Modeling flood event characteristics using D-vine structures

The authors investigate the use of drawable (D-)vine structures to model the dependences existing among the main characteristics of a flood event, i.e., flood volume, flood peak, duration, and peak time. Firstly, different three- and four-dimensional probability distributions were built considering all the permutations of the conditioning variables. The Frank copula was used to model the dependence of each pair of variables. Then, the appropriate D-vine structures were selected using information criteria and a goodness-of-fit test. The influence of varying the data length on the selected D-vine structure was also investigated. Finally, flood event characteristics were simulated using the four-dimensional D-vine structure.     

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.     

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.     

Soil erosion prediction based on land cover dynamics at the Semenyih watershed in Malaysia using LTM and USLE models

This study attempts to identify and forecast future land cover (LC) by using the Land Transformation Model (LTM), which considers pixel changes in the past and makes predictions using influential spatial features. LTM applies the Artificial Neural Networks algorithm) in conducting the analysis. In line with these objectives, two satellite images (Spot 5 acquired in 2004 and 2010) were classified using the Maximum Likelihood method for the change detection analysis. Consequently, LC maps from 2004 to 2010 with six classes (forest, agriculture, oil palm cultivations, open area, urban, and water bodies) were generated from the test area. A prediction was made on the actual soil erosion and the soil erosion rate using the Universal Soil Loss Equation (USLE) combined with remote sensing and GIS in the Semenyih watershed for 2004 and 2010 and projected to 2016. Actual and potential soil erosion maps from 2004 to 2010 and projected to 2016 were eventually generated. The results of the LC change detections indicated that three major changes were predicted from 2004 to 2016 (a period of 12 years): (1) forest cover and open area significantly decreased at rates of almost 30 and 8 km², respectively; (2) cultivated land and oil palm have shown an increment in sizes at rates of 25.02 and 5.77 km², respectively; and, (3) settlement and Urbanization has intensified also by almost 5 km². Soil erosion risk analysis results also showed that the Semenyih basin exhibited an average annual soil erosion between 143.35 ton ha^?1 year^?1 in 2004 and 151 in 2010, followed by the expected 162.24 ton ha^?1 year^?1. These results indicated that Semenyih is prone to water erosion by 2016. The wide range of erosion classes were estimated at a very low level (0–1 t/ha/year) and mainly located on steep lands and forest areas. This study has shown that using both LTM and USLE in combination with remote sensing and GIS is a suitable method for forecasting LC and accurately measuring the amount of soil losses in the future.     

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.     

Investigation of physicochemical parameters in drinking water resources and health risk assessment: a case study in NW Iran

In this study, the amount of cyanide in gold mine tailings is modeled as conceptual, statistical, and mathematical to determine environmental risk level and also to estimate the fate and transport of cyanide in tailings. Therefore, 116 points were selected for sampling from three levels of gold mine tailings and analyzed by colorimetric method. As a matter of fact, new hybrid modeling methods such as AdaBoost, Support Vector Machine, Linear Discriminant Analysis, and Random Forest were used in estimation. In current study, The AdaBoost method was qualified as the best one by minimum estimation error (less than 10%). The model derived from the AdaBoost method shows an average variation of 581 g/day in the volume of cyanide tailings. Thus, the important results of this paper are the presentation of 3D numerical and especially conceptual models according to the 3D cyanide variation in the sulfide gold mine tailings and governing physic/chemical parameters. These qualitative and quantitative results can be used for the management of tailings dam and prevention of the contaminant extension.     

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).