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nsw-2016-storm-impact
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Add Holman 1986 to list of runup functions

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develop
Chris Leaman 6 years ago
parent 7526ab1f3c
commit 0adbf68635
3 changed files with 134 additions and 21 deletions
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32
Makefile
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@ -110,6 +110,16 @@ impacts: ./data/interim/impacts_forecasted_foreshore_slope_sto06.csv ./data/inte
--profile-type "prestorm" \
--output-file "./data/interim/twl_mean_slope_sto06.csv"
./data/interim/twl_mean_slope_hol86.csv: ./data/interim/waves.csv ./data/interim/tides.csv ./data/interim/profiles.csv ./data/interim/sites.csv ./data/interim/profile_features_crest_toes.csv
$(PYTHON_CLI) create-twl-forecast \
--waves-csv "./data/interim/waves.csv" \
--tides-csv "./data/interim/tides.csv" \
--profiles-csv "./data/interim/profiles.csv" \
--profile-features-csv "./data/interim/profile_features_crest_toes.csv" \
--runup-function "hol86" \
--slope "mean" \
--profile-type "prestorm" \
--output-file "./data/interim/twl_mean_slope_hol86.csv"
### IMPACTS
@ -132,10 +142,15 @@ impacts: ./data/interim/impacts_forecasted_foreshore_slope_sto06.csv ./data/inte
--forecasted-twl-csv "./data/interim/twl_foreshore_slope_sto06.csv" \
--output-file "./data/interim/impacts_forecasted_foreshore_slope_sto06.csv"
./data/interim/impacts_forecasted_mean_slope_hol86.csv: ./data/interim/profile_features_crest_toes.csv ./data/interim/twl_mean_slope_hol86.csv
$(PYTHON_CLI) create-forecasted-impacts \
--profile-features-csv "./data/interim/profile_features_crest_toes.csv" \
--forecasted-twl-csv "./data/interim/twl_mean_slope_hol86.csv" \
--output-file "./data/interim/impacts_forecasted_mean_slope_hol86.csv"
### GEOJSONs
geojsons: ./data/interim/impacts_forecasted_mean_slope_sto06.geojson ./data/interim/impacts_forecasted_mean_slope_sto06_R_high.geojson ./data/interim/profile_features_crest_toes.geojson ./data/interim/sites.geojson
geojsons: ./data/interim/impacts_forecasted_mean_slope_hol86.geojson ./data/interim/impacts_forecasted_mean_slope_hol86_R_high.geojson ./data/interim/impacts_forecasted_mean_slope_sto06.geojson ./data/interim/impacts_forecasted_mean_slope_sto06_R_high.geojson ./data/interim/profile_features_crest_toes.geojson ./data/interim/sites.geojson
./data/interim/impacts_forecasted_mean_slope_sto06.geojson: ./data/interim/impacts_forecasted_mean_slope_sto06.csv ./data/interim/impacts_observed.csv
$(PYTHON_CLI) impacts-to-geojson \
@ -152,6 +167,21 @@ geojsons: ./data/interim/impacts_forecasted_mean_slope_sto06.geojson ./data/inte
--impacts-csv "./data/interim/impacts_forecasted_mean_slope_sto06.csv" \
--output-geojson "./data/interim/impacts_forecasted_mean_slope_sto06_R_high.geojson"
./data/interim/impacts_forecasted_mean_slope_hol86.geojson: ./data/interim/impacts_forecasted_mean_slope_hol86.csv ./data/interim/impacts_observed.csv
$(PYTHON_CLI) impacts-to-geojson \
--sites-csv "./data/interim/sites.csv" \
--observed-impacts-csv "./data/interim/impacts_observed.csv" \
--forecast-impacts-csv "./data/interim/impacts_forecasted_mean_slope_hol86.csv" \
--output-geojson "./data/interim/impacts_forecasted_mean_slope_hol86.geojson"
./data/interim/impacts_forecasted_mean_slope_hol86_R_high.geojson: ./data/interim/impacts_forecasted_mean_slope_hol86.csv
$(PYTHON_CLI) r-high-to-geojson \
--sites-csv "./data/interim/sites.csv" \
--profiles-csv "./data/interim/profiles.csv" \
--crest-toes-csv "./data/interim/profile_features_crest_toes.csv" \
--impacts-csv "./data/interim/impacts_forecasted_mean_slope_hol86.csv" \
--output-geojson "./data/interim/impacts_forecasted_mean_slope_hol86_R_high.geojson"
./data/interim/profile_features_crest_toes.geojson: ./data/interim/profile_features_crest_toes.csv
$(PYTHON_CLI) profile-features-crest-toes-to-geojson \
--sites-csv "./data/interim/sites.csv" \

87
src/analysis/forecast_twl.py
Unescape Escape View File

@ -41,13 +41,16 @@ def forecast_twl(
# Process each site_id with a different process and combine results at the end
with Pool(processes=n_processes) as pool:
results = pool.starmap(
foreshore_slope_for_site_id, [(site_id, df_twl, df_profiles) for site_id in site_ids]
foreshore_slope_for_site_id,
[(site_id, df_twl, df_profiles) for site_id in site_ids],
)
df_twl["beta"] = pd.concat(results)
elif slope == "mean":
df_temp = df_twl.join(
df_profile_features.query("profile_type=='{}'".format(profile_type)).reset_index(level="profile_type"),
df_profile_features.query(
"profile_type=='{}'".format(profile_type)
).reset_index(level="profile_type"),
how="inner",
)
df_temp["mhw"] = 0.5
@ -59,19 +62,26 @@ def forecast_twl(
df_temp.dune_toe_z.isnull(), "dune_crest_z"
]
df_temp["top_x"] = df_temp["dune_toe_x"]
df_temp.loc[df_temp.dune_toe_x.isnull(), "top_x"] = df_temp.loc[df_temp.dune_toe_x.isnull(), "dune_crest_x"]
df_temp.loc[df_temp.dune_toe_x.isnull(), "top_x"] = df_temp.loc[
df_temp.dune_toe_x.isnull(), "dune_crest_x"
]
with Pool(processes=n_processes) as pool:
results = pool.starmap(
mean_slope_for_site_id,
[(site_id, df_temp, df_profiles, "top_elevation", "top_x", "mhw") for site_id in site_ids],
[
(site_id, df_temp, df_profiles, "top_elevation", "top_x", "mhw")
for site_id in site_ids
],
)
df_twl["beta"] = pd.concat(results)
# Estimate runup
R2, setup, S_total, S_inc, S_ig = runup_function(
Hs0=df_twl["Hs0"].tolist(), Tp=df_twl["Tp"].tolist(), beta=df_twl["beta"].tolist()
Hs0=df_twl["Hs0"].tolist(),
Tp=df_twl["Tp"].tolist(),
beta=df_twl["beta"].tolist(),
)
df_twl["R2"] = R2
@ -80,7 +90,9 @@ def forecast_twl(
# Estimate TWL
df_twl["R_high"] = df_twl["tide"] + df_twl["R2"]
df_twl["R_low"] = df_twl["tide"] + 1.1 * df_twl["setup"] - 1.1 / 2 * df_twl["S_total"]
df_twl["R_low"] = (
df_twl["tide"] + 1.1 * df_twl["setup"] - 1.1 / 2 * df_twl["S_total"]
)
# Drop unneeded columns
# df_twl.drop(columns=["E", "Exs", "P", "Pxs", "dir"], inplace=True, errors="ignore")
@ -89,7 +101,13 @@ def forecast_twl(
def mean_slope_for_site_id(
site_id, df_twl, df_profiles, top_elevation_col, top_x_col, btm_elevation_col, profile_type="prestorm"
site_id,
df_twl,
df_profiles,
top_elevation_col,
top_x_col,
btm_elevation_col,
profile_type="prestorm",
):
"""
Calculates the foreshore slope values a given site_id. Returns a series (with same indicies as df_twl) of
@ -135,7 +153,9 @@ def foreshore_slope_for_site_id(site_id, df_twl, df_profiles):
"""
# Get the prestorm beach profile
profile = df_profiles.query("site_id =='{}' and profile_type == 'prestorm'".format(site_id))
profile = df_profiles.query(
"site_id =='{}' and profile_type == 'prestorm'".format(site_id)
)
profile_x = profile.index.get_level_values("x").tolist()
profile_z = profile.z.tolist()
@ -175,8 +195,12 @@ def foreshore_slope_from_profile(profile_x, profile_z, tide, runup_function, **k
# Initalize estimates
max_number_iterations = 30
iteration_count = 0
averaged_accuracy = 0.03 # if slopes within this amount, average after max number of iterations
acceptable_accuracy = 0.01 # if slopes within this amount, accept after max number of iterations
averaged_accuracy = (
0.03
) # if slopes within this amount, average after max number of iterations
acceptable_accuracy = (
0.01
) # if slopes within this amount, accept after max number of iterations
preferred_accuracy = 0.001 # if slopes within this amount, accept
beta = 0.05
@ -212,7 +236,15 @@ def foreshore_slope_from_profile(profile_x, profile_z, tide, runup_function, **k
iteration_count += 1
def slope_from_profile(profile_x, profile_z, top_elevation, btm_elevation, method="end_points", top_x=None, btm_x=None):
def slope_from_profile(
profile_x,
profile_z,
top_elevation,
btm_elevation,
method="end_points",
top_x=None,
btm_x=None,
):
"""
Returns a slope (beta) from a bed profile, given the top and bottom elevations of where the slope should be taken.
:param x: List of x bed profile coordinates
@ -260,7 +292,9 @@ def slope_from_profile(profile_x, profile_z, top_elevation, btm_elevation, metho
end_points[end_type]["x"] = intersection_x[-1]
else:
# For bottom elevation, take most landward intersection that is seaward of top elevation
end_point_btm = [x for x in intersection_x if x > end_points["top"]["x"]]
end_point_btm = [
x for x in intersection_x if x > end_points["top"]["x"]
]
if len(end_point_btm) == 0:
# If there doesn't seem to be an intersection seaward of the top elevation, return none.
return None
@ -275,7 +309,10 @@ def slope_from_profile(profile_x, profile_z, top_elevation, btm_elevation, metho
return -(z_top - z_btm) / (x_top - x_btm)
elif method == "least_squares":
profile_mask = [True if end_points["top"]["x"] < pts < end_points["btm"]["x"] else False for pts in profile_x]
profile_mask = [
True if end_points["top"]["x"] < pts < end_points["btm"]["x"] else False
for pts in profile_x
]
slope_x = np.array(profile_x)[profile_mask].tolist()
slope_z = np.array(profile_z)[profile_mask].tolist()
slope, _, _, _, _ = stats.linregress(slope_x, slope_z)
@ -287,12 +324,28 @@ def slope_from_profile(profile_x, profile_z, top_elevation, btm_elevation, metho
@click.option("--tides-csv", required=True, help="")
@click.option("--profiles-csv", required=True, help="")
@click.option("--profile-features-csv", required=True, help="")
@click.option("--runup-function", required=True, help="", type=click.Choice(["sto06"]))
@click.option("--slope", required=True, help="", type=click.Choice(["foreshore", "mean"]))
@click.option("--profile-type", required=True, help="", type=click.Choice(["prestorm", "poststorm"]))
@click.option(
"--runup-function", required=True, help="", type=click.Choice(["sto06", "hol86"])
)
@click.option(
"--slope", required=True, help="", type=click.Choice(["foreshore", "mean"])
)
@click.option(
"--profile-type",
required=True,
help="",
type=click.Choice(["prestorm", "poststorm"]),
)
@click.option("--output-file", required=True, help="")
def create_twl_forecast(
waves_csv, tides_csv, profiles_csv, profile_features_csv, runup_function, slope, profile_type, output_file
waves_csv,
tides_csv,
profiles_csv,
profile_features_csv,
runup_function,
slope,
profile_type,
output_file,
):
logger.info("Creating forecast of total water levels")
logger.info("Importing data")

36
src/analysis/runup_models.py
Unescape Escape View File

@ -10,14 +10,20 @@ def sto06(Hs0, Tp, beta):
:return: Float or list of R2, setup, S_total, S_inc and S_ig values
"""
df = pd.DataFrame({"Hs0": Hs0, "Tp": Tp, "beta": beta}, index=[x for x in range(0, np.size(Hs0))])
df = pd.DataFrame(
{"Hs0": Hs0, "Tp": Tp, "beta": beta}, index=[x for x in range(0, np.size(Hs0))]
)
df["Lp"] = 9.8 * df["Tp"] ** 2 / 2 / np.pi
# General equation
df["S_ig"] = pd.to_numeric(0.06 * np.sqrt(df["Hs0"] * df["Lp"]), errors="coerce")
df["S_inc"] = pd.to_numeric(0.75 * df["beta"] * np.sqrt(df["Hs0"] * df["Lp"]), errors="coerce")
df["setup"] = pd.to_numeric(0.35 * df["beta"] * np.sqrt(df["Hs0"] * df["Lp"]), errors="coerce")
df["S_inc"] = pd.to_numeric(
0.75 * df["beta"] * np.sqrt(df["Hs0"] * df["Lp"]), errors="coerce"
)
df["setup"] = pd.to_numeric(
0.35 * df["beta"] * np.sqrt(df["Hs0"] * df["Lp"]), errors="coerce"
)
df["S_total"] = np.sqrt(df["S_inc"] ** 2 + df["S_ig"] ** 2)
df["R2"] = 1.1 * (df["setup"] + df["S_total"] / 2)
@ -37,6 +43,30 @@ def sto06(Hs0, Tp, beta):
)
def hol86(Hs0, Tp, beta):
df = pd.DataFrame(
{"Hs0": Hs0, "Tp": Tp, "beta": beta}, index=[x for x in range(0, np.size(Hs0))]
)
df["Lp"] = 9.8 * df["Tp"] ** 2 / 2 / np.pi
df["setup"] = 0.2 * df["Hs0"]
df["R2"] = 0.83 * df["beta"] * np.sqrt(df["Hs0"] * df["Lp"]) + df["setup"]
df["S_ig"] = np.nan
df["S_inc"] = np.nan
df["S_total"] = np.nan
return (
float_or_list(df["R2"].tolist()),
float_or_list(df["setup"].tolist()),
float_or_list(df["S_total"].tolist()),
float_or_list(df["S_inc"].tolist()),
float_or_list(df["S_ig"].tolist()),
)
def float_or_list(a):
"""
If only one value in the array, return the float, else return a list

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