Trend Analysis of Hydrological and Meteorological Variables
Summary
Trend analysis of hydrological and meteorological variables has emerged as a critical discipline for understanding how climate change and human activities influence water cycles and weather patterns. By applying non-parametric statistical tests, machine-learning algorithms and change-point detection methods to long-term records of precipitation, temperature, humidity and streamflow, researchers are able to characterise both gradual and abrupt shifts in environmental regimes. These assessments have revealed spatial heterogeneity in trends, with some regions experiencing significant declines in rainfall and runoff, while others show increases in extreme temperature events or seasonal variability. Such analyses serve as a foundation for water resource management, flood and drought risk assessment, ecological conservation and agricultural planning under evolving climatic conditions.
Research from Nature Portfolio
Recent studies have combined advanced trend detection and forecasting frameworks to elucidate the evolution of rainfall regimes at subcontinental scales. One approach integrates non-parametric tests for change-point detection with spatial interpolation and machine-learning forecasting to map past trends and project future conditions. These analyses have identified widespread negative trends in monsoon and annual rainfall across meteorological divisions, with change-points often occurring in the mid-twentieth century. Projections based on trained neural networks indicate a continuation of declining precipitation over the next decades, offering critical guidance for adaptive management of water resources and infrastructure.
Trend Analysis of Hydrological and Meteorological Variables publication trend
The graph below shows the total number of articles in trend analysis of hydrological and meteorological variables across all publications each year (not limited to Nature Index journals).
Technical terms
Mann–Kendall test: Non-parametric method for detecting monotonic trends in time series.
Sen’s slope estimator: Robust procedure for quantifying the magnitude of a trend in ordered data.
Change-point detection: Statistical techniques used to identify points at which the behaviour of a time series changes abruptly.
Pettitt’s test: Non-parametric test for detecting a single change point in a time series.
Artificial neural network: Computational model of interconnected nodes designed to learn complex relationships for forecasting.
References
- Trend analysis and changepoint detection of monthly, seasonal and annual climatic parameters in the Garo Hills of Northeast India. Ecological Informatics (2023).
- Spatiotemporal analysis of precipitation variability in an endorheic basin of Turkey with coordinated regional climate downscaling experiment data. Alexandria Engineering Journal (2024).
- Analyzing trend and forecasting of rainfall changes in India using non-parametrical and machine learning approaches. Scientific Reports (2020).
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