The Use of Central Tendency Measures from an Operational Short Lead-time Hydrologic Ensemble Forecast System for Real-time Forecasts
| dc.contributor.author | Adams, Thomas Edwin III | en |
| dc.contributor.committeechair | Dymond, Randel L. | en |
| dc.contributor.committeemember | McGuire, Kevin J. | en |
| dc.contributor.committeemember | Widdowson, Mark A. | en |
| dc.contributor.committeemember | Ellis, Andrew | en |
| dc.contributor.department | Civil and Environmental Engineering | en |
| dc.date.accessioned | 2018-06-06T08:00:59Z | en |
| dc.date.available | 2018-06-06T08:00:59Z | en |
| dc.date.issued | 2018-06-05 | en |
| dc.description.abstract | A principal factor contributing to hydrologic prediction uncertainty is modeling error intro- duced by the measurement and prediction of precipitation. The research presented demon- strates the necessity for using probabilistic methods to quantify hydrologic forecast uncer- tainty due to the magnitude of precipitation errors. Significant improvements have been made in precipitation estimation that have lead to greatly improved hydrologic simulations. However, advancements in the prediction of future precipitation have been marginal. This research shows that gains in forecasted precipitation accuracy have not significantly improved hydrologic forecasting accuracy. The use of forecasted precipitation, referred to as quantita- tive precipitation forecast (QPF), in hydrologic forecasting remains commonplace. Non-zero QPF is shown to improve hydrologic forecasts, but QPF duration should be limited to 6 to 12 hours for flood forecasting, particularly for fast responding watersheds. Probabilistic hydrologic forecasting captures hydrologic forecast error introduced by QPF for all forecast durations. However, public acceptance of probabilistic hydrologic forecasts is problematic. Central tendency measures from a probabilistic hydrologic forecast, such as the ensemble median or mean, have the appearance of a single-valued deterministic forecast. The research presented shows that hydrologic ensemble median and mean forecasts of river stage have smaller forecast errors than current operational methods with forecast lead-time beginning at 36-hours for fast response basins. Overall, hydrologic ensemble median and mean forecasts display smaller forecast error than current operational forecasts. | en |
| dc.description.abstractgeneral | Flood forecasting is uncertain, in part, because of errors in measuring precipitation and predicting the location and amount of precipitation accumulation in the future. Because of this, the public and other end-users of flood forecasts should understand the uncertainties inherent in forecasts. But, there is reluctance by many to accept forecasts that explicitly convey flood forecast uncertainty, such as, ”there is a 67% chance your house will be flooded”. Instead, most prefer ”your house will not be flooded” or something like ”flood levels will reach 0.5 feet in your house”. We hope the latter does not happen, but due to forecast uncertainties, explicit statements such as ”flood levels will reach 0.5 feet in your house” will be wrong. If by chance, flood levels do exactly reach 0.5 feet, that will have been a lucky forecast, very likely involving some skill, but the flood level could have reached 0.43 or 0.72 feet as well. This research presents a flood forecasting method that improves on traditional methods by directly incorporating uncertainty information into flood forecasts that still appear like forecasts people are familiar and comfortable with and understandable by them. | en |
| dc.description.degree | Ph. D. | en |
| dc.format.medium | ETD | en |
| dc.identifier.other | vt_gsexam:16311 | en |
| dc.identifier.uri | http://hdl.handle.net/10919/83461 | en |
| dc.publisher | Virginia Tech | en |
| dc.rights | In Copyright | en |
| dc.rights.uri | http://rightsstatements.org/vocab/InC/1.0/ | en |
| dc.subject | hydrology | en |
| dc.subject | forecasting | en |
| dc.subject | uncertainty | en |
| dc.subject | precipitation | en |
| dc.subject | Modeling | en |
| dc.subject | ensemble | en |
| dc.title | The Use of Central Tendency Measures from an Operational Short Lead-time Hydrologic Ensemble Forecast System for Real-time Forecasts | en |
| dc.type | Dissertation | en |
| thesis.degree.discipline | Civil Engineering | en |
| thesis.degree.grantor | Virginia Polytechnic Institute and State University | en |
| thesis.degree.level | doctoral | en |
| thesis.degree.name | Ph. D. | en |
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