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To test the consistency of rainfall records, several methods and approaches are commonly used:
Double Mass Curve Analysis
This is a standard and widely used method where cumulative rainfall at the station under test is plotted against the cumulative mean rainfall of surrounding stations. A consistent record shows a linear trend. Any change in slope indicates a change in recording conditions or station environment, which implies inconsistency. The rainfall data can then be adjusted by the ratio of slopes before and after the shift to correct inconsistencies.
Mass Curve Method
Plotting cumulative rainfall over time for a single station helps check for sudden changes or shifts in the rainfall pattern. A straight line plot indicates consistent data, while deviations indicate possible inconsistencies or errors.
Statistical Tests (t-test, F-test)
Statistical methods like the Student’s t-test can check if the rainfall record is statistically consistent with records from neighboring stations, verifying if they come from the same population. This involves assumptions of normal distribution and variance equality.
Spatial Consistency Check
This involves comparing rainfall data from the station with interpolated rainfall estimates from neighboring stations using spatial interpolation methods. Large deviations from expected values indicate possible inconsistencies.
Quality Control of Telemetric Data
For automatic rain gauge networks, real-time quality control schemes assess measurement reliability by multiple checks, including detection of sensor faults, temporal consistency, and comparison between neighboring sensors.
In practice, these methods are combined to ensure that rainfall records are reliable, filling missing data, and correcting deviations using double mass curve adjustments and statistical validations.
Thus, the double mass curve technique remains the cornerstone of rainfall records consistency testing, supported by statistical and spatial validation methods.
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#hydrology #precipitation #rainfall
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