A COMPARATIVE STUDY ON CALIBRATION METHODS OF NASH’S RAINFALL-RUNOFF MODEL TO AMMAMEH WATERSHED, IRAN

Increasing importance of watershed management during last decades highlighted the need for sufficient data and accurate estimation of rainfall and runoff within watersheds. Therefore, various conceptual models have been developed with parameters based on observed data. Since further investigations depend on these parameters, it is important to accurately estimate them. This study by utilizing various methods, tries to estimate Nash rainfall-runoff model parameters and then evaluate the reliability of parameter estimation methods; moment, least square error, maximum likelihood, maximum entropy and genetic algorithm. Results based on a case study on the data from Ammameh watershed in Central Iran, indicate that the genetic algorithm method, which has been developed based on artificial intelligence, more accurately estimates Nash’s model parameters

Large-scale climatic teleconnection for predicting extreme hydro-climatic events in southern Japan

Coordinator: Sameh KantoushPrnicipial Invistegator: Vahid Nouran

Groundwater Level Forecasting Using Wavelet and Kriging

In this research, a hybrid wavelet-artificial neural network (WANN) and a geostatistical method were proposed for spatiotemporal prediction of the groundwater level (GWL) for one month ahead. For this purpose, monthly observed time series of GWL were collected from September 2005 to April 2014 in 10 piezometers around Mashhad City in the Northeast of Iran. In temporal forecasting, an artificial neural network (ANN) and a WANN were trained for each piezometer. Kriging was used in spatial estimations. The comparison of the prediction accuracy of these two models illustrated that the WANN was more efficacious in prediction of GWL for one month ahead. Thereafter, in order to predict GWL in desired points in the study area, the kriging method was used and a Gaussian model was selected as the best variogram model. Ultimately, the WANN with coefficient of determination and root mean square error and mean absolute error, 0.836 and 0.335 and 0.273 respectively, in temporal forecasting and Gaussian model with root mean square, 0.253 as the best fitted model on Kriging method for spatial estimating were suitable choices for spatiotemporal GWL forecasting. The obtained map of groundwater level showed that the groundwater level was higher in the areas of plain located in mountainside areas. This fact can show that outcomes are respectively correct

Evaluation of Leachate Quality and its Effects on Agriculture in the Vicinity of Zanjan Landfill

Landfills can be considered as a potential threat to groundwater resources, considering the potential of groundwater pollution by leachate with the pollutants such as hydrocarbons and heavy metals. In this study, spatial changes in groundwater quality used in agriculture, in the vicinity of landfill site of municipal solid wastes in the southwest of Zanjan city were investigated. For this purpose, analysis of 18 physicochemical, heavy metals and bacterial parameters in leachate and 14 groundwater samples were investigated around the dumpsite up to a 5 km radius from landfill during two sampling periods (i.e., December 2020 and June 2011). In this study, several indices including leachate pollution index, sodium absorption ratio, Killie index, soluble sodium percentage and permeability index were used to investigate groundwater pollution in the study area due to leachate or other sources. According to the results of LPI, none of the groundwater samples were polluted with leachate. Also, the quality of these resources for use in agriculture was evaluated favorably according to SAR, KR and PI indices, however, according to SSP, 64% and 86% of samples during December 2020 and June 2021, respectively, were reported polluted. In general, the results of qualitative study of groundwater samples in dry season (December 2020) were more pronounced than pollutants. Although based on the results of agricultural indicators and LPI in well number 5 as the closest well to the landfill, no contamination by leachate has been reported, however the high chlorine concentration which was at maximum of allowed range, the potential dangers of landfill leachate were shown. It is noted that chlorine acts as a leachate detector in groundwater. Therefore it is recommended that monitoring wells be dug at different depths and at distances of less than one kilometer from the landfill, and sampling be done in successive periods to determine even the smallest effects of leachate on groundwater

The Applications of Soft Computing Methods for Seepage Modeling: A Review

In recent times, significant research has been carried out into developing and applying soft computing techniques for modeling hydro-climatic processes such as seepage modeling. It is necessary to properly model seepage, which creates groundwater sources, to ensure adequate management of scarce water resources. On the other hand, excessive seepage can threaten the stability of earthfill dams and infrastructures. Furthermore, it could result in severe soil erosion and consequently cause environmental damage. Considering the complex and nonlinear nature of the seepage process, employing soft computing techniques, especially applying pre-post processing techniques as hybrid methods, such as wavelet analysis, could be appropriate to enhance modeling efficiency. This review paper summarizes standard soft computing techniques and reviews their seepage modeling and simulation applications in the last two decades. Accordingly, 48 research papers from 2002 to 2021 were reviewed. According to the reviewed papers, it could be understood that regardless of some limitations, soft computing techniques could simulate the seepage successfully either through groundwater or earthfill dam and hydraulic structures. Moreover, some suggestions for future research are presented. This review was conducted employing preferred reporting items for systematic reviews and meta-analyses (PRISMA) method

Application of hydrogeological and biological research for the lysimeter experiment performance under simulated municipal landfill condition

The size and chemical composition of leachates migrating into the aquifer are dependent on the parameters of the waste and the storage conditions. Lysimeter studies allow us to determine the size and chemical composition of leachates as well as the leachate water balance. Lysimeter studies were conducted on a 230-L municipal waste sample for 6 months. During the tests, the specific electrolyte conductivity, pH, Eh, and temperature, as well as the chemical composition, microbiological analysis, and profiling of physiological population level using EcoPlate™ microarrays were measured in collected leachate samples. During the entire experiment, the amounts of inflow and outflow from lysimeters were measured. To assess the existence of significant differences in the chemical component concentrations in leachates, use of Principal Component Analysis was taken into account. The maximum EC value from leachate from the lysimeter was 33 mS/cm. High concentrations of ammonium ion (up to approx. 1400 mg dm−3), chlorides (up to approx. 6800 mg dm−3), and iron (up to approx. 31 mg dm−3) were observed in the effluents. The number of enterococci in May reached 53,000 cells/100 ml. By contrast, the number of these microorganisms was about 15,000 and 16,000 CFU/100 ml in January and April, respectively. Community-level physiological profiling indicates that the activity and functional diversity of microorganisms were higher in the leachate samples obtained in winter compared to effluents collected from lysimeters in spring

Application of Z-numbers to teleconnection modeling between monthly precipitation and large scale sea surface temperature

The teleconnection modeling of hydro-climatic events is a complex problem with highly uncertain circumstances. In contrast to the classic fuzzy logic methods, by using the Z-number in addition to the constraint of information, and by evaluating the data reliability, it is possible to characterize the degree of ambiguity of data. In this regard, this study investigates the performance of the Z-number-based model (ZBM) in prediction of classified monthly precipitation (MP) events of two synoptic stations in Iran (up to five months in advance). To this end, the sea surface temperature (SST) of adjacent seas was used as a predictor. The suggested model, by using Z-number directly and applying fuzzy Hausdorff distance to determine weights of if-then rules, predicted MP events of both the stations with over 70% confidence. Analysis of the results in the test step showed that the ZBM compared to the traditional fuzzy approach improved the results by 69% for Kermanshah and 112% for Tabriz. Overall, the Z-number concept by assessing events reliability can be used in various sectors of water resources manage- ment such as decision-making and drought monitoring

Reconstructing Daily Discharge in a Megadelta Using Machine Learning Techniques

In this study, six machine learning (ML) models, namely, random forest (RF), Gaussian process regression (GPR), support vector regression (SVR), decision tree (DT), least squares support vector machine (LSSVM), and multivariate adaptive regression spline (MARS) models, were employed to reconstruct the missing daily-averaged discharge in a mega-delta from 1980 to 2015 using upstream-downstream multi-station data. The performance and accuracy of each ML model were assessed and compared with the stage-discharge rating curves (RCs) using four statistical indicators, Taylor diagrams, violin plots, scatter plots, time-series plots, and heatmaps. Model input selection was performed using mutual information and correlation coefficient methods after three data pre-processing steps: normalization, Fourier series fitting, and first-order differencing. The results showed that the ML models are superior to their RC counterparts, and MARS and RF are the most reliable algorithms, although MARS achieves marginally better performance than RF. Compared to RC, MARS and RF reduced the root mean square error (RMSE) by 135% and 141% and the mean absolute error by 194% and 179%, respectively, using year-round data. However, the performance of MARS and RF developed for the climbing (wet season) and recession (dry season) limbs separately worsened slightly compared to that developed using the year-round data. Specifically, the RMSE of MARS and RF in the falling limb was 856 and 1, 040 m3/s, respectively, while that obtained using the year-round data was 768 and 789 m3/s, respectively. In this study, the DT model is not recommended, while the GPR and SVR models provide acceptable results