From 25fc9241936fbe3e3359d67cdc605898cb0d2e0c Mon Sep 17 00:00:00 2001 From: Albert Kettner Date: Fri, 4 Feb 2022 09:42:01 -0700 Subject: [PATCH] First version SedCas --- Hydro.out | 158545 +++++++++++++++++++++++++++++++++++++++++ MonthlySedYield.png | Bin 0 -> 60552 bytes README.md | 35 + SedCas.py | 200 + Sediment.out | 158545 +++++++++++++++++++++++++++++++++++++++++ climate.met | 158545 +++++++++++++++++++++++++++++++++++++++++ modules.py | 1115 + parameters.par | 72 + run.py | 17 + sedcas.yml | 192 + 10 files changed, 477266 insertions(+) create mode 100644 Hydro.out create mode 100644 MonthlySedYield.png create mode 100644 README.md create mode 100644 SedCas.py create mode 100644 Sediment.out create mode 100644 climate.met create mode 100644 modules.py create mode 100644 parameters.par create mode 100644 run.py create mode 100644 sedcas.yml diff --git a/Hydro.out b/Hydro.out new file mode 100644 index 0000000..1669324 --- /dev/null +++ b/Hydro.out @@ -0,0 +1,158545 @@ 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z${fZNOU(S*L1~eGMh2+8nvCx0onW+}4x>B}6b>X7zGJ1Pr9sO$FMx9a!S?IzD*qLk&-2C6!)U*8*a4s zBnIMj+StyCe@6-Vw>Do69(lWs8aZh1S(#9TCU*8n&Bmq-kJ*G%6DCDbTn2h|so`&z zZJ{q9^c4PhOE2QKerajxL^)?-w!gps#IM=HZp0#e24ikC+(QXB13HpXgD3Tl!t-?= zwNOa(RL{sgH*Ma`gn;EVlBlpD)1nSX5VxdqW1vWf)XTD@I$2+1!|&@H`aW;i<h9YJsj7*Z27%;sZg?puk)&@cM;Ai*Jzpu$3sZ|V| zu+?L@rN`=dVeXI@3^)4eMNlvijQEr(^PdNwyV6rW`ERLS3L+ z=d^bGjf8x~)TmHT*oKy4o8w#0V|o{#Haq}=__euf9!K+d^8v7&2@T{b`09vjQvc2H2+-cj!Ya7aiJBTz|A;i|d#5u`GeidpV#DaDBV@ zSX(y&ayN2>vfg3&xB{zq(*6+H^PwmiJ7@L2-CU@ghjp8?fPNG(bNS=FzP=2U6MqGF zFM~w1tI!Feba53`n z3|Ql=P3oIm3=mF+0nnv@Z>!b?k*eQc8fFz22O56MfGjPI{{k{oRh`ReS7ol@$rFdrBF3)m$%QD$z&3l z?iP+}a)yrj4m4$5DV`muGvfsM|Ist5PXdea(p-J 0]) + sls = mod.small_ls(n_days, N, self.ls_xmin, self.area, seed=seed) # small landslides + sls.index = lrgls.index # date index for small landslides + sed_run = mod.sedcas(lrgls, sls, self.hydro, self.qdf, self.smax, self.rhc, self.shcap, self.area, 'exp', self.LStrig, self.Tpr, shinit=self.shcap, mindf=self.mindf, smax_nodf=self.smax_nodf, b=self.b) + sed.ls[:,m] = sed_run.ls.values + sed.sc[:,m] = sed_run.sc + sed.sh[:,m] = sed_run.sh + sed.so[:,m] = sed_run.so + sed.sopot[:,m] = sed_run.sopot + sed.dfs[:,m] = sed_run.dfs + seed += 1 + sed.dfspot[:] = sed_run.dfspot + + self.sed = sed + + def save_output(self): + + self.hydro.to_csv('Hydro.out', header=True) + + sedout = pd.DataFrame(index=self.sed.index) + quants = [0, 10, 25, 50, 75, 90, 100] + for q in quants: + c = 'Q%i'%q + sedout[c] = np.percentile(self.sed.so, q, axis=1) + sedout.to_csv('Sediment.out', header=True) + + def plot_sedyield_monthly(self, save=True): + + cf = (self.area*10**6) * 10**-3 # km2 to m2 and mm to m + + sy = pd.DataFrame(data = self.sed.dfs*cf, index=self.sed.index) + syp = pd.DataFrame(data = self.sed.dfspot*cf, index=self.sed.index) + + # monthly sediment yields + sym = sy.resample('m').sum() + sypm = syp.resample('m').sum() + + # mean monthly sediment yield + symm = sym.groupby(by=sym.index.month).mean() + sypmm = sypm.groupby(by=sypm.index.month).mean() + + # quantiles + Q = [0,10,25,50,75,90,100] # quantiles + SY = pd.DataFrame(index=symm.index) + SYP = pd.DataFrame(index=sypmm.index, data=sypmm.values[:,0]) # only one potential since only one climate + + for q in Q: + SY['Q'+str(q)] = np.percentile(symm.values, q,axis=1) + # SYP['Q'+str(q)] = np.percentile(sypmm.values, q,axis=1) + + # # plot + ca = 'steelblue' # color actual sedyield + cp = 'darkred' # color potential sedyield + x = np.arange(1,13) + + fig, ax = plt.subplots() + ax.fill_between(x, SY.Q25, SY.Q75, color=ca, alpha=.5) + ax.plot(x, SY.Q10, color=ca, ls='--', alpha=.5) + ax.plot(x, SY.Q90, color=ca, ls='--', alpha=.5) + ax.plot(x, SY.Q50, color=ca, lw=2) + + ax.plot(x, SYP.values, color=cp, lw=1, zorder=-1) + + # # legend + l1 = plt.Line2D(x, SY.Q50, color=ca, lw=2, label = 'median supply-limited') + l2 = plt.Line2D(x, SY.Q90, color=ca, ls='--', alpha=.5, label = 'Q10/Q90 supply-limited') + l3 = mpatches.Patch(color=ca, label='Q25-Q75 supply-limited') + l4 = plt.Line2D(x, SYP, color=cp, lw=2, label='transport-limited') + ax.legend(handles=[l1, l2, l3, l4], fontsize='small', frameon=False) + + # # ax limits + ax.set_xlim(1,12) + ax.set_ylim(0,) + + # # axis labels + ax.set_xlabel('Months', fontsize=12) + ax.set_ylabel('Debris-flow yield [m$^3$/month]', fontsize=12) + + # # scientific notation + ax.ticklabel_format(axis='y', style='sci', scilimits=(3,3)) + ax.get_yaxis().get_offset_text().set_visible(False) + exponent_axis = 3 + ax.annotate(r'$\times$10$^{%i}$'%(exponent_axis), xy=(0.0, 1.005), xycoords='axes fraction', fontsize=6) + + if save: + fig.savefig('MonthlySedYield.png') diff --git a/Sediment.out b/Sediment.out new file mode 100644 index 0000000..fbf7cff --- /dev/null +++ b/Sediment.out @@ -0,0 +1,158545 @@ +D,Q0,Q10,Q25,Q50,Q75,Q90,Q100 +1999-10-01 00:00:00,0.0,0.0,0.0,0.0,0.0,0.0,0.0 +1999-10-01 01:00:00,0.0,0.0,0.0,0.0,0.0,0.0,0.0 +1999-10-01 02:00:00,0.0,0.0,0.0,0.0,0.0,0.0,0.0 +1999-10-01 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15:00:00 0.0 6.6 147.0 0.0 +2017-10-31 16:00:00 0.0 4.2 22.0 +2017-10-31 17:00:00 0.0 2.2 2.0 +2017-10-31 18:00:00 0.0 1.1 2.0 +2017-10-31 19:00:00 0.0 1.4 1.0 +2017-10-31 20:00:00 0.0 1.4 1.0 +2017-10-31 21:00:00 0.0 1.4 1.0 +2017-10-31 22:00:00 0.0 1.4 1.0 +2017-10-31 23:00:00 0.0 1.7 1.0 diff --git a/modules.py b/modules.py new file mode 100644 index 0000000..8611e45 --- /dev/null +++ b/modules.py @@ -0,0 +1,1115 @@ +#!/usr/bin/env python3 +# -*- coding: utf-8 -*- +""" +Created on Thu Feb 3 16:07:40 2022 + +@author: hirschbe +""" + +import pandas as pd +import numpy as np +import math +import random + +#%% Snow Degree-Day-Module +def degree_day_model(T, P, m, Ta, Tm, s0=0, Asnow=0.8, Asoil=0.3): + ''' + Computing snow depth in SWE based on a degree-day-snow-melt-model + ------------------------------------------------- + + Inputs + ------ + T : Temperature time series [degreeC] + P : Precipitation time series [mm] + m : melt rate factor [mm/degreeC/t] + Ta : temperature threshold for snow accumulation [degreeC] + Tm : temperature threshold for snow melt [degreeC] + s0 : initial snow depth [mm] + Asnow : snow albedo [-] + Asoil : soil albedo [-] + + Output + ------ + df : dataframe containing + - ddepth : daily snow pack change SWE [mm] + - depth : modelled SWE snow depth SWE [mm] + - acc : snow pack accumulation SWE [mm] + - melt : melt from snow pack SWE [mm] + + Note: acc and melt is not necessary equal to ddepth because there can be both + at the same time step (if accumulation and melt temperature threshold are not equal) + ''' + index = P.index + + T = T.values + P = P.values + + # snow accumulation + cond = T <= Ta + acc = P + acc[~cond] = 0. # no snow pack accumulation where temperature is above + + # snow melt + Tgrad = T - Tm # melting gradient + cond = T > Tm + Tgrad[~cond] = 0. # no snowmelt where temperature is below + melt = m * Tgrad # potential snowmelt based on temperature + + # compute actual snow depth + depth = np.zeros(len(P)) + ddepth = np.zeros(len(P)) + for i in range(1,len(P)): + depth[i] = depth[i-1] + acc[i] - melt[i] + if depth[i] < 0: + melt[i] = melt[i] - abs(depth[i]) + depth[i] = 0 + ddepth[i] = depth[i] - depth[i-1] + + # Albedo + A = np.zeros(len(depth)) + cond = depth > 0 + A[cond] = Asnow + A[~cond] = Asoil + + data = {'depth' : depth, + 'ddepth' : ddepth, + 'sacc' : acc, + 'smelt' : melt, + 'albedo' : A + } + + df = pd.DataFrame(data = data, index = index) + + return df + +#%% Potential Evapotranspiration Module +def ET_PM_PT(dt,Rsw,Ta,N,A,Ele,U,OPT,d,z=np.nan,Ws=np.nan,rs_min=np.nan,zom=np.nan): + ''' + # Function for calculation of Evapotranspiration using either + # Penmann-Monteith or Priestly Taylor. + + # MATLAB code by Simone Fatichi + ------------------------------- + INPUTS + ------ + dt = [h], temporal resolution, e.g. [24] for daily + U =[0-1], ? e.g. 0.8 + Rsw [W/m^2], radiation + Ta [degreeC], time series temperature + N [-], cloud cover fraction + A = [-], Albedo time series + Ele = [m a.s.l.] + z = [m], e.g. 1 + zom =[m], e.g. 0 + Ws = [m/s], e.g. 1 + rs_min [s/m], e.g. 0 + OPT = mehtod, 'PT' or 'PM' + d = ?, e.g. 1, not used in method 'PT' + + OUTPUTS + ------- + EPT : PET [mm] + Epot : + Tpot : + EP_en : + EP_aer : + ''' + esat=611*np.exp(17.27*Ta/(237.3+Ta)) ## Vapor Pressure Saturation + ea = U*esat + #### Net Radiation + k=0.4 # von Karman constant + si = 5.6704*(10**-8) # Stefan-Boltzman Constant [W/m**2.K**4] + ###### + K = 0.1 +0.9*(1-0.6*(N**2.5)) #### Emissiivty coefficient cloud + ei= 0.34 -0.14*np.sqrt(ea/1000) ## Net emissivity humidity + ###### + D_Rlw=ei*K*si*((Ta-273.15)**4) ## Net Longwave Radiatio W/m^2 + Rn = Rsw*(1-A) - D_Rlw ## Net Radiation W/m^2 + ################################################# + ##### COMBINED ENERGETIC AND AERODYNAMIC METHOD + ### PARAMETERS + #p_p0 = exp(-Ele/8434.5) ### correction for differences in pressure between basin and seal level + #Pre = P0*p_p0 + Pre0=101325 ##[Pa] + Pre = Pre0*np.exp((-9.81/287)*(Ele-0)/(Ta+273.15)) #[Pa] + #############################5 + ro = (Pre/(287.04*(Ta+273.15)))*(1-(ea/Pre)*(1-0.622)) ## dry air density [kg/m^3] + row = 1000 # water density [kg/m^3] + cp=1005 + ((Ta +23.15)**2)/3364 ## specific heat air [J/kg K] + L= 1000*(2501.3 - 2.361*(Ta)) ### Latent heat vaporization/condensaition [J/kg] + ############## + ###################################### + if OPT == 'PM': #### PENMANN-MONTEITH + #B=(0.622*(k^2)*ro*Ws)/(Pre*row*(log((z-d)/zom))^2) ## [m/s.Pa] + B=(0.622*(k**2)*ro*Ws)/(Pre*row*(np.log((z-d)/zom)*np.log((z-d)/(0.1*zom)))) ## [m/s.Pa] + #ra= ((log((z-d)/zom))^2)/(Ws*k^2) ## ## aerodynamic resistence [s/m] Neutal Condition + ra= (np.log((z-d)/zom)*np.log((z-d)/(0.1*zom)))/(Ws*k**2) ## ## aerodynamic resistence [s/m] Neutal Condition + EP_en=1000*3600*Rn/(L*row) ## [mm/h] evaporazione da bilancio energetico + EP_aer=1000*3600*B*(esat-ea) # [mm/h] evaporazione da bilancio aerodinamico + G=cp*Pre/(0.622*L) ## Pa/c costante psicrometrica + D=(4098*esat)/((237.3+Ta)**2) ## Pa/C +# EP=(D/(D+G))*EP_en+(G/(D+G))*EP_aer ## [mm/h] Penmann Monteith combined method + ############### + Tpot=(1000*3600/row)*(1/L)*(D*Rn + ro*cp*(esat-ea)/ra)/(D +G*(1+rs_min/ra)) ## Evapotraspirazione potenziale [mm/h] + Epot=(1000*3600/row)*(1/L)*(D*Rn + ro*cp*(esat-ea)/ra)/(D +G) ## Evaporazione potenziale [mm/h] + ##### obviously here Epot == EP + EPT = 1.26*(D/(D+G))*EP_en ## [mm/h] Priestly-Taylor + + elif OPT=='PT': ######### PRIESTLY-TAYLOR + EP_en=1000*3600*Rn/(L*row) ## [mm/h] evaporation + G=cp*Pre/(0.622*L) ## Pa/c psicrometric costant + D=(4098*esat)/((237.3+Ta)**2) ## Pa/C + EPT= 1.26*(D/(D+G))*EP_en ## [mm/h] Priestly-Taylor + EP_aer = np.nan + Epot=np.nan + Tpot=np.nan + else: + raise TypeError('Supplied ET OPT was not recognised!') + if not np.isclose(dt, 1.): + EPT = EPT*dt ##[mm/dt] + EP_aer = EP_aer*dt + EP_en = EP_en*dt + Tpot=Tpot*dt + Epot=Epot*dt + + # EPT may be negative due to dew in the winter. However, we do not consider dew and are just + # interested in positive values. + #EPT[EPT<0] = 0 + + return EPT, Epot, Tpot, EP_en, EP_aer + +def ET_PT(dt,Rsw,Ta,N,A,Ele,U,d,z=np.nan,Ws=np.nan,rs_min=np.nan,zom=np.nan): + ''' + # Function for calculation of Evapotranspiration using either Priestly Taylor. + + # modified from: MATLAB code by Simone Fatichi + ------------------------------- + INPUTS + ------ + dt = [h], temporal resolution, e.g. [24] for daily + U =[0-1], ? e.g. 0.8 + Rsw [W/m^2], radiation + Ta [degreeC], time series temperature + N [-], cloud cover fraction + A = [-], Albedo time series + Ele = [m a.s.l.] + z = [m], e.g. 1 + zom =[m], e.g. 0 + Ws = [m/s], e.g. 1 + rs_min [s/m], e.g. 0 + OPT = mehtod, 'PT' or 'PM' + d = ?, e.g. 1, not used in method 'PT' + + OUTPUTS + ------- + EPT : PET [mm] + Epot : + Tpot : + EP_en : + EP_aer : + ''' + esat=611*np.exp(17.27*Ta/(237.3+Ta)) ## Vapor Pressure Saturation + ea = U*esat + #### Net Radiation + si = 5.6704*(10**-8) # Stefan-Boltzman Constant [W/m**2.K**4] + ###### + K = 0.1 +0.9*(1-0.6*(N**2.5)) #### Emissiivty coefficient cloud + ei= 0.34 -0.14*np.sqrt(ea/1000) ## Net emissivity humidity + ###### + D_Rlw=ei*K*si*((Ta-273.15)**4) ## Net Longwave Radiatio W/m^2 + Rn = Rsw*(1-A) - D_Rlw ## Net Radiation W/m^2 + ################################################# + ##### COMBINED ENERGETIC AND AERODYNAMIC METHOD + ### PARAMETERS + #p_p0 = exp(-Ele/8434.5) ### correction for differences in pressure between basin and seal level + #Pre = P0*p_p0 + Pre0=101325 ##[Pa] + Pre = Pre0*np.exp((-9.81/287)*(Ele-0)/(Ta+273.15)) #[Pa] + #############################5 + row = 1000 # water density [kg/m^3] + cp=1005 + ((Ta +23.15)**2)/3364 ## specific heat air [J/kg K] + L= 1000*(2501.3 - 2.361*(Ta)) ### Latent heat vaporization/condensaition [J/kg] + ############## + ###################################### + + EP_en=1000*3600*Rn/(L*row) ## [mm/h] evaporation + G=cp*Pre/(0.622*L) ## Pa/c psicrometric costant + D=(4098*esat)/((237.3+Ta)**2) ## Pa/C + EPT= 1.26*(D/(D+G))*EP_en ## [mm/h] Priestly-Taylor + EP_aer = np.nan + Epot=np.nan + Tpot=np.nan + + if not np.isclose(dt, 1.): + EPT = EPT*dt ##[mm/dt] + EP_aer = EP_aer*dt + EP_en = EP_en*dt + Tpot=Tpot*dt + Epot=Epot*dt + + # EPT may be negative due to dew in the winter. However, we do not consider dew and are just + # interested in positive values. + EPT[EPT<0] = 0 + + return EPT + + +def ET_Hamon(J, T, phi): + ''' + ALTERNATIVE CALCULATION OF EPT if you don't have data on solar radiation or cloud cover. + Hamon PET from Hornberger and Wiberg: Numerical Methods in the Hydrological Sciences + ---------------------------------------------------------------------------------------- + + Inputs + ------ + phi : latitude [degrees] + J : array of Julian day + T : array of Temperatures + + ''' + #phi = 46.27 # Latitude in degrees + delta = 0.4093*math.sin((2*np.pi/365)*J-1.405) # Solar declination + omega_s = math.acos(-math.tan(2*np.pi*phi/360.)*math.tan(delta)) # sunset hour angle + Nt = 24*omega_s/np.pi # hours in day + a = 0.6108 + b = 17.27 + c = 237.3 + es = a*np.exp(b*T/(T+c)) # saturation vapor pressure + PET = ((2.1*(Nt**2)*es)/(T+273.3)) # Hamont PT in mm/m2 + i_cold = T <= 0 + PET[i_cold] = 0 + + return PET + +#%% HYDROLOGICAL MODEL + +def hydmod(snow, PET, Pr, Ta, a, l, params): + ''' + ...work in progress... + linear reservoir hydrological model accounting for snow, ET, soil water storage, discharge. + Unlike the other hydmod module this one is extended by another bucket in the sence of an upper and a lower bucket. + ------------------------------------------------- + + Inputs + ------ + snow : data frame from degree-day-model + PET : potential ET from PET module + P : Precipitation + T : Temperature + a : parameter for efficiency of ET dependent on saturation of upper storage (not like before from Tuttle and Salvucci, 2012) + l : number of reservoirs + params : dict containing + k : factor for release from linera reservoir, i.e. residence time + Scap : water storage capacity [mm] + S0 : initial condition [mm] + example: if n=2 the input shoud be params = dict('k' : [k1, k2], 'Scap' : [sc1, sc2], 'S0' : [s01, s02]) + + Output + ------ + hyd : dataframe containing time series of... + - Q : dischagre [mm] + - Qs : discharge from overland flow [mm] + - Qss : discharge from subsurface flow (outflow from last bucket in the cascasde) [mm] + - Vw : state of soil water storage [mm] + - snow : snow depth SWE [mm], already in input + - PET : Potential ET [mm] + - AET : Actual ET [mm] + ''' + #%% initialization + index = snow.index + n = len(snow) + sdepth = snow.depth.values + dsdepth = snow.ddepth.values + sacc = snow.sacc.values + smelt = snow.smelt.values + PET = PET.values + Pr = Pr.values + Ta = Ta.values + + Q, Qss, Qs = np.zeros(n), np.zeros(n), np.zeros(n) # dischage + Qper = np.zeros(shape=(n,l-1)) # percolation between storages the first column is for the flow between the most upper and the second... + + # assign storage properties + class storage(object): + ''' + makes class from parameter inputs + ''' + def __init__(self, dictionary): + for key in dictionary: + setattr(self, key, dictionary[key]) + + s = storage(params) + + Vw = np.zeros((n, l)) # array for storage time series, each column represents one reservoir (from bottom to top) + AET = np.zeros(n) + for i in range(l): + Vw[0,i] = s.S0[i] # initial condition + + #%% transient storage, discharge computation + # changes from precipitation and snowmelt (both positive, because they are inputs to the storage) + # to keep this order is important because there can be snow accumulation and melt on the same day + dVw = Pr.copy() # add precipitation + dVw = dVw - sacc # when snow accumulation, precipitation is not added to the storage + dVw = dVw + smelt # where snow melt, add it + + # loop through each time step + ## scheme: + # 1) add everything to the most upper storage + # 2) compute all internal and outflows based on state afer input + # 3) subtract outflows, account for internal flow + # 4) if lower is full, it is pushed to the upper + # 5) check that capacity of upper is not exceeded, else more surface runoff + + # loop through time steps + # i --> time index + for i in range(1,n): + # 1) + Vw[i,0] = Vw[i-1,0] + dVw[i] + + # 2) + # AET + if Ta[i] > 0: + b = 1. - np.exp(-a*Vw[i,0]/float(s.Scap[0])) + AET[i] = b * PET[i] + + # this is out of temperature loop, should not make a difference, but ensures continuity of water just in case + if Vw[i,0] > s.Scap[0]: + Qs[i] = Vw[i,0] - s.Scap[0] # the amount exceeding the capacity is surface runoff + + if Ta[i] > 0: # condition for subsurface flow, else frozen + # percolation + # j --> storage index + for j in range(l-1): + Qper[i,j] = Vw[i,j] * 1/s.k[j] + + # 3) + # most upper bucket + if l==1: + Vw[i,0] = Vw[i,0] - AET[i] - Qs[i] + elif l==2: + Vw[i,0] = Vw[i,0] - AET[i] - Qs[i] - Qper[i] + else: + Vw[i,0] = Vw[i,0] - AET[i] - Qs[i] - Qper[i,0] + + # check for continuity of mass + if Vw[i,0] < 0: + d = abs(Vw[i,0]) + Vw[i,0] = Vw[i,0] + AET[i] + AET[i] = AET[i] - d + if AET[i] < 0: + AET[i] = 0 + Vw[i,0] = Vw[i,0] - AET[i] + if Vw[i,0] < 0: + d = abs(Vw[i,0]) + Vw[i,0] = Vw[i,0] + Qs[i] + Qs[i] = Qs[i] - d + Vw[i,0] = Vw[i,0] - Qs[i] + if Vw[i,0] < 0: + Qs[i] = 0 + Vw[i,0] = 0 + + + # lower buckets + # for only two buckets, this does nothing + if l > 1: + if l == 2: + Vw[i,1] = Vw[i-1,1] + Qper[i] + + # check for continuity of mass + if Vw[i,1] < 0: + d = abs(Vw[i,0]) + Vw[i,1] = Vw[i,1] + Qper[i,2] + AET[i] = Qper[i,2] - d + if Qper[i,2] < 0: + Qper[i,2] = 0 + Vw[i,1] = Vw[i,1] - Qper[i,2] + if Vw[i,1] < 0: + Qper[i,2] = 0 + Vw[i,1] = 0 + + elif l > 2: + for j in range(1,l): + Vw[i,j] = Vw[i-1,j] + Qper[i,j] - Qper[i,j+1] + + # check for continuity of mass + if Vw[i,j] < 0: + d = abs(Vw[i,0]) + Vw[i,j] = Vw[i,j] + Qper[i,j+1] + AET[i] = Qper[i,j+1] - d + if Qper[i,j+1] < 0: + Qper[i,j+1] = 0 + Vw[i,j] = Vw[i,j] - Qper[i,j+1] + if Vw[i,j] < 0: + Qper[i,j+1] = 0 + Vw[i,j] = 0 + + ## for lowest bucket + + # release from lowest + Qss[i] = Vw[i,-1] * 1/s.k[-1] + + if l == 1: + Vw[i,0] = Vw[i,0] - Qss[i] + elif l == 2: + Vw[i,-1] = Vw[i,-1] - Qss[i] + else: + Vw[i,-1] = Vw[i,-1] - Qss[i] + + # check for continuity of mass + if Vw[i,-1] < 0: + d = abs(Vw[i,-1]) + Vw[i,-1] = Vw[i,-1] + Qss[i] + Qss[i] = Qss[i] - d + if Qss[i] < 0: + Qss[i] = 0 + Vw[i,-1] = Vw[i,-1] - Qss[i] + if Vw[i,-1] < 0: + Qs[i] = 0 + Vw[i,-1] = 0 + + # 4) + # loop from lowest to most upper to check for capacitiy exceedence + for j in reversed(range(1,l)): + if Vw[i,j] > s.Scap[j]: + up = Vw[i,j] - s.Scap[j] + Vw[i,j] = s.Scap[j] + Vw[i,j-1] = Vw[i,j-1] + up + + # 5) for most upper bucket + if l == 1: + pass + elif Vw[i,0] > s.Scap[0]: + q = Vw[i,0] - s.Scap[0] + Qs[i] = Qs[i] + q + Vw[i,0] = s.Scap[0] + + Q[i] = Qs[i] + Qss[i] + +# if any(Vw<0): +# Warning('Negative Soil Water Storage') + + #%% output + + # total system storage + Vw_tot = np.sum(Vw, axis=1) + + data = {'Q' : Q, + 'Qs' : Qs, + 'Qss' : Qss, + 'Vw' : Vw_tot, + 'snow' : sdepth, + 'snowacc' : dsdepth, + 'PET' : PET, + 'AET' : AET, + 'Pr' : Pr, + 'Ta' : Ta + } + + # single bucket storage + i = 0 + for col in Vw.T: + name = 'Vw%i'%i + data[name] = col + i += 1 + + hyd = pd.DataFrame(data=data, index=index) + + return hyd + + +#%% RANDOM LANDSLIDES FROM HEAVY-TAILED DISTRIBUTION +#def randht(n,**kwargs): +def randht(n, *varargin, seed='none'): + ''' + RANDHT generates n observations distributed as some continous heavy-tailed distribution. Options are power law, log-normal, stretched exponential, power law with cutoff, and exponential. Can specify lower cutoff, if desired. + --------------------- + + Input + ----- + n : generate n observations + *args: + xmin : + Type : type of distribution as string + - PL : pwerlaw, reqires alpha + - PC : cutoff, requires alpha and Lambda (?) + - EX : exponential, requires Lambda + - LN : log-normal, requires mu and sigma + - ST : stretched, requires Lambda and beta + + seed : initialize random number generator; for reproducibility; by default it is 'none', which means new randomness each time, else set a number. + + Output + ------ + x : + + Details + ------- + original source : http://www.santafe.edu/~aaronc/powerlaws/ + Ported to python by Joel Ornstein (2011 August), joel_ornstein@hmc.edu + ''' + #%% update variables +# v = {'Type': '', +# 'xmin': 1, +# 'alpha': 2.5, +# 'beta': 1, +# 'Lambda': 1, +# 'mu': 1, +# 'sigma': 1} +# +# for key, value in kwargs.items(): +# if key not in v.keys(): +# raise NameError('Invalid keyword argument input.') +# +# v.update(kwargs) +# globals().update(v) +# +# +# if n<1: +# raise AttributeError('(RANDHT) Error: invalid ''n'' argument.') +# +# if xmin < 1: +# raise AttributeError('(RANDHT) Error: invalid ''xmin'' argument.') + + Type = ''; + xmin = 1; + alpha = 2.5; + beta = 1; + Lambda = 1; + mu = 1; + sigma = 1; + + + # parse command-line parameters; trap for bad input + i=0; + while i=xmin,y) + q = len(y)-n; + if np.isclose(q, 0.): + break + + if q>0.: + r = range(len(y)); + random.shuffle(r) + ytemp = [] + for j in range(len(y)): + if j not in r[0:q]: + ytemp.append(y[j]) + y=ytemp + break + if (q<0.): + for j in range(10*n): + y.append(math.exp(mu+sigma*random.normalvariate(0,1))) + + x = y + + elif Type =='ST': + x=[] + for i in range(n): + x.append(pow(pow(xmin,beta) - (1./Lambda)*math.log(1.-random.random()),(1./beta))) + elif Type == 'PC': + + x = [] + y=[] + for i in range(10*n): + y.append(xmin - (1./Lambda)*math.log(1.-random.random())) + while True: + ytemp=[] + for i in range(10*n): + if random()0): + r = range(len(x)) + random.shuffle(r) + + xtemp = [] + for j in range(len(x)): + if j not in r[0:q]: + xtemp.append(x[j]) + x=xtemp + break; + + if (q<0.): + y=[] + for j in range(10*n): + y.append(xmin - (1./Lambda)*math.log(1.-random.random())) + + + else: + x=[] + for i in range(n): + x.append(xmin*pow(1.-random.random(),-1./(alpha-1.))) # random.random() is uniform distribution [0,1] + + return x +############################################################################### + +#%% PROBABILISTIC HILLSLOPE EROSION +def large_ls(T, Pr, snow, Tsd, Tpr, Tsa, xmin, alpha, cutoff, Tfreeze, LStrig, area = 10.**6, seed='none'): + ''' + Generation of large landslides by thermal trigger (procedure 1 in Bennett et al., 2014). + Parameters taken from Bennett et al. (2012/13) are not altered. + --------------------- + + Input + ----- + T : Temperature [degreeC] + Pr : Precipitation [mm] + snow : data frame from degree-day-model [mm SWE] + Tsd : threshold snowdepth for landslides to be triggered [mm SWE] + Tpr : threshold liquid precipitation for landslides to be triggered [mm] + Tsa : Snow temperature accumulation threshold [°C] + xmin : + alpha : + cutoff : + Tfreeze : + LStirg : Landslide triggering mechanism ['thermal', 'rainfall', 'random'] + area : catchment area [km2], default is 10^6, i.e. output can also be interpreted in [m3] + seed : initialize random number generator + + Output + ------ + lrg_ls : time series of large land slides, [m3] if area not provided, else [mm] + + ''' + # parameters for large landslides distribution, from Bennett et al. (2012) + + if (LStrig == 'thermal') or (LStrig == 'random'): + # T and snow have to be resampled to daily mean + T_day = T.resample('24h').mean() + #T_day_1 = T_day.shift(1) # this is T of 1 day before + idx = T_day.index + T_day = T_day.values + #T_day_1 = T_day_1.values + snow_day = snow.resample('24h').mean() + snow_day = snow_day.values + + # a LS is triggered when T is subfreezing, the day before was not freezing and the snow depth is below threshold + cond1 = T_day < Tfreeze # freezing days + #cond2 = T_day_1 > 0 # positive T days + cond3 = snow_day < Tsd # days of only little snow + #lsdays = cond1 & cond2 & cond3 # boolean array with days of possible landslides + lsdays = cond1 & cond3 + N = len(lsdays[lsdays == True]) # number of big lansdslides + + lrg_ls = np.zeros(len(T_day)) + + if LStrig == 'rainfall': + Prl = Pr.copy() + Prl[T<=Tsa] = 0 # liquid precipitation + Prl_day = Prl.resample('24h').sum() # daily sums + idx = Prl_day.index + lsdays = Prl_day > Tpr + N = len(lsdays[lsdays == True]) + + lrg_ls = np.zeros(len(Prl_day)) + + if LStrig == 'random': + # N from thermal triggering + nt = len(T_day) + dt = int(nt/N) # mean (?) spacing between small landslides + dtexp = np.random.exponential(dt, N) + dtexp = np.ceil(dtexp) # get full days, in this case max one per day + ids = np.cumsum(dtexp) + if max(ids) >= nt: + nids = ids/max(ids) * (nt-2) # if the days go beyond the time series length, rescale to length of time series // (t-2) to ensure max value after ceiling in next line as well + nids = np.ceil(nids) + ids = nids + ids = [int(i) for i in ids] + lsdays = ids + + # generate N large landslide magnitudes (volume, m3). iteration is needed in order to avoid unreasonable large volumes, greater than cutoff + # this is not effective computation...condition should be in randth + cond=False + while not cond: + mags = randht(N,'xmin',xmin,'powerlaw', alpha, seed=seed) + cond = max(mags)= t: + nids = ids/max(ids) * (t-2) # if the days go beyond the time series length, rescale to length of time series // (t-2) to ensure max value after ceiling in next line as well + nids = np.ceil(nids) + ids = nids + + # generate small landslides + mags_teo = np.random.lognormal(mu, sigma, 10**6) # draw many from random distribution, should represent the theoretical distribution + mags_con = mags_teo[mags_teo <= xmin] # represents theoretical distribution but constrained by xmin + mags = np.random.choice(mags_con,n) # n samples from constrained distribution + + #output + ids = [int(i) for i in ids] + s_ls[ids] = mags + s_ls = s_ls / area * 10.**-3 # convert m3 to mm + + data = {'mag': s_ls} + sls = pd.DataFrame(data=data) + + return sls + +#%% SEDIMENT TRANSFER MODEL + +def sedcas(Lls, Sls, hyd, qdf, smax, rhc, shcap, area, method, LStrig, Tpr, shinit=0, scinit=0, **kwargs): + ''' + Sediment cascade from hillslope to channel to outlet. + Note: inactive storage not considered yet, but it's not really needed... + --------------------- + + Input + ----- + Lls : time series of sum of large landslides + Sls : time series of sum of small landslides + q : discharge from hydro model [mm] + snow : snow depth SWE [mm] from hydro model + smax : maximim potential volumetric ration of sediment (with density of bedrock) to water in the flow + rhc : redopsition rate from hillslope to channel [-] + shcap : hillslope storage capacity [mm] + area : catchment area [km2] + method : method for sediment transport, ['lin' 'exp'] + qdf : critical discharge for triggering of debris flow [mm/t?] + shinit : initial hillslope storage [mm] + scinit : initial channel storage [mm] + kwargs : depending on the method + if method is 'lin' semdiment transport starts when a critical discharge is exceeded: + no additional inputs required (only qdf) + if method is 'exp' sediment transport is follows discharge in an exponential relationship: + (a : scaling parameter , determined automatically) + b : shape parameter + mindf : if given, sediment output will also be given in terms of debris flows [mm] + smax_nodf : max sediement concentration for sub-critical flow conditions + + Output + ------ + sed : data frame containing... + sh : hillslope storage time series [mm] + sc : channel storage time series [mm] + so : catchment sediment output time series [mm] + sopot : potential sediment output based on discharge [mm] + dfs : debris flows, sediment output above minimum threshold and concentration of debris flows[mm] + conc : sediment concentration in flow [-] + ''' + q = hyd.Qs.copy() + snow = hyd.snow.copy() + + # check if given kwargs are valid + valid_kwargs = ['b','mindf', 'smax_nodf'] + for key in kwargs.keys(): + if not key in valid_kwargs: + raise AttributeError('%s is not a valid property.'%key) + + # check if required arguments are povided, else raise error + if method == 'exp': + if not ('a' and 'b' and 'smax_nodf' and 'mindf') in kwargs.keys(): + raise AttributeError('for method "exp" keyword arguments a and b must be provided.') + + # unpack kwargs + def unpack_kwargs(kwargs, key): + try: + var = kwargs[key] + except KeyError: + var = np.nan + return var + b = unpack_kwargs(kwargs, 'b') + mindf = unpack_kwargs(kwargs, 'mindf') + smax_nodf = unpack_kwargs(kwargs, 'smax_nodf') + + # determine 'a' and 'Qmin_nondf' + # this is based on two facts + # 1) the sediment concentration for sub-critical bedload transport cannot exceed the concentration given by smax_nodf + # 2) the volume of the sediment transported cannot exceed the minimal debris-flow solid volume + + if method == 'exp': + Qmin_nodf = qdf - (mindf*(1-smax_nodf) / (smax_nodf*qdf))**(1/(1-b)) + if Qmin_nodf < 0: + Qmin_nodf = 0 + a = smax_nodf * qdf / ((qdf-Qmin_nodf)**b*(1-smax_nodf)) + + # landslides (ls) are daily, needs to be padded + freq = q.index[1] - q.index[0] # desired frequency + delta = pd.to_timedelta('1 day') - freq + dates = Lls.index.values.copy() + dates[-1] = dates[-1] + delta + if 'datetime' in str(Lls.index.dtype): + dates = pd.to_datetime(dates) + elif 'timedelta' in str(Lls.index.dtype): + dates = pd.to_timedelta(dates, unit='h') + else: + raise AttributeError('Your input index must be of type "timedelta" or "datetime"') + + if (LStrig == 'thermal') or (LStrig == 'random'): + ls = Lls.mag.copy() + Sls.mag.copy() + ls.index = dates # the last value is now the same as for the sub-daily time-series, needed for padding + ls = ls.resample(freq).pad() # this is the time series at desired frequency + + if 'datetime' in str(ls.index.dtype): + cond = ls.index.time == pd.to_datetime('12:00').time() # hillslope failure always happen at noon + ls[~cond] = 0 # set the other hours to 0 + elif 'timedelta' in str(ls.index.dtype): + cond1 = ls.index.astype('timedelta64[h]').astype('int64') % 12 == 0 + cond2 = ls.index.astype('timedelta64[h]').astype('int64')/12 % 2 == 1 + ls[~(cond1 & cond2)] = 0 + + elif LStrig == 'rainfall': + ### NOT SURE THIS IS GENERIC FOR ALL TEMPORAL RESOLUTIONS AND DATA TYPES + Prc = hyd.Pr.groupby(pd.Grouper(freq='24h')).cumsum() # daily cumsum + Prc[Prc <= Tpr] = np.nan # set all smaller than the triggering threshold to nan + + daily_min = Prc.groupby(pd.Grouper(freq='24h')).min() # this is the minimum of each day + daily_min.index = dates + daily_min = daily_min.resample(freq).pad() # assign min for every modeling time step + + diff = Prc - daily_min + cond1 = diff == 0 # where the difference to the min is 0, is the first time the precipitation exceeds the triggering threshold on that day + dfcond = pd.DataFrame(data=cond1) + dfcond.columns = ['cond1'] + dfcond['cond2'] = np.nan + dfcond['cond2'][1:] = dfcond.cond1[:-1] + dfcond.cond2.iloc[0] = False + cond1 = dfcond.cond1.values + cond2 = dfcond.cond2.values + + Lls.index = dates + Lls = Lls.resample(freq).pad() + Lls[~cond1] = 0 # where the difference is not 0 + Lls[cond2] = 0 # where the the difference is 0, but the previous one is already tagged + + Sls.index = dates + Sls = Sls.resample(freq).pad() + if 'datetime' in Sls.index.dtype_str: + cond = Sls.index.time == pd.to_datetime('12:00').time() # hillslope failure always happen at noon + Sls[~cond] = 0 # set the other hours to 0 + elif 'timedelta' in Sls.index.dtype_str: + cond1 = Sls.index.astype('timedelta64[h]').astype('int64') % 12 == 0 + cond2 = Sls.index.astype('timedelta64[h]').astype('int64')/12 % 2 == 1 + Sls[~(cond1 & cond2)] = 0 + + ls = Lls.mag.copy() + Sls.mag.copy() + + # test if too long + i = np.argwhere(ls.index == q.index[-1])[0][0] + ls = ls[:i+1] + + # convert to arrays if Data Frames or Series + try: + idx = ls.index + ls = ls.values + q = q.values + snow = snow.values + except AttributeError: + pass + + # initialize + t = len(q) # length of time series + sh, sc, so, sopot = np.zeros(t), np.zeros(t), np.zeros(t), np.zeros(t) # initialize output arrays + + # initial conditions + sh[0] = shinit + sc[0] = scinit + + # parameters for large landslides distribution, from Bennett et al. (2012) + xmin=233 # Minimum landslide volume from the power-law tail + alpha=1.65 # Power law scaling exponent in landslide distribution + + for i in range(1,t): + + ## TRANSFER NUMERICAL SCHEME: outputs at t depend on storage states at t, i.e. first the storages, considering inputs, are recomputed, then the debris flow triggering is computed. + + # hillslope-channel transfer + # concept: from each landslide a fraction defined by the rhc parameter is redeposited in the channel storage, the rest of the landslide goes directly into the channel + redep = ls[i]*rhc # redeposition according to redeposition rate + sh[i] = sh[i-1] + redep # hillslope storage change + sc[i] = sc[i-1] + ls[i] - redep # channel storage change + if sh[i] > shcap: + ls_sh = shcap*2 + while ls_sh >= shcap: + ls_sh = randht(1,'xmin',xmin,'powerlaw',alpha)[0] + ls_sh = ls_sh / area * 10.**-3 # convert m3 to mm + sh[i] = sh[i] - ls_sh + sc[i] = sc[i] + ls_sh + + # channel-outlet transfer + # # there are two methods for sediment entrainment: lin and exp... + # # Note + # # qdf is equivalent to the critical shear stress + # # q-qdf is equivalent to the excess shear stress + if method == 'lin': + # #(1) If runoff exceeds a threshold, Qdf, a debris flow is triggered + if q[i] >= qdf: + sopot[i] = smax/(1-smax) * q[i]# - qdf) # potential sediment output + + if method == 'exp': + if (q[i] < qdf) and (q[i] >= Qmin_nodf): + sopot[i] = a * (q[i] - Qmin_nodf) ** b # exponential function for small flows + elif q[i] >= qdf: + sopot[i] = smax/(1-smax) * q[i] # linear function for debris flows (flows above qdf) + + # # the sediment flow might, however, not be initiated because... + # case 1: there is snow --> no DF + if snow[i] > 0: + so[i] = 0 + sopot[i] = 0 + # case 2: transport limited, channel storage is big enough + elif sc[i] >= sopot[i]: + so[i] = sopot[i] + sc[i] = sc[i] - so[i] + # case 3: supply limited, channel storage is too small + else: + so[i] = sc[i] + sc[i] = 0 + + ############ + + # output is debris flow when 1) the volume is larger than mindf and 2) the sediment concentration is larger than smax_nodf + if ('mindf') in kwargs.keys(): + + def get_dfs(q,s,mindf): + q2 = q.copy() + q2[q2==0] = np.nan + conc = s/(s+q2) # volumetric sediment concentration in flow + cond1 = s >= mindf # first condition, sediment output must be greater than the minimum possibld DF + cond2 = conc > smax_nodf # second condition, the sediment concentration must be greater than for fluvial transport + dfs = s[cond1 & cond2] + idxdfs = idx[cond1 & cond2] + dt = idxdfs[1:] - idxdfs[:-1] # get spacing between debris flows + dt = dt.insert(0,pd.NaT) # put a NaT at the first position becuase it doesn't have a valiue before + + # if there are consecutive values, add them + dfsnew = dfs.copy() + for i in range(len(dt)-1,0,-1): + if dt[i] == pd.to_timedelta('1 hour'): # this should mean, that when using daily data, the values are not added + dfsnew[i-1] = dfsnew[i-1] + dfsnew[i] # add to the previous hour + dfsnew[i] = 0 + idxdfs = idxdfs[dfsnew>0] + dfsnew = dfsnew[dfsnew>0] + + # insert values in full modelling time series + dfs = pd.Series(np.zeros(len(idx)), index=idx) + dfs.loc[idxdfs] = dfsnew + dfs = dfs.values + + return dfs, conc + + df, conc = get_dfs(q,so,mindf) + dfp, concp = get_dfs(q,sopot,mindf) + + # output + data = {'sh': sh, + 'sc': sc, + 'so': so, + 'sopot': sopot, + 'ls': ls, + 'dfs': df, + 'conc': conc, + 'dfspot': dfp, + 'concpot': concp} + + sed = pd.DataFrame(data=data, index=idx) + + return sed diff --git a/parameters.par b/parameters.par new file mode 100644 index 0000000..1a2b96d --- /dev/null +++ b/parameters.par @@ -0,0 +1,72 @@ +SedCas Parameters + +n_HRU: 2 +HRUs: ['forest', 'bedrock'] +Vwcaps: [[72,27], [4]] +ks: [[94, 235], [23]] +shares: [0.56, 0.44] +Asnow: [0.4, 0.65] +Anosnow: [0.15, 0.25] +Ele: 1600 +area: 4.83 +Tsa: 0.6 +Tsm: 0.5 +mrate: 0.08 +alphaET: 20.0 +LStrig: 'thermal' +Tpr: 7.9 +Tsd: 20.0 +Tfreeze: 0 +qdf: 2.40 +smax: 0.57 +smax_nodf: 0.4 +a: auto +b: 1.5 +Qmin_nondf: auto +rhc: 0.12 +minDF: 2000 +rho_b: 2600 +rho_dry: 2000 +rho_bulk: 2000 +shcap: 750000 +ls_xmin: 233 +ls_alpha: 1.69 +ls_cutoff: 3*10**6 +M: 100 + + +Description +----------- +n_HRU >>> Number of hydrological response units (HRU) [-] +HRUs >>> Description of hydrological response units +Vwcaps >>> Water storage capacities of HRUs +ks >>> Residence time parameters for HRUs +shares >>> Relative share of HRUs to total area +Asnow >>> Albedos for HRUs when covered with snow +Anosnow >>> Albedos for HRUs when not covered with snow +Ele >>> (Mean) catchment elevation [m a.s.l.] +area >>> Total catchment area [km2] +Tsa >>> Temperature threshold for snow accumulation [degreeC] +Tsm >>> Temperature threshold for snow melt [degreeC] +m >>> Melt rate factor [-] +alphaET >>> Evaptranspiration parameter [-] +LStrig >>> Landslide triggering mechanism e['thermal', 'rainfall', 'random'] +Tpr >>> Precipitation threshold for rainfall-landslides to be triggered [mm] +Tsd >>> Snow-depth threhsold for landslides to be triggered [mm SWE] +Tfreeze >>> Temperature threshold for landslide triggered by freezing [degreeC] +qdf >>> Critical discharge for debris flows to be triggered [mm] +smax >>> max volumetric ratio of sediment to water [mm/mm], typically <= 0.8 +smax_nodf >>> max possible volumetric sediment concentration for bedload transport (for sub-critical flows) [mm/mm], typically <= 0.4 +a >>> Scaling parameter for bedload transport in sub-critical flow conditions, determined automatically +b >>> Shape parameter for bedload transport in sub-critical flow conditions, typically =1.5 +Qmin_nodf >>> min discharge for bedload transport initiation, determined automatically +rhc >>> Deposition rate from hillslope to channel, i.e. fraction of landslide going into hillslope storage and 1-rhc is the fraction going into the channel storage. +minDF >>> min (measured) debris-flow volume (water + sediment) [m3] +rho_b >>> bedrock density [kg/m3] +rho_dry >>> density of stored sediments, dry unit weight (sediment and air) [kg/m3], e.g. 2000 means porosity of 23% +rho_bulk >>> density of debris flows (sediment and water) [kg/m3], i.e. porosity of 38% +shcap >>> hypothetical threshold [m3] for collapse of hillslope storage, considered density is 'rho_dry' +ls_xmin >>> Minimum landslide volume from the power-law tail +ls_alpha >>> Power law scaling exponent in landslide distribution +ls_cutoff >>> The maximum potential landslide volume (m^3) +M >>> number of stochastic simulations for hillslope module diff --git a/run.py b/run.py new file mode 100644 index 0000000..f1b092f --- /dev/null +++ b/run.py @@ -0,0 +1,17 @@ +#!/usr/bin/env python3 +# -*- coding: utf-8 -*- +""" +Created on Thu Feb 3 16:56:57 2022 + +@author: hirschbe +""" + +from SedCas import SedCas + +model = SedCas() +model.load_climate() +model.load_params() +model.run_hydro() +model.run_sediment() +model.save_output() +model.plot_sedyield_monthly() diff --git a/sedcas.yml b/sedcas.yml new file mode 100644 index 0000000..7511a04 --- /dev/null +++ b/sedcas.yml @@ -0,0 +1,192 @@ +name: sedcas +channels: + - defaults +dependencies: + - alabaster=0.7.12 + - appdirs=1.4.4 + - applaunchservices=0.2.1 + - appnope=0.1.2 + - arrow=0.13.1 + - astroid=2.6.6 + - async_generator=1.10 + - atomicwrites=1.4.0 + - attrs=21.4.0 + - autopep8=1.6.0 + - babel=2.9.1 + - backcall=0.2.0 + - binaryornot=0.4.4 + - black=19.10b0 + - blas=1.0 + - bleach=4.1.0 + - bottleneck=1.3.2 + - brotli=1.0.9 + - brotlipy=0.7.0 + - ca-certificates=2021.10.26 + - certifi=2021.10.8 + - cffi=1.15.0 + - chardet=4.0.0 + - charset-normalizer=2.0.4 + - click=8.0.3 + - cloudpickle=2.0.0 + - colorama=0.4.4 + - cookiecutter=1.7.2 + - cryptography=36.0.0 + - cycler=0.11.0 + - debugpy=1.5.1 + - decorator=5.1.1 + - defusedxml=0.7.1 + - diff-match-patch=20200713 + - docutils=0.17.1 + - entrypoints=0.3 + - flake8=3.9.2 + - fonttools=4.25.0 + - freetype=2.11.0 + - giflib=5.2.1 + - icu=58.2 + - idna=3.3 + - imagesize=1.3.0 + - importlib-metadata=4.8.2 + - importlib_metadata=4.8.2 + - inflection=0.5.1 + - intel-openmp=2021.4.0 + - intervaltree=3.1.0 + - ipykernel=6.4.1 + - ipython=7.29.0 + - ipython_genutils=0.2.0 + - isort=5.9.3 + - jedi=0.18.1 + - jinja2=2.11.3 + - jinja2-time=0.2.0 + - jpeg=9d + - jsonschema=3.2.0 + - jupyter_client=6.1.12 + - jupyter_core=4.9.1 + - jupyterlab_pygments=0.1.2 + - keyring=23.4.0 + - kiwisolver=1.3.1 + - lazy-object-proxy=1.6.0 + - lcms2=2.12 + - libcxx=12.0.0 + - libffi=3.3 + - libpng=1.6.37 + - libsodium=1.0.18 + - libspatialindex=1.9.3 + - libtiff=4.2.0 + - libwebp=1.2.0 + - libwebp-base=1.2.0 + - lz4-c=1.9.3 + - markupsafe=1.1.1 + - matplotlib=3.5.0 + - matplotlib-base=3.5.0 + - matplotlib-inline=0.1.2 + - mccabe=0.6.1 + - mistune=0.8.4 + - mkl=2021.4.0 + - mkl-service=2.4.0 + - mkl_fft=1.3.1 + - mkl_random=1.2.2 + - munkres=1.1.4 + - mypy_extensions=0.4.3 + - nbclient=0.5.3 + - nbconvert=6.3.0 + - nbformat=5.1.3 + - ncurses=6.3 + - nest-asyncio=1.5.1 + - numexpr=2.8.1 + - numpy=1.21.2 + - numpy-base=1.21.2 + - numpydoc=1.2 + - olefile=0.46 + - openssl=1.1.1m + - packaging=21.3 + - pandas=1.3.5 + - pandocfilters=1.5.0 + - parso=0.8.3 + - pathspec=0.7.0 + - pexpect=4.8.0 + - pickleshare=0.7.5 + - pillow=8.4.0 + - pip=21.2.4 + - pluggy=1.0.0 + - poyo=0.5.0 + - prompt-toolkit=3.0.20 + - psutil=5.8.0 + - ptyprocess=0.7.0 + - pycodestyle=2.7.0 + - pycparser=2.21 + - pydocstyle=6.1.1 + - pyflakes=2.3.1 + - pygments=2.10.0 + - pylint=2.9.6 + - pyls-spyder=0.4.0 + - pyopenssl=22.0.0 + - pyparsing=3.0.4 + - pyqt=5.9.2 + - pyrsistent=0.18.0 + - pysocks=1.7.1 + - python=3.9.7 + - python-dateutil=2.8.2 + - python-lsp-black=1.0.0 + - python-lsp-jsonrpc=1.0.0 + - python-lsp-server=1.2.4 + - python-slugify=5.0.2 + - python.app=3 + - pytz=2021.3 + - pyyaml=6.0 + - pyzmq=22.3.0 + - qdarkstyle=3.0.2 + - qstylizer=0.1.10 + - qt=5.9.7 + - qtawesome=1.0.3 + - qtconsole=5.1.1 + - qtpy=1.11.2 + - readline=8.1.2 + - regex=2021.11.2 + - requests=2.27.1 + - rope=0.22.0 + - rtree=0.9.7 + - setuptools=58.0.4 + - sip=4.19.13 + - six=1.16.0 + - snowballstemmer=2.2.0 + - sortedcontainers=2.4.0 + - sphinx=4.4.0 + - sphinxcontrib-applehelp=1.0.2 + - sphinxcontrib-devhelp=1.0.2 + - sphinxcontrib-htmlhelp=2.0.0 + - sphinxcontrib-jsmath=1.0.1 + - sphinxcontrib-qthelp=1.0.3 + - sphinxcontrib-serializinghtml=1.1.5 + - spyder=5.1.5 + - spyder-kernels=2.1.3 + - sqlite=3.37.0 + - testpath=0.5.0 + - text-unidecode=1.3 + - textdistance=4.2.1 + - three-merge=0.1.1 + - tinycss=0.4 + - tk=8.6.11 + - toml=0.10.2 + - tornado=6.1 + - tqdm=4.62.3 + - traitlets=5.1.1 + - typed-ast=1.4.3 + - typing_extensions=3.10.0.2 + - tzdata=2021e + - ujson=4.2.0 + - unidecode=1.2.0 + - urllib3=1.26.8 + - watchdog=2.1.6 + - wcwidth=0.2.5 + - webencodings=0.5.1 + - wheel=0.37.1 + - whichcraft=0.6.1 + - wrapt=1.12.1 + - wurlitzer=3.0.2 + - xz=5.2.5 + - yaml=0.2.5 + - yapf=0.31.0 + - zeromq=4.3.4 + - zipp=3.7.0 + - zlib=1.2.11 + - zstd=1.4.9 \ No newline at end of file