Quickstart - pfra-hydromet

Overview of tools:

Example input and output parameters/files.

Example Inputs

filenames and paths for the precipitation frequency table, DataRepository, and location to save the outputs.

Vector File covering area of interest (hydraulic domain).

NOAA data Atlas 14 Volume number, region, and duration.

CN Curve Number calculated for the area of interest.

Convolution Parameters (*epsilons)

tempEpsilon The number of hours over which to resample the incremental excess rainfall during the first convolution.

tempEpsilon2 The number of hours over which to resample the incremental excess rainfall during the final convolution.

convEpsilon The maximum allowable percent difference in incremental excess between two event at any given resampled time step.

volEpsilon The maximum allowable percent difference between the two events total runoff volume.

*Suggested epsilon values are given. Epsilon values should be adapted for each project area by analyzing the curve fitting results. Plots can be shown for every group by setting display_plots=True

Optional Features

Check functions for default options for using a known seed, testing, debugging, etc. (e.g. seed=88)

Example Outputs

PrecipTable

Precipitation Frequency estimates for AOI.

Tr	Lower 90%	Expected Value	Upper 90%
2	5.25	6.32	7.60
5	6.85	8.27	9.96
10	8.20	9.95	12.05
25	10.06	12.45	15.86
50	11.48	14.56	18.75
100	12.85	16.86	22.22
200	14.20	19.33	26.19
500	16.20	22.89	31.84
1000	17.71	25.79	36.11

Random Events Table

Randomly chosen events & metadata before convolution.

Return Period	Ann. Exc. Prob.	ARI	Log10_ARI	Expected Value	Lower (90%)	Upper (90%)	Quartile	Sigma	Fitted Lower (90%) Limit	Fitted Upper (90%) Limit	Random Precipitation
2.000240382	0.499939912	1.442945202	0.366686304	3.15751785	2.876612547	3.507262528	3	0.078317368	2.855989829	3.49088042	3.308578113
2.001189958	0.499702687	1.44393337	0.367370897	3.157996455	2.877045943	3.507791811	3	0.078224163	2.856763942	3.490992541	3.331452983
2.00267425	0.49933233	1.445477879	0.368439979	3.158744005	2.877722879	3.508618516	3	0.078224124	2.857440325	3.491818745	3.453517084
...	...	...	...	...	...	...	...	...	...	...	...
923.2492014	0.001083131	922.749111	6.827357379	13.55155136	11.4917289	14.67457848	3	0.087869372	12.10830888	15.16682025	12.88399871
1119.173615	0.000893516	1118.67354	7.019898923	14.11398151	11.92280701	15.27555124	3	0.088696172	12.59748493	15.81303532	14.49259104
3000	0.000333333	2999.499972	8.006200878	17.38236261	14.3978362	18.76211492	3	0.092819505	15.43292272	19.57804981	18.76211492

Curve Number Table

Randomly chosen CN number and metadata.

Random Sample	Lower	Expected Value	Upper	Sigma	Fitted Lower Limit	Fitted Upper Limit	Random CN
1	67	83	93	0.116325785	71.50469526	96.34332368	88
2	67	83	93	0.116325785	71.50469526	96.34332368	89
3	67	83	93	0.116325785	71.50469526	96.34332368	93
...	...	...	...	...	...	...	...
5107	67	83	93	0.116325785	71.50469526	96.34332368	90
5108	67	83	93	0.116325785	71.50469526	96.34332368	87
5109	67	83	93	0.116325785	71.50469526	96.34332368	93

Final Events Table

Final events to be modeled.

hours	E0001	E0002	E0003	...	E0395	E0396	E0397	E0398	E0399
0	0	0	0	...	0	0	0	0	0
0.5	0.010	0.0047	0.0030	...	0.01359	0.01038	0.00674	0.00234	0.00555
1	0.013	0.00948	0.0043	...	0.0447	0.0584	0.0124	0.00356	0.008906
1.5	0.10	0.067680	0.0294	...	0.1660	0.15102	0.1220	0.0198	0.0433
...	...	...	...	...	...	...	...	...	...
22	0.13	0.1534	0.0807	...	0.1878	0.1707	0.234	0.07389	0.149328
23.5	0.103	0.21576	0.14100	...	0.1373	0.1408	0.317	0.15162	0.221391
24	0.08732	0.25004	0.1836	...	0.1132	0.1293	0.3403	0.2053	0.2430

Event Weight Table

	Weight
E0001	0.006207654
E0002	0.005313752
...	...
E0964	0.000019172
E0965	0.000071544

Metadata

Additional data developed in intermediated calculations included for traceability/reproducibility.

[{
    # Run Data (Event_Duration_Quartile_Decile_CurveNumber)
    'RunInfo': 

            {'E60001': 'E1_6Hr_Q1_D10_CN79',
             'E60002': 'E2_6Hr_Q1_D90_CN78',
             ...}

    # Cumulative (raw) precipitation time-series
    'precip':  

            {'E60001': {'0.0': 0.0,
                        '0.5': 0.8010830472988398,
                        '1.0': 1.4541398793359375,
                        '1.5': 1.8372665541310351,
                        '2.0': 2.057129020916858,
                        '2.5': 2.1550875457224223,
                        '3.0': 2.170325538469955,
                        '3.5': 2.176856106790326,
                        '4.0': 2.176856106790326,
                        '4.5': 2.176856106790326,
                        '5.0': 2.176856106790326,
                        '5.5': 2.176856106790326,
                        '6.0': 2.176856106790326},
             'E60002': { ...
             }


    # Cumulative excess-precipitation time-series
    'cum_excess':

            {'E60001': {'0.0': 0.0,
                        '0.5': 0.02479673948334961,
                        '1.0': 0.23766036959891948,
                        '1.5': 0.4300482251417235,
                        '2.0': 0.5562285716308201,
                        '2.5': 0.6155457935761868,
                        '3.0': 0.6249312656222388,
                        '3.5': 0.6289662984090281,
                        '4.0': 0.6289662984090281,
                        '4.5': 0.6289662984090281,
                        '5.0': 0.6289662984090281,
                        '5.5': 0.6289662984090281,
                        '6.0': 0.6289662984090281},
             'E60002': { ...
             }

    # Incremental excess-precipitation time-series
    'incr_excess'

            {'E60001': {'0.0': 0.0,
                        '0.5': 0.02479673948334961,
                        '1.0': 0.21286363011556986,
                        '1.5': 0.19238785554280402,
                        '2.0': 0.1261803464890966,
                        '2.5': 0.059317221945366705,
                        '3.0': 0.009385472046052001,
                        '3.5': 0.004035032786789294,
                        '4.0': 0.0,
                        '4.5': 0.0,
                        '5.0': 0.0,
                        '5.5': 0.0,
                        '6.0': 0.0},

             'E60002': { ...
             }

    # Curve groups (similar excess-precipitation time-series)
    'groups'

        {'E0001': ['E60002',
                   'E60930',
                   'E60551',
                      ...  ,
                    E64421',
                    E64423',
                   'E64483'],
         'E0002': [...
         ]
        }


    # Test Statistic 
    'test_stat'
      {'E0001': [0.235358,
                 0.251304,
                 0.214125,
                    ...
                 0.18715,
                 0.184935,
                 0.174473],
       'E0002': [...
     ]
}]