Studi Perbandingan Metode Standardized Precipitation Index (SPI) dan Rainfall Anomaly Index (RAI) untuk Mengestimasi Kekeringan pada DAS Welang

2021 ◽  
Vol 1 (2) ◽  
pp. 489-500
Author(s):  
Aulia Rahmawati Muarifah ◽  
◽  
Donny Harisuseno ◽  
Ery Suhartanto ◽  
◽  
...  
Keyword(s):  
Standardized Precipitation Index ◽  
Rainfall Anomaly ◽  
Precipitation Index ◽  
Anomaly Index

Pada DAS Welang terdapat 20 desa di 5 kecamatan di Kabupaten Pasuruan serta 19 desa di 9 kecamatan di Kabupaten Malang yang termasuk daerah rawan kekeringan. Maka dari itu perlu dilakukan analisis kekeringan. Studi ini bertujuan untuk mengetahui hasil perbandingan indeks kekeringan meteorologi metode Standardized Precipitation Index (SPI) dan Rainfall Anomaly Index (RAI) serta kesesuaian metode apabila diterapkan di DAS Welang dalam mengestimasi kekeringan. Selain itu analisis spasial dengan bantuan software ArcGIS 10.3 bertujuan untuk mengetahui daerah yang berpotensi terdampak kekeringan pada DAS Welang. Hasil analisis kekeringan Metode SPI menghasilkan indeks kekeringan paling minimum pada stasiun hujan Purwodadi sebesar -4,09 pada periode 3 bulanan. Sedangkan metode RAI menghasilkan nilai indeks minimum sebesar -3,93 pada periode 1 bulanan. Setelah dilakukan analisis hubungan kesesuaian dengan indeks kekeringan hidrologi menggunakan metode Standardized Streamflow Index (SSI), didapatkan hasil korelasi yang bersifat lemah. Maka dari itu dipilih opsi perbandingan kualitatif dengan membandingkan pola debit dan data curah hujan bulanan dan metode RAI yang lebih sesuai. Hasil penggambaran peta sebaran kekeringan menggunakan metode IDW pada tahun dengan jumlah kejadian kering terparah yaitu tahun 2007 dan 2015, didapatkan sebanyak 42 desa berpotensi terdampak kekeringan dengan bulan kering yaitu bulan Agustus, September, dan Oktober.

scholarly journals Trend Analysis in Standardized Precipitation Index and Standardized Anomaly Index in the Context of Climate Change in Southern Togo

2017 ◽  
Vol 07 (04) ◽  
pp. 401-423 ◽  
Author(s):  
Komlan Koudahe ◽  
Adewumi J. Kayode ◽  
Awokola O. Samson ◽  
Adekunle A. Adebola ◽  
Koffi Djaman
Keyword(s):  
Climate Change ◽  
Trend Analysis ◽  
Standardized Precipitation Index ◽  
Precipitation Index ◽  
Anomaly Index

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

scholarly journals Prediction of Multi-Scalar Standardized Precipitation Index by Using Artificial Intelligence and Regression Models

Climate ◽  
2021 ◽  
Vol 9 (2) ◽  
pp. 28
Author(s):  
Anurag Malik ◽  
Anil Kumar ◽  
Priya Rai ◽  
Alban Kuriqi
Keyword(s):  
Artificial Intelligence ◽  
Fuzzy Inference ◽  
Standardized Precipitation Index ◽  
Meteorological Drought ◽  
Vital Role ◽  
Precipitation Index ◽  
Visual Interpretation ◽  
Inference System ◽  
Drought Prediction ◽  
Optimal Functioning

Accurate monitoring and forecasting of drought are crucial. They play a vital role in the optimal functioning of irrigation systems, risk management, drought readiness, and alleviation. In this work, Artificial Intelligence (AI) models, comprising Multi-layer Perceptron Neural Network (MLPNN) and Co-Active Neuro-Fuzzy Inference System (CANFIS), and regression, model including Multiple Linear Regression (MLR), were investigated for multi-scalar Standardized Precipitation Index (SPI) prediction in the Garhwal region of Uttarakhand State, India. The SPI was computed on six different scales, i.e., 1-, 3-, 6-, 9-, 12-, and 24-month, by deploying monthly rainfall information of available years. The significant lags as inputs for the MLPNN, CANFIS, and MLR models were obtained by utilizing Partial Autocorrelation Function (PACF) with a significant level equal to 5% for SPI-1, SPI-3, SPI-6, SPI-9, SPI-12, and SPI-24. The predicted multi-scalar SPI values utilizing the MLPNN, CANFIS, and MLR models were compared with calculated SPI of multi-time scales through different performance evaluation indicators and visual interpretation. The appraisals of results indicated that CANFIS performance was more reliable for drought prediction at Dehradun (3-, 6-, 9-, and 12-month scales), Chamoli and Tehri Garhwal (1-, 3-, 6-, 9-, and 12-month scales), Haridwar and Pauri Garhwal (1-, 3-, 6-, and 9-month scales), Rudraprayag (1-, 3-, and 6-month scales), and Uttarkashi (3-month scale) stations. The MLPNN model was best at Dehradun (1- and 24- month scales), Tehri Garhwal and Chamoli (24-month scale), Haridwar (12- and 24-month scales), Pauri Garhwal (12-month scale), Rudraprayag (9-, 12-, and 24-month), and Uttarkashi (1- and 6-month scales) stations, while the MLR model was found to be optimal at Pauri Garhwal (24-month scale) and Uttarkashi (9-, 12-, and 24-month scales) stations. Furthermore, the modeling approach can foster a straightforward and trustworthy expert intelligent mechanism for projecting multi-scalar SPI and decision making for remedial arrangements to tackle meteorological drought at the stations under study.

scholarly journals Hydrological response to different time scales of climatological drought: an evaluation of the Standardized Precipitation Index in a mountainous Mediterranean basin

2005 ◽  
Vol 9 (5) ◽  
pp. 523-533 ◽  
Author(s):  
S. M. Vicente-Serrano ◽  
J. I. López-Moreno
Keyword(s):  
Time Scales ◽  
Hydrological Cycle ◽  
Standardized Precipitation Index ◽  
Precipitation Index ◽  
Drought Indices ◽  
Long Time ◽  
The Standardized Precipitation Index ◽  
Hydrological Droughts ◽  
Different Time Scales

Abstract. At present, the Standardized Precipitation Index (SPI) is the most widely used drought index to provide good estimations about the intensity, magnitude and spatial extent of droughts. The main advantage of the SPI in comparison with other indices is the fact that the SPI enables both determination of drought conditions at different time scales and monitoring of different drought types. It is widely accepted that SPI time scales affect different sub-systems in the hydrological cycle due to the fact that the response of the different water usable sources to precipitation shortages can be very different. The long time scales of SPI are related to hydrological droughts (river flows and reservoir storages). Nevertheless, few analyses empirically verify these statements or the usefulness of the SPI time scales to monitor drought. In this paper, the SPI at different time scales is compared with surface hydrological variables in a big closed basin located in the central Spanish Pyrenees. We provide evidence about the way in which the longer (>12 months) SPI time scales may not be useful for drought quantification in this area. In general, the surface flows respond to short SPI time scales whereas the reservoir storages respond to longer time scales (7–10 months). Nevertheless, important seasonal differences can be identified in the SPI-usable water sources relationships. This suggests that it is necessary to test the drought indices and time scales in relation to their usefulness for monitoring different drought types under different environmental conditions and water demand situations.

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