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Abdullah, Meshal; Asadalla, Naseraldeen; Gharabi, Yassien; Mohan, Midhun; Al Naabi, Sara; Al Ali, Zahraa; Al Hashash, Nouf; Srinivasan, Shruthi; Al Awadhi, Talal; Abulibdeh, Ammar
Journal of arid environments, August 2024, 2024-08-00, Volume: 223Journal Article
Green water is primarily associated with the appearance of annual plants and plays a significant role in biomass production in both arid and semi-arid ecosystems. Herein, we aim to estimate the optimal threshold for determining the presence or absence of annual plants and use them as an ecological indicator to assess potential green water areas in Kuwait as a case study. We integrate remote sensing techniques and MaxEnt modeling. The AUC for the annual plant distribution with all examined factors is 0.847, and the standard deviation is 0.050. The results demonstrated that potential locations with high levels of green water cover <20% of the country. The annual plant distribution was significantly correlated with several types of perennial plants, maximum temperature, precipitation, and sandy soils. It was also found that annual plants are controlled by the spring and winter temperature decline and the timing of precipitation occurrence, especially the pattern and amount of rainfall received in November. Sandy loam and loam soils were found to be ideal for annual plants, although land depressions and soil types are crucial factors in determining annual plant distribution. Additionally, annual plants enhanced the growth of several perennial communities. To reiterate, our study's model helped to comprehend the significance of annual plants as an ecological indicator in sustaining soil moisture over a prolonged period, as well as factors controlling the distribution of annual plants. The developed model and indicators could support decision-makers in determining appropriate locations with adequate levels of green water for revegetation planning in arid landscapes. •The distribution of annual plants, suitable soils, and green water are significantly interconnected.•Machine learning techniques are useful for selecting suitable locations for revegetation.•Revegetation of native plants could help grazing pastures to enhance food security.
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