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Streamflow of 2008--Water year summary

The maps and graphs appearing in this summary describe streamflow conditions for water-year 2008 (October 1, 2007 to September 30, 2008) in the context of the 79-year period 1930-2008, unless otherwise noted. The illustrations are based on observed data from the U.S. Geological Survey's (USGS) National Streamflow Information Program. The period 1930-2008 was used because prior to 1930, the number of streamgages was too small to provide representative data for computing statistics for most regions of the country. In the summary, reference is made to the term "runoff," which is the depth to which a river basin, State, or other geographic area would be covered with water if all the streamflow within the area during a single year was uniformly distributed upon it. Runoff quantifies the magnitude of water flowing through the Nation's rivers and streams in measurement units that can be compared from one area to another. The runoff value for a geographic area is computed as the median runoff value for all streamgages in that geographic area. For example, the runoff value for a State is the median for all streamgages in that State, and the median for the Nation is the median value for all streamgages in the Nation. Each of the maps and graphs below can be expanded to a larger view by clicking on the image. In all the graphics, a rank of 1 indicates the highest flow of all years analyzed.

Fact Sheet

Impacts of grade control structure installations on hydrology and sediment transport as an adaptive management strategy

The goal of this research was to examine the impacts of Grade Control Structure (GCS) installations at the Heard Scout Pueblo (HSP) study site in the City of Phoenix, Arizona, USA. The study site is around a high-use trail system and is comprised of eroded and incised channels that conduct high flows and associated sediments into a residential neighborhood downstream, a noted stormwater control problem. We established baseline conditions associated with rainfall/runoff response before structures were installed so we could have some data for comparison afterwards. Innovative monitoring equipment, including video cameras and pressure transducers (to calculate discharge); digital terrain models, sediment samplers and sediment chains (to measure erosion and deposition); soil moisture sensors in monitoring wells (to document infiltration and potential recharge); and weather stations (to track temperature and relative humidity) were established and a small Unmanned Aircraft System (sUAS) survey was completed by July, 11, 2017, in time for the typical summer monsoon season which officially runs from June 15th to September 30th. Only one pre-GCS installation rain event incurred a significant flow event (October 13, 2018). Natural Channel Design (NCD), a landscape restoration company with decades of experience, was hired through a competitive bid process to develop a novel layout of ~30 GCS installations (sills, modified one-rock dams (ORD), and plugs, as well as a modified Zuni-bowl). The American Conservation Experience (ACE) hand-built the structures based on these designs in the main channel from November 13, 2018 through December 1, 2018. ACE built another ten structures in locations adjacent to the channel from January 15 through January 18, 2019. NCD worked with the landscape forensics to identify a historic channel and reinstate it using GCS. A surface-water model was also applied, using some of the baseline measurements (terrain and hydraulic conductivity) to track the flows of water and potential infiltration associated with rainfall events before GCS installation, to assist NCD in their design. The same model was applied using the installed GCS locations to simulate impacts of the structures on flow and infiltration. Our model was able to predict the slight reduction and delay in peak flows for small events and simulate infiltration, which was measured and occurred in the channel. Results demonstrated that structures could increase infiltration by ~15% over time. More data describing geomorphology and hydrology after repeated rainfall events will allow for increased analyses. Innovative monitoring, including the large‐scale particle image velocimetry (LSPIV) were invaluable to this research. Given the arid-land location and added drought conditions, the water levels were not high enough to compute, even using the continuous slope-area method, so discharge was calculated solely using the LSPIV. The careful redundancy of data acquisition is extremely important when studying dryland hydrology. Weather data indicated that the HSP GCS installations created roughly a three-degree microclimate cooling effect for at least two days following rainfall events, as compared with the untreated channel. The cooling was attributed to increased moisture, evaporation, and latent heat expulsion from the evaporation.

Arizona

Geographic Names of Iceland's Glaciers: Historic and Modern

Climatic changes and resulting glacier fluctuations alter landscapes. In the past, such changes were noted by local residents who often documented them in historic annals; eventually, glacier variations were recorded on maps and scientific reports. In Iceland, 10 glacier place-names are to be found in Icelandic sagas, and one of Iceland's ice caps, Snaefellsjokull, appeared on maps of Iceland published in the 16th century. In the late 17th century, the first description of eight of Iceland's glaciers was written. Therefore, Iceland distinguishes itself in having a more than 300-year history of observations by Icelanders on its glaciers. A long-term collaboration between Oddur Sigurdsson and Richard S. Williams, Jr., led to the authorship of three books on the glaciers of Iceland. Much effort has been devoted to documenting historical glacier research and related nomenclature and to physical descriptions of Icelandic glaciers by Icelanders and other scientists from as far back as the Saga Age to recent (2008) times. The first book, Icelandic Ice Mountains, was published by the Icelandic Literary Society in 2004 in cooperation with the Icelandic Glaciological Society and the International Glaciological Society. Icelandic Ice Mountains was a glacier treatise written by Sveinn Palsson in 1795 and is the first English translation of this important scientific document. Icelandic Ice Mountains includes a Preface, including a summary of the history and facsimiles of page(s) from the original manuscript, a handwritten copy, and an 1815 manuscript (without maps and drawings) by Sveinn Palsson on the same subject which he wrote for Rev. Ebenezer Henderson; an Editor's Introduction; 82 figures, including facsimiles of Sveinn Palsson's original maps and perspective drawings, maps, and photographs to illustrate the text; a comprehensive Index of Geographic Place-Names and Other Names in the treatise; References, and 415 Endnotes. Professional Paper 1746 (this book) is the second of the three books; it is being published in both English and Icelandic editions. This book provides information about all named glaciers in Iceland, historic and modern. Descriptions, with geographic coordinates, and bibliographic citations to all glacier place-names on published maps, books, and scientific articles are included. Maps, oblique aerial photographs, ground photographs, and satellite images document each of the 269 modern named glaciers of Iceland. The third book, Glaciers of Iceland, is Chapter D of the 11-chapter [volume] U.S. Geological Survey Professional Paper 1386-A-K. Chapter D includes a 1:500,000-scale Map of the Glaciers of Iceland; it is a comprehensive historical and modern review and assessment of what is currently known about glaciers in Iceland's eight Regional Glacier Groups from a review of the scientific literature and from analysis of maps and remotely sensed data (ground, airborne, and satellite); topics include geology and geography, climate and climate variability, types of glaciers, history of glacier variation (including the 21 surge-type glaciers), and frequency and magnitude of volcanic and lacustrine jokulhlaups.

Professional Paper

Water-quality conditions during low flow in the lower Youghiogheny River basin, Pennsylvania, October 5-7, 1998

In October 1998, a chemical synoptic survey was conducted by the U.S. Geological Survey, in cooperation with the U.S. Department of Energy, National Energy Technology Laboratory, in the Lower Youghiogheny River Basin in Pennsylvania to give a snapshot of present (1998) water quality during low-flow conditions. Water samples from 38 sites—12 mainstem sites, 22 tributaries, and 4 mine discharges that discharge directly to the Youghiogheny River—were used to identify sources of contaminants from mining operations. Specific conductance, water temperature, pH, and dissolved oxygen were measured in the field at each site and concentrations of major ions and trace elements were measured in the laboratory.z Unaccounted for gains and losses in streamflow were measured during the study. Unaccounted for losses in streamflow might be attributed to water loss through streambed fractures. Extensive mine tunnels are present in the basin and loss of water to these tunnels seems likely. Unaccounted for gains in streamflow may be from unmeasured tributaries or surface seeps, but most of the gains are suspected to come from artesian flow through fractures in the streambed from underground mine pools. Influent flows of rust-colored water were noted in some river sections. The pH values for all the samples collected during this survey were above 5.8, and most (33 of 38 samples) were above 7.0. Samples from the four mine-discharge sites also had pH values between 6.3 and 6.7. The lowest pH (5.8) was in a tributary, Galley Run. All 38 sampling sites had net alkalinity. The alkalinity load in the Youghiogheny River increased between Connellsville and McKeesport from 35 to 79 tons per day. Above Smithton, the measured alkalinity load in the Lower Youghiogheny River agreed well with the estimated alkalinity load. Below Smithton, measured alkalinity loads in the Lower Youghiogheny River are greater than calculated loads, resulting in unaccounted for gains in alkalinity. These gains are believed to be from seeps in the streambed. Approximately one-third of the load of total alkalinity in the Youghiogheny River at McKeesport is attributed to Sewickley Creek, which contributes 14 tons per day. Sulfate concentrations in the Youghiogheny River steadily increase from 33 milligrams per liter at Connellsville to 77 milligrams per liter near McKeesport. The measured concentrations of sulfate exceeded Pennsylvania water-quality standards at four tributary sites (Galley Run, Hickman Run, Sewickley Creek, and Gillespie Run) and all four mine-discharge sites but not at any main-stem sites. A large increase in sulfate load between West Newton and Sutersville can be attributed almost entirely to the contribution from Sewickley Creek (49 tons per day). Approximately 25 percent of the load measured between Connellsville and McKeesport is unaccounted for. These gains are believed to be from seeps in the streambed from underground mine pools. Similar patterns also were observed for loads of sodium, calcium, and magnesium. Unmeasured inputs from mine drainage are believed to be the source of these loads. Elevated concentrations (above background levels) of chemicals associated with drainage from coal-mining operations were measured in samples from tributaries, especially from Galley Run, Gillespie Run, and Sewickley Creek, and from the mine-discharge sites. The synoptic survey conducted for this study was successful in identifying generalized reaches of the Youghiogheny River where unaccounted for loads of constituents associated with mining activities are entering the river. However, the survey was not able to pinpoint the location of these loads. Remote-sensing techniques, such as thermal infrared imaging by the National Energy Technology Laboratory, could be useful for determining the precise locations of these inputs.

Water-Resources Investigations Report

An Automated Cropland Classification Algorithm (ACCA) for Tajikistan by combining Landsat, MODIS, and secondary data

The overarching goal of this research was to develop and demonstrate an automated Cropland Classification Algorithm (ACCA) that will rapidly, routinely, and accurately classify agricultural cropland extent, areas, and characteristics (e.g., irrigated vs. rainfed) over large areas such as a country or a region through combination of multi-sensor remote sensing and secondary data. In this research, a rule-based ACCA was conceptualized, developed, and demonstrated for the country of Tajikistan using mega file data cubes (MFDCs) involving data from Landsat Global Land Survey (GLS), Landsat Enhanced Thematic Mapper Plus (ETM+) 30 m, Moderate Resolution Imaging Spectroradiometer (MODIS) 250 m time-series, a suite of secondary data (e.g., elevation, slope, precipitation, temperature), and in situ data. First, the process involved producing an accurate reference (or truth) cropland layer (TCL), consisting of cropland extent, areas, and irrigated vs. rainfed cropland areas, for the entire country of Tajikistan based on MFDC of year 2005 (MFDC2005). The methods involved in producing TCL included using ISOCLASS clustering, Tasseled Cap bi-spectral plots, spectro-temporal characteristics from MODIS 250 m monthly normalized difference vegetation index (NDVI) maximum value composites (MVC) time-series, and textural characteristics of higher resolution imagery. The TCL statistics accurately matched with the national statistics of Tajikistan for irrigated and rainfed croplands, where about 70% of croplands were irrigated and the rest rainfed. Second, a rule-based ACCA was developed to replicate the TCL accurately (∼80% producer’s and user’s accuracies or within 20% quantity disagreement involving about 10 million Landsat 30 m sized cropland pixels of Tajikistan). Development of ACCA was an iterative process involving series of rules that are coded, refined, tweaked, and re-coded till ACCA derived croplands (ACLs) match accurately with TCLs. Third, the ACCA derived cropland layers of Tajikistan were produced for year 2005 (ACL2005), same year as the year used for developing ACCA, using MFDC2005. Fourth, TCL for year 2010 (TCL2010), an independent year, was produced using MFDC2010 using the same methods and approaches as the one used to produce TCL2005. Fifth, the ACCA was applied on MFDC2010 to derive ACL2010. The ACLs were then compared with TCLs (ACL2005 vs. TCL2005 and ACL2010 vs. TCL2010). The resulting accuracies and errors from error matrices involving about 152 million Landsat (30 m) pixels of the country of Tajikistan (of which about 10 million Landsat size, 30 m, cropland pixels) showed an overall accuracy of 99.6% (k hat = 0.97) for ACL2005 vs. TCL2005. For the 3 classes (irrigated, rainfed, and others) mapped in ACL2005, the producer’s accuracy was >86.4% and users accuracy was >93.6%. For ACL2010 vs. TCL2010, the error matrix showed an overall accuracy on 96.2% (k hat = 0.96). For the 3 classes (irrigated, rainfed, and others) mapped in ACL2010, the producer’s and user’s accuracies for the irrigated areas were ≥82.9%. Any intermixing was overwhelmingly between irrigated and rainfed croplands, indicating that croplands (irrigated plus rainfed areas) as well as irrigated areas were mapped with high levels of accuracies (∼90% or higher) even for the independent year. The ACL2005 and ACL2010, each, were produced using ACCA algorithm in ∼30 min using a Dell Precision desktop T7400 computer for the entire country of Tajikistan once the MFDCs for the years were ready. The ACCA algorithm for Tajikistan is made available through US Geological Survey’s ScienceBase: http://www.sciencebase.gov/catalog/folder/4f79f1b7e4b0009bd827f548 or at: https://powellcenter.usgs.gov/globalcroplandwater/content/models-algorithms . The research contributes to the efforts of global food security through research on global croplands and their water use (e.g., https://powellcenter.usgs.gov/globalcroplandwater/ ). The above results clearly demonstrated the ability of a rule-based ACCA to rapidly and accurately produce cropland data layer year after year (hindcast, nowcast, forecast) for the country it was developed using MFDCs that consist of combining multiple sensor data and secondary data. It needs to be noted that the ACCA is applicable to the area (e.g., country, region) for which it is developed. In this case, ACCA is applicable for the Country of Tajikistan to hindcast, nowcast, and forecast agricultural cropland extent, areas, and irrigated vs. rainfed. The same fundamental concept of ACCA applies to other areas of the World where ACCA codes need to be modified to suite the area/region of interest. ACCA can also be expanded to compute other crop characteristics such as crop types, cropping intensities, and phenologies.

Remote Sensing