Discussion of “Interfacing the geographic information system,remote sensing,and the soil conservation service-curve number method to estimate curve number and runoff volume in the ASIR region of Saudi Arabia” by Fawzi S. Mohammad,Jan Adamowski

The soil conservation service curve number (SCS-CN) method is one of the most commonly used methods to compute the direct runoff from a rainfall event. Since the method was established, numerous researches were undertaken to improve the method through accurate estimation of its parameter and especially the curve number (CN). However, the essence of the SCS method, as an event-based Hortonian mechanism method, remained unchanged. The main assumption of the method related to the rainfall input is that the rainfall is continuous in time and uniform over the watershed. Mohammad and Adamowski (2015) paper apparently used the SCS method to estimate the annual runoff using the annual rainfall as one cumulative rainfall input value, which is a violation of the event-based principle of the method and of the assumption of the continuity of the rainfall event.To re-estimate the average annual runoff more realistically for the Asir region, Saudi Arabia, daily rainfall data from 14 rainfall stations are used for calculating the resulting runoff depths, on a daily event-by-event rainfall basis, throughout the whole simulation period. The resulting runoff depths are added for each year, and the total cumulative annual runoff values for each year are averaged to get the average annual runoff. The runoff values based on the previously mentioned procedure are an upper limit of the actual average annual runoff as the underlying SCS equations discard evaporation and similar long-term losses. Nevertheless, the average runoff values obtained in the discussion paper are an order of magnitude (at least five to tenfold) lower than the ones of the original paper. An equation is proposed to obtain a more realistic estimate of the average annual runoff, to be used with the average annual rainfall as an input, if the annual value is the only available rainfall information.     

Regional modeling of dust storm of February 8, 2015 in the southwest of Iran

According to World Meteorological Organization report in 2015, the southwest of Iran has become one of the dust sources in the region. And the objective of this research is to study the dust storms originating in this region. For this purpose, based on the weather data of 14 stations, the dust storms of the region were investigated, and the dust storm of February 7, 2015, was selected due to its very high concentration of dust particles (66 times the normal values). For the analysis of source areas and storm paths, the FNL data was used. The regional models of Navy Aerosol Analysis and Prediction System (NAAPS), Barcelona Supercomputing Centre-Dust Regional Atmospheric Model 8b (DREAM 8b), Non-hydrostatic Multiscale Model Barcelona Supercomputing Center (NMMB/BSC), and hybrid single-particle Lagrangian integrated trajectory (HYSPLIT) were used to study and analyze the selected storm. The results showed that the dust event in February 8, 2016, has been the result of the polar front jet stream (PJF) caused by western immigrant system that had been over the Sahara in Africa, the deserts of Iraq, Syria, Saudi Arabia, and finally southwest of Iran in making the extreme dust event. According to the Moderate Resolution Imaging Spectroradiometer data and point models of NAAPS, optical depth was very high. The DREAM 8b and NMMB/BSC models confirmed the impact of the local factors and closeness to the dust source regions. The backward tracking of the model with the HYSPLIT model showed three tracks transporting the dust particles to the region. This software also showed that the dust particles occupied an atmospheric tunnel of 1.5 km in diameter.     

Applying an optimized proxy-based workflow for fast history matching

History matching is still one of the main challenging parts of reservoir study especially in giant brown oil fields with lots of wells. In these cases, history matching with conventional manual technique needs many runs and takes months to get a match. In this work, an innovative approach was suggested for fast history matching in a real brown field. The workflow was employed based on an optimized proxy model for history matching of a field consisting of 14 active wells with multiple responses (which are production rate and pressure data) in the south part of Iran. The main important features of the proposed algorithm were defining a proxy model which is response surface method in which 21 model parameters were incorporated based on cubic centered face method. The proxy model was then optimized by one of the most famous algorithms which is genetic algorithm. Proxy model was successfully performed using 256 samples leading into p- value of 0.531 and R ² of 0.91 dataset. As a result, the proposed workflow and algorithm showed good and acceptable results for history matching of studied real model.     

Assessment of proposed approaches for bathymetry calculations using multispectral satellite images in shallow coastal/lake areas: a comparison of five models

Bathymetric information for shallow coastal/lake areas is essential for hydrological engineering applications such as sedimentary processes and coastal studies. Remotely sensed imagery is considered a time-effective, low-cost, and wide-coverage solution for bathymetric measurements. This study assesses the performance of three proposed empirical models for bathymetry calculations in three different areas: Alexandria port, Egypt, as an example of a low-turbidity deep water area with silt-sand bottom cover and a depth range of 10.5 m; the Lake Nubia entrance zone, Sudan, which is a highly turbid, unstable, clay bottom area with water depths to 6 m; and Shiraho, Ishigaki Island, Japan, a coral reef area with varied depths ranging up to 14 m. The proposed models are the ensemble regression tree-fitting algorithm using bagging (BAG), ensemble regression tree-fitting algorithm of least squares boosting (LSB), and support vector regression algorithm (SVR). Data from Landsat 8 and Spot 6 satellite images were used to assess the performance of the proposed models. The three models were used to obtain bathymetric maps using the reflectance of green, red, blue/red, and green/red band ratios. The results were compared with corresponding results yielded by two conventional empirical methods, the neural network (NN) and the Lyzenga generalised linear model (GLM). Compared with echosounder data, BAG, LSB, and SVR results demonstrate higher accuracy ranges from 0.04 to 0.35 m more than Lyzenga GLM. The BAG algorithm, producing the most accurate results, proved to be the preferable algorithm for bathymetry calculation.     

End Bearing Capacity of Drilled Shafts in Sand: A Numerical Approach

In this paper, a modeling procedure is carried out to numerically analyze the end bearing capacity of drilled shafts in sand. The Mohr–Coulomb elastic plastic constitutive law with stress dependent elastic parameters is used for all numerical analyses performed in this study. The numerical results are compared with the available experimental equations. It is seen that numerical results are in good agreement with experimental equations. The variation of the end bearing capacity of drilled shafts versus embedment depth is also studied. Numerical results show that with increase in pile embedment depth, the end bearing capacity increases. However, the rate of increase becomes smaller as the pile embedment depth increases. Also, numerical analyses show that, for equal settlement, the end bearing decreases with increase in the pile diameter. Finally, a sensitivity analysis is performed to obtain the separate effect of each sand parameter on the end bearing capacity of drilled shafts, and the parameters that are most influential are identified.     

Evaluation of climate change in northern Iran during the last four centuries by using dendroclimatology

Natural Hazards - Climate change is currently one of the most important environmental issues. Dendrochronology is frequently used to identify the climatic changes most closely associated with...     

The Effects of Hydrophilic Polymer and Soil Salinity on Corn Growth in Sandy and Loamy Soils

The interaction effects of different applied ratios of a hydrophilic polymer (Superab A200) (0, 0.2, 0.6% w/w) under various soil salinity levels (initial salinity, 4 and 8 ms/cm) were evaluated on available water content (AWC), biomass, and water use efficiency for corn grown in loamy sand and sandy clay loam soils. The results showed that the highest AWC was measured at the lowest soil salinity. The application of 0.6% w/w of the polymer at the lowest salinity level increased the AWC by 2.2 and 1.2 times greater than those of control in the loamy sand and sandy clay loam soils, respectively. The analysis of variance of data showed that the effect of salinity was significant on biomass and water use efficiency of corn in the loamy sand and sandy clay loam soils. The highest amounts of these traits were measured in soils with the lowest salinity level. Application of polymer at the rate of 0.6% in the loamy sand soil and at the rate of 0.2% in the sandy clay loam soil resulted in the highest aerial and root biomass and water use efficiency for corn. At these polymer rates the amounts of water use efficiency for corn were 2.6 and 1.7 times greater than those of control in the loamy sand and sandy clay loam soils, respectively. Thus, the use of hydrophilic polymer in soils especially in the sandy soils increases soil water holding capacity, yield, and water use efficiency of plant. On the other hand, decreases the negative effect of soil salinity on plant and helps for irrigation projects to succeed in arid and semi‐arid areas.     

Landslide susceptibility analysis with a bivariate approach and GIS in Northern Iran

Globally, landslides cause hundreds of billions of dollars in damage and hundreds of thousands of deaths and injuries each year. A landslide susceptibility map describes areas where landslides are likely to occur in the future by correlating some of the principal factors that contribute to landslides with the past distribution of landslides. A case study is conducted in the mountainous northern Iran. In this study, a landslide susceptibility map of the study area was prepared using bivariate method with the help of the geographic information system. Area density (bivariate) method was used to weight landslide-influencing data layers. An overlay analysis is carried out by evaluating the layers obtained according to their weight and the landslide susceptibility map is produced. The study area was classified into five hazard classes: very low, low, moderate, high, and very high. The percentage distribution of landslide susceptibility degrees was calculated. It was found that about 26% of the study area is classified as very high and high hazard classes.