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Abstract The Mississippi River basin drains nearly one-half of the contiguous United States, and its rivers serve as economic corridors that facilitate trade and transportation. Flooding remains a perennial hazard on the major tributaries of the Mississippi River basin, and reducing the economic and humanitarian consequences of these events depends on improving their seasonal predictability. Here, we use climate reanalysis and river gauge data to document the evolution of floods on the Missouri and Ohio Rivers—the two largest tributaries of the Mississippi River—and how they are influenced by major modes of climate variability centered in the Pacific and Atlantic Oceans. We show that the largest floods on these tributaries are preceded by the advection and convergence of moisture from the Gulf of Mexico following distinct atmospheric mechanisms, where Missouri River floods are associated with heavy spring and summer precipitation events delivered by the Great Plains low-level jet, whereas Ohio River floods are associated with frontal precipitation events in winter when the North Atlantic subtropical high is anomalously strong. Further, we demonstrate that the El Niño–Southern Oscillation can serve as a precursor for floods on these rivers by mediating antecedent soil moisture, with Missouri River floods often preceded by a warm eastern tropical Pacific (El Niño) and Ohio River floods often preceded by a cool eastern tropical Pacific (La Niña) in the months leading up peak discharge. We also use recent floods in 2019 and 2021 to demonstrate how linking flood hazard to sea surface temperature anomalies holds potential to improve seasonal predictability of hydrologic extremes on these rivers.
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In search of non-stationary dependence between estuarine river discharge and storm surge based on large-scale climate teleconnections
Compound floods may happen in low-lying estuarine environments when sea level above normal tide co-occurs with high river flow. Thus, comprehensive flood risk assessments for estuaries should not only account for the individual hazard arising from each environmental variable in isolation, but also for the case of bivariate hazard. Characterization of the dependence structure of the two flood drivers becomes then crucial, especially under climatic variability and change that may affect their relationship. In this article, we demonstrate our search for evidence of non-stationarity in the dependence between river discharge and storm surge along the East and Gulf coasts of the United States, driven by large-scale climate variability, particularly El-Niño Southern Oscillation and North Atlantic Oscillation (NAO). Leveraging prolonged overlapping observational records and copula theory, we recover parameters of both stationary and dynamic copulas using state-of-the-art Markov Chain Monte Carlo methods. Physics-informed copulas are developed by modeling the magnitude of dependence as a linear function of large-scale climate indices, i.e., Oceanic Niño Index or NAO index. After model comparison via suitable Bayesian metrics, we find no strong indication of such non-stationarity for most estuaries included in our analysis. However, when non-stationarity due to these climate modes cannot be neglected, this work highlights the importance of appropriately characterizing bivariate hazard under non-stationarity assumption. As an example, we find that during a strong El-Niño year, Galveston Bay, TX, is much more likely to experience a coincidence of abnormal sea level and elevated river stage.
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- PAR ID:
- 10617245
- Publisher / Repository:
- Elsevier
- Date Published:
- Journal Name:
- Advances in Water Resources
- Volume:
- 195
- Issue:
- C
- ISSN:
- 0309-1708
- Page Range / eLocation ID:
- 104858
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript
- Journal Article:
- https://doi.org/10.1016/j.advwatres.2024.104858
