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| import sys
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|
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| _ = [sys.path.append(i) for i in [".", ".."]]
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| import numpy as np
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| import pandas as pd
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| from ..core import *
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|
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| def prepare_lars_weather(
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| file,
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| year,
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| generated=True,
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| order=["year", "jday", "minTemp", "maxTemp", "precip", "rad"],
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| wind_speed=3.4,
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| ):
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| """
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| Uses FAO-PM to calculate reference evapotranspiration for LARS generated and baseline input data.
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|
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| """
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|
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| def vap_pres(t):
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| return 0.6108 * np.exp((17.27 * t) / (t + 237.3))
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|
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| df = pd.read_csv(file, sep='\s+', header=None)
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|
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| if generated:
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| df.columns = order
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| df["tdelta"] = pd.to_timedelta(df.jday, unit="D")
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| df["date"] = pd.to_datetime(f"{year-1}/12/31") + df["tdelta"]
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| psyc = 0.054
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| tmean = (df.maxTemp + df.minTemp) / 2
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| e_s = (vap_pres(df.maxTemp) + vap_pres(df.minTemp)) / 2
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| e_a = vap_pres(df.minTemp)
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| slope = 4098 * vap_pres(tmean) / (tmean + 237.3) ** 2
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| R_ns = (1 - 0.23) * df.rad
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| sb_const = 4.903e-9
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| R_nl = (
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| sb_const
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| * 0.5
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| * ((df.maxTemp + 273.15) ** 4 + (df.minTemp + 273.15) ** 4)
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| * (0.34 - 0.14 * (e_a) ** 0.5)
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| * (1.35 * 0.77 - 0.35)
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| )
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| Rn = R_ns - R_nl
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| u2 = wind_speed
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| eto = 0.408 * slope * Rn + (psyc * 900 * u2 * (e_s - e_a) / (tmean + 273)) / (
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| slope + psyc * (1 + 0.34 * u2)
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| )
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| df["eto"] = eto
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| df.eto = df.eto.clip(0.1)
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| df = df[["simyear", "minTemp", "maxTemp", "precip", "eto", "date"]]
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| df.columns = ["simyear", "MinTemp", "MaxTemp", "Precipitation", "ReferenceET", "Date"]
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|
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| else:
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| df.columns = order
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| df["date"] = pd.to_datetime(df.year, format="%Y") + pd.to_timedelta(df.jday - 1, unit="d")
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| psyc = 0.054
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| tmean = (df.maxTemp + df.minTemp) / 2
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| e_s = (vap_pres(df.maxTemp) + vap_pres(df.minTemp)) / 2
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| e_a = vap_pres(df.minTemp)
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| slope = 4098 * vap_pres(tmean) / (tmean + 237.3) ** 2
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| R_ns = (1 - 0.23) * df.rad
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| sb_const = 4.903e-9
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| R_nl = (
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| sb_const
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| * 0.5
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| * ((df.maxTemp + 273.15) ** 4 + (df.minTemp + 273.15) ** 4)
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| * (0.34 - 0.14 * (e_a) ** 0.5)
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| * (1.35 * 0.77 - 0.35)
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| )
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| Rn = R_ns - R_nl
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| u2 = wind_speed
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| eto = 0.408 * slope * Rn + (psyc * 900 * u2 * (e_s - e_a) / (tmean + 273)) / (
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| slope + psyc * (1 + 0.34 * u2)
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| )
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| df["eto"] = eto
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| df.eto = df.eto.clip(0.1)
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| df = df[["minTemp", "maxTemp", "precip", "eto", "date"]]
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| df.columns = ["MinTemp", "MaxTemp", "Precipitation", "ReferenceET", "Date"]
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| return df
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| def select_lars_wdf(df, simyear):
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| temp = df[df.simyear == simyear][["MinTemp", "MaxTemp", "Precipitation", "ReferenceET", "Date"]]
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| return temp.reset_index(drop=True)
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|
