Research papersEvaluating robustness of waste load allocation under climate change using multi-objective decision making
Graphical abstract
Introduction
Lately, various approaches have been developed to address the uncertainties associated with water resources management. Looking for the most robust design alternatives that minimize deviation from future plans is one of the most critical elements in water resources management. This challenge is exacerbated by deep uncertainties so that the set of all possible future events, as well as their associated probability distributions themselves, contain uncertainties. Though, many robustness frameworks have expanded lately include: information gap decision theory (Ben-Haim, 2004), decision scaling (Brown et al., 2012, Brown, 2010), (many-objective) Robust Decision Making (Lempert, 2002, Kasprzyk et al., 2013, Lempert et al., 2013, Lempert et al., 2006, Lempert and Collins, 2007, Groves and Lempert, 2007, Weaver et al., 2013).
However, various researches have studied and organized ordinary methodological elections in robustness-focused decision support frameworks (Dittrich et al., 2016, Herman et al., 2015, Kwakkel and Haasnoot, 2019, Maier et al., 2016).
Among all these approaches, MORDM is vastly superior in implementing interactive visual analytics to distinguish proper answers, trade-offs recognition among various plans, and evaluate their operation under the influence of deep uncertainties. This method has so far been used to solve several water management issues (Singh et al. 2015).
Problem formulation is the first stage in MORDM, in which the operation goals, the specific uncertainties, and the computational model are received from the beneficiaries. Furthermore, critical constraints or other assumptions of the operation of the system are expressed. In the next step, the previous stage becomes a multi-objective search to create design alternatives for the problem.
For determining the robust performance rules of a water supply system, Kang Ren et al. (2019) utilized the MORDM framework. The uncertainty analysis and multi-objective optimization tools were employed first to show the trade-offs between the conflicting objectives and, second, reveal the sensitive factors for the possible states of the system. Results determined that the robust operating rules offer significant insights into the hurdles posed by deep uncertainties and provide a management template for decision making on climate change and complicated human activities.
Moallemi et al. (2019) examined two frameworks: Epoch–Era Analysis and Robust Decision Making. They deduced that the supremacy of each framework is not inevitable, and depends on the circumstances and the problem. While Robust Decision Making is a more impressing framework under contested and obscure information about the future, Epoch–Era Analysis can operate better when stakeholder knowledge about the future provides a deliberative specification of future scenarios.
Trindade et al. (2017) developed a multi-stakeholder MORDM framework to better account for deeply uncertain factors when recognizing collaborative drought management strategies. Their results revealed that appropriately designing adaptive risk-of-failure action triggers needed stressing them with a broad sample of deeply uncertain factors in the computational search condition of MORDM. The results from their research had a general benefit for areas where neighboring municipalities can profit from joint regional water portfolio planning.
Newly, Multi-Objective Evolutionary Algorithms (MOEAs) (Reed et al., 2013) have been considered in water resources management. These algorithms have been advanced to obtain the most suitable trade-off answers. Instead of gaining a solution that can optimize all objectives concurrently (Coello Coello et al., 2007), MOEAs can explore multiple Pareto-optimal results in a profoundly complicated search space because of their capability to employ the identities of recombinant answers (Zitzler and Thiele, 1999).
For a complex allocation system, MOEAs provide the optimization output as an approximate set of Pareto answers, instead of a single best solution. As managers' portfolio preferences diverged as trade-off information increased (Smith et al., 2019), choosing the most robust solutions among these alternatives creates new challenges for policymakers.
Previous studies have employed a variety of methods to select the robust alternatives in water resources management, among them are geometric angle-based pruning algorithm (Sudeng and Wattanapongsakorn, 2014), visually interactive decision-making and design using evolutionary multi-objective optimization (VIDEO) (Kollat and Reed, 2007) and MORDM (Kasprzyk et al., 2013).
Yan et al. (2017) advanced a model framework combining MORDM and biophysical modeling. Their case study was the Pearl River basin (PRB) China, where to reduce saltwater intrusion in the dry season, adequate flow to the delta was needed. Before recognizing and evaluating robust water allocation plans for the future, the performance of ten ultra-modern MOEAs was assessed for allocating water in the PRB The Borg MOEA, which is a self-adaptive optimization algorithm, had superior achievement through the historical periods. Therefore, it was elected to create new water allocation plans for the future (2079–2099). The study noted that the framework could work inadequately because of unpredictable climate change influences on water resources.
The purpose of this paper is to advance a model framework that combines many-objective robust decision making with hydrological modeling to identify robust waste load allocation plans under climate change. Through these analyses, four climate scenarios have been adopted to demonstrate how the methodological choice of them changes the resulting candidate planning solutions.
In this study, the OpenMORDM is utilized, which is an open-source application of MORDM in the R programming language. OpenMORDM provides a platform for constructive decision support, enabling analysts to interactively identify promising options and possible vulnerabilities while balancing contrasting objectives (Hadka et al., 2015). R language was chosen because of its widespread use in environmental modeling, and access to pre-built analytical and statistical packages selected.
In addition to the preceding, forecasting future climate change is also subject to uncertainty. (Dessai and Hulme, 2007). One of the effects of climate change is on hydrological cycles which, result in changing flow and discharge of rivers. These factors cause changes in the transmission and distribution of pollutants in the environment and generally affect the quantity and quality of water. All of these changes will introduce uncertainties that will pose significant challenges for water resource policymakers and managers.
For example, when 20 General Circulation Models (GCMs) were employed to create 39 runs of the 21st-century for the Murray-Darling Basin in Lim and Roderick (2009) study, the results indicated that 22 runs showed growth in yearly mean precipitation by the end of the 21st century, while 17 runs were showing this decline.
Robust optimization was practiced by Mortazavi-Naeini et al. (2015), to protect bulk water supply quality in an urban area against drought and climate change uncertainties and measured the amount of potential evapotranspiration (PET) and future rainfall. For the 23 GCMs obtained from a previous study CSIRO-BoM (2007), the Multi-Site Stochastic Model was used to generate 10,000 50-year replicates of daily precipitation and PET based on these values. It should be noted that just one emission scenario (A1F1) was involved in their study.
Culley et al. (2016) expanded a bottom-up framework to determine the maximum operational acceptance capacity of water resource systems regarding the space of future climate exposure which was based on seven GCMs and six regional climate models under three representative concentration pathways (RCPs).
There is no agreement on what will occur in the future climate change, and this will cause problems with making decisions for managing water resources efficiently.
Uncertainty about future weather forecasts is unlikely to diminish significantly in the near future. For managing water resources under uncertainty in climate change, it is essential to implement forecasting for a variety of emission scenarios resulting from multiple GCMs (Pierce et al., 2009, Teutschbein et al., 2015).
The remainder of the paper is structured as follows. In Section 2, the model framework has been defined, including models and datasets utilized in this paper. The Golgol River basin, Iran, has been presented in Section 3. As a case study. Conclusion in Section 4 contains lessons learned from this study and proposals for the future.
Section snippets
Model framework
Fig. 1 depicted the model framework combining various modeling tools and data employed in this research. This complex framework has a significant role as a tool to facilitate robust waste load allocation in the Golgol River basin, which includes a model for generating random climate data (Lars-WG), a hydrological model (SWAT), a state-of-the-art optimization algorithm (Borg MOEA), and an open-source software (OpenMORDM).
To study the effects of climate change on various systems in future
Case study
The study area in this research is Golgol watershed with an area of 279.53 km2 in the Ilam province, Iran. It is one of the subbasins of the Ilam Dam, and it is located between 46° 16′ 36′' and 46° 38′ 32′' eastern longitudes and between 33° 23′ 46″ and 33° 38′ 12′' northern latitudes (Fig. 2). The maximum height of the basin is 2065 m, its minimum height is 1052 m, and the average height is 1578 m above sea level. The average slope of the basin is 39.5%, and the average precipitation of the
Formulating the optimization problem
From Lars-WG and SWAT outputs, it has been observed that because of climate change, river flow (Fig. 3), and rainfall (Fig. 4) of the dry season are likely to decrease in the future within all climate scenarios. This may result in decreasing the water quality of the dam reservoir by the increase in the concentration of BOD discharge from upstream stakeholders (Table 1), so seeking an appropriate pollution strategy for BOD discharge to prevent further contamination of the dam reservoir has led
Conclusion
The model framework presented in this study combines MORDM with hydrological modeling to recognize robust waste load allocation strategies under four climate scenarios in the future, and it contains several parts.
First, the selected AOGCM data were down-scaled by the Lars-WG statistical model in order to predict temperature and precipitation based on climate change's effects. The utilized models in this study are HADCM3 and ECHAM5-OM, which are applied under two scenarios (A2 and B1) and in one
Declaration of Competing Interest
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
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