QX/T 551-2020 in English
VALIDQuality control of meteorological observation data - Soil moisture
- Issued on:2020-06-16
- Implemented on:2020-09-01
- File Format:PDF
- Delivery:Via email within 1~3 business days
$204.00
本标准规定了土壤水分观测资料质量控制的内容、方法和步骤。
本标准适用于频域反射法土壤水分小时观测资料的质量控制，烘干法土壤水分观测资料也可参照执行。
Introduction
Technical background and scope of application of the standard
As a special supplement to QX/T118-2020, this standard establishes a quality control system for hourly soil moisture observation data using the frequency domain reflectometry method. The technical evolution integrates the automated quality control concept of the International Soil Moisture Observation Network (ISMN) and refers to the localized practice of the Chinese Automatic Soil Moisture Observation System.
Analysis of the core terminology system
| Terms | Units of measurement | Typical abnormal scenarios |
|---|---|---|
| Soil volumetric moisture content | % | Data invalidation under frozen state |
| Soil effective moisture storage | mm | Sudden change detection during continuous irrigation |
Key points for implementing quality control methods
4.2.3 Technical specifications for limit value inspection
Use dynamic threshold algorithm: When the theoretical upper limit (1-θ_w)×100 calculation result is greater than 60%, 60% is used as the upper limit value. Where θ_w represents the soil wilting moisture (weight moisture content).
4.2.5 Temporal consistency check case
Precipitation scenario example: When the precipitation R=5mm within 2 hours, the volume moisture content of the surface soil increases by more than 5% in 1 hour, triggering a suspicious flag. This rule effectively identifies abnormally wet data caused by automatic irrigation system failures.
Implementation suggestions and precautions
- For the drying method data, the impact of laboratory measurement errors on the limit value needs to be considered
- It is recommended to establish a soil freezing state database to assist in internal consistency checks
- Time series analysis should use a sliding window algorithm to optimize mutation detection
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