May 2nd
Boudreau's
Typology work
General
Objective: to develop an initial typology to fit into the evolution of a useful
classification for upscaling the LOICZ biogeochemical budgets to global
estimates.
Detailed
Objective: Provide the best possible representation of nutrient inputs and
water exchange rates from the existing database.
Note:
West Coast of South America is lost from the dataset as a result of eliminating
positive water depth.
Accepted
conditions:
1) some
cells may be unique with no other similar cells (maybe Upper Gulf of Thailand?)
2)
cells may have significant variation in p-r and nfix-denit within. A
classification that groups cells with large variance together is a useful as
one that group cells with similar levels. Hopefully a single clustering can
result in a calssification that does both.
Criteria
for evaluating results:
2)
Provide a suitable pattern of clusters along the coastline to represent
headlands/inlets. Avoid clusters that
paint long expanses of coastline in a one type such is often the case with
variables that change slowly along the coast, such as SST. It is expected that
headlands will alternate bays.
3)
Isolate urban centres (using basin_population_density) and agriculture (using
%_cropland) as two classes. These classes are expected to be associated with
areas of high nutrient loading.
4)
Isolate cells with large rivers (using basin_runoff) that are expected to be
more flow through systems with characteristic biogeochemistry.
5)
Areas used for tuning include: Eastern Canada (various), Northern (in
consulation with Crossland) and Eastern Australia (in consultation with Smith),
Europe, Western Canada and Alaska.
The
three main variables and their anticipated proxies are:
1)
basin runoff/area of water in the cell - amount of water input relative to the
system size;
2)
basin population density/area of water in the cell - amount of human input
relative to the system sie;
3) area
of water in the cell/tidal height - exchange rate.
Tests
showed that the calculation of the water area in the cell did not improve the
results.
Variables
and weighting used:
PRECIP_TO_CELL: on
1
SUB_BASIN_POPULATION_DENSITY: on
100
SST_MAX_MONTH: on
1
SST_MIN_MONTH: on
1
TIDAL_RANGE: on
100
SUB_BASIN_RUNOFF: on
100
BASIN_PERCNT_CROPLAND: on
1
BASIN_PERCNT_URBAN: on
1
Results:
1) two
clusters represented 46 "urban sites" including for example: L.A.,
Vancouver, New York, Miami, Sydney, Wellington, etc.
2) one
cluster with 41 cells had high basin runoff such as the Amazon, ;
3) One
cluster described as: "high SST_MIN_MONTH, low BASIN_PERCNT_CROPLAND, low
BASIN_PERCNT_URBAN" had 2597 cells. This is much of the tropical area and
will require additional refinement based on other parameter.
Cluster
0: low PRECIP_TO_CELL, high SST_MAX_MONTH, high SST_MIN_MONTH
Cluster
1 & 5: low PRECIP_TO_CELL, high TIDAL_RANGE, low SUB_BASIN_RUNOFF
Clusters
2 & 12 & 13 & 16: medium PRECIP_TO_CELL
Cluster
3: high PRECIP_TO_CELL
Cluster
4: low PRECIP_TO_CELL, high TIDAL_RANGE, low BASIN_PERCNT_CROPLAND
Cluster
6 & 19: low PRECIP_TO_CELL, low SST_MIN_MONTH, low TIDAL_RANGE
Cluster
7: low PRECIP_TO_CELL, low SST_MAX_MONTH, low SUB_BASIN_RUNOFF
Cluster
8: low PRECIP_TO_CELL, high BASIN_PERCNT_CROPLAND, low BASIN_PERCNT_URBAN
Cluster
9 & 15: low PRECIP_TO_CELL, low TIDAL_RANGE, low SUB_BASIN_RUNOFF
Cluster
10: high BASIN_PERCNT_URBAN
Cluster
11: low PRECIP_TO_CELL, low BASIN_PERCNT_URBAN
Cluster
14: low PRECIP_TO_CELL, high SUB_BASIN_RUNOFF, low BASIN_PERCNT_URBAN
Cluster
17: low PRECIP_TO_CELL, high SST_MAX_MONTH, low TIDAL_RANGE
Cluster
18: low PRECIP_TO_CELL, low SUB_BASIN_RUNOFF, low BASIN_PERCNT_URBAN
Future
work:
1)
retry a water exchange index - calculate depth/tide.
2) with
urban cells - ensure that a reasonable number of "cities" are pulled
out of the data and within the cells, look for possible additional distinctions
- maybe GDP as a proxy for potential treatment or lack of same.
3) for
cropland - look at proxies that might distinquish the different types of
cropland and its influence input into the coastal cells.