Assessment of check dams’ role in flood hazard mapping in a semi-arid environment

This study aimed to examine flood hazard zoning and assess the role of check dams as effective hydraulic structures in reducing flood hazards. To this end, factors associated with topographic, hydrologic and human characteristics were used to develop indices for flood mapping and assessment. These indices and their components were weighed for flood hazard zoning using two methods: (i) a multi-criterion decision-making model in fuzzy logic and (ii) entropy weight. After preparing the flood hazard map by using the above indices and methods, the characteristics of the change‐point were used to assess the role of the check dams in reducing flood risk. The method was used in the Ilanlu catchment, located in the northwest of Hamadan province, Iran, where it is prone to frequent flood events. The results showed that the area of ‘very low’, ‘low’ and ‘moderate’ flood hazard zones increased from about 2.2% to 7.3%, 8.6% to 19.6% and 22.7% to 31.2% after the construction of check dams, respectively. Moreover, the area of ‘high’ and ‘very high’ flood hazard zones decreased from 39.8% to 29.6%, and 26.7% to 12.2%, respectively

A new threshold free dust storm detection index based on MODIS reflectance and thermal bands

Wind and soil confrontation under specific atmospheric conditions leads to the horrendous phenomenon of dust storms, particularly in arid lands. The negative impact of storms on the health and life of living beings is evident, resulting in many losses in the economic, social, and environmental sectors. Dust storm detection is the most important topic in dust studies, where many algorithms and indices have already been proposed using satellite imageries and remote sensing techniques. Despite the advantage of dust detection, these algorithms present drawbacks. This includes the need to determine different thresholds for different regional events or simply better respond to oceanic dust storms. The objective of this study is to develop a new Dust Storm Detection Index, called DSDI, without the need to determine different thresholds for each event. To establish the new index, it was necessary to distinguish the properties of dust, clouds, and land surfaces in the images of the MODIS sensors. These properties have been characterized by analyzing their spectral and thermal profiles at different wavelengths during dust storms from 2004 to 2009 in Yazd province, Central Iran, which is constantly coping with severe storms. Spectral and thermal ranges of 0.46, 0.56, 3.9, 1.4, 11, 12, and 13.6 μm were the most suitable discriminating bands of dust from the cloud and land surfaces. The brightness temperature difference of the thermal bands and the spectral ratio of reflectance bands, i.e. DSDI, has developed an appropriate relationship for separating land surfaces and clouds of dust particles. There was a significant correlation between DSDI and horizontal visibility (P-value= 0.05 & 0.01). It confirmed the success of this algorithm to detect storms in the study area, as an arid land