A Review of “Hazards,Vulnerability, and Environmental Justice”

A relatively simple modeling approach for estimating spatially distributed surface energy fluxes was applied to two small watersheds, one in a semi-arid climate region and one in a sub-humid region. This approach utilized a combination of ground-based meteorological data and remotely sensed data to estimate ‘instantaneous’ surface energy fluxes at the time of the satellite or aircraft overpasses. The spatial resolution in the watershed grid cells, which was on the order of 100-400 km, was selected to be compatible with ground measurements used for validation. The model estimates of surface energy fluxes compared well with ground-based measurements of surface flux (typically within approximately 40 Wm^?2). The model accuracy may be slightly less for bare soil surfaces due to an overestimation of the soil heat flux. In addition to demonstrating the feasibility of computing spatially distributed values of surface energy fluxes, these maps were used to qualitatively infer the dominant factors controlling the energy fluxes for the time period shortly following precipitation events in the basins. For the semi-arid watershed, values of sensible heat flux varied considerably over the watershed and displayed a pattern very similar to that of the spatially variable cumulative precipitation for at least one to eight days prior to the image acquisition. Due to the large fraction of exposed bare soil in a semi-arid ecosystem, even very small precipitation events had a strong influence on the pattern of sensible heat fluxes observed shortly after the event (less than 24 hours). For the sub-humid watershed, the fluxes tended to be more uniform across the watershed, and were influenced by a combination of precipitation total and land cover type.     

Forecasting of meteorological drought using Wavelet-ANFIS hybrid model for different time steps (case study: southeastern part of east Azerbaijan province, Iran)

Drought is accounted as one of the most natural hazards. Studying on drought is important for designing and managing of water resources systems. This research is carried out to evaluate the ability of Wavelet-ANN and adaptive neuro-fuzzy inference system (ANFIS) techniques for meteorological drought forecasting in southeastern part of East Azerbaijan province, Iran. The Wavelet-ANN and ANFIS models were first trained using the observed data recorded from 1952 to 1992 and then used to predict meteorological drought over the test period extending from 1992 to 2011. The performances of the different models were evaluated by comparing the corresponding values of root mean squared error coefficient of determination (R ²) and Nash–Sutcliffe model efficiency coefficient. In this study, more than 1,000 model structures including artificial neural network (ANN), adaptive neural-fuzzy inference system (ANFIS) and Wavelet-ANN models were tested in order to assess their ability to forecast the meteorological drought for one, two, and three time steps (6 months) ahead. It was demonstrated that wavelet transform can improve meteorological drought modeling. It was also shown that ANFIS models provided more accurate predictions than ANN models. This study confirmed that the optimum number of neurons in the hidden layer could not be always determined using specific formulas; hence, it should be determined using a trial-and-error method. Also, decomposition level in wavelet transform should be delineated according to the periodicity and seasonality of data series. The order of models with regard to their accuracy is as following: Wavelet-ANFIS, Wavelet-ANN, ANFIS, and ANN, respectively. To the best of our knowledge, no research has been published that explores coupling wavelet analysis with ANFIS for meteorological drought and no research has tested the efficiency of these models to forecast the meteorological drought in different time scales as of yet.     

Estimating magnetic dike parameters using a non‐linear constrained inversion technique: an example from the Särna area,west central Sweden

In this paper, we describe a non‐linear constrained inversion technique for 2D interpretation of high resolution magnetic field data along flight lines using a simple dike model. We first estimate the strike direction of a quasi 2D structure based on the eigenvector corresponding to the minimum eigenvalue of the pseudogravity gradient tensor derived from gridded, low‐pass filtered magnetic field anomalies, assuming that the magnetization direction is known. Then the measured magnetic field can be transformed into the strike coordinate system and all magnetic dike parameters – horizontal position, depth to the top, dip angle, width and susceptibility contrast – can be estimated by non‐linear least squares inversion of the high resolution magnetic field data along the flight lines. We use the Levenberg‐Marquardt algorithm together with the trust‐region‐reflective method enabling users to define inequality constraints on model parameters such that the estimated parameters are always in a trust region. Assuming that the maximum of the calculated g_zz (vertical gradient of the pseudogravity field) is approximately located above the causative body, data points enclosed by a window, along the profile, centred at the maximum of g_zz are used in the inversion scheme for estimating the dike parameters. The size of the window is increased until it exceeds a predefined limit. Then the solution corresponding to the minimum data fit error is chosen as the most reliable one. Using synthetic data we study the effect of random noise and interfering sources on the estimated models and we apply our method to a new aeromagnetic data set from the Särna area, west central Sweden including constraints from laboratory measurements on rock samples from the area.     

Airborne Gravimetry Survey for the Marine Area of the United Arab Emirates

The Military Survey Department (MSD) of the United Arab Emirates (UAE) undertook an airborne gravity survey project for the marine area of the country in 2009, especially to strengthen the marine and coastal geoid in the near-shore regions. For the airborne gravity survey, 5 km spacing coast-parallel flight lines were planned and surveyed. These lines were supplemented by cross-lines in order to assess the quality of the airborne gravity surveys. The flight lines were extended 10 km, spacing lines further offshore. A Beech King Air 350 aircraft was used for the surveys, collecting data at a typical flight speed of 170 knots and a typical flight elevation of 900–1500 m, depending on weather conditions and topography. Gravity was measured with a ZLS-modified LaCoste and Romberg gravimeter (S-99), augmented with a Honeywell strap-down inertial navigation system unit. The estimated accuracy for the airborne gravity data is better than 2.0 mGal r.m.s., as judged from the airborne track crossovers. The new airborne gravimetry data changed the UAE coastal geoid by up to 30 cm in some regions, highlighting the importance of airborne gravity coastal surveys.     

Soil loss and displacement by heavy equipment in forest road subgrading projects

Forest soil is an important component of the natural environment, and is a primary medium for many biological activities. In this study, soil loss and displacement by excavator and bulldozer （heavy equipments） were measured on cut and fills slopes of forest roads located in Mazandaran province, lran. The volumes of soil losses were estimated by prismoidal analyses of cut and fill slopes deformation between two time treatments （under subgrading and two years later） in slope classes of 30-50% and 50-70%. Weights of soil losses were calculated by multiplying the volumes of soil losses （cm^3） to the general bulk density （1.3g/cm^3）. Soil displaced area by heavy equipment was evaluated according to earth working width. Results indicated that heavy equipment has significant effect on deformation of cut slope gradient and fill slope length （p〈0.0001）. During the two-year period, the cut （p〈0.0002） and fill （p〈0.0001） slope gradients were significantly deformed in different slope classes. The average soil loss by excavator and bulldozer were 160.35 t/ha·yr and 429.09 t/ha·yr, respectively. Moreover, the soil displaced area during the subgrading process by bulldozer was greater than excavator in both two slope classes （p〈0.05）. Soil loss and displacement in forest roads can be rednced by applying powerful excavators in subgrading project, especially in steep terrains.     

Landslide susceptibility assessment at the Wuning area,China: a comparison between multi-criteria decision making,bivariate statistical and machine learning methods

Natural Hazards - The aim of this research is to investigate multi-criteria decision making [spatial multi-criteria evaluation (SMCE)], bivariate statistical methods [frequency ratio (FR), index of...     

Extracting of prospective groundwater potential zones using remote sensing data,GIS, and a probabilistic approach in Bojnourd basin,NE of Iran

Various groundwater potential zones for the assessment of groundwater availability in the Bojnourd basin have been investigated using remote sensing, GIS, and a probabilistic approach. Five independent groundwater factors, including topography, ground slope, stream density, geology units, lineament density, and a groundwater productivity factor, i.e., springs’ discharge, were applied. Discharge rates of 226 springs over the area were collected, and the probabilistic model was designed by the discharge rates of springs as the dependent variable. For training the probabilistic model, a ratio of 70/30% of springs’ discharge was applied and discharge rates of 151 springs were selected to randomly train the model. The frequency ratio for each factor was calculated, and the groundwater potential zones were extracted by summation of frequency ratio maps. The groundwater potential map was also classified into four classes, viz., “very good” (with a frequency ratio of >6.75), “good” (5.5FR6.75), “moderate” (4.75FR5.5), and “poor” (FR4.75). Then, the model was verified based on a success-rate curve method which resulted in obtaining an accuracy ratio of 75.77%. Finally, sensitivity analysis was applied by a factor removal method in five steps. Results reveal that topography factor has the biggest effect on the groundwater potential map and removing this factor eventuates in the lowest accuracy of the final map (AUC = 63. 73%). The groundwater potential map is fairly affected by removing the lineament density factor with an accuracy of 68.80%. Removing the lineament density factor has the lowest effect on the final map with accuracy of 68.80%.