Predicting saturated hydraulic conductivity using particle swarm optimization and genetic algorithm

Soil saturated hydraulic conductivity (K_s) is considered as soil basic hydraulic property, and its precision estimation is a key element in modeling water flow and solute transport processes both in the saturated and vadose zones. Although some predictive methods (e.g., pedotransfer functions, PTFs) have been proposed to indirectly predict K_s, the accuracy of these methods still needs to be improved. In this study, some easily available soil properties (e.g., particle size distribution, organic carbon, calcium carbonate content, electrical conductivity, and soil bulk density) are employed as input variables to predict K_s using a fuzzy inference system (FIS) trained by two different optimization techniques: particle swarm optimization (PSO) and genetic algorithm (GA). To verify the derived FIS, 113 soil samples were taken, and their required physical properties were measured (113 sample points?×?7 factors?=?791 input data). The initial FIS is compared with two methods: FIS trained by PSO (PSO-FIS) and FIS trained by GA (GA-FIS). Based on experimental results, all three methods are compared according to some evaluation criteria including correlation coefficient (r), modeling efficiency (EF), coefficient of determination (CD), root mean square error (RMSE), and maximum error (ME) statistics. The results showed that the PSO-FIS model achieved a higher level of modeling efficiency and coefficient of determination (R²) in comparison with the initial FIS and the GA-FIS model. EF and R² values obtained by the developed PSO-FIS model were 0.69 and 0.72, whereas they were 0.63 and 0.54 for the GA-FIS model. Moreover, the results of ME and RMSE indices showed that the PSO-FIS model can estimate soil saturated hydraulic conductivity more accurate than the GA-FIS model with ME?=?10.4 versus 11.5 and RMSE?=?5.2 versus 5.5 for PSO-FIS and GA-FIS, respectively.     

Surface irrigation performance of date palms under water scarcity in arid irrigated lands

In this paper, a study on the performance of surface irrigation of date palms in a Tunisian arid area (Douz oasis) is presented. The study is conducted in 16 plots with various sizes and soil textures over a 4-year period (2012–2015). In the first step, an assessment of total water requirements of the date palms is carried out. Then, the surface irrigation performance is analyzed using three indicators, i.e., the relative water supply (RWS) indicator, the uniformity index of water distribution (D _U ), and the water application efficiency (E _a ). Finally, the irrigation management problems are identified. The results indicate that in the arid Tunisian Saharan oases, the soil texture, plot size, and farmers’ practices (especially irrigation duration) have significant effects on surface irrigation performance. The average annual net irrigation requirements of date palms are about 2400 mm. The RWS increases from 1.8 in the smaller plots (0.5 ha) to 3.6 in the largest plots (2.5 ha), implying that the increase in the plot size requires an excessive water supply. D _U decreases from 80.7 in the 0.5 ha plots to 65.4 in the 2.5 ha plots; however, no significant difference in the E _a is observed. The results show that the soil texture has no influence on the RWS and D _U , but the E _a is significantly higher in the loamy-sand soils (46.7%) compared to the sandy soils (36.3%). Overall, RWS indicator is higher than 1 (RWS?=?2.6) implying excessive irrigation supply to the system. Although D _U is relatively uniform (>?60%), E _a is relatively low (<?50%) indicating that the current irrigation management is inefficient. These findings have a paramount importance for improving irrigation water management in the Tunisian Saharan oases.     

Dynamic soil properties for microzonation of Delhi,India

Delhi, the capital of India, has experienced mild seismic shaking during several earthquakes in the past. The large variations of depth to bedrock and ground water table coupled with different soil types at different locations of Delhi necessitate a seismic microzonation study. Dynamic soil properties such as shear wave velocity, modulus reduction and damping characteristics of local soils are the basic and essential input parameters for conducting even a preliminary ground response analysis which is an essential input in microzonation studies. Shear wave velocity is not measured routinely due to its high cost and lack of the required expertise. Several researchers in the past developed correlations between shear wave velocity (V _s ) and routinely measured N values. In the present study, shear wave velocity profiles measured in the field at more than 80 borehole locations to a depth of about 20 to 32m using Spectral Analysis of Surface Waves (SASW) are presented and correlations between shear wave velocity and N values are also presented for use by engineers and designers. Results of strain and stress controlled cyclic triaxial tests on remoulded samples of sand-silt mixtures in the high strain range are used for generating the modulus reduction and damping curves and are compared with the well-known curves in the literature. The results presented in this article can be used for microzonation studies as well as site specific ground response analyses at Delhi.     

Geospatial approach in mapping soil erodibility using CartoDEM – A case study in hilly watershed of Lower Himalayan Range

Soil erodibility is one of the most important factors used in spatial soil erosion risk assessment. Soil information derived from soil map is used to generate soil erodibility factor map. Soil maps are not available at appropriate scale. In general, soil maps at small scale are used in deriving soil erodibility map that largely generalized spatial variability and it largely ignores the spatial variability since soil map units are discrete polygons. The present study was attempted to generate soil erodibilty map using terrain indices derived from DTM and surface soil sample data. Soil variability in the hilly landscape is largely controlled by topography represented by DTM. The CartoDEM (30 m) was used to derive terrain indices such as terrain wetness index (TWI), stream power index (SPI), sediment transport index (STI) and slope parameters. A total of 95 surface soil samples were collected to compute soil erodibility factor (K) values. The K values ranged from 0.23 to 0.81 t ha^?1R^?1 in the watershed. Correlation analysis among K-factor and terrain parameters showed highest correlation of soil erodibilty with TWI (r ²= 0.561) followed by slope (r ²= 0.33). A multiple linear regression model was developed to derive soil erodibilty using terrain parameters. A set of 20 soil sample points were used to assess the accuracy of the model. The coefficient of determination (r ²) and RMSE were computed to be 0.76 and 0.07 t ha^?1R^?1 respectively. The proposed methodology is quite useful in generating soil erodibilty factor map using digital elevation model (DEM) for any hilly terrain areas. The equation/model need to be established for the particular hilly terrain under the study. The developed model was used to generate spatial soil erodibility factor (K) map of the watershed in the lower Himalayan range.     

Developing synergy regression models with space-borne ALOS PALSAR and Landsat TM sensors for retrieving tropical forest biomass

Forest stand biomass serves as an effective indicator for monitoring REDD (reducing emissions from deforestation and forest degradation). Optical remote sensing data have been widely used to derive forest biophysical parameters inspite of their poor sensitivity towards the forest properties. Microwave remote sensing provides a better alternative owing to its inherent ability to penetrate the forest vegetation. This study aims at developing optimal regression models for retrieving forest above-ground bole biomass (AGBB) utilising optical data from Landsat TM and microwave data from L-band of ALOS PALSAR data over Indian subcontinental tropical deciduous mixed forests located in Munger (Bihar, India). Spatial biomass models were developed. The results using Landsat TM showed poor correlation (R² = 0.295 and RMSE = 35 t/ha) when compared to HH polarized L-band SAR (R² = 0.868 and RMSE = 16.06 t/ha). However, the prediction model performed even better when both the optical and SAR were used simultaneously (R² = 0.892 and RMSE = 14.08 t/ha). The addition of TM metrics has positively contributed in improving PALSAR estimates of forest biomass. Hence, the study recommends the combined use of both optical and SAR sensors for better assessment of stand biomass with significant contribution towards operational forestry.     

Bearing strength surface for bridge caisson foundations in frictional soil under combined loading

The problem of estimating the bearing capacity of massive caisson foundations in frictional soil under combined vertical (N), horizontal (Q) and moment (M) loading is examined numerically by means of three-dimensional finite element analyses. The analysis is performed with due consideration to the foundation’s depth-to-width ratio (D/B), the magnitude of the vertical load and the caisson-soil contact interface conditions. The constitutive law for soil behavior is appropriately validated against experimental results from 1-g small-scale tests, available in the literature. The ultimate limit states are presented in the form of a bearing strength surface in dimensionless and normalized form, while detailed discussion is provided on the physical and geometrical interpretation of the kinematic mechanisms that accompany failure. A generalized closed-form expression for the failure envelope in M–Q–N space is then fitted to the numerical results with use of an appropriately trained artificial neural network. An upper-bound limit equilibrium solution for a certain failure mechanism (designated as the “sliding” mechanism) associated with maximum horizontal bearing capacity is also developed for verification purposes. One of the originalities of the paper lies with respect to the post-failure response of the caissons, where it is shown that the incremental displacement vector is accurately reproduced by assuming normality on the bearing strength surface irrespective of the considered plastic flow rule (associative or non-associative) at the microscale (soil element).     

Spatial information of soil hydraulic conductivity and performance of cokriging over kriging in a semi-arid basin scale

Unlike the studies in small parcels by systematic measurements, the spatial variability of soil properties is expected to increase in those over relatively large areas or scales. Spatial variability of soil hydraulic conductivity (K _h) is of significance for the environmental processes, such as soil erosion, plant growth, transport of the plant nutrients in a soil profile and ground water levels. However, its variability is not much and sufficiently known at basin scale. A study of testing the performance of cokriging of K _h compared with that of kriging was conducted in the catchment area of Sarayköy II Irrigation Dam in Cank?r?, Turkey. A total of 300 soil surface samples (0–10 cm) were collected from the catchment with irregular intervals. Of the selected soil properties, because the water-stable aggregates (WSA) indicated the highest relationship with the hydraulic conductivity by the Pearson correlation analysis, it is used as an auxiliary variable to predict K _h by the cokriging procedure. In addition, the sampling density was reduced randomly to n = 175, n = 150, n = 75 and n = 50 for K _h to determine if the superiority of cokriging over kriging would exist. Statistically, the results showed that all reduced K _h was as good as the complete K _h when its auxiliary relations with WSA were used in cokriging. Particularly, the results of the “Relative Reduction in MSE” (RMSE) revealed that the reduced data set of n = 75 produced the most accurate map than the others. In this basin-scaled study, there was a clear superiority of the cokriging procedure by the reduction in data although a very undulating topography and topographically different aspects, two different land uses with non-uniform vegetation density, different parent materials and soil textures were present in the area. Hence, using the statistically significant auxiliary relationship between K _h and WSA might bring about a very useful data set for watershed hydrological researches.     

Mathematical expression of discharge capacity of compound open channels using MARS technique

In this paper, analytical methods, artificial neural network (ANN) and multivariate adaptive regression splines (MARS) techniques were utilised to estimate the discharge capacity of compound open channels (COC). To this end, related datasets were collected from literature. The results showed that the divided channel method with a coefficient of determination (R ²) value of 0.76 and root mean square error (RMSE) value of 0.162 has the best performance, among the various analytical methods tested. The performance of applied soft computing models with R ²=0.97 and RMSE = 0.03 was found to be more accurate than analytical approaches. Comparison of MARS with the ANN model, in terms of developed discrepancy ratio (DDR) index, showed that the accuracy of MARS model was better than that of MLP model. Reviewing the structure of the derived MARS model showed that the longitudinal slope of the channel (S), relative flow depth (H _r ) and relative area (A _r ) have a high impact on modelling and forecasting the discharge capacity of COCs.     

New record of coralline algae from the Holocene sediments of Agatti Island,Lakshadweep, India

The present paper examines the coralline algal assemblage recovered from the Holocene sediments of Agatti Island, Lakshadweep, India. The assemblage comprises nine species of coralline algae which include Amphiroa fragilisma, Lithophyllum nitorum, Lithophyllum incrustans, Lithoporella melobesioids, Spongites sp., Porolithon craspedium, Aethesolithon problematicum, Clathromorphum parcum, Melobesoideae gen. et spec. indet. Clathromorphum parcumis an endophytic coralline alga reported for the first time from the Indian subcontinent. Out of these, seven species belong to the family Corallinaceae and the two represent the family Hapalidiaceae of the class Rhodophyceae. Coralline algal association of this area is dominated by Lithophylloideae, Melobesioideae and Mastophoroideae which include encrusting to fragmented growth forms. The associated branching corals are well developed and adapted to shallow, warm water, low turbid, protected lagoonal environment.     

Determination and prediction of standardized precipitation index (SPI) using TRMM data in arid ecosystems

Drought over a period threatens the water resources, agriculture, and socioeconomic activities. Therefore, it is crucial for decision makers to have a realistic anticipation of drought events to mitigate its impacts. Hence, this research aims at using the standardized precipitation index (SPI) to predict drought through time series analysis techniques. These adopted techniques are autoregressive integrating moving average (ARIMA) and feed-forward backpropagation neural network (FBNN) with different activation functions (sigmoid, bipolar sigmoid, and hyperbolic tangent). After that, the adequacy of these two techniques in predicting the drought conditions has been examined under arid ecosystems. The monthly precipitation data used in calculating the SPI time series (SPI 3, 6, 12, and 24 timescales) have been obtained from the tropical rainfall measuring mission (TRMM). The prediction of SPI was carried out and compared over six lead times from 1 to 6 using the model performance statistics (coefficient of correlation (R), mean absolute error (MAE), and root mean square error (RMSE)). The overall results prove an excellent performance of both predicting models for anticipating the drought conditions concerning model accuracy measures. Despite this, the FBNN models remain somewhat better than ARIMA models with R?≥?0.7865, MAE?≤?1.0637, and RMSE?≤?1.2466. Additionally, the FBNN based on hyperbolic tangent activation function demonstrated the best similarity between actual and predicted for SPI 24 by 98.44%. Eventually, all the activation function of FBNN models has good results respecting the SPI prediction with a small degree of variation among timescales. Therefore, any of these activation functions can be used equally even if the sigmoid and bipolar sigmoid functions are manifesting less adjusted R² and higher errors (MAE and RMSE). In conclusion, the FBNN can be considered a promising technique for predicting the SPI as a drought monitoring index under arid ecosystems.     

Estimating groundwater evapotranspiration rates using diurnal water-table fluctuations in a semi-arid riparian zone

In semi-arid climates, phreatophytes draw on shallow aquifers, and groundwater evapotranspiration (ET_G) is a principal component of groundwater budgets. Diurnal water table fluctuations, which often are a product of ET_G, were monitored in the riparian zone of Red Canyon Creek, Wyoming, USA. These fluctuations were higher in a riparian wetland (2–36 mm) than a grass-covered meadow (1–6 mm). The onset and cessation of water-table fluctuations correspond to daily temperatures relative to freezing. Spatial differences were due to vegetation type and specific yield, while temporal changes were due to vegetation dormancy. Ratios of ET_G to potential evapotranspiration (PET), K _c,GW, were similar to ratios of actual evapotranspiration (ET) to PET, K _c, in semi-arid rangelands. Before vegetation senescence, K _c,GW increased between precipitation events, suggesting phreatophytes pull more water from the saturated zone as soil moisture decreases. In contrast, K _c decreases with soil moisture following precipitation events as ET becomes increasingly water-limited. Error in ET_G is primarily from estimates of specific yield (S _y), which is difficult to quantify in heterogeneous sediments. ET_G values may be more reliable because the range of acceptable S _y is smaller than K _c and S _y does not change with vegetation type or soil moisture.     

Seismic reliability-based analysis and GIS mapping of cyclic mobility of clayey soils of Mumbai city,India

Cyclic mobility is a mechanism of ground failure due to lateral spreading of soils during an earthquake that usually occurs in soft or medium stiff saturated soils. The simplified procedures developed by the researchers give a factor of safety for judging the cyclic mobility potential. However, the simplified procedures do not take into account the uncertainty in the parameters required to estimate the cyclic stresses in the soil. In this study, a reliability framework based on the simplified procedure, considering the parameter uncertainty, has been proposed for computing the probability of cyclic mobility of clay deposits for a metro city of India, i.e., Mumbai city (latitudes 18°53′N–19°19′N and longitudes 72°47′E–72°58′E). Extensive geotechnical borehole data from 1028 boreholes across 50 locations in the city area of 390 km² and laboratory test data are collected and analyzed thoroughly. A correlation between undrained shear strength (Su) and other parameters such as natural water content (w), SPT N value, liquid limit (LL) and plasticity index (PI) has been established for Mumbai city and has been used in the proposed approach. The sensitivity analysis of the proposed approach predicts that Su has significant influence in the evaluation of the cyclic mobility. Cyclic mobility hazard maps are prepared using the geo-statistical analysis tool in GIS, and it shows that the clayey soils at few locations have a 60–90 % probability of cyclic mobility for a moment magnitude (M _w) of an earthquake of 7.5. These hazard maps can be used by the geotechnical engineers for the cyclic mobility hazard assessment of Mumbai city.     

Determination of atrazine isotherms on agricultural soils

For determination of atrazine isotherms in agricultural soils of Fars Province, composite soil samples from 0 to 5 cm depth with textures of silty clay loam, clay loam and loam were collected. In order to form the atrazine isotherms, 10, 50 and 100 µg atrazine g^?1 soil was added to the soil samples. Soluble atrazine in water:soil ratios of 10:1, 50:1 and 200:1 was measured after 3-h shaking. Finally, for each cases of applied atrazine, water extractable atrazine was determined and quantified using gas chromatography instrument. The results indicated that there was a linear relationship between the logarithms of water extractable atrazine and added atrazine for different water:soil ratios. A general equation of WEA = K(WS) ^α (AA) ^β is obtained experimentally between water extractable atrazine, µg g^?1(WEA), and added atrazine, µg g^?1 (AA), where K, α and β are absorption constants; WS is the water:soil ratio, g g^?1. For the loam, silty clay loam and clay loam soil textures, the α were 0.49, 0.23 and 0.13, respectively, the β were 0.55, 0.806 and 0.21, respectively, and the K were 1.44, 0.78 and 25.38, respectively.     

A modified normalized model for predicting effective soil thermal conductivity

Effective soil thermal conductivity (λ _eff) describes the ability of a multiphase soil to transmit heat by conduction under unit temperature gradient. It is a critical parameter for environmental science, earth and planetary science, and engineering applications. Numerous models are available in the literature, but their applicability is generally restricted to certain soil types or water contents (θ). The objective of this study was to develop a new model in the similar form of the Johansen 1975 model to simulate the λ _eff(θ) relationship of soils of various soil textures and water contents. An exponential type model with two parameters is developed and a new function for calculating dry soil thermal conductivity is presented. Performance of the new model and six other normalized models were evaluated with published datasets. The results show that the new model is able to well mimic λ _eff(θ) relationship of soils from sand to silt loam and from oven dry to full saturation. In addition, it has the best performance among the seven models under test (with root-mean-square error of 0.059 W m^?1 °C^?1, average deviations of 0.0009 W m^?1 °C^?1, and Nash–Sutcliffe efficiency of 0.994). The new model has potential to improve the reliability of soil thermal conductivity estimation and be incorporated into numerical modeling for environmental, earth and engineering studies.     

Assessment of bacterial diversity associated with crude oil-contaminated soil samples from Assam

In this study, we are reporting bacterial diversity in crude oil-contaminated soil of Assam, India. Integration of physiological community profiling, culture-dependent and culture-independent (metagenome) approaches, was employed to obtain a complete picture of the total bacterial diversity. Samples collected from 10 sites contaminated with crude oil ranging from 0.22 to 89.36% were analysed, and altogether 160 culturable bacteria were isolated (117 Gram-positive and 43 Gram-negative bacteria). Molecular identification showed the predominance of genera Lysinibacillus, Alcaligenes, Bacillus, Clostridium, Enterobacter and Pseudomonas. Conversely, denaturing gradient gel electrophoresis (DGGE) profiles of 16S rDNA phylotypes showed the predominance of Sphingomonas, Ralstonia, Sphingobium, Massilia, Acinetobacter and Pseudomonas. Both culture-dependent and culture-independent approaches resulted in 11 genera of which Bacillus and Pseudomonas were the key inhabitants creating most favourable bacterial milieu in the presence of crude oil contaminants. Metabolic fingerprints data depicted in PCA plot demonstrated that sites CTF-D-1 and Core-10 were most diverged. It was further confirmed that variations of bacterial species dominance in different sites were due to origin of hydrocarbon contamination. We here claim that the present findings is a first-hand report on combined physiological community profiling, culture-based and culture-independent approaches in assessing total bacterial diversity in crude oil-contaminated soil of Assam.     

Trend analysis of evapotranspiration over India: Observed from long-term satellite measurements

Owing to the lack of consistent spatial time series data on actual evapotranspiration (ET), very few studies have been conducted on the long-term trend and variability in ET at a national scale over the Indian subcontinent. The present study uses biome specific ET data derived from NOAA satellite’s advanced very high resolution radiometer to investigate the trends and variability in ET over India from 1983 to 2006. Trend analysis using the non-parametric Mann–Kendall test showed that the domain average ET decreased during the period at a rate of \(0.22\,\hbox {mm year}^{-1}\). A strong decreasing trend (\(m = -1.75\, \hbox {mm year}^{-1}\), \(F = 17.41\), \(P\) 0.01) was observed in forest regions. Seasonal analyses indicated a decreasing trend during southwest summer monsoon (\(m= -0.320\, \hbox {mm season}^{-1}\,\hbox {year}^{-1})\) and post-monsoon period (\(m= -0.188\, \hbox {mm season}^{-1 }\,\hbox {year}^{-1})\). In contrast, an increasing trend was observed during northeast winter monsoon (\(m = 0.156 \,\hbox {mm season}^{-1 }\,\hbox {year}^{-1})\) and pre-monsoon (\(m = 0.068\, \hbox {mm season}^{-1 }\,\hbox {year}^{-1})\) periods. Despite an overall net decline in the country, a considerable increase ( \(4 \,\hbox {mm year}^{-1}\)) was observed over arid and semi-arid regions. Grid level correlation with various climatic parameters exhibited a strong positive correlation (\(r \!>\!0.5\)) of ET with soil moisture and precipitation over semi-arid and arid regions, whereas a negative correlation (\(r\) \(-0.5\)) occurred with temperature and insolation in dry regions of western India. The results of this analysis are useful for understanding regional ET dynamics and its relationship with various climatic parameters over India. Future studies on the effects of ET changes on the hydrological cycle, carbon cycle, and energy partitioning are needed to account for the feedbacks to the climate.     

Accumulation and phytostabilization of As,Pb and Cd in plants growing inside mine tailings reforested in Zacatecas,Mexico

The aim of this research was to identify plant species with potential to accumulate and stabilize arsenic (As), lead (Pb) and cadmium (Cd) in mine tailings reforested and naturally recolonized locations in a semiarid region of Zacatecas, Mexico. Plant shoots from 44 species and their rhizospheric soils were analyzed for As, Pb and Cd concentration using atomic absorption spectroscopy. Most represented plant families were Asteraceae, Poaceae, Fabaceae and Cactaceae. The highest concentrations in shoots were As, followed by Pb and Cd. Among herbaceous species, Bouteloua gracilis showed the highest bioconcentration factor (BCF) of As, while Plantago lanceolata showed the highest bioconcentration factor of Pb. The shrub species with highest concentration of As in the rhizospheric soil were Opuntia robusta, Melilotus alba, Baccharis neglecta and Arundo donax (near BCF to 1.0). Similar results were observed in trees Casuarina equisetifolia, Prosopis laevigata, Fraxinus uhdei and Eucalyptus globulus. In addition, Tillandsia recurvata showed a suitable indicator of atmospheric deposition to As. In general, the results suggest that these species can be effective for tailings reforestation with the possibility to enclose potentially toxic elements. Specially, C. equisetifolia which is abundant, having the potential for future applications in other contaminated sites with different types of mine tailings or abandoned mines from arid and semiarid zones.     

Spatial and decadal variations in satellite-based terrestrial evapotranspiration and drought over Inner Mongolia Autonomous Region of China during 1982–2009

Evapotranspiration (ET) plays an important role in exchange of water budget and carbon cycles over the Inner Mongolia autonomous region of China (IMARC). However, the spatial and decadal variations in terrestrial ET and drought over the IMARC in the past was calculated by only using sparse meteorological point-based data which remain quite uncertain. In this study, by combining satellite and meteorology datasets, a satellite-based semi-empirical Penman ET (SEMI-PM) algorithm is used to estimate regional ET and evaporative wet index (EWI) calculated by the ratio of ET and potential ET (PET) over the IMARC. Validation result shows that the square of the correlation coefficients \((R^{2})\) for the four sites varies from 0.45 to 0.84 and the root-mean-square error (RMSE) is  \(0.78\) mm. We found that the ET has decreased on an average of 4.8 mm per decade (\(p=0.10\)) over the entire IMARC during 1982–2009 and the EWI has decreased on an average of 1.1% per decade (\(p=0.08\)) during the study period. Importantly, the patterns of monthly EWI anomalies have a good spatial and temporal correlation with the Palmer Drought Severity Index (PDSI) anomalies from 1982 to 2009, indicating EWI can be used to monitor regional surface drought with high spatial resolution. In high-latitude ecosystems of northeast region of the IMARC, both air temperature \((T_{a})\) and incident solar radiation \((R_{s})\) are the most important parameters in determining ET. However, in semiarid and arid areas of the central and southwest regions of the IMARC, both relative humidity (RH) and normalized difference vegetation index (NDVI) are the most important factors controlling annual variation of ET.     

Fluoride and heavy metal accumulation by vegetation in the fluoride affected area of Talupula,Anantapur district,Andhra Pradesh

A detailed survey showed that groundwater fluoride was 6.1ppm in the Talupula area of the Anantapur district in southern India which is higher than is normally observed in groundwater elsewhere in India but not so extreme as that reported in Andhra Pradesh (up to 21 mg L^-1). Groundwater is used for irrigating agricultural crops as well as for drinking which may expose its population to higher concentrations of fluoride through their diet. A preliminary study examined the concentrations of fluoride in five agricultural crops in the Talupula area and the soil beneath agricultural crops. The stem and leaves of Heliathusannuas, Arachishypogaea, Morusalba, Lychopersiconesculantum, and Phaseolus vulgaris were examined for fluoride and heavy metal accumulation. Heavy metal (Zn, Cu and Pb) accumulation in the stem and leaves were examined to determine if there was any correlation between their uptake and that of fluoride. In the soil beneath the crops, fluoride concentrations ranged from 6.53±0.44 to 8.23±1.22 ppm and a significant accumulation of fluoride was observed in the stems (13.2±2.0 to 23.9±3.6 ppm; p < 0.001) and leaves (16.4±1.9 to 61.5±4.5 ppm) of the agricultural plants. A significant accumulation of fluoride was observed from the stem to the leaves for all plants (p < 0.03) except Helianthus annuas. Copper also displayed significant accumulation from the soil to the stems in some of the plants tested but there was no significant accumulation of zinc or lead. A significant translocation from the stem and accumulation in the leaves was not observed for Cu or Pb but there was significantly higher zinc in the leaves of Arachishypogaea and Morusalba when compared to their stems. This study demonstrated that agricultural plants grown under field conditions and irrigated with groundwater high in fluoride will accumulate fluoride, but that fluoride is unlikely to have a significant effect on Cu, Zn or Pb accumulation in the stems and leaves of agricultural plants of the area. While drinking water is still the major cause of fluorosis in southern India, consumption of foods with high fluoride concentrations may also increase the risk of fluorosis.     

Development of Analytical Models to Estimate Pile Setup in Cohesive Soils Based on FE Numerical Analyses

This paper presents the numerical simulation of pile installation and the subsequent increase in the pile capacity over time (or setup) after installation that was performed using the finite element software Abaqus. In the first part, pile installation and the following load tests were simulated numerically using the volumetric cavity expansion concept. The anisotropic modified Cam-Clay and Dracker–Prager models were adopted in the FE model to describe the behavior of the clayey and sandy soils, respectively. The proposed FE model proposed was successfully validated through simulating two full-scale instrumented driven pile case studies. In the second part, over 100 different actual properties of individual soil layers distracted from literature were used in the finite element analysis to conduct parametric study and to evaluate the effect of different soil properties on the pile setup behavior. The setup factor A was targeted here to describe the pile setup as a function of time after the end of driving. The selected soil properties in this study to evaluate the setup factor A include: soil plasticity index (PI), undrained shear strength (S _u ), vertical coefficient of consolidation (C _v ), sensitivity ratio (S _r ), and over-consolidation ratio (OCR). The predicted setup factor showed direct proportion with the PI and S _r and inverse relation with S _u , C _v and OCR. These soil properties were selected as independent variables, and nonlinear multivariable regression analysis was performed using Gauss–Newton algorithm to develop appropriate regression models for A. Best models were selected among all based on level of errors of prediction, which were validated with additional nineteen different site information available in the literature. The results indicated that the developed model is able to predict the setup behavior for individual cohesive soil layers, especially for values of setup factor greater than 0.10, which is the most expectable case in nature.