Analysis of multidimensional aspects of agricultural droughts in Zimbabwe using the Standardized Precipitation Index (SPI)

Manatsa, Desmond; Mukwada, Geoffrey; Siziba, Emmanuel; Chinyanganya, Tafadzwa

doi:10.1007/s00704-010-0262-2

Cited by 85 publications

(53 citation statements)

References 23 publications

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“…Heim [24] reveals that, even though index was developed purposely for use in Colorado, it can be applied universally to any location. Furthermore, Manatsa et al [23] found the index to be temporarily and spatially comparable, independent of geographical and topographical differences, and even relevant in regions with diverse rainfall patterns.…”

Section: Methodsmentioning

confidence: 99%

“…Since its development, the index has gained increasing acceptance in the United States and other parts of the world as a valuable tool for monitoring drought. It is currently being used by the U.S National Drought Mitigation Center, the Western Regional Climate Center, as well as the Colorado Climate Center [22,23]. The index uses only precipitation data thus making the analysis possible even in the absence of other parameters.…”

Section: Methodsmentioning

confidence: 99%

“…1 month SPI, 3 month SPI, 6 month SPI, 12 month SPI, 24 month SPI etc. ); short term durations of the order of months may be important to agricultural interests while long term durations spanning years may be important to water resources management purpose because of the slow inherent responses in water bodies to rainfall changes [22][23][24]. The method is capable of returning essential parameters after the analysis such as severity, magnitude, and frequency of the drought.…”

Section: Methodsmentioning

confidence: 99%

“…Inspection from the regional SPI value obtained reveals that, the wettest season in record was that of 1978/1979 which can be classified as Severely wet (SPI value of 1.62) and the driest season was that of 1999/ 2000 which can be classified as Moderate drought (SPI value of −1.26) according to Manatsa et al [23] classification presented in Table 2. Table 3 shows five (5) wettest and driest years in record for the region during the study period.…”

Section: Regional Standardized Precipitation Indexmentioning

confidence: 97%

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Climate Characteristics over Southern Highlands Tanzania

Mbululo¹,

Nyihirani²

2012

ACS

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This study was conducted to examine the climate characteristic of southern highland Tanzania (Latitude 6˚S -12˚S and Longitude 29˚E -38˚E). The study findings reveal that rainfall over the region is linked with SST over the Indian Ocean, where warmer (cooler) western Indian Ocean is accompanied by high (low) amount of rainfall over Tanzania. During wet (dry) years, weaker (stronger) equatorial westerlies and anticyclone (cyclonic) anomaly over the southern tropics act to reduce (enhance) the export of equatorial moisture away from East Africa. The wettest (driest) season was found to be 1978/79 (1999/00) which can be classified as the severely wet (moderate drought). Two different modes of rainfall have been identified at time scale of 1.5 and 6 years which have been associated with the quasi biennial oscillation (QBO) and El Nino Southern Oscillation (ENSO), respectively.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Regional Standardized Precipitation Indexmentioning

confidence: 97%

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Climate Characteristics over Southern Highlands Tanzania

Mbululo¹,

Nyihirani²

2012

ACS

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“…Among the several indices, two of the most commonly used are the standardized precipitation index (SPI), which transforms monthly precipitation time series into a standardized normal distribution, and the drought severity index (DSI), which uses accumulated monthly precipitation anomalies. In particular, the SPI has found widespread application in different countries of the world [29][30][31][32][33][34][35], in the Mediterranean basin [36][37][38] and also in Central [39] and Southern Italy [40][41][42][43][44][45][46]. The SPI is easier to calculate than more complex indices because it is based on precipitation alone for estimating wet or dry conditions [38,47].…”

Section: Introductionmentioning

confidence: 99%

An Analysis of the Occurrence Probabilities of Wet and Dry Periods through a Stochastic Monthly Rainfall Model

Caloiero

Sirangelo

Coscarelli

et al. 2016

Water

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Stochastic simulators can effectively generate the intrinsic variability of the rainfall process, which is an important issue in the analysis of the projections uncertainties. In this paper, a procedure for stochastic modeling of precipitation at monthly scale is proposed. The model adopts variable transformations, which are finalized to the deseasonalization and the Gaussianization of the monthly rainfall process, and includes a procedure for testing the autocorrelation. The model was applied to a homogeneous database of monthly rainfall values registered in 12 rain gauges in the region of Calabria (Southern Italy). After the estimation of the model parameters, a set of 10 4 years of monthly rainfall for each rain gauge was generated by means of a Monte Carlo technique. Then, dry and wet periods were analyzed through the application of the standardized precipitation index (SPI). Some results, confirmed through the application of the drought severity index (DSI), showed that the proposed model provided a good representation of the monthly rainfall for the considered rain gauges. Moreover, the results of the SPI application indicate a greater probability of dry conditions than wet conditions, especially when long-term precipitation patterns are considered.

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Monitoring and forecasting drought impact on dryland farming areas

Arshad¹,

Morid²,

Mobasheri³

et al. 2012

Intl Journal of Climatology

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Frequent drought amplifies the need for a warning system and forecasting models for damage to crop yields. This study developed an operational model to assess agricultural drought impact. The dryland areas of Kermanshah Province (Iran) were selected to test the proposed modelling system. The model predicted the consequences of drought damage on wheat crop during critical stages of growth (emergence, vegetative growth, initiation of flowering, grain filling, and maturity) as a drought loss indicator. Two types of input were evaluated to correlate climate conditions versus drought losses. The first group comprises the Palmer Drought Severity Index, Z-index, Crop Moisture Index, Crop-Specific Drought Index (CSDI), Standardized Precipitation Index, and Effective Drought Index with one-to three-month timescales used as meteorological indices. The second group, which is consistent of the vegetation condition index and temperature condition index, is based on satellite data. Also a new satellite-based version of CSDI, so-called standardized CSDI (S-CDSI), where evapotranspiration was estimated using surface energy balance algorithm for land, is used. Adaptive Neuro-Fuzzy Inference Systems (ANFIS) technique was used for forecasting with genetic algorithms applied to select appropriate inputs from among the large number of indices. It was concluded that the combination of meteorological and satellite indices performed best in forecasting crop yield. As expected, accuracy improved over the growth stages as the crop developed. Enhancement of the model with a GIS platform made it possible to present the results more suitably, hence helping users to make more realistic decisions.

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Analysis of multidimensional aspects of agricultural droughts in Zimbabwe using the Standardized Precipitation Index (SPI)

Cited by 85 publications

References 23 publications

Climate Characteristics over Southern Highlands Tanzania

Climate Characteristics over Southern Highlands Tanzania

An Analysis of the Occurrence Probabilities of Wet and Dry Periods through a Stochastic Monthly Rainfall Model

Monitoring and forecasting drought impact on dryland farming areas

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