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The prevailing challenge centers on the limited application of single‐purpose flood control reservoirs transitioning to multi‐purpose reservoirs, which, despite their potential, often fall short in addressing the escalating demands for diverse water resource management. These challenges are compounded by outdated operational rules and changing environmental conditions. This research develops a framework to enhance the dual functionality of reservoirs, initially designed for flood control, to also support water supply through the determination of maximum safe water levels (MSWLs). Utilizing historical inflow data and reservoir simulation models, the study identifies opportunities for optimizing United States Army Corps of Engineers Louisville District reservoirs. It highlights certain reservoirs as ideal for augmenting water supply capabilities without compromising flood control performance. Others remain critical for flood management due to limited water supply potential, underscoring the importance of maintaining a focus on flood control. The findings illuminate the intricate balance required between managing flood risks and enhancing water supply, indicating that precise operational adjustments can significantly improve reservoir sustainability and efficiency. This method offers a viable pathway to convert single purpose reservoirs into multi‐purpose reservoirs, meeting growing water demands while ensuring robust flood mitigation, and making a step toward better water utilization. [ABSTRACT FROM AUTHOR]

Climate change induces significant changes in storm characteristics, particularly for short‐duration extreme storms (heavy rain features), impacting their intensity and spatio‐temporal distribution. Although alterations in precipitation intensity are well documented, past studies examining changes in spatio‐temporal distribution of storms (storm rainrates) were region‐specific and focused on isolated aspects of change in space or time, eluding a comprehensive understanding of the precise nature and extent of these changes. Bridging this gap, this study introduces a novel grid‐based measure of storm homogeneity, "spatio‐temporal homogeneity" metric and investigates the global patterns of change in combined spatio‐temporal characteristics of extreme storms. Analyzing the 30 min × 0.1° × 0.1° resolution Global Precipitation Measurements, the study finds that extreme storms are shrinking in both space and time due to rising surface air temperatures, predominantly in tropics. In contrast, temperate regions experience expanded extreme storms with increasing temperatures. The study also identifies a global trend toward more front‐loading in storms with rising temperatures, driven by a substantial increase in tropics and southern temperate regions. Conversely, storms in northern temperate regions become slightly more rear‐loaded as temperature increases. Furthermore, the study finds that characteristics of short–duration storms (6–12 hr) are more sensitive to temperature changes. Overall, this study contributes valuable insights into the global spatio‐temporal changes of extreme storms, highlighting regions most susceptible to alterations in storm patterns due to climate change. These findings are essential for developing effective adaptation strategies and flood management practices to cope with the changing nature of extreme storms in a warming climate. Plain Language Summary: Extreme rainfall can lead to floods, affect ecosystems, and challenge water management. How intense the rain is, how wide an area it covers, and how long it lasts are key factors in determining flood risk. Additionally, whether the rainfall starts strong and tapers off (front‐loaded) or begins gently and then fades quickly (rear‐loaded)—also plays a role. As the climate changes, it's crucial to understand how these extreme storm characteristics are evolving to better prepare for the future. This study introduces a new way to measure changes in the size and duration of extreme storms in response to rising temperatures worldwide. It also examines whether warmer temperatures make storms more likely to start strong and taper off or the opposite. The findings show that in tropical regions, extreme storms are becoming effectively shorter and covering effectively smaller areas as temperatures increase. Conversely, in temperate regions, storms are lasting longer and covering effectively larger areas. Additionally, storms in areas below 30°N are tending toward more front‐loading, while those above 30°N are becoming more rear‐loaded. This research helps us understand how warming temperatures are changing storm patterns and can inform better flood preparedness and water management strategies. Key Points: Introduces Spatio‐Temporal Homogeneity metric to effectively track comprehensive changes in storm characteristics across both space and timeRising temperature causes "spatio‐temporally peakier" storms in tropics, intense precipitation burst in smaller area over shorter durationRising temperature leads to more front‐loading in storms, notably in tropics and southern temperate regions, increasing flash flood risk [ABSTRACT FROM AUTHOR]

As all kinds of numerical models are emerging in hydrologic and hydraulic engineering, Bayesian model averaging (BMA) is one of the popular multi‐model methods used to account for various uncertainties in the flood modeling process and generate robust ensemble predictions. The reliability of BMA parameters (weights and variances) determines the accuracy of BMA predictions. However, the uncertainty in BMA parameters with fixed values, which are usually obtained from Expectation‐Maximization (EM) algorithm, has not been adequately investigated in BMA‐related applications over the past few decades. Given the limitations of the default EM algorithm, Metropolis‐Hastings (M‐H) algorithm, which is one of the most widely used algorithms in Markov Chain Monte Carlo (MCMC), is proposed to estimate BMA parameters. Both numerical experiments and one‐dimensional Hydrologic Engineering Center‐River Analysis System models are employed to examine the applicability of M‐H algorithm with multiple independent Markov chains. The performances of EM and M‐H algorithms are compared based on the daily water stage predictions from 10 model members. Results show that BMA weights estimated from both algorithms are comparable, while BMA variances obtained from M‐H algorithm are closer to the given variances in the numerical experiment. Moreover, the normal proposal distribution used in M‐H algorithm can yield narrower distributions for BMA weights than those from the uniform proposal distribution. Overall, MCMC approach with multiple chains can provide more information associated with the uncertainty of BMA parameters and its performance is better than the default EM algorithm in terms of both deterministic and probabilistic evaluation metrics as well as algorithm flexibility. Key Points: Multiple Markov Chains Monte Carlo method is applicable for estimating Bayesian model averaging weights and variancesThe performance of Metropolis‐Hastings algorithm is better than that of Expectation‐Maximization algorithm in ensemble flood modelingMultiple Markov Chains Monte Carlo method can provide more information about the uncertainty of Bayesian model averaging parameters [ABSTRACT FROM AUTHOR]