Reverse Flood Routing in Natural Channels using Genetic Algorithm

Establishing a clear overview of data discharge availability for water balance modelling in basins is a priority in Europe, and in the particular in the framework of the system of Economic and Environmental Accounts for Water (SEEAW) developed by the EU Directorate-General for the Environment. Howev...

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Published inWater resources management Vol. 29; no. 12; pp. 4241 - 4267
Main Authors Zucco, G., Tayfur, G., Moramarco, T.
Format Journal Article
LanguageEnglish
Published Dordrecht Springer Netherlands 01.09.2015
Springer Nature B.V
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ISSN0920-4741
1573-1650
1573-1650
DOI10.1007/s11269-015-1058-z

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Abstract Establishing a clear overview of data discharge availability for water balance modelling in basins is a priority in Europe, and in the particular in the framework of the system of Economic and Environmental Accounts for Water (SEEAW) developed by the EU Directorate-General for the Environment. However, accurate discharge estimation at a river site depends on rating curve reliability usually defined by recording the water level at a gauged section and carrying out streamflow measurements. Local stage monitoring is fairly straightforward and relatively inexpensive compared to the cost to carry out flow velocity measurements which are, in addition, hindered by high flow. Moreover, hydraulic models may not be ideally suitable to serve the purpose of rating curve extension or its development at a river site upstream/downstream where the discharge is known due to their prohibitive requirement of channel cross-section details and roughness information at closer intervals. Likewise, rainfall-runoff transformation might be applied but its accuracy is tightly linked to detailed information in terms of geomorphological characteristics of intermediate basins as well as rainfall pattern data. On this basis, a procedure for reverse flood routing in natural channels is here proposed for three different configurations of hydrometric monitoring of a river reach where lateral flow is significant and no rainfall data are available for the intermediate basin. The first considers only the downstream channel end as a gauged site where discharge and stages are recorded. The second configuration assumes the downstream end as a gauged site but only in terms of stage. The third configuration envisages both channel ends equipped to recording stages. The channel geometry is known only at channel ends. The developed model has basically four components: (1) the inflow hydrograph is expressed by a Pearson Type-III distribution, involving parameters of peak discharge, time to peak, and a shape factor; (2) the basic continuity equation for flow routing written in the characteristic form is employed; (3) the lateral flow is related to stages at channel ends. (4) the relation between local stage and remote discharge as found by Moramarco et al. (2005b) is exploited. The parameters, coefficients and exponents of the model are obtained, for each configuration, using the genetic algorithm method. Three equipped river branches along the Tiber River in central Italy are used to validate the procedure. Analyses are carried out for three significant flood events occurred along the river and where the lateral flow was significant. Results show the good performance of the procedure for all three monitoring configurations. Specifically, the discharge hydrographs assessed at channel ends are found satisfactory both in terms of shape with a Nash-Sutcliffe ranging overall in the interval (0.755–0.972) and in the reproduction of rating curves at channel ends. Finally, by a synthetic test the performance of the developed procedure is compared to that of the hydraulic model coupled with a hydrologic model. Two river reaches are considered, the first along the Tiber River and the second one located in the Rio Grande basin which is a tributary of the Tiber River. Detailed channel geometry data are available for both the river sections. Results showed the effectiveness of the reverse flood routing to reproducing fairly well the hydrographs simulated by the hydraulic model in the three monitoring investigated configurations.
AbstractList Establishing a clear overview of data discharge availability for water balance modelling in basins is a priority in Europe, and in the particular in the framework of the system of Economic and Environmental Accounts for Water (SEEAW) developed by the EU Directorate-General for the Environment. However, accurate discharge estimation at a river site depends on rating curve reliability usually defined by recording the water level at a gauged section and carrying out streamflow measurements. Local stage monitoring is fairly straightforward and relatively inexpensive compared to the cost to carry out flow velocity measurements which are, in addition, hindered by high flow. Moreover, hydraulic models may not be ideally suitable to serve the purpose of rating curve extension or its development at a river site upstream/downstream where the discharge is known due to their prohibitive requirement of channel cross-section details and roughness information at closer intervals. Likewise, rainfall-runoff transformation might be applied but its accuracy is tightly linked to detailed information in terms of geomorphological characteristics of intermediate basins as well as rainfall pattern data. On this basis, a procedure for reverse flood routing in natural channels is here proposed for three different configurations of hydrometric monitoring of a river reach where lateral flow is significant and no rainfall data are available for the intermediate basin. The first considers only the downstream channel end as a gauged site where discharge and stages are recorded. The second configuration assumes the downstream end as a gauged site but only in terms of stage. The third configuration envisages both channel ends equipped to recording stages. The channel geometry is known only at channel ends. The developed model has basically four components: (1) the inflow hydrograph is expressed by a Pearson Type-III distribution, involving parameters of peak discharge, time to peak, and a shape factor; (2) the basic continuity equation for flow routing written in the characteristic form is employed; (3) the lateral flow is related to stages at channel ends. (4) the relation between local stage and remote discharge as found by Moramarco et al. (2005b) is exploited. The parameters, coefficients and exponents of the model are obtained, for each configuration, using the genetic algorithm method. Three equipped river branches along the Tiber River in central Italy are used to validate the procedure. Analyses are carried out for three significant flood events occurred along the river and where the lateral flow was significant. Results show the good performance of the procedure for all three monitoring configurations. Specifically, the discharge hydrographs assessed at channel ends are found satisfactory both in terms of shape with a Nash-Sutcliffe ranging overall in the interval (0.755–0.972) and in the reproduction of rating curves at channel ends. Finally, by a synthetic test the performance of the developed procedure is compared to that of the hydraulic model coupled with a hydrologic model. Two river reaches are considered, the first along the Tiber River and the second one located in the Rio Grande basin which is a tributary of the Tiber River. Detailed channel geometry data are available for both the river sections. Results showed the effectiveness of the reverse flood routing to reproducing fairly well the hydrographs simulated by the hydraulic model in the three monitoring investigated configurations.
Author Tayfur, G.
Moramarco, T.
Zucco, G.
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  organization: Department. Civil Engineering, Izmir Institute of Technology
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  surname: Moramarco
  fullname: Moramarco, T.
  organization: Researcher, National Research Council, Research Institute for Geo-Hydrological Protection
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Issue 12
Keywords Hydrograph generation
Flood wave
Simulation
Peak rate
Genetic algorithm
River reach
Time to peak
Hydraulic modelling
Reverse routing
Language English
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References MoramarcoTPandolfoCSinghVPAccuracy of kinematic wave approximation for flood routing. II. Unsteady analysis”J Hydrol Eng200813111089109610.1061/(ASCE)1084-0699(2008)13:11(1089)
BroccaLMeloneFMoramarcoTDistributed rainfall-runoff modelling for flood frequency estimation and flood forecasting”Hydrol Process201125182801281310.1002/hyp.8042
TajiKMiyakeTTamuraH“On error back propagation algorithm using absolute error function”Int Conf Syst Man Cybern IEEE SMC '99Confer Proc19995401406
ChengCTZhaoMYChauKWWuXYUsing genetic algorithm and TOPSIS for Xinanjiang model calibration with a single procedureJ Hydrol20063161–412914010.1016/j.jhydrol.2005.04.022
EliRNWiggertJMContractorDNReverse flow routing by the implicit method”Water Resour Res197410359760010.1029/WR010i003p00597
TayfurGBarbettaSMoramarcoTGenetic Algorithm-Based Discharge Estimation at Sites Receiving Lateral Inflows”J Hydrol Eng200914546347410.1061/(ASCE)HE.1943-5584.0000009
Artichowicz W, Szymkiewicz R (2009) “Inverse integration of open channel flow equation”. International Symposium on Water Management and Hydraulic Engineering, Ohrid, Macedonia
MoramarcoTSaltalippiCSinghVPEstimation of mean velocity in natural channels based on Chiu’s velocity distribution equation”J Hydrol Eng200491425010.1061/(ASCE)1084-0699(2004)9:1(42)
Perumal M, Moramarco T, Sahoo B, Barbetta S (2010) “On the practical applicability of the VPMS routing method for rating curve development at ungauged river sites”. Water Resour Res, 46, W03522, doi:10.1029/2009WR008103, 2010, pp. 9
ChengCTOuCPChauKWCombining a fuzzy optimal model with a genetic algorithm to solve multiobjective rainfall-runoff model calibration”J Hydrol20022681–4728610.1016/S0022-1694(02)00122-1
GoldbergDEGenetic Algorithms”1999USAAddison-Wesley
GuanJAralMMRemediation System Design with Multiple Uncertain Parameters using Fuzzy Sets and Genetic Algorithm”J Hydrol Eng200510538639410.1061/(ASCE)1084-0699(2005)10:5(386)
HejaziMICaiXMBorahDKCalibrating a watershed simulation model involving human interference: an application of multi-objective genetic algorithms”J Hydroinf20081019711110.2166/hydro.2008.010
Perumal M, Moramarco T, Sahoo B, Barbetta S (2007) “A methodology for discharge estimation and rating curve development at ungauged river sites”. Water Resour Res, 43, W02412, doi:10.1029/2005WR004609, 2007, pp. 22
SinghRMDattaBIdentification of Groundwater Pollution Sources Using GA-based Linked Simulation Optimization Model”J Hydrol Eng200611210110910.1061/(ASCE)1084-0699(2006)11:2(101)
Danish Hydraulic Institute (DHI) (2003) “User’s manual and technical references for MIKE 11” (version 2003). Hørsholm, Denmark
SahooBPerumalMMoramarcoTBarbettaSRating Curve Development at Ungauged River Sites using Variable Parameter Muskingum Discharge Routing Method”Water Resour Manag20142820143783380010.1007/s11269-014-0709-9
DasAReverse stream flow routing by using Muskingum models”Sadhana200934348349910.1007/s12046-009-0019-8
MoramarcoTMeloneFSinghVPAssessment of flooding in urbanized ungauged basins: a case study in the Upper Tiber area, Italy”Hydrol Process200519101909192410.1002/hyp.5634
LiongSYChanWTShreeRamJPeak flow forecasting with genetic algorithm and SWMM”J Hydraul Eng ASCE1995121861361710.1061/(ASCE)0733-9429(1995)121:8(613)
BarbettaSFranchiniMMeloneFMoramarcoTEnhancement and comprehensive evaluation of the Rating Curve Model for different river sites”J Hydrol2012464–46537638710.1016/j.jhydrol.2012.07.027
ChengCTWuXYChauKWMultiple criteria rainfall-runoff model calibration using a parallel genetic algorithm in a cluster of computer”Hydrol Sci J200550610691088
TayfurGSoft Computing in Water Resources Engineering”2012SouthamptonWIT Press
TayfurGSinghVPPredicting Mean and Bankfull Discharge from Channel Cross-Sectional Area by Expert and Regression Methods”Water Resour Manag20112551253126710.1007/s11269-010-9741-6
GoldbergDEGenetic algorithms for search, optimization, and machine learning”1989USAAddison-Wesley
JainABhattacharjyaRKSanagaSOptimal design of composite channels using genetic algorithm”J Irrig Drain Eng2004130428629510.1061/(ASCE)0733-9437(2004)130:4(286)
SzymkiewiczRNumerical stability of implicit four-point scheme applied to inverse linear flow routing”J Hydrol1996176132310.1016/0022-1694(95)02785-8
SahooBField application of the multilinear Muskingum discharge routing method”Water Resour Manag20132720131193120510.1007/s11269-012-0228-5
TayfurGMoramarcoTSinghVPPredicting and forecasting flow discharge at sites receiving significant lateral inflow”Hydrol Process2007211848185910.1002/hyp.6320
BruenMDoogeJCIHarmonic analysis of the stability of reverse routing in channels”Hydrol Earth Syst Sci200711155956810.5194/hess-11-559-2007
Palisade Corporation“Evolver, the genetic algorithm solver for Microsoft Excel 2012”2013New YorkNewfield
SenZOztopalAGenetic algorithms for the classification and prediction of precipitation occurrence”Hydrol Sci J200146225526710.1080/02626660109492820
TayfurGGA-optimized method predicts dispersion coefficient in natural channels”Hydrol Res2009401657810.2166/nh.2009.010
TayfurGMoramarcoT“Predicting hourly-based flow discharge hydrographs from level data using genetic algorithms”J Hydrol20083521–2779310.1016/j.jhydrol.2007.12.029
AytekAKisiOA genetic programming approach to suspended sediment modelling”J Hydrol20083513–428829810.1016/j.jhydrol.2007.12.005
DoogeJCIBruenMProblems in reverse routing”Acta Geol Pol2005534357371
WuCLChauKWA flood forecasting neural network model with genetic algorithm”Int J Environ Pollut2006283–426127310.1504/IJEP.2006.011211
D’OriaMTandaMGReverse flow routing in open channels: A Bayesian geostatistical approach”J Hydrol2012460–46113013510.1016/j.jhydrol.2012.06.055
MoramarcoTBarbettaSMeloneFSinghVPRelating local stage and remote discharge with significant lateral inflow”J Hydrol Eng2005101586910.1061/(ASCE)1084-0699(2005)10:1(58)
B Sahoo (1058_CR28) 2014; 28
SY Liong (1058_CR19) 1995; 121
JCI Dooge (1058_CR12) 2005; 53
RM Singh (1058_CR30) 2006; 11
M Bruen (1058_CR5) 2007; 11
MI Hejazi (1058_CR17) 2008; 10
T Moramarco (1058_CR23) 2008; 13
K Taji (1058_CR32) 1999; 5
G Tayfur (1058_CR33) 2009; 40
L Brocca (1058_CR4) 2011; 25
G Tayfur (1058_CR37) 2007; 21
J Guan (1058_CR16) 2005; 10
1058_CR10
Palisade Corporation (1058_CR24) 2013
T Moramarco (1058_CR22) 2005; 10
B Sahoo (1058_CR27) 2013; 27
CL Wu (1058_CR39) 2006; 28
1058_CR1
T Moramarco (1058_CR20) 2004; 9
RN Eli (1058_CR13) 1974; 10
DE Goldberg (1058_CR14) 1989
R Szymkiewicz (1058_CR31) 1996; 176
A Aytek (1058_CR2) 2008; 351
CT Cheng (1058_CR8) 2006; 316
Z Sen (1058_CR29) 2001; 46
G Tayfur (1058_CR34) 2012
CT Cheng (1058_CR7) 2005; 50
S Barbetta (1058_CR3) 2012; 464–465
DE Goldberg (1058_CR15) 1999
1058_CR25
G Tayfur (1058_CR38) 2009; 14
A Jain (1058_CR18) 2004; 130
CT Cheng (1058_CR6) 2002; 268
1058_CR26
A Das (1058_CR11) 2009; 34
G Tayfur (1058_CR35) 2008; 352
G Tayfur (1058_CR36) 2011; 25
M D’Oria (1058_CR9) 2012; 460–461
T Moramarco (1058_CR21) 2005; 19
References_xml – reference: ChengCTZhaoMYChauKWWuXYUsing genetic algorithm and TOPSIS for Xinanjiang model calibration with a single procedureJ Hydrol20063161–412914010.1016/j.jhydrol.2005.04.022
– reference: LiongSYChanWTShreeRamJPeak flow forecasting with genetic algorithm and SWMM”J Hydraul Eng ASCE1995121861361710.1061/(ASCE)0733-9429(1995)121:8(613)
– reference: MoramarcoTSaltalippiCSinghVPEstimation of mean velocity in natural channels based on Chiu’s velocity distribution equation”J Hydrol Eng200491425010.1061/(ASCE)1084-0699(2004)9:1(42)
– reference: GoldbergDEGenetic Algorithms”1999USAAddison-Wesley
– reference: GoldbergDEGenetic algorithms for search, optimization, and machine learning”1989USAAddison-Wesley
– reference: DasAReverse stream flow routing by using Muskingum models”Sadhana200934348349910.1007/s12046-009-0019-8
– reference: EliRNWiggertJMContractorDNReverse flow routing by the implicit method”Water Resour Res197410359760010.1029/WR010i003p00597
– reference: TayfurGSoft Computing in Water Resources Engineering”2012SouthamptonWIT Press
– reference: BroccaLMeloneFMoramarcoTDistributed rainfall-runoff modelling for flood frequency estimation and flood forecasting”Hydrol Process201125182801281310.1002/hyp.8042
– reference: SinghRMDattaBIdentification of Groundwater Pollution Sources Using GA-based Linked Simulation Optimization Model”J Hydrol Eng200611210110910.1061/(ASCE)1084-0699(2006)11:2(101)
– reference: JainABhattacharjyaRKSanagaSOptimal design of composite channels using genetic algorithm”J Irrig Drain Eng2004130428629510.1061/(ASCE)0733-9437(2004)130:4(286)
– reference: ChengCTWuXYChauKWMultiple criteria rainfall-runoff model calibration using a parallel genetic algorithm in a cluster of computer”Hydrol Sci J200550610691088
– reference: MoramarcoTMeloneFSinghVPAssessment of flooding in urbanized ungauged basins: a case study in the Upper Tiber area, Italy”Hydrol Process200519101909192410.1002/hyp.5634
– reference: TayfurGSinghVPPredicting Mean and Bankfull Discharge from Channel Cross-Sectional Area by Expert and Regression Methods”Water Resour Manag20112551253126710.1007/s11269-010-9741-6
– reference: TajiKMiyakeTTamuraH“On error back propagation algorithm using absolute error function”Int Conf Syst Man Cybern IEEE SMC '99Confer Proc19995401406
– reference: ChengCTOuCPChauKWCombining a fuzzy optimal model with a genetic algorithm to solve multiobjective rainfall-runoff model calibration”J Hydrol20022681–4728610.1016/S0022-1694(02)00122-1
– reference: D’OriaMTandaMGReverse flow routing in open channels: A Bayesian geostatistical approach”J Hydrol2012460–46113013510.1016/j.jhydrol.2012.06.055
– reference: Artichowicz W, Szymkiewicz R (2009) “Inverse integration of open channel flow equation”. International Symposium on Water Management and Hydraulic Engineering, Ohrid, Macedonia
– reference: Perumal M, Moramarco T, Sahoo B, Barbetta S (2007) “A methodology for discharge estimation and rating curve development at ungauged river sites”. Water Resour Res, 43, W02412, doi:10.1029/2005WR004609, 2007, pp. 22
– reference: BarbettaSFranchiniMMeloneFMoramarcoTEnhancement and comprehensive evaluation of the Rating Curve Model for different river sites”J Hydrol2012464–46537638710.1016/j.jhydrol.2012.07.027
– reference: SahooBPerumalMMoramarcoTBarbettaSRating Curve Development at Ungauged River Sites using Variable Parameter Muskingum Discharge Routing Method”Water Resour Manag20142820143783380010.1007/s11269-014-0709-9
– reference: TayfurGBarbettaSMoramarcoTGenetic Algorithm-Based Discharge Estimation at Sites Receiving Lateral Inflows”J Hydrol Eng200914546347410.1061/(ASCE)HE.1943-5584.0000009
– reference: Palisade Corporation“Evolver, the genetic algorithm solver for Microsoft Excel 2012”2013New YorkNewfield
– reference: TayfurGMoramarcoTSinghVPPredicting and forecasting flow discharge at sites receiving significant lateral inflow”Hydrol Process2007211848185910.1002/hyp.6320
– reference: MoramarcoTPandolfoCSinghVPAccuracy of kinematic wave approximation for flood routing. II. Unsteady analysis”J Hydrol Eng200813111089109610.1061/(ASCE)1084-0699(2008)13:11(1089)
– reference: Danish Hydraulic Institute (DHI) (2003) “User’s manual and technical references for MIKE 11” (version 2003). Hørsholm, Denmark
– reference: AytekAKisiOA genetic programming approach to suspended sediment modelling”J Hydrol20083513–428829810.1016/j.jhydrol.2007.12.005
– reference: BruenMDoogeJCIHarmonic analysis of the stability of reverse routing in channels”Hydrol Earth Syst Sci200711155956810.5194/hess-11-559-2007
– reference: DoogeJCIBruenMProblems in reverse routing”Acta Geol Pol2005534357371
– reference: GuanJAralMMRemediation System Design with Multiple Uncertain Parameters using Fuzzy Sets and Genetic Algorithm”J Hydrol Eng200510538639410.1061/(ASCE)1084-0699(2005)10:5(386)
– reference: WuCLChauKWA flood forecasting neural network model with genetic algorithm”Int J Environ Pollut2006283–426127310.1504/IJEP.2006.011211
– reference: SahooBField application of the multilinear Muskingum discharge routing method”Water Resour Manag20132720131193120510.1007/s11269-012-0228-5
– reference: TayfurGGA-optimized method predicts dispersion coefficient in natural channels”Hydrol Res2009401657810.2166/nh.2009.010
– reference: MoramarcoTBarbettaSMeloneFSinghVPRelating local stage and remote discharge with significant lateral inflow”J Hydrol Eng2005101586910.1061/(ASCE)1084-0699(2005)10:1(58)
– reference: TayfurGMoramarcoT“Predicting hourly-based flow discharge hydrographs from level data using genetic algorithms”J Hydrol20083521–2779310.1016/j.jhydrol.2007.12.029
– reference: Perumal M, Moramarco T, Sahoo B, Barbetta S (2010) “On the practical applicability of the VPMS routing method for rating curve development at ungauged river sites”. Water Resour Res, 46, W03522, doi:10.1029/2009WR008103, 2010, pp. 9
– reference: SzymkiewiczRNumerical stability of implicit four-point scheme applied to inverse linear flow routing”J Hydrol1996176132310.1016/0022-1694(95)02785-8
– reference: HejaziMICaiXMBorahDKCalibrating a watershed simulation model involving human interference: an application of multi-objective genetic algorithms”J Hydroinf20081019711110.2166/hydro.2008.010
– reference: SenZOztopalAGenetic algorithms for the classification and prediction of precipitation occurrence”Hydrol Sci J200146225526710.1080/02626660109492820
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Snippet Establishing a clear overview of data discharge availability for water balance modelling in basins is a priority in Europe, and in the particular in the...
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SubjectTerms Algorithms
Atmospheric Sciences
Basins
Channels
Civil Engineering
Discharge
Discharge hydrographs
Downstream
Earth and Environmental Science
Earth Sciences
Environment
Environmental monitoring
equations
European Union
Flood peak
Flood routing
Floods
Flow measurement
Flow velocity
Genetic algorithms
Geometry
Geotechnical Engineering & Applied Earth Sciences
High flow
Hydraulic models
Hydraulics
Hydrogeology
hydrograph
Hydrologic data
Hydrologic models
Hydrology
Hydrology models
Hydrology/Water Resources
Italy
Mathematical models
meteorological data
monitoring
Parameter estimation
rain
Rainfall-runoff relationships
Ratings
reproduction
Rivers
roughness
Stream discharge
Stream flow
Water balance
Water inflow
Water levels
Water resources
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