Research Areas

HydroClimX Lab, at the Department of Hydrology, Indian Institute of Technology Roorkee focuses on four key areas:

A) Methodological Advancements,

B) Physical Understanding,

C) Societal Relevance, and

D) Capacity Building.

Our Lab has devised several novel methods such as multi-scale event synchronization (MSES), wavelet multi-scale correlation (WME), wavelet multi-scale entropy (WME), data merging algorithm, and climate-informed flood forecasting among others (for more information please refer to https://www.hydroclimx.com/research-areas). Wavelet-based algorithms have allowed him to demonstrate the importance of multi-scale interactions in climatology and hydrometeorology (A).

Additionally, they have been applied to better comprehend compound dry, hot extremes in India, spatial diversity of Indian rainfall teleconnections, rare extreme events, atmospheric rivers (in review), and the impact of Arctic melting on Indian precipitation (B).

Leveraging these methods and process understanding, HydroClimX Lab is actively contributing to flood forecasting, drought characterization and monitoring, Design flood estimation, Urban Flood Mitigation (Low Impact Development/Best Management Practices), Climate Change and Risk Perception, impact-based forecasting (started recently), landslide susceptibility mapping (C).

HydroClimX Lab is also deeply involved in capacity building, having conducted courses on compound extremes sponsored by ISRO (India, 2022) and fluvial processes sponsored by WMO (Myanmar, 2021). Ankit has organized significant events including the 'National Hazard Symposium on the Himalayan Region' (2021), the 'Roorkee Water Conclave' (2022), and a session at EGU 2022. Furthermore, he initiated an online lecture series titled "Understanding the Dynamics of Natural Hazard in the Himalayan" (D).

The current research (A-C) of HydroClimX is focused on addressing global to local hydro-climatological extremes and risks using novel methods and interdisciplinary perspectives. On the basis of interdisciplinary understanding, the following research foci are being addressed in our group:

List of main domains

Data Mining and advanced methods
Machine Learning and Artificial Intelligence
Hydroclimatic Compound Extremes
Forecasting of (rare-) extreme events
Arctic Melting and Linkages to Indian Summer Monsoon
Climate-Informed Flood Forecasting
Hydrologic - hydrodynamic modelling and remote sensing data in flood hydrology
Atmosphere Moisture Transport Mechanisms - Atmospheric Rivers and Low Level Jet Systems
Impact-based Forecasting (Hazard, Risk and Vulnerability)

Methodological Advancement

Customized sea-surface temperature indicators linking to streamflow

Construction of the multiscale network by integrating wavelet transform and complex networks.

SST regions identified using a multiscale approach go unnoticed while using the single timescale.

Customized SST regions that influence the streamflow variability of Germany can help evaluate local hydrology under future climatic conditions.

Quantile-based Bayesian Model Averaging approach towards merging of precipitation products

A Quantile Based Bayesian Model Averaging (QBMA) approach is developed to merge three satellite precipitation products (TRMM-3B42V7, PERSIANN-CDR, and CMORPH)
The QBMA approach has no significant improvement over the Simple Model Averaging approach regarding the Probability of Detection. At the same time, the bias-corrected QBMA product has shown a lower False Alarm Ratio.
The developed QBMA approach with bias-corrected inputs outperforms the IMERG product in terms of RMSE.

Wavelet Entropy Energy Measure (WEEM) for quantification of intrinsic predictability of time series

A novel measure, the Wavelet Entropy Energy Measure (WEEM), is introduced based on wavelet transformation and information entropy.

WEEM quantify the intrinsic predictability of time series, and its reliability is demonstrated by applying it to the logistic map and real-world data.

Standardized Variability Index: A multiscale entropy measure

A normalized entropy measure, Standardized Variability Index, is proposed.
Using SVI, the spatiotemporal variability of rainfall is investigated at different timescales.
Trend analysis shows one-third of the country experienced a significant increase in rainfall variability.
Coupling the mean annual rainfall with SVI enables a relative assessment of the water resources availability.

Space-time framework for precipitation regionalization based on self-organizing maps coupled with information entropy

A framework is proposed for precipitation regionalization, considering both precipitation magnitude and its temporal variability.
The variability is quantified using SVI, and the regions were clustered using Self-organing maps.
Combining a self-organizing map with SVI reveals the unique seasonal distribution of precipitation for each cluster.
The temporal evolution of clusters unravels a new emerging pattern of the Indian Summer Monsoon across Central India.

Application of complex networks to the optimization of hydrometric monitoring networks

A normalized entropy measure, Standardized Variability Index, is proposed.
Using SVI, the spatiotemporal variability of rainfall is investigated at different timescales.
Trend analysis shows one-third of the country experienced a significant increase in rainfall variability.
Coupling the mean annual rainfall with SVI enables a relative assessment of the water resources availability.

Wavelet-based multiscale similarity measure

A similarity measure combining wavelet and Pearson’s correlation has been proposed and termed Wavelet multiscale correlation (WMC).
The efficacy of the proposed measure is tested with two synthetic case studies and one real-world example.

Multiscale event synchronization for unravelling climate process

Wavelet-based multiscale event synchronization (MSES) method is proposed by combining the wavelet transform and event synchronization.
The method is tested on synthetic and real-world time series, and the results indicate that MSES can capture relationships between processes at different timescales.

Wavelet-based entropy method for catchment regionalization

A novel multiscale entropy method is proposed and applied to streamflow data from 530 stations in the United States.
14 clusters of catchments, with distinct wavelet multiscale entropy, are identified.
The tests for homogeneity reveal that the proposed approach works very well in regionalization.

Development of homogeneous regions using complex networks

A similarity measure combining wavelet and Pearson’s correlation has been proposed and termed Wavelet multiscale correlation (WMC).
The efficacy of the proposed measure is tested with two synthetic case studies and one real-world example.

Physical Understanding

Spatial diversity of compound dry and hot extremes over India

CDHE likelihood depends strongly on the type and strength of precipitation-temperature dependence.
Low soil moisture preconditioned by dry extremes contributes to CDHE occurrence and its spatial diversity.

Study Finds a Widespread Three-fold Rise in Compound Dry and Hot Summer Monsoon Extremes during the Past Decades Over India

The study finds a widespread three-fold rise in compound dry and hot extremes (CDHE) in recent decades and is likely to pose a substantial challenge to future food security.
This increasing pattern of CDHE during monsoon season is observed as follows: Western India and North-eastern India (in June), South-eastern coastlines (in July), North-central India and North-eastern India (in August) and parts of north-central India and South India (in September).

Soil moisture-precipitation extremes as a possible precursor to floods

Event Coincidence Analysis identifies Soil Moisture-Precipitation (SM-P) spatiotemporal dependence patterns.
SM-P is a possible precursor of annual floods over European catchments.

Characterization of Soil moisture-precipitation dependence over India

The application of Event Coincidence Analysis was utilized to measure the impact of soil moisture (SM) anomalies on the occurrence of precipitation extremes.
The relationship between wet soil extremes and the occurrence of precipitation extremes, known as soil moisture-precipitation coupling, is primarily responsible for such events in central India.

Ranking of Multi-day extreme precipitation events over Western Himalayas

Precipitation extremes over multi-day accumulated periods were ranked based on the areal extent and magnitude of precipitation anomalies.
Multi-day ranking scheme quantified and tracked the spatial and temporal propagation of extreme precipitation events.
In addition to identifying multi-day precipitation extremes, this ranking scheme also evaluated the timing of the maximum impact.

Drought characterization over India using GRACE

Two indices were considered to characterize droughts: Combined Climatological Deviation Index (CCDI) utilizes precipitation and TWS anomalies, while GRACE-DSI uses TWS anomalies only.
GRACE-DSI exhibits significant negative trends over most of the Indian sub-basins compared to CCDI, indicating that most of the drought events are due to the depletion of TWS.

Teleconnections of extreme precipitation over India

Extreme precipitation’s decadal and interdecadal oscillations (8–16 years and > 16 years) were more significant than interannual (2–8 years) oscillations.
The influence of PDO on extreme precipitation is modulated through Niño 3.4, while the IOD’s influence is independent of Niño 3.4.

Do the regulated and unregulated basins behave similarly regarding streamflow variability and teleconnections?

Intra-annual and annual variability, i.e., 0.5 and 1 year, are prominent in unregulated in contrast with the appearance of intra-decadal scale in the regulated streamflow stations.
Hydroclimatic teleconnections of unregulated stations are prevalent up to inter-annual scale, whereas they existed up to intra-decadal scale for regulated stations.

Long and short-range connections of sea surface temperature on different scales

Networks are constructed using wavelet multiscale correlation
In addition to confirming the existing connections to PDO and ENSO regions, new insights are gained into the long-range teleconnections between ENSO and the IOD.

Spatial diversity of Indian precipitation teleconnections

A non-linear, multiscale approach, based on wavelets and event synchronization, is applied for unraveling teleconnection influences on precipitation.
The influence of climate index on the region and its time scale mentioned in the parenthesis are as follows: ENSO (Southeast at interannual), IOD (Southeast at decadal), NAO (northern regions), PDO (across the country), and AMO (central arid and semi-arid regions).

Societal Relevance

Farmer typology to understand differentiated climate change adaptation in the Himalayas

The results of the study highlight that the perception of farmers on the impacts of climate change is mediated by their household resource base and production orientation for agriculture.
We reiterate the need for the adaptation policies to be nuanced for the diversity within farming communities so as to not reinforce the existing structural inequality (based on caste, tenancy, and wealth).
The study will serve as a guide for inclusive adaption planning in the Himalayan region.

Analysis of climate change and risk perception among farmer types of Indian Western Himalayas

Novel Approaches

We at HydroClimX use recent techniques and approaches to solve emerging challenges in Geosciences. A few remarkable methods that we developed are as follows: