CAMELS Comes to India
It begins in Peninsular India, where the monsoon transforms dusty fields into shimmering green carpets and where rivers shift between modest trickles and spirited torrents. In their paper, “CAMELS-IND: hydrometeorological time series and catchment attributes for 228 catchments in Peninsular India,” authors Nikunj K. Mangukiya, Kanneganti Bhargav Kumar, Pankaj Dey, Shailza Sharma, Vijaykumar Bejagam, Pradeep P. Mujumdar, and Ashutosh Sharma introduce a data trove to decode these rhythms. Their compilation merges daily rainfall, temperature, wind speed, humidity, and other atmospheric observations with notes on soils, vegetation, and dam operations. The dataset spreads across a region about as large as France, encompassing some catchments that measure around 125 square kilometers (close to the area of Washington, D.C.) and others that top 300,000 square kilometers (similar to Italy).
Life in this varied land hinges on the mood of the monsoon. When the rainy season arrives, parched farmland in places like central Maharashtra can get more than 80 percent of its annual rainfall in roughly four months. That intensity shapes local streams and broader rivers, requiring extensive management in the form of dams, canals, and irrigation networks. “We envision that CAMELS-IND will provide a strong foundation for a community-led effort,” remark the authors, underscoring the desire to explore how these water systems behave in different conditions.
The data come at a time when climate change concerns are mounting. Coastal districts in southern India have seen floods arrive more often, while some interior tracts face unreliable precipitation. The new dataset, known as CAMELS-IND, addresses the Critical Zone, that thin slice of Earth extending from tree canopies down to groundwater. This layer is where food grows, where most terrestrial life thrives, and where water, rocks, and living organisms coexist. Understanding the Critical Zone in India involves knowing how rainfall moves from the atmosphere into soils, how rivers feed communities, and how groundwater supports crops in drier seasons. The paper highlights the significance of bridging these perspectives. “Such a dataset is essential for understanding hydrologic processes over multiple spatiotemporal scales,” write the authors.
One of the dataset’s main strengths is its broad range of catchment variables. The authors detail soils—from the gravel-rich Western Ghats to the clay-heavy plains—and track the fraction of cropland, tree cover, urban development, and hydroelectric dam capacity. They compare the number of dams (some catchments contain over 1,200) and illustrate how reservoirs alter river flow. In large regions such as the Godavari basin, these interventions blend with natural forces to shape flooding and water availability. For context, one of the basins here covers over 300,000 square kilometers, which is more than half the size of California. This scale underscores the magnitude of the data-collection task.
The dataset covers the years 1980 to 2020, making it valuable for spotting climate trends—shifts in rainfall amount, prolonged dry spells, or sharper temperature changes. “We also derived catchment attributes representing human influences, including the number of dams and their utilization,” note the authors. By pairing observed flow records with variables like wind, soil moisture, or leaf area indices, investigators can see how different factors interact. If a particular season brings record-high temperatures and stronger wind speeds, for example, the data can help estimate whether more water is lost to evaporation, and how that may affect farmers in downstream communities.
Because not every region has complete records, the authors trained a computer model called a long short-term memory (LSTM) network to predict streamflow in data-limited spots. This tool, fueled by the daily meteorological time series, adds consistency where official observations are scarce or unavailable. The LSTM approach, tested on over 150 catchments, showed respectable performance in reproducing daily river flows. For climate researchers, it suggests that we can fill data gaps without waiting for old notebooks and local reports to surface.
The dataset’s importance in Critical Zone science is twofold. First, it shows how monsoon-driven rivers, landscapes, and people are linked. Water seldom stays in neat categories—rain can go straight to surface runoff or seep through soils into groundwater. Second, it offers a basis for climate adaptation strategies by revealing patterns of drought, flooding, and land-use changes. This has implications for greenhouse gas emissions too, given the energy demands for pumping and storage.
CAMELS-IND opens up pathways to examine climate impacts, evaluate surface–groundwater interactions, and compare India’s catchments with those in other CAMELS projects around the globe. The synergy of local knowledge, global frameworks, and open data fosters deeper inquiry into how water shapes our planet and daily life. The dataset’s authors believe it can energize collaborative studies and, down the road, guide water managers who must balance irrigation, hydropower, and flood control. Their hope is that more researchers, students, and planners will use the results.
Credit: “CAMELS-IND: hydrometeorological time series and catchment attributes for 228 catchments in Peninsular India” by Nikunj K. Mangukiya, Kanneganti Bhargav Kumar, Pankaj Dey, Shailza Sharma, Vijaykumar Bejagam, Pradeep P. Mujumdar, and Ashutosh Sharma.