A copula-based multivariate flood frequency analysis under climate change effects.

Floods are among the most severe natural hazards, causing substantial damage and affecting millions of lives. These events are inherently multi-dimensional, requiring analysis across multiple factors. Traditional research often uses a bivariate framework relying on historical data, but climate change is expected to influence flood frequency analysis and flood system design in the future.

An improved MOPSO algorithm for multi-objective optimization of reservoir operation under climate change.

Gradually, the previously proposed water resource management schemes and reservoir operating policies adjusted to the historically experienced climatic conditions are losing their validity and efficacy, urging building up the models compatible with the likely climatic change conditions at the future. This paper aims at optimizing the reservoir operation under climate change conditions targeting the objectives including (1) minimizing the shortages in meeting the reservoir downstream water demands and (2) maximizing the sustainability of the reservoir storage. For evaluating the effects of the climate change, six general circulation models (GCMs) built up under the representative concentration pathway (RCP4.

Fusion-based framework for meteorological drought modeling using remotely sensed datasets under climate change scenarios: Resilience, vulnerability, and frequency analysis.

Severe drought events in recent decades and their catastrophic effects have called for drought prediction and monitoring needed for developing drought readiness plans and mitigation measures. This study used a fusion-based framework for meteorological drought modeling for the historical (1983-2016) and future (2020-2050) periods using remotely sensed datasets versus ground-based observations and climate change scenarios. To this aim, high-resolution remotely sensed precipitation datasets, including PERSIANN-CDR and CHIRPS (multi-source products), ERA5 (reanalysis datasets), and GPCC (gauge-interpolated datasets), were employed to estimate non-parametric SPI (nSPI) as a meteorological drought index against local observations.

Multi-objective optimization for optimal extraction of groundwater from a nitrate-contaminated aquifer considering economic-environmental issues: A case study.

This paper focuses on the multi-objective optimization of the groundwater extraction scheme in the Bouein-Myandasht aquifer (Iran) in order to reduce the concentration of nitrate, originating from agricultural activities and wastewater absorbent wells. A simulation-optimization model coupling an artificial neural network (ANN) as the simulator with the non-dominated sorting genetic algorithm-type II (NSGA-II) as the optimizer, are employed. The simulator is trained by help of data generated by process-based simulation models for groundwater flow (MODFLOW) and solute transport (MT3D).

Assessment of the management scenarios for groundwater quality remediation of a nitrate-contaminated aquifer.

Nitrate contaminant degrades groundwater quality and threatens the health of the humans, livestock, and the environment. Damaneh-Daran aquifer is located at upstream of the Zayandehrood reservoir in west-central Iran. This aquifer has been highly contaminated by nitrate and is still rapidly being contaminated.

f-MOPSO/Div: an improved extreme-point-based multi-objective PSO algorithm applied to a socio-economic-environmental conjunctive water use problem.

In this paper, a new version of the multi-objective particle swarm optimizer named the Diversity-enhanced fuzzy multi-objective particle swarm optimization (f-MOPSO/Div) algorithm is proposed. This algorithm is an improved version of our recently proposed f-MOPSO. In the proposed algorithm, a new characteristic of the particles in the objective space, which we named the "extremity," is also evaluated, along with the Pareto dominance, to appoint proper guides for the particles in the search space.

Risk assessment of an industrial wastewater treatment and reclamation plant using the bow-tie method.

At present, wastewater reuse and recycling in water-stressed countries such as Iran has become a vital option for better management of water scarcity. However, despite the benefits of wastewater reuse, there is a probability of incomplete treatment of wastewater in accordance with the standards. Risk assessment of a wastewater treatment plant is required in order to determine the causes and consequences of failure in the treatment system.

Prediction and assessment of drought effects on surface water quality using artificial neural networks: case study of Zayandehrud River, Iran.

Although drought impacts on water quantity are widely recognized, the impacts on water quality are less known. The Zayandehrud River basin in the west-central part of Iran plateau witnessed an increased contamination during the recent droughts and low flows. The river has been receiving wastewater and effluents from the villages, a number of small and large industries, and irrigation drainage systems along its course.

Groundwater vulnerability assessment using fuzzy logic: a case study in the Zayandehrood aquifers, Iran.

Groundwater is an important source of water, especially in arid and semi-arid regions where surface water is scarce. Groundwater pollution in these regions is consequently a major concern, especially as pollution control and removal in these resources are not only expensive but at times impossible. It is, therefore, essential to prevent their contamination in the first place by properly identifying vulnerable zones.