Modelling, GIS and remote sensing

Transcription

1 Modelling, GIS and remote sensing Part 1 Integrated catchment modelling 05 November 2008 Andrew Wade (a.j.wade@reading.ac.uk) Department of Geography The University of Reading, UK University of Reading 2008

2 Introduction Principles Issues and complexity Water resource management Modelling approach Examples INCA N Inclusion of remote sensing data Farming Effluent Climate change Limitations of models Summary

3 Why model? Barnsley 2008, Environmental Modelling, Chapter 1 Building a well designed model forces one to examine the Component elements of an environmental system Processes and structures that govern the relationships and interactions between them Spatial and temporal scales over which the processes and structures operate Scenario analysis for management 3

4 Definitions Spatial Scale Plot: 1 m 2 Small scale, research: 10 km 2 Meso scale: 100 km 2 Large scale: km 2 Regional scale: km 2 National scale: km 2 (UK based definitions)

5 Nitrogen Issues Loch Muick, Aberdeenshire Steinkreuz, southern Germany Haygarth PM, Jarvis, SC Little Ouse, Norfolk Neal et al Neal et al. 2003

6 Variability River Dee, Aberdeenshire

7 Newson MD Land, Water and Development: River basin systems and their sustainable management Routledge, 2 nd Edition, pp 351

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9 National Scale Problem km 1 m Equifinality point

10 N Models: Upland MAGIC and MERLIN Advantages Acidification Dynamic Process representation Disadvantages High data requirements Upland systems MAGIC has simplistic N component

11 N models: Agricultural SUNDIAL Advantages Dynamic Simulate hydrological and chemical processes Disadvantages High data requirements Field scale only

12 N Models: Empirical Export Co efficient Advantages Low computational requirement Easy to apply at different spatial scales Disadvantages No hydrological or chemical process representation

13 N models: In stream QUASAR Advantages Dynamic In stream processes and pollutant routing Disadvantages In stream only

14 What is INCA? Integrated Catchment model N dynamics in complex river systems Terrestrial In stream Point and diffuse Process based, mass balance Daily INCA screen shot Simulates NO 3, NH 4 Wade AJ, Durand P et al Whitehead et al. 1998

15 GIS Database INCA

16 How does the model work?

17 Land Cell: Hydrological Model P AET Soil Quick Quick flow Throughflow Groundwater Groundwater flow

18 Ammonium + Nitrate fertiliser Ammonium + Nitrate deposition Nitrogen Fixation Urban waste to River Nitrate Addition Plant uptake Ammonium Addition Plant uptake denitrification nitrification NO 3 NH 4 Net mineralisation Organic N Reactive Soil Zone Leaching to river Leaching to river NO 3 NH 4 Groundwater Zone

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20 P AET Land Cell: Hydrological Model Soil q soil Quick Quick flow, q quick q soil Groundwater Throughflow, (1 )q soil Groundwater flow, q gw dq quick dt dq dt soil q = α = p eff soil RT RT soil q quick q soil quick dq dt gw q = β soil RT gw q gw

21 Ammonium + Nitrate fertiliser Ammonium + Nitrate deposition Nitrogen Fixation Urban waste to River Nitrate Plant Ammonium Plant Addition uptake Addition uptake denitrification nitrification Net mineralisation NO 3 NH 4 Organic N Reactive Soil Zone Leaching to river Leaching to river dno3 + C dt 4 S 1 soil nitrification = NH 4 10 Vol NO 3 Input C 1 S 1 q NH 4 Groundwater Zone soil. NO3 Vol NO C Vol denitrification soil Output C 3 S 1 S 2 Non biological fixation Plant Uptake NO3 Vol soil 10 6

22 Temperature dependencies Rate coefficients are temperature dependent where θ s = soil temperature C n = C n ( θ 20 ) θ = θ where θ a = air temperature s a C 16 sin 3 dayno. π C 16 is the maximum temperature difference between winter and summer (Green and Harding, 1979)

23 reach instream in instream RT q q dt dq = b reach reach aq L RT = Vol q NH C Vol q NO C Vol NO q Input dt dno instream instream instream instream ireach instream instream stream instream =

24 Lambourn Upper Kennet Catchment Sewage Treatment Works River Kennet at Mildenhall

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29 Atmospheric N Deposition Model MATADOR N (National Power plc) + Deposition Velocities (David Fowler, ITE) + Land Use = Atmospheric N Deposition

30 Total Nitrogen Deposition Kg ha 1 yr

31 Calibration

32 East Shefford Boxford Shaw

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34 Demo

35 Advantages and disadvantages of the INCA N approach Advantages Process based, dynamic, spatial variability Catchment scale, range of issues Terrestrial and in stream Data readily available User interface Reach and land use based output Quick to run Links to aquatic biology (INCA P) European wide use by research groups Common structure for variants (Sed, N, P, C, Hg, Mine)

36 Advantages and disadvantages of the INCA N approach Disadvantages Point sources on tributaries Fully distributed routing Relatively data intensive GIS and modelling skills Equifinality

37 Summary Range of nutrients (nitrogen) issues Complexity of catchments Need for integrated modelling Example of INCA N Demo Scenarios Next Uncertainty What makes uncertainty? How can remote sensing and GIS help?