9.4 Deployment of Adequate Diligence for the Consistency
of Data
All data collection for water resource management are local. The large number of
local data observations has to be transformed into a global picture which can be
deployed in global level assessments and projections. Water data is strongly
affected by the physical parameters of the measurement sites and set-ups.
Hydrometry which forms the backbone of water resources data is affected by the
hydraulic conditions at the measurement site. The observed values are the end
products of the water inputs of the entire upstream basin area and utilization spread
over the same area. With the continual changes in land and water usages, the data
collection points and the interpretation of the data have to modify itself for the
consistency. Reassessment of the suitability of observation site from the hydraulic
and hydrologic consistency is an exercise not carried out frequently. Instances of
changes in data due to construction of a dam/barrage/bridge often vitiate the
observations. Operation of such structures will completely disturb the level discharge relationships. Such data, if incorporated within the database without any
reference to the changes, can lead to inconsistent results. Equal diligence is required
for verifying the data which has been generated by agencies, which may have
different focus. A good example being the reporting of the irrigated areas by
groundwater and canals. The conclusions were based on the data collected by the
authorities in charge of land revenue collection where the non-availability of data
on canal irrigation was reported as 0 (zero) for the area. This has led to a lopsided
conclusion about declining share of canals irrigated area over the period of time.
This serves as a lesson for ensuring the consistency of data generated by different
agencies before incorporating the same in the analysis. Another example is being a
study where the sedimentation and implication about long-term silting of Ganga
river were drawn based on a single sample collected at random time instant in a
year. The sampling method is completely away from the actual reality of variation
in sediment load in an alluvial river over the year and methodology established for
integrating the variation of the sediment concentrations over a large flow cross
section. These examples demonstrate the attention required to be paid to the diligence of collection and consistency in context of the conclusions to be drawn.
For ensuring the integrity of the data, technical auditing of the consistency of the
methods used for collecting the data is necessary. The context in which the data has
been collected and its use in the present scenario should be carefully analysed.
10 Leveraging New Technologies and Approaches
Efficient data management requires efficient collection and processing also. With
the advent of sensor, telemetry and satellite as well as ground networks-based
communication technologies, the collection of data over the wide geographic area
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A. B. Pandya
of Data
All data collection for water resource management are local. The large number of
local data observations has to be transformed into a global picture which can be
deployed in global level assessments and projections. Water data is strongly
affected by the physical parameters of the measurement sites and set-ups.
Hydrometry which forms the backbone of water resources data is affected by the
hydraulic conditions at the measurement site. The observed values are the end
products of the water inputs of the entire upstream basin area and utilization spread
over the same area. With the continual changes in land and water usages, the data
collection points and the interpretation of the data have to modify itself for the
consistency. Reassessment of the suitability of observation site from the hydraulic
and hydrologic consistency is an exercise not carried out frequently. Instances of
changes in data due to construction of a dam/barrage/bridge often vitiate the
observations. Operation of such structures will completely disturb the level discharge relationships. Such data, if incorporated within the database without any
reference to the changes, can lead to inconsistent results. Equal diligence is required
for verifying the data which has been generated by agencies, which may have
different focus. A good example being the reporting of the irrigated areas by
groundwater and canals. The conclusions were based on the data collected by the
authorities in charge of land revenue collection where the non-availability of data
on canal irrigation was reported as 0 (zero) for the area. This has led to a lopsided
conclusion about declining share of canals irrigated area over the period of time.
This serves as a lesson for ensuring the consistency of data generated by different
agencies before incorporating the same in the analysis. Another example is being a
study where the sedimentation and implication about long-term silting of Ganga
river were drawn based on a single sample collected at random time instant in a
year. The sampling method is completely away from the actual reality of variation
in sediment load in an alluvial river over the year and methodology established for
integrating the variation of the sediment concentrations over a large flow cross
section. These examples demonstrate the attention required to be paid to the diligence of collection and consistency in context of the conclusions to be drawn.
For ensuring the integrity of the data, technical auditing of the consistency of the
methods used for collecting the data is necessary. The context in which the data has
been collected and its use in the present scenario should be carefully analysed.
10 Leveraging New Technologies and Approaches
Efficient data management requires efficient collection and processing also. With
the advent of sensor, telemetry and satellite as well as ground networks-based
communication technologies, the collection of data over the wide geographic area
160
A. B. Pandya