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This research aims to assess hydroclimatic variability using a modified trend analysis in the Manjira River Basin.

May 10, 2026Journal of Water and Climate Change0 citationsOpen Access

Modified innovative trend analysis for hydroclimatic variability in the Manjira River Basin, India

SKSachin KumarMaulana Azad National Institute of Technology MCMahendra Kumar ChoudharyGovind Ballabh Pant University of Agriculture and Technology TTThomas ThomasNational Law Institute University

Key Points

This research aims to assess hydroclimatic variability using a modified trend analysis in the Manjira River Basin.
Comparative trend analysis using four methods: Mann-Kendall (MK), Modified Mann-Kendall (MMK), Innovative Trend Analysis (ITA), and Modified Innovative Trend Analysis (MITA).
Analysis of nine hydroclimatic indices from 1971 to 2021 based on high-resolution gridded data across 62 grid points.
Statistical assessments included Z-values and significance levels with focus on temporal sequencing to enhance trend accuracy.
Mann-Kendall detected significant rainfall declines and warming trends (Z > 6.0, p < 0.01, slope: +0.014 to +0.017 °C year−1).
MMK findings adjusted the significance of rainfall trends, leading to reduced intensity; confirmed declines with Z = −2.32 to −2.56, p < 0.01.
MITA revealed pronounced drying trends (−26.7 to −126.6 mm), aligning with MK/MMK estimates and emphasizing accurate temporal sequencing for water resource planning.

Abstract

ABSTRACT Graphical abstract summarizing a comparative trend analysis of nine hydroclimatic indices from 1971 to 2021 using MK, MMK, ITA, and MITA tests. The top row presents the workflow: input time-series data are processed through trend detection methods, MK/MMK significance testing with variance and autocorrelation adjustment, and ITA/MITA scatter-plot based trend assessment after splitting each series into two halves. The bottom row compares test outcomes using a radar chart of trend-detection percentages and stacked bar charts of grid counts across rainfall and temperature indices. Understanding hydroclimatic trends in semi-arid river basins is vital for sustainable water management under changing climate conditions. This study introduces a modified innovative trend analysis (MITA) to address the temporal ordering limitations of the conventional innovative trend analysis (ITA). MITA complements established nonparametric approaches – Mann–Kendall (MK), modified Mann–Kendall (MMK), and Sen's slope estimator – applied here in a four-method comparative framework. We applied these four methods to nine hydroclimatic indices – annual, monsoon, and non-monsoon rainfall; wet-day frequency and intensity; and temperature extremes – derived from high-resolution gridded data (1971–2021) across 62 grid points in the Manjira River Sub-basin (MRSB), India. Comparative analyses revealed distinct patterns: The Mann-Kendall test detected significant rainfall declines and notable warming trends (Z 6.0, p 0.01, slope: +0.014 to +0.017 °C year−1). Accounting for autocorrelation, MMK reduced the significance of rainfall trends. ITA, which disrupts chronology by sorting data halves, produced artificially rising rainfall trends. Conversely, MK/MMK indicated declining rainfall (Z = −2.32 to −2.56, p 0.01), while MITA – by correcting ITA's artificial sorting bias and preserving temporal order – revealed pronounced drying (−26.7 to −126.6 mm), consistent with the conservative but realistic estimates of MK/MMK. Cohen's kappa coefficients (κ = 0.89 for MK–MMK; κ = 0.76 for ITA–MITA) underscore that preserving temporal sequences yields more realistic trend detection, which is crucial for accurate water resources planning under climate change.

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