Assessment of the response of the Himalayan river flows to climate change is complex due to multiple contributors: rainfall, snowmelt, and glacier‐melt. The number of studies is limited in this direction due to lack of data availability as well as non‐availability of models considering all the above‐mentioned components. As for example, the state‐of‐the‐art variable infiltration capacity (VIC) model does not account for the glacier melt. Here we integrate a glacier‐melt model with VIC and validate the model output with observed streamflow in five river basins in the Himalayas, at daily scale. Our model simulates the streamflow with Nash‐Sutcliffe estimates greater than 0.65 in all basins. The sensitivity analysis shows that the contribution from snowmelt decreases substantially in all the five basins with highest decrease of 36% in Dudh Kosi (DK), in a warm and dry scenario. The glacier‐melt increases (15%–70%) in a warmer environment with its present volume, but decreases (3%–38%) substantially, when the volumes are reduced to half. However, such a decrease is found to be compensated by increased precipitation in a wetter scenario with a net increase of 3%–13%. Climate model simulations show a decrease in the spring onset times for Sutlej basin while increase for DK basin for both RCP4.5 and RCP8.5 scenarios. Sutlej and Arun basins show decreases of more than 6 days in the center of volume of streamflow, which suggests that there will be increased flows in the early part of year and reduced flows later in the year.

Variations in the uptake of atmospheric carbon by vegetation over India, the second-highest contributor to global greening, have enormous implications for climate change mitigation. Global studies conclude that temperature and total water storage (TWS) cause interannual variations of carbon uptake based on the correlation coefficient, which is not a causality measure. Here, we apply a statistically rigorous causality approach, Peter Clark momentary conditional independence, to the monthly observed satellite and station-based gridded dataset of India’s climate and carbon uptake variables. We find no existence of causal connections from TWS to gross primary production (GPP) or net photosynthesis (PSN). Causal relationships exist from precipitation to GPP and PSN. Since shallow-rooted croplands dominate India’s green cover, impacts of precipitation on carbon capture of the the land ecosystem are immediate and not via TWS. Our results identify the key climate drivers of GPP/PSN variability and highlight interactions between water and the carbon cycle in India. Our results also highlight the need for formal causal analysis using climate and earth sciences observations rather than the conventional practices of inferring causality from correlations.

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.

Quantile mapping based bias correction and spatial disaggregation (BCSD) have emerged as the de facto standard for rectifying bias and scale-mismatch in global climate models (GCMs) leading to novel climate science insights and new information for impacts and adaptation. Focusing on critical variables crucial for understanding climate dynamics in India and the United States, our evaluation challenges the premise of BCSD approach. We find that BCSD overcorrects GCM simulations to observed patterns while minimizing or even nullifying science-informed projections generated by GCMs. Furthermore, we show that BCSD incorrectly captures extremes and complex climate signals. Our evaluation in the context of the Walker circulation suggests that this inability to adequately capture multivariate and spatial-temporal dependence patterns may at least partially explain the challenges with BCSD.

Increasing droughts and heat waves as a result of global warming pose a major threat to forests and croplands in India. Monitoring the dynamics of vegetation during a drought and its recovery is essential for the Indian socio-economy and biodiversity. We investigate vegetation recovery from a stressed state in the pre-monsoon (May) period to the end of the monsoon period (September). We then attribute net change during the monsoon period to climate drivers such as temperature, precipitation, and soil moisture. To delineate non-linear interactions, we use an information-theoretic metric to understand the relative association of climate variables with vegetation productivity on a daily scale. We found that pre-monsoon vegetation stress is influenced by soil moisture (r = 0.8, p < 0.01), which is driven by variations in temperature and precipitation. During the monsoons, precipitation contributes to vegetation recovery from pre-monsoon stress through soil moisture recharge while inhibiting vegetation productivity by limiting the amount of radiation available for photosynthesis. Linear regression shows the significant negative dependence of vegetation recovery on precipitation (β = –0.7, p < 0.01) and positive dependence on soil moisture (β = 0.4, p < 0.1) indicating radiation limitation on photosynthesis...