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.
Respirogram technology has been widely applied for aerobic process, however, the response of respirogram to anoxic denitrification is still unclear. To reveal such response may help to design a new method for the evaluation of the performance of denitrification. The size distribution of flocs measured at different denitrification moments demonstrated a clear expansion of flocs triggered by denitrification, during which higher specific endogenous and quasi-endogenous respiration rates (SOURe and SOURq) were also observed. Furthermore, SOURq increases exponentially with the specific denitrification rate (SDNR), suggesting that there should be a maximum SDNR in conventional activated sludge systems. Based on these findings, an index Rq/t, defined as the ratio of quasi-endogenous (OURq) to maximum respiration rate (OURt), is proposed to estimate the denitrification capacity that higher Rq/t indicates higher denitrification potential, which can be readily obtained without complex measurement or analysis, and it offers a novel and promising respirogram-based approach for denitrification estimation and control by taking measures to extend anoxic time to maintain its value at a high level within a certain range. 相似文献
