Role of land surface parameter change in dust emission and impacts of dust on climate in Southwest Asia

Natural Hazards - Spatial–temporal changes of land surface parameters (land cover change, net primary production, and vegetation phenology) affect the characteristics of atmospheric dust....     

Spatial assessment of the potential of groundwater quality using fuzzy AHP in GIS

In this study, fuzzy AHP method is used for extracting the water quality indicators based on the Schuler standard and World Health Organization (WHO) guidelines during a 20-year period. For this purpose, the best fit of the zoning model was performed. Furthermore, by comparing the standard errors, the continuous Raster layer was extracted from the important parameters used in generating the qualitative potential assessment index. The classified layer was generated by integrating continuous layers in the GIS environment and with the use of Python programming. The similarity of the outputs of both methods indicates the presence of large sections of aquifers in the middle and southwestern regions of Iran in the “temporarily drinkable” and “bad” classes. The calculations showed that the majority of aquifers that were located in the “inappropriate” class during the first 10 years fell to less valuable class types. Based on the results of the model, there is a direct correlation between the drop in water resources and the decline in the quality indices. In addition, in the Urmia and Bushehr coastal aquifers, due to excessive water withdrawal and salty water penetration, the quality of the table water is in critical condition. Based on the results of the research, the aquifers in the range of Zagros and Alborz mountains show the least change in water quality. The reason for this is the depth of the aquifer and the ability to recharge it.     

Estimation of soil hydraulic properties and their uncertainty through the Beerkan infiltration experiment

The Beerkan method based on in situ single‐ring water infiltration experiments along with the relevant specific Beerkan estimation of soil transfer parameters (BEST) algorithm is attractive for simple soil hydraulic characterization. However, the BEST algorithm may lead to erroneous or null values for the saturated hydraulic conductivity and sorptivity especially when there are only few infiltration data points under the transient flow state, either for sandy soil or soils in wet conditions. This study developed an alternative algorithm for analysis of the Beerkan infiltration experiment referred to as BEST‐generalized likelihood uncertainty estimation (GLUE). The proposed method estimates the scale parameters of van Genuchten water retention and Brooks–Corey hydraulic conductivity functions through the GLUE methodology. The GLUE method is a Bayesian Monte Carlo parameter estimation technique that makes use of a likelihood function to measure the goodness‐of‐fit between modelled and observed data. The results showed that using a combination of three different likelihood measurements based on observed transient flow, steady‐state flow and experimental steady‐state infiltration rate made the BEST‐GLUE procedure capable of performing an efficient inverse analysis of Beerkan infiltration experiments. Therefore, it is more applicable for a wider range of soils with contrasting texture, structure, and initial and saturated water content. Copyright © 2015 John Wiley & Sons, Ltd.     

Wetland cover change detection using multi-temporal remotely sensed data

Lake Urmia, located in northwest Iran, contains a number of wetlands significantly affecting the environmental, social, and economic conditions of the region. The ecological condition of Lake Urmia has degraded during the past decade, due to climate change, human activities, and unsustainable management. The poor condition of the lake has also affected the surrounding wetlands. This study analyzes the land cover change of one of the wetlands in the southern part of Lake Urmia, known as Ghara-Gheshlagh wetland, in the period 1989–2015 using post-classification change detection and machine learning image classification. For this analysis, three Landsat images, acquired in 1989 (TM), 2001 (TM), and 2015 (Landsat-8), were used for the classification and change detection. Support vector machine learning algorithm, a supervised learning method, is employed, and images are classified into four main land cover classes namely “water,” ”barren,” “salty land,” and “agriculture and grassland.” Change detection was carried out for pairs of years 1989 to 2001 and 2001 until 2015. The results of this classification show that there is a sharp increase in the area of salt-saturated land as well as a decrease in the area of water resources. Overall classification accuracy obtained were high for the individual years: 1989 (91.48%), 2001 (90.63%), and 2015 (88.6%). Also, the Kappa coefficients for individual maps were high: 1989 (0.89), 2001 (0.8742), and 2015 (0.84). After that, the land cover change map of the study area is obtained between 1989 to 2001 and then 2001 to 2015. The results of this analysis suggest that more efforts should be taken to effectively manage water resources in the region and point to potential locations for focused management actions within the wetland area.     

A Unified Computational Method for Simulating Dynamic Behavior of Planing Vessels

High speed planing hulls have complex hydrodynamic behaviors. The trim angle and drafts are very sensitive to speed and location of the center of gravity. Therefore, motion simulation for such vessels needs a strong coupling between rigid body motions and hydrodynamic analysis. In addition, free surface should be predicted with good accuracy for each time step. In this paper, velocity and pressure fields are coupled by use of the fractional step method. On the basis of integration of the two-phase viscous flow induced stresses over the hull, acting loads (forces and moments) are calculated. With the strategy of boundary-fitted body-attached mesh and calculation of 6-DoF motions in each time step, time history of ship motions including displacements, speeds and accelerations are evaluated. For the demonstration of the software capabilities, circular cylinder slamming is simulated as a simple type of water slamming. Then, a high-speed planing catamaran is investigated in the case of steady forward motion. All of the results are in good concordance with experimental data. The present method can be widely implemented in design as well as in performance prediction of high-speed vessels.     

Active Control of A Piston-Type Absorbing Wavemaker with Fully Reflective Structure

China Ocean Engineering - Multiple reflections of the waves between structure and wavemaker in hydraulic flumes could change the frequency content of the desired incident wave or result in...     

Rainfall and rainy days trend in Iran

In this study, long-term annual and monthly trends in rainfall amount, number of rainy days and maximum precipitation in 24?h are investigated based on the data collected at 33 synoptic stations in Iran. The statistical significance of trend and climate variability is assessed by the Mann-Kendall test. The Linear trend analysis and the Mann-Kendall test indicate that there are no significant linear trends in monthly rainfall at most of the synoptic stations in Iran. However, the maximum number of stations with negative trends have been observed in April (29 station), and then in May (21 stations) and February (21 stations) and with positive trends in December (26 stations) and July (24 stations). The significant linear trends, with a significant level of 0.05, in annual rainfall have been noticed only at five stations. The monthly number of rainy days does not show any significant linear trend for most areas in Iran. The maximum number of stations with monthly negative trends in rainy days has also been observed in April with the minimum in December. In April, out of 24 stations with negative trends, 12 stations have a significant negative trend. Contrary to that, in October there is no significant linear trend. Most stations have positive trends in annual number of rainy days. Also, the monthly maximum precipitation in 24?h does not show any significant linear trend for most areas in Iran. The maximum number of stations with monthly negative trends in maximum precipitation has also been observed in February with the minimum in December. In spite of that, there are almost no significant precipitation variations in Iran during the last 50-odd years, the tendency of decreasing rainfall amount in April and increasing rainfall amount in December and July could indicate an eventual climate change in this area in the future.