Construction of a Flood Risk Management System in a Watershed by Using Statistical Models Case Study

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Construction of a Flood Risk Management System
in a Watershed by Using Statistical Models (Case
Study Kardeh Watershed)

• Azadeh Ramesh1, Alireza Shahabfar2
• 1. Msc. of Geography, North Khorasan
  Meteorological Administration, I.R.of Iran
  Meteorological Organization (IRIMO)
• 2. Msc. of Civil Engineering, North Khorasan
  Meteorological Administration, I.R.of Iran
  Meteorological Organization (IRIMO)

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Introduction

• By occurrence of climate change phenomenon
  and increasing of human s interference on global
  climate, two natural disasters such as

• have effected on different parts of the earth. In
  the recent years, our country was alternatively
  witness in occurring of floods and severe
  droughts in most of places, specially occurring
  of these natural disasters together, improve each
  other as because of severe droughts, vegetative
  coverage and humidity of soil are spoiled that is
  facilitation agent for flowing destructive
  floods. On the other hand, occurring of severe
  floods have caused destroyed of agricultural
  lands and leaching of fertile soils and have
  amplified the effective of drought in these
  places.

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In a watershed which has high submergible
potential, with an alternative and correct
management, we can reduce the effects and damages
of flood and use of it for increasing of water
potential , for example by

• increasing of soil moisture
• discharging of aquifer
• increasing of water resources of lake of dams.

For succession in these actions,
an alternative and optimum flood risk management
in that watershed
is necessary.
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This research has focused on understanding the
nature of likely changes in flood risk across the
Iran ?and in the North Khorasan
The location of Kardeh basin over the map
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Statistical Models of Climate Variability

To study the impact of different representations
of climate variability on flood risk assessment
and project evaluation, this paper considers 3
reasonable models that can forecast flood risk
1- Log-Normal i.i.d. Model (LN i.i.d.)
2- Log-Normal Trend Model (LN Trend)
3- Log-Normal ARMA Model (LN ARMA)
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1- Log-Normal i.i.d. Model (LN i.i.d.)

• This model assumes that the maximum annual
  floods (Qt) are independent and identically
  distributed random variables where

In this equation Qt Maximum annual flow for
year t. ? Log scale mean of the maximum annual
flow ? Long run standard deviation This is a
traditional model used for flood risk management.
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2- Log-Normal Trend Model (LN Trend)

• This model assumes that the maximum annual
  floods (Qt )have a log normal distribution around
  a linear trend, so that,

In this equation Qt Maximum annual flow for
year t. ? Log scale mean of the maximum annual
flow ? Log scale slope of the trend in the
Log-Normal trend model T Year index t Year
index t Average of years in the design period ?
Error process
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3- Log-Normal ARMA Model (LN ARMA)

• This model assumes that the maximum annual
  floods (Qt) are generated by a stationary
  low-order Autoregressive Moving-Average process
  ARMA (p,q) for the log-flood series

In this equation Qt Maximum annual flow for
year t. ? Log scale mean of the maximum annual
flow ARMA Auto Regressive Moving-Average p
order of Auto Regressive q order of
Moving-Average
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• Risk Forecasts
• for The Kardeh Basin

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• 1- Log-Normal i.i.d. Model (LN i.i.d.)

• For the LN i.i.d. model , threshold is a
  straight horizontal line that was almost exceeded
  in 2005. The LN i.i.d. model predicts the lowest
  risk levels over the design period.

Mean and 20-years flood levels over historical
and planning period for Kardeh record ?using
three flood risk models
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• 2- Log-Normal Trend Model (LN Trend)

• For the LN Trend model the 2 exceedance
  threshold is a straight line with a positive
  slope and it extends from 1984 to 2001 for LN
  Trend (Ts) and from 1984 to 2005 for LN Trend
  (Tc). The Log-Normal model with the assumption
  that the trend continues, LN Trend/Tc, includes
  the statistically significant positive trend (?
  0.3119) . as a result the forecasted exceedance
  probability would converge eventually to 1.
  Because ? is small, this happens slowly and for
  the design period (20 years) the results may be
  unreasonable. This model predicts the highest
  risk over the planning horizon. LN Trend/Ts
  predicts lower flooding risks than the LN
  Trend/Tc model, but higher risk levels than the
  LN i.i.d. and LN ARIMA models.

Mean and 20-years flood levels over historical
and planning period for Kardeh record ?using
three flood risk models
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• 3- Log-Normal ARMA Model (LN ARMA)

• the ARIMA(1,2,3) model conditional mean forecast
  delays to the long-term mean after a decade or
  so. For the mean, the linear variation extends
  from 1984 to 2005 .The LN ARIMA(1,2,3) model
  forecasts flood risk as an average of the
  long-run unconditional risk and the risk in the
  last years of record. Because the last years of
  record at Kardeh experienced several large
  floods, the predicted annual flood tends to be
  much higher than the long-run unconditional mean.
  The LN ARIMA(1,2,3) model is very responsive to
  the observed values at the end of the record. It
  would predict lower risks than the LN i.i.d.
  model if the last years of record experienced
  only smaller floods.

Mean and 20-years flood levels over historical
and planning period for Kardeh record ?using
three flood risk models
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• Management of flood risk in a world with
  variable climate would be aided by simple summary
  measures of flood risk. A set of such simple risk
  measures was developed and used to compare three
  risk forecasts. The investigation demonstrated
  that stationary time series models are very
  flexible and produce a reasonable interpretation
  of historical records.
• The i.i.d. model is included within this
  larger class of models. Stationary time series
  allow risk to vary but preserve the assumption
  that hydrology is stationary in the long run. In
  this framework, perceived trends in a flood
  record can be interpreted as the result of
  natural and stationary variations. When
  stationary time series models are used for risk
  forecasting, the predicted risk returns to the
  unconditional long run average as the forecasting
  horizon is extended. The resulting variation in
  flood risk is likely to affect flood risk
  management if decision parameters can be adjusted
  on a year-to-year basis however variations in
  flood risk are likely to have disappeared before
  major construction projects can be designed,
  authorized and completed.

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• Conclusions

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In this research we consider three simple
statistical models 1- LN i.i.d Model 2- LN
Trend Ts Tc Model 3- LN ARIMA.As a result we
understand that

• 1) LN i.i.d model have large conclusion and have
  no economic benefits and amount of flood risk is
  permanent per time so the result of this model
  isn't logical.
• 2) LN Trend Ts and LN Trend Tc models have not
  actual conclusion because it doesn't consider
  climate variability and application of this
  conclusion isn't possible in actual conditions
  and they don't offer acceptable responses because
  they obey from one general trend.
• 3) LN ARIMA model doesnt have disadvantages of
  previous models and it's results are very
  reasonable and argumentative for predictions and
  simulations of flood risk in Kardeh basin. But,
  we must consider that we can use time series
  model just for one or two time steps and
  applications of long predictions in this model
  isn't valuable and in this conditions we must
  update the model by replacing of observed data
  instead of simulated values. Therefore, by
  considerations of advantage and disadvantage,
  this model is very suitable and useful for Kardeh
  basin.

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Thank you for attention