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Determining optimal probability distributions for gridded precipitation data based on L-moments.
Ming Li1, Guiwen Wang2, Fuqiang Cao2
1College of Geographical Sciences, Shanxi Normal University, Taiyuan 030031, China; Key Laboratory of Geographical Processes and Ecological Security of Changbai Mountains, Ministry of Education, School of Geographical Sciences, Northeast Normal University, Changchun 130024, China.
The Science of the Total Environment
|April 26, 2023
Summary
Precipitation probability distributions vary across the Loess Plateau by location and time. Statistical methods confirm reliable rainfall estimations for climate prediction and hydraulic engineering using gridded datasets.
Area of Science:
- Hydrology and Climatology
- Statistical Analysis of Environmental Data
Background:
- Accurate precipitation probability distributions are vital for climate prediction and hydraulic infrastructure design.
- Traditional regional frequency analysis, while useful, has limitations with the growing availability of high-resolution gridded precipitation data.
- The probability distributions of these advanced datasets remain underexplored.
Purpose of the Study:
- To identify and analyze precipitation probability distributions across the Loess Plateau using high-resolution gridded data.
- To evaluate the reliability of statistical models for estimating precipitation across different temporal scales (annual, seasonal, monthly).
- To provide pixel-wise distribution parameters and quantiles for enhanced precipitation analysis.
Main Methods:
- Utilized L-moments and goodness-of-fit tests to analyze precipitation data.
- Examined five 3-parameter distributions: General Extreme Value (GEV), Generalized Logistic (GLO), Generalized Pareto (GPA), Generalized Normal (GNO), and Pearson type III (PE3).
- Employed the leave-one-out method for accuracy assessment and generated pixel-wise fit-parameters and quantiles.
Main Results:
- Precipitation probability distributions exhibit significant spatial and temporal variability.
- Annual precipitation distributions varied regionally: GLO in humid/semi-humid, GEV in semi-arid/arid, and PE3 in cold-arid zones.
- Seasonal and monthly precipitation distributions showed distinct patterns, with GLO, GEV, GPA, and PE3 being prevalent depending on season, region, and precipitation amount.
Conclusions:
- Fitted probability distribution functions provide reliable estimates of precipitation for various return periods.
- The study enhances understanding of precipitation patterns in the Loess Plateau, crucial for water resource management.
- Findings offer valuable insights for future research utilizing gridded precipitation datasets and robust statistical methodologies.
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