Spatial downscaling of climate variables using three statistical methods in Central Iran

Spatial downscaling methods are widely used for the production of bioclimatic variables(e.g. temperature and precipitation) in studies related to species ecological niche and drainage basin management and planning. This study applied three different statistical methods, i.e. the moving window regression(MWR), nonparametric multiplicative regression(NPMR), and generalized linear model(GLM), to downscale the annual mean temperature(Bio1) and annual precipitation(Bio12) in central Iran from coarse scale(1 km × 1 km) to fine scale(250 m ×250 m). Elevation, aspect, distance from sea and normalized difference vegetation index(NDVI) were used as covariates to create downscaled bioclimatic variables. Model assessment was performed by comparing model outcomes with observational data from weather stations. Coefficients of determination(R2), bias, and root-mean-square error(RMSE) were used to evaluate models and covariates. The elevation could effectively justify the changes in bioclimatic factors related to temperature and precipitation. Allthree models could downscale the mean annual temperature data with similar R2, RMSE, and bias values. The MWR had the best performance and highest accuracy in downscaling annual precipitation(R2=0.70; RMSE=123.44). In general, the two nonparametric models, i.e. MWR and NPMR, can be reliably used for the downscaling of bioclimatic variables which have wide applications in species distribution modeling.     

Using advanced soft computing techniques for regional shoreline geoid model estimation and evaluation

This study aims at evaluating the global geoid model for a regional shoreline fitting using advanced soft computing techniques and global navigation satellite system/leveling measurements. Artificial neural networks, fuzzy logic, and least square support vector machine models are developed and used to fit the global geoid model for the north coastal Egyptian line. In addition, a novel estimation geoid model is designed and evaluated based on the latest global geoid models. The results of the three estimation models show that they can be used to correct the shoreline geoid model, in terms of root mean square error that ranges from 1.7 to 8.5?cm. Moreover, it is found that the least square vector machine model is a competitive approach with certain advantage in solving complex problems represented by missing data.     

Investigation of rangeland indicator species using parametric and non-parametric methods in hilly landscapes of central Iran

This study aimed to identify indicator species and explore the most important environmental and management variables contributing to vegetation distribution in a hilly upper dam landscape in Zagros Mountain chain, Iran. A stratified random sampling method was used to collect topographic, edaphic, management and vegetation data. The density and cover percentage of perennial species were measured quantitatively. Indicator species were identified using the two-way indicator species analysis. Besides calculating physiognomic factors in sample sites, 24 soil samples were collected from 0 to 30 cm of soil depth and analyzed in terms of gravel percentage, texture, saturation moisture, organic matter, pH and electrical conductivity in saturation extract, lime percentage, soluble calcium and magnesium, available phosphorus, Cation Exchange Capacity(CEC) and soluble sodium and potassium. Multivariate techniques including Canonical Correspondence Analysis and Multi-Dimensional Scaling were used to explore the relationships of species with environmental and management variables. Seven plants were identified as indicator species due to being significantly correlated with management(grazing or non-grazing) and edaphic variables such as CEC, soil texture, pH, CaCO_3 percentage and physiographic variable including slope, elevation, and convex and concave formations(p 0.05). Overall, overgrazing and its subsequent effects on soil characteristics, loss of vegetation cover and trampling were found as the major causes of deterioration. Sustainable and integrated management practices such as the implementation of appropriate grazing systems were suggested to enhance soil quality and reduce the accelerated erosion in upper dam zones.     

Hydrodynamic Tidal Model of the Persian Gulf Based on Spatially Variable Bed Friction Coefficient

The aim of this study is to develop a two-dimensional hydrodynamic tidal model for the Persian Gulf (PG2017) using 2D-MIKE21 software. The advantages of present study is accounting for the spatial variation of bed friction coefficient besides a precise bathymetry together with a 23-year of combined records of satellite altimetry data. We found that the bed friction coefficient has a significant effect on sea level changes in the region under our modeling consideration. Since the tidal behavior in the northern part of the Qeshm Island is significantly different from the other parts of the Persian Gulf, to present a more accurate hydrodynamic tidal model, the Gulf is divided into two regions where the bed friction coefficient is modeled separately for each region. The root mean square value of the differences between the amplitude of dominant constituents; M2, S2, K1, and O1 derived from the PG2017 model and that of 98 altimetry and coastal tide gauge stations are respectively equal to 1.6, 1.9, 2.8, and 1.3?cm. Moreover, comparing the PG2017 model efficiency with the FES2014, OSU12, EOT11a, DTU10, and Admiralty models shows that the PG2017 model has an improvement of 22.1%, 47.2%, 43.2%, 44.2%, and 57.6% in terms of relative error, respectively.     

Analytical Reduced Models for the Non-stationary Diabatic Atmospheric Boundary Layer

Geophysical boundary-layer flows feature complex dynamics that often evolve with time; however, most current knowledge centres on the steady-state problem. In these atmospheric and oceanic boundary layers, the pressure gradient, buoyancy, Coriolis, and frictional forces interact to determine the statistical moments of the flow. The resulting equations for the non-stationary mean variables, even when succinctly closed, remain challenging to handle mathematically. Here, we derive a simpler physical model that reduces these governing unsteady Reynolds-averaged Navier–Stokes partial differential equations into a single first-order ordinary differential equation with non-constant coefficients. The reduced model is straightforward to solve under arbitrary forcing, even when the statistical moments are non-stationary and the viscosity varies in time and space. The model is successfully validated against large-eddy simulation for, (1) time-variable pressure gradients, and (2) linearly time-variable buoyancy. The new model is shown to have a superior performance compared to the classic Blackadar solutions (and later improvements on these solutions), and it covers a much wider range of conditions.     

Land use/land cover change impact on groundwater quantity and quality: a case study of Ajman Emirate,the United Arab Emirates,using remote sensing and GIS

Ajman is a rapidly urbanizing emirate with land development succeeding at a fast pace. This study aims to monitor land use/land cover changes and assesses the impact of these changes on groundwater quality and quantity of the shallow aquifer using multitemporal remote sensing data and geographic information system (GIS). To monitor the land use/land cover changes, the Spectral Angle Mapper (SAM) and the Normalized Difference Vegetation Index (NDVI) algorithms were utilized. The obtained maps were correlated against a set of total dissolved solid (TDS); Mg, Cl, and NO₃ groundwater quality index; and depth to the groundwater table maps constructed from groundwater data. The spatial analysis revealed a sharp depletion in groundwater quality and quantity related to the increase in the land use/land cover classes. The mean total TDS is from 21,971 to 26,450 mg/L and depth to groundwater level from ?12.33 to ?17.2 m over a period of 15 years. Maps of normalized difference and groundwater quality sustainability showed that the eastern side of the study area has a high value of groundwater quality sustainability and normalized difference, while the western side of the study area has a minimal value of groundwater quality sustainability and normalized difference. This study is of great assistance for decision makers and land developers to relate to municipal land allotment in rapidly developing regions such as Ajman.     

Evaluating the structural model from ambient vibration recordings and shallow seismic refraction techniques. A case study of Zagazig City,Egypt

This study uses the seismic refraction and noise measurements to investigate the velocity structure of the subsurface and emphasize the advantage of ambient vibration over the conventional seismic refraction technique. Field measurements were carried out at nine sites in and around Zagazig city. Shallow seismic refraction data were interpreted using the delay time method to obtain the two-dimension ground model at each site. Ambient vibration arrays are used to infer the one-dimensional compressional and shear wave velocity profiles through two main steps. The first step is to derive the dispersion curve from the recorded signals using the frequency-wavenumber method. The second is to invert the dispersion curve to obtain the site velocity profiles. The results of the compressional wave velocities obtained from seismic refraction technique showed that the subsurface consists of a number of layers ranging from two to four layers and give a good agreement with the results of the seismic wave velocities obtained from the ambient vibration arrays. The ambient vibration arrays gave a deeper depth of penetration than the other method, providing more information on the subsurface structure without any disturbance to the environment. This work provides reliable estimates of the seismic velocity structures of both shallow and deep sedimentary layers within the area of interest.     

Automatic Feature Extraction Module for Change Detection in Al Ain,UAE: Analysis by Means of Multi-temporal Remote Sensing Data

Monitoring new changes in cities adjacent to dynamic sand dunes requires precise classifier technique. Unlike traditional techniques of supervised classification which use training sites, the integration of image transformation tasseled cap and automatic feature extraction module based on spectral signatures has provided to be sensitive and realistic techniques with time and cost effective. The proposed module was applied to Al Ain district, United Arab Emirates (UAE). The module consists of four steps in terms of segmentation, thresholding and clustering and computing attributes. The obtained greenness and classified maps were then enhanced by applying a 3?×?3 Sobel filter. The new changes were detected by combining the multi-temporal greenness and classification maps. Accuracy assessment and quantitative analysis were performed using confusion matrix and ground truthing. The results showed significant increasing in urban and agricultural areas from the year from 1990 to 2000 compared with the period of time from the year 2000 to 2006. The image difference showed that the vegetation and building classes had increased 7.58 and 20.28 km² respectively. This study showed that image difference and fuzzy logic approach are the most sensitive techniques for detecting new changes in areas adjacent to dynamic sand dunes.