Nature based solutions & mitigation measures

Ability of models to simulate effects of mitigation measures

When mathematical modeling is chosen as a basic tool for a certain study / purpose, we need to select the most appropriate model(s) based on the goals of the study, data availability, modeling expertise and other conditions (Waveren et al., 1999; Farkas and Hagyó, 2010). The outcome of a particular simulation-based study strongly depends on:

•  model selection 
•  the quality, resolution, and amount of the input data available
•  the expert knowledge about locally prevailing conditions
•  the validity of any assumptions that are inevitably made while parameterizing the model (Waveren et al., 1999; Farkas et al., 2016).

At NIBIO, we have developed a matrix that could be used in the future by the NIBIO modelers for model selection in projects/studies when the effect of various mitigation measures is in focus. At present, it incorporates 18 different field- and catchment- scale models and 22 in-field and out-field measures. We defined three levels of representing the measures within a model:

• The model cannot simulate the effects of the measure
• The model can indirectly simulate the effects of the measure
• The measure can be directly incorporated in the modelling system.

Relevant  project(s):

• NORRA
• OPTAIN

Parametrisation of the measures

The table above shows that (some) in-field mitigation measures (management measures) can be directly implemented in several models (e.g SWAP), by using/adopting specific parameters.  When it comes to out-field mitigation measures such as constructed wetlands, grass waterways etc, only spatially distributed models (LISEM, SWAT+ etc.) have the capacity to directly incorporate them in the model.

In addition, there are options to account for both in-field and out-field mitigation measures and estimate the effects of such measures indirectly, using expert judgments and specific parameterisation (INCA, PERSiST, SWAP).

To account for measures in modeling approaches, their parametrisation is key. It can be very intuitive in some cases, in others it may need serious adaptation in model setups. Within the OPTAIN project the guidelines for parameterisation of measures were prepared (under review)

Here one can find detailed descriptions how to include mitigation measures into SWAT+ and SWAP model. See OPTAIN project website for more information about the measures studies in the Norwegian Case Study.

Relevant project(s): 

• OPTAIN 

Location of the measures

One of the most common questions asked, when working with mitigation measures, is about the  optimal location that would lead to reaching expected efficiency of these measures. Terrain analysis, based on readily available geodata, can provide a starting point for answering these questions.

Find out more about selection of optimal location for measures in the three documents listed below:

• Presentation from OPTAIN Modeling webinar - on the left hand side of this page
• A relevant chapter of SirkVann internal report - on the left hand side of this page
• Report by Stolte and Barneveld (2020)

NOTE: The premise for all methods presented here is that the required data are readily available in Norway’s national geodata repositories, and therefore applicable at any spatial scale without additional data acquisition.

Relevant project(s): 

• OPTAIN