2008 S.C. Water Resources Conference - Addressing Water Challenges Facing the State and Regio

Cook, John B

Daamen, Ruby C

Roehl, Ed, Jr

English

Clemson University: TigerPrints

A Novel Approach in Determining Changes in Consumptive Usefor River Basins2008 SC Water Resources ConferenceCharleston, SCRuby Daamen John Cook, PEEd RoehlAdvanced Data Mining InternationalGreenville, SCObjective: To understand hydrologic changes over time in a River Basin and to answer questions:• Has there been a net water loss                      or gain over the period of record?• If so, by how much and why? • What caused the greatest impacts?• Separate rainfall variability from other inputs to determine ―consumptive use‖What is ―consumptive use‖?• ―Consumptive Use‖ defined as: Difference between water withdrawn from a basin and withdrawn water returnedPotential sinks for consumptive use?• Water plant withdrawals and discharges to septic tanks (varies as percentage)• Evaporative losses for chillers, cooling towers for institutional buildings; large use for large cities• Example: ―guest-imate‖ for Charleston Metro: 1-2 MGD; for NYC, think 30 MGDPotential consumptive use, cont…• Historic changes to hydrologic patterns due to changes in land use• Irrigation of lawns and agricultural ET losses• A special case: Inter-basin transfers usually defined as separate impact item   Potential consumptive use, cont…• Power plant cooling by evaporation• Many MGDs• Can be hugelosses—15,000 gpd  per 1 MWSchematic of river basin Example SegmentQinQouttributarytributaryWTPWWTPGAGE    Potential hydrological data sources to be mined• USGS gaging stations• Numerous weather stations in and contiguous to basin--more are better • Known interbasin transfers• The longer the record, outcomes more robustProposed Approach• Divide the river basin into segments (segments determined by available flow gages) and calculate a flow difference for each segment• Determine effects of weather on flow variability using artificial neural networks (ANNs); remove rainfall effects• Calculate change in consumptive use over period of recordNomenclature for process• Qout = Flow measured at the output of the segment• Qin = Flow measured at the input of the segment• ΔQout = Change in segment discharge flow over historical record; calculated using linear regressionNomenclature, cont…• Qdiff = Qout - Qin• ΔQdiff = Change in difference over historical record, Delta (Qout – Qin) determined using linear regression• ΔRain = Change in rainfall over period of record, calculated using linear regression• ΔQrain = Drainage area x ΔRain Nomenclature, cont…• ΔQconsumptive = Change in flow (Qout –Qin) that is not attributable to weather, i.e.,change in ―consumptive use‖• Data record = period of record for a particular segment/gage within the period of recordRaw flow data includes:• Effects on flow from changes in rainfall over time• Effects on flow from consumptive use over time• Therefore, must determine way to remove effects from changes in rainfall and groundwater on basin flowsExample Segment Qout:• In the Example Segment, monitoring data shows flow out of Segment• For many possible reasons, Qout has diminished over period of concernQout over data record for Example Segment - Qout cfs1000900070005000100030001100090007000500030002000 4000 6000 8000 10000Qout (cfs)DaysExample: rainfall summary for 10 weather stations (WS) in the southeastern United States, inches/yearΔRain (inches/year)Weather Station30 Year Change (Inches/Year)-8-6-4-202468CLEV CUMM TAYL NORC BOLT HART NEWN WEST THOM COLUWS1       WS2       WS3       WS4       WS5       WS6       WS7       WS8       WS9      WS10Practical issues to overcome• Temporal variability in rainfall• Travel time of rainfall and groundwaterRainfall takes different time-steps to become river flow, with many losses along the path to riverAnother problem: rainfall is very erratic across any area, not falling uniformly over space or timeDealing with spatial variability• Spatial variability– Multiple weather stations– Correlation analysis of rainfall stations and river flows is used to select stations with strongest correlation using ANNs– Weather data is decorrelated from each other using ANNsDealing with temporal variability• Time variability– Include varying sizes of moving window averages and derivatives; use signal processingExample Segment showing possible weather stationsQinQouttributarytributaryWEATHERSTATION (WS)GAGE    WS1WS2WS3Inspired by the Brainget complicated behaviors from lots of ―simple‖ interconnected devices - neurons and synapsesnon-linear, multivariate curve fittingmodels are synthesized from historical datax1x2x3x4x5y1y2inputs outputsUse Artificial Neural Networks (ANNs) to determine non-linear relationshipsExample Segment–Qdiff v. timeQout - Qin = Qdiff120010008006002000-200-400400QDIFF (cfs)DAYS 1100090007000500030001000 2000 4000 6000 8000 10000Model Qdiff with ANN for Example Segment using rainfall variability over time and space and drainage basin areas Example Segment –Qdiff_Measured vs.Qdiff_Predicted using ANN120010008006002000-200-400400QDIFF (cfs)DAYS7000500030001000 2000 4000 6000 8000 9000Take difference between modeled Qdiff and actual Qdiff to yield ―residual Qdiff‖ (Qres)Model ―residual‖ = Qdiff_measured –Qdiff_predicted• Model residual is an estimate of the variability due to non-rainfall causes• Other causes could include:– Measurement error– Noise (randomness)– ―Other disturbances‖ = consumptive useCalculating ΔQconsumptive over period of record• ANN model predicts weather effects on flow using multiple weather stations• Model Qdiff and calculate Qdiff ―residual‖ between modeled and actual• Linear regression of Qres is used to calculate Qres at the start and end of the data record• ΔQconsumptive = Qresend - Qresbegin[could be (+) or (-) over period of record]Example Segment – residual change is ΔQconsumptive over data record+Q cfs500100-100-300300DAYS 1100090007000500030001000 2000 4000 6000 8000 10000RES_QDiff (cfs)-350-300-250-200-150-100-5001For river basin Example Segment, reduction of flow due to CU and rain-Qconsumptive MGD-Qrain MGDΔQconsumptive v. ΔQrain Q (cfs)Thank you and questions…• Contact: John Cook john.cook@advdmi.com; 843.513.2130• Or: Ruby Daamen ruby.daamen@advdmi.com 

A Novel Approach in Determining Changes in Consumptive Use

https://tigerprints.clemson.edu/cgi/viewcontent.cgi?filename=0&article=1204&context=scwrc&type=additional

A Novel Approach in Determining Changes in Consumptive Use

Abstract

Similar works

Full text

Available Versions

Clemson University: TigerPrints