hydrobm.benchmarks.bm_adjusted_smoothed_precipitation_benchmark
- hydrobm.benchmarks.bm_adjusted_smoothed_precipitation_benchmark(data, cal_mask, precipitation='precipitation', streamflow='streamflow', optimization_method='brute_force')[source]
Calculate the adjusted smoothed precipitation benchmark model as a predictor of runoff-from-precipitation for each timestep in the whole dataframe.
- Parameters:
- datapandas DataFrame
Input data containing precipitation and streamflow columns.
- cal_maskpandas Series
Boolean mask for the calculation period.
- precipitationstr, optional
Name of the precipitation column in the input data. Default is [‘precipitation’].
- streamflowstr, optional
Name of the streamflow column in the input data. Default is [‘streamflow’].
- optimization_methodstr, optional
Optimization method to use. Default is [‘brute_force’]. See optimize_aspb() for further options.
- Returns:
- bm_vals: tuple
Rainfall-runoff ratio value for the calculation period and the optimized lag and window values.
- qbmpandas DataFrame
Benchmark flow time series for the adjusted smoothed precipitation benchmark model. Computed as long-term RRR multiplied by precipitation at each timestep, lagged for a number of timesteps and smoothed with a moving average filter (Schaefli & Gupta, 2007).
References
Schaefli, B. and Gupta, H.V. (2007), Do Nash values have value?. Hydrol. Process., 21: 2075-2080. https://doi.org/10.1002/hyp.6825