Abstract. Spatially distributed hydrological models are commonly employed to optimize the locations of engineering control measures across a watershed. Yet, parameter screening exercises that aim to reduce the dimensionality of the calibration search space are typically completed only for gauged locations, like the watershed outlet, and use screening metrics that are relevant to calibration instead of explicitly describing the engineering decision objectives. Identifying parameters that describe physical processes in ungauged locations that affect decision objectives should lead to a better understanding of control measure effectiveness. This paper provides guidance on evaluating model parameter uncertainty at the spatial scales and flow magnitudes of interest for such decision-making problems. We use global sensitivity analysis to screen parameters for model calibration, and to subsequently evaluate the appropriateness of using multipliers to adjust the values of spatially distributed parameters to further reduce dimensionality. We evaluate six sensitivity metrics, four of which align with decision objectives and two of which consider model residual error that would be considered in spatial optimizations of engineering designs. We compare the resulting parameter selection for the basin outlet and each hillslope. We also compare basin outlet results for four calibration-relevant metrics. These methods were applied to a RHESSys ecohydrological model of an exurban forested watershed near Baltimore, MD, USA. Results show that (1) the set of parameters selected by calibration-relevant metrics does not include parameters that control decision-relevant high and low streamflows, (2) evaluating sensitivity metrics at the basin outlet misses many parameters that control streamflows in hillslopes, and (3) for some multipliers, calibrating all parameters in the set being adjusted may be preferable to using the multiplier if parameter sensitivities are significantly different, while for others, calibrating a subset of the parameters may be preferable if they are not all influential. Thus, we recommend that parameter screening exercises use decision-relevant metrics that are evaluated at the spatial scales appropriate to decision making. While including more parameters in calibration will exacerbate equifinality, the resulting parametric uncertainty should be important to consider in discovering control measures that are robust to it.
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Spatiotemporal patterns of alcohol outlets and violence: A spatially heterogeneous Markov chain analysis
Alcohol-related violence remains a serious social and public health problem in the United States. A large corpus of work suggests a positive statistical relationship between alcohol outlet density and violence. However, questions remain as to how neighborhood violence evolves in response to varying access to alcohol outlets. This paper introduces an approach for analyzing the spatial and temporal dynamics of violence and its association with alcohol outlets by embedding the evolution of assault events and outlet density within a spatially heterogeneous Markov chain framework. This framework enables the exploration of spatiotemporal dynamics of alcohol outlets and violence and controls for potentially confounding impacts and spatial heterogeneity. Using a case study at the block group level in Seattle, Washington, the results of this paper suggest that violence is spatially heterogeneous at the local level and locations with sparsely distributed alcohol outlets are less likely to see an increase in violence when compared to areas with higher densities of outlets. Further, the modeling approach helps identify locations that might “tip” into more violent conditions if more outlets were allowed to operate. This paper concludes with a brief discussion of how the methods and results can help improve the management, licensing, and policy development for alcohol outlets in a community.
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- Award ID(s):
- 1831615
- PAR ID:
- 10296752
- Date Published:
- Journal Name:
- Environment and Planning B: Urban Analytics and City Science
- ISSN:
- 2399-8083
- Page Range / eLocation ID:
- 239980832096556
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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