Modeling level change in Lake Urmia using hybrid artificial intelligence approaches

Theoretical and Applied Climatology - The investigation of water level fluctuations in lakes for protecting them regarding the importance of these water complexes in national and regional scales...     

Estimating the economic potential for agricultural soil carbon sequestration in the Central United States using an aggregate econometric-process simulation model

The purpose of this paper is to develop and apply a new method to assess economic potential for agricultural greenhouse gas mitigation. This method uses secondary economic data and conventional econometric production models, combined with estimates of soil carbon stocks derived from biophysical simulation models such as Century, to construct economic simulation models that estimate economic potential for carbon sequestration. Using this method, simulations for the central United States show that reduction in fallow and conservation tillage adoption in the wheat-pasture system could generate up to about 1.7 million MgC/yr, whereas increased adoption of conservation tillage in the corn–soy–feed system could generate up to about 6.2 million MgC/yr at a price of $200/MgC. About half of this potential could be achieved at relatively low carbon prices (in the range of $50 per ton). The model used in this analysis produced estimates of economic potential for soil carbon sequestration potential similar to results produced by much more data-intensive, field-scale models, suggesting that this simpler, aggregate modeling approach can produce credible estimates of soil carbon sequestration potential. Carbon rates were found to vary substantially over the region. Using average carbon rates for the region, the model produced carbon sequestration estimates within about 10% of those based on county-specific carbon rates, suggesting that effects of spatial heterogeneity in carbon rates may average out over a large region such as the central United States. However, the average carbon rates produced large prediction errors for individual counties, showing that estimates of carbon rates do need to be matched to the spatial scale of analysis. Transaction costs were found to have a potentially important impact on soil carbon supply at low carbon prices, particularly when carbon rates are low, but this effect diminishes as carbon prices increase. This research was supported in part by the Montana State Agricultural Experiment Station, by the EPA STAR Climate Change program and by the Consortium for the Agricultural Mitigation of Greenhouse Gases. Although the research described in this article has been funded wholly or in part by the United States Environmental Protection Agency through grant R-82874501-0 to Montana State University, it has not been subjected to the Agency’s required peer and policy review and therefore does not necessarily reflect the views of the Agency and no official endorsement should be inferred.     

Spatial prediction model and its application to chemistry of atmospheric precipitation in Jordan

This study is concerned with the spatial variability of some wet atmospheric precipitation parameters such as; pH, conductivity (EC). The study also depicts the spatial variability of some ions (cations and anions) of atmospheric precipitation in Jordan such as, Ca²⁺, Mg²⁺, Na⁺ and K⁺, HCO₃⁻, Cl⁻, NO₃⁻ and SO₄²⁻. The basis of the work is to establish a relationship through the cumulative semivariogram technique between the distance ratios and the spatial dependence structure of the chemical composition of atmospheric precipitation. All semivariogram models are constructed in this study in order to understand the behavior of the spatial distribution. The spatial distributions of rainwater parameters show differences from station to station which is expressed in terms of angle, where the larger the angle the weaker the correlation. The semivariogram (SV) models are constructed to show the variation of the rainfall chemistry in Jordan. The SV models show weak correlation between mountain and leeside mountain stations, i.e. mountain and desert stations. On the other hand, good correlations are observed when transferring from south to north of the country. The larger is the found angle, the weaker is the correlation. For most of the SV model the correlation is found to be very weak between desert and mountainous locality. The Standard Regional Dependence Factor (SRDF) is used for prediction of the distribution of rain fall parameters. It shows the relative error between observed and predicted values of rainwater parameters. The overall regional relative error between the observed and estimated concentrations remains less than 15%.     

Physically based modelling of sheet erosion (detachment and deposition processes) in complex hillslopes

A one‐dimensional uncoupled model governed by this research is a physics‐based modelling of the rainfall‐runoff induced erosion process. The presented model is composed of three parts of a three‐dimensional (3D) hillslope geometry, a nonlinear storage (kinematic wave) model for hillslope hydrological response, and an unsteady physically based surface erosion model. The 3D hillslope geometry model allows describing of the hillslope morphology by defining their plan shape and profile curvature. By changing these two topographic parameters, nine basic hillslope types are derived. The modelling of hillslope hydrological response is based on a flow continuity equation as the relation of discharge and flow depth is passed on kinematic wave approximation. The erosion model is based on a mass conservation equation for unsteady flow. The model assumes that suspended sediment does not affect flow dynamics. The model also accounts for the effect of flow depth plus loose soil depth on soil detachment. The presented model was run for two different precipitations, slope content, and length, and results were plotted for sediment detachment/deposition rate. Based on the obtained results, in hillslopes with convex and straight profile curvatures, sediment detachment only occurred in the whole length of the hillslope. However, in concave ones, sediment detachment and deposition only occurred together in hillslope. The hillslopes with straight profiles and convergent plans have the highest rate of detachment. Also, results show that most detachment rates occur in convex profile curvatures, which are about 15 times more than in straight profiles. Copyright © 2015 John Wiley & Sons, Ltd.     

Sensitivity of the Humboldt Current system to global warming: a downscaling experiment of the IPSL-CM4 model

The impact of climate warming on the seasonal variability of the Humboldt Current system ocean dynamics is investigated. The IPSL-CM4 large scale ocean circulation resulting from two contrasted climate scenarios, the so-called Preindustrial and quadrupling CO₂, are downscaled using an eddy-resolving regional ocean circulation model. The intense surface heating by the atmosphere in the quadrupling CO₂ scenario leads to a strong increase of the surface density stratification, a thinner coastal jet, an enhanced Peru–Chile undercurrent, and an intensification of nearshore turbulence. Upwelling rates respond quasi-linearly to the change in wind stress associated with anthropogenic forcing, and show a moderate decrease in summer off Peru and a strong increase off Chile. Results from sensitivity experiments show that a 50% wind stress increase does not compensate for the surface warming resulting from heat flux forcing and that the associated mesoscale turbulence increase is a robust feature.     

Investigation of Biosorption Behavior of Methylene Blue on Pleurotus ostreatus (Jacq.) P. Kumm.

The effect of varying parameters such as dye concentration, adsorbent dose, pH and temperature on the adsorption capacity of Pleurotus ostreatus is investigated. The commonly available white rot fungus Pleurotus ostreatus is investigated as a viable biomaterial for the biological treatment of synthetic basic methylene blue effluents. The results obtained from the batch experiments reveal the ability of the fungus to remove methylene blue. The performance is dependent on the dye concentration, pH, and fungal biomass. The equilibrium and kinetics of adsorption are investigated and the Langmuir equation is used to fit the equilibrium isotherm. The adsorption isotherm of methylene blue follows only the Langmuir model with a correlation coefficient of ca. 0.96–0.99. The maximum adsorption capacity is ca. 70 mg of dye per g of dry fungus at pH 11, 70 mg L^–1 dye, and 0.1 g L^–1 fungus concentration, respectively. This study demonstrates that the fungus could be used as an effective biosorbent for the treatment of dye‐containing wastewater streams.     

Introducing landscape accuracy metric for spatial performance evaluation of land use/land cover change models

Performance evaluation is a critical step for land use/land cover (LULC) change modelling. It can be conducted through pixel quantity and its geographical location according to majority of current approaches. It is hence important to know to what extent spatial patterns of a given landscape are properly replicated in simulated LULC maps. Therefore, a new validation metric, named as landscape accuracy metric (LAM), is introduced by inspiration from landscape ecology. Unlike pixel quantity validation metrics, model performance is measured by LAM through quantifying spatial patterns including structure, composition and configuration attributes. The functionality of LAM was studied to assess the performance of the built-up change simulation under historical, ecological and stochastic scenarios, applying Cellular Automata Markov model. LAM is a flexible measure such that modellers can apply this metric through adding or eliminating various metrics of their interest in a selective manner and under different environmental circumstances.     

On lattice reduction algorithms for solving weighted integer least squares problems: comparative study

Decorrelation or reduction theory deals with identifying appropriate lattice bases that aid in accelerating integer search to find the optimal integer solution of the weighted integer least squares problem. Orthogonality defect has been widely used to measure the degree of orthogonality of the reduced lattice bases for many years. This contribution presents an upper bound for the number of integer candidates in the integer search process. This upper bound is shown to be a product of three factors: (1) the orthogonality defect, (2) the absolute value of the determinant of the inverse of the generator matrix of the lattice, and (3) the radius of the search space raised to the power of the dimension of the integer ambiguity vector. Four well-known decorrelation algorithms, namely LLL, LAMBDA, MLAMBDA, and Seysen, are compared. Many simulated data with varying condition numbers and dimensions as well as real GPS data show that the Seysen reduction algorithm reduces the condition number much better than the other algorithms. Also, the number of integer candidates, before and after the reduction process, is counted for all algorithms. Comparing the number of integer candidates, condition numbers, and orthogonality defect reveals that reducing the condition number and the orthogonality defect may not necessarily result in decreasing the number of integer candidates in the search process. Therefore, contrary to the common belief, reducing the orthogonality defect and condition number do not always result in faster integer least squares estimation. The results indicate that LAMBDA and MLAMBDA perform much better in reducing the number of integer candidates than the other two algorithms, despite having a larger orthogonality defect and condition number in some cases. Therefore, these two algorithms can speed up the integer least squares estimation problem in general and the integer ambiguity resolution problem in particular.