A systematized review exploring the map of publications on the health impacts of drought

Natural Hazards - Climate change is likely to increase the risk of drought which impacts on health are not quite known well due to its creeping nature. This study maps the publications on the...     

Spatial distribution and biodiversity of macrofauna in the southeast of the Caspian Sea, Gorgan Bay in relation to environmental conditions

Spatial distribution and biodiversity of macrofauna in the Gorgan Bay, southeast of the Caspian Sea, were studied at fifteen stations in June 2010. Also, depth, temperature, salinity, dissolved oxygen, total organic matter content and sediment particle size were measured in each station. A total 3,356 individuals belonged to eight families and ten species were identified. Polychaeta were numerically dominated groups and Streblospio gynobranchiata, was constant and dominant species with 60.28% of total individuals but Bivalvia with four species had highest species number, though the density of them were low. The maximum density (4,500 ind/m²) was obtained at station 1 while the minimum (411 ind/m²) was observed at station 6. There was not significant correlation between the density of macrofauna with all environmental conditions. In total, six feeding group were considered but surface deposit feeder and deposit feeder were dominant in all stations. The maximum mean species number, diversity, richness, and evenness were obtained, 6.33, 1.46, 1.38 and 0.87, respectively. Based on the M-AMBI and the AMBI classification it seems that bentic environment in Gorgan Bay was not bad but the results of Shannon-Wiener, Margalef and Simpson indices the results were vice versa. In general, the values of the mentioned indices decreased from the western to the eastern part of the bay. Furthermore, the nonmetric multidimensional scaling (nMDS) showed that the structure of the macrofaunal assemblages was divides to six groups.     

Gorgan Bay:a microcosm for study on macrobenthos species-environment relationships in the southeastern Caspian Sea

The relationship between spatial patterns of macrobenthos community characteristics and environmental conditions(salinity, temperature, dissolved oxygen, organic matter content, sand, silt and clay) was investigated throughout the Gorgan Bay in June 2010. Principal components analysis(PCA) based on environmental data separated eastern and western stations. The maximum(4500 ind./m²) and minimum(411 ind./m²) densities were observed at Stas 1 and 6, respectively. Polychaeta was the major group and Streblospio gynobranchiata was dominant species in the bay. According to Distance Based Linear Models results, macrofaunal total density was correlated with silt percentage and salinity and these two factors explaining 64% of the variability while macrofaunal community structure just correlated with salinity(22% total variation). In general, western part of the bay showed the highest number of species and biodiversity while, the highest density was found at Sta. 1 and in the middle part of the bay. Furthermore, relationship between diversity indices and macrobenthic species with measured factors is also discussed. Our results confirm the effect of salinity as an important factor on distribution of macrobenthic fauna in south Caspian brackish waters.     

Competitive heavy metals adsorption on natural bed sediments of Jajrood River,Iran

The presence of heavy metal concentrations was examined in natural sediments from four sites along the Jajrood river in northeast of Tehran, the capital of Iran. Besides determination of elemental concentrations (Pb, Cu, Zn, Cd, Ni and Cr), X-ray fluorescence and X-ray diffraction tests were carried out to determine other chemical components in these adsorbents. Also the ability of sediments to adsorb these heavy metal ions from aqueous solutions was investigated. Results show that the extent of adsorption increases with increase in adsorbent concentration. The amount of adsorbed Pb, Cu and Zn in sediments was much greater than that of the other metals, and Cr was adsorbed much less than others. The adsorbabilities of sediments to heavy metals increased in the order of Pb > Cu > Zn > Cd > Ni > Cr. Based on the adsorption data, equilibrium isotherms were determined at selected areas to characterize the adsorption process. The adsorption data followed Freundlich and Langmuir isotherms in most cases. Correlation and cluster analysis was performed on heavy metals adsorption and sediment components at each site to evaluate main adsorbing compounds in sediments for each metal. Results demonstrated that heavy metals sorption is mostly related to load of organic matter in the Jajrood river sediments.     

Seismic risk assessment of a steel building supported on helical pile groups

Acta Geotechnica - Designing structures to be the least vulnerable within earthquake-prone areas is a serious challenge for structural engineers. One common and useful tool that structural...     

Comparison of momentum roughness lengths of the WRF-SWAN online coupling and WRF model in simulation of tropical cyclone Gonu

Ocean Dynamics - The surface enthalpy fluxes (latent and sensible heat fluxes) provide the necessary energy to intensify tropical cyclones (TCs). The surface momentum fluxes modify the intensity of...     

Probabilistic Convective Initiation Nowcasting with Reduced Satellite - NWP Predictors over Iran

Geostationary satellites are able to nowcast Convective Initiation (CI) for the next 0–6 h. Compared to using satellite predictors only, the incorporation of satellite and Numerical Weather Prediction (NWP) predictors can provide the possibility to reduce false alarm rates in 0–1:30 Convective Initiation Nowcasting (COIN). However, the correlation among these predictors not only can cause error in COIN, but also increases the runtime. In this study for the first time, all effective predictors in Satellite Convection Analysis and Tracking version 2 (SATCASTv2) and NWP were applied over Iran from 22nd March 2015 to 9th January 2016. In applying SATCASTv2 over Iran, it was necessary to make some modifications to the algorithm, such as removing case specific thresholds of satellite predictors and rearranging COIN predictors. Then, SATCASTv2 was tested and evaluated with both the full and reduced set of predictors. The results suggested that using fixed thresholds for temporal difference predictors could miss COIN in some cases. To investigate the possibility of improving computational efficiency, a dimension reduction was conducted by Factor Analysis (FA) and the number of predictors was reduced from 22 to 11. The NWP-satellite, reduced NWP-satellite, and satellite predictors were used as input in Random Forest (RF), as a parametric machine learning method, for COIN evaluation. The Combination of NWP model and satellite predictors had lower false alarm rates in contrast with satellite predictors. This is in agreement with previous studies. The results from statistical metrics showed that the reduced NWP-satellite predictors had comparable performance to the NWP-satellite predictors over study area, but decreased the run time by almost 50%. The results indicated that Convective Inhibition (CIN) was the most significant predictor when the reduced set of predictors was used.     

Stream Temperature Modeling and Fiber Optic Temperature Sensing to Characterize Groundwater Discharge

The Ngongotaha Stream was used as a case study to assess the applicability of fiber optic distributed temperature sensing (FODTS) to identify the location of springs and quantify their discharge. Thirteen springs were identified, mostly located within a 115 m reach, five discharged from the right bank and eight from the left bank. To quantify groundwater discharge, a new approach was developed in which the one-dimensional transient heat transport model was fitted to the FODTS measurements, where the main calibration parameters of interest were the unknown spring discharges. The spatial disposition of the groundwater discharge estimation problem was constrained by two sources of information; first, the stream gains ∼500 L/s as determined by streamflow gauging. Second, the temperature profiles of the left and right banks provide the spatial disposition of springs and their relative discharges. FODTS was used to measure stream temperature near the left and right banks, which created two temperature datasets. A weighted average of the two datasets was then calculated, where the weights reflected the degree of mixing between the right and left banks downstream of a spring. The new approach in this study marks a departure from previous studies, in which the general approach was to use the steady-state thermal mixing model (Selker et al. 2006a; Westhoff et al. 2007; Briggs et al. 2012) to infer groundwater discharge, which is then used as an input into a transient model of the general form of equation to simulate stream temperature (Westhoff et al. 2007).     

Optimal fusion of optical and SAR high-resolution images for semiautomatic building detection

Building detection from different high-resolution aerial and satellite images has been a notable research topic in recent decades. The primary challenges are occlusions, shadows, different roof types, and similar spectral behavior of urban covers. Integration of different data sources is a solution to supplement the input feature space and improve the existing algorithms. Regarding the different nature and unique characteristics of optical and radar images, there are motivations for their fusion. This paper is aimed to identify an optimal fusion of radar and optical images to overcome their individual shortcomings and weaknesses. For this reason, panchromatic, multispectral, and radar images were first classified individually, and their strengths and weaknesses were evaluated. Different feature-level fusions of these data sets were then assessed followed by a decision-level fusion of their results. In both the feature and decision levels of integration, artificial neural networks were applied as the classifiers. Several post-processing methods using normalized different vegetation index, majority filter, and area filter were finally applied to the results. Overall accuracy of 92.8% and building detection accuracy of 89.1% confirmed the ability of the proposed fusion strategy of optical and radar images for building detection purposes.     

Projections of temperature changes over South America during the twenty-first century using CMIP6 models

A 22-member ensemble from CMIP6 is used to analyze the projected changes and seasonal behavior in surface air temperature over South America during the twenty-first century. In the future projections, CMIP6 models shown a high dependency to the socioeconomic pathway over each country of South America. The multimodel ensemble projects a continuous increase in the annual mean temperature over South America during the twenty-first century under the three future scenarios (SSP1-2.6, SSP2-4.5 and SSP5-8.5). Besides, it was possible to identify consistent positive trends across all the models, with values between 0.45 ± 0.05 and 2.05 ± 0.31 °C cy⁻¹ under the historical experiment, however largest trends occurs for the projection periods (near, mid and far future), with values between − 0.87 ± 0.84 to 2.88 ± 0.60 °C cy⁻¹ (SSP1-2.6), 1.41 ± 0.88 to 5.32 ± 0.81 °C cy⁻¹ (SSP2-4.5) and 4.75 ± 0.58 to 8.76 ± 0.74 °C cy⁻¹ (SSP5-8.5) with maximum values at Bolivia, Brasil, Paraguay and Venezuela whilst minimum values for Argentina and Uruguay, regardless of the SSP scenario used. From the seasonal behavior analysis was possible to identify maximum values between January and March whilst minimum between June and July, except in Brasil, Venezuela and Guyana–Surinam–French Guayana, with annual range decreasing as the latidude decreases. By the end of the twenty-first century the annual mean temperature over South america is projected to increase between 0.92–2.11 °C, 0.97–3.37 °C and 1.27–6.14 °C under SSP1-2.6, SSP2-4.5 and SSP5-8.5 projection scenarios respectively. This projected increase of temperature across the continent will produce negative repercussions in the social, economic and political spheres. The results obtained in this study provide insights about the CMIP6 performance over this region, which can be used to develop adaptation strategies and might be useful for the adaptation to the climate change.