Hydrological Modelling in Data Sparse Environment: Inverse Modelling of a Historical Flood Event

被引:12
|
作者
Bardossy, Andras [1 ]
Anwar, Faizan [1 ]
Seidel, Jochen [1 ]
机构
[1] Univ Stuttgart, Inst Modelling Hydraul & Environm Syst, D-70569 Stuttgart, Germany
关键词
inverse modelling; data uncertainty; parameter uncertainty; data scarcity; RAINFALL; RECONSTRUCTION; OPTIMIZATION; CALIBRATION; DISCHARGES; RISK;
D O I
10.3390/w12113242
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
We dealt with a rather frequent and difficult situation while modelling extreme floods, namely, model output uncertainty in data sparse regions. A historical extreme flood event was chosen to illustrate the challenges involved. Our aim was to understand what the causes might have been and specifically to show how input and model parameter uncertainties affect the output. For this purpose, a conceptual model was calibrated and validated with recent data rich time period. Resulting model parameters were used to model the historical event which subsequently resulted in a rather poor hydrograph. Due to the bad model performance, a spatial simulation technique was used to invert the model for precipitation. Constraints, such as taking the precipitation values at historical observation locations in to account, with correct spatial structures and following the observed regional distributions were used to generate realistic precipitation fields. Results showed that the inverted precipitation improved the performance significantly even when using many different model parameters. We conclude that while modelling in data sparse conditions both model input and parameter uncertainties have to be dealt with simultaneously to obtain meaningful results.
引用
收藏
页数:19
相关论文
共 50 条
  • [21] Spatio-temporal hydrological modelling in a GIS environment
    Dayawansa, N.D.K.
    De Silva, R.P.
    Taylor, J.C.
    IAHS-AISH Publication, 2000, (267): : 433 - 438
  • [22] Spatio-temporal hydrological modelling in a GIS environment
    Dayawansa, NDK
    De Silva, RP
    Taylor, JC
    REMOTE SENSING AND HYDROLOGY 2000, 2001, (267): : 433 - 438
  • [23] Modelling hydrological data with and without long memory
    Burlando, P
    Montanari, A
    Rosso, R
    MECCANICA, 1996, 31 (01) : 87 - 101
  • [24] ATHYS: A hydrological environment for spatial modelling and coupling with GIS
    Bouvier, C
    Delclaux, F
    APPLICATION OF GEOGRAPHIC INFORMATION SYSTEMS IN HYDROLOGY AND WATER RESOURCES MANAGEMENT, 1996, (235): : 19 - 27
  • [25] Scaling input data by GIS for hydrological modelling
    Thieken, AH
    Lücke, A
    Diekkrüger, B
    Richter, O
    HYDROLOGICAL PROCESSES, 1999, 13 (04) : 611 - 630
  • [26] Reconstruction of historical flood discharges from meteorological and hydrological data
    Sudhaus, Dirk
    Buerger, Katrin
    Dostal, Paul
    Imbery, Florian
    Seidel, Jochen
    Konold, Werner
    Mayer, Helmet
    Glaser, Ruediger
    HYDROLOGIE UND WASSERBEWIRTSCHAFTUNG, 2008, 52 (04): : 198 - 202
  • [27] REVIEW OF THREE DATA-DRIVEN MODELLING TECHNIQUES FOR HYDROLOGICAL MODELLING AND FORECASTING
    Oyebode, Oluwaseun
    Otieno, Fred
    Adeyemo, Josiah
    FRESENIUS ENVIRONMENTAL BULLETIN, 2014, 23 (07): : 1443 - 1454
  • [28] Use of historical rainfall series for hydrological modelling - Workshop summary
    Einfalt, T
    Arnbjerg-Nielsen, K
    Frankhauser, R
    Rauch, W
    Schilling, W
    Nguyen, VTV
    Despotovic, J
    WATER SCIENCE AND TECHNOLOGY, 1998, 37 (11) : 1 - 6
  • [29] Hydrological and hydraulic modelling applied to the mapping of flood-prone areas
    Omena Monte, Benicio Emanoel
    Costa, Denis Duda
    Chaves, Mahelvson Bazilio
    Magalhaes, Louis de Oliveira
    Uvo, Cintia B.
    RBRH-REVISTA BRASILEIRA DE RECURSOS HIDRICOS, 2016, 21 (01): : 152 - 167
  • [30] Inverse flood risk modelling under changing climatic conditions
    Cunderlik, Juraj M.
    Simonovic, Slobodan P.
    HYDROLOGICAL PROCESSES, 2007, 21 (05) : 563 - 577