scholarly journals Evaluation of Climatic and Anthropogenic Impacts on Dust Erodibility: A Case Study in Xilingol Grassland, China

2020 ◽  
Vol 12 (2) ◽  
pp. 629
Author(s):  
Jing Wu ◽  
Yasunori Kurosaki ◽  
Chunling Du
Keyword(s):  
Land Surface ◽  
Wind Erosion ◽  
Anthropogenic Impacts ◽  
P Value ◽  
Effective Measure ◽  
Surface Conditions ◽  
Goat Population ◽  
Xilingol Grassland ◽  
Autumn And Winter

Aeolian dust is dependent on erosivity (i.e., wind speed) and erodibility (i.e., land surface conditions). The effect of erodibility on dust occurrence remains poorly understood. In this study, we proposed a composite erodibility index (dust occurrence ratio, DOR) and examined its interannual variation at a typical steppe site (Abaga-Qi) in Xilingol Grassland, China, during spring of 1974–2018. Variation in DOR is mainly responsible for dust occurrence (R2 = 0.80, p-value < 0.001). During 2001–2018, DOR values were notably higher than those during 1974–2000. There was also a general declining trend with fluctuations. This indicates that the land surface conditions became vulnerable to wind erosion but was gradually reversed with the implementation of projects to combat desertification in recent years. To understand the relative climatic and anthropogenic impacts on erodibility, multiple regression was conducted between DOR and influencing factors for the period of 2001–2018. Precipitation (spring, summer, and winter) and temperature (summer, autumn, and winter), together with livestock population (June) explained 82% of the variation in DOR. Sheep and goat population made the greatest contribution. Therefore, reducing the number of sheep and goat could be an effective measure to prevent dust occurrence in Xilingol Grassland.

scholarly journals Fuzzy or Non-Fuzzy? A Comparison between Fuzzy Logic-Based Vulnerability Mapping and DRASTIC Approach Using a Numerical Model. A Case Study from Qatar

Water ◽  
2021 ◽  
Vol 13 (9) ◽  
pp. 1288
Author(s):  
Husam Musa Baalousha ◽  
Bassam Tawabini ◽  
Thomas D. Seers
Keyword(s):  
Fuzzy Logic ◽  
Numerical Model ◽  
Vulnerability Assessment ◽  
Anthropogenic Impacts ◽  
Groundwater Vulnerability ◽  
Vulnerability Mapping ◽  
Anthropogenic Contamination ◽  
Weighted Overlay ◽  
Vulnerability Maps

Vulnerability maps are useful for groundwater protection, water resources development, and land use management. The literature contains various approaches for intrinsic vulnerability assessment, and they mainly depend on hydrogeological settings and anthropogenic impacts. Most methods assign certain ratings and weights to each contributing factor to groundwater vulnerability. Fuzzy logic (FL) is an alternative artificial intelligence tool for overlay analysis, where spatial properties are fuzzified. Unlike the specific rating used in the weighted overlay-based vulnerability mapping methods, FL allows more flexibility through assigning a degree of contribution without specific boundaries for various classes. This study compares the results of DRASTIC vulnerability approach with the FL approach, applying both on Qatar aquifers. The comparison was checked and validated against a numerical model developed for the same study area, and the actual anthropogenic contamination load. Results show some similarities and differences between both approaches. While the coastal areas fall in the same category of high vulnerability in both cases, the FL approach shows greater variability than the DRASTIC approach and better matches with model results and contamination load. FL is probably better suited for vulnerability assessment than the weighted overlay methods.

A geostatistical approach to winter road surface condition estimation using mobile RWIS data

2019 ◽  
Vol 46 (6) ◽  
pp. 511-521
Author(s):  
Lian Gu ◽  
Tae J. Kwon ◽  
Tony Z. Qiu
Keyword(s):  
Robust Regression ◽  
Surface Condition ◽  
Road Surface ◽  
Road Maintenance ◽  
Surface Conditions ◽  
Modelling Framework ◽  
Geostatistical Approach ◽  
Winter Road ◽  
Systematic Framework

In winter, it is critical for cold regions to have a full understanding of the spatial variation of road surface conditions such that hot spots (e.g., black ice) can be identified for an effective mobilization of winter road maintenance operations. Acknowledging the limitations in present study, this paper proposes a systematic framework to estimate road surface temperature (RST) via the geographic information system (GIS). The proposed method uses a robust regression kriging method to take account for various geographical factors that may affect the variation of RST. A case study of highway segments in Alberta, Canada is used to demonstrate the feasibility and applicability of the method proposed herein. The findings of this study suggest that the geostatistical modelling framework proposed in this paper can accurately estimate RST with help of various covariates included in the model and further promote the possibility of continuous monitoring and visualization of road surface conditions.

Sign in / Sign up

Export Citation Format

Share Document