Flood risk mapping and crop-water loss modeling using water footprint analysis in agricultural watershed,northern Iran

Natural Hazards - Spatial information on flood risk and flood-related crop losses is important in flood mitigation and risk management in agricultural watersheds. In this study, loss of water bound...     

Credibility of design rainfall estimates for drainage infrastructures: extent of disregard in Nigeria and proposed framework for practice

Natural Hazards - Rainfall intensity or depth estimates are vital input for hydrologic and hydraulic models used in designing drainage infrastructures. Unfortunately, these estimates are...     

Addressing the epistemic uncertainty in seismic hazard analysis as a basis for seismic design by emphasizing the knowledge aspects and utilizing imprecise probabilities

Bulletin of Earthquake Engineering - Epistemic uncertainty in seismic hazard analysis is traditionally addressed by utilizing a logic-tree structure with subjective probabilities for branches....     

Prediction of safety factors for slope stability: comparison of machine learning techniques

Natural Hazards - Because of the disasters associated with slope failure, the analysis and forecasting of slope stability for geotechnical engineers are crucial. In this work, in order to forecast...     

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.     

Effects of an outer thin stably stratified layer on planetary dynamos

The presence of outer stably stratified layers in planetary cores has been suggested for Earth, Saturn and Mercury. In this study, we use a 3-D numerical dynamo model to investigate the effects of a thin stable layer surrounding a convecting interior on the produced magnetic field. We find that a stable layer can destabilize the field morphology through a thermal wind that produces unfavorable zonal flows throughout the core. The direction of these zonal flows is prograde in equatorial regions, unlike a model with no stable layer that has retrograde equatorial flows. Our models therefore suggest that the Earth does not have a stable layer since we observe a westward drift as opposed to an eastward drift. For Saturn, we find that due to coupling of the flows in the stable and unstable layers, the layer does not act to shear out the non-axisymmetry in the observed magnetic field, and therefore cannot explain Saturn’s axisymmetric magnetic field. For Mercury, we find that if the stable layer is thin, it can actively produce strong or weak surface fields and not necessarily attenuate smaller scale features through the skin effect.     

A rock physics model to map gross lithology using compaction

Differential compaction has long been used by seismic interpreters to infer subsurface geology using knowledge of the relative compaction of different types of sediments. We outline a method to infer the gross fraction of shale in an interval between two seismic horizons using sandstone and shale compaction laws. A key component of the method involves reconstruction of a smooth depositional horizon by interpolating decompacted thicknesses from well control. We derive analytic formulae for decompaction calculations using known porosity–stress relations and do not employ discrete layer iterative methods; these formulae were found to depend not only upon the gross fraction of shale but also on the clay content of the shales and the thickness of the interval. The relative merits of several interpolation options were explored, and found to depend upon the structural setting. The method was successfully applied to an oil sands project in Alberta, Canada.     

Simulating soil loss rate in Ekbatan Dam watershed using experimental and statistical approaches

Reservoir sedimentation resulting fromwater erosion is an important environmental issue inmany countries where storage of water is crucial for economic and agricultural development.Therefore,this paper reports results from analysis of the soil hydrological response,i.e.soil water erosion,to simulated rainfall resulting in sediment accumulation at the reservoir of Ekbatan Dam(Hamedan province,Iran).Also,another objective of this study was to simulate the future trends in reservoir sedimentation(soil loss rate;SLR)from indoor rainfall simulator data by multiple linear regression(MLR)and Artificial Neural Networks(ANNs).For this research,three sampling points with different types of soils were chosen including clayey sand soil(SC-SM),silty soil(ML),and clayey soil(CL).The input parameters were slope gradient(sin θ),soil type(St),water content(w),dry density(γd),shear strength(τ),unconfined compressive strength(qu),permeability(k),and California bearing ratio(CBR).Using MLR and ANN methods,7 models were developed with 2 constant predictors(i.e.sin θ and St)and 6 free predictors which were added in each step one by one.Among MLR models,model 5 with St,sin θ,γd,τ,w,and qu as input parameters was statistically significant.Among ANN models,model 4 with St,sin θ,?d,τ,and w as input parameters,9 nodes,and 1 hidden layer was statistically significant.The root mean square error(RMSE),mean error(ME),and correlation coefficient(R)values were 1.433 kg/m^2 h,0.0195 kg/m^2 h,and 0.698 for the MLRmodel and 0.38 kg/m^2 h,0.151 kg/m^2 h,and 0.98 for the ANN model,respectively.These results show that the ANN model could better predict the SLR in comparison to the MLR model.The results also demonstrate that shear strength,among the strength parameters,had a greater impact on the SLR than compressive strengths(qu and CBR).Last but not the least,the reservoir sedimentationwas estimated for all methods and compared with the observed data.The results indicate that the ANN model is more appropriate for forecasting/simulating the sediment yield for a small watershed.     

Long-term change of surface temperature in water bodies around Iran – Caspian Sea,Gulf of Oman,and Persian Gulf – using 2001–2015 MODIS data

ABSTRACT

To answer the question “what has changed the surface temperatures of water bodies around Iran under climate change conditions,” Moderate Resolution Imaging Spectroradiometer (MODIS) sea surface temperature (SST) data with 9.2-km spatial resolution and monthly time step were evaluated. A significant increasing trend was obtained using Pearson’s correlation coefficient and Mann-Kendall trend test on data from the Caspian Sea (June) and the Southern Gulfs (September). High correlations of 0.69 and 0.97 between remotely sensed data and ERA-Interim reanalysis data were also obtained for June and September, respectively. To assess the impact of some climatic parameters on SST variability in those two months, zonal and meridional wind at 10 m, wind speed at 10 m, air temperature at 2 m, and mean sea level pressure were evaluated. High correlations between meridional wind and the Southern Gulf SST, and meridional wind and Caspian SST were ?0.86 and 0.75, respectively. Moreover, the results showed an important role of air temperature (0.96) and sea level pressure (?0.77) in the rising temperature trends of the Southern Gulfs. Wind speed was also detected in both regions as a booster of the trend.