Discovering linkages between catchment characteristics and water quality using catchment classification Ankit Deshmukh 1 , Riddhi Singh 2 , Ashok Samal 3 1 Indian Institute of Technology Hyderabad, India 2 Indian Institute of Technology Bombay, India 3 University of Nebraska, Lincoln, USA
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Discovering linkages between catchment characteristics and water quality using catchment classification
Ankit Deshmukh1, Riddhi Singh2, Ashok Samal3
1 Indian Institute of Technology Hyderabad, India
2 Indian Institute of Technology Bombay, India
3 University of Nebraska, Lincoln, USA
Water Future Conference 2019.
Classification helps to organize knowledge
2
adapted and recreated from Whittaker, R.H., 1969. New
concepts of kingdoms of organisms. Science, 163(3863),
pp.150-160.
adapted from RH Whittaker, Communities and
Ecosystems, 1975
Water Future Conference 2019.
Classification help to guide modelling studies in ungauged catchments and it also used to understand regional drivers of hydrology
3
Pechlivanidis and Arheimer, 2015, Hydrology and Earth System Sciences.
Clustering based on flow signaturesClustering based on physio-climatic characteristics
Water Future Conference 2019.
However key gaps remain:
4
Clustering of variables other than streamflow quantity derived metrics
Multiplicity of clustering algorithms
Relative utility of clustering/ modelling/correlation studies in disentangling
catchment behaviour
Water Future Conference 2019.
In this study, we:
5
Clustering of variables other than streamflow quantity derived metrics: explore
the value of classification in understanding drivers of water quality
Multiplicity of clustering algorithms: explore the impact of different
algorithmic choices on results
Develop a generic framework to perform classification using
multiple algorithms
Water Future Conference 2019.
A classification study to reveal how water quality metrics are explained by different catchment properties
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Explanatory variables
Groupings based
on water quality
indicator
Groupings based
on properties
Target variables
Data cleaning,
common period
Independent
metrics/ properties
Clusters of water quality/
catchment propertiesClustering
algorithms
Catchment properties;
Water quality metrics
Select high performing
clusters Similarity
metrics
Deshmukh et al., In Preparation
Water Future Conference 2019.
a. We develop physio-climatic characteristics
dataset for 567 Indian catchments. [CC]
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Category #Propertie
s
Climate 39
Geology 16
Hydrology 09
Land cover 48
Land use 19
Socio Economic 08
Soil 38
Topography 14
b. Water quality data is
obtained from WRIS India for
358 catchments [WQ]
Water quality data set is monthly dataset
with 33 indicators. Data availability is
different for each case.
c. We are able to find 254
common catchments, in both
datasets (CC and WQ).
We further reduce the catchments based on
the data availability in the water quality
dataset.
Water Future Conference 2019.
We clean water quality dataset and shortlist 6 indicator with 88 catchment across
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Deshmukh et al., In Preparation
Water Future Conference 2019.
Grouping of water quality indices and catchment characteristics