Recent increases in life loss, destruction and property damages caused by flood at global scale, have inevitably highlighted the pivotal considerations of sustainable development through flood risk management. Throughout the paper, a practical framework to prioritize the flood risk management alternatives for Gorganrood River in Iran was applied. Comparison between multi criteria decision making (MCDM) models with different computational mechanisms provided an opportunity to obtain the most conclusive model. Non-parametric stochastic tests, aggregation models and sensitivity analysis were employed to investigate the most suitable ranking model for the case study. The outcomes of these mentioned tools illustrated that ELimination and Et Choice Translating Reality (ELECTRE III), a non-compensatory model, stood superior to the others. Moreover, Eigen-vector's performance for assigning weights to the criteria was proved by the application of Kendall Tau Correlation Coefficient Test. From the technical point of view, the highest priority among the criteria belonged to a social criteria named Expected Average Number of Casualties per year. Furthermore, an alternative with pre and post disaster effectiveness was determined as the top-rank measure. This alternative constituted flood insurance plus flood warning system. The present research illustrated that ELECTRE III could deal with the complexity of flood management criteria. Hence, this MCDM model would be an effective tool for dealing with complex prioritization issues. Abbreviations MCDMMulti criteria decision making VIKOR VlseKriterijumska optimizacija I Kompromisno Resenje TOPSIS Technique for order preference by similarity to ideal solution ELECTRE I and ELECTRE III Elimination et choice translating reality Water Resour Manage

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Most of the arid and semi-arid regions are located in the developing countries, while the availability of water in adequate quantity and quality is an essential condition to approach sustainable development. In this research, "enhanced Driving force-Pressure-State-Impact-Response (eDPSIR)" sustainability framework was applied to deal with water shortage in Yazd, an arid province of Iran. Then, the Decision Making Trial and Evaluation Laboratory (DEMATEL) technique was integrated into the driven components of eDPSIR, to quantify the inter-linkages among fundamental anthropogenic indicators (i.e. causes and effects). The paper's structure included: (1) identifying the indicators of DPSIR along with structuring eDPSIR causal networks, (2) using the DEMATEL technique to evaluate the inter-relationships among the causes and effects along with determining the key indicators, (3) decomposing the problem into a system of hierarchies, (4) employing the analytic hierarchy process (AHP) technique to evaluate the weight of each criterion, and (5) applying complex proportional assessment with Grey interval numbers (COPRAS-G) method to obtain the most conclusive adaptive policy response. The systematic quantitative analysis of causes and effects revealed that the root sources of water shortage in the study area were the weak enforcement of law and regulations, decline of available freshwater resources for development, and desertification consequences. According to the results, mitigating the water shortage in Yazd could be feasible by implementation of such key adaptive policy-responses as providing effective law enforcement, updating the standards and regulations, providing social learning, and boosting stakeholders' collaboration.

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An appropriate streamflow forecasting method is a prerequisite for implementation of efficient water resources management in the water-limited, arid regions that occupy much of Iran. In the current research, monthly streamflow forecasting was combined with three data-driven methods based on large input datasets involving 11 precipitation stations, a natural streamflow, and four climate indices through a long period. The major challenges of rainfallrunoff modelling are generally attributed to complex interacting processes, the large number of variables, and strong nonlinearity. The sensitivity of data-driven methods to the dimension of input/output datasets would be another challenge, so large datasets should be compressed into independently standardized principal components. In this study, three pre-processing techniques were applied: singular value decomposition (SVD) provided more efficient forecasts in comparison to principal component analysis (PCA) and average values of inputs in all networks. Among the data-driven methods, the multi-layer perceptron (MLP) with 1-month lag-time outperformed radial basis, and fuzzy-based networks. In general, an increase in monthly lag-time of streamflow forecasting resulted in a decline in Downloaded by [Rutgers University] at 15:43 27 August 2015A c c e p t e d M a n u s c r i p t 2 forecasting accuracy. The results reveal that SVD was highly effective in preprocessing of data-driven evaluations.

Drought is a slow and creeping worldwide phenomenon which has adversely affected arid and semi-arid regions of the world. Drought indices like Streamflow Drought Index (SDI) and Standardized Precipitation Index (SPI) offer quantitative methods for combating probable consequences of drought. In this article, the results of the drought indices trend showed that the case study suffers from hydrological drought more than meteorological drought. The correlation analysis between hydrological and meteorological drought was assessed in monthly and seasonal time scales. To this end, some multivariate techniques were used to summarize the SPI and SDI series of all stations into one new dataset. Three assessment criteria involving higher correlation among drought indices, higher eigenvalue in expansion coefficients, and following fluctuation and variation of original data were used to find the best new datasets and the best multivariate method. Results asserted the superiority of singular value decomposition (SVD) over other multivariate methods. EC1 in the SVD method was able to justify about 80% of the variability in drought indices for monthly time scales, as well as summer and spring for seasonal time series, which followed all fluctuations in original datasets. Therefore, the SVD method is recommended for aggregating drought indices.

It is necessary to incorporate Sustainable Development Goals into the selection of Flood Risk Management Plans (FRMPs). This paper is assessing the FRMPs by grouped Sustainable Development Criteria (SDC) criteria. The utilized Multi-Criteria Decision Making (MCDM) models to evaluate FRMPs were chosen from three groups of compensatory, Semi-non compensatory, and non-compensatory models. Sensitivity and uncertainty analysis of input data such as SDC's weights and Criteria' Weight Assessment (IWA) methods were used to compare the results in different groups of MCDMs, IWAs, and their constructions. The results showed that Step-wise Weight Assessment Ratio Analysis (SWARA) had reasonable results as IWA method, while Shannon's Entropy had not satisfied outcomes. The compensatory model was affected by changes in input data, while Semi-noncompensatory and noncompensatory models had more stable results. Based on the SWARA-PROMETHEE model the combination of designing the warning system and employing a flood insurance program was in the first rank, flood peak alleviation by Golestan-I reservoir is in the second rank, designing a warning system was in the third rank, building a diversion canal and levees in the study area were fourth and fifth rank, respectively. Employing a flood insurance program was in the last priority by SWARA-PROMETHEE model. The combination of SWARA-PROMETHEE (Preference Ranking Organization Method for Enrichment Evaluations) model is recommended for the ranking of FRMPs because of the better incorporating SDC in flood management plan in comparison to other models, and demonstrating better results in IWA sensitivity analysis as well as Criteria' Weight (IW) uncertainty assessment.