Our local community engagement continued and expanded in 2021-2022 with in-person and online workshops where we shared knowledge and answered questions on a number of areas including land use, forestry, carbon forests, water, and government policy. We determined and described the different components (e.g., actors, resources, and drivers or dynamics processes of change) of the local socio-hydrological system.
We now have a fully functional, integrated biophysical hydrological model specifically for New Zealand’s planted forests. CABALA-W was recoded into a common computer language, C# so it can be easily used on multiple platforms and provide “longevity” to the model. A GitHUB platform was created to provide programme accessibility to the public at the end of the programme. CABALA-W was successfully integrated with a hillslope model. The integrated model provides, for the first time, a ‘fit-for-purpose” model for forest hydrology for New Zealand’s planted forests that can simulate water use, water storage and release. Once the calibration and validation is completed, the integrated model can be used to predict water yield from existing and new forests, test tradeoffs between planted forests and water yield, and test the potential impact of climate change. The model will be an important tool for engagement with local communities and for the development of a decision-making framework for end-users and next-users.
We achieved the first flight campaign over our primary research sites deploying the SlimSAR L- and P-Band multi-frequency polarimetric synthetic aperture radar (SAR). The SlimSAR system can measure soil moisture at a fine resolution to a soil depth of up to 150 cm. This is the first time it has been deployed over forests with steep and complex topography. Analysis of test data shows we have good calibration data for the SlimSAR system for New Zealand and preliminary results demonstrating that the raw SAR data can be transformed into soil moisture data. SlimSAR will provide revolutionary new detailed insights of key hydrological process both for planted forests, as well as other land-uses.
Real time terrestrial data from the research sites first came online in October 2021. Scion’s FlowLab and NIWA’s LoRA wireless sensor networks are fully operational at all sites. Both networks use New Zealand designed and built technology and collect over 360,000 observations per day from 1,717 instruments across seven sites. This big data is managed in the cloud with the Kafka Big Data pipeline. This approach will greatly simplify the data analysis phase for researchers and has sped up analysis and outputs compared to traditional data management techniques. This innovative approach will provide new insights into planted-forest hydrological processes faster than traditional analysis approaches. If successful, this could provide a template for New Zealand researchers for collecting and analysing real time big data for other environments.