Simply looking at the month and year ‐ allowing for temporal fixed effects ‐ provides little predictive accuracy. However, we have data that allows us to model seasonal difference directly (the amount of water present in the river at the time of a vessel’s lockage). Water flow varies seasonally ‐ at some points during the late winter and spring, faster and deeper currents prevail, whereas slower and shallower conditions are more typical during the summer months. Adding climate and weather variables when building a fully specified model at the outset makes sense. This is because they are sources of systematic uncertainty that policymakers often cannot address through the introduction of new regulations, while other sources of delay can be added in at later stages. Shifts in climatic forces may alter seasonal patterns in the long run. Control of tributaries that feed the Ohio River allows the U.S. Army Corps of Engineers to somewhat regulate water flow. In the short term, Corps officials and policymakers are stuck adapting to what nature provides them. That is (technically speaking) river characteristics are for the most part causally antecedent to human behavior on the river system and can thus be treated as exogenous factors. We can begin with Regression Table 2, which adds a new explanatory variable. This variable is the observed discharge river gages near lock facilities.
The information is available from the U.S. Geological Survey’s Water Resources Data.109 Including discharge as a parameter significantly improves the prediction of Queue Delay and Lockage Delay ‐ in both cases shortening delays. When river discharge is higher, vessels move faster. Two possible reasons for this fall‐off in delays are listed below: Higher discharges improve downriver navigation. If that is the reason then the effect of discharge should depend on which direction the vessel wishes to move – or, to put it technically, discharge should interact with direction. For that reason, Regression Table 3 adds a dummy variable (capturing whether the vessel is moving downriver, and an interaction between that dummy variable and discharge). Results for this idea are mixed. When predicting Queue Delays, they tend to be somewhat shorter on
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The Water Resources Data provide Discharge data for five relevant stations. We apply the data from USGS 03611500 (Metropolis, IL) for Lock 52 and Lock 53. We apply data from USGS 03399800 to Smithland. USGS 03381700 at Old Shawneetown applies to Newburgh and J.T. Myers. Finally, USGS 03303280, USGS 03294500, and USGS 03277200 apply to Cannelton, McAlpine, and Markland respectively.
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downriver journeys. But the tendency of greater discharge to shorten delays applies roughly equally irrespective of travel direction ‐ as indicated by the small and insignificant interaction term for direction and discharge. The same is not true when predicting Lockage Delays. Downstream‐moving lockages are faster, and the advantages brought by greater water flow are even more pronounced when moving in that direction. This is indicated by the negative and significant coefficient on the interaction term.
A second possibility is the volume of water moving through the Ohio River. This matters because travel becomes treacherous at either high (flood) or low (drought) water levels. The data reveals more drought‐related problems than flood‐related delays. If it is true that we are mistaken in giving discharge a single coefficient because the effect will be negative at both high and low values, then one simple way to capture that trend in the systematic component of the regression is to allow a bend in the effect. That is, to include a quadratic measure of discharge. Regression Table 4 shows that the data does support this idea. The coefficients on discharge are negative, but those on the squared measure of discharge are negative. That means that at low (drought) discharge, an increase in the amount of discharge will shorten delays. However, as discharges begin approaching very high flood levels, the delays start increasing rapidly again.
Of course, the volume of water could produce effects simultaneously, both aiding downriver travel in the abstract yet generally hindering travel when approaching either extreme. However, when we tried combining the interactive effect with the quadratic effect, the data did not support a robust conclusion that both phenomena were taking place at once (and sacrificing the model’s parsimony in this fashion did not add notably to the predictive power). Our best guess is that water volume works in one or both of the ways hypothesized. In later models, we will keep only the quadratic bend and not the interaction.
The speed at which water moves through the river system may not seem the most direct way to determine the impact of weather and climate on inland movements. Both floods and droughts matter because they affect the water depth (or stage) of the river. As discussed in a previous section, if the river falls too low then the navigation channel may not allow the passage of vessels. Whereas if the river experiences very high discharges, a new set of problems arise. Unfortunately, the available data concerning water depths does not meet the same scope and quality as the discharge data.
Our original source only applied gage depth for one site (Old Shawneetown) and the data did not cover the entire region. The River Gages data provided by USACE broaden the scope, because they took readings from stations close to each lock and dam in the Louisville Region of the Ohio River Valley. However, the data only started for most of the locks on July 12th 2007. In the case of the gage near Cannelton, data only started on September 5th 2008 and ended
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before our data ran out. This forced us to substitute data for a different gage upriver.110 Regression Table 5 presents models that include river stage as a predictor of delay time.
The listwise deletion (i.e. the exclusion of data records because of missing values) it forces due to missing data wreck the models, taking out several years of data (and therefore the dummy variables associated with those years). These models, however, certainly are suggestive: a higher river stage strongly and significantly predicts shorter delays both in queue and later when passing through the lock. However, the variable takes such a terrible toll on the data. This weakens any other conclusions we might wish to draw, and it makes the most sense to remove the river stage variable again and let discharge volume serve as the proxy for water flow.111
Before moving on, though, we must consider two possible wrinkles in how river stage could work. First, the effect of stage might be greater with vessels that have a deeper draft. But it could matter less with a craft that sits higher in the water (especially recreational vessels). Regression Table 6 includes the vessel’s draft when loaded, first adding that variable alone, then allowing the effect to vary depending on river stage. It shows that the vessel’s draft when loaded does indeed slow progress somewhat. When we allow the effect of draft to depend on river stage, the negative interaction term means that draft does not delay a trip as much when the water is high. Or to put it another way, the effect of low water is less pronounced when vessels have less draft. Regression Table 7 drops the measures of draft, and instead looks at the effect of wicket dams (specifically looking at Lock 52 and Lock 53). First, we look at the impact of adding a dummy variable for wicket dams alone and then allowing their effect to depend on river stage. Wicket dams can be raised and lowered, so that they block river flow and force a lockage some of the time. At other times the wicket dam allows vessels to float right over them. We would expect travel past a wicket dam to be especially speedy when no lockage is required, but if for no other reason than because these lock chambers are the oldest on the river. Because the auxiliary chambers are only half as long we expect delays to be lengthier than usual when they are required. Thus, we can add a dummy variable for the wicket dam, and also include the effect of having that type of dam to hinge on river stage. 110 Specifically, we use Gage 4 (Markland), Gage 6 (McAlpine), Gage 10 supplemented with Gage 8 for Cannelton. Gage 13 (Newburgh), Gage 16 (J.T. Myers), Gage 17 (Smithland), Gage 20 (Lock 52), and Gage 23 (Lock 53). 111 If future research does not turn up a better source of data on River Stage, a valid alternative would be to impute the State using the Old Shawneetown Gage Depth, the discharge seen at various places along the river, and possibly other predictors.
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The first two models show that, on average, passing a wicket dam takes less time than passing the other dams on the river. On the other hand, the second two models with negative interaction terms suggest that the benefit actually only applies when the river stage is high. Lockage delays are not shorter otherwise, and if anything, the queue delays are even longer at wicket dams when river stage is low. For now, we drop the draft and wicket variables along with river stage, but we will return to those explanatory variables soon.