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Cet ouvrage, à la dimension d’un traité philosophique, au sens propre, est amené avant tout, de dérouler une variété des structures en concepts à travers des véracités fondés sur un ensemble des raisonnements déductifs, observationnels et logiques, entretenant des perspectives transversales, longitudinales et pyramidales pour mettre en œuvre non seulement des solutions transparentes à l’égard des problèmes complexes, mais aussi formuler des sous-ensembles des théorèmes, propriétés, axiomes, postulats, assomptions et hypothèses, qui s’agglomèrent pour édifier à leur tour les piliers des théories Adaniennes.
En termes simples, cet ouvrage initie l’universalité de la scientificité en philosophie, en mettant sur pied des préceptes dont la plupart sont novatrices, afin d’émanciper la multiplicité en recherche dans le domaine de la philosophie et les disciplines adjacentes et d’autres part, redéfinir les bases connues en principes de la littérature en philosophie. Les théories développés et améliorés dans cet ouvrage sont à la fois amplificatrice et simplificatrice pour une accessibilité à un large public leur offrant une vue d’ensemble à 360°.
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Description:
Table 1. Overall change
of rainfall for future climate generated using CanESM2, AFR44 and CanESM5 models
in RCP 4.5 and RCP 8.5 scenarios.
Figure 3. Historical and projected
average monthly rainfall at Djibouti airport station (1953-2021) using
statistical downscaling of CanESM2 and CanESM5 and corrected regional model
CORDEX from 2006–2100.
Table 2. Trend of ETCDDI indices for
extreme precipitation (historical (1980-2017) and projected with AFR44.CanRCM4.CHIRPS.RCP45
(2018-2099) at the Djibouti airport station and detection of the stationarity
period with the Pettitt test (Pettitt, 1979) and standard normal homogeneity (SNHT,
Alexandersson 1986).
Figure 4. Projected Interannual
variations (line) and linear trends (dash) in mean annual 3-month RAI at
Djibouti airport station from downscaled CMIP5, CMIP6 and CORDEX based on
observation, CHIRPS and ERA5Land datasets (right panels) and Characteristics of
drought (left panels).
Figure 5. Variation of
ETCDDI indices for projected extreme precipitation (2018-2099) at the Djibouti airport
station with the CanESM2 (CHIRPS.RCP45 and ERA5Land. RCP85), CanESM5 (CHIRPS. RCP45
and ERA5Land. RCP45) and CORDEX (CHIRPS. RCP45 and ERA5Land. RCP45) models.
Figure 5. Projected rainfall changes
relative to the baseline period 1953–2021 based on the downscaled CanESM2-CMIP5
and CanESM5-CMIP6 using Observation, CHIRPS and ERA5Land datasets. Colored
shaded areas represent the areas of uncertainty (standard deviation) for the scenarios
RCP1.9, RCP.2.6 and RCP.7.0 for CMIP5 and SSP1-RCP2.6, SSP3-RCP7.0 and SSP5-RCP8.5
for CMIP6. The solid sphere in the boxplot represents
the mean, and the interquartile range spans from Q1 to Q3 within the box
square. Horizontal lines above and below denote the minimum and maximum.
Irregular dotted indicate extreme values.
Table 3. Evaluation of precipitation projections (very short term) from 2006 to 2021
under the RCP 4.5 scenario simulated at the Djibouti airport station by
Canadian Earth System models derived from CMIP5, CORE-CORDEX and CMIP6.
Table 4. Evaluation of precipitation projections (very short term) from 2006 to 2021
under the RCP 8.5 scenario simulated at the Djibouti airport station by
Canadian Earth System models derived from CMIP5, CORE-CORDEX and CMIP6.
Table 5. Overall change of average rainfall for future climate generated using CanESM2, AFR44 and CanESM5 model RCP 4.5 scenarios.
RAINFALL TRESHOLD (Djibouti Case)
·
Q12.5% less than 35.75 mm/annum (Dry
events)
·
Q87.5% more than
291.60/annum (Wet events)
·
Q50% less than 1 mm/month (Dry
events)
·
Q90% more than 32.72 mm/month (Wet
events)
·
Light rainfall (0–0.2(q95) mm/day)
·
Moderate rainfall events (0.2–8(q99) mm/day)
·
Heavy rainfall events (> 8 mm/day).
Abdi-Basid ADAN, 2024
Understanding changes in mangrove ecosystems driven by human activities, climate change, and environmental variations is essential for effective ecological management. The study analyze the spatiotemporal variability of the Normalized Difference Vegetation Index (NDVI) and their responses to parameters such as sea level (SL), Potential Evapotranspiration (PET), rainfall (RF), Standardized Precipitation Index (SPI-1 month), soil moisture (SM), minimum temperature (TN), and maximum temperature (TX) within the study area. Trends, relative influences, spatial autocorrelation, and relationships between NDVI and climatic-environmental variables, as well as partial correlations, were analyzed using the Mann-Kendall monotonic trend test (MKMT), Relative Weight Analysis (RWA), partial correlation coefficients (PCC), and Multiple Linear Regression (MLR) methods. The spatiotemporal patterns of NDVI, EVI, and SAVI display similar dynamics, showing a reduction in bare soil and an increase in sparse and dense vegetation from 1987 to 2022. Nevertheless, zones of degradation were observed, particularly in southern in 2022 compared to 1987, as indicated by NDVI, EVI, and SAVI. These zones coincide with areas of increased salinity concentration according to the VSSI index over the same period. A notable deterioration in NDVI (> 0.2) was recorded from 2000 to 2012. The interannual trend of NDVI is slightly declining. Additionally, analyses with Mann-Kendall and Theil-Sen slope reveal that TN, TX, PET, and SPI-1 show increasing trends, though not statistically significant, while SM and LST show decreasing trends. For environmental variables, SL indicates an upward trend. Further, partial correlation analysis identifies SL, TN, SPI-1, TX, and PET as the primary climatic factors controlling vegetation dynamics during the JJAS season, with PCC values of -0.89, 0.87, 0.77, 0.76, -0.75, and 0.86 with NDVI, respectively. These findings underscore the significant influence of select environmental factors on the spatiotemporal dynamics of mangrove vegetation, providing insights critical for conservation and management efforts.
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The study covers a range of datasets, including satellite-based, hybrid (satellite + gauge), and reanalysis products:
CHIRPS v2 (0.05°, 1981–present): Hybrid product combining infrared satellite data with rain gauge observations.
GSMaP PRT V6 (0.1°, 2000–present): Satellite-based product using passive microwave and infrared sensors.
IMERG LR & FR (0.1°, 2000–present): Satellite-based datasets from NASA’s GPM mission; Late Run provides near–real-time data, while Final Run is bias-corrected with gauge data.
TRMM 3B42 (0.25°, 1998–2019): Satellite-derived dataset from the Tropical Rainfall Measuring Mission.
MSWEP v2.8 (0.1°, 1979–present): Multi-source hybrid product merging gauge, satellite, and reanalysis data.
ERA5 (0.25°, 1979–present): Global reanalysis from ECMWF.
ERA5-Land (0.1°, 1981–present): High-resolution reanalysis optimized for land-surface applications.
ERA5-Ag (~0.1°, 1979–present): Reanalysis dataset derived from ERA5, tailored for agricultural applications.
This study investigates hydroclimatic variability over the period 1961–2021 using the Standardized Precipitation Index (SPI) across multiple temporal scales. Long-term drought dynamics are assessed through the spatial slope of SPI12 and its relationship with total precipitation, while short- to medium-term variability is explored through SPI3 heatmaps, contrasting monthly and annual patterns as well as seasonal fluctuations (JF, MAM, JJAS, and OND).
A comparative analysis between a station with missing precipitation records (and consequently incomplete SPI3 series) and the corresponding regional SPI3 signal was conducted to evaluate the robustness and representativeness of local trends.
Furthermore, correlations between SPI /SPEI (3, 6, 9, and 12 months) and vegetation-related indices — NDVI (Normalized Difference Vegetation Index), EVI (Enhanced Vegetation Index), VCI (Vegetation Condition Index), and SEDI (Standardized Evapotranspiration Deficit Index) — highlight the interactions between climatic drought and ecosystem responses. Finally, the analysis incorporates the influence of major climate teleconnections, namely ENSO (El Niño–Southern Oscillation) and the IOD (Indian Ocean Dipole, particularly during OND), to assess their role in modulating regional SPI variability.
The findings provide an integrated understanding of the links between precipitation variability, vegetation dynamics, and large-scale ocean-atmosphere drivers, with implications for drought monitoring, prediction, and sustainable water resource management.


















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