Robust decision making, a growing approach to infrastructure planning under climate change uncertainty, aims to evaluate infrastructure performance across a wide range of possible conditions and identify the most robust strategies and designs. Robust decision making seeks to find potential weaknesses in systems in order to gird these through a combination of policy, infrastructure, and, in some cases, resilient or recovery strategies. A system can be explored by simulating many combinations of uncertain climatic and economic parameters; statistical clustering can identify parameter thresholds that lead to unacceptable performance. Often, however, uncertain variables are correlated, complicating the robustness analysis and casting doubt upon the thresholds identified. Here, we evaluate the impact of ordinary, hidden correlations in uncertainty parameters that drive simulation in robust decision making. We induced correlations between temperature and key climatic and economic parameters. We tested correlations of 0%, 30%, 60%, and 90% between temperature and the absolute value of precipitation, coefficient of variation, and downward surface solar radiation, and negative correlations between temperature and net variable benefit and the discount rate. We used a calibrated simulation model of a dam system regulating Lake Tana, Ethiopia, to compute the agricultural supply and net present value of the reservoirs. As the correlation strength increased, the results converged in a smaller region. We found that strong correlations depressed robustness scores of lower-performing alternatives and conversely increased results of the higher-performing alternatives. As the correlations increased in favorable alternatives, the failure thresholds became more extreme, speciously suggesting that only intense changes would result in poor performance. This overall analysis highlights the degree to which correlations of an interconnected climatic and economic system can impact outcomes of robust decision making and suggests methods to avoid confounding results.
The anaerobic-anoxic oxidation ditch (A2/O OD) process is popularly used to eliminate nutrients from domestic wastewater. In order to identify the existence of denitrifying
phosphorus removing bacteria (DPB), evaluate the contribution of DPB to biological nutrient removal, and enhance the denitrifying
phosphorus removal in the A2/O OD process, a pilot-scale A2/O OD plant (375 L) was conducted. At the same time batch tests using sequence batch reactors (12 L and 4 L) were operated
to reveal the significance of anoxic phosphorus removal. The results indicated that: The average removal efficiency of COD,
NH4+, PO43−, and TN were 88.2%, 92.6%, 87.8%, and 73.1%, respectively, when the steady state of the pilotscale A2/O OD plant was reached during 31–73 d, demonstrating a good denitrifying phosphorus removal performance. Phosphorus uptake
took place in the anoxic zone by poly-phosphorus accumulating organisms NO2− could be used as electron receptors in denitrifying phosphorus removal, and the phosphorus uptake rate with NO2− as the electron receptor was higher than that with NO3− when the initial concentration of either NO2− or NO3− was 40 mg/L. 相似文献