About
Post-doctoral researcher studying collective behaviour, movement, social influence, decision-making and mate-choice in animal societies.
Research topics
- Geography
- Environmental science
- Computer Science
- Artificial Intelligence
- Geology
- Machine Learning
- Meteorology
- Statistics
- Physical geography
- Engineering
- Remote sensing
- Water resource management
- Environmental resource management
- Mathematics
- Geotechnical engineering
- Cartography
- Ecology
- Geomorphology
Selected publications
Theoretical and Applied Climatology · 2026-02-17
articleOpen accessCorrespondingLarge-scale climate oscillations shape interannual variability in Indian cereal yields, yet most assessments consider single indices, assume stationarity, or omit explicit tests of incremental value when combining multiple climate drivers with local meteorology. We analyze rice, wheat, and maize yields (1966–2017) for representative districts across representative districts of fourteen agro-climatic zones using a structured wavelet-coherence framework. First, Bivariate Wavelet Coherence (BWC) quantifies time–scale-localized associations between yields and four teleconnections—the El Niño–Southern Oscillation (ENSO), Indian Ocean Dipole (IOD), North Atlantic Oscillation (NAO), and Pacific Decadal Oscillation (PDO)—summarized by Average Wavelet Coherence (AWC) and the Percentage of Significant Coherence (PoSC). Second, we have implemented Multiple Wavelet Coherence (MWC) through a systematic coherence elimination approach (CEA) supplemented by a rigorous Gain-Loss (GL) analysis of PoSC measure. In the GL analysis of two-, three-, and four-factor combinations we retain combinations only for 5% PoSC change relative to the best combination to screen out incidental gains. Finally, we append annual rainfall and temperature to dominant teleconnection sets to test the added value of proximal meteorology. Across zones and crops, ENSO is the most frequent dominant single predictor, while PDO rarely dominates alone but materially strengthens pairs—especially ENSO–PDO—at 4–8-year scales. Adding rainfall and temperature yields zone-specific PoSC gains, most notably where irrigation is limited or late-season heat risk is high. Phase diagnostics indicate predictor-leading relationships at interannual scales, implying seasonal-to-annual predictability windows. The framework provides a transparent, reproducible path from teleconnection diagnostics to parsimonious, operational predictors for crop-yield early warning and climate-smart agronomy.
SWAT-Based Development of Soil and Water Conservation Best Management Practices
Water · 2026-04-23
articleOpen accessCorrespondingStreamflow and sediment yield are key components of river systems and are strongly influenced by anthropogenic land use changes. Soil erosion remains a critical environmental concern, degrading crop productivity, water quality, aquatic ecosystems, and river morphology. Sediment transported from croplands to rivers and reservoirs introduces contaminants and exacerbates water pollution. This study evaluates the effectiveness of Best Management Practices (BMPs) in the Nagavali and Vamsadhara watersheds using a calibrated and validated Soil and Water Assessment Tool (SWAT) model, targeting high sediment-yielding areas. BMP scenarios—including filter strips, sedimentation ponds, contour farming, and contour stone bunding—were assessed at watershed and sub-watershed scales. At the watershed scale, 10 m filter strips reduced sediment yield by 29% and 53% in the Nagavali and Vamsadhara watersheds, respectively. Combined BMP implementation further reduced sediment yield by 37% and 72%, and streamflow by 16.5% and 54%, respectively. These reductions persisted under future climate scenarios. The results highlight the potential of targeted BMP implementation to enhance watershed sustainability and support informed land and water management decisions.
The Hydrology of the Congo Basin
2026-01-01
book-chapterOpen accessAbstract The chapter provides a comprehensive review of the hydrology of the Congo River Basin (CRB), emphasizing its physical features, current data and models, hydrological processes, environmental pressures, and the evolving scientific understanding. Water resources of the CRB support vital ecosystem and societal services that include agriculture, fisheries, hydropower, navigation, water supply, biodiversity conservation, and maintenance of vulnerable ecosystems such as peatlands and flooded forests that are crucial for carbon storage and climate resilience. The CRB hydrology is shaped by its diverse physiographical and geomorphological features, linked through a complex river network encompassing wetlands, lakes, and groundwater systems. Connectivity between headwaters, the Cuvette Centrale, and major tributaries (Kasai, Lualaba, Oubangui, and Sangha) plays a central regulatory role. Our current understanding of these processes remains very limited, which restricts our ability to implement policies for water security and address the impacts of change on physical systems and society. Increasing pressures from deforestation, mining, land use, and climate change threaten hydrological stability, livelihoods, and ecosystem resilience. Heightened vulnerability to hydro-climatic extremes, such as floods, droughts, and landslides, and biological risks like Ebola outbreaks raises concerns of an approaching hydro-ecological tipping point. The chapter calls for urgent investment in monitoring networks, remote sensing, data integration, and predictive modeling to support sustainable water resources management and development. Fifteen major hydrological research challenges are identified , underscoring the need for robust scientific investment. Beyond the CRB, findings will enhance global understanding of tropical forest hydrology and reinforce the basin’s critical role in the Earth System.
Climate Change and Ecosystem Functions and Services in the Congo Basin
2026-01-01
book-chapterOpen accessSenior authorAbstract The Congo Basin, home to the world’s second-largest tropical rainforest, is a biodiversity hotspot and a critical carbon sink. Its varied habitats—including rainforests, savannas, miombo woodlands, freshwater systems, peatlands, and coastal mangroves—support essential ecosystem functions. The Cuvette Centrale peatlands alone store an estimated 30 gigatons of carbon, underscoring the Basin’s climate significance. This review examines climate change impacts on the Basin’s ecosystems, focusing on ecological processes, carbon dynamics, and species responses. The region is increasingly vulnerable to climate change and human pressures. Temperatures are rising faster than the global average, projected to increase 1.5–2 °C by century’s end, while rainfall is becoming more erratic, driving longer dry seasons, flash droughts, and altered flood cycles. These changes, combined with deforestation and land-use conversion, disrupt ecosystem processes. Forests show shift in phenological cycles affecting frugivores and herbivores; savannas face woody encroachment under rising CO₂; freshwater systems experience reduced water quality and declining fish populations; peatlands risk carbon release from lowered water tables; and mangroves are threatened by sea-level rise and reduced freshwater inflows. These ecological shifts have cascading effects, altering carbon and water cycles, nutrient dynamics, and species interactions. Changes in evapotranspiration and rainfall recycling further influence global climate patterns, including the Intertropical Convergence Zone, highlighting the Basin’s role in Earth system regulation. Maintaining the Congo Basin’s ecological integrity is critical for regional resilience and global climate mitigation. Integrated research, targeted conservation, and sustainable land-use practices are urgently needed to safeguard these ecosystems against intensifying climatic and anthropogenic pressures.
The Climate and Land-Use Change Feedback
2026-01-01
book-chapterOpen accessAbstract The Congo Basin, home to the world’s second-largest tropical rainforest, plays a pivotal role in regulating global climate through carbon sequestration, rainfall recycling, and temperature regulation. However, the acceleration of the land-use changes—driven by agriculture, logging, mining, and urbanization—is eroding these vital ecosystem services. This chapter examines potential feedbacks between land-use change and climate, focusing on how deforestation and degradation disrupt biogeophysical processes and amplify the regional and global climate risks. Using observational evidence and modeling approaches, we show that continued forest loss could transform the Congo Basin from a net carbon sink to a carbon source, exacerbate extreme events such as droughts and floods, and destabilize regional rainfall systems. Future scenarios indicate that deforestation could reduce rainfall by up to 10% and raise surface temperatures by about 0.7 °C by mid-century (~2050). Unfortunately, these impacts extend beyond central Africa and influence tropical circulation and global climate stability. To safeguard the Congo Basin’s ecological integrity and climate functions, urgent action is required—through integrated land-use planning, forest conservation, climate-smart agriculture, and robust governance. The chapter underscores that the Congo Basin is both a frontline victim of global change and a critical lever for achieving climate resilience worldwide.
Nature-Based Solutions in the Congo Basin
2026-01-01
book-chapterOpen accessSenior authorAbstract Nature-based approaches have the potential to address challenges such as climate change, biodiversity loss, pollution, flood and drought mitigation, land and soil restoration, and to sustain agricultural productivity, including maintaining pollinators, and regulation of pests and diseases. Case studies from the Congo Basin with the potential to scale up across countries to support efforts being invested in other parts are presented. This chapter highlights the role of local, indigenous, and endogenous knowledge in addressing some of the most intractable current ecological, economic, and social challenges. Although engaging local communities directly is sometimes difficult, their knowledge remains highly valuable. Building trust between the scientific and indigenous communities is crucial to scaling up the contributions of traditional knowledge in evaluating the impact of various nature-based solutions (NbS) across different biomes in the Congo Basin. To support its argument about how science can contribute to sound decision-making processes, we demonstrate in this chapter how NbS can generate non-climate benefits, including biodiversity conservation, which contribute to climate benefits (global cooling, rainfall pattern), food security, water quality improvement, better water penetration into soil, food for livestock, and fuel wood energy supply. We also show how NbS help countries contribute to international commitments, such as the Paris Agreement, 4 per 1000 Initiative “soils for food security and climate,” the Convention on Biological Diversity (CBD), and the Agenda 2030 Sustainable Development Goals (SDGs).
Frontiers in Earth Science · 2026-03-09
articleOpen accessSenior authorLandslide mapping in Kerala is often conducted on spatially aggregated areas, limiting understanding of how landslide vary across different terrain types. This study addresses this gap by performing a comparative analysis of landslide susceptibility across five physiographically distinct catchments in Kerala, namely, Muthirapuzha, Pooyamkutty, Meenachil, Kuttiyadi, and Chaliyar using two different modeling approaches namely Random Forest (RF), representing machine learning techniques, and the Frequency Ratio (FR) method, representing conventional statistical approaches. Landslide susceptibility maps were developed by categorizing catchment areas into five classes, from Very Low to Very High . The conventional statistical approach, indicates that high and very high susceptibility zones together occupy 38.19% of Muthirapuzha, 20.21% of Meenachil, 22.12% of Kuttiyadi, 28.43% of Pooyamkutty, and 23.54% of Chaliyar. In contrast, the RF model produces a more differentiated spatial pattern, with high and very high susceptibility classes covering 31.49%, 25.42%, 38.58%, 32.99%, and 33.61% of the respective catchments. Model performance evaluation demonstrates the robustness of the RF approach, with AUC–ROC values of 0.87 (Chaliyar), 0.83 (Pooyamkutty), 0.93 (Muthirapuzha), 0.95 (Meenachil), and 0.96 (Kuttiyadi), and corresponding classification accuracies of 0.82, 0.76, 0.87, 0.90, and 0.88. Comparison with observed landslide inventories shows that actual landslide occurrences are comparable with the results obtained from RF method. These findings emphasise the influence of geomorphological, geological, and land-use characteristics on landslide occurrence in Kerala’s monsoon-dominated environment. The study highlights the capability of machine learning to capture complex, non-linear interactions among conditioning factors, offering improved tools for landslide hazard mapping and regional disaster risk management.
Tracing the haze: satellite-based assessment of stubble burning and air quality in Delhi
Air Quality Atmosphere & Health · 2026-01-01 · 1 citations
articleOpen accessSenior authorCorrespondingAbstract New Delhi, the capital city of India, routinely records hazardous fine-particle concentrations during the post-monsoon season, yet the quantitative link between regional crop-residue burning and episodic haze remains contested. This study integrates multi-sensor satellite products with atmospheric trajectory modelling to attribute the late-October–early-November aerosol enhancement over the capital during 2020–2024. Columnar aerosol optical depth (AOD) at 550 nm was extracted from MODIS Terra–Aqua (10 km); active-fire detections were taken from VIIRS S-NPP (375 m); harvest dynamics were approximated from MODIS NDVI (250 m); and 120 h forward air-mass trajectories at 500–1000 m a.g.l. were generated with NOAA-HYSPLIT driven by GDAS 1° fields. Seasonal-trend decomposition and Theil–Sen statistics revealed a consistent AOD surge of 0.35 ± 0.06 above pre-monsoon levels ( p < 0.05). Punjab contributed 90% of regional fire counts in 2020 but only 40% in 2024, whereas Haryana showed a marginal decline. Daily fire counts within Punjab–Haryana explained 78% of Delhi AOD variance during October–November ( r = 0.78, p ≪ 0.01). NDVI differencing confirmed harvest-related vegetation loss across > 90% of cropland pixels in week 43 each year. Cluster analysis indicated that 60% of trajectories originating overactive-burn zones intersected Delhi within 36 h, increasing the probability of AOD > 1.2 by a factor of five. These convergent lines of evidence identify stubble combustion as the primary driver of Delhi’s recurring autumn haze. Accelerated deployment of in-situ straw incorporation, baler-mulcher systems, and regional burning-ban enforcement, supported by real-time satellite surveillance, is recommended to achieve National Clean Air Programme particulate-matter targets and to safeguard regional public health. Economic co-benefits are anticipated through fuel savings, improved soil organic carbon, and rural air-quality gains across the Indo-Gangetic Plain.
Harnessing social media data and geospatial tools for flood hazard and risk assessment
Environmental Modelling & Software · 2026-02-17
articleSenior author2025-05-21
book-chapterSenior authorAerosols significantly impact cloud lifetime and solar radiation reflectance in clouds. However, the effect of aerosols on cloud lifetime might be ambiguous without considering meteorological conditions. The region’s rapid industrialization and urbanization have increased aerosol formation in Southern India. Aerosol optical depth (AOD) is used to comprehend the impact of aerosols on cloud fraction (CF) and precipitation for the southwest monsoon from 2005 to 2019 under various atmospheric stability states (K-index) over Southern India. The analysis is performed for light, moderate, and heavy rainfall regimes. The low, warm clouds are analyzed based on cloud top pressure and temperature data from the MODIS (Moderate Resolution Imaging Spectrometer) satellite data. A positive relationship between AOD and CF was observed from the analysis. This might be due to the presence of dispersive aerosols. The impact of atmospheric stability on cloud formation is noticeable for isolated thunderstorm states (20<K<25) and widely scattered thunderstorms (25<K<30) state. The impact is negligible for numerous thunderstorm states (K>35). From the land cover change analysis, cropland and urban areas increased, while grassland was in decline. Forests play an important role in modulating precipitation variability, and the observed decrease in precipitation since 2005 can be partially attributed to the increase in urban areas, which reduces evapotranspiration and, subsequently, rainfall. This shift in land use is primarily driven by agricultural expansion and urban development.
Frequent coauthors
- 33 shared
Kenneth G. Hubbard
- 20 shared
K. Venkata Reddy
National Institute of Technology Warangal
- 19 shared
Hyunwoo Kang
- 17 shared
Jinshing You
University of Nebraska–Lincoln
- 17 shared
Dennis Todey
- 16 shared
Rezaul Mahmood
University of Nebraska–Lincoln
- 15 shared
Syed Azhar Ali
- 12 shared
Dennis P. Lettenmaier
Labs
Education
- 2001
Ph.D, Biosystems Engineering
Oklahoma State University
- 1994
M.Eng., School of Civil Engineering
Asian Institute of Technology
- 1991
B.E, Agricultural Engineering
Tamil Nadu Agricultural University
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