A national-scale GIS-based system for modelling impacts of land use on water quality
Graphical abstract
Introduction
Intensification of agricultural land use threatens water quality in New Zealand (Davies-Colley, 2013, McDowell and Laurenson, 2014, McDowell and Wilcock, 2008) as in other parts of the world. This has led to the development of policy and legislative tools such as the Total Maximum Daily Load (TMDL) requirements in the United States Clean Water Act, the European Union Water Framework Directive (European Parliament and Council of the European Union, 2000) and the New Zealand National Policy Statement for Freshwater Management (NPS-FM; New Zealand Government, 2014). Such policies call for predictions of water quality under scenarios of future land use and associated management practices, which modelling can provide.
Predictive water quality models at a range of spatial and temporal scales and complexity are available for management purposes (Elliott et al., 2014c, Shoemaker et al., 2005, Yang and Wang, 2010). In New Zealand, central government agencies promoted the development of the Catchment Land Use for Environmental Sustainability model (CLUES) to bring together selected existing model components and public data sources within a geographic information system (GIS) platform to predict water quality and economic implications of land use at spatial extents ranging from small catchment (∼10 km2) to national (268,000 km2) scale (Woods et al., 2006). The CLUES framework consists of a geo-database containing model inputs and outputs; a user interface for river reach selection, scenario creation, run control, and output reporting options; a suite of models; and reporting and display tools. These components mean that CLUES can be described as a spatial decision support system (SDSS; Densham, 1991, Geertman and Stillwell, 2003, van Delden et al., 2011). The use of SDSSs for environmental management has been discussed by Argent et al. (2009) with regard to water quality modelling and by van Delden et al. (2011) with regard to policy support.
The development of CLUES has been led by NIWA (National Institute for Water and Atmospheric Research) with support from Landcare Research, AgResearch and Plant and Food Research, and Harris Consulting for the Ministry for Primary Industries (MPI). The intended users were catchment modellers in universities, research institutes and regional and central government charged with freshwater management, especially at a planning level, although there has subsequently been some use by environmental consultants.
A steady-state annual time-step modelling approach was adopted for CLUES, in contrast to dynamic time-stepping models such as the Soil and Water Assessment Tool (SWAT; Neitsch et al., 2009) or Source Catchments (Dutta et al., 2013). The dynamic models are more suited for problems where the timing of inputs to receiving water bodies is critical, but they require considerable model setup time and computational effort. The steady-state approach in CLUES has the advantage of providing rapid model setup and scenario evaluation, whilst allowing the identification of the main contaminant source areas within a catchment and tracking of accumulation through the catchment. Similarly, CLUES is primarily of an empirical/statistical nature, whereas other models such as SWAT have more of a physical basis, requiring entry of a large number of model parameters and intensive calibration compared with CLUES.
Simplified versions of two key existing farm-scale models are used in CLUES in conjunction with a catchment-scale model for the estimation of mean annual catchment loads of nutrients (i.e., total nitrogen, TN and total phosphorus, TP), the microbial indicator E. coli, and sediment. A further model component was added to indicate the socio-economic implications of rural land use and farm management. More recently, the modelling approach has been extended to include an estuary mixing model component due to the interest in effects in marine receiving environments. In this paper we emphasise the freshwater quality aspects of CLUES, with only introductory mention of the socio-economic and estuary components.
The use of visualisation and mapping tools within the GIS platform (ArcGIS, ESRI, Redlands California) was also considered important to enable communication of spatial information with decision-makers in both policy and stakeholder catchment deliberation processes. The GIS platform enables users to display their own geospatial data, create land use and farm practice scenarios and conduct further geo-spatial analyses of input and output data. A full interoperable modelling system was not necessary as there were only a small number of models and datasets and no immediate requirement for extension or re-usability.
Other systems that couple existing models in a framework or GIS system include: eWater Source, which implements various catchment model components within an extensible interoperable modelling environment (Welsh et al., 2013); BASINS (Better Assessment Science Integrating point & Non-point Sources), which couples various models with a desktop GIS MapWindow (https://www.epa.gov/exposure-assessment-models/basins); the PIT (Phosphorus Indications Tool) model, which uses core GIS functions (Liu et al., 2014); and a web-based implementation of predictive aspects of the SPARROW (SPAtially Referenced Regression On Watershed attributes) model (Booth et al., 2011).
CLUES differs from previous integrated modelling efforts in that it combines a number of features including: a) national-scale coverage with default datasets such as a drainage network, land use and soils information along with pre-determined parameters calibrated to national data; b) integration of a farm-scale nutrient loss model to estimate nutrient losses rather than using statistical export coefficient approaches; c) inclusion of contaminant decay in streams and lakes; d) integration with an estuarine mixing model; e) a user interface to enable rapid scenario setup and model running; f) integration with an economic and social integration component; and g) inclusion of multiple contaminants.
While the set-up of CLUES is specific to New Zealand, many of the concepts and implementation details are applicable internationally. This paper summarises the individual components of CLUES and the modelling environment and user interface, with reference to more detailed documentation where appropriate. The paper also evaluates the model against the criteria put forward by Bennett et al. (2013). Finally, the paper presents some examples of how the model has been used in a variety of planning applications, along with discussion of areas for model improvement and extension. Evaluation of CLUES in relation to needs of the National Policy Statement on Freshwater Management has been presented in Semadeni-Davies et al. (2014).
Section snippets
Description of CLUES
The structure of the CLUES framework showing the component parts is illustrated in Fig. 1. These components are discussed in this section.
CLUES software is written primarily in VB.Net, with some FORTRAN code for selected geospatial processes and engine components. All the input data required to run CLUES are provided with the software. These data are summarised in Section 2.1. The CLUES modelling suite contains three catchment model components for estimating stream water quality: OVERSEER®
Model application and evaluation
In this section, we evaluate CLUES using quantitative metrics, qualitative evaluation criteria based on Bennett et al. (2013), and also taking account of other literature on model evaluation (Inman et al., 2011, Kloprogge et al., 2011, McIntosh et al., 2011). Lessons are also drawn from model applications, CLUES is compared with other models of a similar class (budget-based models within a SDSS framework) and opportunities for model improvement are summarised.
Conclusions
The design and implementation of CLUES, a model for predicting water quality at national scale and reach resolution as a loosely coupled component within a GIS, has been demonstrated in this paper. The model meets most of the original needs identified when the model was released initially in 2005, mainly as a rapid quantitative tool for assessment of land use and land management options for water quality (TN, TP, sediment and E. coli) at mean annual timescales. The simple modelling approaches
Software availability
Software is available for free download from ftp://ftp.niwa.co.nz/clues/. The ArcGIS GIS v. 10.3.1 package (ESRI) and a Microsoft Windows operating system is required. A user manual is also available (Semadeni-Davies et al., 2016).
Acknowledgment
This paper was prepared with funding from the Ministry of Business, Innovation and Employment contract C10X1006. We also wish to acknowledge funding for CLUES from the Ministry of Business, Innovation and Employment (contract C10X1006 and predecessors, Envirolink Fund, Crown Research Institute core funding) the Ministry for the Environment and Ministry for Primary Industries.
References (79)
- et al.
A new approach to water quality modelling and environmental decision support systems
Environ. Model. Softw.
(2009) - et al.
Characterising performance of environmental models
Environ. Model. Softw.
(2013) - et al.
Escherichia coli survival in waters: temperature dependence
Water Res.
(2013) A flexible framework for environmental policy assessment at the catchment level
Comput. Electron. Agric.
(2015)- et al.
A new river system modelling tool for sustainable operational management of water resources
J. Environ. Manag.
(2013) - et al.
Development of a New Zealand SedNet model for assessment of catchment-wide soil-conservation works
Geomorphology
(2016) - et al.
A box model of the seasonal exchange and mixing in regions of restricted exchange: application to two contrasting Scottish inlets
Environ. Model. Softw.
(2013) - et al.
A geospatial framework to support integrated biogeochemical modelling in the United Kingdom
Environ. Model. Softw.
(2015) - et al.
A controlling factor approach to estuary classification
Ocean Coast. Manag.
(2007) - et al.
Perceived effectiveness of environmental decision support systems in participatory planning: evidence from small groups of end-users
Environ. Model. Softw.
(2011)