This case study provides an example of four applications of the Land Utilisation and Capability Indicator (LUCI) in water quality modelling to the Tuapaka catchment, a largely agricultural area located to the east of Palmerston North.
Each application uses increasingly detailed input data and the results are compared to OVERSEERTM predictions, as well as instream nitrogen (N) and phosphorus (P) measurements.
LUCI is applied in this case study to help address the growing need for New Zealand farmers and land managers to reduce nutrient run off leaving their land into surrounding waterways. This change in land use is in response to recent changes to legislation which aim to improve water quality throughout New Zealand.
The LUCI model is well suited to explore options in this setting as it is a GIS framework that considers the impacts of land use on multiple ecosystem services in a holistic and spatially explicit manner. The fact that LUCI was able to model at the resolution of the underlying input DEM meant that its application could both be at a catchment scale as well as providing detailed information for individual farms.
The four modelling applications to the catchment used increasing detail and catchment specific input data with each subsequent application becoming being more comprehensive than its former;
LUCI water quality modelling provided maps and data for both water quality instream as well on land nutrient loads (N&P). Outputs are presented as a table and series of maps which provided comparisons between each scenario of increasing detail as well as to the OVERSEERtm model and measured results. Map outputs included; nitrogen loads, accumulated nitrogen, phosphorus loads & accumulated phosphorus.
As can be seen in the map outputs, the quality of input data has a very noticeable effect on the quality and resolution of the outputs that LUCI can produce. The addition of actual farm nutrient inputs as well as soil classifications, as in applications 3 & 4, produced more detailed representations of nutrient levels and pathways. These results highlighted where opportunities exist to implement mitigation strategies to intercept nutrients before they enter local waterways.
Accumulated nitrogen load maps from LUCI Application 1 (3a), Application 2 (3b), Application 3 (3c) and Application 4 (3d)
Phosphorus load maps from LUCI Application 1 (4a), Application 2 (4b), Application 3 (4c) and Application 4 (4d)
Modelling showed that the highest P loads were sourced from steeper pastoral grassland while the lowest loads in the catchment came from forested land and flatter land in the upper sections of the catchment. Modelled results were significantly different to measured data indicating room for refinement in the modelling process.
LUCI modelled results were however very similar to prior OVERSEERtm results considering applications 1&2. Notable differences between the two were modelled in applications 3&4 suggesting that additional detail used in these LUCI scenarios may refine the modelling significantly.
Measured and modelled specific load for the Tuapaka catchment. Note for Row 1 mean is presented with range in brackets.
This case study using LUCI in the Tuapaka catchment highlighted the effect of data resolution and detail on N and P exports. This application has shown that using data specific to a catchment or farm is preferable when using LUCI if the data is available.
While LUCI was comparable to existing models in terms of outputs, clear differences still exist between measured nutrient losses and LUCI predictions at the catchment scale. Improved understanding of nutrient attenuation in the catchment is likely to improve the accuracy of LUCI predictions.
Further modelling improvements as shown in this case study include the addition of detailed soil data (Application 3). This had a clear impact on sources and loads of P within the catchment. Detailed farm input data also decreased loads for both N and P. These differences indicate that using data specific to a catchment or farm is preferable for use in LUCI. Additionally, this highlights the ease with which actual and specific data can be incorporated for use in LUCI.
Trade-offs between outputs for the individual ecosystem services are modelled and mapped, with an option for the user to preferentially weight services of higher importance.
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