Prediction of Watershed Runoff. www.bren.ucsb.edu/academics/courses/235/ Lectures/ WA%205_%20Prediction%20of%20Watershed%20Runoff.ppt -. Prediction of watershed storm runoff. What do we want to know?. Total volume of storm runoff What does the hydrograph look like?
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Prediction of Watershed Runoff
www.bren.ucsb.edu/academics/courses/235/ Lectures/
WA%205_%20Prediction%20of%20Watershed%20Runoff.ppt -
What do we want to know?
It is assumed that a watershed can hold a certain maximum amount of precipitation, Smax
(1)
where F∞ is the total amount of water retained as t becomes very large (i.e. in a long, large storm.) It is the cumulative amount of infiltration
It is also assumed that during the storm (and particularly at the end of the storm)
(2)
(3)
(4)
(5)
The few values actually tabulated in the ‘original’ report are 0.15-0.2 Smax.
(6)
(7)
for all P > Ia . ELSE R = 0.
Thus, the problem of predicting storm runoff depth is reduced to estimating a single value, the maximum retention capacity of the watershed, Smax.
Designers of the procedure must have known that they needed R to respond to P in approximately as follows: SCS (1972) storm runoff relationship
or
or
A spatially weighted average CN is computed for a watershed.
Hydrologic Soil Groups are defined in SCS County Soil Survey reports
1. Method entrenched in runoff prediction practice and is acceptable to regulatory agencies and professional bodies.2. Attractively simple to use.3. Required data available in SCS county soil maps in paper and digital form.4. Method packaged in handbooks and computer programs5. Appears to give ‘reasonable’ results --- big storms yield a lot of runoff, fine-grained, wet soils, with thin vegetation covers yield more storm runoff in small watersheds than do sandy soils under forests, etc.6. No easily available competitor that does any better. The method is already hidden in various larger “computer models”, such as HEC-HMS).7. The task for a watershed analyst or regulator is to decide how to interpret and use the results.
Can be predicted deterministically or estimated probabilistically (i.e. the risk of them can be imagined)
t75
Unspoken conceptual model is Horton overland flow
Watershed boundary
t45
t60
Isochrones of runoff
t15
t30
tequilib= 75 minutes
Qpeak
Q
t = 0
tequilib
t
Qpeak = C I A
Qpeak = 0.278C I A
Australian study 271 small basins:
mI is the rank of the ith flood peak in a set of n peaks
the Weibull formula
T is the recurrence interval (yr). The long-term average interval between floods greater than Qi
See examples in Water in Environmental Planning, pp. 307-308.
Note that this procedure involves fitting a theoretical probability distribution to an observed sample drawn from an imaginary (but not well-understood) population
The “true” theoretical probability distribution of flood discharges is not known, and we have no reason to believe it is simple or has only 1 or parameters.
Plotting the data set on various types of graph paper with different scales, designed to represent various theoretical probability distributions as straight lines, yields graphs of different shapes, which when extrapolated beyond the limits of measurement predict a range of peak flood discharges.
•
where A = drainage area
Ei are watershed characteristics, such as mean annual precipitation, average elevation, average slope, etc.
The equivalent years of record is a measure of the predictive ability
of the regression equation, expressed as the number of years of actual
peak-flow data required to achieve results equal to those obtained from
the regression equation. A is in sq miles, P is in inches (annual), Q is cfs