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Abstract 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.

Abstract India is the second-highest contributor to the post-2000 global greening. However, with satellite data, here we show that this 18.51% increase in Leaf Area Index (LAI) during 2001–2019 fails to translate into increased carbon uptake due to warming constraints. Our analysis further shows 6.19% decrease in Net Primary Productivity (NPP) during 2001–2019 over the temporally consistent forests in India despite 6.75% increase in LAI. We identify hotspots of statistically significant decreasing trends in NPP over the key forested regions of Northeast India, Peninsular India, and the Western Ghats. Together, these areas contribute to more than 31% of the NPP of India (1274.8 TgC.year−1). These three regions are also the warming hotspots in India. Granger Causality analysis confirms that temperature causes the changes in net-photosynthesis of vegetation. Decreasing photosynthesis and stable respiration, above a threshold temperature, over these regions, as seen in observations, are the key reasons behind the declining NPP. Our analysis shows that warming has already started affecting carbon uptake in Indian forests and calls for improved climate resilient forest management practices in a warming world.

Abstract The increase in water holding capacity of the atmosphere with temperature, given by the Clausius‐Clapeyron (CC) relationship, describes the changes in extreme rainfall intensities at warmer atmospheric states. We study the characteristics of extreme rainfall events (EREs) during the Indian summer monsoon season with respect to thermodynamic changes and precipitation‐scaling over the Indian subcontinent and its homogeneous rainfall zones. We utilize outputs from a present‐day climate simulation and a time‐slice future climate change projection experiments of a high‐resolution global climate model. Large changes are seen for very EREs (vEREs) which suggests their sensitivity to warmer temperatures. In future, the altered radiative forcing will heat up the upper atmosphere, stabilize it and offset the effect of increasing humidity on precipitation intensity. Our analysis also suggests that more convective clouds and the interplay of increased moisture content and circulation will result in future changes in EREs.

Abstract The monsoons in Pakistan have been exceptionally harsh in recent decades, resulting in extraordinary drought conditions and record flooding events. The changing characteristics of extreme events are widely attributed to climate change. However, given this region’s long history of floods and droughts, the role of natural climate variability cannot be rejected without a careful diagnosis. Here, we examine how oceanic and atmospheric variability has contributed to unusual precipitation distributions in West South Asia. Variations in sea surface temperatures in the tropical Pacific and northern Arabian Sea, and internal atmospheric variability related to the circumglobal teleconnection pattern and the subtropical westerly jet stream, explain more than 70% of monthly summer precipitation variability in the 21st century. Several of these forcings have co-occurred with record strength during episodes of extreme monsoons, which have exacerbated the overall effect. Climate change may have contributed to increased variability and the in-phase co-occurrences of the identified mechanisms, but further research is required to confirm any such connection.

Abstract 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.

Abstract India is the world’s second largest producer of wheat, with more than 40% increase in production since 2000. Increasing temperatures raise concerns about wheat’s sensitivity to heat. Traditionally-grown sorghum is an alternative rabi (winter season) cereal, but area under sorghum production has declined more than 20% since 2000. We examine sensitivity of wheat and sorghum yields to historical temperature and compare water requirements in districts where both cereals are cultivated. Wheat yields are sensitive to increases in maximum daily temperature in multiple stages of the growing season, while sorghum does not display the same sensitivity. Crop water requirements (mm) are 1.4 times greater for wheat than sorghum, mainly due to extension of its growing season into summer. However, water footprints (m3 per ton) are approximately 15% less for wheat due to its higher yields. Sensitivity to future climate projections, without changes in management, suggests 5% decline in wheat yields and 12% increase in water footprints by 2040, compared with 4% increase in water footprint for sorghum. On balance, sorghum provides a climate-resilient alternative to wheat for expansion in rabi cereals. However, yields need to increase to make sorghum competitive for farmer profits and efficient use of land to provide nutrients.

Indian Summer Monsoon Rainfall (ISMR) is one of the most well-documented areas of hydrometeorology; however, the processes associated with ISMR are not well understood. This attributes to the complexities associated with ISMR at multiple spatio-temporal scales. This further results in inconsistencies across the literature to assess the impacts of global warming on the monsoon, though this has huge relevance as a huge population of South Asia is dependent on the same. Here, we review and assess the existing literature on the Indian monsoon, its variability, and its trajectory in a warming scenario. We further synthesize the literature on its impacts on the hydrology of major river basins in South Asia. We also identify a few research questions, addressing which will add value to the understanding of the Indian monsoon and the associated water cycle. We have highlighted that there is a significant lack of understanding of how different large-scale and regional factors affect ISMR at different timescales. These impacts, in turn, get translated into hydrology and water sector in India. There is a need to know where we stand to combat the impacts of climate change on ISMR, which can be translated to adaptation by policy-making processes and water management practices in India. HIGHLIGHTS The study reviews the literature regarding the Indian monsoon in changing climate over time.; Studies regarding the changing characteristics and the factors affecting the Indian monsoon have been reviewed.; Few research questions are discussed that can be addressed to improve the understanding of the Indian monsoon.; Study can be a guideline for future research regarding the simulation and prediction/projection of the Indian monsoon.;

Abstract Taking the examples of Hurricane Florence (2018) over the Carolinas and Hurricane Harvey (2017) over the Texas Gulf Coast, the study attempts to understand the performance of slab, single‐layer Urban Canopy Model (UCM), and Building Environment Parameterization (BEP) in simulating hurricane rainfall using the Weather Research and Forecasting (WRF) model. The WRF model simulations showed that for an intense, large‐scale event such as a hurricane, the model quantitative precipitation forecast over the urban domain was sensitive to the model urban physics. The spatial and temporal verification using the modified Kling‐Gupta efficiency and Method for Object based Diagnostic and Evaluation in Time Domain suggests that UCM performance is superior to the BEP scheme. Additionally, using the BEP urban physics scheme over UCM for landfalling hurricane rainfall simulations has helped simulate heavy rainfall hotspots.

Abstract Concurrent extreme rainfall events, or synchronous extremes, during Indian Summer Monsoon Rainfall (ISMR), cause significant damage, but their spatiotemporal evolution remains unclear. Using the event synchronization approach to examine the synchronicity of extreme rainfall events from 1901 to 2019, we find that Central India consistently hosts strongly connected synchronous extreme hubs with localized connections, indicating the geographical trapping of these concurrent events in the region. We observe a moderate positive correlation between network cohesiveness and El Niño Southern Oscillations (ENSO), and a negative correlation between ENSO and link lengths, suggesting localized synchronicity during El Niño dominant decades and opposite patterns in La Niña periods. Despite increasing ISMR variability and spatial nonuniformity, the persistence of hubs and network attributes could offer insights for predicting synchronous extremes, informing effective adaptation and risk management strategies during the monsoon season.

Abstract India receives more than 70% of its annual rainfall in the summer monsoon from June to September. The rainfall is scanty and scattered for the rest of the year. Combining satellite data and model simulations, we show that the soil-vegetation continuum works as a natural capacitor of water, storing the monsoon pulse and releasing the moisture to the atmosphere through evapotranspiration over approximately 135 days when the moisture supply from precipitation is less than the evapotranspiration losses. The total Gross Primary Productivity of vegetation in India during the capacitor period accounts for almost 35% of the total annual GPP value. It primarily depends on the soil moisture at the beginning of the period, a measure of moisture capacitance of soil, with a correlation of 0.6. Given that India is the second largest contributor to recent global greening, its soil-vegetation water capacitance plays a significant role in the global carbon balance.

The impact of soil moisture (SM) and vapor pressure deficit (VPD) on gross primary productivity (GPP) variability in ecosystems is a topic of significant interest. Previous studies have predominantly focused on real-time associations between SM, VPD, and carbon uptake, attributing SM as the principal driver of GPP variability due to its direct and indirect effects through VPD. Using an information theory-based process network approach, we discovered that the influence of past VPD, mediated through its effects on SM, emerges as the primary driver of GPP variability across tropical regions. The past VPD conditions influence GPP directly and also affect SM in real-time alongside GPP, which subsequently impacts GPP variability. Examining land-atmosphere feedback using information theory reveals that past VPD conditions influence SM, but not the reverse. These causal structures explain the consistent decline in GPP with increasing VPD trends observed in tropical regions, which are not consistent with SM trends. Our findings emphasize the importance of considering the influence of past VPD mediated by SM when analyzing complex land-vegetation-atmosphere interactions.

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.

Evapotranspiration ( ET ) is one of the most important yet highly uncertain components of the water cycle. Available modeled ET products do not necessarily agree with each other at various spatiotemporal scales, either due to limitations on input data and/or due to model assumptions and simplifications. Therefore, using the water budget equation to estimate ET has gained attention. However, numerous water budget combinations with large uncertainties are available, which increases ambiguity in choosing the best ET estimate. Here, the Kalman filter is employed to ingest 96 water budget-based ET estimates, and produce a global ET product with uncertainty $ {\lt}2$ mm month ^−1 , and capture the general spatiotemporal pattern of ET and the inter-annual variability over all continents. Since the water budget includes storage changes due to human interventions, our ET estimates are superior over regions with strong irrigation signals, such as the Ganges basin. We verify our claim by using a modified variable infiltration capacity model that also simulates irrigation activities. Our ET estimates have a global mean positive trend of $0.18 \, \pm \, 0.02 $ mm yr ^−1 with larger regional variations, which we discuss.

Despite significant development in the Earth system models (ESMs) and releases of several coupled model intercomparison projects (CMIPs), the evolving patterns of Indian summer monsoon rainfall and its future trajectory is still uncertain, with low confidence in its direction. This could be because of differential impacts from increasing greenhouse gas (GHG) and aerosol concentrations. We found that the observed pre-2000 (1951–2000) declining monsoon was likely attributed to the increasing aerosol concentrations. On the contrary, the reported revival of post-2000 monsoon rainfall is due to GHG dominance. These are spatiotemporally consistent with individual CMIP Phase 6 (CMIP6) ESM simulations with GHG and aerosols separately. Similar results were obtained for temperature in India, which showed no to low warming signal in pre-2000 due to aerosol-driven cooling. The dominance of GHG impacts has increased India’s warming trend in post-2000. This research highlights a notable trend in temperature and precipitation across the Indian subcontinent during the past two decades, emphasizing the dynamic character of climate change explained by contrasting anthropogenic influences, including GHGs and aerosols.

Abstract Continuous remote-sensed daily fields of ocean color now span over two decades; however, it still remains a challenge to examine the ocean ecosystem processes, e.g., phenology, at temporal frequencies of less than a month. This is due to the presence of significantly large gaps in satellite data caused by clouds, sun-glint, and hardware failure; thus, making gap-filling a prerequisite. Commonly used techniques of gap-filling are limited to single value imputation, thus ignoring the error estimates. Though convenient for datasets with fewer missing pixels, these techniques introduce potential biases in datasets having a higher percentage of gaps, such as in the tropical Indian Ocean during the summer monsoon, the satellite coverage is reduced up to 40% due to the seasonally varying cloud cover. In this study, we fill the missing values in the tropical Indian Ocean with a set of plausible values (here, 10,000) using the classical Monte-Carlo method and prepare 10,000 gap-filled datasets of ocean color. Using the Monte-Carlo method for gap-filling provides the advantage to estimate the phenological indicators with an uncertainty range, to indicate the likelihood of estimates. Quantification of uncertainty arising due to missing values is critical to address the importance of underlying datasets and hence, motivating future observations.

New study finds that Summer Indian heatwaves are controlled by the large scale atmospheric circulation associated with the Pacific Meridional Mode.

Pre-monsoon irrigation over the Indo-Gangetic Plain is often misrepresented in model-driven hypothesis. Using actual census-based data and realistic model simulations, the authors show that irrigation has limited role in enhancing heat stress in the region.

Water is a vital resource essential for both sustaining life and a healthy environment as well as being a critical hazard in the form of floods or droughts which can destroy people’s livelihoods and property. This gives rise to a multi-faceted set of concerns and issues that affect everybody. For example, when contaminated with pathogens, wastewater can carry and rapidly transmit disease. The global distribution of freshwater is uneven and the problems this creates are likely to get worse due to climate change and the uncertainties associated with changing rainfall patterns and the emergence of more extreme weather events. Water has been described as the “new oil,” with potential conflicts arising out of disputed access to scarce water resources in the rest of this century. Billions of people around the world still do not have access to adequate safe water supplies or basic sanitation facilities, so in bringing basic water services to all there is much still to be done.

The Extended Range Predictions (ERP) provide Sub-seasonal to Seasonal (S2S) prediction of meteorological variables at a lead time of multiple weeks. Despite the considerable improvements in the ERP system, their applications in real-time irrigation water management are limited. Such limited use of ERP is due to uncertainty in predictions and a spatial mismatch in scales between the model grids and farms. Here, we develop a chance-constrained optimization framework for farm-scale irrigation water management planning for multiple weeks in advance. The degrading model performance with time and the related uncertainty are addressed by generating large ensembles using weather generators. Working with grape farmers in India has revealed that such an advance planning could save water by 3–25 % at a farm-scale without losing yield, compared with the existing approach requiring near real-time decisions. Moreover, the co-development of such irrigation tools with farmers increases the use and usability of S2S predictions.

Rivers form an essential component of the earth system, with ~36,000 km3 of riverine freshwater being dumped into the global oceans every year. The role of rivers in controlling the sea-surface salinity and ensuing air-sea interactions in the Bay of Bengal (BoB) is well-known from observational studies; however, attempts to include rivers in coupled models used for seasonal prediction have been limited. This study reports the benefits of river routing in coupled models over prescribing observational river discharge and the impact on the Indian Summer Monsoon (ISM) simulation. Seasonal hindcasts are carried out using a state-of-the-art global coupled ocean-atmosphere-land-sea ice model, Climate Forecast System version 2, coupled to a runoff routing model. It is demonstrated that such a coupling leads to a better representation of the upper ocean stratification in northern BoB, causes mixed layer warming during July-August, and imparts a significant inter-annual variability to the mixed layer heat budget. The rainfall-runoff coupled feedback associated with ISM is captured better, and remote teleconnections with the equatorial Pacific are enhanced. Improved seasonal mean temperature and salinity profiles in the northern BoB lead to the formation of a thicker barrier layer, which is closely tied to the freshwater from rivers. These processes result in an overall enhancement of the ISM rainfall simulation skill, which stems from scale interactions between the sub-seasonal and seasonal variability of ISM. A significant community effort is required to reduce biases in land-surface processes to improve streamflow simulations, along with better parameterization of mixing of river water with the ocean.

Higher warming will affect more regions globally with intensified agricultural and ecological droughts. Higher CO _2 concentration improves vegetation’s water use efficiency (WUE), but its potential to alleviate extreme agricultural and ecological droughts is unclear. India is the second-highest contributor to global greening, having two of the eight global hottest biodiversity hotspots. Here, for the first time, using the CMIP6 earth system models (ESMs), we found an increase in the net vegetation productivity in India at the rate of 10.552 TgC year ^−1 with 1% per year increase in atmospheric CO _2 concentration from 285 ppm to 1140 ppm, contrary to global trends. The improved WUE resulting from carbon fertilization and higher rain under warming will supersede the increased evapotranspiration water loss due to radiative effects. We found that the substantial increase in vegetation productivity in India attributes to plant physiology, and such factor needs to be considered in the drought projections.

Atmospheric aridity (vapor pressure deficit, VPD) and soil moisture (SM) deficit limit plant photosynthesis and, thus, affect vegetation carbon uptake. The strong correlation between SM and VPD makes it challenging to delineate their relative contributions to regional vegetation productivity. Addressing this gap is vital to understand the future trajectory of plant productivity in India—the second-highest contributor to global greening. Here, we separate the controls of SM and VPD on the Indian vegetation using statistical and causal analysis. We found that vegetation productivity in India is primarily controlled by SM limitation (87.66% of grids) than VPD limitation (12.34% of grids). Vegetation has a varying association with SM and VPD across different agroecological regions in India. The negative impact of VPD on vegetation carbon uptake is not visible in high-rainfall areas of India. These findings advance our understanding of vegetation dynamics under regional dryness stress and can enhance dynamic vegetation model estimates for India under changing climate scenarios.

Abstract There is an emerging understanding toward the importance of land‐atmosphere interactions in the monsoon system, but the effects of specific land and water management practices remain unclear. Here, using regional process‐based experiments, we demonstrate that monsoon precipitation is sensitive to the choice of irrigation practices in South Asia. Experiments with realistic representation of unmanaged irrigation and paddy cultivation over north‐northwest India exhibit an increase in the late season terrestrial monsoon precipitation and intensification of widespread extreme events over Central India, consistent with changes in observations. Such precipitation changes exhibit substantially different spatial patterns in experiments with a well‐managed irrigation system, indicating that increase in unmanaged irrigation might be a factor driving the observed changes in the intraseasonal monsoon characteristics. Our findings stress the need for accurate representation of irrigation practices to improve the reliability of earth system modeling over South Asia.

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.

Indian river basins are intensively managed with country-specific agricultural practices of cultivating submerged paddy and uncontrolled groundwater (GW) irrigation. Numerical experiments with the state-of-the-art land surface models, such as variable infiltration capacity (VIC), without incorporating region-specific practices, could be misleading. Here, we coupled VIC with 2D GW model AMBHAS, incorporating India-specific irrigation practices and crop practices, including submerged paddy fields. We performed numerical experiments to understand the causal factors of GW depletion in the northwest Indo-Gangetic plain (IGP). We identify widespread flood irrigation and cultivation of water-intensive paddy as critical drivers of the declining GW scenario. Our numerical experiments suggest that the introduction of drip irrigation reduces GW depletion in the northwest, but does not change the sign of GW level trends. The GW levels in the non-paddy fields of the middle IGP are less sensitive to irrigation practices due to the high return flow to GW for flood irrigation.

The Ganga river basin, being one of the largest river basins in South-East Asia, with area over 1 million Km2 and population over 400 million, is highly vulnerable to water scarcity due to climate change and rapid growth in agriculture, industrialization, and urbanization. To understand the potential impact of climate and land use changes on regional terrestrial water balance has become crucial for ensuring appropriate water management strategies for adaptation and mitigation purposes. In this study we employ an RCP-SSP (Representative Concentration Pathways—Shared Socioeconomic Pathways) scenario framework (1.5 and 2°C warming scenarios and SSP1–5) to explore the relative impacts of projected twenty-first century climate and land use changes on the surface hydrology of the Ganga river basin. By statistically comparing the hydrological responses of each combination of socioeconomic and climate mitigation pathways against a control scenario, we distinguish between the impacts of each scenario. We also analyze our data in a conceptual framework to understand how climatic and land use factors impact the basin characteristics and which one among them is projected to be the dominant factor in our study region. Our results show that, in terms of hydrologic impact assessment, climate change mitigation pathways are the dominant factor and the land use changes associated with socio-economic pathways contribute little to the projected future changes.

Abstract Skillful quantitative precipitation forecast using the numerical weather prediction model relies on an accurate estimate of the atmospheric state as an initial condition. Variational assimilation methods (VAR) have the potential to provide improved initial state estimation to the numerical weather prediction model using observations, prior data (background), and their respective error covariance. The quality of variational assimilation hinges on the background error statistics (BES) as it weights the error in prior state and determines the spread of assimilated observations in model space. Traditional approaches used to model stationary BES in a three‐dimensional variational assimilation system often fail to represent the model error in BES. In this study, we have proposed an ensemble method using Stochastically Perturbed Parameterization Tendency to represent the model error in BES. The characteristics of the proposed BES are compared with the traditional approaches using the National Meteorological Centre method for different control variables choices. We have further tested the performance of the proposed method in improving the skill of precipitation forecast for an extreme rainfall event, which caused devastating flood over Chennai city, India, on December 2015. Results demonstrate that the use of the proposed method results in better forecast skill of convective precipitation in terms of both position and intensity than traditional National Meteorological Centre‐based BES. Best results are obtained when zonal and meridional momentum control variables are used for modeling ensemble‐based BES.

Against the backdrop of a declining monsoon, the number of extreme rain events is on the rise over central India. Here the authors identify a threefold increase in widespread extreme rains over the region during 1950–2015, driven by an increasing variability of the low-level westerlies over the Arabian Sea.

The evacuation of the population from flood-affected regions is a non-structural measure to mitigate flood hazards. Shelters used for this purpose usually accommodate a large number of flood evacuees for a temporary period. Floods during a pandemic result in a compound hazard. Evacuations under such situations are difficult to plan as social distancing is nearly impossible in the highly crowded shelters. This results in a multi-objective problem with conflicting objectives of maximizing the number of evacuees from flood-prone regions and minimizing the number of infections at the end of the shelter’s stay. To the best of our knowledge, such a problem is yet to be explored in literature. Here we develop a simulation-optimization framework, where multiple objectives are handled with a max–min approach. The simulation model consists of an extended Susceptible—Exposed—Infectious—Recovered—Susceptible model. We apply the proposed model to the flood-prone Jagatsinghpur district in the state of Odisha, India. We find that the proposed approach can provide an estimate of people required to be evacuated from individual flood-prone villages to reduce flood hazards during the pandemic. At the same time, this does not result in an uncontrolled number of new infections. The proposed approach can generalize to different regions and can provide a framework to stakeholders to manage conflicting objectives in disaster management planning and to handle compound hazards.

Decadal predictions have gained immense importance over the last decade because of their use in near-term adaption planning. Computationally expensive coupled model intercomparison project phase 5 general circulation models (GCMs) are initialized every 5 years and they generate the decadal hindcasts with moderate skill. Here we test the hypothesis that computationally inexpensive data-driven models, such as multi-variate singular spectrum analysis (MSSA), which takes care of trends and oscillations, performs similar to GCMs. We pick up one of the most complex variables having low predictability, Indian summer monsoon rainfall (ISMR) and its possible causal sea surface temperatures (SST). We find that the MSSA approach performs similar to the GCMs in simulating SSTs beyond their nonlinear limits of predictability, which is ∼12 months. These SSTs are used for decadal predictions of ISMR and show improved skills compared to the GCMs. We conclude that data-driven models are possible alternatives to computationally expensive GCMs for decadal predictions.

Green Roofs (GRs) are one of the measures considered for Urban Heat Island (UHI) mitigation. The cooling effects of GRs are well studied in the literature. However, previous work has not addressed the impacts of GRs on heavy rainfall in cities. This study develops and tests the hypothesis that incorporating green roofs in urban areas enhances the magnitude of rain for heavy rainfall events. To test this, examples of heavy rainfall events over three different years are examined over Mumbai, India, one of the megapoleis that continues to witness heavy rains and urban flooding. The heavy rain events are simulated using Weather Research and Forecasting (WRF) model for different green roof fraction (GF) scenarios (10%, 25%, 50%, 75%, and 100%) over the urban area. The GF simulations are compared to the ‘no GF’ simulation (control run). The results indicate a consistent increase (1%–72%) in the total accumulated precipitation in all GF scenarios. Additional moisture and increased equivalent potential temperature aided the formation and sustenance of localized pockets of enhanced rain occurrences, contributing to the total amount of rainfall for the rain events for the domain. The increase in rainfall amounts leads to higher runoff and can increase the risk of flash floods. Thus, it is necessary to account for this rainfall-based feedback of GR before adopting it as a mitigation option. The results of this work may be helpful in effective urban planning and managing the urban climate extremes.

Regional water resource modelling is important for evaluating system performance by analyzing the reliability, resilience and vulnerability criteria of the system. In water resource systems modelling, several uncertainties abound, including data inadequacy and errors, modeling inaccuracy, lack of knowledge, imprecision, inexactness, randomness of natural phenomena, and operational variability, in addition to challenges such as growing population, increasing water demands, diminishing water sources and climate change. Recent advances in modelling techniques along with high computational capabilities have facilitated rapid progress in this area. In India, several studies have been carried out to understand and quantify uncertainties in various basins, enumerate large temporal and regional mismatches between water availability and demands, and project likely changes due to warming. A comprehensive review of uncertainties in water resource modelling from an Indian perspective is yet to be done. In this work, we aim to appraise the quantification of uncertainties in systems modelling in India and discuss various water resource management and operation models. Basic formulation of models for probabilistic, fuzzy and grey/inexact simulation, optimization, and multi-objective analyses to water resource design, planning and operations are presented. We further discuss challenges in modelling uncertainties, missing links in integrated systems approach, along with directions for future.

The impacts of hazard events such as extreme rainfall, heatwaves, and droughts are substantial and represent an increasing threat over India. Effective adaptations to these hazards require an in-depth understanding of their physical and socioeconomic drivers. While hazard characteristic models have been substantially improved, compelling evidence of the spatio-temporal analysis of social vulnerability (SoV) throughout India are still lacking. Here, we provide the first analysis of the SoV to disasters at a national-scale for the past two decades using a robust data envelopment analysis framework, which eliminates subjectivity associated with indicator weighting. An interesting result is that SoV has decreased over past decade, which is primarily due to an increase in literacy rate and conversion rate of marginalized groups to main working population, and a decrease in child population due to use of birth control. Contrarily, while analyzing hydro-climatic hazards over India, we notice an increase in probability of their occurrence over significantly large portions all over India, particularly in Karnataka, Maharashtra, Odisha, North-Eastern states and Telangana. The spatial pattern of increase is surprisingly similar for all three considered hazards, viz. extreme precipitation, heatwaves, and drought. Combining the information from SoV and hazard analysis, we further estimate the risk to hydro-climatic extremes. A notable observation is the synchronized increase in hazard and risk in these regions, indicating that hazards are contributing significantly to the increasing risk and not SoV. Further analyses of mortalities induced by different hazards indicate that deaths per million on a decadal-scale have either decreased or remained constant in recent decades, which suggests that mortality is decreasing despite the increasing risk of hazards over India. This also indicates an enhanced capacity for adaptation, which can be attributed to the decadal decrease in SoV observed in the present study.

Indian agriculture is globally well-documented to reflect the impacts of changing climate significantly. However, climate adaptation efforts are often hindered due to the inadequate assessment of coupled human-environment interactions. In this study, we propose a novel unified country-level framework to quantify the decadal agricultural risks derived from multiple hydro-meteorological exposures and adaptive consequences. We identify, for the first time, that rice and wheat risks have increased in the recent decade, with wheat at a twofold higher magnitude than rice. Increasing crops risk is found to be predominantly driven by the decreasing number of cultivators; in particular, the wheat risk is also attributed to increasing minimum temperatures during the crop growing season. We provide convincing evidence indicating that the hydro-climatic hazards related to precipitation extremes and droughts are specifically alarming the crops risk as compared to temperature extremes. These observation-based results highlight the sensitivity of India’s agriculture and the risk associated with multiple agro-ecological and climatic components. We recommend these findings to facilitate the informed planning of adaptive measures and ensure sustainable food security of the nation.

The dynamics of interactions between the environmental and the meteorological variables in an urban region is extremely complex due to continuously evolving coupled human–natural processes in an urban setting. We attempt to understand the same with the networks of variables using information theory, known as process network. We monitored local meteorological variables at half-hourly scale using an eddy covariance observation system combined with available concentration of pollutants from other sources. Both the datasets are for Powai, Mumbai, India, from January to April 2020 that includes pre-lockdown and lockdown periods associated with interventions in response to COVID-19. Analysis of the weekly process networks developed with the same data shows that they are more dominated by memory during the lockdown period. We find that a high concentration of pollutants under no-lockdown scenarios, during specific work commute hours, interrupts the memory of the network. A seasonal transition in temperature during the pre-lockdown period failed to make any major changes. Our analysis shows that the dynamics of pollutant concentration drives the interaction between the variables of urban environmental meteorological system.

It is undeniable that coastal regions worldwide are facing unprecedented damages from catastrophic floods attributable to storm-tide (tidal) and extreme rainfall (pluvial). For flood-risk assessment, although recognizing compound impact of these drivers is a conventional practice, the marginal/individual impacts cannot be overlooked. In this letter, we propose a new measure, Tide-Rainfall Flood Quotient (TRFQ), to quantify the driver-specific flood potential of a coastal region arising from storm-tide or rainfall. A set of inundation and hazard maps are derived through a series of numerical and hydrodynamic flood model simulations comprising of design rainfall and design storm-tide. These experiments are demonstrated on three different geographically diverse flood-affected coastal regions in India. The new measure throws light on existing knowledge gaps on the propensity of coastal flooding induced by the marginal/individual contribution of storm-tide and rainfall. It shall prove useful in rationalizing long-term flood management strategies customizable for storm-tide and pluvial dominated global coastal regions.

Anthropogenic changes are likely to intensify rainfall extremes, posing a risk to human, environmental and urban systems. Understanding the impact of urbanization on rainfall extremes is critical for both reliable climate projections as well as sustainable urban development. This study presents the unexplored impacts of changes arising in urban areas on rainfall extremes over the Contiguous United States. The results show a 2.7-fold higher probability of exceeding a 25% change in 50 year rainfall events over urban areas than over rural areas. Spatially, the changes in rainfall extremes over the central, northeast central, southeast, and northwest central zones were more pronounced due to urbanization. Statistical analyses highlight a positive relationship between changes in rainfall extremes and urbanization within a set of concentric ring buffers around rain gauge stations. Here, we show that urbanization, even though a local feature, influences the mesoscale meteorological setting; and, is statistically associated with an intensification of rainfall extremes across the Contiguous United States.

Crop loss and ensuing social crises can be detrimental for the agriculture-driven economy of India. Though some studies identify country-wide increasing temperatures as the dominant factor for crop loss, the agro-climatic diversity within the country necessitates an understanding of the influence of climate variability on yields at regional scales. We report a complex interplay among rainfall, temperature and cropping choices, with a focus on the drought-prone Marathwada region in Maharashtra. Our analysis based on observations, as well as statistical and process-based modelling experiments, and temperature projections of 1.5 °C and 2 °C warmer worlds show that for the two major cropping seasons, rainfall deficit is the primary cause of crop failure, as compared to rising temperatures. The gradual shift from drought-resilient food crops, such as sorghum and pearl-millet to water-intensive cash crops such as sugarcane in recent years, is seemingly responsible for aggravating this crisis. Our findings warrant strategies promoting drought-resilient food crops, that will be useful, not only for mitigating the immediate agrarian crisis, but also for curbing impending threats to food security in the region under future climate change.

Monsoon climate over India has high degree of spatio-temporal heterogeneity characterized by the existence of multi-climatic zones along with strong intra-seasonal, seasonal, and inter-annual variability. Vegetation growth of Indian forests relates to this climate variability, though the dependence structure over space and time is yet to be explored. Here, we present a comprehensive analysis of this association with quality-controlled satellite-based remote sensing dataset of vegetation greenness and radiation along with station based gridded precipitation datasets. A spatio-temporal time-frequency analysis using wavelets is performed to understand the relative association of vegetation growth with precipitation and radiation at different time scales. The inter-annual variation of forest greenness over the Tropical India are observed to be correlated with the seasonal monsoon precipitation. However, at inter and intra-seasonal scales, vegetation has a strong association with radiation in regions of high precipitation like the Western Ghats, Eastern Himalayas, and Northeast hills. Forests in Western Himalayas were found to be correlated more on the winter precipitation from western disturbances than the south west monsoon precipitation. Our results provide new and useful region-specific information for dynamic vegetation modelling in the Indian monsoon region that may further be used in understanding global vegetation-land-atmosphere interactions.

India's agricultural output, economy, and societal well-being are strappingly dependent on the stability of summer monsoon rainfall, its variability and extremes. Spatial aggregate of intensity and frequency of extreme rainfall events over Central India are significantly increasing, while at local scale they are spatially non-uniform with increasing spatial variability. The reasons behind such increase in spatial variability of extremes are poorly understood and the trends in mean monsoon rainfall have been greatly overlooked. Here, by using multi-decadal gridded daily rainfall data over entire India, we show that the trend in spatial variability of mean monsoon rainfall is decreasing as exactly opposite to that of extremes. The spatial variability of extremes is attributed to the spatial variability of the convective rainfall component. Contrarily, the decrease in spatial variability of the mean rainfall over India poses a pertinent research question on the applicability of large scale inter-basin water transfer by river inter-linking to address the spatial variability of available water in India. We found a significant decrease in the monsoon rainfall over major water surplus river basins in India. Hydrological simulations using a Variable Infiltration Capacity (VIC) model also revealed that the water yield in surplus river basins is decreasing but it is increasing in deficit basins. These findings contradict the traditional notion of dry areas becoming drier and wet areas becoming wetter in response to climate change in India. This result also calls for a re-evaluation of planning for river inter-linking to supply water from surplus to deficit river basins.

The Indian summer monsoon rainfall (ISMR) contributes nearly 80% of the annual rainfall over India and has a significant influence on the country’s gross domestic product through the agricultural sector. Onset of the ISMR displays substantial interannual variability and controls the crop calendar and hence the agricultural output. This variability is traditionally linked to sea surface temperature (SST) anomalies over the tropical Pacific Ocean. The tropical Pacific SST underwent a regime shift during 1976/77. We report a prominent delay in the Indian summer monsoon (ISM) onset following the regime shift. The onset dates are computed with the Hydrologic Onset and Withdrawal Index, based on vertically integrated moisture transport over the Arabian Sea (AS). The shift in onset is found to be due to the change in moisture availability over the AS. A delay in the development of easterly vertical shear reduces northward-propagating intraseasonal variability during May–June, limiting the moisture supply from the equatorial Indian Ocean (IO) to the AS. This, along with enhanced precipitation over the IO during the pre-monsoon, drives a reduction in moisture availability over the AS region from pre- to post-1976/77, delaying the ISM onset in recent decades. Our findings highlight the need for the re-assessment of the crop calendar in India, which is now based on the mean onset date computed from long-term data, without considering the regime shift or trends in onset.