Modeling snow#
R.A. Collenteur, University of Graz / Eawag, November 2021
In this notebook it is shown how to account for snowfall and smowmelt on groundwater recharge and groundwater levels, using a degree-day snow model. This notebook is work in progress and will be extended in the future.
import matplotlib.pyplot as plt
import pandas as pd
import pastas as ps
ps.set_log_level("ERROR")
ps.show_versions()
Pastas version: 1.11.0
Python version: 3.11.12
NumPy version: 2.2.6
Pandas version: 2.3.2
SciPy version: 1.16.1
Matplotlib version: 3.10.6
Numba version: 0.61.2
1. Load data#
In this notebook we will look at some data for a well near Heby, Sweden. All the meteorological data is taken from the E-OBS database. As can be observed from the temperature time series, the temperature regularly drops below zero in winter. Given this observation, we may expect precipitation to (partially) fall as snow during these periods.
head = pd.read_csv("data/heby_head.csv", index_col=0, parse_dates=True).squeeze()
evap = pd.read_csv("data/heby_evap.csv", index_col=0, parse_dates=True).squeeze()
prec = pd.read_csv("data/heby_prec.csv", index_col=0, parse_dates=True).squeeze()
temp = pd.read_csv("data/heby_temp.csv", index_col=0, parse_dates=True).squeeze()
ps.plots.series(head=head, stresses=[prec, evap, temp]);
2. Make a simple model#
First we create a simple model with precipitation and potential evaporation as input, using the non-linear FlexModel to compute the recharge flux. We not not yet take snowfall into account, and thus assume all precipitation occurs as snowfall. The model is calibrated and the results are visualized for inspection.
# Settings
tmin = "1985" # Needs warmup
tmax = "2010"
ml1 = ps.Model(head)
sm1 = ps.RechargeModel(
prec, evap, recharge=ps.rch.FlexModel(), rfunc=ps.Gamma(), name="rch"
)
ml1.add_stressmodel(sm1)
# Solve the Pastas model in two steps
ml1.solve(tmin=tmin, tmax=tmax, fit_constant=False, report=False)
ml1.add_noisemodel(ps.ArNoiseModel())
ml1.set_parameter("rch_srmax", vary=False)
ml1.solve(tmin=tmin, tmax=tmax, fit_constant=False, initial=False)
ml1.plot(figsize=(10, 3));
Fit report Head Fit Statistics
================================================
nfev 34 EVP 39.31
nobs 590 R2 0.39
noise True RMSE 0.13
tmin 1985-01-01 00:00:00 AICc -3225.72
tmax 2010-01-01 00:00:00 BIC -3195.26
freq D Obj 1.22
warmup 3650 days 00:00:00 ___
solver LeastSquares Interp. No
Parameters (7 optimized)
================================================
optimal initial vary
rch_A 2.056697 1.365660 True
rch_n 1.352496 3.102418 True
rch_a 195.831896 53.315065 True
rch_srmax 2.618543 2.618543 False
rch_lp 0.250000 0.250000 False
rch_ks 5075.121811 106.014571 True
rch_gamma 18.639673 1.121170 True
rch_kv 1.046650 1.999805 True
rch_simax 2.000000 2.000000 False
constant_d 77.741547 0.000000 False
noise_alpha 108.925011 1.000000 True
The model fit with the data is not too bad, but we are clearly missing the highs and lows of the observed groundwater levels. This could have many causes, but in this case we may suspect that the occurrence of snowfall and melt impacts the results.
3. Account for snowfall and snow melt#
A second model is now created that accounts for snowfall and melt through a degree-day snow model (see e.g., Kavetski & Kuczera (2007). To run the model we add the keyword snow=True to the FlexModel and provide a time series of mean daily temperature to the RechargeModel. The temperature time series is used to split the precipitation into snowfall and rainfall.
ml2 = ps.Model(head)
sm2 = ps.RechargeModel(
prec,
evap,
recharge=ps.rch.FlexModel(snow=True),
rfunc=ps.Gamma(),
name="rch",
temp=temp,
)
ml2.add_stressmodel(sm2)
# Solve the Pastas model in two steps
ml2.solve(tmin=tmin, tmax=tmax, fit_constant=False, report=False)
ml2.add_noisemodel(ps.ArNoiseModel())
ml2.set_parameter("rch_srmax", vary=False)
ml2.solve(tmin=tmin, tmax=tmax, fit_constant=False, initial=False)
Fit report Head Fit Statistics
================================================
nfev 33 EVP 71.64
nobs 590 R2 0.72
noise True RMSE 0.09
tmin 1985-01-01 00:00:00 AICc -3393.31
tmax 2010-01-01 00:00:00 BIC -3354.20
freq D Obj 0.91
warmup 3650 days 00:00:00 ___
solver LeastSquares Interp. No
Parameters (9 optimized)
================================================
optimal initial vary
rch_A 0.643058 0.562044 True
rch_n 1.199465 1.456297 True
rch_a 160.343870 96.540868 True
rch_srmax 135.033159 135.033159 False
rch_lp 0.250000 0.250000 False
rch_ks 341.101701 307.682220 True
rch_gamma 11.289139 13.508011 True
rch_kv 0.652375 0.706174 True
rch_simax 2.000000 2.000000 False
rch_tt 2.522761 2.800000 True
rch_k 5.854269 6.568692 True
constant_d 78.287976 0.000000 False
noise_alpha 61.310132 1.000000 True
Compare results#
From the fit_report we can already observe that the model fit improved quite a bit. We can also visualize the results to see how the model improved.
ax = ml2.plot(figsize=(10, 3))
ml1.simulate().plot(ax=ax)
plt.legend(
[
"Observations",
"Model w Snow NSE={:.2f}".format(ml2.stats.nse()),
"Model w/o Snow NSE={:.2f}".format(ml1.stats.nse()),
],
ncol=3,
)
<matplotlib.legend.Legend at 0x793295006d50>
Extract the water balance (States & Fluxes)#
df = ml2.stressmodels["rch"].get_water_balance(
ml2.get_parameters("rch"), tmin=tmin, tmax=tmax
)
df.plot(subplots=True, figsize=(10, 10));
References#
Kavetski, D. and Kuczera, G. (2007). Model smoothing strategies to remove microscale discontinuities and spurious secondary optima in objective functions in hydrological calibration. Water Resources Research, 43(3).