Top soil salinity prediction in South-Western part of Urmia Lake with ground water data

Drying of Urmia Lake in the north-west of Iran threatens all the agricultural lands around the Lake. Therefore, soil salinity appears to be the major threat to the agricultural lands in the area. The aim of the present study was to investigate the spatial variation of top soil salinity by taking into account of underground water quality data as secondary information. The research was performed on a grid of 500 m in an area of 5000 ha. Soil samples were gathered during the autumn of 2009 and were repeated in the spring of 2010. Electrical conductivity of soil samples was measured in a 1:2.5 soil to water suspension. Then covariance functions were build for each data set and soil salinity prediction were done on a grid of 100 m using kriging estimator with taking into account the mean variation. Afterwards sodium activity ratio derived from underground water quality database was used as covariate to develop cross-semivarograms in prediction of top soil salinity using cokriging method. Results demonstrated that soil salinity varied from values lower than 0.5 to more than 35 dSm-1 as a function of distance to the Lake. Cross-validating the results from salinity predictions using only kriging estimator to that of cokriging with sodium activity ratio data revealed that kriging offered better estimations with ME of 0.04 for autumn 2009 and -0.12 for spring 2010. Cokriging estimator had more smoother and diffused boundaries than that of kriging and resulted in more bias estimations (ME= -0.11 and -0.21 for first and second data sets). Although kriging method had better performance in top soil salinity prediction, but cokring method resulted in smoother boundaries and reduced the negative effects of mean variation in the area

Edwards–Wilkinson depinning transition in fractional Brownian motion background

Abstract There are various reports about the critical exponents associated with the depinning transition. In this study, we investigate how the disorder strength present in the support can account for this diversity. Specifically, we examine the depinning transition in the quenched Edwards–Wilkinson (QEW) model on a correlated square lattice, where the correlations are modeled using fractional Brownian motion (FBM) with a Hurst exponent of H.We identify a crossover time $$T^*$$ T ∗ that separates the dynamics into two distinct regimes: for $$T>T^*$$ T > T ∗ , we observe the typical behavior of pinned surfaces, while for $$T0.5$$ H > 0.5 ) cases. The critical driving force decreases with increasing H, as the host medium becomes smoother for higher H values, facilitating fluid mobility. This fact causes the asymptotic velocity exponent $$\theta$$ θ to increase monotonically with H

TOP SOIL SALINITY PREDICTION IN SOUTH-WESTERN PART OF URMIA LAKE WITH GROUND WATER DATA

Drying of Urmia Lake in the north-west of Iran threatens all the agricultural lands around the Lake. Therefore, soil salinity appears to be the major threat to the agricultural lands in the area. The aim of the present study was to investigate the spatial variation of top soil salinity by taking into account of underground water quality data as secondary information. The research was performed on a grid of 500 m in an area of 5000 ha. Soil samples were gathered during the autumn of 2009 and were repeated in the spring of 2010. Electrical conductivity of soil samples was measured in a 1:2.5 soil to water suspension. Then covariance functions were build for each data set and soil salinity prediction were done on a grid of 100 m using kriging estimator with taking into account the mean variation. Afterwards sodium activity ratio derived from underground water quality database was used as covariate to develop cross-semivarograms in prediction of top soil salinity using cokriging method. Results demonstrated that soil salinity varied from values lower than 0.5 to more than 35 dSm-1 as a function of distance to the Lake. Cross-validating the results from salinity predictions using only kriging estimator to that of cokriging with sodium activity ratio data revealed that kriging offered better estimations with ME of 0.04 for autumn 2009 and -0.12 for spring 2010. Cokriging estimator had more smoother and diffused boundaries than that of kriging and resulted in more bias estimations (ME= -0.11 and -0.21 for first and second data sets). Although kriging method had better performance in top soil salinity prediction, but cokring method resulted in smoother boundaries and reduced the negative effects of mean variation in the area

Top soil salinity prediction in South-Western part of Urmia Lake with ground water data

Drying of Urmia Lake in the north-west of Iran threatens all the agricultural lands around the Lake. Therefore, soil salinity appears to be the major threat to the agricultural lands in the area. The aim of the present study was to investigate the spatial variation of top soil salinity by taking into account of underground water quality data as secondary information. The research was performed on a grid of 500 m in an area of 5000 ha. Soil samples were gathered during the autumn of 2009 and were repeated in the spring of 2010. Electrical conductivity of soil samples was measured in a 1:2.5 soil to water suspension. Then covariance functions were build for each data set and soil salinity prediction were done on a grid of 100 m using kriging estimator with taking into account the mean variation. Afterwards sodium activity ratio derived from underground water quality database was used as covariate to develop cross-semivarograms in prediction of top soil salinity using cokriging method. Results demonstrated that soil salinity varied from values lower than 0.5 to more than 35 dSm-1 as a function of distance to the Lake. Cross-validating the results from salinity predictions using only kriging estimator to that of cokriging with sodium activity ratio data revealed that kriging offered better estimations with ME of 0.04 for autumn 2009 and -0.12 for spring 2010. Cokriging estimator had more smoother and diffused boundaries than that of kriging and resulted in more bias estimations (ME= -0.11 and -0.21 for first and second data sets). Although kriging method had better performance in top soil salinity prediction, but cokring method resulted in smoother boundaries and reduced the negative effects of mean variation in the area

Spatial Prediction of Soil Salinity Using Kriging with Measurement Errors and Probabilistic Soft Data

In this study it is shown how kriging with measurement errors (KME) is useful as opposed to more conventional kriging methods. The goal of the study was to properly account for field measured soil electrical conductivity (EC) as soft data for the spatial prediction of soil salinity. Samplings were done in autumn 2009 (first dataset), spring and autumn 2010 (second and third datasets) around Uromieh Lake, northwest of Iran. The salinity was measured both in the field and laboratory for the first and second datasets. The first dataset was used for error measurements from which an error variance can be estimated. The measured errors were then used for characterizing probabilistic type soft data using the second dataset. The KME with only soft data (SKME), KME with both soft and hard data (HSKME) and ordinary kriging methods were compared. Validation criteria, mean error (ME) and mean squared error (MSE) were used for comparing the methods. Finally, the SKME method was applied as a way of improving the salinity prediction for the third dataset where only field measured soil salinity data were available. Comparing different kriging methods, Ordinary Kriging (OK) showed the best results among the comparing methods with ME and MSE equal to 0.12 and 0.55 respectively. SKME with ME equal to 0.13 was slightly different from OK and SKME with ME equal to 0.24 resulted in more bias predictions among others. KME method has shown to be useful for soil salinity monitoring and can effectively reduce sampling time

Numerical simulation of wave generation in a tank by wall and floor oscillation

Tsunamis occur every year in different seas and oceans around the world. These waves propagate at high speeds in various directions and, if they reach the shore, cause irreparable damage to these areas and their structures and facilities. Therefore, understanding this complex phenomenon and predicting its behavior can reduce the damages. In the present study, numerical simulation studies of the tsunami phenomenon were carried out. The purpose of the study was to predict the tsunami wave characteristics when reaching the coastal area. The use of numerical simulation greatly reduces the cost of laboratory work and can also be used for complex geometries and models. The tsunami waves were considered as viscous fluid by Navier-Stokes equations for shallow water as governing equations with fluid volume fractionation method for simulating water surface in software. Wave generation was created by simulating a tank that fluctuates once to its left wall and once to its bottom. This work was carried out by Fluent software. In the following, the influence of shaking side wall angles on the generated waves is investigated. The simulation results show a significant increase in wave height due to the oscillating wall angle. The effects of the oscillating bottom wall have also been studied. In this thesis, the method of producing and propagating tsunami waves is described and the equations are defined. Also, since the most important issue in dealing with this phenomenon is their control, a method for controlling tsunami waves is presented in this thesis. Finally, a multi-phase method is used to simulate the movement of waves in a tank with a tremor wall. Finally, the obtained results have been compared to the analytical results by Green equation method and there are good agreements between them. The results showed that there is no change in wave height at distant points and with the oblique wall obliquity being increased by 30 degrees, the wave production increases. In addition, the flow and pressure lines also become almost horizontal