Trend Analysis of Hydro-Climatological Factors Using a Bayesian Ensemble Algorithm with Reasoning from Dynamic and Static Variables

• Published in: Atmosphere Vol. 13; no. 12; p. 1961
• Main Authors: A, Keerthana, Nair, Archana
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.12.2022
• Subjects: Algorithms; Bayesian analysis; Bayesian Ensemble Algorithm; Bayesian theory; Climate change; Climatic analysis; Datasets; Decision making; Environmentalists; Freshwater resources; Geomorphology; Groundwater; Groundwater levels; Groundwater resources; Hydrologic cycle; Hydrologists; Hydrology; Lithology; Mann–Kendall test; Methods; Moisture effects; Normalized difference vegetative index; Probability theory; Rain and rainfall; Rainfall; Soil dynamics; Soil layers; Soil moisture; Soil temperature; temperature; Trend analysis; Trends; Vegetation index; Vegetation zones; Water resources; Water shortages; Wind; India; Tamil Nadu India
• ISSN: 2073-4433; 2073-4433
• DOI: 10.3390/atmos13121961

This study examines the variations in groundwater levels from the perspectives of the dynamic layers soil moisture (SM), normalized difference vegetation index (VI), temperature (TE), and rainfall (RA), along with static layers lithology and geomorphology. Using a Bayesian Ensemble Algorithm, the trend changes are examined at 385 sites in Kerala for the years 1996 to 2016 and for the months January, April, August, and November. An inference in terms of area under the probability curve for positive, zero, and negative trend was used to deduce the changes. Positive or negative changes were noticed at 19, 32, 26, and 18 locations, in that order. These well sites will be the subject of additional dynamic and static layer investigation. According to the study, additional similar trends were seen in SM during January and April, in TE during August, and in TE and VI during November. According to the monthly order, the matching percentages were 63.2%, 59.4%, 76.9%, and 66.7%. An innovative index named SMVITERA that uses dynamic layers has been created using the aforementioned variables. The average proportion of groundwater levels that follow index trends is greater. The findings of the study can assist agronomists, hydrologists, environmentalists, and industrialists in decision making for groundwater resources.