Project Summary

Currently diverse players are doing groundwater monitoring across the country from many government bodies, NGOs, academics, and citizens. This results in large amounts of data on groundwater, which is available in various formats, styles and languages.  Further, this data can be collected using differing methodologies. Given the lack of standardisation that exists today, it is difficult to ascertain how much data exits, where it lies, how useful it is and if it can be accessed .Also, without proper baseline data with which to compare, it is difficult to benchmark the progress that the government is making with their own data collection and aquifer mapping programs. By creating a repository, Arghyam is encouraging players to share data, evaluate what is good, and then ground truth government programs by providing data grounded in standardised data collection whose quality has been proven by civil society.

A  primary audience is the Participatory Groundwater Management (PGWM) coalition ,as these organisations are already a part of Arghyam’s network and are invested in community driven management of groundwater resources, that includes sound data management. This group can be leveraged to use their data to give feedback to government aquifer mapping initiatives. Apart from coalition members, NGOs that do watershed development, groundwater management, and general monitoring of an area, will also be invested in this repository.

Apart from NGOs, practitioners, academics, government agencies, the public will also find this data to be useful. Tools can be developed that allow interested people to choose a location and get an accurate picture of their groundwater situation and ways to participate in maintaining their resources.

By creating a place for aggregating and evaluating data as well as  a system that allows putting in data and then filtering out information based on parameters will be a benefit to all who work in the sector.

Outputs

The data repository that will be created, will allow filtering of data by various parameters, evaluate quality of data and standardise data methodology.