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3.2 IUKWC Workshop Freshwater EO - Mark Cutler - Jun17

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

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3.2 IUKWC Workshop Freshwater EO - Mark Cutler - Jun17

  1. 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. 2. 1000 GloboLakes Total GloboLakes Catchment Area (cumulative) ~ 131,180,824 sq km
  3. 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. 4. (Sorrano et al., 2009)
  5. 5. Catchment drivers of lake change
  6. 6. Catchment drivers for GloboLakes Non-climatic drivers Climatic drivers
  7. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 17. Catchment drivers | Score sheets + lake info form
  18. 18. 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
  19. 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 .. our aim is to release this to the wider community in due course….
  20. 20. Thank you m.e.j.cutler@dundee.ac.uk

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