D Quality flags

Quality flags are constructed on a philosophy of two qualifiers. The first described the origin of the data and the second the quality.

Possible origins of data are:

1. Original: This entails the data value is the original value. It has not been amended by Delft-FEWS

2. Completed: This entails the original value was missing and was replaced by a non-missing value.

3. Corrected: This entails the original value was replaced with another non-missing value.

Possible qualifiers are:

4. Reliable: Data is reliable and valid

5. Doubtful: The validity of the data value is uncertain

6. Unreliable: The data value is unreliable and cannot be used.

Following this specification, the table below gives an overview of quality flag enumerations

Table D.1 Enumeration of quality flags

Enumeration	Description
0	Original/Reliable The data value is the original value retrieved from an external source and it successfully passes all validation criteria set.
1	Corrected/Reliable The original value was removed and corrected. Correction may be through interpolation or manual editing.
2	Completed/Reliable Original value was missing. Value has been filled in through interpolation, transformation (e.g. stage discharge) or a model.
3	Original/Doubtful Observed value retrieved from external data source. Value is valid, but marked as suspect due to soft validation limits being exceeded.
4	Corrected/Doubtful The original value was removed and corrected. However, the corrected value is doubtful due to validation limits.
5	Completed/Doubtful Original value was missing. Value has been filled in as above, but resulting value is doubtful due to limits in transformation/interpolation or input value used for transformation being doubtful.
6	Missing/Unreliable Observed value retrieved from external data source. Value is invalid due to validation limits set. Value is removed
7	Corrected/Unreliable The original value was removed and corrected. However, corrected value is unreliable and is removed.
8	Completed/Unreliable Original value was missing. Value has been filled in as above, but resulting value is unreliable and is removed.
9	Missing value in originally observed series. Note this is a special form of both Original/Unreliable and Original/Reliable.

No difference is made between historic and forecast data. This is not considered a quality flag. The data model of NFFS is constructed such that this difference is inherent to the data type definition.
External sources may either be an actual external source, a forecasting module or a transformation. The convention in NFFS the definition of data series parameter types identifies the data source.

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