HighlightsThe effects of lakes and reservoirs on global daily streamflow are evaluated.Reservoirs affect model performance substantially in the global domain.Lakes’ effects on model performance are limited to few catchments.Lakes and reservoirs reduce return levels discharge thresholds globally.Reservoir parameters contribute to uncertainty of model performance metrics.

This study presents the statistical evaluation of the vegetative filter strip modeling system VFSMOD-W as a tool to design vegetative filter strips to use in the mitigation plans required as a part of phosphate mining permitting process by the State of Florida. A two-step statistical evaluation framework using global techniques is presented based on: (1) a screening method (Morris) for qualitative ranking of parameters, and (2) a variance-based method (extended Fourier Analysis Sensitivity Test-extended FAST) for quantitative sensitivity and uncertainty analyses. Measured characteristics of the central Florida phosphate-mining region are used to construct the 16 probability distributions of input factors. Two design filter lengths (3 and 6 m) and two model structures (VFSM-the filter module alone, and UH/VFSM-combined filter and source area components) are considered and compared to previous local "one-parameter-at-a-time" (OAT) analyses. It was found that for this application the filter's saturated hydraulic conductivity (VKS) was the most important factor controlling the filter runoff response, explaining over 90% of total output variance irrespective of model structure. In the case of the VFSM structure, sediment-related outputs were mainly influenced by three parameters: sediment particle size diameter (DP), effective flow width of the strip (FWIDTH), and VKS. For UH/VFSM, there were six important parameters: DP, the source area erosion and runoff parameters (slope of the source area Y, USLE soil erodibility index K, and runoff curve number CN), FWIDTH, and VKS. The results show the model's additive nature for this specific application, i.e., there are no significant parameter interactions for all model outputs except sediment outflow concentration and sediment wedge geometry. The uncertainty analysis indicates that regardless of the model structure, the probability of meeting a minimum required 75% sediment reduction was acceptable at the 90% confidence level for the 6 m long filter, but not for the 3 m filter. In general the UH/VFSM model structure exhibited larger output uncertainty. Comparison with previous OAT analyses of the model indicates the importance of performing the global evaluation for each specific model application. The results illustrate four main products of the global analysis: ranking of importance of the VFSMOD-W parameters for different outputs, effect of changing modeling structure, type of influence of the important parameters, and assurance of the model's behavior.

Habitat suitability index (HSI) models are commonly used to predict habitat quality and species distributions and are used to develop biological surveys, assess reserve and management priorities, and anticipate possible change under different management or climate change scenarios. Important management decisions may be based on model results, often without a clear understanding of the level of uncertainty associated with model outputs. We present an integrated methodology to assess the propagation of uncertainty from both inputs and structure of the HSI models on model outputs (uncertainty analysis: UA) and relative importance of uncertain model inputs and their interactions on the model output uncertainty (global sensitivity analysis: GSA). We illustrate the GSA/UA framework using simulated hydrology input data from a hydrodynamic model representing sea level changes and HSI models for two species of submerged aquatic vegetation (SAV) in southwest Everglades National Park: Vallisneria americana (tape grass) and Halodule wrightii (shoal grass). We found considerable spatial variation in uncertainty for both species, but distributions of HSI scores still allowed discrimination of sites with good versus poor conditions. Ranking of input parameter sensitivities also varied spatially for both species, with high habitat quality sites showing higher sensitivity to different parameters than low-quality sites. HSI models may be especially useful when species distribution data are unavailable, providing means of exploiting widely available environmental datasets to model past, current, and future habitat conditions. The GSA/UA approach provides a general method for better understanding HSI model dynamics, the spatial and temporal variation in uncertainties, and the parameters that contribute most to model uncertainty. Including an uncertainty and sensitivity analysis in modeling efforts as part of the decision-making framework will result in better-informed, more robust decisions.

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