... | ... | @@ -17,19 +17,13 @@ function (m, a, cs, alp, bet, d, nbd) |
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return(tm)
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}
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```
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where *m* and *a* were the mean and amplitude of the environmental parameter (temperature or flow) over a year, *d* was the Julian day, *cs* was a shifting parameter to adjust the sinusoidal signal and *error(dd)* was an autoregressive random error of the form:
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where *alp* was a non-zero parameter and *esp* was a normal random number centered on 0 and of variance *bet*.
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where *m* and *a* were the mean and amplitude of the environmental parameter (temperature or flow) over a year, *d* was the Julian day, *cs* was a shifting parameter to adjust the sinusoidal signal and *error(dd)* was an autoregressive random error with *alp* was a non-zero parameter and *esp* was a normal random number centered on 0 and of variance *bet*.
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The Scorff River data series (over 15 yrs) allowed a parameterization of these two functions:
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(1) for the river water temperature (T(t));
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(2) for the logarithm of the flow (flow(t) in m3 /s) divided by the module of the river (5.03 m3 /s).
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Table 2 gives the parameters of the adjusted values.
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For further studies simulating climate change, a temporal trend either on the mean and amplitude parameters ( *m* and *a* respectively) could be easily set up.
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For example, using *a=3* for water temperature (i.e. increase of 3°C over 50 years) and *a=1.25* for water flow (i.e. increase of 25% of amplitude over 50 years), we obtain:
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For further studies simulating climate change, a temporal trend either on the mean and amplitude parameters ( *m* and *a* respectively) could be easily set up. For example, using the [river_climate_model](https://github.com/Ibasam/IBASAM/blob/master/IBASAM/R/river_climate_model.R) function and *a=3* for water temperature (i.e. increase of 3°C over 50 years) and *a=1.25* for water flow (i.e. increase of 25% of amplitude over 50 years), we obtain:
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![](CC.png) |
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\ No newline at end of file |