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

Treść / Zawartość
Identyfikatory
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
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.
Wydawca
Rocznik
Tom
Strony
44--52
Opis fizyczny
Bibliogr. 28 poz., rys., tab., wykr.
Twórcy
autor
  • University of Papua, Faculty of Agricultural Technology, Jl. Gn. Salju, Manokwari, West Papua 98314, Indonesia
  • University of Jember, Faculty of Agricultural Technology, Jember, East Java, Indonesia
autor
  • University of Jember, Faculty of Agricultural Technology, Jember, East Java, Indonesia
  • University of Papua, Faculty of Agriculture, Manokwari, West Papua, Indonesia
Bibliografia
  • 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.
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  • 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 International. No. 10 p. 111–120. DOI 10.1080/02508068508686328.
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  • 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.
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Uwagi
Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-c054b4db-d008-48eb-98c2-8b924c3c0875
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