Evaluation of spatial and spatiotemporal estimation methods in simulation of precipitation variability patterns

Identification of spatial and spatiotemporal precipitation variations plays an important role in different hydrological applications such as missing data estimation. In this paper, the results of Bayesian maximum entropy (BME) and ordinary kriging (OK) are compared for modeling spatial and spatiotemporal variations of annual precipitation with and without incorporating elevation variations. The study area of this research is Namak Lake watershed located in the central part of Iran with an area of approximately 90,000 km². The BME and OK methods have been used to model the spatial and spatiotemporal variations of precipitation in this watershed, and their performances have been evaluated using cross-validation statistics. The results of the case study have shown the superiority of BME over OK in both spatial and spatiotemporal modes. The results have shown that BME estimates are less biased and more accurate than OK. The improvements in the BME estimates are mostly related to incorporating hard and soft data in the estimation process, which resulted in more detailed and reliable results. Estimation error variance for BME results is less than OK estimations in the study area in both spatial and spatiotemporal modes.     

A real-time operation optimization model for flood management in river-reservoir systems

In this paper, a new methodology has been developed for real-time flood management in river-reservoir systems. This methodology is based upon combining a Genetic Algorithm (GA) reservoir operation optimization model for a cascade of two reservoirs, a hydraulic-based flood routing simulation model in downstream river system, a Geographical Information System (GIS) based database, and application of K-Nearest Neighbor (K-NN) algorithm for development of optimal operating rules. The GA optimization model estimates the optimal hourly reservoirs’ releases to minimize the flood damages in the downstream river. GIS tools have also been used for specifying different land-uses and damage functions in the downstream floodplain and it has been linked to the unsteady module of HEC-RAS flood routing model using Hec-GeoRAS module. An innovative approach has also been developed using K-NN algorithm to formulate the optimal operating rules for a system of two cascade reservoirs based on optimal releases obtained from the optimization model. During a flood event, the K-NN algorithm searches through the historical flood hydrographs and optimal reservoir storages determined by the optimization model to find similar situations. The similarity between the hydrographs is quantified based on the slopes of rising and falling limbs of inflow hydrographs and reservoir storages at the beginning of each hourly time step during the flood events for two cascade reservoirs. The developed methodology have been applied to the Bakhtiari and Dez River-Reservoir systems in southwest of Iran. The results show that the proposed models can be effectively used for flood management and real-time operation of cascade river-reservoir systems.     

A hybrid multiple criteria decision-making model for the sustainable management of aquifers

In this study, a hybrid multiple criteria decision-making (HMCDM) model was proposed for prioritizing scenarios for managing groundwater use from an aquifer. Three scenarios, including the construction of subsurface dams, the use of artificial recharge and reducing groundwater use by 5% and 10% were considered to assess the most sustainable development approach. The examined MCDM models were: simple additive weighting (SAW); and MTAHP which is a hybridization of the modified TOPSIS and the analytic hierarchy process models. The criteria proposed for determining the order preference of the scenarios included the sustainable development index (I_U) and a modified water exploitation index as well as economic, social and environmental indices. To assess the technical and economic impacts of the management scenarios, modeling of the aquifer was simulated for a 3-year period using these scenarios. The results of the assessment indicated that the scenario of water withdrawal reduction by 10% was the best scenario determined in MTAHP followed by a reduction in groundwater withdrawal by 5%, the use of artificial recharge and the construction of a subsurface dam, respectively. The difference between the results of MTAHP and SAW models was in their first and third ranks, in such a way artificial recharge scored the first rank in SAW model and the third rank in MTAHP model, also withdrawal reduction by 10% scored third rank in SAW model and first rank in MTAHP model. The results of these two models have demonstrated that the construction of a subsurface dam in Shahrekord aquifer is not an appropriate management option. According to the results of this study, MTAHP models can be applied for ranking feasible management scenarios in aquifers using the redefined sustainable development and modified groundwater exploitation indices introduced in this study.     

Integrated risk assessment of urban water supply systems from source to tap

Urban water supply systems (UWSS) are generally composed of water sources, transmission pipes, treatment plants, and distribution networks from source to tap and usually are exposed to variety of uncertain threatening hazards. These threats can be divided to three main groups of natural, human-made, and operational hazards which affect either water quantity or water quality. In order to evaluate the reliability of water supply systems, risk assessment tools must be used to identify threats, their probability, and consequences and vulnerabilities of each element of these systems against the hazards. Due to the complexity and uncertainties affecting water supply systems and threatening hazards, a comprehensive and effective risk assessment method is required. In this study, an integrated fuzzy hierarchical risk assessment model for water supply systems (IFHRA-WSS) is proposed to assess hazards in a complex UWSS using a systematic approach incorporating both water quantity and quality issues. This model uses a hierarchical framework for breaking down the UWSS infrastructures to their interrelated elements to reduce the overall complexity of the system. It also considers uncertainties using Fuzzy Logic approach. Effects of functional interdependencies between different components of the system have also been considered in the vulnerability analysis. IFHRA-WSS incorporates the contributions of urban water experts in a group risk assessment procedure in a way that they can be easily expressed in terms of the qualitative and quantitative risk measures. Efficiency of this model has been examined in a case study which includes a large part of a drinking water supply system in a major city in Iran. This system includes all the elements of the UWSS from the delivery point to the consumption point. In the case study, different components and subcomponents of this system have been ranked based on their estimated risk values. It is envisaged that the results of the proposed model can help the decision makers to plan for effective risk mitigation measures.     

Improvement of multiple linear regression method for statistical downscaling of monthly precipitation

This article aims at proposing an improved statistical model for statistical downscaling of monthly precipitation using multiple linear regression (MLR). The proposed model, namely Monthly Statistical DownScaling Model (MSDSM), has been developed based on the general structure of Statistical DownScaling Model (SDSM). In order to improve the performance of the model, some statistical modifications have been incorporated including bias correction using variance correction factor (VCF) to improve the computed variance pattern. We illustrate the effectiveness of the proposed model through its application to 288 rain gauge stations scattered in different climatic zones of Iran. Comparison between the results of SDSM and the proposed MSDSM has indicated superiority of the proposed model in reproducing long-term mean and variance of monthly precipitation. We found that the weakness of MLR method in estimating variance has been considerably improved by applying VCF. We showed that the proposed model provides a promising alternative for statistical downscaling of precipitation at monthly time scale. An investigation of the effects of climate change in different climatic zones of Iran by the use of Representative Concentration Pathways (RCPs) has shown that the most significant change is an increase in precipitation in fall and that the largest share of this increase belongs to arid climate.     

Risk based optimal design of detention dams considering uncertain inflows

This study deals with the optimal design of detention dams under flood discharge uncertainties using a simulation-based optimization approach. An extended methodology is represented by integrating the ant colony optimization (ACO), artificial neural networks, and various risk measures including: expected flood discharge, value at risk, and conditional value at risk (CVaR). For this purpose, first, the neural network is trained by the results of a hydrodynamic model and then it is used to measure different risk indices under flood uncertainties. The proposed approach is then applied to a real case and optimal designs are determined by the search algorithm-i.e. ACO. Different optimal designs are obtained for the storage detention dams when different risk concepts are implemented as the objective function in the system modeling. Particularly, when the expected value measure is combined with the CVaR, the cost of optimal design is nearly two times smaller than those obtained by the formulation with independent objective functions whereas the obtained solution could efficiently minimize both E(Q _d ) and CVaR. The optimal solutions have especial capabilities in terms of performance and cost levels and this gives the stakeholders and decision makers to construct a framework to choose the final design when there are different attitudes and interests for flood risk management.     

Development of a master plan for industrial solid waste management

Rapid industrial growth in the province of Khuzestan in the south west of Iran has resulted in disposal of about 1750 tons of solid waste per day. Most of these industrial solid wastes including hazardous wastes are disposed without considering environmental issues. This has contributed considerably to the pollution of the environment. This paper introduces a framework in which to develop a master plan for industrial solid waste management. There are usually different criteria for evaluating the existing solid waste pollution loads and how effective the management schemes are. A multiple criteria decision making technique, namely Analytical Hierarchy Process (AHP), is used for ranking the industrial units based on their share in solid waste related environmental pollution and determining the share of each unit in total solid waste pollution load. In this framework, a comprehensive set of direct, indirect, and supporting projects are proposed for solid waste pollution control. The proposed framework is applied for industrial solid waste management in the province of Khuzestan in Iran and a databank including GIS based maps of the study area is also developed. The results have shown that the industries located near the capital city of the province, Ahwaz, produce more than 32 percent of the total solid waste pollution load of the province. Application of the methodology also has shown that it can be effectively used for development of the master plan and management of industrial solid wastes.