Delineation the active subsurface structures at Fayoum-Cairo district, Egypt, using magnetic and magnetotelluric tools

In this work, we present a reconnaissance study to elucidate and delineate subsurface fault structures for an active tectonics area that lies between Cairo and El Fayoum provinces and consider major sources of seismicity in Egypt. Well logging, aeromagnetic, land magnetic, and magnetotelluric data have been used. The well-logging data were used for several drilled wells along W–E direction. The magnetic data were analyzed using trend analysis, 3D magnetic modeling, and Werner deconvolution techniques. The magnetotelluric data were interpreted using 2D (TM–TE) modeling techniques. The results show that there are eight major fault structures having E–W, N–S, and NW–SE directions. These faults extend downward for about 20 km at the Dahshour and Qatrani areas. The epicenter sources of the earthquakes are clustering around the intersections of these structures. The Kattaniya horst structure has been interpreted as a regional structure that exceeds the limits previously determined by geologists. The depth to this horst reaches about 1.8 km at the NW and more than 4.3 km at the southern parts. The interpreted values of magnetic susceptibility at the horst zone indicate that they are ultrabasic/basic intrusion bodies.     

Groundwater abstraction management in Sana’a Basin,Yemen: a local community approach

Overexploitation of groundwater resources in Sana’a Basin, Yemen, is causing severe water shortages associated water quality degradation. Groundwater abstraction is five times higher than natural recharge and the water-level decline is about 4–8 m/year. About 90 % of the groundwater resource is used for agricultural activities. The situation is further aggravated by the absence of a proper water-management approach for the Basin. Water scarcity in the Wadi As-Ssirr catchment, the study area, is the most severe and this area has the highest well density (average 6.8 wells/km²) compared with other wadi catchments. A local scheme of groundwater abstraction redistribution is proposed, involving the retirement of a substantial number of wells. The scheme encourages participation of the local community via collective actions to reduce the groundwater overexploitation, and ultimately leads to a locally acceptable, manageable groundwater abstraction pattern. The proposed method suggests using 587 wells rather than 1,359, thus reducing the well density to 2.9 wells/km². Three scenarios are suggested, involving different reductions to the well yields and/or the number of pumping hours for both dry and wet seasons. The third scenario is selected as a first trial for the communities to action; the resulting predicted reduction, by 2,371,999 m³, is about 6 % of the estimated annual demand. Initially, the groundwater abstraction volume should not be changed significantly until there are protective measures in place, such as improved irrigation efficiency, with the aim of increasing the income of farmers and reducing water use.     

Predicting river water temperatures using stochastic models: case study of the Moisie River (Québec,Canada)

Successful applications of stochastic models for simulating and predicting daily stream temperature have been reported in the literature. These stochastic models have been generally tested on small rivers and have used only air temperature as an exogenous variable. This study investigates the stochastic modelling of daily mean stream water temperatures on the Moisie River, a relatively large unregulated river located in Québec, Canada. The objective of the study is to compare different stochastic approaches previously used on small streams to relate mean daily water temperatures to air temperatures and streamflow indices. Various stochastic approaches are used to model the water temperature residuals, representing short‐term variations, which were obtained by subtracting the seasonal components from water temperature time‐series. The first three models, a multiple regression, a second‐order autoregressive model, and a Box and Jenkins model, used only lagged air temperature residuals as exogenous variables. The root‐mean‐square error (RMSE) for these models varied between 0·53 and 1·70 °C and the second‐order autoregressive model provided the best results. A statistical methodology using best subsets regression is proposed to model the combined effect of discharge and air temperature on stream temperatures. Various streamflow indices were considered as additional independent variables, and models with different number of variables were tested. The results indicated that the best model included relative change in flow as the most important streamflow index. The RMSE for this model was of the order of 0·51 °C, which shows a small improvement over the first three models that did not include streamflow indices. The ridge regression was applied to this model to alleviate the potential statistical inadequacies associated with multicollinearity. The amplitude and sign of the ridge regression coefficients seem to be more in agreement with prior expectations (e.g. positive correlation between water temperature residuals of different lags) and make more physical sense. Copyright © 2006 John Wiley & Sons, Ltd.     

Daily river water temperature forecast model with a k‐nearest neighbour approach

Water temperature is a key abiotic variable that modulates both water chemistry and aquatic life in rivers and streams. For this reason, numerous water temperature models have been developed in recent years. In this paper, a k‐nearest neighbour model (KNN) is proposed and validated to simulate and eventually produce a one‐day forecast of mean water temperature on the Moisie River, a watercourse with an important salmon population in eastern Canada. Numerous KNN model configurations were compared by selecting different attributes and testing different weight combinations for neighbours. It was found that the best model uses attributes that include water temperature from the two previous days and an indicator of seasonality (day of the year) to select nearest neighbours. Three neighbours were used to calculate the estimated temperature, and the weighting combination that yielded the best results was an equal weight on all three nearest neighbours. This nonparametric model provided lower Root Mean Square Errors (RMSE = 1·57 °C), Higher Nash coefficient (NTD = 0·93) and lower Relative Bias (RB = ? 1·5%) than a nonlinear regression model (RMSE = 2·45 °C, NTD = 0·83, RB = ? 3%). The k‐nearest neighbour model appears to be a promising tool to simulate of forecast water temperature where long time series are available. Copyright © 2011 John Wiley & Sons, Ltd.     

Solidification of Tank Bottom Sludge

Tank bottom oily sludge (S) is collected from tank bottoms during cleaning operations and contaminated soil is collected after spills and leakages. Disposal of tank sludge is a significant item of tank maintenance for producers, refiners and transporters of petroleum materials. The objective of this paper is to investigate the use of various additives in the solidification of tank bottom sludge. The sludge was solidified using various combinations of additives including ordinary Portland cement (OPC), cement by-pass dust (CBPD) and quarry fines (QF). Geotechnical and leachability properties of the mixtures were determined. The use of OPC alone as a solidifying agent yielded the best results. This was followed by blends S:OPC:QF of 1:0.5:1.5 and S:CBPD of 1:2. Economically, the latter two mixtures would be considered more cost-effective in solidifying the sludge as the additives are waste by-product materials. The Toxicity Characteristic Leaching Procedure (TCLP) results indicated that no extracts exceeded the threshold TCLP limits established by U.S. Environmental Protection Agency (USEPA). The solidified material can be used in construction of roads, embankments and landfill layers.     

Groundwater level simulation using gene expression programming and M5 model tree combined with wavelet transform

ABSTRACT

In order to understand and adequately manage hydrological stress, it is necessary to simulate groundwater levels accurately. In this research, gene expression programming (GEP) and M5 model tree (M5) are used to simulate monthly groundwater levels. The models are combined with wavelet transform to produce two hybrid models: wavelet gene expression programming (WGEP) and wavelet M5 model tree (WM5). For the simulation, groundwater level, temperature and precipitation values from three observation wells and one meteorological station, located in Iran, are used. The results indicate that the hybrid models, WGEP and WM5, lead to a better performance than the simple models, GEP and M5. The performance of the two hybrid models is similar. It is also observed that selecting a suitable time lag for inputs plays an important role in the accuracy of the simple models. The selection of a suitable decomposition level strongly affects the accuracy of hybrid models.     

Use of Systematic, Palaeoflood and Historical Data for the Improvement of Flood Risk Estimation. Review of Scientific Methods

The catastrophic floods recently occurring in Europe warn of the critical need forhydrologic data on floods over long-time scales. Palaeoflood techniques provideinformation on hydrologic variability and extreme floods over long-time intervals(100 to 10,000 yr) and may be used in combination with historical flood data (last1,000 yr) and the gauge record (last 30–50 yr). In this paper, advantages anduncertainties related to the reconstruction of palaeofloods in different geomorphologicalsettings and historical floods using different documentary sources are described.Systematic and non-systematic data can be combined in the flood frequency analysisusing different methods for the adjustment of distribution functions. Technical toolsintegrating multidisciplinary approaches (geologic, historical, hydraulic and statistical)on extreme flood risk assessment are discussed. A discussion on the potential theoreticalbases for solving the problem of dealing with non-systematic and non-stationary data ispresented. This methodology is being developed using new methodological approachesapplied to European countries as a part of a European Commission funded project (SPHERE).     

Building a Coastal GIS Using Digital Photogrammetry

This paper examines the recent emergence of integrated coastal zone management as an application for the gathering, management and distribution of geospatial data. The requirements of a geospatial database of the coastal zone are outlined and the contribution of digital photogrammetry is examined. A saltmarsh monitoring project, which uses digital photogrammetry and was initiated by the authors, is described     

Bentonite as a Natural Adsorbent for the Sorption of Iron from the Ground Water Exploited from Aswan Area, Egypt

Sorption of dissolved Fe²⁺ on bentonite was studied using a batch technique. The distribution coefficient, K_d , was evaluated for a bentonite-iron system as a function of contact time, pH, sorbent and sorbate concentrations, and temperature. Sorption results were interpreted in terms of Freundlich's and Langmuir's equations. Thermodynamic parameters for the sorption system were determined at three temperatures: 298°, 308°, and 318°K. The values of ΔH°(-4.0 kjmol⁻¹) and ΔG°(-2.46 Kjmol⁻¹) at 298°K (25°C) suggest that sorption of iron on bentonite is an exothermic and a spontaneous process. The ΔG° value became less negative at higher temperatures and, therefore, less iron was sorbed at higher temperatures. The desorption studies with 0.01 M CaCl₂ and deionized water at iron loading on bentonite showed that more than 90 wt% of the iron is irreversibly sorbed, probably due to the fixation of the iron by isomorphous replacement in the crystal lattice of the sorbent.