Aquifer Maps Add Accuracy to Jaltol
Update on the new map layer added to Jaltol v1.1
by Craig Dsouza
Jaltol, our digital tool for easy water balance estimation, is in the second phase of development. We have incorporated a new map layer which improves the accuracy of the tool.
Why monitor groundwater levels
Monitoring groundwater levels is critical to managing the groundwater crisis in rural India. In the rural water security planning process, pre-monsoon and post-monsoon groundwater level changes are measured to estimate groundwater budgets. Water levels by themselves are not enough to understand groundwater storage and availability.
To understand how much groundwater is available, we need to first understand how groundwater is stored. Groundwater is stored in bodies of rock below the ground called aquifers.
Not all groundwater aquifers store and yield the same amount of water. They behave differently based on rainfall and geology.
For instance, Punjab, with alluvial deposits from the tributaries of the Indus river, is underlain by an unconsolidated aquifer containing layers of granular material such as sand, gravel, silt, and clay. Since these aquifers hold more water, the wells have high well yields. Non-coastal Karnataka on the other hand, is underlain by hard-rock aquifers made up of granite, gneiss and charnockite. These aquifers hold only small amounts of water in the fractures and therefore have low well yields.
The amount of water that can be drawn from an aquifer is denoted by its “specific yield”, the ratio of water that can be drawn from a 1m3 volume of saturated aquifer. For instance in hard rock aquifers, only 0.01 m3 of water might be withdrawable from a 1 m3 volume of aquifer (1% specific yield) where an alluvial aquifer might generate 0.1 m3 (10% specific yield) from that same volume. Therefore, to know how much groundwater is available, we need to know the nature of the aquifer being monitored and its specific yield.
In this post, we take a deeper look at the aquifer maps provided by the Central Ground Water Board (CGWB) and document the simple steps we’ve taken to clean up the dataset before incorporating it into Jaltol. This is the most notable update in the latest version (v1.1) of Jaltol. In Jaltol v1.0, specific yield was assumed to be uniformly 1% across the country. We believe that by using aquifer specific yield values we will improve the overall accuracy of water budgets.
Input dataset
CGWB has mapped 16 principal aquifers and 42 aquifer sub-types across the country and each of these have associated specific yield values, given as a min-max range for each aquifer. It is recommended that in the absence of more accurate data, the specific yield should be taken as the mean of the min-max range for each aquifer.
- CGWB Aquifer Maps: https://www.indiawris.gov.in/wris/#/Aquifer (Retrieved on 30 Aug 2021)
- Metadata: http://cgwb.gov.in/Documents/GEC2015_Report_Final%2030.10.2017.pdf (Table 7, Pg 77)
Methodology
The original shapefile (a digital storage format) of the aquifer map of the country was given min, max and mean specific yield attributes as shown in Table 7, Pg 77 of the CGWB data. The following visualisation allows us to see the variation in these values across the country.
Step 1: We converted the shapefile into a raster layer of 1km spatial resolution using QGIS.
Step 2: We then uploaded this raster layer to Google Earth Engine (GEE). The Jaltol plugin accesses the raster layer from GEE and uses it for computing changes in groundwater storage.
The formula used is
Delta GW Storage = (Post-monsoon SWL — Pre-monsoon SWL) * Specific Yield
Output
Using this updated formula, we can now compute changes in groundwater storage with improved accuracy.
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