused by increased populations, as well as increasing nutritional demands, are met by production increases in existing cropland or through virtual food trade, or both. More than 18 countries, territories, departments, or regions had 60 percent or more of their geographic area as cropland: Republic of Moldova, San Marino, and Hungary had more than 80 percent of the country’s area as cropland; Denmark, Ukraine, Ireland, and Bangladesh, 70 to 80 percent; and Uruguay, Netherlands, United Kingdom, Spain, Lithuania, Poland, Gaza Strip, Czechia, Italy, India, and Azerbaijan, 60 to 70 percent. Europe and South Asia can be considered agricultural capitals of the world, on the basis of their percentages of geographic area as cropland. United States, China, and Russia, which all have high cropland areas, are ranked second, third, and fourth in the world; India is ranked first. However, the amount of cropland as a percentage of the country’s geographic area is relatively very low for United States (18.3 percent), China (17.7 percent), and Russia (9.5 percent), whereas it is 60.5 percent for India. Most African and South American countries, territories, departments, or regions have less than 15 percent of their geographic area as cropland. China and India together house 36 percent of the world’s population; however, between 2000 and 2015, the amount of China’s cropland area fell by 18.9 percent, owing to urban expansion and the abandonment of farmlands caused by demographic changes (that is, the movement of population from villages to cities). In contrast, China’s population grew by 10 percent. The amount of India’s cropland increased by 8.5 percent, whereas its population grew by 20 percent. This study showed that, out of the 10 leading cropland countries, Ukraine, Nigeria, Russia, and Indonesia showed an 18 to 31 percent increase in cropland areas, on the basis of GCEP30 by the year 2015, compared to 2000. Nigeria’s cropland area increased by 25 percent, and its population increased by 31 percent in the same period. In these countries, food security is maintained by cropland expansion, productivity increases, and virtual food trade. Nevertheless, this trend of increasing net-cropland area and productivity will likely become difficult to maintain, owing to diminishing arable lands and plateauing of 50 years of continual yield increases, requiring policymakers to explore novel and data-supported approaches to solving future food security issues. The GCEP30 product, which can be browsed at full resolution at www.croplands.org , has been released for public download and use through U.S. Geological Survey (USGS)–National Aeronautics and Space Administration (NASA) Land Processes Distributed Active Archive Center (see https://lpdaac.usgs.gov/news/release-of-gfsad-30-meter-cropland-extent-products/ ).

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