Long-term-robust adaptation strategies for reservoir operation considering magnitude and timing of climate change: application to Diyala River Basin in Iraq - Waheed, Saddam Qahtan, author; Grigg, Neil S., advisor; Ramirez, Jorge A., committee member; Bailey, Ryan T., committee member; Fassnacht, Steven R., committee member (Colorado State University. Libraries, 2020)
Vulnerability assessment due to climate change impacts is of paramount importance for reservoir operation to achieve the goals of water resources management. This requires accurate forcing and basin data to build a valid hydrology model and assessment of the sensitivity of model results to the forcing data and uncertainty of model parameters. The first objective of this study is to construct the model and identify its sensitivity to the model parameters and uncertainty of the forcing data. The second objective is to develop a Parametric Regional Weather Generator (RP-WG) for use in areas with limited data availability that mimics observed characteristics. The third objective is to propose and assess a decision-making framework to evaluate pre-specified reservoir operation plans, determine the theoretical optimal plan, and identify the anticipated best timeframe for implementation by considering all possible climate scenarios. To construct the model, the Variable Infiltration Capacity (VIC) platform was selected to simulate the characteristics of the Diyala River Basin (DRB) in Iraq. Several methods were used to obtain the forcing data and they were validated using the Kling–Gupta efficiency (KGE) metric. Variables considered include precipitation, temperature, and wind speed. Model sensitivity and uncertainty were examined by the Generalized Likelihood Uncertainty Estimation (GLUE) and the Differential Evolution Adaptive Metropolis (DREAM) techniques. The proposed RP-WG was based on (1) a First-order, Two-state Markov Chain to simulate precipitation occurrences; (2) use of Wilks' technique to produce correlated weather variables at multiple sites with conservation of spatial, temporal, and cross correlations; and (3) the capability to produce a wide range of synthetic climate scenarios. A probabilistic decision-making framework under nonstationary hydroclimatic conditions was proposed with four stages: (1) climate exposure generation (2) supply scenario calculations, (3) demand scenario calculations, and (4) multi-objective performance assessment. The framework incorporated a new metric called Maximum Allowable Time to examine the timeframe for robust adaptations. Three synthetic pre-suggested plans were examined to avoid undesirable long-term climate change impacts, while the theoretical-optimal plan was identified by the Non-dominated Sorting Genetic Algorithm II. The multiplicative random cascade and Schaake Shuffle techniques were used to determine daily precipitation data, while a set of correction equations was developed to adjust the daily temperature and wind speed. The depth of the second soil layer caused most sensitivity in the VIC model, and the uncertainty intervals demonstrated the validity of the VIC model to generate reasonable forecasts. The daily VIC outputs were calibrated with a KGE average of 0.743, and they were free from non-normality, heteroscedasticity, and auto-correlation. Results of the PR-WG evaluation show that it exhibited high values of the KGE, preserved the statistical properties of the observed variables, and conserved the spatial, temporal, and cross correlations among the weather variables at all sites. Finally, risk assessment results show that current operational rules are robust for flood protection but vulnerable in drought periods. This implies that the project managers should pay special attention to the drought and spur new technologies to counteract. Precipitation changes were dominant in flood and drought management, and temperature and wind speed changes effects were significant during drought. The results demonstrated the framework's effectiveness to quantify detrimental climate change effects in magnitude and timing with the ability to provide a long-term guide (and timeframe) to avert the negative impacts.
Keywords: Diyala River Basin; robustness; vulnerability; hydrologic modelling uncertainty; climate change; statistical weather generator