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Assessment of flood forecasting lead time based on generalized likelihood uncertainty estimation approach

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Abstract

Real time updating of rainfall-runoff (RR) models is traditionally performed by state-space formulation in the context of flood forecasting systems. In this paper, however, we examine applicability of generalized likelihood uncertainty estimation (GLUE) approach in real time modification of forecasts. Real time updating and parameter uncertainty analysis was conducted for Abmark catchment, a part of the great Karkheh basin in south west of Iran. A conceptual-distributed RR model, namely ModClark, was used for basin simulation, such that the basin’s hydrograph was determined by the superposition of runoff generated by individual cells in a raster-based discretization. In real time updating of RR model by GLUE method, prior and posterior likelihoods were computed using forecast errors that were obtained from the results of behavioral models and real time recorded discharges. Then, prior and posterior likelihoods were applied to modify forecast confidence limits in each time step. Calibration of parameters was performed using historical data while distribution of parameters was modified in real time based on new data records. Two scenarios of rainfall forecast including prefect-rainfall-forecast and no-rainfall-forecast were assumed in absence of a robust rainfall forecast model in the study catchment. The results demonstrated that GLUE application could offer an acceptable lead time for peak discharge forecast at the expense of high computational demand.

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Abbreviations

AK:

Loss rate coefficient at the beginning of the time interval

AL:

Potential loss rate in mm/h during the time interval

C :

Accumulated loss (mm)

d max :

Maximum travel distance to the catchment outlet

DL:

Amount of initial accumulated rain loss (mm)

DK:

Incremental increase in the loss rate coefficient during the first DL of accumulated loss

ER:

Exponent of precipitation that reflects the influence of precipitation rate on basin average loss characteristics and varies from 0.0 to 1.0

L(θ i |Y):

Performance measure for i-th model conditioned on the observed discharges of Y

N :

Parameter of equation

O t :

Observed discharge at t time step

\(\bar O\) :

Average observed discharge during flood period

P :

The hourly precipitation in mm

RK:

Corresponding ratio for snow loss

RL:

Ratio of rain loss coefficient that corresponding to 10 mm more of accumulated loss

Q t :

Simulated discharge at time step t

\(\bar Q\) :

Average simulated discharge during flood period

SR:

Start values of rainfall loss coefficients (mm/h)

SS:

Start values of snowmelt loss coefficients (mm/h)

t c :

Concentration time

w t :

Weight of discharge corresponding to time step t

σ 2obs :

Observed variance for the period of flood hydrograph

η:

Scale coefficient for rescaling of the posterior likelihood

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Authors and Affiliations

  1. Civil and Environmental Department, AmirKabir University of Technology, Tehran, Iran

    A. Heidari, B. Saghafian & R. Maknoon

  2. Soil Conservation and Watershed Management Research Institute, Tehran, Iran

    B. Saghafian

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  1. A. Heidari
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  2. B. Saghafian
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  3. R. Maknoon
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Correspondence to A. Heidari.

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Heidari, A., Saghafian, B. & Maknoon, R. Assessment of flood forecasting lead time based on generalized likelihood uncertainty estimation approach. Stoch Environ Res Ris Assess 20, 363–380 (2006). https://doi.org/10.1007/s00477-006-0032-y

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