Abstract This study examined long-term changes in soil moisture (SM) and soil temperature (ST) across a set of 21 study sites spanning the entire Northeast Indian region (NEI) and adjoining regions for the first time. We used the high-resolution Indian Monsoon Data Assimilation and Analysis (IMDAA) gridded products (12 km; 1 hr resolution) to assess annual and seasonal trends in SM and ST across each location during the period 1979–2020. Parametric tests including simple linear regression (SLR) and non-parametric tests such as Mann–Kendall (MK) test, Theil–Sen estimator and Innovative Trend Analysis (ITA) were applied. Moreover, Sequential MK (SQMK) test was used to detect abrupt shift in each variable. While SM revealed both significant increasing and decreasing trends, ST generally displayed increasing trends. Spatial heterogeneity was observed for SM trends over the entire area, whereas significantly increasing ST trends were mostly concentrated over northeastern stretches. ITA reveals relatively greater percentage of stations exhibiting significant trends in both the variables. SQMK showed clear overall distributional shifts in ST series around 2000; no clear picture emerged out for soil moisture. The findings of this study are expected to have significant implications for climate science, agriculture and environmental policy in NEI. Research Highlights <UnorderedList Mark="Bullet"> <ItemContent> First comprehensive assessment of long-term soil moisture and temperature trends across 21 sites in Northeast India (1979–2020) using high-resolution IMDAA (12 km, hourly) data. </ItemContent> <ItemContent> Combined parametric (linear regression) and non-parametric (Mann–Kendall test, Theil–Sen estimator, innovative trend analysis, Sequential MK test) methods to effectively detect trends and abrupt shifts. </ItemContent> <ItemContent> Soil moisture exhibited spatially heterogeneous trends with both significant increases and decreases. </ItemContent> <ItemContent> Soil temperature showed predominantly significant increasing trends, especially across northeastern stretches. </ItemContent> <ItemContent> Sequential MK detected a marked distributional shift in soil temperature around 2000, with no consistent shift in soil moisture. </ItemContent> </UnorderedList>

Soil moisture and soil temperature trends across 21 sites over Northeast India and nearby areas using statistical tests

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发表日期：

2026年3月30日

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Arup Borgohain,
Rohit Gautam,
Arban Shongdor Youroi,
Shyam Sundar Kundu,
Shiv Prasad Aggarwal

摘要

Abstract

This study examined long-term changes in soil moisture (SM) and soil temperature (ST) across a set of 21 study sites spanning the entire Northeast Indian region (NEI) and adjoining regions for the first time. We used the high-resolution Indian Monsoon Data Assimilation and Analysis (IMDAA) gridded products (12 km; 1 hr resolution) to assess annual and seasonal trends in SM and ST across each location during the period 1979–2020. Parametric tests including simple linear regression (SLR) and non-parametric tests such as Mann–Kendall (MK) test, Theil–Sen estimator and Innovative Trend Analysis (ITA) were applied. Moreover, Sequential MK (SQMK) test was used to detect abrupt shift in each variable. While SM revealed both significant increasing and decreasing trends, ST generally displayed increasing trends. Spatial heterogeneity was observed for SM trends over the entire area, whereas significantly increasing ST trends were mostly concentrated over northeastern stretches. ITA reveals relatively greater percentage of stations exhibiting significant trends in both the variables. SQMK showed clear overall distributional shifts in ST series around 2000; no clear picture emerged out for soil moisture. The findings of this study are expected to have significant implications for climate science, agriculture and environmental policy in NEI.

Research Highlights

First comprehensive assessment of long-term soil moisture and temperature trends across 21 sites in Northeast India (1979–2020) using high-resolution IMDAA (12 km, hourly) data.

Combined parametric (linear regression) and non-parametric (Mann–Kendall test, Theil–Sen estimator, innovative trend analysis, Sequential MK test) methods to effectively detect trends and abrupt shifts.

Soil moisture exhibited spatially heterogeneous trends with both significant increases and decreases.

Soil temperature showed predominantly significant increasing trends, especially across northeastern stretches.

Sequential MK detected a marked distributional shift in soil temperature around 2000, with no consistent shift in soil moisture.