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This paper presents a comparative evaluation of methods for climate-based estimation of the net inflow rate into Lake Okeechobee, Fla. The estimated net inflow rate is used by the South Florida Water Management District (SFWMD) to support the management and operations of the Lake Okeechobee hydrologic system. The first method evaluated in this paper (Croley) uses rainfall outlooks provided by the National Oceanic and Atmospheric Administration’s Climate Prediction Center (CPC) to calculate a weighed average of historical inflow values for each month. The second method evaluated in this paper (SFWMD Empirical) uses a linear regression on statistics of historical data to predict the net inflow rate. These two methods were developed and have been used operationally by the SFWMD since 2000. Three new methods are presented and comparatively evaluated to gauge their ability in estimating net inflow rates. The first two of these methods are based on CPC issued forecasts in decile probability density format. The remaining method is based on a subsampling technique for “peer” wet/dry years in the historical record and is found to yield better results in a retrospective analysis. For extreme climatic events on the historical record, CPC rainfall outlooks are found not to yield a large enough shift in probabilities for forecasts to match observed net inflow rates; this is especially noticeable during El Niño Southern Oscillation events. Recommendations are made for potential improvements to climate-based net inflow rate estimation methods, particularly in regard to their ability to reproduce observed results for net inflow into Lake Okeechobee in the presence of an extreme climatic event, as well as over an extended climatological period. [ABSTRACT FROM AUTHOR]

• Propose an optimization procedure to estimate weight-categorized truck counts. • Demonstrate an easy-to-use method for integrating freight datasets. • Conduct a case study to estimate weight-categorized truck counts in Florida. • Explore scenarios to derive appropriate weightages for the optimization model. Knowledge of spatial distributions of weight-categorized truck flows in a region is critical to the understanding of movements of empty or partially-loaded trucks and devising appropriate strategies to reduce empty or partially-loaded truck flows and improve truck utilization efficiency in the region. However, such disaggregated information cannot be directly obtained from existing data sources and models. In this paper, we propose a compact model for estimating weight-categorized truck origin–destination (OD) flows and link-level truck counts by fusing several freight datasets. The proposed model minimizes the squared errors between the estimated and observed truck OD flows and link volumes considering the flow conservation of trucks and commodity weights. To illustrate a real-world application of this model, a case study is conducted to estimate the spatial distribution of empty or partially-loaded truck flows into, within, and out of the State of Florida. With the case study results, high production- and attraction-zones of empty or partially-loaded truck trips are also identified. Such results can potentially inform freight planning and policy decisions to learn spatial patterns of empty or partially-loaded backhauling truck flows and devise countermeasures to reduce such flows and improve freight transportation efficiency. This is particularly relevant to a terminal state such as Florida that has large volumes of back-hauling truck flow. [ABSTRACT FROM AUTHOR]