Power-law size distributions in geoscience revisited

TL;DR

This paper revisits power-law distributions in geoscience, providing a rigorous statistical method to determine their validity and range, with applications to natural hazard data like earthquakes, wildfires, and rainfall.

Contribution

It introduces an improved statistical fitting method for power-law distributions and applies it to diverse geoscience data sets, clarifying their distribution ranges and tail behaviors.

Findings

Impact fireballs and Californian earthquakes follow untruncated power-laws.

Global earthquakes exhibit a double power-law behavior.

Rainfall and tropical cyclones show truncated power-law regimes.

Abstract

The size or energy of diverse structures or phenomena in geoscience appears to follow power-law distributions. A rigorous statistical analysis of such observations is tricky, though. Observables can span several orders of magnitude, but the range for which the power law may be valid is typically truncated, usually because the smallest events are too tiny to be detected and the largest ones are limited by the system size. We revisit several examples of proposed power-law distributions dealing with potentially damaging natural phenomena. Adequate fits of the distributions of sizes are especially important in these cases, given that they may be used to assess long-term hazard. After reviewing the theoretical background for power-law distributions, we improve an objective statistical fitting method and apply it to diverse data sets. The method is described in full detail and it is easy to implement. Our analysis elucidates the range of validity of the power-law fit and the corresponding exponent, and whether a power-law tail is improved by a truncated log-normal. We confirm that impact fireballs and Californian earthquakes show untruncated power-law behavior, whereas global earthquakes follow a double power law. Rain precipitation over space and time and tropical cyclones show a truncated power-law regime. Karst sinkholes and wildfires, in contrast, are better described by truncated log-normals, although wildfires also may show power-law regimes. Our conclusions only apply to the analyzed data sets, but show the potential of applying this robust statistical technique in the future.