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Market-based approaches are increasingly considered reallocating instruments that put water consumption at its highest economic value among competing water users. Setting up a water market can have a lot of environmental, social, economic, and legal complexities. One of the main issues is the uncertain nature of the available water, which can cause the failure of markets, especially during drought conditions. Therefore, there is a need for market mechanisms to consider and reduce the adverse impacts of available water uncertainty on market outcomes. Accordingly, this paper proposes a new real-time seasonal smart water market framework for basin-wide surface water pricing and allocation. The framework uses the results of the reservoir water allocation optimization models and ANFIS-based monthly river discharge forecasts to better assist the water users with their bidding. The market manager uses updated available information at the beginning of each season to provide users with a more accurate understanding of available water to adjust their tradings for the rest of the year. The applicability and efficiency of the proposed framework are evaluated by applying it to the Gorganrood River basin in Iran. According to the results, the framework increased users' benefits from 721 to 1050 billion rials, which is more efficient than an annual market. Water markets can use this framework to improve their ability to cope with the uncertainty of available water, increase their users' benefits, and encourage them to improve their efficiency. Furthermore, the proposed framework allows the decision-makers in water sectors (e.g., industrial, agricultural, etc.) to discover time and location specific water allocation and price for different water users. [Display omitted] • Proposing a real-time market-based framework for surface water allocation and pricing. • Calculating users' seasonal bid packages and using them in a gross pool option market. • The real-time feature of the market uses seasonal ANFIS-based rive flow predictions. • The framework can help users better cope with the available surface water uncertainty. • The water market applied to a large river basin and increased users' benefits by 45%. [ABSTRACT FROM AUTHOR]

Due to land-use and hydrology changes, people are constantly exposed to floods. The adverse impact of floods is greater on vulnerable populations that disproportionately inhabit flood-prone areas. This paper reports a comprehensive study on flood vulnerability of flood prone areas in residential areas of the Tajan watershed, Iran in two periods before 2006 and after 2006. Flood prone area were determined by the random forest (RF) and K-nearest neighbor (KNN) machine learning methods. To reduce time and cost, the vulnerability was assessed only in areas with very high flood hazard using 4 main criteria (social, policy, economic, infrastructure), 40 items, and 210 questionnaires across 40 villages. Independent t -test, Kruskal-Wallis, and paired t -test were used for statistical analysis of questionnaire data. The results of machine learning models (MLMs) showed that the RF model with AUC = 0.92% is more accurate in determining flood prone areas. The results of paired t -test showed that the three criteria of social (mean P1 = 2.97 and P2 = 3.35), infrastructure (mean P1 = 2.88 and P2 = 3.25), and policy (mean P1 = 3.02 and P2 = 3.50) had significant changes in both periods. The Kruskal-Wallis test also revealed the mean of all four criteria in both periods and all sub-watersheds, except three sub-watersheds 10 (Khalkhil village), 19 (Tellarem and Kerasp villages), and 23 (Dinehsar and Jafarabad), had a significant difference. The results of the t -test also showed a decrease in vulnerability in the second period (before 2006) compared to the first period (after 2006), so the number of sub-watersheds in the very high vulnerability class was more in the first period than in the second period. A vulnerability map was developed using three factors of risk zone area, area of each sub-watershed, and population of each sub-watershed. • Using machine learning methods and field survey to assess vulnerability in flood hazard areas. • Investigating temporal and spatial variability in flood vulnerability of Tajan watershed. • Prioritizing flood-vulnerable areas using related criteria and indicators. [ABSTRACT FROM AUTHOR]

This paper introduces a new framework to evaluate the resilience of lakes under climatic and anthropogenic droughts. The proposed hierarchical structure of criteria for assessing lake's resilience has four levels. The first level includes several indices such as long-term resilience, reliability, and implementation cost. In the second to fourth levels, four main resilience-based criteria (i.e. robustness, resourcefulness, redundancy, and rapidity) and some qualitative and quantitative sub-criteria are defined considering the factors affecting the ecological condition of lakes. To quantify the time series of the sub-criteria, a coupled SWAT-MODSIM-based simulation model has been applied. Also, the values of criteria and sub-criteria have been aggregated using the Evidential Reasoning (ER) approach. After estimating the annual resilience time series, three resilience indices, namely the recovery time (Tr), loss of resilience (LOR), and final resilience (Res f), have been calculated. The normalized values of these indices and reliability criteria have been aggregated to evaluate the overall performance of lake restoration scenarios. To show the applicability of the proposed methodology, the Zarrinehrud river basin and Lake Urmia have been selected as the case study. As one of the largest hypersaline lakes globally, Lake Urmia suffers from drastic changes in its water body and a high level of salinization. Also, the Zarrinehrud river basin, located in the southeastern of Urmia Lake, is the most significant sub-basin of the lake and is responsible for supplying 41% of the total annual inflow of the lake. The restoration scenarios of Lake Urmia have been assessed from 2019 to 2049. Eventually, the most effective scenario, which has an average overall performance of 0.72, the implementation cost of 17.1 million dollars, and the uncertainty band of 0.05, has been selected. • A new methodology is proposed for evaluating resilience of lakes under drought. • Some new criteria are proposed for assessing the lake's ecological condition. • The evidential reasoning approach is utilized for combining resilience sub-criteria. • Three new resilience indices have been proposed for evaluating management scenarios. • The methodology has been applied to Lake Urmia and Zarrinehrud river basin in Iran. [ABSTRACT FROM AUTHOR]