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ARS Home » Plains Area » El Reno, Oklahoma » Oklahoma and Central Plains Agricultural Research Center » Agroclimate and Hydraulics Research Unit » Research » Publications at this Location » Publication #433825
Research Project: Development of a Monitoring Network, Engineering Tools, and Guidelines for the Design, Analysis, and Rehabilitation of Embankment Dams, Hydraulic Structures, and Channels

Location: Agroclimate and Hydraulics Research Unit

Title: Trend and time-frequency analysis of environmental time series using wavelet transform methods

Author
item PULLURI, VARSHITH - University Of Missouri
item Hunt, Sherry
item ALOYSIUS, NOEL - University Of Missouri

Submitted to: Meeting Abstract
Publication Type: Abstract Only
Publication Acceptance Date: 4/10/2026
Publication Date: 4/20/2026
Citation: Pulluri, V., Hunt, S., Aloysius, N. 2026. Trend and time-frequency analysis of environmental time series using wavelet transform methods. Meeting Abstract. University of Missouri Show Me Research Week April 20-24, 2026.

Interpretive Summary:

Technical Abstract: Understanding long-term trends and periodicities in environmental time series data is critical for assessing the impacts of changing weather patterns and on runoff generation and river flows. Many statistical methods, due to the underlying assumptions, often inadequately capture gradual trends and short- and long-term periodic cycles present in such data. Here, we evaluate multi-decadal daily stream flow records to evaluate long-term trends and temporal variability by combining non-parametric trend analysis with time-frequency analysis techniques. To detect monotonic trends, the Mann-Kendall test, a non-parametric method robust to non-normal data and missing values, is employed. In addition, Sen’s slope estimator is used to quantify the magnitude of detected trends. To further analyze the temporal evolution of periodic components, the Continuous Wavelet Transform (CWT) is applied, enabling the identification of localized frequency variations over time. The analysis is conducted using approximately 50 years of daily stream flow time series data covering the period 1970-2025. We obtained data for several locations within the Mississippi River Basin from the US Geological Survey Water Data archives. The implementation is carried out using Python. We will present preliminary results that show evidence of long-term trends, along with wavelet-based scalograms that reveal dominant periodicities and their evolution over time. Our approach provides an assessment of both long-term trends and dynamic patterns in environmental data, offering valuable insights for research in water resource management.