Ice Jam Prediction Ice Engineering Research Division US Army Cold Regions Research and Engineering Laboratory Presented by Kate White For Hydromet 00-2 Thursday, 9 March 2000 Why try to predict ice jams? Improve emergency responseImprove emergency response Increase flood fighting effectivenessIncrease flood fighting effectiveness HindcastHindcast Synthesize a historical record of jams for design or flood warning purposes Ideal ice jam prediction model Will provide a quantitative probability of ice jam occurrence or flooding with enough lead time to institute mitigation measuresWill provide a quantitative probability of ice jam occurrence or flooding with enough lead time to institute mitigation measures Type I error (i.e., a jam occurs when it was not predicted) rate smallType I error (i.e., a jam occurs when it was not predicted) rate small Type II error (cry wolf) rate smallType II error (cry wolf) rate small Variables easily and accurately measured or forecastVariables easily and accurately measured or forecast Ice Jam Prediction Model The ideal: generalized, site-transferable methods which address all issues. The reality: A collection of site-specific methods, each of which addresses only those issues deemed important at that specific site. A wide variety of forecasting techniques are used. Stable Ice Cover Formation Mechanical Breakup: (ice pieces generated) Transport Capacity Exceeded Transport Capacity not Exceeded JamNo Jam Thermal Meltout (ice melts in place) Ice Run No Ice Cover or Insufficient Ice to Form Jam Thermal Decay Ice Jam Prediction Issues Stable ice cover formationStable ice cover formation Ice cover growth, strength, and decayIce cover growth, strength, and decay Mechanical vs. thermal breakupMechanical vs. thermal breakup Ice transportIce transport Ice jam formationIce jam formation Flood levels and rate of riseFlood levels and rate of rise Important Variables Ice Cover FormationIce Cover Formation Strength and DecayStrength and Decay BreakupBreakup Air temperature AFDD Discharge Other met data Air Temp TDD /Sunlight Ice thickness Snow cover Freezeup stage Rate of change in Q and/or stage Antecedent meteorological conditions Important Variables Ice TransportIce Transport Ice Jam FormationIce Jam Formation Flood LevelsFlood Levels Discharge Floe size and strength River plan form River geometry and slope Discharge Ice volume Granular ice strength parameters Downstream stages Discharge Ice volume Modeling Considerations Easier Harder Many Historical Ice Jams Few Consistent Winter Weather Variable One Spring Peak Hydrology Many peaks Long Duration of Ice Cover Short Comprehensive Data Available Little Moderate Ice Jam Severity Extreme Frequent problems with ice-related data Short or interrupted period of recordShort or interrupted period of record Low frequency ice jamsLow frequency ice jams Perception stagePerception stage MisclassificationsMisclassifications Reliability of measurementsReliability of measurements Error in discharge estimationError in discharge estimation Interrelationships among variables understood to varying degreesInterrelationships among variables understood to varying degrees Lack of observationsLack of observations Types of jam prediction models Probabilistic forecastingProbabilistic forecasting Empirical thresholdEmpirical threshold Empirical cluster-type analysisEmpirical cluster-type analysis Multiple linear regressionMultiple linear regression Logistic regressionLogistic regression Discriminant function analysisDiscriminant function analysis DeterministicDeterministic OtherOther Brief review of existing breakup jam prediction models Use a wide variety of variablesUse a wide variety of variables Some focus on small part of processSome focus on small part of process Results variableResults variable Few easily transported to other locationsFew easily transported to other locations Threshold identification Goal is to identify one or more variables for which a threshold exists, or where statistically significant differences are presentGoal is to identify one or more variables for which a threshold exists, or where statistically significant differences are present Shuliakovskii 1963 AFDDs Snow Depth Box & Whisker Plots Missouri River at Williston, ND Missouri River at Williston, ND Wuebben et al. Threshold Models White & Kay (Platte R., North Bend, NE)White & Kay (Platte R., North Bend, NE) AFDD > 400AFDD > 400 Q > 6000 cfs or Q>=.39(JD) 1.9Q > 6000 cfs or Q>=.39(JD) 1.9 whichever is larger whichever is larger Identified 65% of the ice events +-7 daysIdentified 65% of the ice events +-7 days 41% wrong date or Type II error41% wrong date or Type II error One Type I errorOne Type I error Threshold Models Tuthill et. al. (Winooski R., Montpelier, VT)Tuthill et. al. (Winooski R., Montpelier, VT) 1 Dec - 31 March Q > 1,800 cfs No peaks > 1,000 cfs in previous 30 days Time to peak less than 3 days Identified 13 of 17 known historic ice jams Also identified 22 potential jams (I.e., large Type II error) Threshold Models White & Daly (Oil Creek, Oil City, PA)White & Daly (Oil Creek, Oil City, PA) 15 AFDD > 120 1 Q > 1,000 cfs Predicted Predicted Predicted Predicted Jam No Jam___ Jam No Jam___ Actual Jam Actual No Jam Predicted no jam Predicted jam Discriminant Function Analysis White & Daly (Oil Creek, Oil City, PA)White & Daly (Oil Creek, Oil City, PA) LogARQ: Log Allegheny R Discharge Log 1 QOC: Log 1 Day Oil Creek Q 2 AFDD: 2 Day AFDD Predicted Predicted Predicted Predicted Jam No Jam___ Jam No Jam___ Actual Jam Actual No Jam Deterministic Models Stable ice cover formationStable ice cover formation Ice cover strength and decay rateIce cover strength and decay rate Mechanical breakupMechanical breakup Ice transportIce transport Ice jam formationIce jam formation Flood levelsFlood levels 1-D hydraulic models with thermal & ice solar penetration models Onset of breakup Hydraulic models with ice: free drift;discrete parcel; discrete element Hydraulic model: unsteady vs. steady Deterministic Models Uncertainties enter due toUncertainties enter due to parameters input data structural problems Difficult to deal with uncertainty in any direct mannerDifficult to deal with uncertainty in any direct manner Development lagging due to lack of compete analytical modelDevelopment lagging due to lack of compete analytical model Summary Ice jam formation is a very site specific phenomenaIce jam formation is a very site specific phenomena There are a number of issues inhibiting development of ice jam prediction modelsThere are a number of issues inhibiting development of ice jam prediction models The ideal of generalized, site- transferable methods remains a goalThe ideal of generalized, site- transferable methods remains a goal A number of techniques have been used for forecasting jamsA number of techniques have been used for forecasting jams Threshold models have been successfully used in some casesThreshold models have been successfully used in some cases Deterministic models can achieve the goal of a generalized method, but a complete analytical model is required and the problems of uncertainty must be addressedDeterministic models can achieve the goal of a generalized method, but a complete analytical model is required and the problems of uncertainty must be addressed Partnerships between agencies will increase effectiveness of response to ice jams Examples: NWS inclusion of ice jams in Hydromet trainingNWS inclusion of ice jams in Hydromet trainingCorps-NWS partnerships (e.g., St. Paul District, NCRFC)Corps-NWS partnerships (e.g., St. Paul District, NCRFC) CRREL partnerships (e.g., MARFC, NERFC, NWS Glasgow MT)CRREL partnerships (e.g., MARFC, NERFC, NWS Glasgow MT)