The response of intermittent catchments to rainfall is complex and difficult to model. This study uses the spatially distributed CATchment HYdrology (CATHY) model to explore how the frequency of daily rainfall ( ) can affect the hydrologic regime of intermittent catchments. After a multi-objective calibration and validation of CATHY against experimental measurements of streamflow and groundwater levels in a catchment used as a pasture, the role of in affecting streamflow characteristics was explored using different scenarios. With different values of for the dry and wet periods of the year, CATHY showed that a series of frequent rainfall events was often associated with incipient streamflow, independent of the season. Activation of streamflow during the wet season was related to multiple factors and was not often associated with the shallow groundwater levels near the outlet of the catchment.The interplay between rainfall depth and intensity acted as the most important factor for the generation of streamflow. Using the difference between accumulated rainfall and evapotranspiration as a measure of wetness, saturated subsurface flow mechanism generated streamflow in simulations with wetness at least three times larger than mean wetness of other simulations. Although groundwater uprise near the outlet did not effectively contribute to streamflow in the initial days of flow, it strongly correlated with the magnitude of the runoff coefficient. Values of close or equal to the maximum value in the wet season can sustain the connectivity between groundwater and streamflow in the riparian zone. This connectivity increases the catchment wetness, which consequently results in an increase of the generated streamflow. Our study showed that rainfall regimes characterized by different were able to identify distinct flow regimes typical of either intermittent, ephemeral, or nonflowing catchments. Decrease of in the wet season is likely associated with a reduction of streamflow, with a shift of flow regime from intermittent to ephemeral or no-flow.
This work introduces the symbiotic organisms search (SOS) evolutionary algorithm to the optimization of reservoir operation. Unlike the genetic algorithm (GA) and the water cycle algorithm (WCA) the SOS does not require specification of algorithmic parameters. The solution effectiveness of the GA, SOS, and WCA was assessed with a single-reservoir and a multi-reservoir optimization problem. The SOS proved superior to the GA and the WCA in optimizing the objective functions of the two reservoir systems. In the single reservoir problem, with global optimum value of 1.213, the SOS, GA, and WCA determined 1.240, 1.535, and 1.262 as the optimal solutions, respectively.The superiority of SOS was also verified in a hypothetical four-reservoir optimization problem. In this case, the GA, WCA, and SOS in their best performance among 10 solution runs converged to 97.46%, 99.56%, and 99.86% of the global optimal solution. Besides its better performance in approximating optima, the SOS avoided premature convergence and produced lower standard deviation about optima.
Water quality degradation affects socio-economic development inappropriately and has dire effects on human health too. Water quality indexes (WQIs) are the methods widely used for modelling water quality status. However, using these indexes is limited by some constraints like deficiency of necessary database or uncertainty of decision-making. Throughout the ongoing research, fuzzy water quality indexes (FWQIs) were developed based on the Mamdani fuzzy inference system (FIS) to overcome the above-mentioned limitations. In other words, seven FWQIs models with different water quality parameters have been developed based on triangular and trapezoidal membership functions. Later, the developed indexes were employed to evaluate the water quality of 17 wells in Saveh Plain, Iran. Compared to the conventional WQI, the results showed that the elimination of some needed parameters in development of FWQI did not decrease the accuracy of water quality classification. However, omitting some other parameters with undesirable values made the classification of water quality unreliable. According to the results, some 35 % of wells benefitted from proper drinking water quality, while approximately 30 and 35 % of them suffered from unsuitable and very poor drinking water quality, respectively.
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