NON-LINEAR SIGNAL PROCESSING OF RAINFALL- RUNOFF PROCESS USING DATA-BASED MECHANISTIC MODELING (DBM) CASE STUDY: KHERSAN BASIN

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JALAL KAMALI N.; SEDGHI H.

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Journal: IRAN-WATER RESOURCES RESEARCH Year:2008 | Volume:4 | Issue:2 (11) Page(s): 35-45

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Title

NON-LINEAR SIGNAL PROCESSING OF RAINFALL- RUNOFF PROCESS USING DATA-BASED MECHANISTIC MODELING (DBM) CASE STUDY: KHERSAN BASIN

Author(s)

JALAL KAMALI N. | SEDGHI H. | Issue Writer Certificate

Keywords

DATA-BASED MECHANISTIC MODELINGQ4

RAINFALL- RUNOFF PROCESSQ4

LINEAR STORE MODELSQ2

PARALLEL FLOW PROCESSQ4

TIME VARIABLE AND STATE DEPENDENT PARAMETER ESTIMATIONQ4

Abstract

There were many hydrological researches focused on dynamic and linear modeling of rainfall-runoff process. Conversion of rainfall data to runoff consists of nonlinear complex relationships which are resulted from interactions of different sets of hydrological process. The stochastic modeling is therefore seems to be more sensible in this estimate than deterministic ones.In this research observed runoff is modeled against total rainfall. This will mainly avoid misleading theories in breaking the rainfall and runoff time series into the excess rainfall and the direct runoff time series. A Transfer Function (TF) model with single input and single output variable (SISO) is used in this research. This function is transferred to the state space equations. The stochastic DATA-BASED MECHANISTIC MODELING (DBM) method relying upon recursive Kalman filtering algorithm is then used to identify the non-linear relationship between rainfall and runoff.This approach is applied to the Khersan sub basin in the Great Karon catchment south western Iran. The relation between the calibrated parameters and the routine characteristics of the basin flow showed a probable parallel structure of flow routine in this sub basin. Finally the sensitivity analysis is performed using the Monte Carlo Simulation (MCS) in order to quantify the reliability of the model.

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APA: Copy

JALAL KAMALI, N., & SEDGHI, H.. (2008). NON-LINEAR SIGNAL PROCESSING OF RAINFALL- RUNOFF PROCESS USING DATA-BASED MECHANISTIC MODELING (DBM) CASE STUDY: KHERSAN BASIN. IRAN-WATER RESOURCES RESEARCH, 4(2 (11)), 35-45. SID. https://sid.ir/paper/99979/en

Vancouver: Copy

JALAL KAMALI N., SEDGHI H.. NON-LINEAR SIGNAL PROCESSING OF RAINFALL- RUNOFF PROCESS USING DATA-BASED MECHANISTIC MODELING (DBM) CASE STUDY: KHERSAN BASIN. IRAN-WATER RESOURCES RESEARCH[Internet]. 2008;4(2 (11)):35-45. Available from: https://sid.ir/paper/99979/en

IEEE: Copy

N. JALAL KAMALI, and H. SEDGHI, “NON-LINEAR SIGNAL PROCESSING OF RAINFALL- RUNOFF PROCESS USING DATA-BASED MECHANISTIC MODELING (DBM) CASE STUDY: KHERSAN BASIN,” IRAN-WATER RESOURCES RESEARCH, vol. 4, no. 2 (11), pp. 35–45, 2008, [Online]. Available: https://sid.ir/paper/99979/en

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