Comparison of artificial neural network and multivariate linear regression methods for estimation of daily soil temperature in an arid region

Soil temperature (T _S) strongly influences a wide range of biotic and abiotic processes. As an alternative to direct measurement, indirect determination of T _S from meteorological parameters has been the focus of attention of environmental researchers. The main purpose of this study was to estimate daily T _S at six depths (5, 10, 20, 30, 50 and 100?cm) by using a multilayer perceptron (MLP) artificial neural network (ANN) model and a multivariate linear regression (MLR) method in an arid region of Iran. Mean daily meteorological parameters including air temperature (T _a), solar radiation (R _S), relative humidity (RH) and precipitation (P) were used as input data to the ANN and MLR models. The model results of the MLR model were compared to those of ANN. The accuracy of the predictions was evaluated by the correlation coefficient (r), the root mean-square error (RMSE) and the mean absolute error (MAE) between the measured and predicted T _S values. The results showed that the ANN method forecasts were superior to the corresponding values obtained by the MLR model. The regression analysis indicated that T _a, RH, R _S and P were reasonably correlated with T _S at various depths, but the most effective parameters influencing T _S at different depths were T _a and RH.     

Outlier identification and correction for GRACE aggregated data

Gravity measurements within the Gravity Recovery and Climate Experiment (GRACE) provide a direct measure of monthly changes in mass over the Earth’s land masses. As such changes in mass mainly correspond to water storage changes, these measurements allow to close the continental water balance on large spatial scales and on a monthly time scale within the respective error bounds. When quantifying uncertainties, positive and negative peaks are detected in GRACE aggregated monthly time series (from different data providers) that do not correspond to hydrological or hydro-meteorological signals. These peaks must be interpreted as outliers, which carry the danger of signal degradation. In this paper an algorithm is developed to identify outliers and replace them with hydrologically plausible values. The algorithm is based on a statistical approach in which hydrological and hydro-meteorological signals are used to control the algorithm. The procedure of outlier detection is verified by evaluating catchment based aggregated GRACE monthly signals with ground truth from hydrology and hydro-meteorological signals. The results show improvement in the correlation of GRACE versus hydrometeorological and hydrological signals in most catchments. Also, the noise level is significantly reduced over 255 largest catchments.     

Surfactant Enhance DLLME/FO‐LADS: Assay of Malachite Green Level in Aquatic Environment of Trout Fish

Malachite green (MG), a traditional agent used in aquaculture although is not approved; its low cost and high efficacy make illicit use likely. We developed a small‐scale, simple, and sensitive dispersive liquid–liquid microextraction procedure for the assay of trace amounts of MG in aquatic environment of Trout fish. Fiber optic‐linear array detection spectrophotometry with charge‐coupled device detector benefiting from a microcell was used for this purpose. The method is based on enhancement effect of an anionic surfactant on the extraction of MG in to very fine multidroplets of microextraction solvent which made assisted by disperser solvent. Under the optimum conditions, the enrichment factor 77.5 was obtained from a 5‐mL water sample. The calibration graph was linear up to 5 × 10^?7 mol L^?1 with detection limit of 1 × 10^?8 mol L^?1. The relative standard deviation for seven replicate measurements of 4 × 10^?7 and 5 × 10^?8 mol L^?1 of MG were 3.3 and 4.5%, respectively.     

Chromium Removal through Biosorption and Bioaccumulation by Bacteria from Tannery Effluents Contaminated Soil

Four bacterial isolates (two resistant and two sensitive to chromium) were isolated from soil contaminated with tannery effluents at Jajmau (Kanpur), India, and were identified by 16S rDNA gene sequencing as Stenotrophomonas maltophilia, Exiguobacterium sp., Pantoea sp., and Aeromonas sp. Biosorption of chromium by dried and living biomasses was determined in the resistant and sensitive isolates. The effect of pH, initial metal concentration, and contact time on biosorption was studied. At pH 2.5 the living biomass of chromium resistant isolate Exiguobacterium sp. ZM‐2 biosorbed maximum amount of Cr⁶⁺ (29.8 mg/g) whereas the dried biomass of this isolate biosorbed 20.1 mg/g at an initial concentration of 100 mg/L. In case of chromate sensitive isolates, much difference was not observed in biosorption capacities between their dried and living biomasses. The maximum biosorption of Cr³⁺ was observed at pH 4.5. However, biosorption was identical in resistant and sensitive isolates. The data on chromium biosorption were analyzed using Langmuir and Freundlich isotherm model. The biosorption data of Cr⁶⁺ and Cr³⁺ from aqueous solution were better fitted in Langmuir isotherm model compared to Freundlich isotherm model. Metal recovery through desorption was observed better with dried biomasses compared to the living biomasses for both types of chromium ions. Bioaccumulation of chromate was found higher in chromate resistant isolates compared to the chromate sensitive isolates. Transmission electron microscopy confirmed the accumulation of chromium in cytoplasm in the resistant isolates.     

Probabilistic rainfall thresholds for flood forecasting: evaluating different methodologies for modelling rainfall spatial correlation (or dependence)

Rainfall threshold (RT) method is one of the evolving flood forecasting approaches. When the cumulative rainfall depth for a given initial soil moisture condition intersects the threshold rainfall curve, the peak discharge is expected to be equal or greater than the threshold discharge for flooding at the target site. Besides the total rainfall depth, spatial and temporal distribution of rainfall impacts the flood peak discharge and the time to peak. To revisit a previous study conducted by the authors, in which spatially independent rainfall pattern was assumed, the spatial distribution of rainfall was simulated following a Monte Carlo approach. The structure of the spatial dependence among sub‐watersheds' rainfalls was taken into account under three different scenarios, namely independent, bivariate copula (2copula) and multivariate Gaussian copula (MGC). For each set of generated random dimensionless rainfalls, the probabilistic RT curves were derived for dry moisture condition. Results were evaluated with both historical and simulated events. For the simulated events, threshold curves were assessed by means of categorical statistics, such as hit rate, false rate and critical success index (CSI). Results revealed that the best performance based on the CSI criterion corresponded to 50% curve in 2copula and MGC scenarios as well as 90% curve in the independent scenario. The recognition of 50% curve in 2copula and MGC scenarios is in agreement with our expectations that the mean probable curve should have the best performance. Moreover, the proposed inclusion of spatially dependent rainfall scenario improved the performance of RT curves by about 25% in comparison with the presumed spatially uniform rainfall scenario. Copyright © 2011 John Wiley & Sons, Ltd.     

Crustal structure of Northwest Zagros (Kermanshah) and Central Iran (Yazd and Isfahan) using teleseismic Ps converted phases

Receiver functions are widely employed to detect P-to-S converted waves and are especially useful to image seismic discontinuities in the crust. In this study we used the P receiver function technique to investigate the velocity structure of the crust beneath the Northwest Zagros and Central Iran and map out the lateral variation of the Moho boundary within this area. Our dataset includes teleseismic data (M _b ≥ 5.5, epicentral distance from 30° to 95°) recorded at 12 three-component short-period stations of Kermanshah, Isfahan and Yazd telemetry seismic networks. Our results obtained from P receiver functions indicate clear Ps conversions at the Moho boundary. The Moho depths were firstly estimated from the delay time of the Moho converted phase relative to the direct P wave beneath each network. Then, we used the P receiver function inversion to find the properties of the Moho discontinuity such as depth and velocity contrast. Our results obtained from PRF are in good agreement with those obtained from the P receiver function modeling. We found an average Moho depth of about 42 km beneath the Northwest Zagros increasing toward the Sanandaj-Sirjan Metamorphic Zone and reaches 51 km, where two crusts (Zagros and Central Iran) are assumed to be superposed. The Moho depth decreases toward the Urmieh-Dokhtar Cenozoic volcanic belt and reaches 43 km beneath this area. We found a relatively flat Moho beneath the Central Iran where, the average crustal thickness is about 42 km. Our P receiver function modeling revealed a shear wave velocity of 3.6 km/s in the crust of Northwest Zagros and Central Iran increasing to 4.5 km/s beneath the Moho boundary. The average shear wave velocity in the crust of UDMA as SSZ is 3.6 km/s, which reaches to 4.0 km/s while in SSZ increases to 4.3 km/s beneath the Moho.     

Dynamics of hourly sea level at Hillarys Boat Harbour,Western Australia: a chaos theory perspective

Water level forecasting using recorded time series can provide a local modelling capability to facilitate local proactive management practices. To this end, hourly sea water level time series are investigated. The records collected at the Hillarys Boat Harbour, Western Australia, are investigated over the period of 2000 and 2002. Two modelling techniques are employed: low-dimensional dynamic model, known as the deterministic chaos theory, and genetic programming, GP. The phase space, which describes the evolution of the behaviour of a nonlinear system in time, was reconstructed using the delay-embedding theorem suggested by Takens. The presence of chaotic signals in the data was identified by the phase space reconstruction and correlation dimension methods, and also the predictability into the future was calculated by the largest Lyapunov exponent to be 437 h or 18 days into the future. The intercomparison of results of the local prediction and GP models shows that for this site-specific dataset, the local prediction model has a slight edge over GP. However, rather than recommending one technique over another, the paper promotes a pluralistic modelling culture, whereby different techniques should be tested to gain a specific insight from each of the models. This would enable a consensus to be drawn from a set of results rather than ignoring the individual insights provided by each model.     

Seismic design factors for RC special moment resisting frames in Dubai, UAE

This study investigates the seismic design factors for three reinforced concrete (RC) framed buildings with 4, 16 and 32-stories in Dubai, UAE utilizing nonlinear analysis. The buildings are designed according to the response spectrum procedure defined in the 2009 International Building Code (IBC’09). Two ensembles of ground motion records with 10% and 2% probability of exceedance in 50 years (10/50 and 2/50, respectively) are used. The nonlinear dynamic responses to the earthquake records are computed using IDARC-2D. Key seismic design parameters are evaluated; namely, response modification factor (R), deflection amplification factor (Cd), system overstrength factor (Ωo), and response modification factor for ductility (Rd) in addition to inelastic interstory drift. The evaluated seismic design factors are found to significantly depend on the considered ground motion (10/50 versus 2/50). Consequently, resolution to the controversy of Dubai seismicity is urged. The seismic design factors for the 2/50 records show an increase over their counterparts for the 10/50 records in the range of 200%-400%, except for the Ωo factor, which shows a mere 30% increase. Based on the observed trends, period-dependent R and Cd factors are recommended if consistent collapse probability (or collapse prevention performance) in moment frames with varying heights is to be expected.     

Towards quantitative evaluation of gas injection using time‐lapse seismic data

Of particular concern in the monitoring of gas injection for the purposes of storage, disposal or improved oil recovery is the exact spatial distribution of the gas volumes in the subsurface. In principle this requirement is addressed by the use of 4D seismic data, although it is recognized that the seismic response still largely provides a qualitative estimate of moved subsurface fluids. Exact quantitative evaluation of fluid distributions and associated saturations remains a challenge to be solved. Here, an attempt has been made to produce mapped quantitative estimates of the gas volume injected into a clastic reservoir. Despite good results using three accurately repeated seismic surveys, time‐delay and amplitude attributes reveal fine‐scale differences though large‐scale agreement in the estimated fluid movement. These differences indicate disparities in the nature of the two attributes themselves, which can be explained by several possible causes. Of most impact are the effects of processing and migration, wave interference effects and noise from non‐repeatability of the seismic surveys. This subject highlights the need for a more careful consideration in 4D acquisition, amplitude processing and use of true amplitude preserving attributes in quantitative interpretation.     

Coherence among climate signals, precipitation, and groundwater

Climate signals may affect groundwater level at different time scales in different geographical regions, and those patterns or time scales can be estimated using coherence analysis. This study shows that the synthesis effort required to search for patterns at the physical geography scale is possible, and this approach should be applicable in other regions of the world. The relations between climate signals, Southern Oscillation Index, Pacific Decadal Oscillation, North Atlantic Oscillation, North Pacific Pattern (SOI, PDO, NAO, and NP), precipitation, and groundwater level in three geographical areas of Wisconsin are examined using a three-tiered coherence analysis. In the high frequency band (<4(-1) cycles/year), there is a significant coherence between four climate signals and groundwater level in all three areas. In the low frequency band (>8(-1) to ≤23(-1) cycles/year), we found significant coherence between the SOI and NP signals and groundwater level in the forested area, characterized by shallow wells constructed in sand and gravel aquifers. In the high frequency band, there is significant coherence between the four climate signals and precipitation in all three areas. In the low frequency band, the four climate signals have effect on precipitation in the agricultural area, and SOI and NP have effect on precipitation in the forested and driftless areas. Precipitation affects groundwater level in all three areas, and in high, low and intermediate frequency bands. In the agricultural area, deeper aquifers and a more complex hydrostratigraphy and land use dilute the effect of precipitation on groundwater level for interdecadal frequencies.