It’s all we have, so we have to use it!
How do we justify using data we know for a fact are wrong?
In the run up to World Water Day, I am continuing my critiques of Indian hydrology research and application. A related article can be read online in Resonance journal (targeted to Indian undergraduate science students) http://www.ias.ac.in/describe/article/reso/022/03/0303-0313
One of the fun challenges of doing water research in India is the data or rather the lack thereof. The good news is that only half of the data are wrong. The bad news is we just don’t know which half! This fact is well known to every water researcher in India. How on earth do we justify using data, we know for sure is wrong? Yet if I had a rupee for every time I’ve heard “but we have to use the data — its all we’ve got”, I’d be rich.
Groundwater levels in hard-rock peninsular India are a classic example of this blinkered vision. In our field studies, we have consistently found that government monitoring well data to be much shallower than both our own observation data (from borewell scans and water level recorders) and farmer reports. We have our pet theories about these discrepancies, but that’s for another day. This article is about how people react when the discrepancy becomes obvious.
On one hand, everyone understands perfectly that water levels are highly heterogeneous and that two wells a few feet apart could have water levels that differ by hundreds of feet. On the other hand, that does not stop everyone from treating data from randomly located monitoring wells (that are very sparsely distributed) as somehow magically being perfectly representative of the entire 100 sq km region around.
We build entire models of the country using this flawed data and even base infrastructure decisions and insurance prices on it. If the whole castle is built on a weak base, what’s the point? Despite this obvious flaw in logic, most groundwater hydrogeologists will run around in circles trying to explain why the monitoring well data are more reliable and why its OK to use them.
Here are some explanations often offered:
- The monitoring well data represent static water levels, while farmer wells exhibit dynamic water levels. Even if there’s a kernel of truth here, it does not explain the inherent heterogeneity. The unstated assumption in the static-dynamic water level argument is that given enough time, ALL borewells that are not being pumped will reach an equilibrium, even in a hard rock aquifer system. This simply isn’t true; two wells which are 5 feet apart and haven’t been pumped for months, can still show widely different groundwater levels (as much as 200 feet in the case of our two office borewells!).
- Government wells were carefully constructed and therefore more “believable” than farmers’ wells. This is also implausible because the monitoring well levels are extremely shallow. If the static water was really 15–30 feet below the ground as monitoring well data suggest, open wells in the catchment should hold water. Yet in areas where we’ve worked in Arkavathy basin not one open well has held water for the last 25 years at least. And its hard to believe that farmers spend lakhs of rupees drilling hundreds of feet for no other reason than they haven’t figured out how to locate a good driller.
- The monitoring well data are official and therefore the only source admissible in a court of law. Don’t even get me started on this one!
Its quite clear that farmer narratives of depletion in hard rock aquifers diverge sharply from monitoring well data. The question is why. This is a valid line of inquiry.
But progress can only be made if the scientific community recognizes all types of data as being equally valid (within reason). Some Indian journals do not in fact recognize certain types of data as valid. A review comment in an article submitted to an Indian journal (by me) for instance argued that government data were beyond question and NGOs had no business question monitoring well data from a “National Level Scientific Body involving experts”. Citizen science or farmer surveys (“hearsay data”) even when systematically done could not be used in a scientific paper!
This needs to change. The notion that certain ideas, institutions or types of data are inherently superior or inferior should have no place in science. The culture of Indian science needs to become more grounded and driven by a spirit of curiosity and open to any form of data, provided we challenge ourselves to validate the data independently, interpret and communicate clearly what the data mean.