Groundwater is a key source of drinking water in Jordan. This study was conducted to assess the suitability of groundwater in major groundwater basins in Jordan for drinking purposes. The groundwater quality data from sixteen sampling stations within one-year-monitoring period from March 2015 to February 2016 were used. Weighted arithmetic water quality index (WQI) with respect to the Jordanian standards for drinking water was used for quality assessment. Sixteen Physical, chemical and microbiological parameters were selected to calculate WQI. The result showed that all physical and chemical parameters were almost below the maximum allowable level based on the Jordanian standards for drinking. On the other hand, the microbiological parameter (i.e. E.coli count) was exceeded the maximum allowable limit in all the studied locations based on the Jordanian standards for drinking water. The computed WQI values range from 40 to 4295. Therefore, out of 16 studied locations, three locations are classified in the "Excellent water" class, nine locations as a "Good water" class, one as a "Poor water" class, two as a "very poor water" class, and one as a "water unsuitable for drinking purpose" class. Furthermore, Escherichia coli is considered the most effective parameter on the determination of WQI in this study. This result highlighted the importance of including the microbiological parameters in any drinking water assessment, since they reflect with other physical and chemical parameters the actual condition of water quality for different purposes.
The use of treated wastewater for irrigation purposes will be an essential component for sustainable water resources management, especially in the water-stressed countries as in Jordan. In this context, an attempt has been made to determine the suitability of effluent quality of selected wastewater treatment plants in Jordan for the irrigation purposes based on weighted arithmetic water quality index (WQI) approach, according to the Jordanian standards for reclaimed domestic wastewater. The effluent wastewater quality records from 22 wastewater treatment plants within a one-year-monitoring period from March 2015 to February 2016 were used. Fifteen physical, chemical, and microbiological parameters were selected to calculate WQI. According to the WQI scale classification, most of the selected wastewater treatment plants were not in full compliance with the Jordanian standards for the reclaimed domestic wastewater regarding the direct reuse of treated wastewater for the irrigation purposes. Therefore, for category A (i.e., vegetables that are normally eaten cooked, parking areas, sides of roads inside cities, and playgrounds), one plant is classified in the 'Excellent water' class and six plants as a "Good water" class. For category B (i.e., irrigation of fruit trees, green areas, and sides of roads outside the cities), one plant is classified in the 'Excellent water' class and twelve plants as a "Good water" class. For category C (i.e., irrigation of industrial crops, field crops, and forest trees), one plant is classified in the 'Excellent water' class and fifteen plants as a "Good water" class. The effective weight calculations identified that E. coli is considered the most effective parameter in the WQI values in category A, and to a lesser extent, SAR, pH, BOD, and NO 3 −. For category B, the SAR, pH and E. coli parameters are considered the most effective parameters in the WQI values. In turn, for category C, the SAR, pH, and PO 4-3 parameters are considered the most effective parameters in the WQI values. Thus, these parameters based on category are considered as the main parameters which degrade the effluent wastewater quality for irrigation purposes. The results of this study are beneficial for the water managers and policymakers for proper actions on water resources and agricultural management in Jordan.
Estimates of extreme precipitation are commonly associated with different sources of uncertainty. One of the primary sources of uncertainty in the statistical modeling of precipitation extremes comes from extreme data series (i.e., sampling uncertainty). Therefore, this research aimed to quantify the sampling uncertainty in terms of confidence intervals. In addition, this article examined how the data record length affects predicted extreme precipitation estimates and data set statistics. A nonparametric bootstrap resample was utilized to quantify the precipitation quantile sampling distribution at a particular non exceedance probability. This sampling distribution can provide a point estimation of the precipitation quantile and the confidence interval at a particular non exceedance probability. It has been shown that the different types of probability distributions fit the extreme precipitation data series of various weather stations. Therefore, the uncertainty analysis should be conducted using the best-fit probability distribution for extreme precipitation data series rather than a predefined single probability distribution for all stations based on modern extreme value theory. According to the 95% confidence intervals, precipitation quantiles are subject to significant uncertainty and the band of the uncertainty intervals increases with the return period. These uncertainty bounds need to be integrated into any frequency analysis from historical data. The average, standard deviation, skewness and kurtosis are highly affected by the data record length. Thus, a longer record length is desirable to decrease the sampling uncertainty and, therefore, decrease the error in the predicted quantile values. Moreover, the results suggest that a series of at least 40 years of data records is needed to obtain reasonably accurate estimates of the distribution parameters and the precipitation quantiles for 100 years return periods and higher. Using only 20 to 25 years of data to obtain estimates of the higher return period quantile is risky, since it created high sampling variability relative to the full data length.
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