Abstract. Increasing attention is turning to moderating the impact of human activity on the environment, both to preserve the intrinsic value of ecosystems and species for their own sake, and to protect the benefits we derive from nature for future generations. Internationally, various regulations and policies are in place or in development to improve our stewardship of the environment and develop more sustainable and resilient management practices. However, policies formulated at national or regional scales are not always suited to enacting targeted and cost-effective approaches at the local scale due to geoclimatic, topographical, or management constraints. The direct monitoring of the local and upstream impacts of every management unit to determine their net impact is a costly practice, thus emphasising the need for modelling approaches to complement limited on-ground measurements. This paper describes and demonstrates tools (LUCI-EntEx v1.0) that automatically identify the fluvial and terrestrial flow of water in and out of a study area, such as a river that enters a farm that is impacted by upstream management, or terrestrial flow coming from neighbouring property. By identifying the stream entry/exit points, the net impact of land management within the study area can be more easily quantified based on the contribution of neighbouring and upstream areas, aiding in the decision-making process. This algorithm also facilitates the identification of inconsistencies in data such as differences between the legal/official catchment boundaries and the hydrological boundaries determined by the representation of terrain and river networks. If such inconsistencies are not resolved, they can cause further error propagation in later stages of the modelling process. Four case studies of New Zealand management units – two at the farm scale and two at the catchment scale – demonstrate the algorithm's utility in determining fluvial and terrestrial entry/exit points and highlighting potential data inconsistencies. The farm case studies also use the Land Utilisation and Capability Indicator (LUCI) framework to demonstrate how this algorithm can be embedded in other models for further value: in this case, we show its potential to improve predictions and enhance management of nutrients and sediment.
This preprint has been withdrawn.
Received: 04 Aug 2020 – Discussion started: 23 Nov 2020
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There is an increasing will to preserve nature for its own sake and to protect its benefits for future generations. Various policies encourage more sustainable land management practices to protect rivers and lakes. Separating out broad scale from local impacts is difficult, but necessary for informed land management outcomes. We present tools automatically identifying flows of water, sediment and chemicals in and out of farms, forestry blocks, etc to enable smarter future management.
There is an increasing will to preserve nature for its own sake and to protect its benefits for...
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