Studying the significant impacts of drought on vegetation is crucial to understand its dynamics and interrelationships with precipitation, soil moisture, and temperature. In North and West Africa regions, the effects of drought on vegetation have not been clearly stated. Therefore, the present study aims to bring out the drought fluctuations within various types of Land Cover (LC) (Grasslands, Croplands, Savannas, and Forest) in North and West Africa regions. The drought characteristics were evaluated by analyzing the monthly Self-Calibrating Palmer Drought Severity Index (scPDSI) in different timescale from 2002 to 2018. Then, the frequency of droughts was examined over the same period. The results have revealed two groups of years (dry years and normal years), based on drought intensity. The selected years were used to compare the shifting between vegetation and desert. The Vegetation Condition Index (VCI), the Temperature Condition Index (TCI), the Precipitation Condition Index (PCI), and the Soil Moisture Condition Index (SMCI) were also used to investigate the spatiotemporal variation of drought and to determine which LC class was more vulnerable to drought risk. Our results revealed that Grasslands and Croplands in the West region, and Grasslands, Croplands, and Savannas in the North region are more sensitive to drought. A higher correlation was observed among the Normalized Difference Vegetation Index (NDVI), Land Surface Temperature (LST), Tropical Rainfall Measuring Mission (TRMM), and Soil Moisture (SM). Our findings suggested that NDVI, TRMM, and SM are more suitable for monitoring drought over the study area and have a reliable accuracy (R2 > 0.70) concerning drought prediction. The outcomes of the current research could, explicitly, contribute progressively towards improving specific drought mitigation strategies and disaster risk reduction at regional and national levels.

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Abstract:States of ecological maturity and temporal trends of drylands in Morocco, Algeria and Tunisia north of 28˝N are reported for [1998][1999][2000][2001][2002][2003][2004][2005][2006][2007][2008]. The input data were Normalized Difference Vegetation Index databases and corresponding climate fields, at a spatial resolution of 1 km and a temporal resolution of one month. States convey opposing dynamics of human exploitation and ecological succession. They were identified synchronically for the full period by comparing each location to all other locations in the study area under equivalent aridity. Rain Use Efficiency (RUE) at two temporal scales was used to estimate proxies for biomass and turnover rate. Biomass trends were determined for every location by stepwise regression using time and aridity as predictors. This enabled human-induced degradation to be separated from simple responses to interannual climate variation. Some relevant findings include large areas of degraded land, albeit improving over time or fluctuating with climate, but rarely degrading further; smaller, but significant areas of mature and reference vegetation in most climate zones; very low overall active degradation rates throughout the area during the decade observed; biomass accumulation over time exceeding depletion in most zones; and negative feedback between land states and trends suggesting overall landscape persistence. Semiarid zones were found to be the most vulnerable. Those results can be disaggregated by country or province. The combination with existing land cover maps and national forest inventories leads to the information required by the two progress indicators associated with the United Nations Convention to Combat Desertification strategic objective to improve the conditions of ecosystems and with the Sustainable Development Goal Target 15.3 to achieve land degradation neutrality. Beyond that, the results are also useful as a basis for land management and restoration.

Land cover, land use, soil salinization, and sand encroachment, which are desertification-indicating features, were integrated in a diachronic assessment, obtaining quantitative and qualitative information on the ecological state of the land, particularly degradation tendencies. In arid and semi-arid study areas of Algeria and Tunisia, sustainable development requires the understanding of these dynamics as it withstands the monitoring of desertification processes. Both visual interpretation and automated classification approaches have been set up for salt and sand features extraction using historical and present Landsat imagery. The automated one includes a decision tree classifier and an unsupervised classification applied to the principal components extracted from Knepper ratios composite. New spectral indices are employed in the decision tree classifier for the extraction of features of interest. The validation of the classification methods showed that the decision tree had an overall accuracy over 85% in both areas. Integrating results with ancillary spatial data, we could identify driving forces and estimate the metrics of desertification processes. In the Biskra area (Algeria), it emerged that the expansion of irrigated farmland in the past three decades has been contributing to an ongoing secondary salinization of soils, with an increase of over 75%. In the Oum Zessar area (Tunisia), there has been substantial change in several landscape components in the last decades related to increased anthropic pressure and settlement, agricultural policies, and national development strategies. One of the most concerning aspects is the expansion of sand-encroached areas over the last three decades of around 27%.

Droughts are one of the world’s most destructive natural disasters. In large regions of Africa, droughts can have strong environmental and socioeconomic impacts. Understanding the mechanism that drives drought and predicting its variability is important for enhancing early warning and disaster risk management. Taking North and West Africa as the study area, this study adopted multi-source data and various statistical analysis methods, such as the joint probability density function (JPDF), to study the meteorological drought and return years across a long term (1982–2018). The standardized precipitation index (SPI) was used to evaluate the large-scale spatiotemporal drought characteristics at 1–12-month timescales. The intensity, severity, and duration of drought in the study area were evaluated using SPI–12. At the same time, the JPDF was used to determine the return year and identify the intensity, duration, and severity of drought. The Mann-Kendall method was used to test the trend of SPI and annual precipitation at 1–12-month timescales. The pattern of drought occurrence and its correlation with climate factors were analyzed. The results showed that the drought magnitude (DM) of the study area was the highest in 2008–2010, 2000–2003, and 1984–1987, with the values of 5.361, 2.792, and 2.187, respectively, and the drought lasting for three years in each of the three periods. At the same time, the lowest DM was found in 1997–1998, 1993–1994, and 1991–1992, with DM values of 0.113, 0.658, and 0.727, respectively, with a duration of one year each time. It was confirmed that the probability of return to drought was higher when the duration of drought was shorter, with short droughts occurring more regularly, but not all severe droughts hit after longer time intervals. Beyond this, we discovered a direct connection between drought and the North Atlantic Oscillation Index (NAOI) over Morocco, Algeria, and the sub-Saharan countries, and some slight indications that drought is linked with the Southern Oscillation Index (SOI) over Guinea, Ghana, Sierra Leone, Mali, Cote d’Ivoire, Burkina Faso, Niger, and Nigeria.