CLUES - Catchment Land Use for Environmental Sustainability
MAF in association with Ministry for the Environment has been working with NIWA and their subcontractors to develop a modelling system to assess the effects of land-use changes on water quality and socio-economic indicators.
CLUES models nitrogen and phosphorus loads in streams in specific locations under different land-use scenarios. Links to socio-economic models mean that the effects of a large-scale change in land-use, say from grazing livestock to viticulture, on local communities can also be predicted.
The CLUES project includes creating national maps of land use, soils, and pollution risk, plus extensive databases predicting nitrogen leaching for many combinations of crop, fertiliser, climate, and soils. Land-use types which can be analysed include arable, horticulture, forestry, and several sheep, beef, dairy, and deer farming variations.
The initial impetus for CLUES' development came from MAF, who wanted 'what if' scenarios to be modelled at large scales. A number of existing modelling and mapping procedures, developed by various research organisations, have been amalgamated to produce CLUES. The first stage of the work which is reported here was funded by the Foundation for Research, Science and Technology (FRST) as part of the Cross Departmental Research Pool.
- Predicting the effects of landuse on water quality - Stage 1
NIWA Client report for project MAF04501 to Ministry of Agriculture and Forestry, August 2004.
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- Predicting the effects of land-use on water quality - Stage II
NIWA Client report for project MAF5502 to Ministry of Agriculture and Forestry, June 2006.
PDF Download [4.8M PDF]
- Tutorial manual for CLUES 1.4
NIWA Client report for project MAF07205 to Ministry of Agriculture and Forestry, November 2007
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The next phase of the project, funded by FRST's EnviroLink (applied environmental research on behalf of regional councils) and Pastoral 21 (remote monitoring and assessment of pasture growth) will involve making the system more suitable for use by regional agencies - for example, by incorporating seasonality, predicted microbe and sediment changes in water courses, and local calibrations of the underlying water quality prediction model.
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