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• Relative impacts of human activities and climate change on river flow are assessed. • The proposed methodology is a stochastic version of the fixing-changing method. • The uncertainties of the models' parameters are analyzed using the DREAM algorithm. • To evaluate the methodology, it was applied to the Zayandehrud basin in Iran. • Results show the applicability and efficiency of the proposed methodology. Climate change and human activities are two important factors affecting surface runoff. In water resource planning and management, it is usually important to separate the contribution of the mentioned factors to the runoff change. In this paper, a new methodology is proposed to obtain the relative impact of human activities and climate change on streamflow under uncertainty. In this methodology, the breakpoint of the annual time series of the observed runoff is estimated and the time series is divided into two study periods; before and after the breakpoint, namely the "natural" and "impacted" periods. The Jazim monthly water balance model is used for monthly runoff simulation. To incorporate the effect of snow in simulating runoff, two Jazim-based monthly water balance models with Rao and Al Wagdany (1995) and McCabe and Markstrom (2007) snow modules are used. Then, the monthly water balance models are calibrated and verified for the natural and impacted periods. The main parameters of the water balance models are optimized using the Genetic Algorithms. The uncertainty of the parameters of the models is analyzed using differential evolution adaptive Metropolis (DREAM) algorithm, which is based on the Markov Chain Monte Carlo (MCMC) algorithm. This algorithm provides the posterior probability distribution functions (PDFs) of parameters of the runoff simulation models. Next, the PDFs of the contribution rate (CR) of the climate change and human activities to runoff are determined using the fixing-changing method by taking into account the PDFs of the models' parameters. To show the applicability and efficiency of the proposed methodology, it is applied to the Zayandehrud sub-basin in Iran. Using the Standard Normal Homogeneity Test (SNHT), Pettitt and Buishand range tests, one breakpoint at 2006 is detected in runoff time series. Through the monthly rainfall-runoff simulations and the fixing-changing method, it is estimated that decrease in the runoff after the breakpoint can be attributed to climate change by 57.8% and 45%, using the Jazim-Rao & Al Wagdany model and the Jazim-McCabe & Markstrom model, respectively. The results of the uncertainty analysis of the parameters of the water balance models show that the parameters of LSMX (maximum moisture capacity of the lower soil layer) and K 3 (deep percolation coefficient) are the most significant parameters in the uncertainty of runoff simulated by both the models. According to the PDFs of contribution rate (CR) of climate change and human activities which are obtained using each rainfall-runoff model, it can be inferred that the Jazim-McCabe & Markstrom model significantly decreased the uncertainty of estimations. [ABSTRACT FROM AUTHOR]

Highlights • Monthly precipitation was predicted using soft computing methods including ANFIS with GA, ACOR, PSO and DE. • Suggested algorithms improved the ANFIS performance for modeling the precipitation. • Suggested methods decreased the event of trapping in local optima. • ANFIS-ACOR model with least error and most correlation showed highest performance in monthly precipitation forecast. Abstract Existent algorithms to train adaptive neuro-fuzzy inference system (ANFIS) such as gradient descent and least-square methods are useful, but they have problems such as trapping in local optimum as well as high-volume of computations. This paper examines the application of genetic algorithm (GA), ant colony optimization for the continuous domain (ACO R), particle swarm optimization (PSO) and differential evolution (DE) for improving the performance of ANFIS models in simulating monthly rainfall magnitudes. Monthly rainfall records of nine foothills to arid weather stations of Isfahan province, central Iran were used for evaluating the proposed methodology. The correlation coefficient and non-linear sensitivity analysis were performed on the available rainfall records. Monthly rainfall modelling was carried out based on three input groups and used separately in five applied models (e.g. ANFIS, ANFIS-PSO, ANFIS-ACO R , ANFIS-DE, and ANFIS-GA). The obtained results showed that proposed hybrid models had better accuracy than the simple ANFIS model in escaping local optima. The best model was ANFIS-ACO R (with R2 = 0.92, RMSE = 2.73 mm and SI = 0.26), while sole ANFIS model (with R2 = 0.33, RMSE = 9.98 mm and SI = 0.90) presented the worst output. Moreover, the obtained results showed that: (1) Coupling the optimization algorithms with ANFIS can improve its performance in modeling the monthly rainfall time series; (2) the correlation coefficient and sensitivity analysis of input data provided a valid input selection that could improve the model accuracy with neglecting the redundant input parameters. [ABSTRACT FROM AUTHOR]

Highlights • Simulating daily ET o in island regions is reported here. • Mass transfer-based models showed promising results. • Selecting one year as minimum test size could not show the error variations properly. • Using mass transfer-based data from exogenous coastal sites gave promising results. • Local and external GEP models gave promising results. Abstract Among the reference evapotranspiration (ET o) equations, temperature-based and radiation-based equations have been accepted as promising approaches when the available meteorological data are limited. The present paper aimed at assessing this general implication in island environments, which might be generally volatile than the interior/coastal zones. Using data from five island locations of Iran, daily ET o values were calculated by the benchmark FAO-Penman-Monteith (FAO56-PM) model, then the commonly used temperature-, radiation- and mass transfer-based ET o equations were evaluated against the benchmark values. An additional heuristic modeling of ET o was also performed through employing gene expressions programming (GEP), evaluated by k-fold testing. The obtained outcomes showed that the calibrated mass transfer-based equations and their corresponded GEP models generally surpassed the other applied models in the studied locations. This criticized the commonly accepted implication about applying temperature/radiation-based models in case of data scarcity for different locations. Nevertheless, analysis of the temporal variations of models performance among test years showed that fixing the minimum test size as 1 year for local k-fold testing (that is the case in similar studies) might be misleading as it could not show the fluctuations in modeling accuracy well. A minimum affordable size of at least one growing season was instead selected, tested and recommended in the current research. [ABSTRACT FROM AUTHOR]

• This paper shows the role of Social Learning (SL) water resources systems. • A new method is presented for quantifying the SL. • The relations of SL, social network, and stakeholder analysis are evaluated. • The influential stakeholders are identified in social learning diffusion. • The proposed approach is applied to the Kan river basin in Iran. In this paper, six critical topics in social learning (SL) definition and applications are discussed: 1- Clarification of the SL definition; 2- The role of SL in the management of shared water resources; 3- The definition of "learning" in SL; 4- The quantification of SL; 5- The relation of SL with outputs of Social Networks Analysis (SNA) and stakeholder Analysis (SA); 6- Identifying and ranking of key stakeholders (i.e., influencers) for SL diffusion. After defining SL, we present SL's role using a well-known game theory problem. We have addressed the term "learning" in SL using an existing method, namely reinforcement learning. We present a new approach for quantifying SL within a hydrological environment and finding key stakeholders for the SL diffusion. For the first time, we consider a combination of SL, SA, and SNA to propose a comprehensive social-based approach to investigate SL diffusion. The western part of Tehran city, which is suffering from some water and environmental issues such as overexploitation of groundwater and land subsidence, is the case study of this paper. The results show a significant relationship between SL and the outputs of SNA. The suggested method for identifying key stakeholders has been able to identify key stakeholders in terms of various water resources management issues in the study area. The results can be used for developing effective policies towards sustainable management of water and environmental resources in the studied socio-hydrological system. [ABSTRACT FROM AUTHOR]

• Several snow-rain phase separation models was evaluated at various synoptic stations of Iran. • A new sigmoidal model was proposed for snow-rain phase separation. • The proposed model performed better than the existing models for snow-rain phase discrimination. This paper presents the use of a double sigmoidal model for precipitation phase partitioning in mountains. For the model development and evaluation, daily rainfall, minimum and maximum air temperatures, and snow depth data from twelve synoptic stations in the mountainous regions of Iran are used. The model performance is compared against six air temperature-based methods using the Taylor diagram, the coefficient of determination (R2), and the Root Mean Square Error (RMSE) performance indicators. Comparing the frequency of snowfall events estimated by the proposed model with those of the existing models and observation at the selected stations showed better performance of the proposed model in precipitation phase discrimination at the studied stations. The average R2 statistic that relates the frequency of snowfall occurrences estimated by the proposed model with the frequency of snowfall events observed at the studied stations is 0.989, which is the highest compared to other available models. Its average error across the stations is 4%, the lowest among the examined models. A split sample approach was used to ensure that the parameters of the proposed model are appropriately estimated. The result showed that the R2 and RMSE values of both calibration and validation subsets are not vastly different from each other, suggesting that the parameters of the two experiments are stable and the corresponding results are robust. The proposed model also performs better than other existing models in accurately estimating air temperature thresholds required for precipitation phase discrimination as well as the snow depth data in all studied stations. [ABSTRACT FROM AUTHOR]

• The traditional probabilistic approach fails to design an ogee crest accurately. • Hydrologic and hydraulic uncertain parameters are endowed with TFNs. • Approximation methods are linear approximation for Fuzzy multiplication and division. • Fuzzy Approach needed at least three rainfall data related to flood events to perform. • Fuzzy method incorporates any possible values of the spillway coefficient with a TFN. This paper provides a Fuzzy methodology based on calculating the hydrological return period discharge and the resistance-load theory for designing an ogee spillway in a diversion dam. Hence, the uncertainties of rainfall intensity, catchment runoff coefficient, and spillway discharge coefficient are endowed with triangular Fuzzy numbers (TFNs), describing possible values the uncertain parameters may have. To do so, the rational formula and, later, the spillway discharge equation are analyzed here to obtain the Fuzzy products of the peak runoff and spillway discharge capacity. On the basis that both must match, the risk is calculated using the difference between the obtained Fuzzy product quantities with three Fuzzy arithmetic operations. It is observed that the standard approximation closely tracks the result of the alpha-cut (exact) method, providing a time-saving tool for Fuzzy calculations. Since the return period (T) and risk of failure are related quantities, the spillway dimensions are calculated for several recurrence intervals, assuming risk equal to 1/T. Then, the Fuzzy approach is compared with the conventional return-period approach for the design of an ogee crest in three experimental catchments in Iran with adequate historical data. The result indicates that the Fuzzy technique outperforms the conventional return-period approach for designing an ogee spillway, obtaining an average 29% larger spillway dimension in all case studies. Beyond that, the Fuzzy procedure is applied to another catchment with short rainfall-runoff data, which is impossible to implement the conventional return-period method for the hydrologic design of an ogee spillway. [ABSTRACT FROM AUTHOR]

• This paper presents a novel approach for considering social attachments in water allocation. • The proposed approach takes into account the effects of social attachments on agents' decisions. • Social Network Analysis (SNA) is applied to evaluate the coordination level between the agents. • To evaluate the applicability of the approach, it is applied to the Kan River Basin in Iran. Although economic and environmental considerations have been well integrated into water resources management, the social aspects have been usually neglected. Quantifying and embedding social attachments in the water resource management models are so challenging. In this paper, a novel approach is proposed to quantify and consider social attachments for water and wastewater allocation in urban areas. In the management of shared resources, one of the main issues is the level of cooperation among agents. In a multi-agent environment, stakeholders can decide non-aligned or depending on their interests and social attachment, have some cooperation and coordination in the decision making process. In the proposed approach, an effort has been made to take into account the effects of social attachments on agents' decisions. In this paper, social network analysis is applied to evaluate the coordination level between agents in the use of shared water resources. A paired physical and behavioral simulation model is developed to assess water and wastewater allocation scenarios. A reinforcement learning approach, namely Q-learning is used to develop the behavioral model, which is linked with social attachments. To evaluate the applicability of the proposed approach, the Kan River Basin in the western part of the Tehran metropolitan area is studied. By using the proposed approach for a 25-year simulation horizon and 63 water and treated wastewater allocation scenarios, it is shown that it has merits to find the best scenarios in different conditions and can enhance the sustainability of the water resources in the study area. According to the results, improving the social attachments in the study area can reduce the over exploitation of groundwater and improve the groundwater quality. [ABSTRACT FROM AUTHOR]

[Display omitted] • DDA and weighted RVA approaches were developed to assess hydrologic alterations. • DDA outperforms in detecting and calculating hydrologic alterations. • Flood control measures have altered the Gharnaveh watershed's hydrologic regime. • A high conformity exists between DDA and weighted RVA results and other approaches. Assessing the natural or man-made alterations in the hydrological characteristics of watershed systems is a prerequisite to develop watershed management, restoration, and conservation strategies and plans. The Range of Variability Approach (RVA), based on a suite of Indicators of Hydrologic Alteration (IHA), is most widely used to assess hydrologic alterations. However, the traditional RVA and its revised versions and alternatives all suffer from major challenges such as binning the data and using only the target range to compare the probability distributions of the hydrologic variables in the pre- and post-impact periods through applying simplified statistical analyses. This paper proposes the Density Difference Approach (DDA) and the weighted RVA to overcome these challenges. Despite the previous approaches, which divide the time series of the continuous variables into discrete groups, DDA uses the continuous probability density function (pdf) estimated by the Gaussian Kernel Density Estimation (KDE) technique. It considers the magnitude, frequency, and order position of all individual data in the alteration analysis. Furthermore, to account for the variations in the values and frequencies of the variables beyond the target range, which is neglected in the traditional RVA, the weighted RVA approach has been proposed. These suggested approaches, along the other widely used alteration assessment approaches, have been applied to quantify the hydrologic alteration in the Gharnaveh watershed in the northeast of Iran using 50 years of daily discharge data recorded at the Tamar hydrometry station at the outlet of the watershed. The performance of the alteration analysis approaches was further evaluated using the randomly generated time series with the arbitrary changes in the probability distribution parameters. The results indicate a significant difference in the alteration values and ranks calculated by different alteration analysis approaches. Compared to the traditional RVA, the weighted RVA resulted in higher alteration degrees for most of the IHA indicators. DDA outperformed the other alteration analysis approaches for both the observed and randomly generated data. It better accounted for the alteration in the key characteristics of the probability distribution (including the location, dispersion, and skewness parameters) of the randomly generated data series. We recommend DDA as an appropriate hydrologic alteration assessment approach that assists water resource managers and decision-makers through accounting for multi-dimensional attributes of hydrologic alterations. [ABSTRACT FROM AUTHOR]

• Six DNNs and three corrected NWP models are used for adaptive rainfall nowcasting. • Outputs of individual models are fused using some ensemble techniques. • KGE, R 2 , and RMSE indices are used to determine the best adaptive nowcasting model. • Different events are selected to evaluate the forecasting abilities of the models. • Rainfall nowcasting models are applied to the eastern drainage catchment of Tehran. Accurate rainfall nowcasting is necessary for real-time flood management in urban areas. In this paper, some deep neural networks (DNNs) are developed for rainfall nowcasting with lead-time of 5 min. To increase the accuracy of the DNNs, their outputs are fused with the predictions of some numerical weather prediction (NWP) models using three ensemble models (i.e., bagging, random forest, and adaboost). The bias of NWPs is corrected using the quantile mapping technique. The ensemble models are compared with the DNNs and NWP models in terms of the accuracy of predictions. To reduce the run time of the proposed rainfall nowcasting models to be applicable for real-time urban flood management, they are executed using a parallel computing algorithm on the central processing unit (CPU) and graphics processing unit (GPU). To evaluate the efficiency of the proposed models, they were applied to the eastern drainage catchment (EDC) of Tehran city in Iran. More than 29,000 observed rainfall data were used for training and cross validating the proposed models. The results illustrated that the accuracy, coefficient of determination, of the ensemble models is at least 10% better than the DNNs in most rainfall events. [ABSTRACT FROM AUTHOR]

• We evaluate two distinct types of hydrological similarity in this study: (i) the apparent similarity measured by similarity distance based on observable catchments descriptors (CDs) and (ii) behavioral similarity, which is determined by highest-performance of transferred model parameters between gauged donor catchment and ungauged target catchment (best-donor case). • More than 75% of our physically similar catchments have a hydrological similarity. • The climatic (aridity index), topographic (mean elevation), and physiographic (catchment area) properties exert a greater influence on parameter transfer to ungauged catchments than do other CDs. A key goal of hydrological science is to understand the climatic controls on catchment hydrological response behavior. Progress toward this goal would result in improved model transferability from gauged to gauged catchments. In this sense, the effectiveness of the model's transferability is contingent on the proper selection of donor and target catchment pairs. Thus, using a rainfall-runoff model, we evaluate two distinct types of hydrological similarity in this study: (i) the apparent similarity measured by similarity distance based on observable catchments descriptors (CDs) and a Euclidean distance based on physical similarity (PS) method and (ii) behavioral similarity, which is determined by highest-performance of transferred model parameters between gauged donor catchment and ungauged target catchment (best-donor case (BD)). It is believed that catchments that apparently to be similar in terms of CDs, have a similar hydrological behavior. We wish to see if that assumption is valid in this paper. Spatial proximity (SP) is also implemented to see if it might be used as an alternative for PS where there is no apparent physical similarity between catchments. To test the study's assumptions, the HBV conceptual rainfall-runoff model is used in 576 catchments across four climate regions in Iran. The results indicate that: (1) as expected, the best-donor (BD) case performs the best, and the more than 75 % of our physically similar catchments have a hydrological similarity (the overlap was ≥ 70 %), (2) the superiority of PS over SP demonstrates that the CDs exert a great influence on transferability within each climate region than geographical distance. However, we demonstrated that the SP is superior, when spatial distance between donor and target catchments is reduced (nearest neighbor ≤ 20 km), (3) consistent with CDs, when utilizing SP method, geographical distance has a varying effect on model transferability within wetter and drier regions, such that SP performs better in wetter regions than it does in dry interior regions, (4) throughout Iran, the dominant controls on model transferability differ by region. Thus, the climatic (aridity index or PET/P), topographic (mean elevation), and physiographic (catchment area) properties exert a greater influence on parameter transfer to ungauged catchments than do other CDs, and (5) the runoff ratio (streamflow signature) confirmed the superiority of the wetter regions over the drier regions in terms of control on the parameters transfer. [ABSTRACT FROM AUTHOR]

• A stakeholder-based framework is proposed for groundwater resources management. • Some resilience-based criteria are used for evaluating management scenarios. • Power and interest of each stakeholder is evaluated using the rough set theory. • Evidential reasoning approach incorporates quantitative and qualitative criteria. • The methodology can be easily applied to other groundwater systems. In this paper, a new stakeholder-based framework is proposed for groundwater resources management in arid regions. In this framework, rough set theory is used for the probabilistic classification of stakeholders considering their powers and interests, which are obtained from stakeholder analysis. Then, a system dynamics model is developed to evaluate the physical, economic, environmental, and social conditions of the system corresponding to each defined water resources management scenario. Moreover, an evidential reasoning approach is used to estimate the resilience of scenarios based on seven generic resilience principles. Finally, based on the results of stakeholder analysis, system dynamics modeling, and resilience analysis, groundwater management scenarios are ranked using a proposed social choice procedure called Electoral College Voting. The results of applying the methodology to the Rafsanjan plain in Iran illustrate that a scenario that includes modern irrigation systems and water transfer is the socially optimal scenario in the study area. Implementing this scenario reduces water consumption by 40%, improves the resilience of groundwater by about 50% and helps the aquifer to be recovered. [ABSTRACT FROM AUTHOR]

• MSDI is used to incorporates the meteorological and agricultural droughts across Iran. • 2007–08 and 2000 are the strongest and broadest droughts over the latest 38 years. • Pacific Ocean oscillations more controls droughts across Iran rather Atlantic Ocean. • Iran's extreme droughts resulted from strong La Nina coupled with cold PDO and positive NAO. Drought may affect more people than any other climate extremes worldwide, especially in (semi)arid climates such as Iran. In this paper, the empirical Multivariate Standardized Drought Index (MSDI) that incorporates the meteorological and agricultural drought conditions is first computed. The wavelet analysis is then used to identify teleconnections between ocean–atmosphere oscillations from Atlantic (AMO, AO, and NAO) and Pacific (ENSO, PDO, and PNA) oceans and Iran's drought at a highly dense network of regularly gridded points (2485) during 1979–2016. It is found that among the droughts of 1980, 1985–86, 1990–91, 1998–99, 2000, 2007–08, and 2010–11, both the 2007–08 and 2000 are the broadest and strongest across the country. Overall, those droughts that occurred in the 2000–2016 period (average MSDI = -1.14) were, on the whole, stronger than those happened over the latest 20 years of the 20th century (average MSDI = -0.77), reflecting the strengthening of the influence that ENSO has on the Middle East climate in the most recent decades. According to the wavelet coherence results, unlike the Atlantic-based oscillations that almost have an anti-correlation with droughts, there is a positive correlation between Pacific-based oscillations and Iran's drought. It is also identified that the severe historical droughts across the country were usually associated with a strong La Niña coupled with cold PDO and positive NAO phases in the latest 30 years. Overall, droughts in Iran tend to be substantially controlled by multiple interacting of climate indices since three-coupled indices have stronger coherence with drought than either two-coupled indices or single ones. A combination of the three indices of ENSO, PDO, and NAO has the strongest coherence with Iran's drought compared to all the other three- and two-coupled, and all single indices, indicating that integrated impact of climate indices can better capture hydroclimate teleconnections. These multiple interacting impacts from the different large-scale climate circulation patterns mark a significant step towards an improved prediction of severe droughts across Iran and can also be used for other parts of the world. [ABSTRACT FROM AUTHOR]

• We applied social choice rules to the Graph Model for Conflict Resolution outcomes. • We introduced the Related game, Leading game, and Consequential game concepts. • The presented algorithm is applied to two water resource conflicts. • Our case studies are on the Tigris and Euphrates Rivers and the Shatt al-Arab River. • Agents and policymakers can use the proposed model and corresponding analyses. Due to the lack of resources, individuals, groups, or countries are more involved in common resource conflicts. This paper uses the Graph Model for Conflict Resolution methodology to model a conflict based on the Nash solution concept, General Meta-Rationality, Symmetric Meta-Rationality, Sequential Stability, Limited h Move Stability, and Non-Myopic Stability definitions and then sieve the socially optimal outcomes from the results of the graph model. We propose to add a new analytical stage as a sequential next step to the equilibria in the Graph Model for Conflict Resolution and utilize social choice voting rules such as the Borda score, the Plurality rule, the Median voting rule, Copeland's method, Simpson's rule, and Fallback bargaining. We also apply a new approach called Related games, where we show how the Leading game impacts the Consequential game's outcome and equilibria. To demonstrate the proposed concepts, we applied these ideas to two case studies; (1) the Tigris and Euphrates Rivers conflict between Turkey, Syria, and Iraq, and (2) the Shatt al-Arab, which is on the border of Iran and Iraq. Results confirm that adding analytical stages in the Graph Model for Conflict Resolution and considering the Related games concept help decision and policy makers to gain a clearer insight into the conflict. [ABSTRACT FROM AUTHOR]

• Developing a novel forensic engineering approach to improve reservoir operation on flood control. • Developing criteria to guide assessment of reservoirs. • Developing forecast-based prereleases of water from reservoir to reduce the flooding. • Developing two key performance criteria (PDR and FVR). Floods have various impacts, including loss of life and damage to property. Flood- management reservoirs can help mitigate floods, but their operation can also worsen flood impacts. This paper presents a novel forensic engineering approach to assess the role of reservoir operation on flood control. Fourteen criteria are employed for assessing forecast-based prereleases of water from reservoir storage to reduce the impact of flooding. The proposed approach is applied for forensic assessment of the system performance of reservoirs during the large flood of 2019 in southwestern Iran (the Great Karun Basin). The two main study areas are in the sub-basins of Karun and Dez. Results concerning two key performance criteria (the peak discharge reduction (PDR) and flood volume reduction (FVR)) show the PDR criterion in the Karun sub-basin multi-reservoir system reached about 79% (where 100% is the theoretically best performance) under historic operations (actual operating conditions in 2019), and improved from 8 to 19% if various prerelease operations were made. The FVR achieved about 33% in the historical situation and improved from 20 to 59% under prerelease operations scenarios, respectively. The PDR criterion achieved 26% under the historical scenario, but with better operation could exceed 55% in the Dez sub-basin multi-reservoir system, whereas FVR was as low as 11% in 2019 but could be raised to between 15 and 25% under prerelease operations. This forensic work's assessments establish that improved reservoir operation could be achieved by applying specialized operation approaches. [ABSTRACT FROM AUTHOR]

• This is the first time that cultural capital has become quantified. • Cultural capital was used to test farmers' intention toward water conservation. • Cultural capital was equally good at illumination intention and behavior. • Cultural capital could be a serious competitor to the current behavioral models. The objective of this paper is to empirically test the explanatory ability of cultural capital, which is generally considered more qualitatively and rarely quantified, for farmers' water conservation behavior. In applying this approach, we reflect the growing interest in the role of culture and identity in influencing farmer's decision-making. The study design involved a cross-sectional survey of growers in the Aleshtar county in Western Iran. The sample consisted of 360 farmers. The results of person correlation test showed significant relationships between all forms of cultural capital and both intention to behave and behavior. Furthermore, Structural Equation Model analysis revealed that the embodied and objective forms of capital predicted about 43% of the variance in farmers' intention. Moreover, the institutionalised, objective, intension and family experiences predicted about 45% of the variance in farmers' behavior. Based on the research results, recommendations for water conservation were provided. [ABSTRACT FROM AUTHOR]

• Social learning capacity corresponds to process and outcome variables. • Social learning can lead to constructive, neutral, and destructive learning. • How policies are imposed affects social learning in the level of exploiters. • It is more important to see what the target groups learn as the final outcome. • Policies should be designed respecting the position of learning concepts. Adaptive water governance systems are underpinned by enhanced social learning capacity of the society of actors to understand and adapt to changes properly. Thus, as a first step, it is crucial to assess the learning capacity of water actors in different levels, as human components of a socio-ecological system (SES), to know how to improve the water resources system adaptive capacity. Aiming at practicing assessment of learning capacity in a society of water actors, the present paper focuses on local water users, one the society of farmers using the groundwater resource in Rafsanjan Plain and the other one the society of farmers in Lakes Tashk-Bakhtegan Basin, both situated in the southern Iran. A methodological framework was developed and adopted to assess the learning capacity of water users corresponding to both the process of learning (represented by social justice, perception of interdependency, mutual trust, and interaction among all stakeholders) and the learning outcomes (including width, direction, depth and orientation of learning). The required data were collected using semi-structured interviews. The results showed how the responses of the local water users are affected by the mechanisms which are rooted in the social memory, technology, path dependency, and the degree of access to water. Although most of the researchers have addressed the social learning to have positive and constructive outcomes, the results of this research revealed the process of social learning resulted in neutral and destructive outcomes among water users in the study areas. Assessment of learning capacity in the water associated societies, using the methodological framework developed in this research, can help set off removing learning barriers and enhancing the socio-ecological system adaptive capacity. [ABSTRACT FROM AUTHOR]

• A methodology for detecting breakpoints in runoff time series considering uncertainties is developed. • An optimal set of breakpoint locations are found for several generated runoff time series. • The objective function based on the MDL is minimized. • Corresponding breakpoints are estimated and the PDF of breakpoint locations is derived. • The results are analyzed by investigating the remote sensing data and the impacts of inter-basin water transfer projects. Most conventional hydrological time series models, used for forecasting or synthetic data generation, are based on the stationary hypothesis. Hydrologic variables such as surface runoff discharge exhibit non-stationary behavior due to the effects of climate change or human activities. In this paper, a new methodology is proposed to analyze the uncertainty of the number and location of breakpoints in runoff time series. The non-stationary runoff time series is simulated by segmenting the series into stationary pieces, which follow an Auto-Regressive Moving Average (ARMA) process, based on the minimum description length (MDL) principle. The length of each piece and the order of the piecewise ARMA model are optimized using the Genetic Algorithm (GA). Several runoff time series with variable lengths are generated based on the observed data and the probability distribution function(s) (PDFs) of breakpoint location(s) are constructed. An optimal set of breakpoint locations are found for each generated runoff time series by minimizing the objective function based on the MDL. Finally, the PDF(s) of breakpoint location(s) is (are) constructed using the results of the mentioned optimization model. To evaluate the proposed methodology, it is applied to the case study of Zayandehrud River in Iran. According to the obtained breakpoint PDF for the runoff time series, three breakpoints around years of 1987, 1996 and 2006 are identified. The results are analyzed by investigating the time series of agricultural lands derived using remote sensing data and studying the impacts of interbasin water transfer projects implemented in the study area. Overall, investigating human-induced changes in the study area confirms the obtained shape of the PDF of runoff breakpoint and shows the good performance of the developed method for finding the number and location of breakpoints in the runoff time series. [ABSTRACT FROM AUTHOR]

• Precipitation forecasts of four NMME models were compared over Sistan Basin. • Seasonal and subseasonal precipitation predictions were improved. • Statistical-dynamical improvement methods were applied: The GrandNMME, QM, Copula. • A novel hybrid approach for ensemble weighting was developed. • The proposed techniques provide more accurate forecasts. Poor forecasting of climate variables at river basin scale, leads to situations which entail considerable risks and losses in various sectors such as the agriculture, the environment and water resources. Lack of accurate sub-seasonal and seasonal precipitation predictions are major management and operation predicaments in the transboundary Sistan Basin, located in Afghanistan and Iran, and selected as the case study in this paper. The outputs of the North-American Multi Model Ensemble (NMME), as a seasonal forecasting system, were utilized in the target region. In order to evaluate the performance of the raw outputs of four NMME models (namely: NCEP-CFSv2, CMC-CanCM3, CMC2-CanCM4, NCAR-CCSM4), the individual model ensemble mean was compared to the observations. Results highlighted the need for post-processing of the NMME outputs. For this purpose, three different commonly used methods, including GrandNMME, bias correction quantile mapping (QM) and Copula approaches, alongside a novel hybrid technique, were applied to improve future predictions of precipitation patterns for the hindcast period of 1982–2010 and forecast period of 2012–2016. All four methods were performed for each month in each 0.5-degree cell over the following one to six months. The findings demonstrated the performance of different NMME models were not the same over time and space. Among the models, the NCEP-CFSv2 and CMC2-CanCM4 seemed to best capture the spatial variability of precipitation in the study area. In addition, they performed better in estimating the amounts of precipitation. Among the methods, the QM, the Copula and the hybrid were all successful in improving the geographic patterns of the data. Furthermore, they were all able to enhance the accuracy of the data. At different timescales, the hybrid method showed substantial improvements over the seasonal predictions. Overall, the findings illustrated that collective use of methods may narrow the potential uncertainty in projections of regional changes. [ABSTRACT FROM AUTHOR]

• We propose a probabilistic framework for estimating water budgets in low runoff regions. • We investigate the locations and time scales at which the water balance calculated provides most closure. • The validity of the results is examined through an innovative application of the FORM. • The failure probability of water budge closure is chiefly dependent on the accuracy of evapotranspiration estimations. Utilizing ground-based measurements to obtain water budget components, especially in large scale basins, is challenging due to the limitation in the spatiotemporal availability of in-situ data. In this paper, we propose a probabilistic framework for estimating water budgets in low runoff regions using remote sensing products. By studying water budgets in the Central Basin of Iran (CBI) over 8 years period (2009–2016), we investigate the locations and time scales at which the water budget calculated from satellite products provides most closure. To this end, we use precipitation from the Tropical Rainfall Measuring Mission (TRMM), evapotranspiration from the Water Productivity Open Access Portal (WaPOR) and terrestrial water storage change from the Gravity Recovery and Climate Experiment (GRACE). The results show better closure and consistency of water budget in the center and South East of the basin at seasonal and annual time scales. Due to the uncertainty initiated from different data sources as well as the mismatch between the spatiotemporal resolutions of various satellite products, the validity of the results is examined through an innovative application of the First Order Reliability Method (FORM). Furthermore, the reliability sensitivity analysis also reveals that the failure probability of water budge closure is chiefly dependent on the accuracy of evapotranspiration estimations than the other components involved in the water budget equation. [ABSTRACT FROM AUTHOR]

• A practical framework towards RWH suitability assessment is presented. • A comprehensive, and comparative investigation of RWH in Iran at basin scale. • Theoretical, socio-economic and practical aspects of RWH systems were considered. • The implication of RWH systems on water crisis alleviation in MPB was discussed. Rainwater Harvesting (RWH) is becoming one of the most promising alternative sources of fresh water which can be potentially captured and utilized, particularly in arid and semi-arid regions (ASARs). However, identification of potential sites for RWH on a large scale has always been a great challenge, and therefore, requires further studies. Most recent studies on this topic select RWH zones employing Geographic Information Systems (GIS), combined with multi-criteria analysis and hydrological models. Nevertheless, there are a few studies, mostly in small catchments, that locate potential zones considering socio-economic factors. In this paper, a GIS-based Multi-Criteria Decision Analysis (MCDA) is presented to identify potential RWH areas incorporating socio-economic factors including distance from drainage networks, roads, urban areas, faults, farms and wells. The proposed methodology was implemented at a large catchment scale in Mashhad Plain Basin with an area of 9762 (km2) located in the northeast of Iran. Results show that 48% of the study area is not suitable for RWH. The rest of the study area is divided into three potential zones that are poor, moderate and good for RWH, covering 31%, 43%, and 26% of the potential areas, respectively. Ponds and pans, check dams and terracing were considered as the best practices for the good RWH potential areas. Preliminary estimates suggest that up to 158, 135 and 110 M m3 of precipitation could be received respectively by each system. [ABSTRACT FROM AUTHOR]

• Lower Upper Bound Estimation method used for constructing prediction intervals. • General circulation models applied for downscaling precipitation and temperature. • Results compared with the results of classic Bootstrap method. • Performance of methods was assessed by the criteria of coverage and width. • Prediction intervals for temperature were narrower than precipitation. In this paper, point prediction and prediction intervals (PIs) of artificial neural network (ANN) based downscaling for mean monthly precipitation and temperature of two stations (Tabriz and Ardabil in North West of Iran) were evaluated using general circulation models (GCMs). PIs were constructed by novel Upper Lower Bound Estimation (LUBE) method in which an ANN with two outputs was constructed for estimating the prediction bounds. Also, Bootstrap method as a classic technique for assessing uncertainty of ANN was used to further examine the proposed LUBE method. In this way, the accuracy of PIs was quantified by coverage and width criteria. Three GCMs, Can-ESM2, BNU-ESM, INM-CM4 and ensemble-GCM (ensemble of mentioned models) were used in four grid points around each of station for evaluating ANN-based downscaling of precipitation and temperature parameters. Comparison between the results of two methods indicated that LUBE method could lead to more reliable results than the Bootstrap method. PIs width and coverage probability were 10–40% lower and 2–10% higher than the Bootstrap method for different GCMs, respectively. Ensemble-GCM led to more accurate results so that computed PIs width and coverage probability were 10–60% lower and 2–20% higher than those for the single GCMs. [ABSTRACT FROM AUTHOR]