scholarly journals Studi Kekeringan Meteorologi dengan Menggunakan Metode Standardized Precipitation Index (SPI) dan China Z Index (CZI) di DAS Lekso Kabupaten Blitar

2021 ◽  
Vol 1 (2) ◽  
pp. 648-660
Author(s):  
Firda Novita ◽  
◽  
Donny Harisuseno ◽  
Ery Suhartanto ◽  
◽  
...  
Keyword(s):  
Southern Oscillation Index ◽  
Southern Oscillation ◽  
Standardized Precipitation Index ◽  
Precipitation Index

Kabupaten Blitar merupakan salah satu daerah yang rawan terjadi kekeringan, salah satunya yaitu DAS Lekso. Kekeringan yang terjadi di Kabupaten Blitar disebabkan oleh minimnya intensitas curah hujan yang turun, maka dari itu dibutuhkan upaya awal untuk memitigasi kekeringan meteorologi dengan cara memantau dan menganalisis kekeringan yang terjadi pada lokasi studi. Metode yang digunakan dalam menganalisis kekeringan yaitu metode Standardized Precipitation Index (SPI)) dan metode China Z Index (CZI) yang kemudian dibandingkan dengan data Southern Oscillation Index (SOI). Hasil indeks kekeringan kedua metode yang telah dikomparasi dengan data SOI akan digunakan sebagai penggambaran peta sebaran kekeringan menggunakan Sistem Informasi Geografis (SIG) dengan interpolasi Kriging. Pada hasil analisa perbandingan indeks kekeringan dengan data SOI bulanan didapatkan hasil persentase pendekatan metode CZI sebesar 57.45% dan metode SPI sebesar 42.55%. Pada perbandingan indeks kekeringan dengan SOI rerata tahunan didapatkan persentase metode CZI sebesar 63% dan metode SPI sebesar 60%. Pada hasil analisa korelasi indeks kekeringan yang dikomparasi dengan data hujan didapatkan nilai korelasi metode CZI memiliki tingkat hubungan korelasi mendekati positif sempurna dan metode SPI memiliki korelasi yang cukup. Sehingga metode CZI dipilih sebagai penggambaran peta sebaran kekeringan menggunakan interpolasi kriging yang kemudian didapatkan desa-desa yang terdampak kekeringan di Kabupaten Blitar khusunya di DAS Lekso.

scholarly journals Studi Analisa Kekeringan Metode Standardized Precipitation Index (SPI) dan Palmer Drought Severity Index (PDSI) di DAS Kemuning Kabupaten Sampang

2021 ◽  
Vol 1 (2) ◽  
pp. 535-548
Author(s):  
Alfian Firdaus ◽  
◽  
Donny Harisuseno ◽  
Ery Suhartanto ◽  
◽  
...  
Keyword(s):  
Southern Oscillation Index ◽  
Palmer Drought Severity Index ◽  
Southern Oscillation ◽  
Standardized Precipitation Index ◽  
Severity Index ◽  
Precipitation Index ◽  
Drought Severity ◽  
Distance Weighting ◽  
Inverse Distance ◽  
Drought Severity Index

Kekeringan ialah bencana alam yang terjadi secara perlahan dan berdampak buruk untuk kelangsungan hidup penduduk Kabupaten Sampang. Mengingat hal tersebut, perlu dilakukan analisa indeks kekeringan serta pemetaan sebarannya sebagai upaya mitigasi bencana kekeringan. Studi ini bertujuan untuk mengetahui tingkat keparahan kekeringan dengan metode Standardized Precipitation Index (SPI) dan Palmer Drought Severity Index (PDSI), serta kesesuaiannya dengan data Southern Oscillation Index (SOI) yang mampu mempresentasikan kejadian El Nino Southern Oscillation (ENSO). Setelah itu, Indeks kekeringan yang lebih sesuai dengan pola SOI dipetakan dengan metode Inverse Distance Weighting (IDW) untuk mengetahui sebaran kekeringan. Metode SPI menghasilkan indeks kekeringan terparah di bulan April 2004 sebesar -3,651 pada periode defisit 1 bulanan. Metode PDSI menghasilkan indeks kekeringan terparah di bulan September 2001 sebesar - 20,628. Berdasarkan hasil analisa rerata PDSI periode 1998-2017, diketahui bahwa bencana kekeringan umumnya bermula sejak bulan Juli dan berakhir di bulan Oktober, sedangkan puncak kekeringan terjadi pada bulan September. Metode PDSI juga memiliki kesesuaian sebesar 60% terhadap nilai SOI berdasarkan penggambaran grafik surplus dan defisit indeks rerata tahunan, lebih baik daripada metode SPI yang hanya bernilai 53%. Penggambaran peta sebaran kekeringan berdasarkan indeks kekeringan PDSI menunjukkan bahwa Kecamatan Sampang, Torjun, dan Camplong perlu diprioritaskan dalam upaya mitigasi bencana kekeringan di masa mendatang karena memiliki potensi bencana kekeringan lebih besar jika dibandingan kecamatan lainnya.

scholarly journals Spatial and Temporal Analysis of Drought in Nepal using Standardized Precipitation Index and its Relationship with Climate Indices

1970 ◽  
Vol 7 (1) ◽  
pp. 59-74 ◽  
Author(s):  
M Sigdel ◽  
M Ikeda
Keyword(s):  
Principal Component Analysis ◽  
Southern Oscillation ◽  
Climatic Variability ◽  
Standardized Precipitation Index ◽  
Principal Component ◽  
Component Analysis ◽  
Precipitation Index ◽  
Climate Indices ◽  
Indian Ocean Dipole Mode ◽  
Time Space

Drought over Nepal is studied on the basis of precipitation as a key parameter. Using monthly mean precipitation data for a period of 33 years, Standardized Precipitation Index (SPI) is produced for the drought analysis with the time scale of 3 months (SPI-3) and 12 months (SPI-12) as they are applicable for agriculture and hydrological aspects, respectively. Time-space variability is explored based on Principal Component Analysis (PCA) along with Rotated PCA (RPCA). Four rotated components were explored for both SPI-3 and SPI-12 representing climatic variability with cores over eastern, central and western Nepal separately. Droughts associated with SPI-3 occurred almost evenly over these regions. Droughts associated with SPI-12 were consistent with SPI-3 for summer, since summer precipitation dominates annual precipitation. Connection between SPI and the climate indices such as Southern Oscillation Index (SOI) and Indian Ocean Dipole Mode Index (DMI) was studied, suggesting that one of the causes for summer droughts is El Nino, while the winter droughts could be related with positive DMI. Keywords: Standardized Precipitation Index; Nepal; Principal component analysis; Drought DOI: http://dx.doi.org/10.3126/jhm.v7i1.5617 JHM 2010; 7(1): 59-74

scholarly journals ANALISIS KEKERINGAN DI KABUPATEN SRAGEN DENGAN METODE PALMER, THORNTHWAITE, DAN STANDARDIZED PRECIPITATION INDEX

2021 ◽  
Vol 17 (2) ◽  
pp. 111-124
Author(s):  
Safrudin Nor Aripbilah ◽  
Heri Suprapto
Keyword(s):  
Drought Index ◽  
Southern Oscillation ◽  
Standardized Precipitation Index ◽  
Severity Index ◽  
Precipitation Index ◽  
Suitable Method ◽  
Drought Severity ◽  
Severe Drought ◽  
Drought Hazard ◽  
Dry Conditions

El Nino and La Nina in Indonesia are one of the reasons that caused climate changes, which has possibility of drought and flood disasters. Sragen Regency wherethe dry season occurs, drought happened meanwhile other areas experience floods and landslides. A study on drought needs to be carried out so as to reduce the risk of losses due to the drought hazard. This study is to determine the drought index in Sragen Regency based on several methods and the correlation of each methods and its suitability to the Southern Oscillation Index (SOI) and rainfall. Drought was analyzed using several methods such as Palmer Drought Severity Index (PDSI), Thornthwaite-Matter, and Standardized Precipitation Index (SPI) then correlated with SOI to determine the most suitable method for SOI. The variables are applied in this method are rainfall, temperature, and evapotranspiration. The results showed that the drought potential of the Palmer method is only in Near Normal conditions, which is 1%, Severe drought conditions are 29% for the Thornthwaite-Matter method, and Extreme Dry conditions only reach 1,11% for the SPI method. The PDSI and SPI methods are inversely proportional to the Thornthwaite-Matter method and the most suitable method for SOI values or rainfall is the SPI method. These three methods can be identified the potential for drought with only a few variables so that they could be applied if they only have those data.Keywords: Drought, PDSI, Thornthwaite-Matter, SPI, SOI

scholarly journals Analisis Kekeringan Meteorologi dengan Menggunakan Metode Standardized Precipitation (SPI) dan Reconnaissance Drought Index (RDI) di DAS Lekso Kabupaten Blitar

2021 ◽  
Vol 1 (2) ◽  
pp. 672-685
Author(s):  
Amifta Farah Listya ◽  
◽  
Donny Harisuseno ◽  
Ery Suhartanto ◽  
◽  
...  
Keyword(s):  
Drought Index ◽  
Southern Oscillation ◽  
Standardized Precipitation Index ◽  
Precipitation Index ◽  
Reconnaissance Drought Index

Kekeringan dapat didefinisikan pengurangan persediaan air yang bersifat sementara secara signifikan di bawah normal. Bencana kekeringan yang terjadi di Indonesia saat ini mengakibatkan daerah kekurangan suplai air untuk kebutuhan hidup, pertanian, dan kegiatan ekonomi dalam masa yang berkepanjangan. Meninjau dampak yang ditimbulkan, maka diperlukan analisis untuk daerah-daerah yang memiliki potensi terjadinya bencana kekeringan. Terdapat beberapa metode yang dikembangkan untuk menganalisis kekeringan, seperti SPI (Standardized Precipitation Index) dan RDI (Reconnaissance Drought Index), sehingga mengetahui tingkat dan karakteristik kekeringan suatu daerah. Setelah melakukan analisis dengan kedua indeks tersebut dilakukan pengambaran peta sebaran kekeringan menggunakan Sistem Informasi Geografi sehingga mempermudah menginterpretasikan daerah yang mengalami potensi kekeringan pada DAS Lekso , serta dapat melakukan upaya-upaya pencegahan dan penanggulangan bahaya bencana kekeringan. hasil penelitian menunjukkan puncak kekeringan metode SPI periode defisit 1 bulan terjadi Mei tahun 2005 dengan wilayah desa yaitu Desa Slumbung, Balerejo, Semen, Tulungrejo dan Soso. Sedangkan pada metode RDI , puncak kekeringan terjadi pada bulan Mei tahun 2005 dengan wilayah desa yang mengalami kekeringan yaitu Desa Slumbung, Balerejo, Semen, Tulungrejo dan Soso. Berdasarkan analisis kesesuaian antara indeks kekeringan dengan data Southern Oscillation Indeks, disimpulkan bahwa perhitungan indeks kekeringan metode RDI memiliki prosentase tingkat kesesuaian lebih tinggi dibandingkan dengan metode indeks kekeringan SPI.

scholarly journals A Non-Stationary and Probabilistic Approach for Drought Characterization Using Trivariate and Pairwise Copula Construction (PCC) Model

2021 ◽  
Author(s):  
Soumyashree Dixit ◽  
K V Jayakumar
Keyword(s):  
Drought Index ◽  
Southern Oscillation ◽  
Standardized Precipitation Index ◽  
Precipitation Index ◽  
Drought Indices ◽  
Climate Indices ◽  
Copula Model ◽  
Reconnaissance Drought Index ◽  
Drought Characteristics ◽  
Recurrence Intervals

Abstract Under the variable climatic conditions, the conventional Standardized Precipitation Index (SPI) and Reconnaissance Drought Index (RDI) are inadequate for predicting extreme drought characteristics. So in the present study, two indices namely, Non-stationary Standardized Precipitation Index (NSPI) and Non-stationary Reconnaissance Drought Index (NRDI) are developed by fitting non-stationary gamma (for precipitation series) and lognormal (for initial values,δ0) distributions. The Generalized Additive Model in Location, Scale and Shape (GAMLSS) framework, with time varying location parameters considering the external covariates, is used to fit the non-stationary distributions. This includes various large scale climate indices namely Multivariate ENSO Index (MEI), Southern Oscillation Index (SOI), Sea Surface Temperature (SST), and Indian Ocean Dipole (IOD) as external covariates for the non-stationary drought assessment. The performances of stationary and non-stationary models are compared based on the Akaika Information Criterion (AIC). Additionally, the drought characteristics are evaluated using Run theory analysis for both stationary and non-stationary drought indices. The study also concentrated on the trivariate copula as well as the Pairwise Copula Construction (PCC) models to estimate the drought recurrence intervals. The comparison of two copula models revealed that the PCC model performed better than the trivariate Student’s t copula model. The recurrence intervals arrived at for the drought events are different for trivariate copula model and PCC model. The area taken for the study is the Upper and Lower sub basins of the Godavari River basin. This study shows that non-stationary drought indices will be helpful in the accurate estimate of the drought characteristics under the changing climatic scenario.

scholarly journals Meteorological Drought and its Relationship with Southern Oscillation Index (SOI)

2020 ◽  
Vol 6 (10) ◽  
pp. 1864-1875 ◽  
Author(s):  
Donny Harisuseno
Keyword(s):  
Southern Oscillation Index ◽  
Drought Index ◽  
Southern Oscillation ◽  
Pearson Correlation ◽  
Standardized Precipitation Index ◽  
Meteorological Drought ◽  
Drought Risk ◽  
Drought Event ◽  
Association Analyses ◽  
The Standardized Precipitation Index

Drought monitoring, including its severity, spatial, and duration is essential to enhance resilience towards drought, particularly for overcoming drought risk management and mitigation plan. The present study has an objective to examine the suitability of the Standardized Precipitation Index (SPI) and Percent of Normal Index (PN) on assessing drought event by analyzing their relationship with the Southern Oscillation Index (SOI). The monthly rainfall data over twenty years of the observation period were used as a basis for data input in the drought index calculation. The statistical association analyses, included the Pearson Correlation (r), Kendal tau (τ), and Spearman rho (rs) used to assess the relationship between the monthly drought indexes and SOI. The present study confirmed that the SPI showed a more consistent and regular pattern relationship with SOI basis which was indicated by a moderately high determination coefficient (R2) of 0.74 and the magnitude of r, τ, and rs that were of 0.861, 0.736, and 0.896, respectively. Accordingly, the SPI showed better compatibility than the PN for estimating drought characteristics. The study also revealed that the SOI data could be used as a variable to determine the reliability of drought index results.

Economical exposition to drought events (1980-2001)

2017 ◽  
Author(s):  
Chloé Meyer
Keyword(s):  
World Bank ◽  
Standardized Precipitation Index ◽  
Research Unit ◽  
Precipitation Index ◽  
Climatic Research Unit ◽  
Columbia University ◽  
East Anglia ◽  
Gis Modeling ◽  
The World Bank ◽  
Year 2000

Estimation of the annual economical exposition to drought based on Standardized Precipitation Index. It is based on three sources: 1) A global monthly gridded precipitation dataset obtained from the Climatic Research Unit (University of East Anglia). 2) A GIS modeling of global Standardized Precipitation Index based on Brad Lyon (IRI, Columbia University) methodology. 3) A Global Domestic Product grid for the year 2010, provided by the World Bank. Unit is expected average annual GDP (2007 as the year of reference) exposed in (US $, year 2000 equivalent). For more information, visit: http://preview.grid.unep.ch/ Cost Drought Exposure Risk

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