Land degradation drivers of anthropogenic sand and dust storms

Zucca, Claudio; Fleiner, Renate; Bonaiuti, Enrico; Kang, Utchang

doi:10.1016/j.catena.2022.106575

Cited by 19 publications

(4 citation statements)

References 92 publications

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“…Regarding the LD processes addressed, priority was given to dryland processes and particularly to soil erosion by wind. Dust emission by wind and sand and dust storms (SDS) are widespread in arid and semiarid regions and are increasingly recognized to be strongly associated with human-induced degradation of agropastoral and seminatural systems (Zucca et al, 2021(Zucca et al, , 2022. Thus, we included the functionality to trace SDS as a sub-component of our framework.…”

Section: Explicitly Elucidate the Relationship Between Drivers And In...mentioning

confidence: 99%

Implementation of a multi‐layered land degradation monitoring and forecasting framework toward integrated countermeasure planning in drylands

et al. 2023

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Various earth observation (EO) data and related spatial technologies are employed to monitor land degradation (LD) and its impacts on the drylands. However, a comprehensive framework that integrates multiple data sources and metrics to generate a consistent output still needs to be explored. Furthermore, the lack of consistency and the limited reliabilities of existing approaches make it challenging to identify proper countermeasures (avoid, reduce, reverse LD) and support decisions to combat LD in line with the Land Degradation Neutrality (LDN) Framework. The “Multi‐Layered Land Degradation Tracer (ML‐LDT)” is a framework that addresses the above research gaps. It is based on a comprehensive framework that integrates data extraction, LD monitoring, and forecasting capacities and consists of three functional layers, that is, a base data processor, monitoring/modeling components, and a forecasting layer employing machine learning (ML) algorithms. ML‐LDT has a state‐of‐art capacity to model wind erosion together with sand and dust storms (SDS), making it particularly suited to the arid lands most prone to these processes. It is implemented over cloud processors with open‐source components based on industry standards. Therefore, the routines are easily adaptable to user requirements in LDN planning. We evaluated the ML‐LDT framework in the Indian Thar Desert and Inner Mongolian drylands. The results showed the overall robustness of the procedures and that ML‐based models provide good insights to forecast future LD/SDS patterns that can be used to simulate LDN scenarios and targets. We validated the forecasting results against LD maps and remote sensing observations.

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Section: Explicitly Elucidate the Relationship Between Drivers And In...mentioning

confidence: 99%

Implementation of a multi‐layered land degradation monitoring and forecasting framework toward integrated countermeasure planning in drylands

et al. 2023

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“…This has led to the destruction of the soil environment and given rise to a series of ecological and environmental problems. For example, soil degradation can accelerate the degradation of local ecosystems and increase the risk of dust storm formation [7,8]. Some scholars have found through research that soil degradation can cause a reduction in the capacity of local ecosystem services and an increased risk of soil erosion [9].…”

Section: Introductionmentioning

confidence: 99%

Driving Forces on the Distribution of Urban Ecosystem’s Non-Point Pollution Reduction Service

Shu

Han

et al. 2023

Atmosphere

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In the context of increasing urbanization and worsening environmental pollution, nonpoint source pollution during high-frequency rainfall has become a major ecological problem that endangers residents in cities. This study takes Shenzhen as an example. On the basis of a large number of soil sample test data, and combined with relevant environmental variables, it has drawn the high-resolution, high-precision spatial distribution maps of soil attributes within the city. In addition, this paper combines the revised universal soil loss equation and the GeoDetector model to evaluate the supply capacity of nonpoint source reduction services in the city’s ecological space and the main driving factors of spatial distribution characteristics for different types of land. The study found that increasing soil point density and combining environmental variables can help improve the accuracy of spatial mapping for soil attributes. The ME, MSE, ASE, RMSE, and RMSSE of spatial mapping all meet the accuracy evaluation criteria and are better than many existing studies; the spatial distribution characteristics of soil attributes and nonpoint source reduction services show significant differences among the whole city, secondary administrative regions, and different types of land; the GeoDetector results show that among the three main types of land use (forested land, industrial land, and street town residential land), topographic factors, habitat-quality factors, and ecosystem types have the greatest impact on the spatial differentiation characteristics of nonpoint source reduction services. Among climate factors, only precipitation factors have the greatest impact on the spatial differentiation characteristics of services. Facing the above factors, the q-values calculated by the GeoDetector are all higher than 10%. The results of this study can provide information for making better decisions on regional ecological system management and soil protection and on restoration work aimed at improving nonpoint source reduction services.

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“…For example, SRDSFs have a two-way relationship with deforestation and desertification. One of the major causes of the formation of sources and dust storms is deforestation and desertification; on the other hand, dust storms also cause deforestation and desertification [32][33][34][35]. Trees and vegetation play a crucial role in stabilizing the soil and preventing erosion.…”

Section: Introductionmentioning

confidence: 99%

A New Risk-Based Method in Decision Making to Create Dust Sources Maps: A Case Study of Saudi Arabia

Alsubhi,

Qureshi,

Siddiqui

2023

Remote Sensing

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Dust storms are one of the major causes of the destruction of natural ecosystems and human infrastructure worldwide. Therefore, the identification and mapping of susceptible regions to dust storm formation (SRDSFs) is of great importance. Determining SRDSFs by considering the concept of risk in the decision-making process and the kind of manager’s attitude and planning can be very valuable in dedicating financial resources and time to identifying and controlling the negative impacts of SRDSFs. The purpose of this study was to present a new risk-based method in decision making to create SRDSF maps of pessimistic and optimistic scenarios. To achieve the purpose of this research, effective criteria obtained from various sources were used, including simulated surface data, satellite products, and soil data of Saudi Arabia. These effective criteria included vegetation cover, soil moisture, soil erodibility, wind speed, precipitation, and absolute air humidity. For this purpose, the ordered weighted averaging (OWA) model was employed to generate existing SRDSF maps in different scenarios. The results showed that the wind speed and precipitation criteria had the highest and lowest impact in identifying dust centers, respectively. The areas identified as SRDSFs in very pessimistic, pessimistic, neutral, optimistic, and very optimistic scenarios were 85,950, 168,275, 255,225, 410,000, and 596,500 km2, respectively. The overall accuracy of very pessimistic, pessimistic, neutral, optimistic, and very optimistic scenarios were 84.1, 83.3, 81.6, 78.2, and 73.2%, respectively. The very pessimistic scenario can identify the SRDSFs in the study area with higher accuracy. The overall accuracy of the results of these scenarios compared to the dust sources obtained from the previous studies were 92.7, 94.2, 95.1, 88.4, and 79.7% respectively. The dust sources identified in the previous studies have a higher agreement with the results of the neutral scenario. The proposed method has high flexibility in producing a wide range of SRDSF maps in very pessimistic to very optimistic scenarios. The results of the pessimistic scenarios are suitable for risk-averse managers with limited financial resources and time, and the results of the optimistic scenarios are suitable for risk-taking managers with sufficient financial resources and time.

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Land degradation drivers of anthropogenic sand and dust storms

Cited by 19 publications

References 92 publications

Implementation of a multi‐layered land degradation monitoring and forecasting framework toward integrated countermeasure planning in drylands

Implementation of a multi‐layered land degradation monitoring and forecasting framework toward integrated countermeasure planning in drylands

Driving Forces on the Distribution of Urban Ecosystem’s Non-Point Pollution Reduction Service

A New Risk-Based Method in Decision Making to Create Dust Sources Maps: A Case Study of Saudi Arabia

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