The Himalayan foothills are entering a new hydroclimatic state

Uttarakhand, once defined by forested valleys and small agrarian settlements, has transformed dramatically over the past three decades. Expanding urban centres, industrial corridors, and the relentless rise of tourism have altered its ecological rhythm.

This author, from the School of Environment and Disaster Management, Ramakrishna Mission Vivekananda Educational and Research Institute (RKMVERI), led a recent study in collaboration with colleagues from The University of Queensland, the Hadley Centre for Climate, Met Office (UK), and the University of Petroleum and Energy Studies (India).

We set out to uncover how these human-induced changes are influencing rainfall behaviour and hydroclimatic stability.

Published in Earth Systems and Environment, the research integrates machine learning with geospatial analytics to examine four decades (1984-2023) of precipitation data across Uttarakhand—one of India’s most ecologically fragile yet rapidly urbanising Himalayan states.

Our motivation stemmed from a simple but urgent question: how are human-driven landscape transformations altering rainfall extremes in a region so vital to South Asia’s hydrological balance?

Urbanisation introduces impervious surfaces, heat islands, and atmospheric aerosols—all of which interact with regional circulation and moisture patterns. Yet, the precise links between these changes and rainfall behaviour in mountainous terrains remained uncertain.

Our research bridges this gap by combining statistical climatology, extreme event analysis, and artificial intelligence. We wanted to capture how urban-driven climate shifts manifest in data-scarce, topographically complex environments like the Himalayas.

A data-driven framework for climate insight

To achieve this, we developed a hybrid analytical framework that merges classical non-parametric statistics with modern AI techniques. We used the Mann–Kendall and Sen’s slope tests to detect long-term rainfall trends and combined them with machine learning classifiers—Random Forest (RF) and Support Vector Machine (SVM)—to identify and predict extreme precipitation events.

Three methodological advances define our approach. First, we created a hybrid statistical–AI model that captures both historical and predictive rainfall patterns—marking a first-of-its-kind application in Himalayan climate studies. Second, we implemented fine-scale spatial mapping across all 13 districts of Uttarakhand, distinguishing urban centers like Dehradun and Haridwar from rural zones such as Tehri Garhwal and Champawat. This allowed us to pinpoint localised climate regimes shaped by land-use and elevation. Third, we incorporated global extreme climate indices—Consecutive Dry Days (CDD) and Consecutive Wet Days (CWD)—to quantify persistent dry and wet spells and correlate them with meteorological parameters such as humidity, dew point, and surface pressure.

When cities rain more—and dry longer

Our findings reveal a striking paradox. Urban districts are now experiencing both heavier rainfall and longer dry spells than their rural counterparts. Haridwar and Dehradun recorded mean rainfall totals of 377.64 mm and 158.4 mm, respectively—far exceeding those in non-urban districts like Tehri Garhwal (116.18 mm). Dehradun alone exhibited a steep upward trend in rainfall with a Sen’s slope of 9.06 × 10⁻⁵, reflecting accelerated hydroclimatic shifts.

Yet, this intensification coexists with prolonged dry phases. In 2022, Dehradun recorded up to 81 consecutive dry days, followed by wet spells lasting nearly two months the following year. This oscillation—between drought and deluge—signals the emergence of what we call a climate duality. Such volatility poses serious challenges for water security, flood management, and disaster preparedness in rapidly urbanising mountain systems.

Machine learning models further substantiated these findings. The Random Forest classifier predicted extreme rainfall events with nearly 80 per cent accuracy, slightly outperforming the Support Vector Machine model, especially in the industrialised district of Udham Singh Nagar. Correlation analyses revealed that relative humidity, dew point temperature, and surface pressure are dominant climatic controls influencing rainfall variability. These variables act as precursors to extreme events. Their strong correlations with rainfall confirm how even subtle shifts in local meteorology can trigger significant hydrological consequences.

Global significance and policy relevance

Though rooted in the Himalayas, our results resonate globally. They echo the Intergovernmental Panel on Climate Change (IPCC, AR6) warnings of intensifying hydroclimatic extremes under global warming. However, our study extends that narrative by showing that urbanisation itself can locally amplify these global trends—transforming even mid-altitude mountain regions into hotspots of hydroclimatic instability.

The Himalayan foothills are entering a new hydroclimatic state. Urban expansion and land-cover change are now as influential as global warming in modulating rainfall. Future adaptation strategies must integrate both global and local drivers of climate change.

Our data-driven framework carries direct policy implications. It complements India’s National Action Plan on Climate Change (NAPCC) and supports Sustainable Development Goals 6 (Clean Water and Sanitation) and 13 (Climate Action). The findings can inform regional early-warning systems, climate-resilient urban design, and flood-drought management strategies for mountain cities where development and fragility intersect.

A wake-up call for a warming Himalaya

In essence, our research deciphers the evolving hydroclimatic fingerprint of the urbanising Himalayas. We find that the very process fueling economic progress—urban growth—is simultaneously intensifying rainfall extremes, hydrological stress, and disaster vulnerability.

By uniting data science with environmental climatology, this work represents an important step toward building a predictive and resilient understanding of mountain climate systems. The synthesis of statistical rigor, AI precision, and policy relevance not only enhances our grasp of how cities in the clouds are shaping their own weather—it also serves as a timely reminder. If urbanisation continues unchecked, the Himalayas may soon become both a cradle of opportunity and a crucible of climate risk.

Sumanta Das is an Assistant Professor at the School of Environment and Disaster Management, Ramakrishna Mission Vivekananda Educational and Research Institute (RKMVERI). His research focuses on climate–land interactions, hydroclimatic extremes, and the use of geospatial and artificial intelligence tools for environmental monitoring and sustainable mountain development

Views expressed are the author’s own and don’t necessarily reflect those of Down To Earth