Готові списки джерел за темами / Rain measuring

Добірка наукової літератури з теми "Rain measuring"

Автор: Grafiati
Опубліковано: 14 березня 2023

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Статті в журналах з теми "Rain measuring"

1

Costa-Patry, Etienne, Sven Holzendorf, and Matthias Pätsch. "Car Rain Sensors as Mobile Measuring Stations in Heavy-rain Forecasting." ATZ worldwide 123, no. 5-6 (May 2021): 70–75. http://dx.doi.org/10.1007/s38311-021-0655-0.

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2

Madden, L. V., L. L. Wilson, and N. Ntahimpera. "Calibration and Evaluation of an Electronic Sensor for Rainfall Kinetic Energy." Phytopathology® 88, no. 9 (September 1998): 950–59. http://dx.doi.org/10.1094/phyto.1998.88.9.950.

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A novel sensor for measuring the kinetic energy of impacting raindrops, developed based on a soil-mass erosion sensor, was tested in the laboratory, with a rain simulator, and in the field. Drop impactions on the sensor—consisting of a piezoelectric crystal and associated electronics—produce an electrical charge that equals a fixed amount of energy. Calibration of the sensor was done in the laboratory using water drops of known diameter impacting with known velocity, and thus, with known kinetic energy. The relationship between pulse-count output of the sensor minus the background pulse counts when no drops were impacting (O; per min) and kinetic energy flux density (i.e., power [P; mJ cm-2 min-1]) was found to be described by the formula P; = (0.204 + 0.065 · O)0.67. The measurement threshold was 0.34 mJ cm-2 min-1. Using the sensor, generated rains with intensities of 23 to 48 mm/h were found to have powers of 0.4 to 2.2 mJ cm-2 min-1. In 2 years of field testing, 85 individual rain episodes were monitored, with mean intensities ranging from 0.1 to 42 mm/h. These rains had mean powers ranging from 0 to 5 mJ cm-2 min-1, and the highest power for a 5-min sampling period was 10 mJ cm-2 min-1. Both power and intensity varied greatly over time within rain episodes, and there was considerable variation in power at any given rain intensity, emphasizing the importance of measuring rather than simply predicting power. Although there was no known true power measurements for the generated or natural rains, estimates were realistic based on theoretical calculations, assuming that the gamma distribution represents raindrop sizes. The sensor is important in assessing the risk of rain splash dispersal of plant pathogens.
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3

Hamed, Asmaa M., and Basim I. Wahab. "Measuring the Acid Rain in Heet City of Iraq." IOP Conference Series: Earth and Environmental Science 1060, no. 1 (July 1, 2022): 012022. http://dx.doi.org/10.1088/1755-1315/1060/1/012022.

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Abstract Acid rain is a consequence of pollution in the atmosphere. When any sort of fuel is burned, a number of chemicals are released. The smoke from a fire, factories and the fumes from a car exhaust contain a lot of invisible gases that can be far more toxic to our environment than the smoke grey particles that can be seen. Where in this search, used the data from European Center for Medium - Range Weather Forecasts (ECMWF) was depend on. The search is concerned with studying and measuring acid rain in Heet city for three point in same region (Industrial, residential, agricultural) by using values PH that has been measured by devices (PH (ATC), PH (laboratory), paper PH and TDS (Measures the concentration of basic and acidic salts). Determination the amount of the rain in units (mm/h, inches) and period begin fall the rain for a rainy season (from October to May). Study the behavior of polluting gases in sampling regions (CO, CO2, NO2, NO, SO2, O3) due to it has most influence on the formation of the acid rain and determine the most regions which contain pollutants. After measuring the acid rain samples it was found the highest acidity value was recorded in Kabaisa Cement Factory, which is a polluted industrial area, where the PH value was 4.00, meaning that it is one of the most common areas in which acid rain spreads. While agricultural areas have lowest acidity values where PH between (7.01- 7.05), because they are far from sources of pollutants emission and they contain plants and trees(as is well known plants absorb polluting gases) which makes them areas free of all kinds of polluting gases. Also, residential areas contain acidic PH values where PH between 5.06 - 6.03 at lower rates, and the reason for this is the frequent use of cars and motors and the more widespread Restaurants, bread ovens.
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4

Sekaranom, Andung Bayu, and Hirohiko Masunaga. "Comparison of TRMM-Derived Rainfall Products for General and Extreme Rains over the Maritime Continent." Journal of Applied Meteorology and Climatology 56, no. 7 (July 2017): 1867–81. http://dx.doi.org/10.1175/jamc-d-16-0272.1.

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AbstractProperties of the rain estimation differences between Tropical Rainfall Measuring Mission (TRMM) Precipitation Radar (PR) 2A25, TRMM Microwave Imager (TMI) 2A12, and TRMM Multisatellite Precipitation Analysis (TMPA) 3B42 are investigated with a focus on distinguishing between nonextreme and extreme rains over the Maritime Continent from 1998 to 2014. Statistical analyses of collocated TMI 1B11 85-GHz polarization-corrected brightness temperatures, PR 2A23 storm-top heights, and PR 2A25 vertical rain profiles are conducted to identify possible sources of the differences. The results indicate that a large estimation difference exists between PR and TMI for the general rain rate (extreme and nonextreme events). The PR–TMI rain-rate differences are larger over land and coast than over ocean. When extreme rain is isolated, a higher frequency of occurrence is identified by PR over ocean, followed by TMI and TMPA. Over land, TMI yields higher rain frequencies than PR with an intermediate range of rain rates (between 15 and 25 mm h−1), but it gives way to PR for the highest extremes. The turnover at the highest rain rates arises because the heaviest rain depicted by PR does not necessarily accompany the strongest ice-scattering signals, which TMI relies on for estimating precipitation over land and coast.
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5

Wolff, David B., D. A. Marks, E. Amitai, D. S. Silberstein, B. L. Fisher, A. Tokay, J. Wang, and J. L. Pippitt. "Ground Validation for the Tropical Rainfall Measuring Mission (TRMM)." Journal of Atmospheric and Oceanic Technology 22, no. 4 (April 1, 2005): 365–80. http://dx.doi.org/10.1175/jtech1700.1.

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Abstract An overview of the Tropical Rainfall Measuring Mission (TRMM) Ground Validation (GV) Program is presented. This ground validation (GV) program is based at NASA Goddard Space Flight Center in Greenbelt, Maryland, and is responsible for processing several TRMM science products for validating space-based rain estimates from the TRMM satellite. These products include gauge rain rates, and radar-estimated rain intensities, type, and accumulations, from four primary validation sites (Kwajalein Atoll, Republic of the Marshall Islands; Melbourne, Florida; Houston, Texas; and Darwin, Australia). Site descriptions of rain gauge networks and operational weather radar configurations are presented together with the unique processing methodologies employed within the Ground Validation System (GVS) software packages. Rainfall intensity estimates are derived using the Window Probability Matching Method (WPMM) and then integrated over specified time scales. Error statistics from both dependent and independent validation techniques show good agreement between gauge-measured and radar-estimated rainfall. A comparison of the NASA GV products and those developed independently by the University of Washington for a subset of data from the Kwajalein Atoll site also shows good agreement. A comparison of NASA GV rain intensities to satellite retrievals from the TRMM Microwave Imager (TMI), precipitation radar (PR), and Combined (COM) algorithms is presented, and it is shown that the GV and satellite estimates agree quite well over the open ocean.
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6

Kovaříček, P., R. Šindelář, J. Hůla, and I. Honzík. "Measurement of water infiltration in soil using the rain simulation method." Research in Agricultural Engineering 54, No. 3 (August 20, 2008): 123–29. http://dx.doi.org/10.17221/711-rae.

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: For the measurement of the infiltration speed under operational conditions, we were equipped by a rain simulator with the measuring surface of 0.5 m<sup>2</sup>. The infiltration speed is determined from the defined rain intensity and water surface runoff from the measured surface. The retained water mass from the surface runoff is recorded at regular time intervals over the whole measuring period. The beginning of the water runoff from the measured surface indicates the beginning of elutriation. The measuring time is finished after the infiltration speed has been stabilised. The beginning of elutriation and infiltration speed stabilisation are typical and mutually comparable parameters for defined soil properties at the site followed.
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7

Chai, Hui, Shixuan Liu, Xianglong Yang, Xiaozheng Wan, Shizhe Chen, Jiming Zhang, Yushang Wu, Liang Zheng, and Qiang Zhao. "Development of Capacitive Rain Gauge for Marine Environment." Journal of Sensors 2021 (April 2, 2021): 1–8. http://dx.doi.org/10.1155/2021/6639668.

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At present, the methods and instruments for measuring the precipitation on land may not effectively work in the offshore corrosion environment which is characterized by salt and humid. The research look at investigating the reliable and precision approach of measuring rainfall and a capacitive rain gauge for the marine environment is developed. Firstly, according to the working principle and performance requirements of the capacitive rain gauge, the modular mechanical structure and capacitance voltage conversion circuit of the rain gauge are designed, and the calibration and stability experiments of the prototype are tested to evaluate the measurement error and exam the accuracy of the rain gauge measurement results changing over time. Then, environmental adaptability experiment is carried out on the capacitive rain gauge to explore its performance in the environment of temperature and humidity changes as well as salt spray. Finally, the feasibility of the rain gauge used in the marine environment is verified by the sea test of the prototype. The measurement error of the developed capacitive rain gauge is less than 1 mm, which provides a realization method for the measurement of precipitation in the marine environment.
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8

Fisher, Brad L. "Statistical Error Decomposition of Regional-Scale Climatological Precipitation Estimates from the Tropical Rainfall Measuring Mission (TRMM)." Journal of Applied Meteorology and Climatology 46, no. 6 (June 1, 2007): 791–813. http://dx.doi.org/10.1175/jam2497.1.

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Abstract Monthly rainfall estimates inferred from the NASA Tropical Rainfall Measuring Mission (TRMM) satellite contain errors due to discrete temporal sampling and remote spaceborne rain retrievals. This paper develops a regional-scale error model that uses the rain information in the ground data to disentangle the sampling and retrieval errors in the satellite estimate statistically. The proposed method computes a mean rain rate from monthly rainfall statistics for each TRMM rain sensor by subsampling high-resolution ground-based rain data at satellite overpass times. This additional rain-subsampled parameter plays an essential role in the statistical decomposition of the total error distribution into its sampling and retrieval error components. Using the statistical formalism developed in this study, an error analysis was performed on 5 yr of monthly rain estimates produced by the TRMM Microwave Imager (TMI) and precipitation radar (PR) rain sensors aboard TRMM over a quasi 2° × 2° region of the TRMM ground validation (GV) site at Melbourne, Florida. Annual retrieval and sampling error statistics were computed for the TMI and PR using monthly rainfall estimates derived from two independent ground-based sensors: a regional rain gauge network and the Next-Generation Weather Radar (NEXRAD). Subsampled ground-based rainfall estimates produced for the radar and the gauges were highly correlated with the TMI and PR rainfall estimates, and both GV sensors produced relatively consistent error estimates. The PR-to-TMI sampling error ratio was equal to about 1.3, which was in close agreement with prelaunch predications, and the TMI-to-PR retrieval error ratio was about 2.0. For the TMI, a seasonally alternating rainfall bias was also observed that was negative during winter and positive during summer.
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9

Hardjianto, Mardi, Dimas Ariyanto, and Agnes Aryasanti. "Penerapan Sensor Ultrasonik HC-SR04 dan Hujan untuk Memantau Ketinggian Air dan Pendeteksi Hujan." JURNAL MEDIA INFORMATIKA BUDIDARMA 6, no. 1 (January 25, 2022): 251. http://dx.doi.org/10.30865/mib.v6i1.3486.

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Indonesia is a country with high rainfall. The rainy season can last for four months of the year. Development in urban areas in increasing resulting, resulting in reduced water absorption areas. People's habit of throwing garbage in waterways is also one of the factors that cause flooding. The current obstacle is the difficulty of monitoring water levels when heavy rains cause water to overflow onto roads and residents' houses. Checking the water level is still done manually, only looking at the measuring limit or ruler found on the river and not giving warning messages when the water level rises and it rains. Devices are needed to monitor water levels during heavy rains and send alerts to officers when it rains and the water level has exceeded the limit. Researchers found the idea to make a water level monitor and rain detector using the Wemos D1 R2 Mini microcontroller, supported by the HC-SR04 sensor and Internet of Things (IoT) based rain sensor. The Blynk application receives data from Wemos to be forwarded as a notification to officers. This system provides real-time information about river water levels and conditions when it rains to help avoid or reduce losses due to flooding. The accuracy value generated by the ultrasonic sensor reaches 99.89%, while from the rain sensor, it reaches 100%.
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10

Korzekwa, Kaine. "Measuring Evapotranspiration in Rain Gardens with Soil Moisture Sensors." CSA News 66, no. 11 (October 3, 2021): 3–5. http://dx.doi.org/10.1002/csan.20601.

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Дисертації з теми "Rain measuring"

1

Seed, Alan William. "Statistical problems in measuring convective rainfall." Thesis, McGill University, 1989. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=74251.

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Simulations based on a month of radar data from Florida, and a summer of radar data from Nelspruit, South Africa, were used to quantify the errors in the measurement of mean areal rainfall which arise simply as a result of the extreme variability of convective rainfall, even with perfect remote sensing instruments. The raingauge network measurement errors were established for random and regular network configurations using daily and monthly radar-rainfall accumulations over large areas. A relationship to predict the measurement error for mean areal rainfall using sparse networks as a function of raining area, number of gauges, and the variability of the rainfield was developed and tested. The manner in which the rainfield probability distribution is transformed under increasing spatial and temporal averaging was investigated from two perspectives. Firstly, an empirical relationship was developed to transform the probability distribution based on some measurement scale, into a distribution based on a standard measurement length. Secondly, a conceptual model based on multiplicative cascades was used to derive a scale independent probability distribution.
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2

Kundgol, A. S. "A novel technique for measuring and sensing rain." Thesis, University of Salford, 2015. http://usir.salford.ac.uk/35418/.

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Rainfall passing over a given area is a highly dynamic process; it changes constantly in form and intensity. It varies constantly on short spatial and temporal scales that makes real time measurements of the amount of rainfall challenging. Measuring and sensing rain is important to be able to understand and control our urban environment. Traditionally, rainfall analysis for hydrologic modelling use spatial measurements collected at various sparsely spread observation points using rain gauges working on various principles such as weighing type, tipping bucket, capacitive type etc. An accurate representation of spatial model of rainfall is essential for hydrological operational purposes such as forecasting of river flow, flood irrigation planning and modelling of catchment areas. Measurement of drop size distribution are also exploited to investigate microphysics of precipitation and to improve rainfall remote sensing estimation techniques. However, the high initial costs of convectional rain gauges prevent collection of data with high spatial resolution. The research looks at investigating the sensor stack to be a part of an integrated sensor approach to develop a device architecture for the development of low cost integrated rain sensing and measuring the rain. The device architecture consists of three main stacks – energy generation layer, sensing layer, processing layer. The raindrop on impact causes vibration on the device surface. This force exerted by the raindrop causes a deflection and is measured indirectly by the use of a thin film piezo sensor. As part of the work, we find there is a good correlation between the vibrations caused and the size or volume of the raindrop by indirectly measuring the impact force of the raindrop. The working range of the device is between 100hz and 2000hz, which includes the first modal peak of the impact that acts as an amplification to the drop's impact. Using this information, the device is able to calculate the raindrop size distribution and the rain intensity. Calibration of the device is key as we are measuring the impact force of the rain drops and correlating it to the size of the drop. Primary, not all rain drops will fall on the device at terminal velocity (the main assumption for calibration of the device), as the fall velocity of the droplet may also be affected by the wind. Secondly, the spatial variation of the frequency response function in Volts/Newton in decreasing order from the centre of the plate.
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3

DeMoss, Jeremy. "Changes in Tropical Rainfall Measuring Mission (TRMM) retrievals due to the orbit boost estimated from rain gauge data." [College Station, Tex. : Texas A&M University, 2006. http://hdl.handle.net/1969.1/ETD-TAMU-1732.

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4

Chapman, Michael Alan. "Measuring the Effectiveness of a Green Infrastructure Pilot Program in Wyoming, Ohio." Miami University / OhioLINK, 2011. http://rave.ohiolink.edu/etdc/view?acc_num=miami1323547160.

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5

Crockett, Jonathan. "Understanding and measuring perceptions of convenience in rail travel." Thesis, University of Southampton, 2004. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.403877.

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6

Király, Péter [Verfasser], Gerhard [Akademischer Betreuer] Lauer, Gerhard [Gutachter] Lauer, Marco [Gutachter] Büchler, and Ramin [Gutachter] Yahyapour. "Measuring metadata quality / Péter Király ; Gutachter: Gerhard Lauer, Marco Büchler, Ramin Yahyapour ; Betreuer: Gerhard Lauer." Göttingen : Niedersächsische Staats- und Universitätsbibliothek Göttingen, 2019. http://d-nb.info/119198897X/34.

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7

Hopkinson, P. G. "Methods for measuring environmental disturbances affecting residents and pedestrians - a contribution to rail project appraisal." Thesis, University of Leeds, 1988. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.233810.

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8

李子琪 and Chi-kei Li. "An exploration into the development of an evaluation system using RAI for measuring casework effectiveness." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 1992. http://hub.hku.hk/bib/B31977078.

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Li, Chi-kei. "An exploration into the development of an evaluation system using RAI for measuring casework effectiveness." [Hong Kong : University of Hong Kong], 1992. http://sunzi.lib.hku.hk/hkuto/record.jsp?B13409451.

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10

Warsop, Thomas E. "Three-dimensional scene recovery for measuring sighting distances of rail track assets from monocular forward facing videos." Thesis, Loughborough University, 2011. https://dspace.lboro.ac.uk/2134/8994.

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Rail track asset sighting distance must be checked regularly to ensure the continued and safe operation of rolling stock. Methods currently used to check asset line-of-sight involve manual labour or laser systems. Video cameras and computer vision techniques provide one possible route for cheaper, automated systems. Three categories of computer vision method are identified for possible application: two-dimensional object recognition, two-dimensional object tracking and three-dimensional scene recovery. However, presented experimentation shows recognition and tracking methods produce less accurate asset line-of-sight results for increasing asset-camera distance. Regarding three-dimensional scene recovery, evidence is presented suggesting a relationship between image feature and recovered scene information. A novel framework which learns these relationships is proposed. Learnt relationships from recovered image features probabilistically limit the search space of future features, improving efficiency. This framework is applied to several scene recovery methods and is shown (on average) to decrease computation by two-thirds for a possible, small decrease in accuracy of recovered scenes. Asset line-of-sight results computed from recovered three-dimensional terrain data are shown to be more accurate than two-dimensional methods, not effected by increasing asset-camera distance. Finally, the analysis of terrain in terms of effect on asset line-of-sight is considered. Terrain elements, segmented using semantic information, are ranked with a metric combining a minimum line-of-sight blocking distance and the growth required to achieve this minimum distance. Since this ranking measure is relative, it is shown how an approximation of the terrain data can be applied, decreasing computation time. Further efficiency increases are found by decomposing the problem into a set of two-dimensional problems and applying binary search techniques. The combination of the research elements presented in this thesis provide efficient methods for automatically analysing asset line-of-sight and the impact of the surrounding terrain, from captured monocular video.
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Книги з теми "Rain measuring"

1

Goddard DAAC (Goddard Space Flight Center) and Uchū Kaihatsu Jigyōdan (Japan), eds. Tropical Rainfall Measuring Mission. Greenbelt, MD: NASA Goddard Distributed Active Archive Center, 2000.

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2

Center, Goddard Space Flight, ed. Tropical Rainfall Measuring Mission Office. Greenbelt, MD: NASA Goddard Space Flight Center, 1993.

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3

Branch, Ontario Water Resources. Measuring the trophic status of lakes sampling protocols. Toronto: Queen's Printer for Ontario, 1992.

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4

Center, Goddard Space Flight, ed. Tropical Rainfall Measuring Mission: Learning how rainfall affects climate change. Greenbelt, Md: National Aeronautics and Space Administration, Goddard Space Flight Center, 1998.

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5

Wei-Kuo, Tao, and Adler Robert, eds. Cloud systems, hurricanes, and the Tropical Rainfall Measuring Mission (TRMM): A tribute to Dr. Joanne Simpson. Boston: American Meteorological Society, 2003.

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6

Tropical Rainfall Measuring Mission. Science Steering Group. Report of the Science Steering Group for a Tropical Rainfall Measuring Mission (TRMM). Greenbelt, Md: Goddard Space Flight Center, 1988.

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7

United States. National Aeronautics and Space Administration., ed. Four-dimensional oceanic and atmospheric data assimilation with tropical rainfall measuring mission data: April 15, 1992-November 30, 1996. [Washington, D.C: National Aeronautics and Space Administration, 1997.

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8

United States. National Aeronautics and Space Administration., ed. Four-dimensional oceanic and atmospheric data assimilation with tropical rainfall measuring mission data: April 15, 1992-November 30, 1996. [Washington, D.C: National Aeronautics and Space Administration, 1997.

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9

National Research Council (U.S.). Committee on the Future of the Tropical Rainfall Measuring Mission. Assessment of the benefits of extending the tropical rainfall measuring mission: A perspective from the research and operations communities : interim report. Washington, D.C: National Academies Press, 2006.

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10

Crucini, Mario J. Measuring business cycles by saving for a rainy day. Cambridge, MA: National Bureau of Economic Research, 2010.

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Частини книг з теми "Rain measuring"

1

Awaka, Jun, Toshio Iguchi, and Ken'ichi Okamoto. "Rain Type Classification Algorithm." In Measuring Precipitation From Space, 213–24. Dordrecht: Springer Netherlands, 2007. http://dx.doi.org/10.1007/978-1-4020-5835-6_17.

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2

Bizzarri, Bizzarro, Albin J. Gasiewsk, and David H. Staelin. "Observing Rain by Millimetre–Submillimetre Wave Sounding from Geostationary Orbit." In Measuring Precipitation From Space, 675–92. Dordrecht: Springer Netherlands, 2007. http://dx.doi.org/10.1007/978-1-4020-5835-6_50.

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3

Inyukhin, V. S., and S. A. Kushchev. "Measuring Precipitation Fields Using Radar and Rain Gaugers." In Springer Proceedings in Earth and Environmental Sciences, 195–202. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-19012-4_18.

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4

Anagnostou, Emmanouil N. "Assessment of Satellite Rain Retrieval Error Propagation in the Prediction of Land Surface Hydrologi." In Measuring Precipitation From Space, 357–68. Dordrecht: Springer Netherlands, 2007. http://dx.doi.org/10.1007/978-1-4020-5835-6_28.

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Shiba, S., R. Ito, and T. Sueishi. "Effect of Rainfall Intensity on Acid Rain Formation by Absorption of Sulfur Dioxide." In Water Pollution: Modelling, Measuring and Prediction, 735–48. Dordrecht: Springer Netherlands, 1991. http://dx.doi.org/10.1007/978-94-011-3694-5_51.

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6

Smith, Eric A., and Throy D. Hollis. "Performance Evaluation of Level-2 TRMM Rain Profile Algorithms by Intercomparison and Hypothesis Testing." In Cloud Systems, Hurricanes, and the Tropical Rainfall Measuring Mission (TRMM), 207–22. Boston, MA: American Meteorological Society, 2003. http://dx.doi.org/10.1007/978-1-878220-63-9_19.

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Huq, Md Enamul, Zhenfeng Shao, Ahmed Abdullah Al Dughairi, Md Nazirul Islam Sarker, Cai Bowen, Abdullah Al Mamun, Nayyer Saleem, Akib Javed, and Md Mahabubur Rahman. "Measuring Vulnerability to Flash Flood of Urban Dwellers." In Natural Disaster Science and Mitigation Engineering: DPRI reports, 317–54. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-2904-4_12.

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AbstractFlash floods are unexpected, localized flood events that occur when an exceptional amount of rain falls happens over a short period of time. In South Asia, it is mostly disastrous, for example, in 2017 flash floods killed approximately 1200 people from India, Nepal, and Bangladesh. However, it is also common in Dhaka megacity, Bangladesh due to its geographic location, monsoon climatic condition and surrounding rivers. Though it is impossible to avoid them, the losses and damages of hazards can be reduced effectively by using appropriate techniques. This study aims to determine the responsible factors and measure the household vulnerability to flash flood as a tool of mitigation. The study has been conducted based on primary data. Therefore, data were collected from both slum and non-slum population to cover the entire urban habitats. Data were collected with a structured questionnaire based on five factors (social, economic, institutional, structural, and environmental) of vulnerability to flash flood. The key feature of this paper is to provide an insight into real picture of vulnerability to flash flood for urban habitants. Moreover, this practical approach is useful to quantify hazard-induced vulnerabilities not only for Dhaka megacity but also for other cities of the globe.
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Siewwuttanagul, Somsiri, Takuro Inohae, and Nobuo Mishima. "Measuring the Changes of Subway Accessibility Through the Service Area Territories: A Case Study of Fukuoka Subway Network." In Urban Rail Transit, 207–19. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-5979-2_11.

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Wang, Chao, Yanfu Li, Dawei Liu, and Hongli Liu. "Profile Calibration of Dynamically Measuring Rail Wear Using LIS." In Lecture Notes in Electrical Engineering, 37–45. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-10-7989-4_4.

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Mokhlas, H., N. A. Hamid, M. Mustafa, and R. Sham. "Measuring Walkability Attributes of Pedestrian Rail Commuter: A Pilot Study." In Proceedings of the 1st AAGBS International Conference on Business Management 2014 (AiCoBM 2014), 393–405. Singapore: Springer Singapore, 2016. http://dx.doi.org/10.1007/978-981-287-426-9_34.

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Тези доповідей конференцій з теми "Rain measuring"

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Acciari, G., M. Caruso, R. Miceli, L. Riggi, P. Romano, G. Schettino, and F. Viola. "Measuring rain energy with the employment of “Arduino”." In 2016 IEEE International Conference on Renewable Energy Research and Applications (ICRERA). IEEE, 2016. http://dx.doi.org/10.1109/icrera.2016.7884504.

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Ramos, B., M. D'Amico, J. Santos, I. Nolivos, A. Manzoni, R. Ponguillo, J. Gomez, and T. Chavez. "Measuring rain with microwave links: A pilot experiment in Ecuador." In 2015 IEEE-APS Topical Conference on Antennas and Propagation in Wireless Communications (APWC). IEEE, 2015. http://dx.doi.org/10.1109/apwc.2015.7300149.

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Xu, Kai, Xiang Shunxiang, and Huang Linshu. "Measuring and analyzer of rain attenuation for satellite communication in Ku band." In 2009 3rd IEEE International Symposium on Microwave, Antenna, Propagation and EMC Technologies for Wireless Communications (MAPE). IEEE, 2009. http://dx.doi.org/10.1109/mape.2009.5355890.

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Paulson, K. S., and G. R. Rimven. "Prediction of One-Minute Rain Rate Distributions from Tropical Rainfall Measuring Mission (TRMM)." In 5th Colloquium on Antennas, Wireless and Electromagnetics (CAWE 2017). Institution of Engineering and Technology, 2017. http://dx.doi.org/10.1049/ic.2017.0014.

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Raharjo, Slamet Rahman, Mamok Suprapto, and Cahyono Ikhsan. "Rain design analysis using TRMM (tropical rainfall measuring mission) method (case study: Cisadane watershed)." In Proceedings of the 17th International Conference on Ion Sources. Author(s), 2018. http://dx.doi.org/10.1063/1.5054465.

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Zhang, Xiong, Zhouhu Deng, Shuai Lu, Wenqiong Zhang, Baopeng Lai, Zhiyong Zhang, and Junfeng Yan. "Research and Realization of Acid Rain Measuring Method Based on Internet of Things Technology." In 2015 Sixth International Conference on Intelligent Systems Design and Engineering Applications (ISDEA). IEEE, 2015. http://dx.doi.org/10.1109/isdea.2015.157.

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Quérel, Arnaud, Pascal Lemaitre, Marie Monier, Emmanuel Porcheron, and Andrea Flossmann. "Study of Aerosol Scavenging by Rain in Case of a Radioactive Contamination of the Atmosphere." In 2012 20th International Conference on Nuclear Engineering and the ASME 2012 Power Conference. American Society of Mechanical Engineers, 2012. http://dx.doi.org/10.1115/icone20-power2012-54239.

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The analysis of the radioactive aerosol scavenging by rain after the Chernobyl accident highlights some differences between the modelling studies and the environmental measurements. Part of this gap is due to the uncertainties on the scavenging efficiency of aerosol particles by raindrops, in particular for drops with a diameter larger than one millimeter. The IRSN (Institut de Radioprotection et de Sûreté Nucléaire) has decided to launch an experimental study to measure with a better accuracy the scavenging efficiency of large raindrops. The scavenging efficiency of aerosol has been determined by measuring precisely the mass of aerosol particles collected by a single drop after its path through an atmosphere loaded with particles. The collection efficiencies for drop diameters of 2 mm and 2.6 mm (previously unknown for atmospheric aerosols) are measured. The impact of these new data on modeling of the washout of the atmosphere by the rain is noticed.
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Srinivasa Ramanujam, K., and C. Balaji. "A Fast Polarized Microwave Radiative Transfer Model for a Raining Atmosphere." In 2010 14th International Heat Transfer Conference. ASMEDC, 2010. http://dx.doi.org/10.1115/ihtc14-22228.

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Retrieval of vertical rain structure and hence the estimation of surface rain rate is of central importance to various missions involving remote sensing of the earth’s atmosphere. Typically, remote sensing involves scanning the earth’s atmosphere at visible, infra red and microwave frequencies. While the visible and infra red frequencies can scan the atmosphere with higher spatial resolution, they are not suited for scanning under cloudy conditions as clouds are opaque under these frequencies. However, the longer wavelength microwave radiation can partially penetrate through the clouds without much attenuation thereby making it more suitable for meteorological purposes. The retrieval algorithms used for passive microwave remote sensing involve modeling of the radiation in the earth’s atmosphere where in the clouds and precipitating rain (also known as hydrometeors) emit / absorb / scatter. Additionally, it has been observed that the rain droplets tend to polarize the microwave signal emitted by the earth’s surface. In view of this, the first step in the development of a rainfall retrieval algorithm for any satellite mission is to simulate the radiances (also known as brightness temperatures) that would have been measured by a typical radiometer for different sensor frequencies and resolutions. Towards this, a polarized microwave radiation transfer code has been developed in house for a plane parallel raining atmosphere (henceforth called as forward model) that depicts the physics as seen by a satellite. Physics based retrieval algorithm often involves repeated execution of the forward model for various raining scenario. However, due to the complexity involved in the radiation modeling of the raining atmosphere which is participating in nature, the forward model suffers from the drawback that it requires enormous computational effort. In the present work, a much quicker alternative is proposed wherein the forward model can be replaced with an Artificial Neural Network (ANN) based Fast Forward Model (AFFM). This AFFM can be used in conjunction with an appropriate inverse technique to retrieve the rain structure. Spectral microwave brightness temperatures at frequencies corresponding to the Tropical Rainfall Measuring Mission (TRMM) of National Aeronautics and Space Administration (NASA) and Japan Aerospace Exploration Agency (JAXA) are first simulated using an in-house polarized radiate on transfer code for sixteen past cyclones in the North Indian Ocean region in the period (2000–2005), using the hydrometeor profiles retrieved from the Goddard Profiling Algorithm (GPROF) of the Tropical Rainfall Measuring Mission (TRMM)’s Microwave Imager (TMI). This data is split into two sets: while the first set of data is used for training the network, the remainder of the data is used for testing the ANN. The results obtained are very encouraging and shows that neural network is capable of predicting the brightness temperature accurately with the correlation coefficient of over 99%. Furthermore, the execution of the forward model on an Intel Core 2 Quad 3.0 GHz processor based, 8 GB DDR3 RAM workstation took 3 days, while the AFFM delivers the results in 10 seconds.
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Kong, Changduk, Keonwoo Kim, and Jihyun Kim. "Development of GUI Type On-Line Condition Monitoring Program for a Turboprop Engine Using LabVIEW®." In ASME 2011 Turbo Expo: Turbine Technical Conference and Exposition. ASMEDC, 2011. http://dx.doi.org/10.1115/gt2011-45337.

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Recently, the health monitoring system has been developed for precaution and maintenance action against faults or performance degradations of the advanced propulsion system which may be occurred in severe environments such as high altitude, foreign object damage particles, hot and heavy rain and snowy atmospheric conditions. However to establish this health monitoring system, the on-line condition monitoring program is firstly required, and the program must monitor the engine performance trend through comparison between measuring engine performance data and base performance results calculated by base engine performance model. This work aims to develop a GUI type on-line condition monitoring program for the PT6A-67 turboprop engine of a high altitude and long endurance operation UAV using LabVIEW. The base engine performance of the on-line condition monitoring program is simulated using component maps inversely generated from the limited performance deck data provided by engine manufacturer. The base engine performance simulation program is evaluated because analysis results by this program are well agreed with the performance deck data. The proposed on-line condition program can monitor the real engine performance as well as the trend through precise comparison between clean engine performance results calculated by the base performance simulation program and measuring engine performance signals. In the development phase of this monitoring system, a signal generation module is proposed to evaluate the proposed on-line monitoring system. For user friendly purpose, all monitoring program are coded by LabVIEW, and monitoring examples are demonstrated using the proposed GUI type on-condition monitoring program.
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Harrison, Harold. "Producing and Measuring the 3rd Body Layer." In 2020 Joint Rail Conference. American Society of Mechanical Engineers, 2020. http://dx.doi.org/10.1115/jrc2020-8095.

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Abstract Decades of measuring friction conditions at the wheel-rail interface have resulted in widely varying answers produced by different measurement methods and devices. This variation is a result of many factors, some of which are difficult to quantify and control, especially under field conditions. One of these variables is the presence of an unknown 3rd body layer material, whose accumulation on the wheel and rail surfaces affect the value of independent tests performed to quantify the nominal state of wheel on rail in actual operating conditions. This paper describes a series of tests undertaken to understand the creation of the 3rd body layer from the starting point of clean wheel and rail surfaces and how that can lead to new standard practices for field testing. A 2 × 2 matrix of wheel and rail conditions is defined to help formalize the discussion of the production and measurement of the 3rd body layer. Case 1 is the nominal virgin state of the two materials. This is actually fairly hard to produce. Almost any process and/or handling will leave some films on both surfaces. Lab tests typically start in this state after cleaning/degreasing the surfaces following machining or sanding. Cases 2 and 3 are potentially the same (clean wheel or rail), however, the more likely case is Case 2 where some existing location on actual track is chosen to determine its current state, thus the use of a Tribometer using a clean wheel. Case 4 represents the nominal state of most track with passing vehicles where the current environmental/ambient conditions encourage a particular set of 3rd body wear products. This formalization helps reveal the prior common practices that have produced assumptions of what is the ‘correct’ value and how that has created a bias in one’s thinking of what really exists in normal revenue service. This issue is particularly consequential in attempting to quantify the benefit of the friction modifiers that are being adopted across the railroad industry.
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Звіти організацій з теми "Rain measuring"

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Meranger, J. C., R. E. Lett, and T. Pickering. An on-site method for measuring aluminium speciation in shallow well water samples from areas exposed to acid rain. Natural Resources Canada/ESS/Scientific and Technical Publishing Services, 1993. http://dx.doi.org/10.4095/193275.

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Agassi, Menahem, Michael J. Singer, Eyal Ben-Dor, Naftaly Goldshleger, Donald Rundquist, Dan Blumberg, and Yoram Benyamini. Developing Remote Sensing Based-Techniques for the Evaluation of Soil Infiltration Rate and Surface Roughness. United States Department of Agriculture, November 2001. http://dx.doi.org/10.32747/2001.7586479.bard.

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The objective of this one-year project was to show whether a significant correlation can be established between the decreasing infiltration rate of the soil, during simulated rainstorm, and a following increase in the reflectance of the crusting soil. The project was supposed to be conducted under laboratory conditions, using at least three types of soils from each country. The general goal of this work was to develop a method for measuring the soil infiltration rate in-situ, solely from the reflectance readings, using a spectrometer. Loss of rain and irrigation water from cultivated fields is a matter of great concern, especially in arid, semi-arid regions, e.g. much of Israel and vast area in US, where water is a limiting factor for crop production. A major reason for runoff of rain and overhead irrigation water is the structural crust that is generated over a bare soils surface during rainfall or overhead irrigation events and reduces its infiltration rate (IR), considerably. IR data is essential for predicting the amount of percolating rainwater and runoff. Available information on in situ infiltration rate and crust strength is necessary for the farmers to consider: when it is necessary to cultivate for breaking the soil crust, crust strength and seedlings emergence, precision farming, etc. To date, soil IR is measured in the laboratory and in small-scale field plots, using rainfall simulators. This method is tedious and consumes considerable resources. Therefore, an available, non-destructive-in situ methods for soil IR and soil crusting levels evaluations, are essential for the verification of infiltration and runoff models and the evaluation of the amount of available water in the soil. In this research, soil samples from the US and Israel were subjected to simulated rainstorms of increasing levels of cumulative energies, during which IR (crusting levels) were measured. The soils from the US were studied simultaneously in the US and in Israel in order to compare the effect of the methodology on the results. The soil surface reflectance was remotely measured, using laboratory and portable spectrometers in the VIS-NIR and SWIR spectral region (0.4-2.5mm). A correlation coefficient spectra in which the wavelength, consisting of the higher correlation, was selected to hold the highest linear correlation between the spectroscopy and the infiltration rate. There does not appear to be a single wavelength that will be best for all soils. The results with the six soils in both countries indeed showed that there is a significant correlation between the infiltration rate of crusted soils and their reflectance values. Regarding the wavelength with the highest correlation for each soil, it is likely that either a combined analysis with more then one wavelength or several "best" wavelengths will be found that will provide useful data on soil surface condition and infiltration rate. The product of this work will serve as a model for predicting infiltration rate and crusting levels solely from the reflectance readings. Developing the aforementioned methodologies will allow increased utilization of rain and irrigation water, reduced runoff, floods and soil erosion hazards, reduced seedlings emergence problems and increased plants stand and yields.
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Crucini, Mario, and Mototsugu Shintani. Measuring Business Cycles by Saving for a Rainy Day. Cambridge, MA: National Bureau of Economic Research, June 2010. http://dx.doi.org/10.3386/w16075.

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Green, John G., and Francis J. Miller. Examining the Effects of Precision Scheduled Railroading on Intercity Passenger and High-Speed Rail Service. Mineta Transportation Institute, March 2022. http://dx.doi.org/10.31979/mti.2022.2016.

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More than just scheduling terminal-to-terminal trips for trains, “Precision Scheduled Railroading” (PSR) creates entire point-to-point trip plans for individual railroad shipments. Since precision execution was first put into practice, the benefits to shipment arrival reliability and to freight railroads’ profitability have been demonstrated by its use in several Class One freight railroads. However, the effects of the PSR operating strategy on passenger railway operations in shared freight/passenger corridors has not been studied in detail. This research examines the effects of PSR railroad operations on passenger railways, including measuring “Host Railroad Minutes of Delay per 10,000 Train-Miles” and “On-Time Performance” of individual passenger railways, both intercity and high-speed.
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Raymond, Kara, Laura Palacios, Cheryl McIntyre, and Evan Gwilliam. Status of climate and water resources at Saguaro National Park: Water year 2019. Edited by Alice Wondrak Biel. National Park Service, December 2021. http://dx.doi.org/10.36967/nrr-2288717.

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Climate and hydrology are major drivers of ecosystems. They dramatically shape ecosystem structure and function, particularly in arid and semi-arid ecosystems. Understanding changes in climate, groundwater, and water quality and quantity is central to assessing the condition of park biota and key cultural resources. The Sonoran Desert Network collects data on climate, groundwater, and surface water at 11 National Park Service units in south-ern Arizona and New Mexico. This report provides an integrated look at climate, groundwater, and springs conditions at Saguaro National Park (NP) during water year 2019 (October 2018–September 2019). Annual rainfall in the Rincon Mountain District was 27.36" (69.49 cm) at the Mica Mountain RAWS station and 12.89" (32.74 cm) at the Desert Research Learning Center Davis station. February was the wettest month, accounting for nearly one-quarter of the annual rainfall at both stations. Each station recorded extreme precipitation events (>1") on three days. Mean monthly maximum and minimum air temperatures were 25.6°F (-3.6°C) and 78.1°F (25.6°C), respectively, at the Mica Mountain station, and 37.7°F (3.2°C) and 102.3°F (39.1°C), respectively, at the Desert Research Learning Center station. Overall temperatures in WY2019 were cooler than the mean for the entire record. The reconnaissance drought index for the Mica Mountain station indicated wetter conditions than average in WY2019. Both of the park’s NOAA COOP stations (one in each district) had large data gaps, partially due to the 35-day federal government shutdown in December and January. For this reason, climate conditions for the Tucson Mountain District are not reported. The mean groundwater level at well WSW-1 in WY2019 was higher than the mean for WY2018. The water level has generally been increasing since 2005, reflecting the continued aquifer recovery since the Central Avra Valley Storage and Recovery Project came online, recharging Central Arizona Project water. Water levels at the Red Hills well generally de-clined starting in fall WY2019, continuing through spring. Monsoon storms led to rapid water level increases. Peak water level occurred on September 18. The Madrona Pack Base well water level in WY2019 remained above 10 feet (3.05 m) below measuring point (bmp) in the fall and winter, followed by a steep decline starting in May and continuing until the end of September, when the water level rebounded following a three-day rain event. The high-est water level was recorded on February 15. Median water levels in the wells in the middle reach of Rincon Creek in WY2019 were higher than the medians for WY2018 (+0.18–0.68 ft/0.05–0.21 m), but still generally lower than 6.6 feet (2 m) bgs, the mean depth-to-water required to sustain juvenile cottonwood and willow trees. RC-7 was dry in June–September, and RC-4 was dry in only September. RC-5, RC-6 and Well 633106 did not go dry, and varied approximately 3–4 feet (1 m). Eleven springs were monitored in the Rincon Mountain District in WY2019. Most springs had relatively few indications of anthropogenic or natural disturbance. Anthropogenic disturbance included spring boxes or other modifications to flow. Examples of natural disturbance included game trails and scat. In addition, several sites exhibited slight disturbance from fires (e.g., burned woody debris and adjacent fire-scarred trees) and evidence of high-flow events. Crews observed 1–7 taxa of facultative/obligate wetland plants and 0–3 invasive non-native species at each spring. Across the springs, crews observed four non-native plant species: rose natal grass (Melinis repens), Kentucky bluegrass (Poa pratensis), crimson fountaingrass (Cenchrus setaceus), and red brome (Bromus rubens). Baseline data on water quality and chemistry were collected at all springs. It is likely that that all springs had surface water for at least some part of WY2019. However, temperature sensors to estimate surface water persistence failed...
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Measuring Rail Market Power in Wheat Transportation Summary, December 4, 2019. U.S. Department of Agriculture, Agricultural Marketing Service, December 2019. http://dx.doi.org/10.9752/ts240.12-2019.

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