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Phytodiversity and Conservation Status of the Nara Desert, Pakistan
Published in Hasnain Nangyal, Muhammad Saleem Khan, Environmental Pollution, Biodiversity, and Sustainable Development, 2020
This is a scorching sandy desert with the temperature fluctuations of 20–45°C. Majority of the area falls in hyperarid to arid region, typified by tremendous temperature (a long dry summer), extreme drought adjunct by high wind velocity, low humidity, high evapotranspiration, and too sparse rainfall. The southern part (Khipro, Sanghar) is relatively semiarid in nature that supports somewhat dense vegetation. The annual rainfall varies from 88 to 135 mm, typically received throughout the monsoon. The water is scarce in the Nara desert and the groundwater resources are lying at the depth of 50–300 feet from the surface (Qureshi and Bhatti, 2005b).
Soil salinity and moisture content under non-native Tamarix species
Published in International Journal of Phytoremediation, 2020
Solomon W. Newete, Mohamed A. Abd Elbasit, Tesfay Araya
The Tamarix tolerance to water stress in a hyperarid environments could be linked to some of its morphological and physiological adaptions which includes cutting evapotranspiration through leaf shedding, reduced leaf structure, (Horton and Campbell 1974) and well-developed deep tap root system that extends up to 50 m long (Baum 1978; Zeng et al.2013). It is, however, not well understood how Tamarix transport water extracted from the groundwater to the plant tissues since most of the fine roots (<2 mm diameter) of desert plants are abundantly located on the top 30 cm of the surface soils (Jackson et al.1996) which implies a limited water uptake due to the low density of fine roots in the subsurface soils (Yu et al.2013). Thus, this study investigated a potential hydraulic lift in Tamarix species during dry conditions as is the case in many desert plants. This study also investigated if the soil salt concentrations under the exotic Tamarix species are leached from Tamarix litters and guttation sap which are believed to act as alellopathic effect that inhibit other plants growing in the vicinity.
Modelling agricultural drought: a review of latest advances in big data technologies
Published in Geomatics, Natural Hazards and Risk, 2022
Ismaguil Hanadé Houmma, Loubna El Mansouri, Sébastien Gadal, Maman Garba, Rachid Hadria
On a global scale, all approaches combined, it is estimated today that there are 150 indices developed for drought assessment, classification, and monitoring (Svoboda and Fuchs 2016). They are most often grouped into two large distinct families: single indices and multivariate composite indices. However, it should be noted that many of these indices have been developed in specific geographical conditions, and therefore, their performance in other geographical regions could be limited. Indeed, the variability in performance of drought indices by geographic region or ecosystem type has been demonstrated in several studies (Mishra and Singh 2010; Naumann et al. 2014; Zhang et al. 2017; Liu et al. 2018; Song et al. 2020; Li et al. 2020b; Hanadé et al. 2022). By comparing the consistency of several indices (SDCI, TVDI, SDI, VCI, TCI, PCI, VHI) over mainland China, Li et al. (2020b) noted that many indices have different applicability across regions in mainland China. Similarly, through statistical evaluation, Liu et al. (2018) found substantial differences in the performance of drought indices (PDSI, SPI, SPEI) for agricultural drought in the North China Plain. Thus, the sensitivity to drought of a region is strongly influenced by its specific characteristics in terms of the energy and water balances (Shahabfar et al. 2012). According to Cartwright et al. (2020), variability in drought sensitivity is influenced by topography, climate, soil characteristics and altitude. These highly variable characteristics in time and space have a direct influence on the biophysical and climatic variables used in the formulation of drought indices. Therefore, in arid and hyperarid regions, the estimation of the different drought indicators is characterized by important uncertainty due to limited rainfall and very low vegetation cover (Naumann et al. 2014). This implies that the indices, particularly those based on remote sensing data; appear more accurate in rain-fed regions (Huang et al. 2020). To support this point through a study on the influence of spatial heterogeneities on the performance of indices, Hanadé et al. (2022) revealed that drought indices have very variable performance depending on whether it is an irrigated, rainfed, desert and mountainous agrosystem. This is particularly true for drought indices based on NDVI (Normalized Difference Index) or LST (Land Surface Temperature). Drought index concordance was higher in semi-arid agrosystems than in the desert zones. Similarly, it was raised in the same geographical region that the drought indices had variable performance depending on the seasons or the time window considered (Chen et al. 2020). This suggests that no multivariate index or model for drought monitoring or prediction can adequately address the multifactorial and multi-temporal stochastic dimension of drought under any climatic condition. Thus, nowadays, monitoring and assessment of drought conditions tends to become a local priority putting forward the hypothesis that the development of local early warning systems or monitoring of agricultural drought at the plot scale could improve strategies to mitigate drought-related impacts (Andersson et al. 2020, Jung et al. 2021; Das et al. 2021).