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Assessing and managing design storm variability and projection uncertainty in a changing coastal environment.

ABSTRACT: Coastal urban infrastructure and water management programs are vulnerable to the impacts of long-term hydroclimatic changes and to the flooding and physical destruction of disruptive hurricanes and storm surge. Water resilience or, inversely, vulnerability depends on design specifications of the storm and inundation, against which water infrastructure and environmental assets are planned and operated. These design attributes are commonly derived from statistical modeling of historical measurements. Here we argue for the need to carefully examine the approach and associated design vulnerability in coastal areas because of the future hydroclimatic changes and large variability at local coastal watersheds. This study first shows significant spatiotemporal variations of design storm in the Chesapeake Bay of the eastern U.S. Atlantic coast, where the low-frequency high-intensity precipitations vary differently to the tropical cyclones and local orographic effects. Average and gust wind speed exhibited much greater spatial but far less temporal variability than the precipitation. It is noteworthy that these local variabilities are not fully described by the regional gridded precipitation used in CMIP5 climate downscaling and by NOAA's regional design guide Atlas-14. Up to 46.4% error in the gridded precipitation for the calibration period 1950-1999 is further exacerbated in the future design values by the ensemble of 132 CMIP5 projections. The total model projection error (δ_M) up to -61.8% primarily comes from the precipitation regionalization (δ₁), climate downscaling (δ₂), and a fraction from empirical data modeling (δ_E). Thus, a post-bias correction technique is necessary. The bias-corrected design wind speed for 10-yr to 30-yr storms has small changes <20% by the year 2100, but contains large spatial variations even for stations of close proximity. Bias-corrected design precipitations are characteristic of large spatial variability and a notable increase of 2-5 year precipitation in the future along western shores of the Lower and Middle Chesapeake Bay. All these accounts point to the potential vulnerability of water infrastructure and water program in coastal areas, when the hydrological design basis using regional values fails to account for significant spatiotemporal precipitation variations in local coastal watersheds.

SUBMITTER: Liang MS

PROVIDER: S-EPMC7487976 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Managing wildlife populations for conservation, control or harvesting involves uncertainty. Nevertheless, decisions need to be made based on the available evidence. The two main sources of uncertainty in population modelling are parameter estimates and structural uncertainty. Structural uncertainty in models is not included as often as parameter uncertainty.We present an approach where parameter and structural uncertainty (strength of density dependence) is included within a model, using the over-wintering English population of cormorants Phalacrocorax carbo L. Because of the damage caused to inland fishery interests by cormorants, there was a change in UK government policy in autumn 2004, increasing the numbers of birds that can be shot under licence.A stochastic Monte Carlo annual population model was produced to examine the effect of changes to the numbers of birds shot each year. Indices of annual population size were converted to population estimates and used to determine annual growth rates and strength of density dependence.There is strong evidence for density dependence in the data, which suggests the population is currently slightly above carrying capacity, with a mean growth rate of 4-6% per annum. The 1300 birds shot under licence in 2004/05 represent about 4.5% of the English population, and if this level of culling continues, the population would be expected to decline by 9% by 2007, compared to the long-term average. The a priori preferred model, which included all uncertainty, gave predictions for 2004/05 and 2005/06 in close agreement with field data.The model was used to produce short-term population projections, with the understanding that Adaptive Resource Management (ARM) will be adopted to iteratively update the parameters and model each year, feeding back into the numbers of available licences.Synthesis and applications. We recommend the approach used in this study of including parameter and structural uncertainty within a single model, where possible, with the proportion of iterations that utilize a particular structure dependent on the weight of evidence for that structure. This will produce results with wider confidence intervals, but ensures that the evidence for any particular model is not over-interpreted.

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Assessing and managing design storm variability and projection uncertainty in a changing coastal environment.

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