Artificial recharge efficiency assessment by soil water balance and modelling approaches in a multi-layered vadose zone in a dry region

To assess recharge through floodwater spreading, three wells, approx. 30 m deep, were dug in a 35-year-old basin in southern Iran. Hydraulic parameters of the layers were measured. One well was equipped with pre-calibrated time domain reflectometry (TDR) sensors. The soil moisture was measured continuously before and after events. Rainfall, ponding depth and the duration of the flooding events were also measured. Recharge was assessed by the soil water balance method, and by calibrated (inverse solution) HYDRUS-1D. The results show that the 15 wetting front was interrupted at a layer with fine soil accumulation over a coarse layer at the depth of approx. 4 m. This seemed to occur due to fingering flow. Estimation of recharge by the soil water balance and modelling approaches showed a downward water flux of 55 and 57% of impounded floodwater, respectively.     

Intentional contamination of water distribution networks: developing indicators for sensitivity and vulnerability assessments

Performing a comprehensive risk analysis is primordial to ensure a reliable and sustainable water supply. Though the general framework of risk analysis is well established, specific adaptation seems needed for systems such as water distribution networks (WDN). Understanding of vulnerabilities of WDN against deliberate contamination and consumers’ sensitivity against contaminated water use is very vital to inform decision-maker. This paper presents an innovative step-by-step methodology for developing comprehensive indicators to perform sensitivity, vulnerability and criticality analyses in case of absence of early warning system (EWS). The assessment and the aggregation of these indicators with specific fuzzy operators allow identifying the most critical points in a WDN. Intentional intrusion of contaminants at these points can potentially harm both the consumers as well as water infrastructure. The implementation of the developed methodology has been demonstrated through a case study of a French WDN unequipped with sensors.     

Wave data assimilation using a hybrid approach in the Persian Gulf

The main goal of this study is to develop an efficient approach for the assimilation of the hindcasted wave parameters in the Persian Gulf. Hence, the third generation SWAN model was employed for wave modeling forced by the 6-h ECMWF wind data with a resolution of 0.5°. In situ wave measurements at two stations were utilized to evaluate the assimilation approaches. It was found that since the model errors are not the same for wave height and period, adaptation of model parameter does not result in simultaneous and comprehensive improvement of them. Therefore, an approach based on the error prediction and updating of output variables was employed to modify wave height and period. In this approach, artificial neural networks (ANNs) were used to estimate the deviations between the simulated and measured wave parameters. The results showed that updating of output variables leads to significant improvement in a wide range of the predicted wave characteristics. It was revealed that the best input parameters for error prediction networks are mean wind speed, mean wind direction, wind duration, and the wave parameters. In addition, combination of the ANN estimated error with numerically modeled wave parameters leads to further improvement in the predicted wave parameters in contrast to direct estimation of the parameters by ANN.     

Limit states and failure mechanisms of viscous dampers and the implications for large earthquakes

Fluid viscous dampers are used to control story drifts and member forces in structures during earthquake events. These elements provide satisfactory performance at the design‐level or maximum considered earthquake. However, buildings using fluid viscous dampers have not been subjected to very large earthquakes with intensities greater than the design and maximum considered events. Furthermore, an extensive database of viscous damper performance during large seismic events does not exist. To address these issues, a comprehensive analytical and experimental investigation was conducted to determine the performance of damped structures subjected to large earthquakes. A critical component of this research was the development and verification of a detailed viscous damper mathematical model that incorporates limit states. The development of this model and the laboratory and simulation results conclude good correlation with the new model and the damper limit states and provide superior results compared with the typical damper model when considering near collapse evaluation of structures. Copyright © 2010 John Wiley & Sons, Ltd.     

Neuro-fuzzy models employing wavelet analysis for suspended sediment concentration prediction in rivers

Abstract

The study of sediment load is important for its implications to the environment and water resources engineering. Four models were considered in the study of suspended sediment concentration prediction: artificial neural networks (ANNs), neuro-fuzzy model (NF), conjunction of wavelet analysis and neuro-fuzzy (WNF) model, and the conventional sediment rating curve (SRC) method. Using data from a US Geological Survey gauging station, the suspended sediment concentration predicted by the WNF model was in satisfactory agreement with the measured data. Also the proposed WNF model generated reasonable predictions for the extreme values. The cumulative suspended sediment load estimated by this model was much higher than that predicted by the other models, and is close to the observed data. However, in the current modelling, the ANN, NF and SRC models underestimated sediment load. The WNF model was successful in reproducing the hysteresis phenomenon, but the SRC method was not able to model this behaviour. In general, the results showed that the NF model performed better than the ANN and SRC models.

Citation Mirbagheri, S. A., Nourani, V., Rajaee, T. & Alikhani, A. (2010) Neuro-fuzzy models employing wavelet analysis for suspended sediment concentration prediction in rivers. Hydrol. Sci. J. 55(7), 1175–1189.     

The Impact of Geology of Recharge Areas on Groundwater Quality: A Case Study of Zhob River Basin,Pakistan

Groundwater is a major source of water supply for domestic and irrigation uses in semiarid, remote but rapidly developing Kilasaifullah district part of Zhob River Basin, located at Pakistan–Afghanistan Border. Zhob River is among few major rivers of perennial nature in Balochistan, which flows from WSW to ENE and falls in Gomal River, a tributary of Indus River. Keeping in view the important geopolitical position and rapid development of the region, this study is primarily focused on groundwater chemistry for contamination sources as well as agriculture development. Water samples from open and tube wells are analyzed and calculated for electrical conductivity (EC), total dissolved solids (TDS), turbidity, pH, K⁺, Na⁺, Ca²⁺, Mg²⁺, HCO, Cl^?, NO, SO, PO, sodium percent (Na%), sodium adsorption ratio (SAR), Kelly's index (KI), and heavy metals (Fe, Cu, Cr, Zn, Pb, and Mn). On the basis of the chemical constituents two zones within the study area are identified and possible causes of the contaminants are pointed out. Two recharge areas were responsible for the different chemical results in groundwater, e.g., zone A was recharged from NNW saline geological formations (Nisai, Khojak, Multana, Bostan formations, and Muslim Bagh ophiolites), which are concentrated with high sodium and chloride. On the other hand Zone B was sourced from SSW from carbonate rich rocks (Alozai, Loralai, Parh formations, and Muslim Bagh ophiolites). The groundwater is classified as C2–S1, C3–S1, C3–S2, C4–S2 on the basis of EC and SAR values which indicate that most of the water of both zones can be used for irrigation safely except the samples plotted in C3–S2 and C4–S2 categories which could be dangerous for soil and crops. Groundwater samples are plotted in good to permissible limits with some samples excellent to good and few samples belong to doubtful category based on sodium percent. Groundwater of zone A is unsuitable for irrigation use due to higher values of KI (more than one) but water of zone B are good for irrigation based on KI. In general, water of both zones is suitable for irrigation but care should be taken during the selection of crops which are sensitive to alkalinity or sodium hazards particularly in zone A.     

APG:A novel python-based ArcGIS toolbox to generate absence-datasets for geospatial studies

One important step in binary modeling of environmental problems is the generation of absence-datasets that are traditionally generated by random sampling and can undermine the quality of outputs.To solve this problem,this study develops the Absence Point Generation(APG)toolbox which is a Python-based ArcGIS toolbox for automated construction of absence-datasets for geospatial studies.The APG employs a frequency ratio analysis of four commonly used and important driving factors such as altitude,slope degree,topographic wetness index,and distance from rivers,and considers the presence locations buffer and density layers to define the low potential or susceptibility zones where absence-datasets are gener-ated.To test the APG toolbox,we applied two benchmark algorithms of random forest(RF)and boosted regression trees(BRT)in a case study to investigate groundwater potential using three absence datasets i.e.,the APG,random,and selection of absence samples(SAS)toolbox.The BRT-APG and RF-APG had the area under receiver operating curve(AUC)values of 0.947 and 0.942,while BRT and RF had weaker per-formances with the SAS and Random datasets.This effect resulted in AUC improvements for BRT and RF by 7.2,and 9.7％from the Random dataset,and AUC improvements for BRT and RF by 6.1,and 5.4％from the SAS dataset,respectively.The APG also impacted the importance of the input factors and the pattern of the groundwater potential maps,which proves the importance of absence points in environmental bin-ary issues.The proposed APG toolbox could be easily applied in other environmental hazards such as landslides,floods,and gully erosion,and land subsidence.     

Estimating wave crest distributions using the method of L-moments

The design of fixed or floating offshore structures requires accurate information of the met-ocean data at the intended offshore site. In the design process it is recognized that this environmental data is modified in the near-field by the interaction with the particular geometrical configuration of the offshore structure. This transformation of the incident wave field around and beneath an offshore structure presents a challenge for ocean engineers when specifying the wave gap elevation to avoid impact loads on the underside of the deck and inundation of the topsides. Thus, the accurate estimation of the wave crest distributions from measurements at various locations near and under the offshore structure during model test studies is essential. A semi-empirical approach is presented herein that builds upon the findings of previous studies and introduces the Method of L-moments. A three parameter model for a wave crest probability distribution function is presented and explicit relationships between the parameters of the distribution and its’ first three L-Moments are established. Furthermore, three narrow-band models from earlier research studies are reviewed and compared with the new model. Wave measurements from a mini-TLP model test program are used as the basis for comparison of the four distributions. The root-mean-square error is used as a metric to quantify the overall fit of the data and its accuracy in the high end tail of the data. The L-Moment model is shown to be more robust in representing the data in both the far-field and beneath the deck of the mini-TLP where the wave field demonstrates increased non-linear behavior.