How to search?
advanced search

Search results

Filters

  • Journals
  • Authors
  • Keywords
  • Date
  • Type

Search results

Number of results: 3
items per page: 25 50 75
Sort by:

Equivalent Climate Temperature Analysis as a Criterion of Climate Hazard Evaluation in Polish Underground Mines

Bartłomiej Głuch
Archives of Mining Sciences | 2018 | vol. 63 | No 4 | 975-988 | DOI: 10.24425/ams.2018.124988
Keywords evaluation of microclimate conditions climate hazard underground mines equivalent climate temperature predicted heat strain model PHS model
Download PDF Download RIS Download Bibtex

Abstract

The article discusses changes in Polish regulations concerning assessment of the climate hazard in underground mines. Currently, the main empirical index representing the heat strain, used in qualification of the workplace to one of the climate hazard levels in Poland is the equivalent climate temperature. This simple heat index allows easy and quick assessment of the climate hazard. To a major extent, simple heat indices have simplifications and are developed for a specific working environments. Currently, the best methods used in evaluation of microclimate conditions in the workplace are those based on the theory of human thermal balance, where the physiological parameters characterising heat strain are body water loss and internal core temperature of the human body. The article describes the results of research on usage of equivalent climate temperature to heat strain evaluation in underground mining excavations. For this purpose, the numerical model of heat exchange between man and his environment was used, taken from PN-EN ISO 7933:2005. The research discussed in this paper has been carried out considering working conditions and clothing insulation in use in underground mines. The analyses performed in the study allowed formulation of conclusions concerning application of the equivalent climate temperature as a criterion of assessment of climate hazards in underground mines.

Go to article

Authors and Affiliations

Bartłomiej Głuch

The Importance of the WF Coefficient in Forecasting Climatic Conditions in Coal Excavations

Marcin Smołka
Archives of Mining Sciences | 2022 | vol. 67 | No 3 | 423-436 | DOI: 10.24425/ams.2022.142408
Keywords longwalls mining aerology climate hazard prognosis heat flux calculations coal entries
Download PDF Download RIS Download Bibtex

Abstract

The article presents the results of research on the formation of the WF coefficient in coal excavations. The WF coefficient determines the share of the wet surface of the excavation sidewall. The wet part of the excavation sidewall is covered partly by the water film, which evaporates, lowering the temperature of this surface. This coefficient is one of the principal parameters used in forecasting the changes in temperature and humidity of the mine air occurring on the way of contact between the excavation sidewall and the flowing air. During the determination of the coefficient value, the criterion of equality of the actual and forecasted ratios of sensible heat to total heat was assumed in the research methodology. Values of the WF coefficient in the examined excavations generally vary within the range of 0,1-0,6, and they are mostly dependent on the parameters related to the period of ventilation.
Go to article

Authors and Affiliations

Marcin Smołka
1
ORCID: ORCID
  1. Central Mining Institute, Plac Gwarków 1, 40-166 Katowice, Poland

Assessment of agricultural drought based on CHIRPS data and SPI method over West Papua – Indonesia

Arif Faisol Indarto Indarto Elida Novita Budiyono Budiyono
Journal of Water and Land Development | 2022 | No 52 | 44-52 | DOI: 10.24425/jwld.2021.139942
Keywords agricultural drought Climate Hazards Group Infrared Precipitation with Stations (CHIRPS) data Standardized Precipitation Index (SPI) method West Papua – Indonesia
Download PDF Download RIS Download Bibtex

Abstract

This study aims to utilise Climate Hazards Group Infrared Precipitation with Stations (CHIRPS) data and Standardised Precipitation Index (SPI) method to assess agricultural drought in West Papua, Indonesia. The data used in this study is monthly CHIRPS data acquired from 1996 to 2019, daily precipitation data recorded from 1996 to 2019 from the five climatological stations in West Papua, Indonesia located at Sorong, Fakfak, Kaimana, Manokwari, and South Manokwari. 3-month SPI or quarterly SPI are used to assess agricultural drought, i.e., SPI January–March, SPI February–April, SPI March-May, SPI April–June, SPI May–July, SPI June–August, SPI July–September, SPI August–October, SPI September–November, and SPI October–December. The results showed that in 2019 agricultural drought in West Papua was moderately wet to severely dry. The most severely dry occurred in September– December periods. Generally, CHIRPS data and SPI methods have an acceptable accuracy in generating drought information in West Papua with an accuracy of 53% compared with climate data analysis. Besides, the SPI from CHIRPS data processing has a moderate correlation with climate data analysis with an average R2 = 0.51.
Go to article

Bibliography

BMKG 2020. The standardized precipitation index December 2019 [online]. Jakarta Pusat. Badan Meteorologi, Klimatologi, dan Geofisika [Access 01.02.2020]. Available at: https://www.bmkg.go.id/iklim/indeks-presipitasi-terstandarisasi.bmkg
BPS Provinsi Papua Barat 2019. Provinsi Papua Barat dalam angka 2019 [Papua Barat Province in figures 2019]. Manokwari. Badan Pusat Statistik Provinsi Papua Barat. ISSN 2089-1563 pp. 527.
BYUN H.R., WILHITE D.A. 1999. Objective quantification of drought severity and duration. Journal of American Meteorological Society. No. 12 p. 2747–2756. DOI 10.1175/1520-0442(1999)0122747:OQODSA>2.0.CO;2.
DAS S., CHOUDHURY M.R., GANDHI S., JOSHI V. 2016. Application of Earth observation data and Standardized Precipitation Index based approach for meteorological drought monitoring, assessment and prediction over Kutch, Gujarat, India. International Journal of Environment and Geoinformatics. No. 3(2) p. 24–37. DOI 10.30897/ijegeo.306468.
DINKU T., FUNK C., PETERSON P., MAIDMENT R., TADESSE T., GADAIN H., CECCATO P. 2018. Validation of the CHIRPS satellite rainfall estimates over Eastern Africa. Quarterly Journal of the Royal Meteorological Society. No. 144 p. 292–312. DOI 10.1002/qj.3244.
EDO 2019. Standardized Precipitation Index (SPI) [online]. Ispra. European Drought Observatory. [Access 02.02.2020]. Available at: https://edo.jrc.ec.europa.eu/documents/factsheets/factsheet_-spi.pdf
FAO 2018.The impact of disasters and crises on agriculture and food security. 1st ed. Rome. Food and Agriculture Organization. ISBN 978-92-5-130359-7 pp. 143.
FUNK C.C., PETERSON P. J., LANDSFELD M. F., PEDREROS D.H., VERDIN J.P., ROWLAND J.D., VERDIN A.P. 2014. A quasi-global precipitation time series for drought monitoring. 1st ed. Virginia. U.S. Geological Survey Data Series. No. 832. ISSN 2327-638X pp. 4. DOI 10.3133/ds832.
GEBRECHORKOS S.H., HÜLSMANN S., BERNHOFER C. 2018. Evaluation of multiple climate data sources for managing environmental resources in East Africa. Journal of Hydrology and Earth System Sciences. No. 22 p. 4547–4564. DOI 10.5194/hess-22-4547-2018.
GUTTMAN N.B. 1999. Accepting the Standardized Precipitation Index: A calculation algorithm. Journal of The American Water Resource Association. Vol. 35(2) p. 311–322. DOI 10.1111/j.1752-1688.1999.tb03592.x.
HEIM R.R. 2002. A review of twentieth-century drought indices used in the United States. Bulletin of American Meteorological Society. No. 83 p. 1149–1165. DOI 10.1175/1520-0477-83.8.1149.
KARAVITIS C.A., ALEXANDRIS S., TSESMELIS D. E., ATHANASOPOULOS G. 2011. Application of the Standardized Precipitation Index (SPI) in Greece. Journal of Water. Vol. 3 p. 787–805. DOI 10.3390/w3030787.
KUMAR M.N., MURTHY C.S., SESHA M.V. R., ROY P.S. 2009. On the use of Standardized Precipitation Index (SPI) for drought intensity assessment. Journal of Meteorological Application. Vol. 16. Iss. 3 p. 381–389. DOI 10.1002/met.136.
MAHARANI T. 2019. Pemodelan bahaya kekeringan meteorologis di Provinsi Jawa Timur menggunakan data CHIRPS [Climate Hazards Group infrared precipitation with station data] [online]. MSc Thesis. Gadjah Mada University. [Access 01.02.2020]. Available at: https://www.google.com/search?client=firefox-b- d&q=Pemodelan+bahaya+kekeringan+meteorologis+di+Provin-si+Jawa+Timur+menggunakan+data+CHIRPS+
MISHRA S.S., NAGARAJAN R. 2011. Spatio-temporal drought assessment in Tel River Basin using Standardized Precipitation Index (SPI) and GIS. Journal of Geomatics, Natural Hazards, and Risk. Vol. 2(1) p. 79–93. DOI 10.1080/19475705.2010.533703.
MISNAWATI 2018. Evaluasi performa Standardized Precipitation Index (SPI) sebagai indikator kekeringan pertanian di Jawa Tengah [An evaluation of Standardized Precipitation Index (SPI) for agricultural drought indicator in Central Java] [online]. Thesis. IPB University. [Access 01.02.2020]. Available at: http://repository. ipb.ac.id/handle/123456789/92614
NOSRATI K., ZAREIEE A.R. 2011. Assessment of meteorological drought using SPI in West Azarbaijan Province, Iran. Journal of Applied Sciences and Environmental Management. Vol. 15(4) p. 563–569.
NUGROHO P.C., PINUJI S.E., ICHWANA A.N., NUGRAHA A., WIGUNA S., SYAUQI, SETIAWAN A. 2018. Data dan informasi bencana Indonesia [Indonesian disaster risk index]. Jakarta. Badan Nasional Penanggulangan Bencana pp. 325.
PALMER W.C. 1965. Meteorological drought. Washington, DC. USDC Weather Burreau. Research Paper. No. 45 pp. 58.
SALEHNIA N., ALIZADEH A., SANAEINEJAD H., BANNAYAN M., ZARRIN A., HOOGENBOOM G. 2017. Estimation of meteorological drought indices based on AgMERRA precipitation data and station-observed precipitation data. Journal of Arid Land. No. 9 (October) p. 797–809. DOI 10.1007/s40333-017-0070-y.
TOPÇU E., SEÇKIN N. 2016. Drought analysis of the Seyhan Basin by using Standardized Precipitation Index (SPI) and L-moments. Journal of Agricultural Sciences. Vol. 22 p. 196–215. DOI 10.1501/TARIMBIL_0000001381.
NOAA 2020. Drought information [online]. National Weather Service. National Oceanic and Atmospheric Administration / National Centers for Environmental Prediction Climate Prediction Center. National Center for Weather and Climate Prediction [Access 01.02.2020]. Available at: https://www.cpc.ncep.noaa.gov/pro-ducts/Drought/
WIDODO N. 2013. Analisis dan pemetaan indeks kekeringan meteorologis menggunakan data satelit TRMM dari 36 titik stasiun BMKG di Pulau Sumatera [Meteorological drought index analysis and mapping using TRMM satellite on Sumatera Island] [online]. Thesis. IPB University. [Access 10.02.2020]. Available at: http://repository.ipb.ac.id/handle/123456789/67416.
WILHITE D.A., GLANTZ M.H. 1985. Understanding the drought phenomenon: The role of definition. Journal of Water Interna-tional. No. 10 p. 111–120. DOI 10.1080/02508068508686328.
WITONO A., CHOLIANAWATI N. 2011. Deteksi dan prediksi kekeringan meteorologis di Sumatera Selatan menggunakan satelit TRMM [Detection and prediction of meteorological drought in South Sumatera using the TRMM satellite]. Seminar nasional sains atmosfer dan antariksa 2011. ISBN 9789791458535.
WMO 2012. Standardized Precipitation Index user guide. 1st ed. (M. Svoboda, M. Hayes, D. Wood). WMO-No. 1090. Geneva. World Meteorological Organization. ISBN 978-92-63-11090-9 pp. 16.
XIA L., ZHAO F., MAO K., YUAN Z., ZUO Z., XU T. 2018. SPI-based analyses of drought changes over the past. Journal of Remote Sensing. No. 10(171) p. 1–15. DOI 10.3390/rs10020171.
ZHU Q., LUO Y., ZHOU D., XU Y., WANG G., GAO H. 2019. Drought monitoring utility using satellite-based precipitation products over the Xiang River Basin in China. Journal of Remote Sensing. Vol. 11 p. 1–17. DOI 10.3390/rs11121483.
Go to article

Authors and Affiliations

Arif Faisol
1
ORCID: ORCID
Indarto Indarto
2
ORCID: ORCID
Elida Novita
2
Budiyono Budiyono
3
  1. University of Papua, Faculty of Agricultural Technology, Jl. Gn. Salju, Manokwari, West Papua 98314, Indonesia
  2. University of Jember, Faculty of Agricultural Technology, Jember, East Java, Indonesia
  3. University of Papua, Faculty of Agriculture, Manokwari, West Papua, Indonesia

This page uses 'cookies'. Learn more