How is rainfall and groundwater level data analysed to predict future groundwater levels?
This is a question from a donor with an MSc in community water supply so replies can be as technical and detailed as possible.
Yours is a very interesting question. We are currently only doing this in a small number of selected projects, mainly in West Africa, though there are developments in East Africa and Pakistan and some examples from India.
It is very difficult to do groundwater level prediction in a meaningful way.
We are obviously hampered by lack of long term data - in particular groundwater levels are tricky to obtain and we often depend on data from other sources, such as water regulating bodies, meteorological services etc. Most of our interventions are boreholes, and rest water levels can often only be obtained during construction or during repairs or breakdowns. Those details are often ad-hoc and not collected in a structured way.
In Burkina Faso, there are some really good examples of community-based water Management - where communities monitor both rainfall and groundwater level. Based on the trends, the community decides how to divide the resource; and place restrictions (e.g. cattle, irrigation etc.) if needed. These calculations are estimates, but are typically for the current year. We often are not in a position to do long term, multi-year forecasting, as that requires considerable data and more detailed analyses (Vincent Casey can surely provide a more detailed answer).
That said, in Tharparkar district, Pakistan, WaterAid is working with the district to get water security plans based on analyses of existing data from a variety of sources. This initiative is however relatively new. In India, we have developed a number of water security plans (typically 3-4 communities together) which focus on all water users, and ground water abstractions.
Proper predictive analyses requires modelling, detailed aquifer information, long-term rainfall data etc. This unfortunately goes beyond the scope of WaterAid's possibilities and resources.
Hope this helps,
As Arjen indicates any sort of accurate prediction requires long term records of rainfall and groundwater level. Even then, a direct correlation is likely to be impossible though a general relationship may emerge with a likelihood of high groundwater levels following a heavy rainy season with a lag of months between the peak rain and the highest groundwater level. The Food and Agricultural Organisation has also worked with community level data collection to establish a relationship for resource allocation. Long term rainfall records may be available locally from an airport, agricultural research station, school or mission but groundwater records are likely to be patchy at best. It is difficult to get clear groundwater levels from a well or borehole that is in use for water supply. Where there are sufficient resources, dedicated observation boreholes are drilled but this is unlikely to be the case in your area.
As often the wikipedia explains it quite well: http://en.wikipedia.org/wiki/Groundwa...
As others have mentioned the availability of long timeseries of groundwater levels from multiple boreholes, nearby river/lake levels and spacial rainfall data is quite important as it is used to calibrate a model. This means that you develop a model based on your available aquifer data (soil/ground composition for a sufficiently large area) and then adjust it's calculated output according to the real data you have from the timelines. If you have high quality ground composition data and detailed spacial rainfall data, the calibation step (that usually involves a lot of guessing to "massage" the model's output to fit to the real data) is a bit less important and you can get away with shorter timeseries of the calibration data.
I undertook a 2 year project analyzing data from over 50 years of monitoring in the Northern Territory of Australia and it was one of the hardest things I've done. Monitoring groundwater levels and correlating with rainfall, recharge and extraction is incredibly difficult let alone the second step of then using that data to make predictions.
As already mentioned it is important to have decent length records of groundwater levels and rainfall, but that is only the start. Two key variables that can be hard to assess are the changes in weather over the years and the volume of water being extracted. Without really good data it is very difficult to accurately predict what will happen to groundwater levels in the future. In areas with shallow and regional aquifers it is essential to have information on the aquifer and where the wells are screened as you can end up monitoring the wrong system. Depending on the location you might be looking at aquifers that are recharged over centuries, and local weather patterns are almost irrelevant (as is the case in the central desert of Australia). As with many things groundwater, it's complicated!
I could go on and on about this, but I'll try not to. The short answer to your question is; Historical rainfall data is one of several variables that can be used in predicting groundwater levels, however the validity of the prediction will depend on factors including the completeness of the data set, the aquifer under investigation, the availability of groundwater level records, the extent and relative location of extraction, and an understanding of future resource use.
In my analysis of data I found it very illuminating to do simple hydrographs with groundwater level against rainfall data over time, where there was understood to be seasonal variation in groundwater levels related to rainfall (e.g. rapid recharge in wet season). It's simplistic but as a visual representation of data it can be very powerful. Trend analysis can then be done showing a few scenarios;
- groundwater level not changing over time
- groundwater level increasing over time
- groundwater level varies with rainfall (seasonal) - can use multiple year data sets to establish if there is an overall rising or falling trend
- groundwater level varies not related to rainfall = extraction influence, over extraction, recharge from location/time outside of monitoring area, bad monitoring data!
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