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Mahkameh Zarekarizi, Hamid Moradkhani,
An Operational Drought Prediction Framework with application of Vine Copula functions Mahkameh Zarekarizi, Hamid Moradkhani, Remote Sensing and Water Resources Lab Portland State University Student Research Symposium, May 2017
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Agriculture and therefore food production is sensitive to drought.
Introduction Framework Methodology Case Studies Conclusion References Background… Droughts unlike other natural disasters such as floods, evolve slowly over time and expand into large areas of land. Early and accurate drought predictions can benefit water resources and emergency managers by enhancing drought preparedness. Agriculture and therefore food production is sensitive to drought. Agricultural drought is characterized by deficit in soil moisture. Soil moisture has a memory; spanning from weeks to months. Introduction Framework Drought Prediction Methodology Goals… Case studies Probabilistic analysis References This study uses the memory of hydrological variables to predict their future states via multivariate statistical models. Here, 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 values of hydrological variables on antecedent drought status.
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Agriculture and therefore food production is sensitive to drought.
Introduction Framework Methodology Case Studies Conclusion References Background… Droughts unlike other natural disasters such as floods, evolve slowly over time and expand into large areas of land. Early and accurate drought predictions can benefit water resources and emergency managers by enhancing drought preparedness. Agriculture and therefore food production is sensitive to drought. Agricultural drought is characterized by deficit in soil moisture. Soil moisture has a memory; spanning from weeks to months. Introduction Framework Drought Prediction Methodology Goals… Case studies Probabilistic analysis References This study uses the memory of hydrological variables to predict their future states via multivariate statistical models. Here, 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 values of hydrological variables on antecedent drought status.
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Soil Moisture Simulation
Introduction Framework Methodology Case Studies Conclusion References Soil Moisture Simulation Forcing: NLDAS Precipitation Forcing: NLDAS Wind Speed Soil Moisture Simulations Forcing: NLDAS Temperature Maximum Spatial resolution: 1/8th degree. Forcing: NLDAS Temperature Minimum Temporal resolution: Daily. NLDAS (North American Land Data Assimilation Systems) datasets.
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Introduction Framework Methodology Case Studies Conclusion References
Persistence
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Real-time Drought Prediction Framework
Introduction Framework Methodology Case Studies Conclusion References Real-time Drought Prediction Framework Monthly Seasonal Oct Nov Dec Jan Feb Mar Apr May Jun 1-Jan 1-Feb Oct Nov Dec Jan Feb Mar Apr May Jun Oct Nov Dec Jan Feb Mar Apr May Jun 1-Mar Oct Nov Dec Jan Feb Mar Apr May Jun 1-Apr
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Real-time Drought Prediction Framework
Introduction Framework Methodology Case Studies Conclusion References Real-time Drought Prediction Framework Monthly Seasonal Oct Nov Dec Jan Feb Mar Apr May Jun 1-Jan 1-Feb Oct Nov Dec Jan Feb Mar Apr May Jun Oct Nov Dec Jan Feb Mar Apr May Jun 1-Mar Oct Nov Dec Jan Feb Mar Apr May Jun 1-Apr
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Prediction Methodology
Introduction Framework Methodology Case Studies Conclusion References Prediction Methodology
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Prediction Methodology
Introduction Framework Methodology Case Studies Conclusion References Prediction Methodology Month t Month t+1
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Prediction Methodology
Introduction Framework Methodology Case Studies Conclusion References Prediction Methodology Training Real-Time Joint PDF of SM of month t and month t+1 Joint PDF of SM of month t and month t+1 Conditional PDF of SM in month t+1 given SM in month t SM Initial Month t Month t CDF Monthly predictions are provided by deriving the conditional distribution of a month’s soil moisture given the previous month’s soil moisture value Month t+1 CDF Elliptical Copula Month t+1
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Prediction Methodology
Introduction Framework Methodology Case Studies Conclusion References Prediction Methodology Joint PDF of SM of month t and month t+1 Joint PDF of SM of month t and month t+1 SM Initial Likelihood Month t PDF Month t CDF Month t+1 CDF Elliptical Copula Month t+1 PDF SM in month t+1 given SM in month t
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Soil Moisture Persistence
Introduction Framework Methodology Case Studies Conclusion References Soil Moisture Persistence Prediction Methodology Drought Category Description Soil Moisture Percentile Normal/Wet Normal 31 to 100 D0 Abnormally Dry 21 to 30 D1 Moderate Drought 11 to 20 D2 Severe Drought 6 to 10 D3 Extreme Drought 3 to 5 D4 Exceptional Drought 0 to 2 Joint PDF of SM of month t and month t+1 Joint PDF of SM of month t and month t+1 Soil Moisture Prediction using the maximum Likelihood Estimation SM Initial Maximum Likelihood Likelihood Month t PDF Month t CDF Soil Moisture Percentile Month t+1 CDF Elliptical Copula Drought Category Month t+1 PDF MLE SM in month t+1 given SM in month t
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Prediction Methodology Soil Moisture Persistence
Introduction Framework Methodology Case Studies Conclusion References Prediction Methodology Soil Moisture Persistence
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The US Drought of 2012 Monitoring Prediction
Introduction Framework Methodology Case Studies Conclusion References The US Drought of 2012 Monitoring Prediction
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The US Drought of 2012 Vine Copula
Introduction Framework Methodology Case Studies Conclusion References The US Drought of 2012 May Jun Month t-2 Month t-1 Vine Copula Jul Aug Month t
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The US Drought of 2012 Elliptical Copula
Introduction Framework Methodology Case Studies Conclusion References The US Drought of 2012 May Jun Month t-1 Elliptical Copula Jul Aug Month t
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Mixed Vine Copula and Elliptical Copula
Introduction Framework Methodology Case Studies Conclusion References Month t-1 Month t Mixed Vine Copula and Elliptical Copula Significant? Elliptical Copula NO May Jun YES Month t-2 Month t-1 Jul Aug Vine Copula Month t
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Mixed Selection Past two months Previous month
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Probability of Drought Persistence
Introduction Framework Methodology Case Studies Conclusion References Probability of Drought Persistence
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Probability of Drought Termination
Introduction Framework Methodology Case Studies Conclusion References Probability of Drought Termination
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Monitoring, Jan 2014 Monitoring, Winter 2014 USDM Monitoring
Introduction Framework Methodology Case Studies Conclusion References Monitoring, Jan 2014 Monitoring, Winter 2014 USDM Monitoring
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Winter 2014 drought in California
Introduction Framework Methodology Case Studies Conclusion References Winter 2014 drought in California MLE predicted drought in Winter 2014 in California Probability of experiencing any type of drought in Winter 2014 in California
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Winter 2014 drought in California
Introduction Framework Methodology Case Studies Conclusion References Winter 2014 drought in California
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Winter 2014 drought in California
Introduction Framework Methodology Case Studies Conclusion References Winter 2014 drought in California Replacing SM with SPI Average Drought Probability over California in Winter 2014
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Introduction Framework Methodology Case Studies Conclusion References
Columbia River Basin
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Winter 2014 drought in California
Introduction Framework Methodology Case Studies Conclusion References Winter 2014 drought in California
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All predictions rely on the persistence of soil moisture.
Introduction Framework Methodology Case Studies Conclusion References Conclusion Probabilistic drought prediction has been performed by conditioning soil moisture on antecedent soil moisture values. All predictions rely on the persistence of soil moisture. Information provided by past two months were employed to predict a subsequent drought condition. To employ as much information as possible for more reliable predictions, we have done a significance analysis where for those areas that only the previous month has shown to be informative, a two dimensional Elliptical copula model is built and employed to issue the predictions while for the rest of the areas where both lag-1 and lag-2 time series have shown significance correlations, a three dimensional Vine Copula model is constructed and used to issue probabilistic predictions. the ability of the proposed model was tested to hindcast the drought of Results were compared with predictions made by only Elliptical Copulas and only Vine Copulas and it was concluded that combining both models depending on the region results in more reliable predictions. Results show that the proposed multivariate models are able to capture the drought onset and persistence of the drought states, which potentially facilitate drought preparation and mitigation.
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Conus Photo from http://www.freestockphotos.biz/stockphoto/11358
Introduction Framework Methodology Case Studies Conclusion References References Cover photo from: Conus Photo from Liang, X., 1994: A Two-Layer Variable Infiltration Capacity Land Surface Representation for General Circulation Models, Water Resour. Series, TR140, 208 pp., Univ. of Washington, Seattle. Photo in the introduction slide:
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Thank You
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