We measured the tree transpiration of 9-year-old, Eucalyptus grandis x Eucalyptus urophylla clonal hybrid (GU) trees in the commercial forestry area of northern KwaZulu-Natal, South Africa. Transpiration was measured using the heat ratio method over two consecutive hydrological years (2019/20 and 2020/21) and up-scaled to a stand level. Leaf area index (LAI), quadratic mean diameter, and soil water content (SWC) were measured over the same period using an LAI 2200 plant canopy analyser, manual dendrometers and CS616 sensors, respectively. The depth to groundwater was estimated to be approx. 28 m, using a borehole next to our study site. Results showed that transpiration followed a seasonal pattern, with daily mean of 2.3 mm-tree-1-day-1 (range: 0.18 to 4.55 mm-tree-1-day-1) and 3.3 mm-tree-1-day-1 (range: 0.06 to 6.6 mm-tree-1-day-1) for 2019/20 and 2020/21, respectively. Annual GU transpiration was higher than that found by international studies under similar conditions, but was within the same transpiration range as Eucalyptus genotypes in the KwaMbonambi area. Plantation water productivity, calculated as a ratio of stand volume to transpiration, was higher than for other published studies, which was attributed to a very high productive potential of the study site. Multiple regression using the random forests predictive model indicated that solar radiation, SWC and air temperature highly influence transpiration. There is a high possibility that our GU tree rooting system extracted water in the unsaturated zone during the dry season. Due to the use of short-term results in this study, the impact of GU on water resources could not be quantified; however, previous long-term paired catchment studies in South Africa concluded that Eucalyptus has a negative impact on water resources. Further research is suggested with long-term measurements of transpiration and total evaporation and an isotope study to confirm the use of water by GU trees in the unsaturated zone.

ABSTRACT Background: The productivity of the coastal Zululand region, which was known as the South African breadbasket for fibre is declining. Climate-related changes are a significant factor contributing to this decline. The 3PG (Physiological Processes Predicting Growth) model was calibrated for E. grandis x E. urophylla hybrids planted in this region to quantify the effect of climate variation x site on their growth and survival. Monthly weather data for the ungauged plantations were estimated using the Random Forest (RF) supervised learning algorithm. A dataset consisting of 17 permanent sample plots (PSPs) and published parameter values for this hybrid in various regions of Brazil were utilized for parameter estimation. Using a parsimonious optimization approach, we developed a novel method called extended Root Mean Square Error (eRMSE) to select the optimal parameter set. Result: The new parameter set yielded accurate predictions for three key variables; quadratic stem diameter (R2 = 0.85, E = 0.73), mean height (R2 = 0.84, E = 0.78), and basal area (R2 = 0.87, E = 0.78). Model performance at 15 independent sites allowed the comparison with three other Brazilian parameter sets for stand volume prediction at a specific age. The optimized parameter set provided a satisfactory, albeit slightly overestimated stand volume (V (m3ha-1), R2 = 0.65, E = -0.32) at the validation sites. Conclusion: The 3PG model can be adapted with parameter set from another region to characterize the growth of E.grandis x E.urophylla stands in South Africa.