Integrated modelling for mapping spatial sources of dust in central Asia - An important dust source in the global atmospheric system
Introduction
Dust storms are one of the most important consequences of wind erosion in arid and semi-arid regions (Lal, 2003; Xiao et al., 2017; Kouchami-Sardoo et al., 2019; Xu et al., 2019) - and have many on-site (e.g., loss of organic matter, degradation of water quality or topsoil structure, burial of plants, abrasion of crop tissue) and off-site (e.g., reduced visibility and traffic safety, deposition of dust in houses, or on agricultural or industrial land) impacts (Goossens, 2003; Miri et al., 2009). Dust storms generated by wind erosion affect terrestrial and marine ecosystems, as well as weather phenomena and climate dynamics (Jish Prakash et al., 2015; Tyagi et al., 2019; Suresh et al., 2021; Francis et al., 2021).
Frequent wind-blown dust events result from severe wind erosion in Central Asia (CA) (Pi et al., 2017; Shen et al., 2016; Wang et al., 2020; Zhang et al., 2020) and may contribute to 17–20% of global dust emissions (Xi and Sokolik, 2015). The highest rates of dust deposition globally have been simulated over CA (Groll et al., 2013; Zhang et al., 2017). The significance of CA dust emissions on local socio-economic performance, human health and ecosystems has been recognized in several previous studies (Indoitu et al., 2015; Issanova and Abuduwaili, 2017; Rupakheti et al., 2020; Li and Sokolik, 2018), which, thereby, motivate further research on the dust problem and associated atmospheric circulation patterns and dynamics (Groll et al., 2013; Shi et al., 2020; Li et al., 2019, 2021a). More specifically, determining land susceptibility classes to wind erosion and/or dust emissions and providing spatial maps for the provenance of dust are essential to help mitigate the deleterious effects of dust storms on the environment, socio-economic affairs and human health in CA (Li et al., 2020).
To address this issue, machine learning algorithms (e.g., random forest, support vector machine, cubist, classification and regression tree) (Gholami et al., 2021a; Boroughani et al., 2020) and deep learning (DL) techniques, including simple recurrent neural network (RNN) and restricted Boltzmann machine (RBM) (Gholami et al., 2021b), have been widely used in aeolian geomorphology for spatial modelling purposes. Many techniques such as copulas, variational autoencoders and generative adversarial networks have been used to generate synthetic data (Patki et al., 2016; Wan et al., 2017; Xu and Veeramachaneni, 2018). In comparison with black-box DL models, training copulas are easy to use and robust. Copula models provide a direct representation of the statistical distribution, which makes them easier to interpret and tweak after training. Overall, the copula-based models can be used effectively for generating synthetic data that are in close agreement with real data (Patki et al., 2016).
The susceptibility of land surfaces to wind erosion hazard is determined by a diverse range of environmental and bioclimatic variables (Parajuli et al., 2016, 2019; Jebali et al., 2021), and therefore, identifying the controlling factors through DL models is essential for a successful modelling exercise. A wide range of machine learning models including random forest, MARS, extreme gradient boosting, maximum entropy, Bayesian additive regression trees, multiple linear regression model and boosted regression trees has been used to identify effective factors for various environmental hazards such as gully erosion (Azareh et al., 2019), landslides (Chen et al., 2017), surface solar radiation (Wang et al., 2021; Yu et al., 2020) and dust emissions from arid land surfaces (Gholami et al., 2020b). Most recently, Mohammadifar et al. (2021a) and Gholami et al. (2021b) reported successful applications of game theory and the boruta algorithm to identify and quantify the relative importance of effective factors for soil erosion by water, and wind from arid land surfaces, respectively.
This study examines the factors that contribute to land degradation, wind erosion and dust emissions in CA and also develops new high-resolution spatial maps for dust sources over the region, by applying two individual novel DL models (i.e., the gcForest and the bidirectional LSTM –Bi-LSTM) and a hybrid copula-gcForest model based on a gcForest and Gaussian copula model. Evaluation of the performance of the individual DL models and a hybrid model is undertaken by applying six statistical indicators namely; accuracy, recall, precision, f1 score, cohens kappa and the receiver operating characteristic-area under curve (ROC-AUC). The analysis quantifies the relative importance of factors controlling dust emissions in CA, and their interactions and contributions to prediction, and finally, generates synthetic data with a copula Gaussian multivariate model. Calibration of the best model is undertaken using synthetic data. High resolution spatial mapping of the areas susceptible to wind erosion over CA may help in mitigating deleterious impacts of dust storms on the atmospheric environment, socio-economics and population health.
Section snippets
Study area description
CA, extending from the Caspian Sea to the East Tianshan Mountains, with an area of about 7.75 M km2 (Cheng et al., 2016), is an arid region with several deserts and thick and expansive loess deposits (Li et al., 2020, 2021a), located in the eastern end of the dust belt (Shi et al., 2021) (Fig. 1). CA includes several deserts such as the Karakum, Kyzylkum, Aralkum, Caspian depression, Balkhash arid land and Taklimakan desert in the Tarim Basin (Zhang et al., 2020; Shi et al., 2021). CA
Variables controlling dust emissions from the land surfaces
Many factors including climatic variables, soil characteristics and physiographic-topographic factors control dust emissions from land surfaces (Shao, 2008; Ge et al., 2016). Here, we examined 14 potential factors that affect land susceptibility to wind erosion and mineral dust emissions, comprising climatic factors (e.g., precipitation and wind speed), soil properties (bulk density, cation exchange capacity -CEC), silt, coarse fragment, sand, clay and organic carbon content, elevation (from a
Feature selection by LOFO importance
The results of feature selection by LOFO are presented in Fig. 5, and eight out of the 14 factors were considered effective for controlling dust emissions in CA, namely; precipitation, bulk density, slope, lithology, CEC, wind speed, silt content and coarse fragment content. On the contrary, six factors comprising sand content, land use, aspect, elevation, clay content and organic carbon content were removed from further analysis. Based on the genetic algorithm, wind speed, elevation, organic
Interaction among the features controlling dust emissions
The results of assessing the interaction of variables controlling dust emissions in CA suggested that bulk density had the strongest interaction strength with slope, coarse fragment content and precipitation, whereas CEC exhibited the strongest interaction with silt content, slope and wind speed. The strongest interaction strength for coarse fragment content was found to be with silt content, precipitation and bulk density, whereas as that for lithology was identified as being with silt
Conclusions
The novel scientific contribution of this study is the introduction of a new integrated modelling approach (LOFO – DL – GCBM – GT approach) for generating spatial maps of the dust sources and assessing the interpretability of the spatial maps over CA. Based on the statistical indicators of model performance, the hybrid copula-gcForest model performed slightly better than both individual DL models in generating spatial maps of dust sources in CA. According to the current modelling results,
Declaration of competing interest
The authors declare that there is no conflict of interests regarding the publication of this article.
Acknowledgements
The authors would like to thank the Faculty of Agriculture and Natural Resources, University of Hormozgan, Iran, for supporting this joint research project. The research was also supported by a collaborative agreement on “fingerprinting sources of aeolian dust and climate and environmental change in the core zone of the Silk Road economic belt based on loess deposits” between the University of Hormozgan and the Institute of Earth Environment, Chinese Academy of Sciences. D.G.K. acknowledges
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