Tushar Sethi
Tushar is Managing Director of Margosa Environmental Solutions Ltd, a UK-based geoinformatics firm that delivers natural resource data products, analytics, and mapping solutions for various applications. He is also a Director at Spatial Ecology, an educational non-profit in the UK specialising in geocomputation. Tushar has an environmental engineering and management background with experience in water management, waste treatment, and energy and materials recovery from waste. He holds an MSc, DIC in Environmental Technology from Imperial College London.
Sessions
In tandem with the monumental increase in geo-data availability from remote sensors, field sensors and various publicly available environmental datasets, state-of-the-art geoinformatics algorithms have evolved to harness earth science data as never before. In the field of computational hydrology, these processes have yielded global information in fine detail, and of exceptional precision.
Hydrography90m is one such data product that pushes the boundaries of computational hydrology in several ways. It is a globally standardised and seamless hydrographic dataset that allows the mapping of headwaters in unprecedented density and detail. With the minimum upstream contributing area set at 0.05km^2, it comprises the highest density of headwaters compared to leading global hydrographic assessments. The dataset contains 1.6 million drainage basins and 726 million stream segments and sub-catchments. It is also designed to overcome the spatial and accessibility constraints of gauged locations and address the limitations of spectral analyses.
As for applications in scientific research, Hydrography90m is well-suited for both global and comparative area-of-interest studies. The dataset contains many essential stream features, such as stream slope, stream distance, types of stream order and flow indices. Hydrography90m thus offers significant utility in the assessment of freshwater quantity and quality, inundation risk, biodiversity and conservation, and resource management objectives, all in a globally comprehensive and standardised manner.
In terms of the underlying computational approach, Hydrography90m is based on a drainage flow algorithm that distributes downhill water flow in a realistic manner, following the concavity and convexity of terrain. Additionally, programming in a variety of open
source software provides unmatched computational power, and the implementation of different scripting procedures allows for bench-marking strategies to check for potential errors. Software employed includes GDAL, Pktools, and GRASS GIS.
The novel computational approach of Hydrography90m broadens the scope for using various remote and field sensor technologies, and the scripting procedure lays the foundation for more complex Machine Learning-based discharge assessments. Its design is a pivotal development for addressing the challenges of overfitting and universal coverage in hydrological modelling. Machine Learning can now enable the massive data integration that is vital for global scale hydrological studies; and hydrographic data with fine detail of headwaters provides an excellent foundation for interbasin connectivity and high-resolution discharge predictions. Meanwhile, other data-driven and ensemble methods that have emerged recently to address these technical challenges still remain limited as tools for basin-specific studies.
Given the multitude of resource and conservation applications, Hydrography90m can be a vital toolkit for achieving several UN Sustainable development goals. Additional uses of the dataset are relevant to freshwater flows and sediment transport computations, pollutant and nutrient concentration assessments, public health, and geopolitical and resource challenges. To date, Hydrography90m has been used in species distribution modelling for aquaculture, vector-borne disease mapping, and various ecological studies. Institutions engaged in water resource management, transnational security and environmental crime monitoring are also starting to derive value from the dataset’s attributes.