a novel approach for groundwater assessment and predictions

1. A novel approach for groundwater
assessment and predictions
Improving the representativeness of sparse
observation networks and enhancing model results
Ezra Haaf, Roland Barthel
Department of Earth Sciences, University of Gothenburg, Göteborg
Grundvattendagarna 2015, Session "Modellering", 14th of October 2015

2. Situation in Sweden
• large country with small population
• many small and medium sized groundwater bodies
 not feasible to monitor everything
 What is happening at the unmonitored locations?
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Similarity based classification to derive the
groundwater state at unmonitored locations

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Previous approaches for groundwater systems
classification
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(Knutsson and Fagerlind 1977)

4. Current status in Sweden
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(Sundén and Maxe, 2010)
(Vikberg et al., 2015)
"snabb" vs "långsamreagerande"
Climate dependent regime curves

5. Current classification
• unclear how to distinguish between e.g. snabb and
långsamreagerande
• representativeness of different regime curves
• classification based on subjective expert assessment
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6. Aims of this study
 develop a classification methodology that is:
– more systematic
– more comprehensive
– makes reproducible use of existing data and knowledge
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7. Available data
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• 77 000 sqkm catchment
• ~ 4000 Observation wells
• daily/weekly interval
• up to 90 years
• well data, metadata
• geology
• hydrometereological data
• land use

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Sorting groundwater time series

9. Classifying groundwater hydrographs into
groups with similar characteristics
– PCA with varimax rotation
 reduce redundance based on maximizing variance, find
significant representations
– k-means clustering
 partitioning method, minimizing the sum of squared errors
within each cluster
– Hierarchical clustering
 hierarchy of clusters based on distance metrics
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10. Example hierarchical clustering
1. Raw time series clustering 2. Clustering of extracted features
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• mean, std
• mean max
GWL date
• Lyapunov
exponent
• …
clustering algorithm clustering algorithm
select distance metric select distance metric

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Example hierarchical clustering

12. What is left to do?
• Tweaking of the grouping
approaches
• connect the similarity of
groundwater hydrographs to
groundwater systems
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13. Conclusions I
• Our approach can be used to:
– predict dynamics in unmonitored groundwater
bodies
– improve climate change impact assessment
on groundwater resources
– supplement numerical groundwater models
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14. Conclusions II
• Clearer definitions of previously weakly defined
classifications such as ”slow and fast responding”
”big and small”
• Provide a scientifically sound background for
”expertbedömning”
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15. THANKS FOR LISTENING!
ezra.haaf@gu.se