Complexity of hydrometeorological variables: false nearest neighbour algorithm

Abstract

ABSTRACT This study examines the complexity of hydrometeorological variables in the Savitri River basin in India. Specifically, it estimates the dimensionality of daily rainfall, runoff, maximum temperature, minimum temperature, pan evaporation, relative humidity, sunshine duration, and wind speed observed during 2000–2010 at two stations (Kangule and Birwadi). The false nearest neighbour (FNN) algorithm is employed to estimate the dimensionality of each variable. The dimensionality represents the number of variables dominantly governing the system. The FNN dimension values of the eight daily hydrometeorological series from each station range between 4 and 7, which may be considered as exhibiting a medium level of complexity. Among the eight series, wind speed is found to be the least complex, whereas minimum temperature and sunshine duration are the most complex. An attempt is also made to examine the effect of temporal scale on the complexity of the hydrometeorological variables, by analysing the hourly rainfall and runoff series. The results indicate that, for both rainfall and runoff, hourly data exhibits greater complexity, with two to three additional influential variables. The present results have important implications for hydrometeorological modelling, prediction, and disaster management in the small and flood-prone Savitri River basin.