Computation of the Standardized Precipitation Index (SPI) and Its Use to Assess Drought Occurrences in Cameroon over Recent Decades

Guenang, Guy Merlin; Kamga, F. Mkankam

doi:10.1175/jamc-d-14-0032.1

Cited by 109 publications

(73 citation statements)

References 32 publications

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“…In this study, a 35-year historical record of precipitation data were used to assure the SPI accuracy. For a more detailed explanation of the theory and calculation of SPI, one may refer to these related studies [14,75,76].…”

Section: Standardized Precipitation Index (Spi)mentioning

confidence: 99%

Mapping the Spatial-Temporal Dynamics of Vegetation Response Lag to Drought in a Semi-Arid Region

Wang

Sui

et al. 2019

Remote Sensing

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Drought, as an extreme climate event, affects the ecological environment for vegetation and agricultural production. Studies of the vegetative response to drought are paramount to providing scientific information for drought risk mitigation. In this paper, the spatial-temporal pattern of drought and the response lag of vegetation in Nebraska were analyzed from 2000 to 2015. Based on the long-term Daymet data set, the standard precipitation index (SPI) was computed to identify precipitation anomalies, and the Gaussian function was applied to obtain temperature anomalies. Vegetation anomaly was identified by dynamic time warping technique using a remote sensing Normalized Difference Vegetation Index (NDVI) time series. Finally, multilayer correlation analysis was applied to obtain the response lag of different vegetation types. The results show that Nebraska suffered severe drought events in 2002 and 2012. The response lag of vegetation to drought typically ranged from 30 to 45 days varying for different vegetation types and human activities (water use and management). Grasslands had the shortest response lag (~35 days), while forests had the longest lag period (~48 days). For specific crop types, the response lag of winter wheat varied among different regions of Nebraska (35-45 days), while soybeans, corn and alfalfa had similar response lag times of approximately 40 days.

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Section: Standardized Precipitation Index (Spi)mentioning

confidence: 99%

Mapping the Spatial-Temporal Dynamics of Vegetation Response Lag to Drought in a Semi-Arid Region

Wang

Sui

et al. 2019

Remote Sensing

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“…Associated with global warming and regional precipitation decreases, the potential for increases in drought has been of particular concern in recent years. Many previous studies focused on the development of indices (Byun and Wilhite, 1999;Mu et al, 2013;Tatli, 2015), variabilities (Dai et al, 2004;Kim et al, 2011;Guenang and Kamga, 2014), possible mechanisms associated with atmospheric circulation (Hryciw et al, 2013;Seager and Hoerling, 2014;Zhang and Zhou, 2015) and the impacts (Zhang and Zhou, 2015;Yan et al, 2016) of drought around the world. Drought variation and its impacts have gained much attention in China (Xin et al, 2006;Zhai et al, 2010;Sun and Yang, 2012;Xu et al, 2015;Yan et al, 2016).…”

Section: Introductionmentioning

confidence: 99%

Spatial and temporal variations of regional drought events in southern China

Wang

Yan

2017

Meteorological Applications

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In this paper, an objective identification technique for regional extreme events (OITREE) is used to identify regional drought events (RDEs) in southern China based on the daily precipitation dataset of 342 stations from 1961 to 2012. Generally RDEs in southern China occur over an entire year, with high frequencies from January to April and peak frequencies in February and March. The spatial distributions of frequency and intensity of RDEs are consistent, with high frequencies of more than 60 and the annual average number of drought days being more than 30 in most regions of southern China east of 105 ° E. The trend distributions of the frequency and intensity of RDEs in southern China are similar, with increasing trends in most western regions and decreasing trends in most eastern regions. The decreasing trends of RDEs in most eastern regions were mainly affected by the weakening of East Asia summer moonsoon (EASM) from the 1960s to the 1990s, which resulted in the summer main rain belts in eastern China moving from North China to southern China and caused the precipitation increasing in eastern regions of southern China from the 1960s to the 1990s. While the increasing trends of RDEs in most western regions were not only affected by the EASM, but also associated with the weakening of the India‐Burma Trough (IBT) that caused less wet air transport to Southwest China and the decrease of precipitation in western regions of southern China.

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“…CRU precipitation data have already been applied in the area. They were used to compute the multi-scalar Standardized Precipitation Index (SPI), and the results were compared to those from observation data [31]. The results showed that CRU precipitation quite well corroborates the observations.…”

Section: Data Usedmentioning

confidence: 91%

Total Water Storage Change in Cameroon: Calculation, Variability and Link with Onset and Retreat Dates of the Rainy Season

2016

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Abstract:Total water storage change (TWSC) was calculated using CRU (Climatic Research Unit) monthly gridded data for the period 1962-1993 over Cameroon. Investigations were conducted to link its annual cycle with both the beginning and the end of the rainy season. A method was derived as an alternative to determine onset and retreat dates of the rainy season. Two methods were used for the calculation of TWSC. The first method used potential evapotranspiration (PET) from the Thornthwaite formula (PET TH ) and the second, CRU gridded PET data estimated from the Penman-Monteith formula (PET PM ). A comparative study of the corresponding TWSC, namely TWSC TH and TWSC PM , respectively, was done. According to the preliminary results, the study area is classified as humid below latitude 8 • N and semiarid above. The results of the spatial and temporal variations showed a close correlation between the two methods, but with a slight gap between their different values, those of TWSC PM being larger and fluctuating less. The annual cycles of TWSC and PR generally showed similar patterns, and their intensities decreased from the southern part of the area (Equatorial forest zone) to the northern part (Sahelian zone). For mean TWSC = 0, two different points were identified: the first and the second corresponding dates matching the onset and retreat months of the rainy season, respectively, except in the arid area (Sahelian zone), where only the retreat month of the rainy season was perfectly determined. The delay observed in the determination of rainfall onset date in that area is assigned to PET formulas that are defined only for humid areas and to the influence of high temperature just before the beginning of the rainy season, promoting the rapid evaporation of soil water immediately after the first rains. Application of the same method (TWSC = 0) for the individual year showed similar performances. Although TWSC is always negative in Zone 3 and positive in Zones 1 and 2, the study of the interannual variabilities revealed an overall declining trend due to a stronger decrease in precipitation compared with PET. Moreover, the decrease during dry months is more remarkable than during wet months.

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Computation of the Standardized Precipitation Index (SPI) and Its Use to Assess Drought Occurrences in Cameroon over Recent Decades

Cited by 109 publications

References 32 publications

Mapping the Spatial-Temporal Dynamics of Vegetation Response Lag to Drought in a Semi-Arid Region

Mapping the Spatial-Temporal Dynamics of Vegetation Response Lag to Drought in a Semi-Arid Region

Spatial and temporal variations of regional drought events in southern China

Total Water Storage Change in Cameroon: Calculation, Variability and Link with Onset and Retreat Dates of the Rainy Season

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