Approximating rainfall-runoff modelling response using a stochastic integral equation

被引:0
作者
Hromadka, TV
Whitley, RJ
机构
[1] Dept of Maths, California State Univ, Fullerton CA 92634, United States
关键词
uncertainty; rainfall-runoff models; hydrology; modelling; stochastic; stochastic integrals;
D O I
暂无
中图分类号
TV21 [水资源调查与水利规划];
学科分类号
081501 ;
摘要
Rainfall-runoff modelling uncertainty can be analysed by the use of a stochastic integral formulation. The stochastic integral equation can be based on the rainfall-runoff model input of model rainfall or model rainfall excess. Similarly, the stochastic integral equation can be based on the rainfall-runoff model output of the modelled runoff hydrograph. The residual between actual measured runoff data and modelled runoff (from the rainfall-runoff model) is analysed here by the use of a stochastic integral equation. This approach is used to develop a set of convolution integral transfer function realizations that represent the chosen rainfall-runoff modelling error. The resulting stochastic integral component is a distribution of possible residual outcomes that may be directly added to the rainfall-runoff model's deterministic outcome, to develop a distribution of probable runoff hydrograph realizations from the chosen rainfall-runoff model.
引用
收藏
页码:1003 / 1019
页数:17
相关论文
共 50 条
  • [41] Rainfall runoff modelling of the Upper Ganga and Brahmaputra basins using PERSiST
    Futter, M. N.
    Whitehead, P. G.
    Sarkar, S.
    Rodda, H.
    Crossman, J.
    ENVIRONMENTAL SCIENCE-PROCESSES & IMPACTS, 2015, 17 (06) : 1070 - 1081
  • [42] Short term rainfall-runoff modelling using several machine learning methods and a conceptual event-based model
    Adnan, Rana Muhammad
    Petroselli, Andrea
    Heddam, Salim
    Guimaraes Santos, Celso Augusto
    Kisi, Ozgur
    STOCHASTIC ENVIRONMENTAL RESEARCH AND RISK ASSESSMENT, 2021, 35 (03) : 597 - 616
  • [43] A semi-distributed approach to rainfall-runoff modelling - a case study in a snow affected catchment
    Kokkonen, T
    Koivusalo, H
    Karvonen, T
    ENVIRONMENTAL MODELLING & SOFTWARE, 2001, 16 (05) : 481 - 493
  • [44] Assessing the performance of an ensemble approach to rainfall-runoff modelling for prediction of the impact of climate change on streamflow
    Silberstein, Richard
    Aryal, Santosh
    20TH INTERNATIONAL CONGRESS ON MODELLING AND SIMULATION (MODSIM2013), 2013, : 2401 - 2408
  • [45] DeepGR4J: A deep learning hybridization approach for conceptual rainfall-runoff modelling
    Kapoor, Arpit
    Pathiraja, Sahani
    Marshall, Lucy
    Chandra, Rohitash
    ENVIRONMENTAL MODELLING & SOFTWARE, 2023, 169
  • [46] A comparative assessment of rainfall-runoff modelling against regional flow duration curves for ungauged catchments
    Kim, Daeha
    Jung, Il Won
    Chun, Jong Ahn
    HYDROLOGY AND EARTH SYSTEM SCIENCES, 2017, 21 (11) : 5647 - 5661
  • [47] Estimation of Volumetric Runoff Coefficients for Texas Watersheds Using Land-Use and Rainfall-Runoff Data
    Dhakal, Nirajan
    Fang, Xing
    Cleveland, Theodore G.
    Thompson, David B.
    Asquith, William H.
    Marzen, Luke J.
    JOURNAL OF IRRIGATION AND DRAINAGE ENGINEERING, 2012, 138 (01) : 43 - 54
  • [48] Study of the rainfall-runoff process in the Andes region using a continuous distributed model
    Braud, I
    Fernandez, P
    Bouraoui, F
    JOURNAL OF HYDROLOGY, 1999, 216 (3-4) : 155 - 171
  • [49] Calibration of a conceptual rainfall-runoff model using a genetic algorithm integrated with runoff estimation sensitivity to parameters
    Wu, Shiang-Jen
    Lien, Ho-Cheng
    Chang, Che-Hao
    JOURNAL OF HYDROINFORMATICS, 2012, 14 (02) : 497 - 511
  • [50] Identifying evapotranspiration relationships for input into rainfall-runoff models using the SWIM model
    Carlile, PW
    Croke, BFW
    Jakemane, AJ
    MODSIM 2003: INTERNATIONAL CONGRESS ON MODELLING AND SIMULATION, VOLS 1-4: VOL 1: NATURAL SYSTEMS, PT 1; VOL 2: NATURAL SYSTEMS, PT 2; VOL 3: SOCIO-ECONOMIC SYSTEMS; VOL 4: GENERAL SYSTEMS, 2003, : 879 - 884