Introduction: Rainfall is one of the key meteorological parameters that plays an important role in the design of hydraulic structures and the effective management of water resources. Therefore, estimating rainfall for long return periods is of great importance. To achieve this, sufficient and continuous data from rain gauge stations are required. Consequently, reconstructing missing or incomplete rainfall data and assessing the associated uncertainty are essential. In this study, an artificial intelligence model was employed to reconstruct rainfall data and analyze the related uncertainty. Methods: In this study, a framework was developed to estimate the maximum daily precipitation per year using the XGBOOST (XGB) model. Also, the confidence interval of each estimate was obtained. Precipitation data at Ghalat, Khanezenyan, and Sazman ab stations were used as input parameters and data from Maroun station were used as target parameters. Then, using the extended data, precipitation with a 100-year return period was calculated. Findings: The results showed that the XGB model has a good ability to predict daily precipitation. The correlation between the extended data of Maroun station and neighboring stations is close to the correlation between the observational data of Maroun station and neighboring stations, which indicates the good power of the XGB model. The confidence interval of the R2 criterion with a 95% confidence level is between 0.877 and 0.936 in the training phase and between 0.756 and 0.929 in the testing phase, which are relatively high numbers and close to one. conclusion: The results of this study indicate that combining the XGB model with Monte Carlo simulation provides relatively reliable estimates for predicting and reconstructing daily rainfall data as well as annual maximum daily rainfall. Furthermore, the findings revealed that the prediction of the annual maximum daily rainfall with a 100-year return period is only slightly sensitive to the training of the XGB model. These characteristics highlight the potential of using XGB-based predictions in the design of hydraulic structures and water resources planning.
Introduction: Sedimentation in dam reservoirs is mainly influenced by turbidity currents, and reduces the useful dam life. Therefore, it is especially important to investigate the turbidity current and try to control it, in order to prevent the turbidity current from reaching the dam, as well as to change the sedimentation pattern in the reservoir to increase the useful life of dam body. Methods: In this study, the effect of the obstacle, its height and position at different concentrations of turbidity current input on the control of velocity, Sedimentary flow discharge of turbidity current has been seen in the laboratory experiments. Information about the velocity of the turbidity current’s body was obtained by acoustic velocity-meter and information about concentration through calibration (ABS method). Findings: The dependent parameters discussed in this study are parameters related to the flow body, including Velocity and sedimentary flux of turbidity current. Finally, the dimensionless relationships were presented to calculate the velocity and sedimentary flow density after the obstacle, based on the independent variables of the problem. Conclusion: The results indicated that the position and location of the obstacle in the sedimentation pattern have been very effective, in a way that the efficiency of the obstacle in controlling the speed of the current and also controlling the sedimentation of turbidity current in the subcritical current has been two folds more than the supercritical current and it is proposed that the obstacle location in the dam reservoir should be in a mile-slopped place with distance from the dam body as far as possible.
Introduction: Along ever-increasing population of the world, the demand for food products, especially the production of oilseeds, including soybeans, is increasing much more than others. Therefore, it is important to improve the productivity of the main production inputs in the agricultural sector, especially the production of oil seeds Methods: The current study attempted to investigate on the rate of return and productivity of inputs (water and fertilizer) in soybean production in target provinces (Golestan, Mazandaran and Ardebil) in 2000-2021. The necessary data was collected through documentary studies from the Ministry of Agriculture Jihad, the Agricultural Research, Education and Extension Organization, and the Statistical Center of Iran. To determine of return used net profit, sail return and rate of return accounting and determination of productivity of inputs used physical and economic productivity criteria. Findings: The results showed that, the percentage of sail return of soybean production in Golestan, Mazadaran, Ardebil provinces and the country was estimated 43.9, 26.5, 52.6 and 43.9 per cent and the rate of return accounting of soybean production in target regions was estimated about 76.7, 35.6, 110.9 and 78.4 per cent respectively. The means of Economic water productivity in Golestan, Mazadaran and Ardebil provinces was estimated 8131.3, 4654.4 and 7951.5 Rial/ m3, respectively, and the means of economic productivity of fertilizer in target regions was estimated 123559.8, 48115.3 and 300702.9 Rial/ Kg respectively. Conclusion: According to the result, It is recommend to cultivate soybeans in areas of the country where the economic productivity of inputs, especially water input is higher.
Introduction: Determining the total sediment load of rivers is important for designing hydraulic structures and managing water resources. Due to the complexity of the sediment transfer phenomenon, the accuracy of the proposed experimental equations is low, so it is necessary to use smart methods to increase the accuracy of the equations. Methods: The innovation of this research is the use of a hybrid of two methods, Fuzzy Clustering Means (FCM) and Group Method of Data Handling (GMDH), for predicting the total sediment load in rivers. In this study, 214 data sets from three equatorial rivers have been collected for predicting the total sediment load. To evaluate machine learning methods, 9 scenarios have been selected. Findings: The results showed that Fuzzy-GMDH methods predict the total sediment load with higher accuracy compared to the two methods XGB and GMDH and also previous relationships. On the other hand, the FCM-GMDH method, with values of R2 = 0.75, NASH = 0.66, MAE = 0.00048, has higher accuracy compared to other methods.
Introduction: Nanotechnology-induced crisis management in the petrochemical industry and its effects on biological environments, especially in harbors and areas that are predominantly petroleum and petrochemical industries, are almost definite. This study aims to manage the crisis caused by nanotechnology in the petrochemical industry within the boundaries of Imam Khomeini Port. Methods: Collecting information in the petrochemical industry regarding the application of Nanoparticles and the problems created through the provision of a questionnaire and providing it on two levels: technical managers and technical experts, and then analysis It also examined the effects of nanotechnology in the petrochemical industry on biological, economic and social environments. In this way, the results showed that more than 90% of the nanoparticles in the Port-Petrochemical Company are critical to the exposure of senior technical staff. Findings: The findings showed that the exposure level was higher than the average of employees with nanoparticles on the mental health of the staff and the quality of worker's work negatively. In addition, according to the results, critical nanoparticles have an increasing effect on global warming and aquatic mortality rates, so if global exposure to critical nanoparticles is exceeded, global warming will increase. The statistical analysis of critical nanoparticles released by Imam Khomeini port's petrochemical industry under The names of the inappropriate sanitary-environmental responses, economic responses, and social responses accounted for 48.43% of the total variance of the responses generated by the release of critical nanoparticles. make. On this basis, it is to be determined that, as far as possible, minimize the exposure time of individuals with nanoparticles, especially in enclosed or controlled areas, and minimize exposure to them.
Introduction: Stabilizing erosion control structures in river bends, especially in navigable waterways, presents a key challenge in hydraulic engineering. Among these, T-shaped groins are crucial for controlling flow and reducing hydraulic stresses. However, understanding the relationship between groin design and riprap properties requires careful assessment. Objective: This study aims to analyze the hydraulic effects of T-shaped groin geometries—specifically, total length and wing length—on the stability of riprap materials in a 90-degree river bend. The analysis spans a range of Froude numbers and riprap particle sizes to assess structural performance under diverse hydraulic conditions. Methodology: The two-dimensional CCHE2D numerical model, utilizing a standard k-ε turbulence closure, was employed to simulate flow patterns and bed shear stress in a laboratory flume. A physical model mimicking the same geometry was constructed to validate the numerical results. In this study, a combination of numerical modeling and physical experiments was used, and their results were compared and validated. A total of 90 test scenarios were performed by varying groin length, wing extension, and riprap size. Results: Extending the wing length of the groin significantly lowers tip velocity and shear stress, thereby reducing the stability index (Nc). Conversely, modifications in the overall groin length had little impact on riprap stability. Increasing riprap diameter from 9.5 mm to 12.7 mm decreased Nc by about 13.5%, indicating increased resistance to hydraulic forces. Conclusion: The combined numerical-physical approach confirms that optimizing groin performance depends on increasing wing length and using larger riprap particle sizes, rather than extending the groin length. The high accuracy of CCHE2D in replicating experimental results supports its application in real-world design scenarios.
Introduction: Providing drinking water to major cities in Mazandaran Province, such as Babol, Babolsar, and Fereydounkenar, is a key concern. The construction of the Kelarud Dam on the Kelarud River in southern Babol County is proposed as a solution. This study evaluates the dam's environmental impacts using the Pastakia Matrix (RIAM), a quantitative assessment method covering physical-chemical, biological-ecological, social-cultural, and economic-executive aspects. Methods: Two scenarios were assessed: implementing or not implementing the project. The findings indicate that while the construction phase has negative environmental impacts, these can be mitigated through environmental management programs. During the operation phase, positive impacts increase significantly, addressing water shortages effectively. Findings: Final scores reveal +348 points for implementing the dam, compared to -485 points for not implementing it. Despite initial challenges, the project's long-term benefits in alleviating water scarcity outweigh its adverse effects, making it a viable solution.
Introduction: Accurate climatic data are essential for crop modeling and yield prediction, particularly under climate change. In developing countries, access to reliable data is constrained by the low density of meteorological stations and limited historical records. Gridded climate datasets can mitigate this limitation; however, as they are not directly measured, uncertainty analysis is necessary. Methods: This study evaluated uncertainty from gridded climate datasets—including CPC Global, CRU TS, ERA-Interim, ERA5, and MERRA-2—in simulating wheat and maize using the AquaCrop model across diverse Iranian climates over 30 years (1989–2019). AquaCrop, developed for water management and agricultural productivity, requires inputs such as precipitation and temperature. Bootstrap method quantified uncertainty in simulated biomass, evapotranspiration, crop water requirement, and yield. P-factor and d-factor indices were also calculated to assess dataset accuracy. Findings: ERA5 consistently showed the lowest uncertainty in AquaCrop simulations. For maize biomass at Ahvaz, p-factor and d-factor were 23.33% and 0.5, respectively. Overall, ERA5 and ERA-Interim exhibited minimal uncertainty across most climates, whereas MERRA-2, with the highest uncertainty, performed worst. Conclusion: Selecting appropriate high-resolution datasets and applying rigorous uncertainty analyses can directly improve AquaCrop prediction accuracy. ERA5 emerged as the most reliable option. These practices enhance modeling precision and support informed decision-making in agricultural and water resource management.
Introduction: Due to frequent droughts, it is usually not possible to increase water allocation and therefore it is the limiting factor of water volume. Considering the limitation of water resources, the use of appropriate techniques to optimize the allocation of water to different products can be a solution. Using deficit irrigation methods is an optimal strategy to deal with the water shortage crisis. Methods: In the current study have been investigated the effects of deficit irrigation on water allocation between crops and regions, system profit, water exchange between regions. For this purpose, the Stackelberg-Nash-Cournot equilibrium model has been used with the aim of allocating water between different irrigated areas at the leader level and allocating water between different crops at the follower level in the condition that the water market is formed and emphasis on deficit irrigation. In this study, three scenarios of 5% deficit irrigation, 10% deficit irrigation and 15% deficit irrigation have been considered to deal with drought conditions in Sistan region. Findings: The results showed that in the condition that full irrigation is done, the most water is allocated to the melon crop and the least amount of water to the wheat crop, and the profit in this case is 2.71×1011 IRR, which increases the profit by applying deficit irrigation scenarios. The results of this research can be used as assistants for network managers and responsible people for water allocation.
Introduction: In countries with arid and semi-arid climates, such as Iran, studies of groundwater resources, the identification and management of aquifer systems, and the efficient and sustainable utilization of groundwater reserves_ recognized as one of the primary sources for meeting water demands—are of critical importance. In the other hand, the decline in groundwater resources caused by pollution and climate change has underscored the essential role of groundwater as a vital water supply across diverse climatic regions. Furthermore, the identification and quantification of groundwater, as well as its comparison with surface water resources, remain challenging due to the limited availability of reliable data and measurements. Methods: In this study, the quantitative and qualitative simulation and modeling of the SEMELQAN Plain aquifer, located in North Khorasan Province, were executed using the GMS 10 software and the MODFLOW model. Groundwater modeling can be applied in various forms; in the present research, groundwater flow was simulated using the MODFLOW software, which is based on the Taylor series expansion. The model was executed under steady-state conditions and in forward mode using the MODFLOW-2005 engine and due to uncertainties in some input parameters, relatively high errors were initially observed in the modeling results. Findings: In the final year, the inflow volume was approximately 6100 m³/day, while the outflow volume was about 5600 m³/day. These values show a decreasing trend over time, indicating a continuous reduction in the groundwater volume of the Semelqan aquifer. The relative RMS/RMSE error was 24.1, demonstrating the high accuracy of the simulation. Its normalized value was 6%, which is below the acceptable threshold of 30% for long-term simulation periods.
Introduction: After reviewing the literature and studying water resources management, it can be acknowledged that the concept of water governance is a debatable topic that many of its aspects have not yet been properly clarified. The present study was conducted with the aim of designing a water governance model in rural communities of Qom province in order to achieve sustainable rural development in Qom province. Methods: The statistical population of the present research included water experts of the Agricultural Engineering Organization of Qom province, which were 163 people (official statistics of the organization, 1401) and water experts of the Qom Water Organization, which were 130 people (official statistics of the organization, 1401). Based on the Cochran's formula and considering the size of the statistical population, the sample size in this research was calculated as 170 people and selected using the simple random sampling method. To collect data, a questionnaire consisting of three sections was used as the main research tool: personal characteristics of the respondents, water governance items, and factors affecting water governance. Findings: It was found that the technical factor was the most effective factor in the research model (β = 0.522, t = 6.642). After that, the institutional and administrative support factor (β = 0.374, t = 7.392), the social factor (β = 0.348, t = 4.353), and the economic factor (β = 0.297, t = 2.960) were the most important factors. Also, legal and regulatory factors were not statistically significant (β = 0.040, t = 0.318). Conclusion: This study, focusing on one of the most important issues in the water sector, has attempted to address the relationship between two very important national issues, namely rural development and water governance, which has been less addressed in previous domestic studies.
Introduction: In arid regions, rainfall shortage and evaporation from reservoir surfaces are among the issues that exacerbate the water scarcity problem in the country. One of the methods for combating and controlling this shortage is to reduce evaporation from reservoirs behind dams. Methods: In this study, as chemical methods, a mixture of octadecane and hexadecane dissolved in ethanol was used. which were sprayed on the surface of the class A evaporation pond every three days. By spraying this powder on small reservoirs prepared to dimensions of cubic meters at the Water and Meteorology Research Station, the quantitative (amount of evaporation reduction) and qualitative (possible changes on some chemical, physical parameters) effects have been investigated and the results are presented. Quantitative and qualitative results are compared over a two-month period between two control and control samples. Findings: The results obtained show a 4% and 2% increase in BOD and COD in the pond with a monolayer of hexadecanol compared to the control pond. In addition, in the control pond, the possibility of contact with air, more incoming radiation and the occurrence of photosynthesis increase the concentration of dissolved oxygen. In contrast, in the pond covered with a monolayer of hexadecanol, the presence of floating coatings reduces the amount of water mixing. These factors cause an 8% decrease in dissolved oxygen in the pond with hexadecanol.
Introduction: Suspended sediment load (SSL) is one of the complex hydrological phenomena, and its prediction is difficult. This study uses the artificial neural network method to predict suspended sediment load. Since the accuracy of artificial neural networks depends on their parameters, the benefit of meta-heuristic algorithms can be effective in increasing their performance. The case study is the catchment area of the Kosar Dam located in the southwest of Iran. Methods: River discharge and rainfall were considered as inputs, and features for predicting models. Five input compounds were selected. OTLBO and PSO meta-heuristic algorithms were used to find the optimal ANN values, and ANN-OTLBO and ANN-PSO prediction models were developed. Predicting models were evaluated using different numerical and visual indicators. Findings: The results show that the ANN-OTLBO model provides higher prediction performance than other models used in this study. Specifically, the ANN-OTLBO-M5 model shows superior values (R=0.96358, RMSE=258.14, PBIAS=2.6752, and NSE=0.92674). Also, based on the Scatter plot, Heat map, and Box plot, the closest predicted data to the observed data belongs to the ANN-OTLBO-M5 model.
Introduction: Water is considered one of the main foundations of sustainable development of societies, while clean water resources are a major prerequisite for environmental protection and economic, political, social and cultural development. The increasing demand for water, increasing living standards and the spread of water resource pollution due to the development of agricultural, urban and industrial activities have led to a chaotic environmental situation and intensified water resource pollution, which will make it difficult to control. Methods: Multivariate statistical methods and data mining have been used to investigate water quality in many studies. Cluster analysis (CA) and discriminant analysis (DA) were used to identify pollution sources in river basins. In order to systematically compare the assumptions of the analytical methods used, the theoretical foundations of each method were examined. Nonparametric methods such as percentage elimination (PR) and sign test (ST) were applicable without the need to assume a specific data distribution, while classical multivariate methods including PCA and FA were used with the assumption of multivariate normality and linear relationships between variables (as confirmed by KMO and Bartlett tests). Machine learning models including Random Forest and XGBoost with the ability to analyze nonlinear relationships and resist collinearity, SVM with sensitivity to feature scaling and the need for separable space, and regression methods such as PLS and Stepwise with the assumption of linear relationships and the need for cross-validation to prevent overfitting were used. Findings: According to the results obtained from the statistical methods of percentage elimination and sign test, it was observed that the wetland plays a fundamental and key role in the entire drainage system; therefore, using the statistical methods of LDA, PCA/FA and HACA, all water quality factors in the wetland are examined. Also, principal component analysis (PCA) plays a positive role in prioritizing the importance of each factor in pollution, so that it places the more important factors in the first component and the less important factors in the subsequent components. The results obtained from the principal component analysis show that the components with more than one eigenvalue are considered the most important components that justify the variance.
Drought is a complex phenomenon that affects hydrological and geohydrological processes. Given the importance of the river as a major water source, studying the impact of drought on flow patterns is very important. In this study, the Atrak River watershed in Iran was analyzed by considering daily data from 1978 to 2018 and using the tools of Streamflow Drought Index (SDI), Lyapunov Exponent (LE), Approximate Entropy (ApEn), and finally Pearson correlation. The findings showed that the river flow had a level of chaos. The river flow was also predictable to an acceptable level. The chaos and the amount of deterministic and random elements in the river flow were dominated by multi-scale (multi-fractal) behavior. Finally, the results revealed that the degree of fractality of hydrological drought had a positive and direct effect on the multi-scale behavior of chaos and the amount of deterministic and random elements of the river flow. The Pearson correlation values between SDI and ApEn and SDI and LE were 0.93 and 0.99, respectively. This study provides a new perspective on the effects of hydrological drought on river flow dynamic patterns. The findings will have great application in the fields of flow forecasting, drought monitoring, and water resources management.
Introduction: The issue of land use changes and their detection, as well as the effects they have on cities and their surrounding environments, has become an important and practical topic, and the use and creation of various up-to-date methods and models to study this issue have become an important matter. In this regard, remote sensing data, by providing up-to-date and reliable information on the status of land cover, are a suitable tool for preparing land cover and use maps. Methods: In this study, the catchment area of the Neyshabur Plain which is a part of the catchment area of the central desert of Iran is considered. Envi 5.6 and ArcMap 10.7 software were used to process, analyze, and retrieve satellite data, and Qgis 3.16 software was used to finalize thematic maps. It should be noted that in this study, the Google Earth Engine, which is known as the most powerful system for processing satellite image time series in remote sensing, was also used. Findings: According to the results of the error matrix obtained, it was found that the neural network method with an overall accuracy of 83.35 and a kappa coefficient of 0.79 for the year 2013, the maximum likelihood method with an overall accuracy of 81.92 and a kappa coefficient of 0.78 for the year 2019, and a neural network with an overall accuracy of 36.79 and a kappa coefficient of 0.75 for the year 2021, have more favorable performance and accuracy for preparing land use maps than other methods.
Introduction:This study investigates the factors influencing sustainable development and water resource management within the agricultural sector of the Mako Free Trade and Industrial Zone. The study highlights the critical role of participatory policies grounded in human rights principles and balanced resource management for optimizing water resource sustainability. Methods: Employing a quantitative, descriptive-survey approach within a pragmatic paradigm, data were collected via a literature review and a validated questionnaire. Participants included experts from agriculture, free trade zone, and natural resource departments, selected through non-probability convenience sampling (n=30). Analysis was performed using SPSS version 29. A Friedman test was used to rank the importance of the factors. Findings:Analysis revealed significant positive relationships between water resource management and eight key factors: education, economics, innovation in consumption, cross-sectoral management, non-structural measures, political and legal frameworks, participatory approaches, and research. These variables collectively explained 67% of the variance in water resource management. Participatory approaches (mean rank 5.68) were identified as the most influential factor, followed by non-structural management and legal frameworks. These findings offer valuable insights for policymakers and organizations seeking to develop effective programs for sustainable water resource development in similar contexts.
Introduction: Determining the actual amount of inflow values into the dam reservoir, as one of the main sources of water supply, is one of the basic components of decision-making in the field of water resources management. Due to limitations of the lack of proper spatial and temporal distribution of data extracted from ground stations, the use of satellite-based data is attractive and interesting. However, the scale of satellite-based data and the need for their exponential scaling are the uncertainties of these data. Methods: In this research, the performance of PERSIANN-CDR and CMC (Canadian Meteorological Centre) satellite data for rainfall and snow estimation and determining the inflow values into the dam reservoir is investigated. Therefore, by considering different combinations of input data, different models are proposed and the input flow to the dam reservoir is predicted using the artificial neural network (ANN) model. Here, the ZayandehRoud dam reservoir of the Gavkhoni drainage basin is selected as a case study. Findings: The results shows that the best R2 and RMSE values for rainfall (snow) estimation data based on the PERSIANN-CDR satellite (CMC) are 0.49 (0.34) and 60.90 (41.56) mm. In other words, the results show the proper performance of satellite-based data for rainfall and snow estimation. Therefore, these data are used for creating the ANN model to determine the inflow values into the reservoir of ZayandehRoud dam reservoir. The results show that the values of R2, RMSE and NES for training data (validation and testing) of ANN model are equal to 0.72 (0.74), 56.08 (75.178) MCM, and 0.85 (0.86) respectively. In other words, the results show the proper performance of satellite-based data for estimating and determining the inflow into the ZayandehRoud dam reservoir using ANN model.
Introduction: Water, as one of the fundamental and vital natural resources, plays an indispensable role in sustainable development and in meeting human, agricultural, and industrial needs. However, population growth, rapid urbanization, climate change, and the unequal distribution of water resources worldwide have created major challenges in managing and optimally allocating these resources. Methods: In this study, a comprehensive ontology was developed with the aim of intelligent modeling and addressing key questions related to the identification and assessment of water resources, resource allocation and analysis, modeling and forecasting, and the use of technology and alternative water sources. The proposed ontology not only covers the technical aspects of water supply and demand but also enables structural analysis and provides answers to questions concerning allocation models, influencing factors, and optimization methods. Findings: The developed ontology considers various types of water demand, water resources, quality of each source, factors influencing resource changes, allocation models and methods, sustainability indicators, and essential constraints and solution techniques. The results revealed that pollution strongly affects water quality, thereby altering the usability of water sources and influencing allocation decisions. Moreover, the quantitative evaluation of the ontology based on three common metrics—Relation Richness (RR=0.24), Attribute Richness (AR=0.38), and Inheritance Richness (IR=1.20)—indicates acceptable diversity in relations, moderate attribute coverage, and a balanced hierarchical structure. A review of previous studies further revealed that most focused on surface water allocation aimed at reducing shortage costs and often relied on linear models, while less attention was given to pollution and groundwater resources. Conclusion: Queries applied to the ontology demonstrated that existing quantitative studies have addressed complex issues such as climate change, hydrogeology, and multi-objective modeling. Although simple models provide preliminary insights, incorporating more realistic factors such as pollution and environmental characteristics leads to more accurate and practical results.
Introduction: In the present study, owing to the substantial effect of the Indian Ocean Dipole (IOD) on the activity of the Madden-Julian Oscillation (MJO), the focus is on improving the estimation of the influence of the IOD on the MJO-related precipitation variabilities over Iran during the October-to-November (ON) periods which coincide with the peak activity of the IOD. Methods: This is achieved by comparing the corresponding active phases of the Real-time Multi-variate MJO (RMM) and Regionally Modified RMM (RM-RMM) indices during the Positive, Neutral and Negative IOD events, separately. Accordingly, in addition to performing a localized significance test at each grid point, the field significance of these tests was also assessed within the study region, Iran, by using the False Discovery Rate (FDR) method. Findings: The significant positive (negative) differences in the precipitation anomaly and occurrence probability are detected during the phases 8 and 1 (phase 4) of the RM-RMM and RMM indices over some parts of Iran, during the Positive IOD (Negative) IOD event which intensifies the MJO’s convection (suppression) and the lower-level convergence (divergence) of the moisture over the western Indian Ocean. However, during the Neutral IOD event no significant precipitation difference between the pertinent phases is observed. These findings suggest that by forecasting the interannual variations in the IOD and anticipating the associated precipitation differences between the equivalent phases of the RM-RMM and RMM indices, there is potential to increase the accuracy of the precipitation forecasts for Iran.
