Massive river interlinking projects are proposed to offset observed increasing droughts and floods in India, the most populated country in the world. These projects involve water transfer from surplus to deficit river basins through reservoirs and canals without an in-depth understanding of the hydro-meteorological consequences. Here, we use causal delineation techniques, a coupled regional climate model, and multiple reanalysis datasets, and show that land-atmosphere feedbacks generate causal pathways between river basins in India. We further find that increased irrigation from the transferred water reduces mean rainfall in September by up to 12% in already water-stressed regions of India. We observe more drying in La Niña years compared to El Niño years. Reduced September precipitation can dry rivers post-monsoon, augmenting water stress across the country and rendering interlinking dysfunctional. Our findings highlight the need for model-guided impact assessment studies of large-scale hydrological projects across the globe.

Interannual variations of Indian summer monsoon rainfall (ISMR) are modulated by external forcings such as El Niño Southern Oscillation, Indian Ocean Dipole, and the Atlantic Niño. Vegetation over land responds to variations in ISMR, but the feedback from vegetation to ISMR variability has not been fully explored yet. To address this gap, we perform two simulations with the regional Weather Research and Forecasting model coupled to the Community Land Surface Model (WRF‐CLM) for the period of 2004–2018. We use the same boundary forcing from ERA‐interim reanalysis for the two experiments, but with two different vegetation prescriptions, (1) observed, interannually varying Leaf Area Index (LAI), obtained from satellite images/data (VAR‐LAI); and (2) climatological Leaf Area Index from the same product, to suppress interannual LAI variations (CLIM‐LAI). We find that the correlation coefficient of simulated total seasonal rainfall with the observed data is higher for VAR‐LAI simulation as compared to CLIM‐LAI. To elicit causality among eco‐hydro‐climatological variables, we develop a network based on information theory, i.e., a process network. We find that LAI plays a major role in influencing precipitation in the network through evapotranspiration. The number of links originating from LAI and evapotranspiration increases during drought years, making the eco‐hydro‐climatological network denser. Our findings indicate that the ISMR predictions and projections need to represent the time‐varying LAI to fully capture the varying feedbacks from evolving vegetation to the atmosphere especially during drought years.

Emerging data-driven techniques, such as Complex Networks (CNs), can identify spatial linkages between droughts on a global scale and can improve early warning systems. Recent studies used CNs to identify hotspots of global drought teleconnections as land drought hubs; however, these studies excluded the ocean regions in CN, an oversight that can upend the insights gained thus far. Here, using a comprehensive global CN analysis on drought onsets, we show that oceanic regions harbor significantly larger drought hubs than land regions. The Indo-Pacific Warm pool (IPWP) in the Maritime continent emerges as the most significant drought hub having the farthest teleconnections. We show that IPWP, together with a few sub-tropical land and ocean regions, exhibit a ‘rich club phenomenon’ in CN. Further, using a causal network learning algorithm, we demonstrate the confounding role of oceans in modulating drought onsets on land regions. Our study reveals insights on the spatiotemporal linkages of global drought onsets and highlights the role of oceans in driving global drought teleconnections and modulation of land drought hubs.

Sundarbans in coastal South Asia, the largest contiguous mangrove forest in the world, face an intensifying compound stress of climate extremes and anthropogenically influenced water pollution. However, our knowledge about the responses of mangroves to these perturbations and their recovery mechanism is largely limited. We address this research gap by delineating causal networks between in situ observations of soil-water chemistry, carbon fluxes, and hydro-meteorological variables from Sundarbans mangroves. Our results show that mangroves recover from physiological stresses caused by weather extremes quickly, within one to two weeks, and maintain stable productivity despite steeply declining nutrient composition due to human-induced water pollution. We demonstrate that mangroves maintain this stable productivity by increasing link strength and memory with the hydro-meteorological variables of the region. Our findings highlight the resilience of Sundarbans mangroves in South Asia to natural and anthropogenic perturbations and the importance of estimating thresholds of their critical transitions.

Causal and attribution studies are essential for earth scientific discoveries and critical for informing climate, ecology, and water policies. However, the current generation of methods needs to keep pace with the complexity of scientific, data availability, and stakeholder challenges, combined with the adequacy of data-driven methods. Unless carefully informed by physics, they run the risk of conflating correlation with causation or getting overwhelmed by estimation inaccuracies. In particular, information-theoretic approaches such as transfer entropy (TE), despite their recent popularity and widespread use, can yield spurious causal links, even when statistical significance testing is applied. To address this, we introduce CAST (Causal Analysis Spuriousness Test), a subsample-based ensemble framework that quantifies the robustness of inferred links via the CAST index. Through extensive simulations across systems with varying dynamics as well as applications to real-world climate datasets, we demonstrate that CAST effectively filters unreliable connections while preserving true causal relationships. Our findings underscore the need for consistency-based evaluation in causal discovery and provide a generalizable strategy to enhance the reliability of TE-based and other data-driven causal methods in Earth sciences.