Title of Poster / Presentation
An Operational Drought Prediction Framework with application of Vine Copula functions
Start Date
10-5-2017 1:00 PM
End Date
10-5-2017 3:00 PM
Subjects
Copulas (Mathematical statistics), Drought forecasting -- Mathematical models, Multivariate analysis
Abstract
Early and accurate drought predictions can benefit water resources and emergency managers by enhancing drought preparedness. Soil moisture memory is shown to contain helpful information for prediction of future values. This study uses the soil moisture memory to predict their future states via multivariate statistical modeling. We present a drought forecasting framework which issues monthly and seasonal drought forecasts. This framework estimates droughts with different lead times and updates the forecasts when more data become available. Forecasts are generated by conditioning future soil moisture values on antecedent drought status. The statistical model is initialized by soil moisture simulations retrieved from a calibrated hydrologic model. The predictability of both agricultural droughts are assessed in this study through the proposed framework. The framework is implemented on the Contiguous United States and the ability of the model to predict officially declared droughts of the region is assessed. Results show that proposed multivariate models are able to capture the drought onset and persistence of the drought states, which potentially facilitate drought preparation and mitigation.
Authors: Mahkameh Zarekarizi, Hamid Moradkhani
Presenter: Mahkameh Zarekarizi
Persistent Identifier
http://archives.pdx.edu/ds/psu/20051
Included in
Hydrology Commons, Mathematics Commons, Remote Sensing Commons
An Operational Drought Prediction Framework with application of Vine Copula functions
Early and accurate drought predictions can benefit water resources and emergency managers by enhancing drought preparedness. Soil moisture memory is shown to contain helpful information for prediction of future values. This study uses the soil moisture memory to predict their future states via multivariate statistical modeling. We present a drought forecasting framework which issues monthly and seasonal drought forecasts. This framework estimates droughts with different lead times and updates the forecasts when more data become available. Forecasts are generated by conditioning future soil moisture values on antecedent drought status. The statistical model is initialized by soil moisture simulations retrieved from a calibrated hydrologic model. The predictability of both agricultural droughts are assessed in this study through the proposed framework. The framework is implemented on the Contiguous United States and the ability of the model to predict officially declared droughts of the region is assessed. Results show that proposed multivariate models are able to capture the drought onset and persistence of the drought states, which potentially facilitate drought preparation and mitigation.
Authors: Mahkameh Zarekarizi, Hamid Moradkhani
Presenter: Mahkameh Zarekarizi