Climate change exerts significant pressure on environmental and socio-economic systems, with water resources being among the most affected. Water, energy, and food form three interdependent pillars within the agricultural sector. Each of these pillars is influenced by climatic variability, and each also contributes to it. Because these components interact in complex ways, their management cannot be separated. So, achieving long-term sustainability requires integrated strategies that consider these interactions. In the Kermanshah Plain, the absence of such an integrated approach has resulted in the unsustainable use of natural resources and growing system instability. System instability denotes the vulnerability of enviroment to disruptions in WEF resourced availability. This research introduces a comprehensive framework for conjunctive surface-groundwater management under climate change conditions using the water-energy-food (WEF) nexus concept. Guided by projections from the Intergovernmental Panel on Climate Change Sixth Assessment Report (IPCC6), the framework simulates future changes in temperature and precipitation. It evaluates the resulting impacts on water availability and develops adaptive strategies for sustainable resource management. A coupled WEAP-MODFLOW model was used to dynamically simulate surface-groundwater interactions across the Kermanshah Plain. Based on these simulations, a multi-objective optimization model was developed using the three-dimensional Multi-Objective Gray Wolf Optimizer (MOGWO-3D). The optimization considered WEF interdependencies and sought the most efficient cropping pattern and resource allocation under projected climate scenarios. System performance was then assessed by comparing the optimized configuration with a reference SSP8.5Hy scenario. The WEF index analysis identified tomatoes, sugar beets, and potatoes as the most influential crops, with correlation coefficients of 0.70, 0.58, and 0.55, respectively. Implementation of the Optimized SSP8.5 Hybrid (Optimized SSP8.5Hy) scenario improved the reliability of meeting agricultural and water demands to 78.4-77.7 %, representing an increase of approximately 19-19.8 % compared with the baseline scenario. Moreover, the simulated groundwater decline was reduced by 9.5 m, indicating a 22 % improvement in subsurface resource stability. Overall, the optimization scenario demonstrated superior performance in maintaining reservoir storage levels, stabilizing groundwater, and sustaining water supply reliability across both dry and wet periods. In addition, the approach mitigated environmental risks associated with agricultural activities, including soil degradation, nutrient runoff, and greenhouse gas emissions. These findings confirm that the proposed simulation-optimization framework provides a robust basis for sustainable water management and agricultural resilience under changing climatic conditions.

Conflicts among operational objectives remain a major challenge in sustainable water resources management. This study develops an integrated simulation–optimization framework that couples a quantitative–qualitative WEAP model with a newly proposed fuzzy multi-objective imperialist competitive algorithm in a three-dimensional response space (F-MOICA3D). The main objective is to improve the sustainable operation of the Dez Dam–River system in southwest Iran. A comprehensive modelling framework was developed to simultaneously simulate and optimize water quantity and quality over a 30-year period under two scenarios: a baseline scenario representing current operational conditions and an optimization scenario generated by the proposed coupled model. Key water quality parameters, including electrical conductivity (EC), pH, biochemical oxygen demand (BOD), dissolved oxygen (DO), nitrate nitrogen (N–NO₃), and ammonium nitrogen (N–NH₄), were analyzed. In addition, system sustainability was evaluated using reliability, resilience, and vulnerability indices. The optimization framework is designed to maximize water supply reliability, minimize violations of water quality standards, and reduce penalties associated with reservoir operational constraints. The results indicate that the optimization scenario significantly improves system performance compared with the baseline case. Water supply reliability increases, water quality conditions improve through a reduction in exceedances of permissible limits, and reservoir storage remains above critical thresholds, preventing failures during dry periods. Moreover, spatial pollution hotspots associated with BOD and EC are effectively identified. Overall, the proposed F-MOICA3D–WEAP framework demonstrates strong capability in addressing complex multi-objective water resources management problems. It enhances system sustainability by simultaneously improving water allocation efficiency, water quality status, and reservoir operation performance, providing a practical and robust decision-support tool for integrated water resources planning.

This study evaluated the impacts of climate change on surface flow in the Dinevar Basin, Iran, via the SWAT model. It was calibrated (1986-2004) and validated (2005-2012) via monthly streamflow data from two hydrometric stations and exhibited satisfactory performance (NSE > 0.6). The model was forced with bias-corrected data from five CMIP6 climate models under the SSP1-2.6 and SSP5-8.5 scenarios for the near-future (2020-2060) and far-future (2061-2100) periods. Sensitivity analysis revealed that 17 key parameters were the most influential. Future climate projections revealed high variability in precipitation patterns among different GCMs, whereas all the models consistently projected increased maximum temperatures, particularly under SSP5-8.5. Runoff simulations revealed substantial uncertainty among GCMs, with multimodel annual averages ranging from 0.96 to 43.8 m(3)/s. Most models predicted an overall reduction in annual runoff, with significant decreases from March to May. Uncertainty assessment using a performance-weighted hybrid multimodel ensemble indicated that increased average monthly flow is projected only in November for both scenarios and future periods, whereas decreases are expected in all other months. This highlights a critical shift in the hydrological regime towards concentrated flow in late autumn, presenting substantial challenges for sustainable water resource management under changing climatic conditions.

Land settlement, sinkholes, and environmental destruction have occurred as a result of excessive extraction of groundwater resources and a severe water table drawdown in various parts of the world, including the plains of Famenin and Kabudarahang in the west of Iran. Simulating the interaction of surface and groundwater is one of the useful and powerful methods to determine the water budget of resources, to calculate more accurately the amount of recharge and discharge from the aquifer in such plains. The purpose of this research is to develop a coupled dynamic model of surface and groundwater to simulate the complete cycle of hydroclimatology in the saturated and unsaturated layers of the aquifer, to investigate the possibility of implementing a conservation reserve program (CRP) in a degraded aquifer and provide a workable solution to control the drop in the groundwater level and solve the crisis of sinkholes. Other goal is investigating the effects of implementing CRP in the sustainable exploitation of groundwater resources under the CRP30 and CRP50 management scenarios along with the reference scenario for a future 15-year period. Investigating the environmental consequences of the emission of greenhouse gases as a result of the implementation of these scenarios in the study area based on Life Cycle Assessment (LCA) is another goal of this research. The results of this research show that the implementation of the CRP30 and CRP50 scenarios can compensate for the lost groundwater reserves to a large extent and will prevent sinkhole crisis in the long term. Taking into account the effect classes of LCA, the implementation of the CRP30 and CRP50 scenarios in the Famenin and kabudarahang plains will play a significant role in reducing the depletion of water resources, reducing the depletion of phosphate resources, and reducing the amount of eutrophication compared to the reference scenario. The success of CRP requires the paradigm of a systemic view and scientific-statistical support, especially cultural, social and economic support from both governments and critics of government policies and farmers cooperation.

Lake Water Surface Area (WSA) plays a vital role in environmental preservation and future water resource planning and management. Accurately mapping, monitoring and forecasting Lake WSA changes are of great importance to regulatory agencies. This study used the MODIS satellite images to extract a monthly time series of WSA of two lakes located in Iran from 2001 to 2019. Following a consequence of image and time series preprocessing to obtain the preprocessed lake surface area time series, the outcomes were modeled by the Long-Short-Term Memory (LSTM) deep learning (DL) method, the stochastic Seasonal Auto-Regressive Integrated Moving Average (SARIMA) method and hybridization of these two techniques with the objective of developing WSA forecasts. After separate standardization and normalization of A L TS and reevaluation of the preprocessed data, the SARIMA (1, 0, 0) (0, 1, 1) 12 model outperformed sole LSTM models with correlation index of (R) 0.819, mean absolute error (MAE) of 49.425 and mean absolute percentage error (MAPE) of 0.106. On the other hand, the hybridization (stochastic-DL) enhanced the reproduction of the primal statistical properties of WSA data and caused better mediation. However, the other accuracy indices did not change markedly (R 0.819, MAE 49.310, MAPE 0.105). The multi-step preprocessing and reevaluation also caused all LSTM models to produce their best results by less than 12 inputs.

The worsening water crisis-driven by climate change, poor consumption management, and the overexploitation of surface and groundwater resources-poses a serious threat to global sustainable water supplies. However, the diverse nature of water scarcity and its effects on resources and communities remain poorly understood. Hence, this study aimed to assess changes in the Water Poverty Index (WPI) affecting the social and economic status of communities and to propose optimal scenarios for sustainable water management. Using geostatistical analysis within a GIS framework, spatio-temporal WPI maps were developed for different regions of Kermanshah province in western Iran. In parallel, various policy scenarios were simulated for the period 2024–2054 using a system dynamics (SD) approach to ensure sustainable water access for human, agricultural, and ecological systems. The results showed that Kermanshah province is under significant water stress, with available resources unable to meet domestic, agricultural, and environmental demands. Notably, a direct relationship was observed between the expansion of water-intensive agriculture and increased WPI levels. Furthermore, spatial-temporal analysis revealed substantial inequality in water resources across different counties. Sensitivity analysis showed that under scenarios 2 and 3, reducing per capita domestic water consumption and agricultural water storage significantly reduced the WPI and had a positive impact on sustainable water supply. Implementing these policies can foster sustainability across human, agricultural, and ecological systems.

Early Warning (EW) is commonly understood as the forecasting of a potentially catastrophic event. The effective use of data for EW plays a vital role in failure management, encompassing tasks like vulnerability mapping, forecasting, warning systems, prevention, planning, and the execution of actions. This study introduces an Early Warning Protocol (EWP) to tackle the potential failure of the Shiadeh Earth Dam in Iran. The dam was commissioned in 1999 and, in 2004, experienced a landslide at the downstream left abutment and the downstream slope. All installed monitoring instruments failed, meaning no deformation data has been recorded. Consequently, the Radial Basis Function (RBF) algorithm was employed to predict the dam's settlement. Satellite imagery was utilized for monitoring and controlling the dam's settlement, and a 3D PLAXIS model was used to review and evaluate the dam's performance under various reservoir conditions during different seasons, including static and dynamic states. In this study, several comprehensive phases were conducted to ensure the reliability and validity of the proposed protocol. According to the analysis results, the action plan was recognized as the central element of the EWP. Furthermore, the study highlighted that the steps of observing, planning, decision-making, and acting are essential prerequisites for the proposed protocol. By integrating satellite monitoring, numerical modeling, radial basis function algorithms, and geodetic surveying of micro-geodetic points and the dam crest alignment, this study presents an innovative and reliable protocol for monitoring the Shiadah earth dam. This approach can play a significant role in enhancing safety and reducing the risk of dam failure. Moreover, the method is applicable to all earth and concrete dams across the country. The assessment results indicate that the dam is currently at alert level 2 (caution status).

Global predictions have shown that the demand for water, food and energy will increase significantly in the coming years due to population growth, politico-economic development, international trade, urban sprawl, food diversity, and social and climate change. Given the severe shortage of resources, decision-making, planning and implementation of policy in the field of resource management should be based on the knowledge of the interdependence of water, food and energy resources. A three-way relationship is between water, food and energy, and the interrelationship between water, food and energy is called the Water-Energy-Food Nexus (WEFN). WEFN helps to better understand the interrelationships between the three sources, so using WEFN we can use and manage limited resources sustainably. The study area in this study is Oshtorinan in Boroujerd and a small sub-basin in the north of Boroujerd basin located in Lorestan Province. This study was conducted aimed to assess the relationship between water, food and energy of 4 crops of cucumber, pumpkin, tomato and potato, which are the spring and dominant crops in the study area, during two consecutive years 2019–2020. Data were collected through interviews with 86 farmers in the study area with an area of 210 hectares. According to the study results, potato had the highest water and energy consumption index and on the other hand, the lowest productivity. Pumpkin had the lowest consumption index and the highest productivity. Also, potato had the worst value of WEFN i.e. 0.00 in 2020 and 0.1 in 2019. Pumpkin had the best value in terms of WEFN i.e. 0.92 in 2020 and 0.84 in 2019.

Nine remote sensing-based surface soil moisture (SSM) estimation models using images from Landsat 8, Sentinel-2 and Sentinel-1 satellites were compared. To evaluate these models, we measured SSM at 179 locations in a 50-ha sunflower field . The result showed that the Water Cloud-based model, a semi-empirical regression model, which used the synergy of Landsat 8 and Sentinel-1 data, was the best model, with an R-2 of 0.73 and RMSE of 0.053 m(3)/m(3). In sum, with the integration of images from multiple satellites, soil moisture maps with suitable spatial resolutions were retrieved that may be used for irrigation planning.

Considering the unsustainability of the current situation in terms of coordinated management of water, food, and energy consumption, per capita water and energy consumption in Iran have a significant gap compared to the consumption patterns in developed countries and the world. In some seasons, water consumption in Iran is 2 to 3 times higher than the global standard. There is no accurate statistical data in Iran regarding water consumption, cultivated land, crop production, and many other agricultural structural information. On the other hand, agriculture and food are associated with water, fertilizer, and energy consumption, which result in significant environmental damage, and as we know, climate change is one of the most challenging environmental issues. To investigate the conditions of sustainable agriculture based on Water, Energy, Food Nexus (WEFN) and environmental impacts, it was decided to develop a model to accelerate computations and create a database. Therefore, in this study, a computer model called MIDtoCP was designed for data receiving and processing, which is based on Water, Energy, Food Nexus. The model was designed using the C# programming language in the Visual Studio 2018 environment, and Microsoft SQL Server Management Studio 2018 was used for data storage in the database. Since the utilization interdependence of Water, Energy, Food is crucial in sustainable management and agriculture, this model utilizes the triple interdependence of these resources as the primary basis for agricultural management. Six indices, including water consumption, energy consumption, water productivity, energy productivity, water economic efficiency, and energy economic efficiency, are calculated using the interdependence concept. These indexes are calculated and utilized in the model through programming. The results demonstrate that the developed model has the capability to receive and process the necessary information for each product separately, at the field or basin level, based on the Water, Energy, Food Nexus. Furthermore, according to the available information, environmental evaluation based on the Life Cycle Assessment (LCA) is another advantage of this model, as it enables the investigation of agricultural impacts on the environment at various field or regional levels.

In the operation of water distribution networks in cities, leakage from pipes always causes problems for human health and for the environment. Leakage openings in pipes may exist in different shapes. Circular holes are common in corroded and punched pipes. In the leakage studies, the area of these openings is usually assumed to be fixed and the leakage exponent is about 0.5. In this study, an analytical equation has been presented with two purposes. First, Examining the changes in the leak area and leakage exponent of circular holes. Second, providing an equation that contains more parameters than the general leakage equations. By using such an equation, the accuracy of leakage estimation is increased due to the direct involvement of the effective parameters. Also, for the possibility of modeling different leakage equations, including the present equation, a new hydraulic analysis model has been developed. This model tries to improve leakage modeling by including more capabilities than the existing hydraulic analysis models. Results showed that the leak area in circular holes is not fixed and changes due to different parameters. Comparison of the present equation and the orifice equation showed a significant difference which confirms that the orifice equation cannot be always used for circular leaks. In the study of leakage exponent, it was found that for polyethylene pipes, the leakage exponent is higher than value of 0.5 mentioned in the other studies and it can take different values depending on the leakage position in the network. Increasing the hole diameter did not affect the leakage exponent, but increased the leakage coefficient. On the other hand, for steel pipes, the leakage coefficient was fixed and the exponent remained around 0.5. Finally, the results showed the usefulness of the developed hydraulic analysis model for implementing the scenarios defined in this study.

Abstract In regions with arid and semi-arid climates, groundwater serves as one of the main sources of agricultural, industrial, and drinking water supply, constantly interacting with surface waters. The purpose of this study is to investigate changes in the level and volume of aquifer storage in Kermanshah by simulating the interaction of surface and groundwaters, using a coupling dynamic model WEAP-MODFLOW. This model is capable of calling and automatically running climate change scenarios and displaying their effects on the entire system. In this method, data and results between the MODFLOW and WEAP models are exchanged on a monthly basis, and the impacts of implementing each of the CMIP5 climate scenarios can be observed in both surface water and groundwater sections. The values of recharge, extraction, runoff, river levels, and water supply from the WEAP model are input into the MODFLOW model to calculate groundwater levels and changes in aquifer storage, with results fed back to the WEAP model. To apply model uncertainties and climate scenarios was developed a hybrid model based on the combination of predictions from 5 different AR5 models. The results showed that over a base period of 27 years (October 1991 to September 2018), the average groundwater level at the end of the period decreased by 4.3 meters, with a reservoir volume reduction of 253 million cubic meters. In the event of aquifer operation, based on the predicted climatic parameters derived from the hybrid model during the 81 years (October 2018 to September 2099), the level of reduction and volume of aquifer storage was predicted under the optimistic scenario of RCP2.6 in order of 2.52m and 251.51MCM and the pessimistic scenario RCP8.5, respectively 8.88m and 769.04 MCM. The results demonstrated that employing an integrated operation model in a dynamic link mode is an effective strategy for better river and aquifer management under climate change conditions. The effects of each climate scenario on the entire system are observable in this model, aiding decision-makers in implementing effective adaptation strategies to climate change.

In this study, the Terrestrial Water Storage Anomaly (TWSA) in the Lake Urmia Basin (LUB) was obtained by using the GRACE satellites. The whole study area was covered by 10 GRACE/GFO pixels, and the TWSA value was calculated for these 10 pixels. Examining the changing trend showed that the value of TWSA in the LUB has a decreasing trend and fluctuates from approximately -200 to +200 compared to the average value. The TWSA was modelled using the group method of data handling (GMDH) by considering six different parameters obtained from the European Centre for Medium-Range Weather Forecasts (ECMWF) Reanalysis V5 (ERA5) and the Global Land Data Assimilation System version 2.0 (GLDAS V2.0). Finally, the best models with two to six input variables were selected. Among the models with different inputs, the GMDH Model 2, with three inputs, had the best performance compared to the other models. After choosing the best inputs in calculating the TWSA value by the GMDH, the TWSA value was also modelled by using the Adaptive Neuro-Fuzzy Inference System (ANFIS), the Extreme Learning Machine (ELM) and the Artificial Neural Network (ANN). The results showed that GMDH not only outperformed other models but also provided some simple equations to apply in practical tasks.

Revealing the contribution of each of the climatic and human factors can help the management of water resources. Firstly, trend analysis and change‐point tests were performed to determine the status of the parameters affecting the groundwater resources of the Mahidasht aquifer during the period from 1981 to 2019 by using statistical methods and the Pettitt test. The GMS model was calibrated as a tool to simulate the Mahidasht aquifer system. After determining the change point, modeling of the groundwater level before and after the change point was done using the GMS model. By removing the effect of human factors in the model, the contribution of the effects of climate variability and human activities on groundwater was obtained. The results showed that there was no significant trend in annual rainfall, but the annual runoff has a significantly decreasing trend at the confidence level of 99%, and the trend of the groundwater level is also significantly decreasing at the confidence level of 99%. Pettitt change point test showed precipitation without a change point, but the runoff in 1998 and the groundwater level in 2001 had a change point. Finally, the modeling of the aquifer showed that the contribution of human activities in the reduction of the groundwater level of the Mahidasht aquifer was 78 and climate variability is 22%.

Climate change has led to significant changes in weather elements in recent years, which, affect the various aspects of human life, including water supply, food security, and so on. In this research, the effects of climate change on the Kor basin in Fars province under different climate scenarios for four parameters of minimum and maximum temperature, mean temperature and precipitation using Beijing Climate Center Climate System Model (BCC-CSM 1.1), Centro Euro-Mediterraneo per Cambiamenti Climatici (CMCC-CM) and Community Earth System Model Contributors CESM-BGC models (Fifth assessment report Intergovernmental Panel on Climate Change, IPCC) is investigated. In the present study, the method of micrometric factor conversion for the period 1979 to 2005 for calibration of these models and from 2006 to 2017 to measure the accuracy of the models is used. In the following, these models are used to predict the parameters of precipitation and temperature in the upcoming period (up to 2100). Among these models, BCC-CSM 1.1 has four climatic scenarios including Representative Concentration Pathways (RCPs) RCP 2.6, RCP 4.5 and RCP 6.0 and RCP 8.5, and CMCC-CM and CESM1-BGC models have two RCP 4.5 and RC P6.0 scenarios. According to the IPCC's fifth report, each of these scenarios has different concentrations of greenhouse gases in predicting the future climate. The results show that the BCC-CSM 1.1 model with a mean absolute relative error (MARE) of 2.619 has the best performance in rainfall estimation and the CESM1-BGC model with MARE of 1.467 has the best performance in evaluation of the minimum temperature and CMCC-CM model with MARE equal to 0.287 and, 0.115 showed the best performance in estimation of the average and maximum temperature. Considering that the issue of climate change and its impact on all climatic parameters such as precipitation and temperature can affect different aspects of human life, by using these models and determining the efficiency of each in estimating its specific climatic component of the model can be useful step by step in planning and managing the region.

Understanding the systemic approach and its potential for decision-making is important for resource management, especially in agriculture in which increasing food demands and environmental and social issues are the main challenges. Therefore, multiple-criteria decision-making methods have a vital role in the optimum combination of resources. Computational models are commonly used to assist resource management decision-making; however, while water-energy-food nexus (WEFN) are increasingly well modeled, the inclusion of social issues has lagged behind. This paper outlines a model based on a multi-objective genetic algorithm (MOGA) that conceptualizes and proceduralizes balancing the goals of sustainable agricultural development highlighting impacts and interactions between social variables and the WEFN index in agriculture. The model was developed using a bottom-up approach, informed through farmer interviews, and secondary data in the Miandarband plain, west Iran. The Compromise Programming (CP) method, which is widely used to solve MOGA models, was applied to optimization algorithms in three-dimensional spaces. The model represents field conditions and provides a tool for policymakers and sustainable resource management. The modeling framework applied to the study area for the comparison of WFEN, life cycle assessment (LCA), and social dimension in current and optimum cultivation patterns. The proposed optimal cultivation pattern in minimum CP will reduce water and energy consumption by 2.56 % and 12.71 % while reducing environmental impacts by 6.82 %, and it will improve the social status of farmers. Results suggest that changes in the basic elements of objective functions will lead to a balance between cultivation patterns that depends on policies and socio-economic conditions. Moreover, proposed cultivation patterns may be sustainable but their viability varies across the periods and also in different human ecologies. However, by analyzing the feedback of the model and interactions between different dimensions, this work highlights that policymakers can decide sustainable agriculture how should be occur by comparing different solutions.

In areas with dry and semi-arid climates, underground water is one of the main sources of water supply for agriculture, industry and drinking. The aim of this research is to investigate hydrodynamic coefficients, plain balance and aquifer thickness in the study area of Kermanshah through aquifer simulation using MODFLOW model. In this method, first the conceptual model and then the numerical model were built in two steady and unsteady states, then it was calibrated and validated. The results show that the maximum thickness of the aquifer in the north and northeast of the plain is about 160.96 meters, and the lowest thickness is 118.14 and 120.69 in the southwest and west of the plain, respectively. Also, the coefficient of hydraulic conductivity varies between 0.09 and 40 m/day and the coefficient of specific yield varies between 1 and 35%. Balance calculations showed the negative water balance in the western and southwestern areas of the plain and the positive balance in the northern, northeastern, and southeastern areas.

During the recent few decades, the use of various models has been regarded as a promising option to predict groundwater level (GWL) in any given region using a wide variety of data and relevant equations. The lack of trustworthy and comprehensive data is, nevertheless, one of the most significant obstacles that must be overcome in order to analyze and anticipate the depletion of groundwater in the context of water management. Because of this, the implementation of artificial intelligence (AI) models that are able to predict the GWL with high accuracy using a reduced amount of data is unavoidable. In this work, the GWL variations of Lur plain were simulated using GMS model by utilizing the available data and maps. The accuracy of model was assessed at both phases i.e. validation and calibration. Following that, GA-ANN and ICA-ANN approaches, together with ELM, ORELM, and GMDH models, were used in order to fulfill the demand for too smaller volumes by AI procedures. According to the results, the ORELM output had the highest correlation with the observed information, which indicates that it is the most accurate model in this regard. The correlation coefficient for this model was 0.976. Because of this, instead of utilizing a complicated GMS model that needs a significant amount of data for the simulation, an ORELM model can be used to reliably forecast the GWL in the Lur plain. This simple model allows the researchers to accurately predict changes in GWL during rainy and non-rainy years compared to other complicated and time-consuming numerical models.

Accurate forecasting of runoff as an important hydrological variable is a key task for water resources planning and management. Given the importance of this variable, in the current study, a multivariate linear stochastic model (MLSM) is combined with a multilayer nonlinear machine learning model (MNMLM) to generate a hybrid model for the spatial and temporal simulation of runoff in the Quebec basin, Canada. Monthly hydrological data from 2001 to 2013, including precipitation and runoff data from nine stations and Normalized Difference Vegetation Index (NDVI) extraction of MODIS data, are applied as input to the proposed hybrid model. At the first step of the hybrid modeling, data normality and stationary were examined by performing various tests. In the second step, MLSM was developed by defining four different scenarios and as a result 15 sub-scenarios. The first and second scenarios were developed based on one exogenous variable (precipitation or NDVI). In contrast, the second and third scenarios were developed based on two additional variables. In the first and third scenarios, the data are modeled without preprocessing. In the second and fourth scenarios, a preprocessing step is performed on the data. Then, in the third step, various combinations based on different time delays from runoff data were applied for developing nonlinear model. The comparisons are made between observed and simulated time series at various stations and based on the root mean squared error (RMSE), mean absolute error (MAE), correlation coefficient (R) and Akaike information criterion (AIC). The efficiency of the proposed hybrid model is compared with a novel machine learning model that was introduced in 2021 by Sultani et al., and it was also compared with the results obtained from the linear and nonlinear models. In most stations, delays (t-1) and (t-24) are identified as the most effective delays in hybrid and nonlinear modeling of runoff. Also, in most stations, the use of climatic parameters and physiographic factors as exogenous variables along with runoff data improves the results compared to the use of one variable. Results showed that at all stations, proposed hybrid model generally leads to more accurate estimates of runoff compared with various linear and nonlinear models. More accurate estimates of peak runoff values at all stations were another excellence of proposed hybrid model than other models.