In this paper, various types of stochastic dynamic programming models (SDP) and also a deterministic dynamic programming (DP) are presented and compared for multipurpose Dez RESERVOIR dam located in southwest of Iran. The impact of INFLOW forecasting uncertainty in optimum RESERVOIR operation is investigated through two types of stochastic dynamic programming models. SDP models are different based on hydrologic state variable and INFLOW conditional or non conditional probability assumptions. A simulation model is developed to investigate the achieved optimum policies in different models. So, average of different operation variables and also performance criteria such as reliability, resiliency and vulnerability are used to compare the results. In two SDP models which apply current INFLOW instead of previous time step INFLOW as hydrologic state variable, real time simulation is done with forecasted and observed INFLOWs. The effect of forecasting accuracy and different forecasting methods in RESERVOIR operation are also studied. In general, objective function is considered as minimizing the sum squared of two sided deviations from target release and storage. This research shows that lack of attention to INFLOW forecasting in models which need it and assuming known values could lead us to unreal and false results and mislead us in selecting type of model. On the other hand, it is noticed that forecasting accuracy plays an important role in optimum RESERVOIR operation.

In this research, a 2D laterally averaged model of hydrodynamics and water quality, CE-QUAL-W2, was applied to simulate water quality parameters in the Ilam RESERVOIR. The water quality of Ilam RESERVOIR was obtained between mesotrophic and eutrophic based on the measured data including chlorophyll a, total phosphorus and subsurface oxygen saturation. The CE-QUAL-W2 model was calibrated and verified by using the data of the year 2009 and 2010, respectively. Nutrients, chlorophyll a and dissolved oxygen were the water quality constituents simulated by the CE-QUAL-W2 model. The comparison of the simulated water surface elevation with the measurement records indicated that the flow was fully balanced in the numerical model. There was a good agreement between the simulated and measured results of the hydrodynamics and water quality constituents in the calibration and verification periods. Some scenarios have been made base on decreasing in water quantity and nutrient inputs of RESERVOIR INFLOWs. The results have shown that the water quality improvements of the Ilam RESERVOIR will not be achieved by reducing a portion of the RESERVOIR INFLOW. The retention time of water in RESERVOIR would be changed by decreasing of INFLOWs and it made of the negative effects on the chlorophyll-a concentration by reduction of nutrient inputs and keeping constant of discharge INFLOW to RESERVOIR, the concentration of total phosphorus would be significantly changed and also the concentration of chlorophyll-a was constant approximately. Thus, the effects of control in nutrient inputs are much more than control in discharge INFLOWs in the Ilam RESERVOIR.

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.

The stochastic property of INFLOW to the RESERVOIR system makes the forecast of operation rules in RESERVOIRs to have great importance for irrigation of farmlands. The aim of simulation technique is to develop a. mathematical model to predict a behavior of RESERVOIR system in future operation periods. Decision makers make a good management for system operation by application of different scenarios. Aras Haran RESERVOIR with 25MMC active volume was constructed for the purpose of irrigating the 3200 ha of Keleyber farmlands. In this research Monte Carlo technique was applied to generate the synthetic INFLOWs by the use of local historical data for the purpose of system behavior prediction. Using the generated data the decision variables (RESERVOIR storage, precipitation, evaporation and spill) were determined and operation policy of RESERVOIR was identified. The results show that in the 15 percent of times storage volume in the RESERVOIR greater than form RESERVOIR total capacity and therfore excess water will spills.

One of the Principles of water resources management is the optimal use of the RESERVOIRs as the main sources of surface water, and this issue has a special importance in the science of water engineering. In this research, the new K-means clustering method to discretize RESERVOIR INFLOW has been presented for the Stochastic Dynamic Programming(SDP). In addition, the Moran's method is used to discretize the RESERVOIR storage. By the programming in the Python environment, the historical RESERVOIR INFLOW in each season is classified to different clusters and obtained the best INFLOW cluster for each season. The effects of this clustering is also considering in the SDP of Jamishan RESERVOIR. In general, the change in INFLOW classification will lead to a fundamental change in the transition probability matrix. Thus, the use of K-means method for the RESERVOIR INFLOW discretization, due to the possibility of optimizing the number of clusters in each time period, can be very useful for the SDP. finally, it is strongly recommended to use K-means method to discretize RESERVOIR INFLOW for RESERVOIR operation by SDP.The k-means clustering algorithm was first used by James McQueen in 1967. K-means is an object-based algorithm that selects representative clusters from the data itself rather than averaging them. Actually, K-means of a cluster is the most central element of a cluster. The purpose of this method is to reduce sensitivity to large values in the data set. In this algorithm, each cluster is introduced with one of the data close to the center. In this algorithm, according to the number of data categories (k), the value of the least squares function is minimized and the data are categorized in the best way. In addition, the Moran's method is used to discretize the RESERVOIR storage. In this method, the upper and lower limit of the range of changes and the upper limit of each category are used as indicators of discretization of the RESERVOIR volume. The study area includes Jamishan RESERVOIR sub-basin with an area of 527.07 km2 located in the southwest of Sanghar city near the Pirsalman hydrometric station. The annual average of rainfall, evaporation and temperature are 441 mm, 1534 mm and 10 degrees Celsius, respectively.Evaluating the performance of the K-means model in 4 different seasons, showed that among the 19 considered clusters, the best result in seasonal classification is obtained by the 5 INFLOW clusters according to the performance rate in fall, winter, spring and summer seasons - 142.57, -176.90, -475.36 and -2.10, respectively. In order to investigate the effect of classification of flow in the results of the first order Markov chain, the possible values are given in Table 1 for 4 seasons in 5 clusters, and in this table, the specified numbers indicate the probability of moving each cluster for each season.In the following, using the backward recursive function, the calculations are continued until reaching the stationary state condition. Finally, the value of l* was obtained for all 4 periods and for different combinations of k and i.The results of steady state condition showed that l* happened mostly in spring up to 5 clusters of the RESERVOIR storage and the least happened in summer with one cluster. Then, the calculations of the RESERVOIR release probability in each period for each class of INFLOW and storage have been made. The highest value has occurred for RESERVOIR storage class 4 in autumn, winter, and spring seasons but in summer season, due to less INFLOW and high water demand, it has happened in RESERVOIR storage class 5.In this research, Stochastic Dynamic Programming(SDP) of Jamishan dam RESERVOIR is discussed using K-means method in classifying the INFLOW discharge seasonally for the 41 years’ historical data. Moran's method is also used to classify the storage volume of the RESERVOIR by 7 classes. To calculate the transition probability matrix during the first-order Markov chain process, it is necessary to have the flow class in each period. For this purpose, the k-means method is used. The RESERVOIR INFLOW in each season is classified from 2 to 20 classes by programming in the Python environment and especially with Scikit-learn library. Evaluating the performance of the K-means model in 4 different seasons, showed that among the 19 considered clusters, the best result in seasonal classification is obtained by the 5 INFLOW clusters. Changing in the number of INFLOW cluster leads to changes in the transition probability matrix and this process would change the results of RESERVOIR operation. It can be said that the use of this flow classification method can have a significant impact on the management and optimization of dam RESERVOIR performance. In general, the use of new classification methods such as K-means method in the discretization of RESERVOIR INFLOW for the RESERVOIR stochastic dynamic programming can be very beneficial and effective

In the management water resources science, optimal Operation of existing water systems, such as dams, every day is more important. Due to budget and operational water resources limitations and environmental problems, optimize operation of these systems are gradually replaced by new systems are constructed. Optimal Operation of water resources is a Complex, nonlinear, multi-constraint and multidimensional optimization Problem, so to solve them robust optimization techniques needs are needed. In this study Genetic Algorithm optimization has been used in operation of the Mahabad RESERVOIR Dam in Northwest of Iran. The objective function is minimization of difference between downstream monthly demand and release. Early; Sensitivity analysis of GA model performed by considering of various parameters. Then the method was applied considering the reduce probability of INFLOW to dam for period 24months in the different scenarios. The results show that, the critical condition of drought could managed by the GA model, the optimized model could satisfy downstream watery demand.

Extended Abstract Introduction Worries about how to plan and exploit water resources in confronting new conditions have increased as evidence of climate change becomes more apparent. This issue has led a significant portion of recent meteorological research to examine climate change's consequences on water resources. One of the most important developments is the change in the INFLOWs to the dam's RESERVOIRs. The Dez Dam was built over the Dez river and is located in southwestern Iran, within 23 kilometers of distance from Andimeshk. Its maximum capacity is 3.3 billion m3. As one of the most important water supply sources in the agricultural and electric energy sector of Khuzestan province, this dam has faced severe droughts in recent years, and the flow to the RESERVOIR has been decreasing. The purpose of this study is to evaluate the amount of INFLOW and RESERVOIR volume of the dam under the conditions of climate change.   Materials and methods To simulate basin temperature and precipitation, the accuracy of 17 Regional Climate Models of the CORDEX - WAS project (South Asia) was evaluated based on the skill score (SS). Then, a combination of ten with the lowest skill score was used to simulate the climatic parameters of temperature and precipitation for future periods. Also, the bias correction of simulated monthly precipitation and temperature data in the historical period and then the future period was done in each station and for each parameter using the change factor method. Simulations of these parameters were conducted for three 20-year periods 2020s (2020-2039), 2050s (2050-2069), and 2080s (2080-2099) under the RCP4.5 and RCP8.5 scenarios for selected stations. In this research, the SWAT semi-distributed hydrological and MODSIM models was used to simulate the INFLOW and RESERVOIR volume, respectively.   Results and Discussion The results of the downscale of the selected models and climatic scenarios indicate an increase in minimum and maximum temperatures in all months of the year and decreased average rainfall in the future. Predictions considered the range of minimum and maximum temperature increase under selected models in the RCP4.5 scenario from 1.5 - 4.2 ° C and 1.5 - 3 ° C, respectively, and in the RCP8.5 scenario from 2.7 - 5.3 ° C, and 1.6 - 5.8 ° C probable. The models also predicted the range of precipitation change to be 11 up 17% under the RCP4.5 scenario and 8 to 18% under the RCP8.5 scenario. After ensuring the hydrological model's accuracy and the general circulation models (RCM) output, the SWAT model was implemented under different scenarios. The outflows indicate a significant reduction in the flow of the Dez River in the future. The reduction rate can be between 49 and 52% in the RCP4.5 scenario and the RCP8.5 scenario was more, especially in the last decade of the 21st century, about 44 to 64%. The highest decrease will occur in the colder months of the year. In other words, the INFLOW decreases in December and even further decreases in April and March. One of the main reasons for the decrease in the volume of flow in the region in these months (March, April, and May) is changes in the precipitation pattern, in addition to the decrease in precipitation. In other words, in these months, the water of snow melting is associated with an increase in the river's discharge in the current climate, but such conditions will change in the future. The decrease in the flow rate entering the dam has caused a decrease in the RESERVOIR volume of the dam, and its volume has decreased under RCP4.5 and RCP8.5 release scenarios. In the RCP4.5 scenario, the average annual volume of the RESERVOIR will reach 1184.4, 1179.8, and 1138.8 billion m3 for the three decades of 2020-2039, 2050-2069, and 2080-2099, respectively. In the RCP8.5 scenario, this average value equals 1293.8, 1070.4, and 1008.9 billion m3, respectively. Therefore, under the two selected scenarios, the RESERVOIR volume was reduced between 47 to 50% for the future decades. This significant decrease in volume in the future decades, which has affected the volume of water discharge of the dam, that is indicate Dez Dam will face significant challenges in come cross  with downstream needs (environmental needs, agriculture, drinking, industry, and electric energy).   Conclusion This study evaluated the effect of climate change on the amount of INFLOW and the RESERVOIR volume of Dez Dam under two climate scenarios. Using the semi-distributed SWAT model, the water flow simulation to the dam is done. After evaluating the model and calibrating and validating the parameters of the hydrological model, by entering the future precipitation and temperature data into the calibrated SWAT model, the flow was simulated for the three future periods under the above scenarios. The results of the climate models illustrated that the average minimum and maximum annual temperature would have increased by 3 and 3.5 degrees Celsius, respectively, for the future decades. The average annual precipitation for the study area will have decreased by 14%. The prediction of the INFLOW to the dam by the SWAT model under two scenarios indicate a significant decrease in the discharge of the Dez River in the future; The amount of this decrease in the RCP4.5 scenario will be between 49 to 52%, and in the RCP8.5 scenario and especially in the last decade of the 21st century, it will be more and around 44 to 64%. The decrease in the flow rate entering the dam has caused a decrease in the volume of the RESERVOIR of the dam, and its volume will have decreased between 47 and 54 percent under the two selected scenarios for the future decades. In general, the results obtained from this study indicate that this region will move towards a climate with lower humidity and higher temperature in the future decades. This situation will increase the shortage of water resources in the basin and will intensify the water crisis in the downstream areas. Therefore, it seems that the water resources management in this basin requires a review for its sustainable development and exploitation. Since, the flow rate of Dez in the future decades will significantly decrease due to, the expected of the climate changes, the Dez dam's primary goals to meet the needs of agriculture, electricity, drinking, etc. will have face significant shortages. It is recommended to implement the policy of reducing demand, changing the cultivation pattern, recommending and developing the cultivation of low water-demanding plants, using new irrigation methods, and using unconventional waters.

The Dorudzan dam INFLOW is assessed using SWAT under climate change. The daily simulated precipitation and temperature data by 22 general circulation models under RCP2. 6, RCP4. 5 and RCP8. 5 are downscaled at five climatic stations using LARS-WG statistical model and transient change factors approach. The precipitation over the watershed will decrease from 751 mm during the observation period to 653, 624 and 630 mm under the RCP2. 6, RCP4. 5 and RCP8. 5, respectively (13%, 17% and 16. 1%). The mean annual evapotranspiration will increase by 13. 6%-16% due to increased mean annual temperature by l. 7-3. 3'C under three RCPs. The mean annual INFLOW will decrease from 28. 6 m3/s to 14. 9, 14. 2 and 14. 3 m3/s under the RCP2. 6, RCP4. 5 and RCP8. 5, respectively (47. 9%, 50% and 49. 9%). The mean monthly RESERVOIR volume will decrease from 490 MCM to 350, 232 and 247 MCM under the RCP2. 6, RCP4. 5 and RCP8. 5, respectively while the dam outflow will decrease from 60 MCM/month to 45. 3, 34 and 35. 9 MCM/month, respectively due to the precipitation reduction by 127 mm (17%) and evapotranspiration increase by 65 mm (16%) over the watershed. The reduction ofRESERVOIR volume will intensify the downstream water shortage and crisis in the future.