FunctionDescriptions.md

Implemented Quality Check Functions

Index of the main documentation of the implemented functions, their purpose and parametrization.

Index

• Miscellaneous
  • range
  • seasonalRange
  • isolated
  • missing
  • clear
  • force
• Spike Detection
  • spikes_basic
  • spikes_simpleMad
  • spikes_slidingZscore
  • spikes_spektrumBased
• Constant Detection
  • constant
  • constants_varianceBased
• Break Detection
  • breaks_spektrumBased
• Time Series Harmonization
  • harmonize_shift2Grid
  • harmonize_aggregate2Grid
  • harmonize_linear2Grid
  • harmonize_interpolate2Grid
  • harmonize_downsample
  • harmonize
  • deharmonize
• Soil Moisture
  • soilMoisture_plateaus
  • soilMoisture_spikes
  • soilMoisture_breaks
  • soilMoisture_byFrost
  • soilMoisture_byPrecipitation
• Machine Learning
  • machinelearning

Soil Moisture

soilMoisture_plateaus

soilMoisture_plateaus(plateau_window_min="12h", plateau_var_limit=0.0005,
                      rainfall_window_range="12h", var_total_nans=np.inf, 
                      var_consec_nans=np.inf, derivative_max_lb=0.0025, 
                      derivative_min_ub=0, data_max_tolerance=0.95, 
                      filter_window_size=None, smooth_poly_order=2)

parameter	data type	default value	description
plateau_window_min	string	"12h"	Options - any offset string Minimum barrier for the duration, values have to be continouos to be plateau canditaes. See condition (1).
plateau_var_limit	float	0.0005	Barrier, the variance of a group of values must not exceed to be flagged a plateau. See condition (2).
rainfall_range	string	"12h"	An Offset string. See condition (3) and (4)
var_total_nans	int or 'inf'	np.inf	Maximum number of nan values allowed, for a calculated variance to be valid. (Default skips the condition.)
var_consec_nans	int or 'inf'	np.inf	Maximum number of consecutive nan values allowed, for a calculated variance to be valid. (Default skips the condition.)
derivative_max_lb	float	0.0025	Lower bound for the second derivatives maximum in rainfall_range range. See condition (3)
derivative_min_ub	float	0	Upper bound for the second derivatives minimum in rainfall_range range. See condition (4)
data_max_tolerance	flaot	0.95	Factor for data max barrier of condition (5).
filter_window_size	Nonetype or string	None	Options: - None - any offset string Controlls the range of the smoothing window applied with the Savitsky-Golay filter. If None is passed (default), the window size will be two times the sampling rate. (Thus, covering 3 values.) If you are not very well knowing what you are doing - do not change that value. Broader window sizes caused unexpected results during testing phase.
smooth_poly_order	int	2	Order of the polynomial used for fitting while smoothing.

NOTE, that the dataseries-to-be flagged is supposed to be harmonized to an equadistant frequency grid.

The function represents a stricter version of the constant_varianceBased test from the constants detection library. The added constraints for values to be flagged (3)-(5), are designed to match the special case of constant value courses of soil moisture meassurements and basically check the derivative for being determined by preceeding rainfall events ((3) and (4)), as well as the plateau for being sufficiently high in value (5).

Any set of consecutive values

$x_k,..., x_{k+n}$

, of a timeseries

$x$

is flagged, if:

1. $n >$
   plateau_window_min
2. $\sigma(x_k, x_{k+1},..., x_{k+n}) <$
   plateau_var_limit
3. $\max(x'_{k-n-s}, x'_{k-n-s+1},..., x'_{k-n+s}) \geq$
   derivative_max_lb, with
   $s$
   denoting periods per rainfall_range
4. $\min(x'_{k-n-s}, x'_{k-n-s+1},..., x'_{k-n+s}) \leq$
   derivative_min_ub, with
   $s$
   denoting periods per rainfall_range
5. $\mu(x_k, x_{k+1},..., x_{k+n}) < \max(x) \times$
   plateau_var_limit

This Function is an implementation of the soil temperature based Soil Moisture flagging, as presented in:

Dorigo, W. et al: Global Automated Quality Control of In Situ Soil Moisture Data from the international Soil Moisture Network. 2013. Vadoze Zone J. doi:10.2136/vzj2012.0097.

All parameters default to the values, suggested in this publication.

SoilMoisture_spikes

soilMoisture_spikes(filter_window_size="3h", raise_factor=0.15, dev_cont_factor=0.2,
                   noise_barrier=1, noise_window_size="12h", noise_statistic="CoVar")

parameter	data type	default value	description
filter_window_size	string	"3h"
raise_factor	float	0.15
dev_cont_factor	float	0.2
noise_barrier	integer	1
noise_window_size	string	"12h"
noise_statistic	string	"CoVar"

The Function is just a wrapper around flagSpikes_spektrumBased, from the spike detection library and performs a call to this function with a parameter set, referring to:

Dorigo, W. et al: Global Automated Quality Control of In Situ Soil Moisture Data from the international Soil Moisture Network. 2013. Vadoze Zone J. doi:10.2136/vzj2012.0097.

soilMoisture_breaks

soilMoisture_breaks(diff_method="raw", filter_window_size="3h",
                   rel_change_rate_min=0.1, abs_change_min=0.01, first_der_factor=10,
                   first_der_window_size="12h", scnd_der_ratio_margin_1=0.05,
                   scnd_der_ratio_margin_2=10, smooth_poly_order=2)

parameter	data type	default value	description
diff_method	string	"raw"
filter_window_size	string	"3h"
rel_change_rate_min	float	0.1
abs_change_min	float	0.01
first_der_factor	integer	10
first_der_window_size	string	"12h"
scnd_der_ratio_margin_1	float	0.05
scnd_der_ratio_margin_2	float	10.0
smooth_poly_order	integer	2

The Function is just a wrapper around flagBreaks_spektrumBased, from the breaks detection library and performs a call to this function with a parameter set, referring to:

Dorigo, W. et al: Global Automated Quality Control of In Situ Soil Moisture Data from the international Soil Moisture Network. 2013. Vadoze Zone J. doi:10.2136/vzj2012.0097.

soilMoisture_byFrost

soilMoisture_byFrost(soil_temp_reference, tolerated_deviation="1h", frost_level=0)

parameter	data type	default value	description
soil_temp_reference	string		A string, denoting the fields name in data, that holds the data series of soil temperature values, the to-be-flagged values shall be checked against.
tolerated_deviation	string	"1h"	An offset string, denoting the maximal temporal deviation, the soil frost states timestamp is allowed to have, relative to the data point to be flagged.
frost_level	integer	0	Value level, the flagger shall check against, when evaluating soil frost level.

The function flags Soil moisture measurements by evaluating the soil-frost-level in the moment of measurement (+/- tolerated deviation). Soil temperatures below "frost_level" are regarded as denoting frozen soil state and result in the checked soil moisture value to get flagged.

This Function is an implementation of the soil temperature based Soil Moisture flagging, as presented in:

Dorigo, W. et al: Global Automated Quality Control of In Situ Soil Moisture Data from the international Soil Moisture Network. 2013. Vadoze Zone J. doi:10.2136/vzj2012.0097.

All parameters default to the values, suggested in this publication.

soilMoisture_byPrecipitation

soilMoisture_byPrecipitation(prec_reference, sensor_meas_depth=0,
                            sensor_accuracy=0, soil_porosity=0,
                            std_factor=2, std_factor_range="24h"
                            ignore_missing=False)

parameter	data type	default value	description
prec_reference	string		A string, denoting the fields name in data, that holds the data series of precipitation values, the to-be-flagged values shall be checked against.
sensor_meas_depth	integer	0	Depth of the soil moisture sensor in meter.
sensor_accuracy	integer	0	Soil moisture sensor accuracy in $\frac{m^3}{m^{-3}}$
soil_porosity	integer	0	Porosoty of the soil, surrounding the soil moisture sensor
std_factor	integer	2	See condition (2)
std_factor_range	string	"24h"	See condition (2)
ignore_missing	bool	False	If True, the variance of condition (2), will also be calculated if there is a value missing in the time window. Selcting Flase (default) results in values that succeed a time window containing a missing value never being flagged (test not applicable rule)

Function flags Soil moisture measurements by flagging moisture rises that do not follow up a sufficient precipitation event. If measurement depth, sensor accuracy of the soil moisture sensor and the porosity of the surrounding soil is passed to the function, an inferior level of precipitation, that has to preceed a significant moisture raise within 24 hours, can be estimated. If those values are not delivered, this inferior bound is set to zero. In that case, any non zero precipitation count will justify any soil moisture raise.

Thus, a data point

x_k

with sampling rate

f

is flagged an invalid soil moisture raise, if:

1. The value to be flagged has to signify a rise. This means, for the quotient
   s =
   (raise_reference /
   f
   ):
   • x_k > x_{k-s}
2. The rise must be sufficient. Meassured in terms of the standart deviation
   V
   , of the values in the preceeding std_factor_range - window. This means, with
   h =
   std_factor_range /
   f
   :
   • x_k - x_{k-s} >
     std_factor
     \times V(x_{t-h},...,x_k{k})
3. Depending on some sensor specifications, there can be calculated a bound
   >0
   , the rainfall has to exceed to justify the eventual soil moisture raise. For the series of the precipitation meassurements
   y
   , and the quotient
   j =
   "24h" /
   f
   , this means:
   • y_{k-j} + y_{k-j+1} + ... + y_{k} <
     sensor_meas_depth
     \times
     sensor_accuracy
     \times
     soil_porosity

This Function is an implementation of the precipitation based Soil Moisture flagging, as presented in:

Dorigo, W. et al: Global Automated Quality Control of In Situ Soil Moisture Data from the international Soil Moisture Network. 2013. Vadoze Zone J. doi:10.2136/vzj2012.0097.

All parameters default to the values, suggested in this publication.

Machine Learning

machinelearning

machinelearning(references, window_values, window_flags, path)

parameter	data type	default value	description
references	string or list of strings		the fieldnames of the data series that should be used as reference variables
window_values	integer		Window size that is used to derive the gradients of both the field- and reference-series inside the moving window
window_flags	integer		Window size that is used to count the surrounding automatic flags that have been set before
path	string		Path to the respective model object, i.e. its name and the respective value of the grouping variable. e.g. "models/model_0.2.pkl"

This Function uses pre-trained machine-learning model objects for flagging. This requires training a model by use of the training script provided. For flagging, inputs to the model are the data of the variable of interest, data of reference variables and the automatic flags that were assigned by other tests inside SaQC. Therefore, this function should be defined last in the config-file, i.e. it should be the last test that is executed. Internally, context information for each point is gathered in form of moving windows. The size of the moving windows for counting of the surrounding automatic flags and for calculation of gradients in the data is specified by the user during model training. For the model to work, the parameters 'references', 'window_values' and 'window_flags' have to be set to the same values as during training. For a more detailed description of the modeling aproach see the training script.