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Wavelet entropy-based evaluation of intrinsic predictability of time series
(AIP, 2020-03) Guntu, Ravikumar
Intrinsic predictability is imperative to quantify inherent information contained in a time series and assists in evaluating the performance of different forecasting methods to get the best possible prediction. Model forecasting performance is the measure of the probability of success. Nevertheless, model performance or the model does not provide understanding for improvement in prediction. Intuitively, intrinsic predictability delivers the highest level of predictability for a time series and informative in unfolding whether the system is unpredictable or the chosen model is a poor choice. We introduce a novel measure, the Wavelet Entropy Energy Measure (WEEM), based on wavelet transformation and information entropy for quantification of intrinsic predictability of time series. To investigate the efficiency and reliability of the proposed measure, model forecast performance was evaluated via a wavelet networks approach. The proposed measure uses the wavelet energy distribution of a time series at different scales and compares it with the wavelet energy distribution of white noise to quantify a time series as deterministic or random. We test the WEEM using a wide variety of time series ranging from deterministic, non-stationary, and ones contaminated with white noise with different noise-signal ratios. Furthermore, a relationship is developed between the WEEM and Nash–Sutcliffe Efficiency, one of the widely known measures of forecast performance. The reliability of WEEM is demonstrated by exploring the relationship to logistic map and real-world data.
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.
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.
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.
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.
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.
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.
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.
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.

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