WINETECH Technical Yearbook 2021

3. The appl ication of regional climate surface interpolation improves the accuracy and processing time. The regional interpolat ion method development for surface temperature was a major milestone in 2020 (see story map https://arcg.is/1q0mTn0). Regionality research was undertaken by delineating and interpolat ing the temperature surfaces on subsections (wine regions) of the Western Cape, resulting in an improved understanding of the topographic differences driving temperature changes. In some areas temperature is strongly affected by covariates, such as distance to coast, elevation, solar radiation, etcetera. The regional interpolation improves the final merged output by allowing the incorporation of locally-tailored covariate relationships per region, compared to applying the same, generalised covariate relationships to all regions (figure 2). The regionality results has allowed for the study area to be expanded to cover 33% of the Western Cape and 97.9% of wine grape vineyards (figure 3). The research results rendered improved accuracies, faster run times, scalable methodology and valuable recommendations for future weather station density and region size (figure 4) for more accurate temperature FIGURE 1. Examples of the dashbo rd and reporting functionality, top and bottom right temperature displays at field and regional level. Bottom left is an example of climatic variables in reporting documentations: (A) Mean daily temperature for multiple seasons (2017 - 2019), (B) mean, minimum and maximum monthly average temperature for the period 2016 - 2019, and (C) example of mean, minimum and maximum daily temperature.

FIGURE 2. A Visual comparison of final temperature surface layers using the regional interpolation (A) compared to standard interpolation without regionalisation (B). The image on the left (A) captures the regional variation with greater accuracy (more very red and very blue areas), compared to the over-generalised output of the standard interpolation (B). FIGURE 2. A Visual comparison of final temperature surface layers using the regional interpolation (A) compared to standard interpolation without regionalisation (B). The image on the left (A) captures the regional variation with greater accuracy (more very red and very blue areas), compared to the over-gen ralised output of the standard interpolation (B).

layers in the future. The spatial distribution of accuracy per region is described in figure 4a, areas in red are greater concern than areas in green. Certain regions identified as “error hotspots” as they have higher RMSE values, i.e. region 1 (Gansbaai) and region 3 (Constantia), highlighting the need for more weather stations to improve accuracy.

FIGURE 3. Shows the current TerraClim study area (left) and updated TerraClim extent (right) as an outcome from the regionality study, the new area covers 33% of the Western Cape and 97.9% of wine grape vineyards.

n the report. The continued aim of the report uide the aid user interpre ation of the graphs the context of climate change and industry bsite www.terraclim.co.za is currently in the mproving continuously as we acquire new user ara@sun.ac.za for feedback).

WINETECH TECHNICAL YEARBOOK 2021 | 13

TION OF REGIONAL CLIMATE ERPOLATION IMPROVES THE