Application of different clustering approaches to hydroclimatological catchment regionalization in mountainous regions,a case study in Utah State

With respect to the different hydrological responses of catchments, even the adjacent ones, in mountainous regions, there are a great number of motivations for classifying them into homogeneous clusters. These motivations include prediction in ungauged basins (PUB), model parameterization, understanding the potential impact of environmental changes, transferring information from gauged catchments to the ungauged ones. The present study investigated the similarity of catchments through the hydro-climatological pure time-series of a 14-year period from 2001 to 2015. Data sets encompass more than 13,000 month-station streamflow, rainfall, and temperature data obtained from 27 catchments in Utah State as one of the eight mountainous states of the USA. The identification, analysis, and interpretation of homogeneous catchments were investigated by applying the four approaches of clustering, K-means, Ward, and SOM (Self-Organized Map) and a newly proposed Wavelet-Entropy-based (WE-SOM) clustering method. By using two clustering evaluation criteria, 3, 5, and 6 clusters were determined as the best numbers of clusters, depending on the method employed, where each cluster represents different hydro-climatological behaviors. Despite the absence of geographic characteristics in input data matrix, the results indicated a regionalization in agreement with topographic characteristics. Considering the dependency of the hydrological behavior of catchments on the physiographic field aspects and characteristics, WE-SOM method demonstrated a more acceptable performance, compared to the other three conventional clustering methods, by providing more clusters. WE-SOM appears to be a promising approach in catchment clustering. It preserves the topological structure of data which can, as a result, be proofed in a greater number of clusters by dividing data into higher numbers of distinct clusters with similar altitudes of catchments in each cluster. The results showed the aptitude of wavelets to quantify the time-based variability of temperature, rainfall and streamflow, in the way contributing to the regionalization of diverse catchments.     

Prediction of riverine suspended sediment discharge using fuzzy logic algorithms, and some implications for estuarine settings

The ability of fuzzy logic algorithms to model relationships between stream flow and suspended sediment discharge was investigated using daily measurements of stream flow and suspended sediment discharge for the Escanaba River mouth station, situated on the shore of Lake Michigan and operated by the US Geological Survey. Three different configurations of inputs were applied, whereby the inputs were fuzzified into fuzzy subsets of variables by means of triangular membership functions. The relationships between inputs and suspended sediment discharge (output) were represented by a set of fuzzy rule expressed in IF–THEN format. The weighted average method served for defuzzification. The commonly used sediment rating curve was also applied to the data, and its performance compared with that of the three models by means of statistical analyses. For all three models, suspended sediment discharge predicted by the fuzzy logic algorithm was in satisfactory agreement with observations. Furthermore, the fuzzy logic algorithms performed better than the sediment rating curve, particularly at higher rates of suspended sediment discharge (in this study, more than 50  × 10⁶ g/day). Considered collectively, the use of fuzzy logic algorithms is suggested as a simple and effective approach for better prediction of suspended sediment discharge, also for estuaries.     

Characterization of hydrogeologic properties of the Tabriz plain multilayer aquifer system,NW Iran

A detailed hydrogeological investigation was carried out in the Tabriz plain in Iran using conventional hydrogeological field investigations and hydrochemistry. The study was carried out because the aquifers are of particular importance as they are more or less the only source of water supply available to the rural population and for agricultural and industrial activities. Analytical and numerical methods were applied to the constant rate pumping test data from the Tabriz airport and the Tabriz Power Station well fields. Two types of aquifers of different water quality were identified in the study area: an unconfined aquifer that extends over the plain and confined aquifers that are found in the deeper layers of the multilayered sediment terraces of the Aji-Chay River course. Therefore, the central part of the Tabriz plain contains both unconfined and confined aquifers, while close to the highlands, there is only an unconfined aquifer. There was evidence of minor leakage in the confined aquifers when the numerical method was used for analysis. The groundwater in the area can be identified by three main geochemical facies: Na-Cl, Ca-HCO₃, and mixed Ca-Mg-Cl-SO₄. The processes responsible for the hydrochemical evolution in the area fall into five categories: dissolution of evaporate minerals, precipitation of carbonate minerals, evaporation, ion exchange, and anthropogenic activity.     

Prediction of rainfall using artificial neural networks for synoptic station of Mashhad: a case study

In this paper, we have utilized ANN (artificial neural network) modeling for the prediction of monthly rainfall in Mashhad synoptic station which is located in Iran. To achieve this black-box model, we have used monthly rainfall data from 1953 to 2003 for this synoptic station. First, the Hurst rescaled range statistical (R/S) analysis is used to evaluate the predictability of the collected data. Then, to extract the rainfall dynamic of this station using ANN modeling, a three-layer feed-forward perceptron network with back propagation algorithm is utilized. Using this ANN structure as a black-box model, we have realized the complex dynamics of rainfall through the past information of the system. The approach employs the gradient decent algorithm to train the network. Trying different parameters, two structures, M₅₃₁ and M₇₄₁, have been selected which give the best estimation performance. The performance statistical analysis of the obtained models shows with the best tuning of the developed monthly prediction model the correlation coefficient (R), root mean square error (RMSE), and mean absolute error (MAE) are 0.93, 0.99, and 6.02 mm, respectively, which confirms the effectiveness of the developed models.     

Land subsidence due to groundwater withdrawal in Arak plain,Markazi province,Iran

With current trends of groundwater exploitation, the plains of Iran have been facing numerous problems, where subsidence is considered as the foremost obstacle. In the present study, the principle aim was to analyze the level of groundwater and induced subsidence effects in different regions of Arak plain from 2002 to 2008. Arak plain located in Meyghan Lake basin is subjected to subsidence process as a result of the declining groundwater levels. Monthly variations of groundwater levels were plotted using ArcGIS software. The results indicated declining water levels especially in Meyghan Lake than the Arak plains, resulting up to 36 m of drop in the groundwater levels of marginal areas in the plains during these years.     

Role of early warning systems for sustainable agriculture in Iran

Drought is a complex phenomenon in meteorology and can affect agriculture. Its impacts vary greatly since they depend not only on the magnitude, timing, duration, and frequency of rainfall deficits but also on the differing responses of various plants to water stress. The essence of good drought management is to use this range of responses to the best advantage. Iran is one of the world’s largest and most productive suppliers of food and fiber. The objective of this study was, therefore, to gather and analyze standardized information on the Role of Early Warning Systems for Sustainable Agriculture for cereals and leguminous and industrial crops in Iran environmental zones. Annual average rainfall (mm year^?1) and ET_O (mm year^?1) are 76.56 and 3001, respectively, in stations with very dry climate; 195.41 and 2249, respectively, in stations with dry climate; 343.9 and 1351, respectively, in stations with semi-dry climate; 583.8 and 1153, respectively, in stations with semi-humid climate; and 1272 and 949, respectively, in stations with humid climate. The maximum and minimum of annual average rainfall happened in Rasht (1337 mm year^?1) and Zabol (57 mm year^?1) stations, and the maximum and minimum for annual average ET_O happened in Chabahar (3909.15 mm year^?1) and Anzali harbor (890.6 mm year^?1), respectively. Therefore, 13.63 % of stations have suitable conditions for crop productions and 86.37 % are in critical conditions.     

Storage dam’s locality placing by MCDM techniques (case study: three dams in Iran)

MCDM (multi-criteria decision making) techniques are used to choose the best alternative among the relevant ones and rank the others. In this research, MCDM techniques were used to choose and rank the best places for constructing storage dams in Iran. To achieve this goal, the relevant information and data about dam construction situations were collected. Then, the recommended places for constructing storage dams were ranked by using MCDM techniques and according to the most suitable criteria which have been chosen based on experts’ opinions. To rank dam constructing project, three MCDM techniques, i.e., TOPSIS, AHP, and DEMATEL, were used. The results showed that the application of MCDM models by putting emphasis on economic, social, political, and technical criteria enables government and watershed managers to choose the best alternative(s) for constructing a storage dam.