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Zeng, H., B. Wu, M. Zhang, N. Zhang, A. Elnashar, L. Zhu, W. Zhu, F. Wu, N. Yan, and W. Liu, Dryland ecosystem dynamic change and its drivers in Mediterranean region, , vol. 48: Elsevier, pp. 59 - 67, 2020. AbstractWebsite

This review describes the latest progress of dryland ecosystem dynamic change in the Mediterranean region. Recent findings indicate that extent of dryland in the Mediterranean region has been expanding in the past decades and will continue to expand in the coming decades due to the stronger warming effect than other regions. The warming trend with intensified human activities has generated a series of negative impacts on productivity, biodiversity, and stability of the dryland ecosystem in Mediterranean region. Increased population, overgrazing and, grazing abandonment intensified the land degradation and desertification. The coverage, richness, and abundance of biological soil crust have been reduced due to the decline of soil water availability and increased animals. Future studies are required to further our understanding of the process and mechanism of the dryland dynamics, including the identification of essential variables, discriminating human and climate-induced changes, and modeling future trajectories of dryland changes.

Zeng, H., A. El Nashar, B. Wu, W. Zhu, F. Tian, and Z. Ma, "A framework for separating natural and anthropogenic contributions to evapotranspiration of human-managed land covers in watersheds based on machine learning", Science of The Total Environment, vol. 823, issue 1, pp. 153726, 2022. AbstractWebsite

Actual EvapoTranspiration (ET) represents the water consumption in watersheds; distinguishing between natural and anthropogenic contributions to ET is essential for water conservation and ecological sustainability. This study proposed a framework to separate the contribution of natural and anthropogenic factors to ET of human-managed land cover types using the Random Forest Regressor (RFR). The steps include: (1) classify land cover into natural and human-managed land covers and then divide ET, meteorological, topographical, and geographical data into two parts corresponding to natural and human-managed land cover types; (2) construct a natural ET (ETn) prediction model using natural land cover types of ET, and the corresponding meteorological, topographical and geographical factors; (3) the constructed ETn prediction model is used to predict the ETn of human-managed land cover types using the corresponding meteorological, topographical and geographical data as inputs, and (4) derive the anthropogenic ET (ETh) by subtracting the natural ET from the total ET (ETt) for human-managed land cover types. Take 2017 as an example, ETn and ETh for rainfed agriculture, mosaic agriculture, irrigated agriculture, and settlement in Colorado, Blue Nile, and Heihe Basin were separated by the proposed framework, with R2 and NSE of predicted ETn above 0.95 and RB within 1% for all three basins. In the semi-arid Colorado River Basin and arid Heihe Basin, human activities on human-managed land cover types tended to increase ET higher than humid Blue Nile Basin. The anthropogenic contribution to total water consumption is approaching 53.68%, 66.47%, and 6.14% for the four human-managed land cover types in Colorado River Basin, Heihe Bain and Blue Nile Basin, respectively. The framework provides strong support for the disturbance of water resources by different anthropogenic activities at the basin scale and the accurate estimation of the impact of human activities on ET to help achieve water-related sustainable development goals.