Staff profile

• 2020 - present: PhD student, Durham University (Early Stage Research, MSCA ITN 'ICONN' project)
• 2019 - 2020: Junior Research Fellow, Water Resources and Hydrology Lab, IISER Bhopal
• 2014 - 2019: BS-MS dual degree (major in Earth and Environmental Sciences), IISER Bhopal

Project Description

Land degradation is a complex phenomenon with non-linear behaviour that is difficult to predict. In recent years, ecogeomorphic frameworks and connectivity-based approaches has been used to understand land degradation in drylands. Ecogeomorphological concepts are based on the idea that animals and plants inhabiting an ecosystem can effectively modulate the flows of energy and matter that characterizes the biogeochemical cycles, sediment transport, and emergence of landscapes.

The aim of this project is to integrate network science and ecogeomorphology to understand the emergence of dryland ecosystems and identify the role of critical nodes with respect to land degradation. This work builds on previous approaches and presents a novel network science based ecogeomorphic framework for understanding the dynamics of land degradation at multiple spatial and temporal scales. The research framework is made up of three interconnected parts. The first part presents an abstract representation of drylands in form of multilayer complex network based on empirical and modelled data. The multilayer network approach allows us to understand the dryland ecogeomorphology, evolution of connectivity (structural/functional), and the spatial and temporal location of critical nodes. The second part outlines the application of a coupled erosion-vegetation dynamic ecogeomorphic model to understand the spatial and temporal propagation of land degradation in drylands. The third part outlines the role of critical nodes in ecogeomorphic systems based on the multilayer network presented in first part. The proposed quantification of node importance is based on (a) landscape connectivity (functional and structural) and (b) feedback dynamics (propagation of land degradation in space and time). The proposed framework will be used to understand how multiple types of climatic perturbations (such as prolonged droughts, changes in rainfall extremes, and projected climate change) can affect the dryland ecosystems. The output of this work will enable a better understanding of land degradation in drylands through a range of ecogeomorphic perspective.

• Critical Nodes
• Complex Network Theory
• Ecogeomorphic Systems and Processes
• Land degradation in Drylands

Journal Article

• Recinos Brizuela, S. S., Funk, A., Tiwari, S., Baldan, D., & Hein, T. (2024). Multilayer networks in landscape ecology: a case study to assess changes in aquatic habitat connectivity for flying and non-flying benthic macroinvertebrates in a Danube floodplain. Landscape Ecology, 39(11), Article 186. https://doi.org/10.1007/s10980-024-01975-0
• Tiwari, S., Recinos Brizuela, S., Hein, T., Turnbull-Lloyd, L., Wainwright, J., & Funk, A. (2024). Water‐controlled ecosystems as complex networks: Evaluation of network‐based approaches to quantify patterns of connectivity. Ecohydrology, Article e2690. https://doi.org/10.1002/eco.2690
• Karittevlis, C., Papadopoulos, M., Lima, V., Orphanides, G. A., Tiwari, S., Antonakakis, M., Papadopoulou Lesta, V., & Ioannides, A. A. (2024). First activity and interactions in thalamus and cortex using raw single-trial EEG and MEG elicited by somatosensory stimulation. Frontiers in Systems Neuroscience, 17, Article 1305022. https://doi.org/10.3389/fnsys.2023.1305022
• Singh, A., Tiwari, S., & Jha, S. K. (2021). Evaluation of quantitative precipitation forecast in five Indian river basins. Hydrological Sciences Journal, 66(15), 2216-2231. https://doi.org/10.1080/02626667.2021.1982138
• Voutsa, V., Battaglia, D., Bracken, L., Brovelli, A., Costescu, J., Diaz Munoz, M., Fath, B. D., Funk, A., Guirro, M., Hein, T., Kerschner, C., Kimmich, C., Lima, V., Messe, A., Parsons, A. J., Perez, J., Poeppl, R., Prell, C., Recinos, S., Shi, Y., …Huett, M.-T. (2021). Two classes of functional connectivity in dynamical processes in networks. Journal of the Royal Society. Interface, 18(183), Article 20210486. https://doi.org/10.1098/rsif.2021.0486
• Tiwari, S., Jha, S. K., & Singh, A. (2020). Quantification of node importance in rain gauge network: influence of temporal resolution and rain gauge density. Scientific Reports, 10(1), Article 9761. https://doi.org/10.1038/s41598-020-66363-5
• Tiwari, S., Kumar Jha, S., & Sivakumar, B. (2019). Reconstruction of daily rainfall data using the concepts of networks: Accounting for spatial connections in neighborhood selection. Journal of Hydrology, 579, Article 124185. https://doi.org/10.1016/j.jhydrol.2019.124185