has become relatively easy. The utilization of these technologies has seen steady
progress across the country, and by now, a large part of the data collection network
of CWC for hydrologic data has been automated. Similarly, the usage of remote
sensing-based regional assessments has provided great help in yield forecasting and
inundation mapping as well as presenting a realistic picture of project progress.
Automation in water quality assessment through instrument assemblies is also
gaining popularity.
While leveraging new technologies, it also becomes important to establish the
correlation with the historical data so that the observations are on the basis of the
same principles and assumptions as the original ones. If this link is broken, the
disconnect between the two datasets leaves both of them unusable. In case of water,
the historical continuity is very important to generate probabilistic models for
planning and forecasting.
Selection of appropriate technologies for the data collection and processing is of
utmost importance.
With the technology, comes the needs for capacity. Any technological solution
adopted for data collection and processing, the capacity to maintain, troubleshoot
and upgrade has to be built in. The capacities have to be built in right from the first
level of operators to the top level consumers. With the advent of plug and play
technologies with advances in self-diagnostic systems built in the sensors and data
collection platforms, the field level maintenance exercises have become relatively
easier with the detailed repairs relegated to offsite workshops.
11 Conclusions
1. In order to manage a random physical resource like water, an approach based
on sound data is required. Hence, a well-thought out strategy for data collection, processing and use is a prime requirement.
2. Water being a ubiquitous resource, present in all the activities of civilization
and environment, collection and processing of multiple domain data, is necessary to generate a holistic picture. The data collection fields span over climatic phenomena like precipitation, flows over or underground, intervention
measures employed and utilization in various fields like agriculture, industry,
human and animal consumption.
3. Contexts of data collection and management are also multifarious. Water is not
only a life giving resource but also a factor causing disasters and disruptions.
Data collection and processing strategies have to account for each such needs.
4. Demand and utilization assessment pose big challenges in data collection due to
their wide spatial extent and divergences generated due to developmental needs
of the society. Direct observation methods face field level implementation
problems necessitating indirect assessments.
Data Usage for Development, Management of Water Resources
161
progress across the country, and by now, a large part of the data collection network
of CWC for hydrologic data has been automated. Similarly, the usage of remote
sensing-based regional assessments has provided great help in yield forecasting and
inundation mapping as well as presenting a realistic picture of project progress.
Automation in water quality assessment through instrument assemblies is also
gaining popularity.
While leveraging new technologies, it also becomes important to establish the
correlation with the historical data so that the observations are on the basis of the
same principles and assumptions as the original ones. If this link is broken, the
disconnect between the two datasets leaves both of them unusable. In case of water,
the historical continuity is very important to generate probabilistic models for
planning and forecasting.
Selection of appropriate technologies for the data collection and processing is of
utmost importance.
With the technology, comes the needs for capacity. Any technological solution
adopted for data collection and processing, the capacity to maintain, troubleshoot
and upgrade has to be built in. The capacities have to be built in right from the first
level of operators to the top level consumers. With the advent of plug and play
technologies with advances in self-diagnostic systems built in the sensors and data
collection platforms, the field level maintenance exercises have become relatively
easier with the detailed repairs relegated to offsite workshops.
11 Conclusions
1. In order to manage a random physical resource like water, an approach based
on sound data is required. Hence, a well-thought out strategy for data collection, processing and use is a prime requirement.
2. Water being a ubiquitous resource, present in all the activities of civilization
and environment, collection and processing of multiple domain data, is necessary to generate a holistic picture. The data collection fields span over climatic phenomena like precipitation, flows over or underground, intervention
measures employed and utilization in various fields like agriculture, industry,
human and animal consumption.
3. Contexts of data collection and management are also multifarious. Water is not
only a life giving resource but also a factor causing disasters and disruptions.
Data collection and processing strategies have to account for each such needs.
4. Demand and utilization assessment pose big challenges in data collection due to
their wide spatial extent and divergences generated due to developmental needs
of the society. Direct observation methods face field level implementation
problems necessitating indirect assessments.
Data Usage for Development, Management of Water Resources
161