Browsing by Author "Guntu, Ravikumar"

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Accounting for temporal variability for improved precipitation regionalization based on self-organizing map coupled with information theory
(Elsevier, 2020-11) Guntu, Ravikumar
Precipitation regionalization deals with an investigation of the seasonality and its temporal variability and is useful for a wide variety of applications in hydro-meteorology. The d homogeneous regions can be used as a basis for transforming the information from gauged to ungauged sites and can reduce the uncertainty in estimating the seasonal characteristics of precipitation across India. Despite several studies stressing the importance of seasonality and temporal variability to the environment, there is a lack of studies on accounting for temporal variability in regionalization. Precipitation regionalization must account for both the precipitation magnitude and its temporal variability at multiple time-scales to extract the seasonality of a region representing coherent local and inter-annual variability. Therefore, in this study, we propose a framework for precipitation regionalization, considering both precipitation magnitude and its temporal variability. High resolution (0.25° × 0.25°) gridded daily precipitation time series over the period 1901–2013 from Indian Meteorological Department (IMD) was used for the evaluation of the framework. First, the historical daily time series was transformed into multiple time scales, i.e., annual, seasonal, and monthly time scales. Entropy-based standardized variability index was used to measure the inter-annual variability of precipitation at each time scale. Regionalization of grid points was performed using self-organizing maps, an artificial neural network. Ten distinct regions were identified that can be tied back to two general categories, such as climate characteristics and physical characteristics. Coupling of the self-organizing map with standardized variability index reveals unique seasonal distribution of precipitation for each region. The temporal evolution of clusters unravels a new emerging pattern across Central India. Consideration of temporal variability plays an insignificant role in the shape, size and stability of south-central India, south-eastern coastlines, and Konkan Coast. Intriguingly, separate Rain-belt and Rain-shadow Western Himalayas are formed due to the difference in topography and seasonal characteristics of precipitation. The temporal evolution of clusters unravels a significant change in the occurrence of the 50th percentile monsoon after the 1940s across the north-western region; a significant increase in the 50th percentile monsoon after the 1940s across western India, and decrease in the 50th percentile monsoon after the 1980s in the north-central Region.
Changing spatiotemporal dependence of the precipitation-temperature during Indian Summer Monsoon using observational and CMIP6 model simulations
(Elsevier, 2025-02) Guntu, Ravikumar
Quantifying precipitation-temperature (P-T) dependence is essential for understanding emerging patterns of compound extremes, especially in climate-vulnerable countries like India. The present study investigates the spatiotemporal variability of P-T dependence during the Indian Summer Monsoon using observational data and CMIP6 model simulations. We evaluated the performance of CMIP6 simulations and projected changes in P-T dependence under SSP1–2.6 and SSP5–8.5 scenarios. New hydrological insight for the region Observations show spatial diversity, with strong negative associations in central, western, and coastal regions, while positive associations are prominent in the Western Ghats and northeastern regions. CMIP6 models show mixed performance in capturing the spatial patterns and temporal evolution of P-T dependence. The EC-Earth model simulations effectively replicate the observed P-T dependence. In contrast, models such as ACCESS-ESM1–5, CanESM5, and ACCESS-CM2 exhibit discrepancies when compared to observations, suggesting that their future projections should be interpreted with caution. Projections under high-emission scenarios indicate a widespread increase in P-T dependence, particularly in northern and central areas, highlighting an increased likelihood of compound extremes.
A complex network approach to study the extreme precipitation patterns in a river basin
(AIP, 2022-01) Guntu, Ravikumar
The quantification of spatial propagation of extreme precipitation events is vital in water resources planning and disaster mitigation. However, quantifying these extreme events has always been challenging as many traditional methods are insufficient to capture the nonlinear interrelationships between extreme event time series. Therefore, it is crucial to develop suitable methods for analyzing the dynamics of extreme events over a river basin with a diverse climate and complicated topography. Over the last decade, complex network analysis emerged as a powerful tool to study the intricate spatiotemporal relationship between many variables in a compact way. In this study, we employ two nonlinear concepts of event synchronization and edit distance to investigate the extreme precipitation pattern in the Ganga river basin. We use the network degree to understand the spatial synchronization pattern of extreme rainfall and identify essential sites in the river basin with respect to potential prediction skills. The study also attempts to quantify the influence of precipitation seasonality and topography on extreme events. The findings of the study reveal that (1) the network degree is decreased in the southwest to northwest direction, (2) the timing of 50th percentile precipitation within a year influences the spatial distribution of degree, (3) the timing is inversely related to elevation, and (4) the lower elevation greatly influences connectivity of the sites. The study highlights that edit distance could be a promising alternative to analyze event-like data by incorporating event time and amplitude and constructing complex networks of climate extremes.
Compound dry and hot extremes: a review and future research pathways for India
(Elsevier, 2024-05) Guntu, Ravikumar
Compound Dry and Hot Extremes (CDHEs) is gaining attention compared to individual dry or hot extremes, due to their amplified impacts on both the population and ecosystems in India. This underscores the importance of transitioning from studying individual extremes to adopting a compound perspective. Despite this, investigation of CDHEs during the Indian summer monsoon remain limited, and a comprehensive review of methodologies for the investigation of CDHE is absent. This review systematically synthesizes recent literature, covering concepts of CDHE with illustrative examples, including identification, characterization, drivers, and prediction. It illustrates three widely used methods for the identification of CDHEs along with their advantages and disadvantages. Furthermore, it describes concepts with illustrative examples to investigate the characteristics (frequency, spatial extent, timing, duration, severity, and likelihood), explores drivers using event coincidence analysis and a complexity-based framework, and discusses the strengths and weaknesses of a logistic regression model for predicting the occurrence of CDHE. In light of the growing significance of CDHEs, we suggest future directions for Indian CDHE research, including an improved characterization of CDHEs across multiple temporal and spatial scales, a deep understanding of the physical mechanism, a robust evaluation of climate models, attribution and projection, and a comprehensive impact assessment. CDHEs are the new normal, and there is an urgent need to advance research on CDHEs in vulnerable regions like India to combat and mitigate their effects.
Deciphering the drivers of direct and indirect damages to companies from an unprecedented flood event: A data-driven, multivariate probabilistic approach
(Copernicus Publications, 2026) Guntu, Ravikumar
Floods are among the most destructive natural hazards, causing extensive damage to companies through direct impacts on assets and prolonged business interruptions. The July 2021 flood in Germany caused unprecedented damage, particularly in North Rhine-Westphalia and Rhineland-Palatinate, affecting companies of all sizes. While the drivers of company damages from riverine flooding are well documented, the drivers of both direct and indirect damages during an extreme flash flood event have not yet been examined. This study addresses this gap using survey data from 431 companies affected by the July 2021 flood. Results show that 62 % of companies incurred direct damages exceeding EUR 100 000. Machine learning models and Bayesian network analyses identify water depth and flow velocity as the primary drivers of both direct damage and business interruption. However, company characteristics (e.g., size premise, number of employees) and preparedness also play critical roles. Companies that implemented precautionary measures experienced significantly shorter business interruption durations – up to 58 % for water depths below 1 m and 44 % for depths above 2 m. These findings offer important insights for policy development and risk-informed decision-making. Incorporation of behavioural indicators into flood risk management strategies and improving early warning systems could significantly enhance business preparedness.
Disentangling increasing compound extremes at regional scale during Indian summer monsoon
(Springer, 2021-08) Guntu, Ravikumar
Compound extremes exhibit greater adverse impacts than their univariate counterparts. Studies have reported changes in frequency and the spatial extent of extremes in India; however, investigation of compound extremes is in the infancy state. This study investigates the historical variation of compound dry and hot extremes (CDHE) and compound wet and cold extremes (CWCE) during the Indian summer monsoon period from 1951 to 2019 using monthly data. Results are analyzed for 10 identified homogeneous regions for India. Our results unravelled that CDHE (CWCE) frequency has increased (decreased) by 1–3 events per decade for the recent period (1977–2019) relative to the base period (1951–1976). Overall, the increasing (decreasing) pattern of CDHE (CWCE) is high across North-central India, Western India, North-eastern India and South-eastern coastlines. Our findings help in identification of the parts of the country affected by frequent and widespread CDHE during the recent period, which is alarming. More detailed assessments are required to disentangle the complex physical process of compound extremes to improve risk management options.
Economic consequences of cascading drought-flood events: evidence from central Europe
(IOP, 2025-10) Guntu, Ravikumar
Cascading drought-flood events (CDFEs), also referred to as ‘drought-to-flood transitions’ or ‘drought–flood abrupt alternations,’ in which a flood follows a period of drought, may have different flood generation mechanisms than floods occurring independently from drought, as the drought could affect soil infiltration rates and, consequently, runoff dynamics. With the increasing frequency of extreme weather events driven by climate change, understanding the cascading nature of drought and flood events has become crucial for effective disaster risk management. However, there is a lack of empirical evidence on how these drought-flood interactions work and translate to economic losses. This study addresses this gap by identifying CDFEs and flood-only events (FEs) across Central Europe and linking them to their flood impacts from the modelled Historical Analysis of Natural Hazards in Europe database. CDFEs are associated with significantly higher maximum daily mean streamflow (58.51 m3 s−1 vs 38.20 m3 s−1), deeper mean water depths (1.90 m vs 1.88 m), and greater economic losses (€33.09 million km−2 vs €29.75 million km−2) compared to FEs. These findings underscore the special features of CDFEs and the need to take them into account in flood risk management.
FLEMOflash – flood loss estimation models for companies and households affected by flash floods
(2025-04) Guntu, Ravikumar
In light of the increasing losses from flash floods intensified by climate change, there is a critical need for improved loss models. Loss assessments predominantly focus on fluvial flood processes, leaving a significant gap in understanding the key drivers of flash floods and the effect of preparedness on losses. To address these gaps, we introduce FLEMOflash—a novel multivariate probabilistic Flood Loss Estimation Model compilation for flash floods. The models are developed for companies and households based on survey data collected after flash flood events in 2002, 2016, and 2021 in Germany. FLEMOflash employs a data-driven feature selection approach, combining machine learning techniques (Elastic Net, Random Forest, XGBoost) to identify key drivers influencing flash flood losses and Bayesian networks to model probabilistic loss estimates, including uncertainty. Model-based findings show that in extreme hazard scenarios, high preparedness can reduce building losses by up to 47 % for large companies. Households who knew exactly what to do during high water depth were able to reduce their building losses by 77 % and contents losses by 55 %. Thus, FLEMOflash can support risk communication and management by providing reliable estimation of flash flood losses along with the loss differential considering the level of risk preparedness.
Flood experience and access to insurance contribute to differences in homeowners’ post-disaster adaptation in a cross-border region of Western Europe
(Springer, 2025-06) Guntu, Ravikumar
The July 2021 floods in Europe stand out as one of the most devastating flood-related disasters to impact the continent in recent years, affecting multiple countries at once. As climate change intensifies, such cross-border disasters are expected to become more frequent. Here we use unique cross-country survey data from flooded homeowners to understand the patterns and limits of how households in different nations respond to shared flood crises. We find evidence of financial, institutional, and psychological limits to household adaptation. Insurance compensation is associated with private adaptation actions shortly after flooding. Households that suffered flood damage are more likely to mitigate future risks to their homes. Yet, this intention encounters limits for extreme flood damage. Once experienced flood damages exceed a threshold of around 60% of the home reconstruction value, homeowners begin to view private adaptation efforts as less effective, prompting a shift toward relocating to safer areas.
Improving the predictability of compound dry and hot extremes through complexity science
(IOP, 2023-11) Guntu, Ravikumar
Compound dry and hot extremes (CDHE) will have an adverse impact on socioeconomic factors during the Indian summer monsoon, and a future exacerbation is anticipated. The occurrence of CDHE is influenced by teleconnections, which play a crucial role in determining its likelihood on a seasonal scale. Despite the importance, there is a lack of studies unraveling the teleconnections of CDHE in India. Previous investigations specifically focused on the teleconnections between precipitation or temperature and climate indices. Hence, there is a need to unravel the teleconnections of CDHE. In this study, we present a framework that combines event coincidence analysis (ECA) with complexity science. ECA evaluates the synchronization between CDHE and climate indices. Subsequently, complexity science is utilized to construct a driver-CDHE network to identify the key drivers of CDHE. To evaluate the effectiveness of the proposed drivers, a logistic regression model is employed. The occurrence of CDHE exhibits distinct patterns from July to September when considering intra-seasonal variability. Our findings contribute to the identification of drivers associated with CDHE. The primary driver for Eastern, Western India and Central India is the indices in the Pacific Ocean and Atlantic Ocean, respectively, followed by the indices in the Indian Ocean. These identified drivers outperform the traditional Niño 3.4-based predictions. Overall, our results demonstrate the effectiveness of integrating ECA and complexity science to enhance the prediction of CDHE occurrences.
Increased likelihood of compound dry and hot extremes in India
(Elsevier, 2023-07) Guntu, Ravikumar
Compound dry and hot extremes (CDHE) are periods of prolonged dry and hot weather. Their joint occurrence typically impacts society and nature stronger compared to the occurrence of the single hazards. Understanding the likelihood, variability and drivers of CDHE is challenging due to the complexity of the climate system involving interactions and feedbacks among atmosphere-land processes. In this study, we first investigate the role of the dependence between precipitation and temperature for the likelihood of CDHEs. We demonstrate that both the dependence strength and its type, i.e. the degree of tail dependence, substantially affect the CDHE likelihood. We then analyze the space-time variation of CDHE characteristics during the Indian Summer Monsoon across India for the period 1961–2014. We find strong negative association and substantial tail dependence between precipitation and temperature in some regions. Event coincidence analysis reveals that low soil moisture preconditioned by dry extremes is responsible for 55–65% of CDHE occurrence. Our analysis of the temporal evolution of CDHE characteristics finds an increasing negative association between precipitation and temperature leading to a 2 to 3-fold rise of CDHE frequency for some regions of India.
Investigation of precipitation variability and extremes using information theory
(MDPI, 2020-11) Guntu, Ravikumar
Quantifying the spatiotemporal variability of rainfall is the principal component for the assessment of the impact of climate change on the hydrological cycle. A better understanding of the quantification of variability and its trend is vital for water resources planning and management. Therefore, a multitude of studies has been dedicated to quantifying the rainfall variability over the years. Despite their importance for modelling rainfall variability, the studies mainly focused on the amount of rainfall and its spatial patterns. The studies investigating the spatial and temporal variability of rainfall across Central India, in general, and at multiscale, in particular, are limited. In this study, we used a Standardized Variability Index (SVI), based on information theory to investigate the spatiotemporal variability of rainfall. SVI is independent of the temporal scale, length of the data and can compare the rainfall variability at multiple timescales. Distinct spatial patterns were observed for information entropies at the monthly and seasonal scale. Grid points with statistically significant trends were observed and vary from monthly to seasonal scale. There is an increase in the variability of rainfall amount from South to North, indicating that spread of the rainfall is high in the South when compared to North of Central India. Trend analysis revealed there is changing behavior in the rainfall amount as well as rainy days, showing an increase in variability of rainfall over Central India, hence the high probability of occurrence of extreme events in the near future.
More than one landslide per road kilometer – surveying and modelling mass movements along the Rishikesh-Joshimath (NH-7) highway, Uttarakhand, India
(2023-01) Guntu, Ravikumar
The rapidly expanding Himalayan road network connects rural mountainous regions. However, the fragility of the landscape and poor road construction practices lead to frequent mass movements along-side roads. In this study, we investigate fully or partially road-blocking landslides along the National Highway (NH-) 7 in Uttarakhand, India, between Rishikesh and Joshimath. Based on an inventory of > 300 landslides along the ~250 km long corridor following exceptionally high rainfall in October and September 2022, we identify the main controls on the spatial occurrence of mass-movement events. Our analysis and modelling approach conceptualizes landslides as network-attached spatial point pattern. We evaluate different gridded rainfall products and infer the controls on landslide occurrence using Bayesian analysis of an inhomogeneous Poisson process model. Our results reveal that slope, rainfall amounts, and lithology are the main environmental controls on landslide occurrence. The individual effects of aggregated lithozones is consistent with previous assessments of landslide susceptibilities of rock types in the Himalayas. Our model spatially predicts landslide occurrences and can be adapted for other rainfall scenarios, and thus has potential applications for efficiently allocating efforts for road maintenance. To this end, our results highlight the vulnerability of the Himalayan road network to landslides. Climate change and increasing exposure along this pilgrimage route will likely exacerbate landslide risk along the NH-7 in the future
Multi-objective optimization for stormwater management by green-roofs and infiltration trenches to reduce urban flooding in central Delhi
(Elsevier, 2022-03) Guntu, Ravikumar
Urban surface runoff management via best management practices (BMP) and low impact development (LID) has earned significant recognition owing to positive environmental and ecological impacts. However, due to the complexity of the parameters involved, the estimation of LID efficiency in attenuating the urban surface runoff at the watershed scale is challenging. A planning analysis of employing Green Roofs and Infiltration Trenches as BMPs/LIDs practices for urban surface runoff control is presented in this study. A multi-objective optimization decision-making framework is established by coupling SWMM (Storm Water Management Model) with NSGA-II models to check the performance of BMPs/LIDs concerning the cost-benefit analysis of LID at the watershed scale. Two urbanized areas belonging to Central Delhi in India were used as case studies. The results showed that the SWMM model is useful in simulating optimization problems for managing urban surface runoff. The optimum scenarios efficiently minimized the urban runoff volume while maintaining the BMPs/LIDs implementation costs and size. With BMPs/LIDs implementation, the reduction in runoff volume increases as expenses increase initially; however, there is no noticeable reduction in flood volume after a certain threshold. Contrasted with the haphazard arrangement of BMPs/LIDs, the proposed approach demonstrates 22%–24% runoff reductions for the same expenditures in watershed 1 and 23%–26% in watershed 2. The result of the study provides insights into planning and management of the urban surface runoff control with LID practices. The proposed framework assists the hydrologists in optimum selection and placements of BMPs/LIDs practices to acquire the most extreme ecological advantages with the least expenses.
Multiscale spatiotemporal analysis of extreme events in the gomati river basin, India
(MDPI, 2021-04) Guntu, Ravikumar
Accelerating climate change is causing considerable changes in extreme events, leading to immense socioeconomic loss of life and property. In this study, we investigate the characteristics of extreme climate events at a regional scale to -understand these events’ propagation in the near future. We have considered sixteen extreme climate indices defined by the World Meteorological Organization’s Expert Team on Climate Change Detection and Indices from a long-term dataset (1951–2018) of 53 locations in Gomati River Basin, North India. We computed the present and future spatial variation of theses indices using the Sen’s slope estimator and Hurst exponent analysis. The periodicities and non-stationary features were estimated using the continuous wavelet transform. Bivariate copulas were fitted to estimate the joint probabilities and return periods for certain combinations of indices. The study results show different variation in the patterns of the extreme climate indices: D95P, R95TOT, RX5D, and RX showed negative trends for all stations over the basin. The number of dry days (DD) showed positive trends over the basin at 36 stations out of those 17 stations are statistically significant. A sustainable decreasing trend is observed for D95P at all stations, indicating a reduction in precipitation in the future. DD exhibits a sustainable decreasing trend at almost all the stations over the basin barring a few exceptions highlight that the basin is turning drier. The wavelet power spectrum for D95P showed significant power distributed across the 2–16-year bands, and the two-year period was dominant in the global power spectrum around 1970–1990. One interesting finding is that a dominant two-year period in D95P has changed to the four years after 1984 and remains in the past two decades. The joint return period’s resulting values are more significant than values resulting from univariate analysis (R95TOT with 44% and RTWD of 1450 mm). The difference in values highlights that ignoring the mutual dependence can lead to an underestimation of extremes.
Network-based exploration of basin precipitation based on satellite and observed data
(Springer, 2021-04) Guntu, Ravikumar
Adequate and efficient precipitation data is a major concern due to its spatiotemporal variability and topographic and climatic factors. Satellite-based products are an alternative for a reliable precipitation estimate in basins having a complicated topography and diverse climate zones. Satellite products with global coverage and continuous data are freely available; however, understanding spatial connections is essential for reliable hydrological applications. In this study, complex network concepts like clustering coefficient, degree, degree distribution, average neighbour and architecture employed to investigate spatial connections in a basin. We also identified influential grid points in the precipitation network using weighted degree betweenness. Our results reveal that the correlation method does not significantly affect the network topology. However, the correlation threshold influences the spatial distribution of the clustering coefficient and degree values of precipitation network. The spatial distribution of clustering coefficient and degree indicated an inverse relationship independent of similarity measures and correlation thresholds. The architecture of precipitation based on satellite and observed data shows small-world behaviour for the certain correlation threshold range. Our findings unravel spatial precipitation connections and provide a way for hydrological applications in further research.
Quantile-based Bayesian model averaging approach towards merging of precipitation products
(Elsevier, 2022-01) Guntu, Ravikumar
Precipitation is a fundamental input for many hydrological and water management studies. Nowadays, a number of satellite precipitation products are easily accessible online at free of cost. Despite so, the utility of such products is still limited owing to their lack of accuracy in capturing the ground truth. To improve the reliability of the satellite precipitation products, we have developed a quantile based Bayesian model averaging (QBMA) approach to merge the satellite precipitation products. QBMA approach was compared with traditional methods, namely, simple model averaging and one outlier removed. We have considered three SPPs (TRMM, PERSIANN-CDR, CMORPH) for QBMA merging during the monsoon season over India's coastal Vamsadhara river basin. QBMA optimal weights were trained using 2001 to 2013 daily monsoon precipitation data and validated for 2014 to 2018. Results indicated that the bias-corrected QBMA outperformed the other methods. On monthly evaluation, it is observed that all the products perform better during July and September than that in June and August. The QBMA approaches do not have any significant improvement over the SMA approach in terms of POD. However, the bias-corrected QBMA products have lower FAR. The developed QBMA approach with bias-corrected inputs outperforms the IMERG product in terms of RMSE.
Rainfall and streamflow variability in North Benin, West Africa, and its multiscale association with climate teleconnections
(Elsevier, 2025-06) Guntu, Ravikumar
Study region Three tributaries of the Niger River, covering 48,000 km² in northern Benin, West Africa. Study focus Understanding rainfall and streamflow variability in a warming world is crucial for drought-prone West Africa, whose economy relies heavily on rain-fed agriculture. This study explores past changes (1970–2020) in catchment rainfall and streamflow and their association with climate teleconnections. New hydrological insights for the region We find consistent rainfall patterns across the three catchments, with a recovery from the 1970s-1980s droughts starting in the 1990s. Total rainfall has increased significantly driven by more rainy days, although the wet day rainfall amount has decreased. These results can be summarized as ‘increased total rainfall, but less intense and more variable in space’. More rain, however, does not mean that the drought situation is alleviated, as high interannual and decadal variability persists. Wavelet coherence reveals that rainfall and streamflow variability are modulated by the climate teleconnections ENSO, AMO, and IOD. For rainfall, we find a tendency of a shift from lower-frequency coherence (4–10 years) in earlier decades to higher-frequency coherence (1–3 years) in recent decades. These patterns are less pronounced for streamflow due to indirect climate influences. Unlike many African studies relying on model simulations, these findings are based on quality-checked, dense station data networks, essential for understanding local climate impacts, water management, and early warning systems.
Spatiotemporal dependence of soil moisture and precipitation over India
(Elsevier, 2022-07) Guntu, Ravikumar
Anthropogenic climate change has impacted almost all phases of the global water cycle. Growing consensus asserts that extreme precipitation events will only rise in the years to come. However, an increase in extreme precipitation events does not necessarily correspond to higher flood risk. Much onus lies on the antecedent conditions before the storm events. Despite the importance of Soil Moisture (SM) – Precipitation (P) dependence in runoff generation, relatively few studies have unraveled the SM – P dependence. Previous studies were constrained by the direct trivial relationship existing between SM and P, and hence there is a need to understand direction and dynamical interdependency. We employed Event Coincidence Analysis (ECA) to identify and quantify the preconditioning of P extremes by soil moisture (SM) anomalies. High precursor coincidence rate (greater than45%) was obtained for traditional flash flood-prone areas over India - Ganga river basin, West-flowing rivers of Kutch and Saurashtra including Luni, inland drainage of Rajasthan and Narmada river basin, indicating the robustness of the approach. The trigger coincidence rate reveals strong SM-P coupling over central India. Our results indicate the applicability of ECA in characterizing the spatiotemporal patterns of SM-P dependence over India.
Spatiotemporal variability of Indian rainfall using multiscale entropy
(Elsevier, 2020-08) Guntu, Ravikumar
Understanding the spatiotemporal variability of rainfall is vital for water resources planning and management, flood and drought mitigation, and erosion control, among others. Despite the progress in this direction, through proposal of many different approaches and their applications to rainfall data at various regions around the world, our knowledge of the spatiotemporal variability of rainfall remains limited. Studies in this direction have largely focused on the amount of rainfall and its spatial patterns, and investigations of spatiotemporal variability at multiscale are limited. In this study, we introduce a novel measure, Standardized Variability Index (SVI), based on the concept of entropy to investigate the spatiotemporal variability of gridded rainfall in the Indian subcontinent at different timescales. The results show distinct spatial patterns in the inter-annual rainfall variability at all timescales. Also, the intra-annual variability of rainfall amount, as well as rainy days, is found to increase from east to west of India. The Mann-Kendall trend test at different timescales reveals significant increase in rainfall variability. In addition, coupling the mean annual rainfall with SVI enables a relative assessment of the water resources availability.