Groundwater Deterioration of Shallow Groundwater Aquifers Due to Overexploitation in Northeast Jordan

Groundwater is the major water resource in Jordan and most of the groundwater basins are already exploited beyond their estimated safe yield. Azraq basin is one of the most important groundwater basins in Jordan, which supplies Amman with drinking water. However, due to overpumping from the shallow groundwater aquifers, the water level dropped dramatically and signs of salinization and depletion are starting to occur. The severe drawdown in the Azraq well‐field caused a reverse in the hydraulic gradient and consequently, the saltwater in the center of the basin (Qa‐Azraq) started to move in the direction of the well‐field. The salinization in the shallow aquifer (basalt/B5/B4) is believed to result from one of the following scenarios: (i) a reverse flow from Sabkha to the AWSA well field, (ii) an upward leakage from the middle aquifer system (B2/A7) and the combined B3 Aquitard‐B2/A7 aquifer, (iii) a dissolution process between the water and rock matrix due to lowering of the dynamic water levels during pumping which reached the mineralized formations underlying the Basalt. The salinization trend of some AWSA wells represented by the gradual increase of major ions is associated with rather constant stable isotopic contents. This indicates that these constituents originate from the main minerals existing in the matrix of the aquifers and thus this scenario is the most likely to occur.     

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%.     

(Im)mobilization of soil heavy metals using CaO,FA, sulfur,and Na<Subscript>2</Subscript>S: a 1-year incubation study

The immobilization of heavy metals (HMs)-contaminated soils using amendments is a cost-effective remediation technique. Therefore, the aim of this study was to evaluate the effectiveness and aging factor of CaO, fly ash (FA), sulfur, and Na₂S on the immobilization of Cd, Cu, and Pb in three different contaminated soils under 1-year incubation. The study sites (S) and amendments treatments (T) are termed as S1, S2, and S3 and T1, T2, T3, and T4, respectively. The TCLP-extractable HMs were analyzed in treated soils after 2, 6, and 12 months. The higher concentrations of CaO, FA, and Na₂S (T3 and T4) efficiently immobilized the Cd. However, a moderate Cd decrease was noted in sulfur-treated samples with no significant difference in ratios as compared to control in used soils. Likewise, CaO decreased moderately Cu content in used soils with gradual increase in Cu mobility. Similarly, FA, sulfur, and Na₂S showed effective immobilization of Cu content with no difference in treatment ratios as compared to control. In addition, CaO, sulfur, and Na₂S decreased the significant content of Pb as compared to control. However, FA treatments showed moderate reduction in Pb content with no difference in ratios. The higher concentrations of alkaline amendments should be avoided in the farmland soils as they increase the soil pH and EC of soil ecosystem. The higher ratios of alkaline amendments would be suitable to remediate the abandoned lands/brownfields. The sulfur amendment would be suitable for immobilization of metals in alkaline soils rather than in acidic soils.     

Critical state behaviour of weakly bonded soil in drained state

A series of consolidated drained triaxial test was performed on weakly bonded soils that were artificially prepared by mixing sand (87%) and kaolin (13%). To create weakly bond strength, samples were fired at 500°C for 5 h. A critical state (CS) concept was used to interpret the results in order to describe the behaviour of the studied soil. Defining the CS is not always straightforward. Several tests showed changing in deviator stress q and volumetric strain ε_v up to the end of tests. Hence, defining the CS requires careful assessments on q–ε_a, Δu–ε_a, q–p′ and v–ln p′ spaces which were performed. A ‘discontinuity’ approach was applied to position the CS on dilatant path in v–ln p′ spaces after considering stress-strain and volumetric strain curves. The critical state line in a v–ln p? space from bonded samples exhibited differences to the destructured samples. This suggests that the influence of cementation bond is substantially clear at lower stresses but as stresses increase beyond the stress yield, cementation degradation becomes more significant. The effect of bonding can be clearly visualised from the normalisation of the stress paths of destructured and bonded samples.     

Potential impacts of climate change on groundwater levels on the Kerdi-Shirazi plain,Iran

It is important to predict how groundwater levels in an aquifer will respond to various climate change scenarios to effectively plan for how groundwater resources will be used in the future. Due to the overuse of groundwater resources and the multi-year drought in the Kerdi-Shirazi plain in Iran, some land subsidence and a drop in groundwater levels has taken place, and without active management, further degradation of the groundwater resource is possible under predicted future climate change scenarios in the country. To determine the potential impacts of climate change on groundwater levels in the region, the groundwater model GMS was coupled with the atmospheric circulation model HADCM3 using scenarios A1B, A2 and B1 for the period 2016–2030. The results of the climate modelling suggest that the Kerdi-Shirazi plain will experience an increase in minimum temperature and maximum temperature of, respectively, between 0.03 and 0.47, and 0.32–0.45 °C for this time period. The results of the groundwater modelling suggest that water levels on the Kerdi-Shirazi plain will continue to decline over the forecast period with decreases of 34.51, 36.57 and 33.58 m being predicted, respectively, for climate scenarios A1B, A2 and B1. Consequently, groundwater resources in the Kerdi-Shirazi plain will urgently need active management to minimize the effects of ongoing water level decline and to prevent saltwater intrusion and desertification in the region.     

Mapping red edge-based vegetation health indicators using Landsat TM data for Australian native vegetation cover

The usefulness of red edge bands, and vegetation indices based on red edge bands, for vegetation health monitoring has already been demonstrated. There are some satellites such as WorldView-2 and Sentinel-2 acquiring images in red edge band data; while, the former data can be expensive and often lack consistent global coverage, the latter does not have a long term archive and consequently cannot be used for a long term time series analysis. This study tests the ability to predict red edge band and red edge-based vegetation indices through freely available Landsat Thematic Mapper data for an Australian Eucalyptus-dominated vegetation cover within and around a mine site. Two modelling strategies including multiple-linear regression as a linear approach and random forests as a non-linear approach were used. The results showed that it is possible to generate red edge derivatives using the Landsat Thematic Mapper data with less than 10% error using both linear and non-linear methods; however, the linear method resulted in higher estimation accuracies than non-linear methods.     

An efficient knowledge-based approach for random variation interpretation in NDVI time series

Nowadays, detecting and interpreting random variation extracted from satellite image time series is a far-reaching real-world issue. A more adequate approach should be designed to deal with this challenge. In this paper, we propose an efficient knowledge-based approach for vegetation monitoring using normalized derivation vegetation index time series. First, a decomposition process is designed to separate seasonal, trend and remainder components. Then, a genetic based schema is proposed to generate association rules. The extracted knowledge is intended to interpret the remainder component extracted during the previous phase by discovering the hidden link between random variation and climate observations. For validation purpose, a database covering the regions in Northwestern Tunisia is used for a period starting from 2000 to 2012. The data have been derived from Moderate resolution Imaging Spectroradiometer and fused with ground climate data (temperature and precipitation) in the form of fuzzy association rules. The obtained results show the efficiency of the proposed approach by reducing the non-stationarity effect.     

Trend analysis of hydroclimatological variables in Urmia lake basin using hybrid wavelet Mann–Kendall and Şen tests

This paper investigates monthly, seasonal, and annual trends in rainfall, streamflow, temperature, and humidity amounts at Urmia lake (UL) basin and analyzes the interaction between these variables and UL’s water level fluctuation during the 1971–2013 period. Two new methods including nonparametric hybrid wavelet Mann–Kendall test and ?en’s methodology have been used to determine potential trends in the variables and their dominant periods. The results showed significant decreasing trends in the water level and streamflow series, moderate decreasing trend in the rainfall and relative humidity series, and increasing trends in the observed temperature data. The 8- , 12-month, and 2-year periods were detected as the dominant periods of the variables in monthly, seasonal, and annual timescales, respectively. The results from the interaction analysis revealed that the main factor influencing the water level at UL is decreasing trend in the streamflow series. Both the monthly series of UL’s water level and the streamflow series of the stations indicated two start points of significant decreasing trend in 1973 and 1998. Furthermore, a comparative analysis among the applied methods indicated a good agreement between the results of hybrid wavelet Mann–Kendall test and ?en’s trend analyzing method.     

Morphometric,statistical, and hazard analyses using ASTER data and GIS technique of WADI El-Mathula watershed,Qena, Egypt

An attempt to carry out morphometric, statistical, and hazard analyses using ASTER data and GIS technique of Wadi El-Mathula watershed, Central Eastern Desert, Egypt. Morphometric analysis with application of GIS technique is essential to delineate drainage networks; basin geometry, drainage texture, and relief characteristics, through detect forty morphometric parameters of the study watershed and its sub-basins. Extract new drainage network map with DEM, sub-basin boundaries, stream orders, drainage networks, slope, drainage density, flow direction maps with more details is very necessary to analyze different morphometric and hydrologic applications for the study basin. Statistical analysis of morphometric parameters was done through cluster analysis, regression equations, and correlation coefficient matrix. Clusters analyses detect three independents variables which are stream number, basin area, and stream length have a very low linkage distance of 0.001 (at very high similarity of 99.95%) in a cluster with the basin width. Main channel length and basin perimeter (at very high similarity of 99.83%) are in a cluster with basin length. Using the regression equations and graphical correlation matrix indicates the mathematical relationships and helps to predict the behavior between any two variables. Hazard analysis and hazard degree assessment for each sub-basin were performed. The hazardous factors were detected and concluded that most of sub-basins are classified as moderately to highly hazardous. Finally, we recommended that the flood possibilities should be taken in consideration during future development of these areas.