def precip_to_runoff_h4(hydro_events:np.ndarray ,atlas14_precip_table_dir: pl.WindowsPath,
precip_data: pd.DataFrame,df_weights_rainfall: pd.DataFrame, display_print = False) -> tuple:
"""Takes the events, precipitation data, atlas 14 temporal distribution, selected CNs and applies the
CN reduction method to obtain a runoff curve for each recurrence interval.
"""
Atlas14_hyetographs = ['q1', 'q2', 'q3', 'q4']
hydro_events = list(df_weights_rainfall.index)
column_names = []
for event in hydro_events:
for hyetograph in Atlas14_hyetographs:
column_names.append(event+'_'+hyetograph)
prep_curves = pd.DataFrame(columns = column_names)
prep_weights = pd.DataFrame(index = column_names, columns= ['Event Weight'])
for hyetograph in Atlas14_hyetographs:
for event in hydro_events:
dist_df, weight_df = get_hyeto_input_data_atlas(atlas14_precip_table_dir, hyetograph, display_print)
dist_df['precip'] = dist_df[hyetograph]*df_weights_rainfall['Rainfall'].loc[event]
s = df_weights_rainfall['Avg. S'].loc[event]
ia = IA_24hr(s)
dist_df = excess_precip(dist_df,ia, s)
prep_curves[event+'_'+hyetograph] = dist_df['hyeto_input']
prep_weights['Event Weight'].loc[event+'_'+hyetograph] = df_weights_rainfall['Event Weight'][event]*weight_df['weight'][hyetograph]
return prep_curves, prep_weights
def precip_to_runoff_h2(hydro_events:np.ndarray ,nrcs_precip_table_dir: pl.WindowsPath,
precip_data: pd.DataFrame,df_weights_rainfall: pd.DataFrame, CN: int, display_print = False) -> pd.DataFrame:
"""Takes the events, precipitation data, nrcs temporal distribution, CN and applies the CN reduction method to
obtain a runoff curve for each recurrence interval. Also applies to events beyond published Atlas 14 values.
"""
prep_curves = pd.DataFrame(columns = hydro_events)
hyeto_graphs = np.where(df_weights_rainfall.index.to_numpy().astype(int)<1000, df_weights_rainfall.index.to_numpy().astype(int), 1000).tolist()
for event, hyetograph in zip(hydro_events, hyeto_graphs):
dist_df = get_hyeto_input_data_nrcs(nrcs_precip_table_dir, hyetograph, display_print)
dist_df['precip'] = dist_df['ratio']*df_weights_rainfall['P_Mean_in'].loc[event]
s = S_24hr(CN)
ia = IA_24hr(s)
dist_df = excess_precip(dist_df,ia, s)
prep_curves[event] = dist_df['hyeto_input']
return prep_curves
def precip_to_runoff_atlas(hydro_events_dict:dict,atlas14_precip_table_dir: pl.WindowsPath,
precip_data: pd.DataFrame, CN: int, quartile:int, display_print = False) -> pd.DataFrame:
"""Takes the events, precipitation data, nrcs temporal distribution, CN and applies the CN reduction method to
obtain a runoff curve for each recurrence interval
"""
#runoff_distros1 = {}
Atlas14_hyetographs = {1:'q1',2: 'q2',3: 'q3',4: 'q4'}
prep_curves = pd.DataFrame(columns = hydro_events_dict.keys())
for event in hydro_events_dict.keys():
dist_df, weight_df = get_hyeto_input_data_atlas(atlas14_precip_table_dir, Atlas14_hyetographs[quartile], display_print)
dist_df['precip'] = dist_df[Atlas14_hyetographs[quartile]]*precip_data['Precip_in'].loc[event]
s = S_24hr(CN)
ia = IA_24hr(s)
dist_df = excess_precip(dist_df,ia, s)
prep_curves[event] = dist_df['hyeto_input']
return prep_curves
def precip_to_runoff_h3(hydro_events:np.ndarray ,nrcs_precip_table_dir: pl.WindowsPath,
precip_data: pd.DataFrame,df_weights_rainfall: pd.DataFrame, display_print = False) -> pd.DataFrame:
"""Takes the events, precipitation data, nrcs temporal distribution, selected CNs and applies the CN
reduction method to obtain a runoff curve for each recurrence interval.
"""
hyeto_graphs = np.where(hydro_events <1000, hydro_events, 1000).astype(int).tolist()
hydro_events = list(df_weights_rainfall.index)
prep_curves = pd.DataFrame(columns = hydro_events)
for event, hyetograph in zip(hydro_events, hyeto_graphs):
dist_df = get_hyeto_input_data_nrcs(nrcs_precip_table_dir, hyetograph, display_print)
dist_df['precip'] = dist_df['ratio']*df_weights_rainfall['Rainfall'].loc[event]
s = df_weights_rainfall['Avg. S'].loc[event]
ia = IA_24hr(s)
dist_df = excess_precip(dist_df,ia, s)
prep_curves[event] = dist_df['hyeto_input']
return prep_curves
def precip_to_runoff_nrcs(hydro_events_dict:dict,nrcs_precip_table_dir: pl.WindowsPath,
precip_data: pd.DataFrame, CN: int, display_print = False) -> pd.DataFrame:
"""Takes the events, precipitation data, nrcs temporal distribution, CN and applies the CN reduction method to
obtain a runoff curve for each recurrence interval
"""
#runoff_distros1 = {}
prep_curves = pd.DataFrame(columns = hydro_events_dict.keys())
for event in hydro_events_dict.keys():
dist_df = get_hyeto_input_data_nrcs(nrcs_precip_table_dir, hydro_events_dict[event], display_print)
dist_df['precip'] = dist_df['ratio']*precip_data['Precip_in'].loc[event]
s = S_24hr(CN)
ia = IA_24hr(s)
dist_df = excess_precip(dist_df,ia, s)
prep_curves[event] = dist_df['hyeto_input']
return prep_curves
def precip_to_runoff_h1(hydro_events:np.ndarray,nrcs_precip_table_dir: pl.WindowsPath,
precip_data: pd.DataFrame, df_weights_rainfall: pd.DataFrame, CN: int, display_print = False) -> pd.DataFrame:
"""Takes the events, precipitation data, nrcs temporal distribution, CN and applies the CN reduction method to
obtain a runoff curve for each recurrence interval
"""
#runoff_distros1 = {}
prep_curves = pd.DataFrame(columns = hydro_events.astype(float))
for evnt in hydro_events:
dist_df = get_hyeto_input_data_nrcs(nrcs_precip_table_dir, evnt, display_print)
dist_df['precip'] = dist_df['ratio']*precip_data['Median'].loc[evnt]
s = S_24hr(CN)
ia = IA_24hr(s)
#runoff_distros1[evnt] = excess_precip(dist_df,ia, s)
dist_df = excess_precip(dist_df,ia, s)
prep_curves[evnt] = dist_df['hyeto_input']
return prep_curves