Search

The list of challenges associated with the operation of the Grand Ethiopian Resistance Dam (GERD) now includes global concern over climate change. Due to their inability to reach a legally enforceable agreement on how to address watershed degradation and water quality, riparian nations are already experiencing peace and security issues. A win–win strategy seeks to develop collaborative solutions to transboundary water management problems while maximizing the benefits for both parties. This paper aims to develop, evaluate, and analyze win–win transboundary water management strategies during the operating stage of GERD. Using value engineering (VE), functional analysis system technique (FAST), and life cycle cost (LCC), the optimal win–win strategy is determined. The study uncovered four distinct value options for various win–win strategies. According to the study results, the third win–win strategy is the most effective way to help both upstream and downstream nations retain peace, as it seeks to encourage riparian countries to collaborate by: i) developing cooperative regulatory policy; ii) switching to renewable energy supported by GERD; and iii) reducing water loss. This study advances our understanding of the topic by providing policymakers with a tool to: i) evaluate win–win strategies for managing transboundary water; and ii) incorporate the added value and functionality into policies regarding transboundary water management.

Water scarcity and climate change are posing new challenges to irrigation management. Climate change increases water demand and decreases crop yields. The aim of this paper is to propose a framework to select the most efficient irrigation strategy to mitigate the impacts of climate change and achieve food security. Value engineering (VE) methodology is utilized to assure the functionality of the strategy and add an element of creativity while creating the value alternatives. The life cycle cost (LCC) technique is utilized to provide the optimum irrigation strategy from an economic perspective. The findings showed three different value alternatives for different crops, soil types, and irrigation systems. This paper contributes to the current state of knowledge by a) utilizing the Value Engineering methodology in irrigation management studies; b) evaluating irrigation strategies to ensure efficient irrigation water management; and c) providing policymakers with a tool to incorporate the added value and functionality into their policies regarding irrigation water.

Abnormal changes in temperature, precipitation, atmospheric carbon dioxide, or solar radiation are examples of climate change that may affect irrigation water demand. Studies show an increasing trend in the earth’s surface temperature, with decreasing trends in rainfall. With most studies focused on predicting the extent of climate change, fewer studies focused on developing appropriate response strategies. This paper aims to propose a systematic approach to identify, analyze, and respond to the risks of climate change on irrigation water in arid regions using the Risk Management process. The compound effect of these risks was analysed using Monte Carlo Simulation, which indicated a 69% loss in crop production due to climate change at a 90% confidence level. The proposed responses to the risks of climate change include strategies to avoid, transfer, mitigate, and/or accept these risks. The study has made three contributions to the state of the art. First, to adopt a well-recognized risk management methodology in climate change studies. Second, to quantify the compound effect of climate change risks on irrigation water in arid regions. Third, to recommend a set of response strategies to help policymakers mitigate the inevitable harmful effect of climate change on irrigation water.

Agricultural drainage is essential to the success of irrigated agriculture because it prevents ponding, waterlogging, and salt. This Paper presents a systematic method for identifying, assessing, and mitigating the risks associated with poor agricultural drainage (PAD). Using Monte Carlo simulation within a risk management process, the cumulative impact of all conceivable PAD risks on agricultural productivity was examined. The minimum and maximum predicted values of loss in crop production as predicted by the Monte Carlo simulation model are 60.2% and 69.2%, respectively. According to the interpretation of these findings, crop production might be reduced to around two-thirds of its current level over a century. This necessitates prompt action to limit the impact of these hazards in the coming years. Key contributions of this work include identifying and categorizing the negative effects of PAD on the environment and crop production, analyzing these risks quantitatively and qualitatively, and developing the appropriate responses.

The Evaluation of construction baseline schedules aims to provide a high-quality project schedule. However, obtaining a project schedule free from technical defects remains a challenge to construction planners and schedulers.The result of having a high-quality schedule is achieving the desired project completion dates and avoid any disputes between the construction project parties which may be present due to the technical defects of the schedule itself. Many organizations and consultants had developed some evaluations techniques for assessing the baseline schedules from different prospects. The aim of this study is to classify and filter out the evaluation metrics and parameters of the project baseline schedule, in order to facilitate the schedule reviewing and evaluating process. This is achieved by introducing a clear, arranged and classified evaluation metrics to let the schedule reviewerto list down all the schedule technical defects and convert them into clear comments in order to rectify them and having a schedule free of defects.

Construction projects suffer from diverse uncertainties that hinder the key objectives’ achievement. These uncertainties represent risks that may appear through the project life cycle. This paper introduces a quantitative model to estimate and rank risks dynamically during the risk planning phase. Such ranking would help decision-makers appropriately respond to and/or control construction risks. The model provides proper risk contingency reserves for both project time and cost that meet decision-makers' selected confidence levels using Monte Carlo Simulation (MCS). In order to quantify the project uncertainty, severities of residual risks are determined and allocated at the project's activities-level using a planning/scheduling spreadsheet model and a MCS tool suitable for spreadsheets. The model is able to calculate the contribution of each risk from the determined contingency at both the project level for both the time and cost at the decision-maker confidence level.The model represents a direct implementation for a Risk Planning Contingency Model (RPCM); which involves four modules as follows: (1) Risk Register (RR), (2) Risk Allocator (RA), (3) Risk Simulator (RS), and (4) Contingency Calculator (CC). These modules are hosted in a critical path model scheduling spreadsheet to facilitate risk management. In addition, a simulation engine add-in is used for analyzing the probability distribution for the project time and cost outcomes. In order to verify the proposed model, the process and analysis have been applied to a case study project. The results show that the RPCM is capable to rank and estimate the residual risks in an easy, fast, and effective way.