Application of the Standardized Precipitation Index and Normalized Difference Vegetation Index for Evaluation of Irrigation Demands at Three Sites in Jamaica

2013 ◽  
Vol 139 (11) ◽  
pp. 922-932 ◽  
Author(s):  
Johanna Richards ◽  
Chandra A. Madramootoo ◽  
Manish Kumar Goyal ◽  
Adrian Trotman
Keyword(s):  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Standardized Precipitation Index ◽  
Precipitation Index ◽  
The Standardized Precipitation Index

scholarly journals Spatial distribution of drought levels in Bantimurung District, Maros Regency, South Sulawesi Province

2020 ◽  
Vol 153 ◽  
pp. 02008
Author(s):  
Mina Senjani ◽  
Eko Kusratmoko ◽  
Yoanna Ristya
Keyword(s):  
Spatial Distribution ◽  
El Niño ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
El Nino ◽  
Standardized Precipitation Index ◽  
Temporal Distribution ◽  
Precipitation Index ◽  
South Sulawesi ◽  
Long Time

Drought is an event that almost occurs every year in several regions of Indonesia. Drought events are often associated with the El Nino phenomenon, namely the lack of rainfall over a certain period of time in a reasonably long time. In 2015, Indonesia experienced drought in several parts of Indonesia, one of which was Bantimurung District in Maros Regency, South Sulawesi Province. This study aimed to map the level of drought in Bantimurung District. Standardized Precipitation Index (SPI) based meteorological and Normalized Difference Vegetation Index (NDVI) were used to determine the spatial-temporal distribution of drought. Then, the NDVI values of weak (2014) and very strong (2015) El Nino year were correlated with SPI. The results show that Bantimurung District experienced drought in 2015 with near normal (July-September) to moderately dry (October-November) drought levels. In 2014, the drought was not so severe compared to 2015 because the level of drought was near normal and moderately wet. In 2014, the moderately wet area was located in the east district including Leang-leang and Kalabbirang villages. In 2015, villages Minasa Baji, Mattoangin, Alangtae, Baruga, Tukamasea, Mangeloreng, west of Kalabbirang and Leang Leang were located in western of the district have moderately dry drought level.

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.

scholarly journals Seasonal forecasts of droughts in African basins using the Standardized Precipitation Index

2013 ◽  
Vol 17 (6) ◽  
pp. 2359-2373 ◽  
Author(s):  
E. Dutra ◽  
F. Di Giuseppe ◽  
F. Wetterhall ◽  
F. Pappenberger
Keyword(s):  
Real Time ◽  
Lead Time ◽  
Standardized Precipitation Index ◽  
Precipitation Index ◽  
Seasonal Forecasting ◽  
Monthly Precipitation ◽  
Seasonal Forecasts ◽  
Blue Nile ◽  
Forecasting System ◽  
The Standardized Precipitation Index

Abstract. Vast parts of Africa rely on the rainy season for livestock and agriculture. Droughts can have a severe impact in these areas, which often have a very low resilience and limited capabilities to mitigate drought impacts. This paper assesses the predictive capabilities of an integrated drought monitoring and seasonal forecasting system (up to 5 months lead time) based on the Standardized Precipitation Index (SPI). The system is constructed by extending near-real-time monthly precipitation fields (ECMWF ERA-Interim reanalysis and the Climate Anomaly Monitoring System–Outgoing Longwave Radiation Precipitation Index, CAMS-OPI) with monthly forecasted fields as provided by the ECMWF seasonal forecasting system. The forecasts were then evaluated over four basins in Africa: the Blue Nile, Limpopo, Upper Niger, and Upper Zambezi. There are significant differences in the quality of the precipitation between the datasets depending on the catchments, and a general statement regarding the best product is difficult to make. The generally low number of rain gauges and their decrease in the recent years limits the verification and monitoring of droughts in the different basins, reinforcing the need for a strong investment on climate monitoring. All the datasets show similar spatial and temporal patterns in southern and north-western Africa, while there is a low correlation in the equatorial area, which makes it difficult to define ground truth and choose an adequate product for monitoring. The seasonal forecasts have a higher reliability and skill in the Blue Nile, Limpopo and Upper Niger in comparison with the Zambezi. This skill and reliability depend strongly on the SPI timescale, and longer timescales have more skill. The ECMWF seasonal forecasts have predictive skill which is higher than using climatology for most regions. In regions where no reliable near-real-time data is available, the seasonal forecast can be used for monitoring (first month of forecast). Furthermore, poor-quality precipitation monitoring products can reduce the potential skill of SPI seasonal forecasts in 2 to 4 months lead time.

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