Abstract:
Huge drawdovm of the groundwater tahle is one of the environmental hazards due to
large scale groundwater withdrawal during dry period. Hand tube wens (HTWs) and
shallow tube wells (STWs) operated under gra~itational foree, arc the main supplier
of drinking water in rural area~ may be dry out due to excessive drawdown, This
worst condition of drinking water supply in rural areas has been found in the northwest
part of Rangladesh, namely in Dinajpur dismet. Therefore, this study was
conducted to identify the areas within Oinajpur district where groundwater goes under
a certain level of safe yield from which water cannot be withdrawn with hand tube
wells. A maximwn of 6m depth to groundwater table from ground surface has been
considered us the safe yield limit to ensure the drinking water supply in the study area
through HTWs and STWs with full operational eft1eiency.
The existing groundwater table of the study area has been analyzed from different
groundwater observation wells of BWDR Jur the last available nine years' data. Three
interpolation methods available in GIS namely, inverse distance weighted (lOW),
rhin-plate Spline and Kriging have been tested \0 construct groundwdter level surface
from the observation well data. Among this three interpolation methods, Kriging with
ordinary linear semi-variance model has given the most accurate result when a few
number of groundwater observation well data available. "lhis study showed that the
most of the critical areas lie in Biral, Dinajpur Sadar, Kaharole and Khansama
upazilus of Dinajpur dismct where armual groundwater level fluctuation is in the
range of 4.0m to Il.Om. Rochagonj, Birganj and Chirirbandar upazilas of Dinajpur
district have been found us negligible "Titical areas where annual groundwater level
fluctuation varies from 1.5m to 6.5m. This ~tudy also developed some customized
tools using Avenue scripts (built in object-oriented programming language) in ESRl's
ArcView CIS 3.2 !\Oflware ""ith Spatial Analyst 2.0 exten~ion to make tbe delineation
of water scarce areas easy for different time on the basis of available data.
