مجله اکو هیدرولوژی
سال دهم شماره 4 (زمستان 1402)

The research conducted was aimed at developing a comprehensive method to evaluate the technical performance of an operator-centered Operation. The study was conducted under different water supply shortage conditions. The NekooAbad irrigation District was selected for simulation purposes using the Integral-Delay model to simulate flow distribution in the canals. The boundary conditions were based on historical statistics of surface water supply at the source. Seven scenarios were created, ranging from normal to severe water shortage. The technical assessment was based on two aspects: temporal analysis of the daily average water distribution adequacy in 13 main and 149 secondary off-takes. The second aspect included spatial analysis of the distribution of the mentioned index throughout the district. The study classified water distribution adequacy under each scenario. The results showed a pattern of reduced water distribution adequacy from the source to the downstream in all 13 secondary and the main canal. The daily average of the surface water distribution adequacy index ranged from over 10% in the normal scenario to less than 40% in the water scarcity scenarios. The percentage changes ranged from -95% to 64%, 90% to 56%, 89% to 54%, 89% to 50%, 86% to 49%, 86% to 46%, and 33% to 77%. The study also revealed a clear pattern of the operational system's inefficiency in the adequate distribution of surface water under water scarcity scenarios. Furthermore, it identified the vulnerable areas of the district through spatial regionalization maps of water distribution adequacy.

In this study, an assessment of the performance of the surface water distribution system in the Abshar Isfahan irrigation network was conducted. For this purpose, the two main right and left channels and ten secondary channels of this network were integrated into an integral delay simulator model developed in MATLAB. The simulation of surface water distribution between the intakes located in the main and secondary channels for an irrigation season, corresponding to the water year 1400 1401 and divided into five dominant operational scenarios, was carried out. The evaluation of surface water distribution performance involves the use of performance assessment indices, specifically the adequacy of water distribution, for each intake, region, and the entire channel. Additionally, the simulated data was imported into GIS software to analyze the spatial distribution of surface water distribution across the entire network, and maps of the average adequacy index dispersion for each operational scenario were extracted and analyzed. The simulation results indicated a predominantly decreasing trend in water delivery adequacy indices from the upstream intakes to the downstream in both the main and secondary channels. The average surface water distribution adequacy index ranged from 98% to 100%, 90% to 100%, 97% to 84%, 96% to 81%, and 93% to 69% in the upstream intakes and from 80% to 85%, 65% to 70%, 41% to 45%, 30% to 34%, and 20% to 28% in the downstream intakes, in scenarios one through five, namely, from high water availability to severe water scarcity. The results obtained highlighted deficiencies in the existing irrigation system's water distribution adequacy, especially in scenarios of low water availability, along the main and secondary channels. Furthermore, the spatial classification maps revealed a distinct pattern of inefficiency in surface water distribution at the network level and identified vulnerable areas within the network.

In this research, in order to select an appropriate model for predicting river flow in the Bashar River basin, data-driven models including multilayer perceptron artificial neural network and fuzzy neural network from the Sugeno fuzzy inference system were used using the clustering reduction method, and the analysis of uncertainty of these models was investigated.The data used in this research includes monthly values of rainfall and average temperature at rain gauge stations, as well as monthly average river discharge at the hydrological station located in the Bashar River basin from the years 1979-1980 to 2018-2019. The sensitivity analysis results on the number of neurons in the hidden layer of the neural network showed that the optimal number of neurons in the hidden layer for the input combination is 13.Based on the root mean square error (RMSE) index, the best combination of input variables for simulating river flow in both the neural network and neural-fuzzy network models was determined to be the input combination consisting of average river discharge with one-month and two-month lag along with monthly rainfall values and monthly rainfall values with one-month and two-month lag.In order to investigate the uncertainty of the models, the artificial neural network and neural-fuzzy network models were employed in the form of Monte Carlo sampling.The results of the uncertainty analysis showed that, for the same random input variables, the deviation from the mean in the output of the neural network model is higher than that of the neural-fuzzy network model. Additionally, the results obtained from calculating the confidence interval indicate that the confidence interval for different confidence levels is smaller in the neural-fuzzy network compared to the neural network. For example, in the neural network model with 98% confidence, the output is within the range of (0.64 and 0.36), whereas in the neural-fuzzy network model with 98% confidence, the output is between the range of (0.69 and 0.53). This indicates a higher level of uncertainty in the results of the neural network model.

Due to global warming and the increase in the population of the planet, maintaining and protecting available water resources is very important. One of the factors that has caused the reduction of water resources today is the increase in the rate of evaporation from the level of water stored in water resources. In this study, using the experimental results obtained from the work of Sina Bashir et al., the evaporation rate of the lake behind the Karkheh Dam has been modeled. This modeling has been done using the neural-adaptive fuzzy inference system. The approach of this system is considered the Mamdani approach in this modeling because this approach has a very good performance in modeling dynamic and natural processes such as evaporation. According to the laboratory results, in the presence of silica nanostructured material at 28, 32 and 40 degrees Celsius and wind conditions of 4 meters per second (similar to the prevailing wind around the Karkheh dam), the evaporation rate decreases by 33, 32 and 30%, respectively. In this modeling, the rate of reduction of evaporation is entered into the modeling as a coefficient according to the laboratory results, and as a result, the rate of evaporation obtained is the result of the decrease in the presence of nanostructured material. In this modeling, considering the creation of a nanostructured thermal insulation cover for only 20% of the lake surface, 2 million cubic meters of water can be saved and saved.

This research aims to investigate the impact of climate change based on the sixth report (CMIP) under two scenarios SSP3_7.0 and SSP5_8.5 on forecasting temperature, wind speed, evaporation-transpiration (ET), and the amount of extractable water (Q) by two CNRM models and ESM was at 16 meteorological stations during the future period of 2025-2044 and 2045-2064. The statistical analysis results showed that the impact of climate change under two scenarios on temperature, ET and Q was significant. CNRM model performed better than ESM in temperature estimation (CC=0.96-0.98). From examining the results of the CNRM model, the maximum and minimum RMSE of temperature in Khormadreh and Zanjan stations were 8.30 and -0.5 , respectively; Also, the RMSE value of wind speed fluctuated between 0.82-0.5 m.s-1. The examination of ESM model showed the fluctuation of RMSE between 2.55-8.45  in temperature parameter and 0.62-0.68 meters per second in wind speed. The maximum and minimum values of Q and ET in the seasonal survey occurred in summer and winter, respectively. Both models had poor performance in predicting wind speed. The maximum ET under the SSP5_8.5 scenario by the CNRM model (first period) at Khorramdare station is equal to 104.29 mm.month-1 and the minimum value by the ESM model (first period) under the SSP3_7.0 scenario at Firuzkoh station is equal to 25.60 mm.month-1 was estimated. The maximum Q under the SSP3_7.0 scenario by the ESM model (first period) at Malair station is equal to 20.70 Lit.day.m-2 and the minimum value by the CNRM model (second period) under the SSP3_7.0 scenario at the Astara station is equal to 0.3 Lit.day.m-2 were estimated.

Drought is a natural disaster which could be repeated, and cause damages in all climates. In Iran, drought has occurred frequently and caused water shortages in different sectors. Fars province geographical location is in the western sought of Iran. Due to the increases cities, villages, industrial and agricultural centers in this province, drought assessment is an urgent need. In this research Z score, Percentage of Normal Precipitation Index (PNPI), Decades of Precipitation Index (DPI), Rainfall Anomaly Index (RAI) and standard precipitation Index (SPI) were evaluated and and compared. Statistical analysis of precipitation showed a stable condition in Doroudzan Dam station and unstable conditions in Lar, Lamerd and Abadeh. Precipitation had a wide variation except in Shiraz, Zarghan and Doroudzan Dam stations, which verifies dominant drought climates in Fars. In order to determine the best index, minimum of rainfall and indicies correlation were used in this study. Results showed that PNPI-Z ,PNPI-SPI SPI-RAI, SPI-Z RAI-Z and PNPI-RAI indices are the most correlated ones, and DPI-SPI , DPI-RAI ,DPI-Z ,DPI-PNPI indices have week correlation. 1 and 12 months average indices showed the most correlation. The results showed that the PNPI, SPI and Z coincided with the date of minimal rainfall, and reported a severe drought in the study stations, therefore they are more efficient than the other indices to determine meteorological drought.

Forecasting future river flow is a critical aspect in efficiently managing water resources, particularly in meeting the diverse downstream requirements of reservoir dams. The significance of predicting inflow to the dam is amplified due to its role in addressing its downstream needs. The present study focuses on predicting the monthly inflow to the Karkheh Reservoir Dam through the utilization of integrated autocorrelated moving average (ARIMA) models, including the seasonal variant (SARIMA). The development of these models involved analyzing 57 years of monthly flow data into the Karkheh dam reservoir. Of this dataset, 47 years were designated for model training, while the remaining 10 years were used for model testing. The determination of optimal ARIMA model parameters involved assessing various combinations of (p, d, q), with selection based on the Akaike information evaluation criterion. Results indicate that the ARIMA model with parameters (8,0,7) yields the lowest Akaike information evaluation criterion. Additionally, recognizing the seasonality in the data, a SARIMA model was constructed and employed for predicting monthly flow into the Karkheh dam reservoir. A comparison of the root mean squared error between the ARIMA and SARIMA methods reveals superior accuracy in predicting monthly flow to the Karkheh dam reservoir with the ARIMA model.