Retrieving catchment variables to explain changes in lake behaviour: the GloboLakes catchment database. Mark Cutler (University of Dundee)

1. Retrieving catchment variables to
explain changes in lake behaviour:
the GloboLakes catchment database
Mark Cutler, John Rowan, & Eirini Politi
Geography, School of Social Sciences, University of Dundee, UK
IUKWC Workshop
Stirling
19th June 2017
m.e.j.cutler@Dundee.ac.uk

2. 1000 GloboLakes
Total GloboLakes Catchment Area (cumulative) ~ 131,180,824 sq km

3. • To begin to explain and interpret change in
lake behaviour we need to understand the
drivers of change.
• We also need to be able to ‘describe’ our lakes
and produce a global lake typology
• To do this globally requires bringing together
global standardised datasets and information
– GloboLakes Catchment database
– Mixture of pre-existing and modelled data for our
1000 Globolakes

4. (Sorrano et al., 2009)

5. Catchment drivers of
lake change

6. Catchment drivers for
GloboLakes
Non-climatic drivers
Climatic drivers

7. 1. Air Temperature (surface) (TMP)
2. Precipitation (PRE)
3. Potential evapotranspiration (PET)
4. Effective rainfall (=PRE-PET)
Climatic Research Unit (CRU)
Time Series v.3.2.2
Monthly | 1971-2013 | 62.5 km (0.5◦)
(1901-2013) Rescaled to 1 km
5. Sunshine duration (SUN)
ECMWF* Re-analysis for the 20th
Century (ERA-20C)
* European Centre for Medium-Range Weather Forecasts
Daily | 1971-2010 | 15 km (0.125◦)
(1900-2010) Rescaled to 1 km
Catchment drivers | Climatic

8. 6. Land cover/use change (LC)
5-year epochs | 1998-2012 | 300 m
ESA Climate Change Initiative
(CCI) Land Cover (LC)
8. Population density (POPDENS)
NASA SEDAC CIESIN**
Gridded Population of the
World (GPW) v.3
5-yearly | 1990-2015 | 5 km (0.04◦)
Rescaled to 1 km
7. Surface runoff (SRO)
ECMWF* 40 year re-analysis
(ERA-40)
Monthly | 1957-2002 | 15 km (0.125◦)
Rescaled to 1 km
Catchment drivers | Non-climatic
* European Centre for Medium-Range Weather Forecasts
** Socioeconomic Data & Applications Center, Center for International Earth Science Information Network

9. Catchment drivers | Non-climatic
9. Irrigated land potential
10. Livestock
11. Fertilisers
Yearly | 1961-2011 (9, 10) | Country level
| 2002-2010 (11) |
Food and Agriculture
Organization (FAO)
12. Water level fluctuations
13. Dams + impoundments
ESA River & Lake; USDA Global Reservoir
and Lake Monitoring (GRLM);
LEGOS Hydroweb
Global Reservoir and
Dam (GRanD) Database
14. NDVI
NOAA AVHRR NDVI
Monthly | 1981-2000 | 12 km (0.1◦)
Rescaled to 1 km

10. Lough Neagh,
UK
Air temperature
(WorldClim, 1950-2000)
Precipitation
(WorldClim, 1950-
2000)
Livestock
(FAO Cattle 2005)
Elevation
(SRTM)
Fertilisers
(FAO)
Socioeconomic indices
(IMF)
Geology
(GLiM)
Ecoregion
(TEOW)
Population density
(SEDAC-CIESIN, 2010)
Soils (HWSD)
Mean Annual
Surface Runoff
(UNH Water Systems
Analysis)
Riparian development
(ESA GlobCover 2009)
Rivers
(HydroSHEDS) &
Dams (GRanD)
Road network
(gROADS)
Vegetation indices
(NOAA AVHRR NDVI, Aug 2000)
Land cover
(ESA GlobCover
2009)
Soil moisture max
capacity (HWSD)

11. Air temperature
(WorldClim, 1950-2000)
Precipitation
(WorldClim, 1950-
2000)
Livestock
(FAO Cattle 2005)
Elevation
(SRTM)
Fertilisers
(FAO)
Socioeconomic indices
(IMF)
Geology
(GLiM)
Ecoregion
(TEOW)
Population density
(SEDAC-CIESIN, 2010)
Soils (HWSD)
Mean Annual
Surface Runoff
(UNH Water Systems
Analysis)
Riparian development
(ESA GlobCover 2009)
Rivers
(HydroSHEDS) &
Dams (GRanD)
Road network
(gROADS)
Vegetation indices
(NOAA AVHRR NDVI, Aug 2000)
Land cover
(ESA GlobCover
2009)
Soil moisture max
capacity (HWSD)
Lake
Balaton

12. Land cover/use change
ESA CCI Standardised Land cover product covering 3 epochs
Lake Reindeer,
Canada
Lough Neagh,
Ireland
Lake Nicaragua,
Nicaragua/Costa
Rica

13. GDP, annual national data
[billion $US]
Livestock (pigs), annual national
data
[total number per ha of agricultural
area]
0
2000
4000
6000
8000
10000
1980
1983
1986
1989
1992
1995
1998
2001
2004
2007
2010
0
0.2
0.4
0.6
0.8
1
1.2
1961
1965
1969
1973
1977
1981
1985
1989
1993
1997
2001
2005
2009
0
200
400
600
800
1000
1200
1400
1961
1965
1969
1973
1977
1981
1985
1989
1993
1997
2001
2005
2009
Total area equipped for
irrigation, annual national data
[1000 ha]
*Excluding China +
Russia
Nitrogen + Phosphate fertilisers, annual
national data [tonnes per 1000 ha]
-50
50
150
250
350
450
Population count, annual
national data [1000 persons]
0
200000
400000
600000
800000
1000000
1200000
1400000
1600000
1961
1968
1975
1982
1989
1996
2003
2010
2017
2024
2031
2038
2045
Population density, interval (5-yr)
mean data from catchment [persons
per sq.km]
0
100
200
300
400
500
600
700
1990 1995 2000
UK
Canada
Malawi
Peru
Nicaragua
Russia
Cambodia
Sweden

14. Catchment drivers | Other catchment + lake data
Gross Domestic Product per capita (GDP)
Catchment altitude (mean)
Catchment relief ratio
Geology
Soil properties
Ecoregion
Lake location
Lake elevation
Lake morphometry (mean-max depth, volume)
Mixing regime
Eutrophication status
Freezing time
Residence time
Shoreline Development Index (SDI)
Continentality
Bathymetry
CatchmentLake
Global Lake
Typology

15. • Independent (published) observations of mean
depth, max. depth and lake volume:
– Search of Web of Science, Lake Databases & Google using
search terms:
• Volume [lake name]
• Depth [lake name]
• Mean depth [lake name]
– 565 lakes where we have at least one of the above
• Allows us to model the relationship between area and depth to
derive information from lakes where no information is readily
available
• Residence time, origin, trophic status etc. also recorded
Catchment drivers | Modelling

16. • Modelled drivers:
Lake morphometry
Catchment drivers | Modelling
Mean depth
R² improves if lakes are
grouped by origin
R² = 0.82
0
1
2
3
0 1 2 3
Observedmeandepth(m,Log10)
Modelled mean depth (m, Log10)
Glacial lakes, n=40
R² = 0.67
0
1
2
3
0 1 2 3
Observedmeandepth(m,Log10)
Modelled mean depth (m, Log10)
Tectonic lakes, n=31
R² = 0.51
0
1
2
3
0 1 2 3
Observedmeandepth(m,Log10)
Modelled mean depth (m, Log10)
All origins, n=129
Heathcote et al., 2015
Log10 V = log10 lake area  0.96 + log10 elevation change25  0.77

17. Catchment drivers | Score sheets + lake info form

19. When finalised, the GloboLakes Catchment Database (GLBL_CDB)
will be the first global lake catchment database and it:
• .. will contain:
– 9 spatial TS (averaged)
– 5 non-spatial TS (i.e. point measurements or data available at country
level)
– 25+ fixed lake and catchment variables
• .. will cover:
– 40+ years of data
– monthly, yearly or 5-yearly intervals
• .. will be delivered in two complementary formats:
– Microsoft Access
– GIS
Catchment drivers | Summary

20. When finalised, the GloboLakes Catchment Database (GLBL_CDB)
will be the first global lake catchment database and it:
• .. will contain:
– 9 spatial TS (averaged)
– 5 non-spatial TS (i.e. point measurements or data available at country
level)
– 25+ fixed lake and catchment variables
• .. will cover:
– 40+ years of data
– monthly, yearly or 5-yearly intervals
• .. will be delivered in two complementary formats:
– Microsoft Access
– GIS
Catchment drivers | Summary
.. our aim is to release this to the wider
community in due course….

21. Thank you
m.e.j.cutler@dundee.ac.uk