Mahrita Harahap has a background in Mathematics, Finance and Statistics and is currently undertaking a PhD in the Ecological Modelling Research Group at the School of Life Sciences. Mahrita is an accredited statistician and a researcher in applied statistics and collaborates widely with colleagues in other disciplines to analyse, model and make sense of the data collected.
Mahrita completed a Bachelor of Mathematics and Finance (Honours) at UTS with a thesis on "Pricing Barrier Options using the Eigenfunction Expansion Approach". She also completed a Master of Statistics at UNSW with a thesis on "Statistical modelling of extreme rainfall in South-West Western Australia using Extreme Value Theory and Spatial Analysis".
Her current PhD project is about pattern identification on the drivers in carbon flux and water flux data at Australian savannas over multiple spatial and time scales.
Mahrita is a member of:
- The Statistical Society of Australia (SSAI)
- Australian Mathematical Society (AMS)
- Terrestrial Environment Research Network (TERN)
- Australian Mathematical Science Institute (AMSI)
- American Statistical Association (ASA)
Project Title: Pattern identification of the interactive impacts of climatic variables on the coupling of carbon and water fluxes at Australian savannas
Supervisors: Prof. Qiang Yu, (University of Technology, Sydney)
Co supervisor: Prof. Jason Beringer, (University of Western Australia)
In light of recent concerns of climate change, it is important to conserve natural ecosystems with vast spatial extent, such as savannas, that contribute to binding the increasing additional carbon dioxide in our atmosphere due to anthropogenic activities, into the future. They account for approximately 25% global productivity (GPP), this makes them a key terrestrial biome. Therefore, there is a current need to improve the land surface models that quantify carbon capture and storage capacity to provide an improvement to savanna productivity estimates.
These models of primary production in terrestrial ecosystems cannot rely on a single limiting factor but instead must consider multiple potentially limiting coupled processes on multiple time and spatial scales. Spatial heterogeneity assures that exchange processes and concentration gradients commonly exhibit a complex three-dimensional structure. Driving mechanisms of climate dynamics and landscape evolution operate on a variety of time scales from diurnal to season to yearly but instrumental measurements do not capture the full range of time scale variability and so the models have limitations in the uncertainty of climate representations in the data. The lack of flux data and the failure of models to capture and account for the spatial, temporal and multiple timescale effects of this land-surface variability on atmospheric exchanges are recognised as a major source of uncertainty in the current land surface models.
We now live in a data driven technological advanced society where we can extract meaningful insights from data with the proliferation of the speed of computers and ability of storing large datasets. We apply these machine learning techniques to seven OzFlux sites on a spatial sub-continental scale rainfall gradient of more than 1100km in length, known as the North Australian Tropical Transect (NATT) in Australia to understand the vegetation dynamics and patterns.
Hopefully the models will provide robust future estimates of carbon and water that will aid our management of savannas to ensure viable water resources and carbon sequestration, to provide tools for land managers to account for the consequences of climate change and elevated atmospheric CO2 concentrations on ecosystem viability and hopefully inform the policy decision makers on the importance on conserving the savannas natural ecosystem. As this project deals with the environmental sustainability of Australian ecosystems, it will be of considerable social benefit for Australia.
Mahrita Harahap is also interested in extreme value modelling, statistical modelling of carbon capture & sequestration methods and renewable energy technologies that will help mitigate against climate change.
- Technology Research Methods
- Data Algorithms and Meaning
- Statistical Thinking for Data Science
- Statistical Methods
- Introduction To Data Analysis
- Business Statistics
- Linear Dynamical Systems
- Statistical Design Analysis
- Regression Analysis
- Introduction To Statistics
- Mathematical Modelling 2
- Statistics and Mathematics for Science
- Foundation Mathematics
- UTS Maths Study Centre Tutor
He, L, Li, J, Harahap, M & Yu, Q 2018, 'Scale-Specific Controller of Carbon and Water Exchanges Over Wheat Field Identified by Ensemble Empirical Mode Decomposition', International Journal of Plant Production, vol. 12, no. 1, pp. 43-52.View/Download from: UTS OPUS or Publisher's site
© 2017, Springer International Publishing AG, part of Springer Nature. The exchange of carbon and water in the ecosystem is influenced not only by weather and climatic perturbations but also by vegetation dynamics. The relationship between carbon and water exchange and environment in agro-ecosystem across different temporal scales is not very often been quantified. Spectral analysis of eddy covariance measurements can identify the interactions between environmental and biological factors at multi-temporal scales. Here, we used a new method, ensemble empirical mode decomposition (EEMD), to study the temporal covariance between ecosystem exchange of carbon dioxide (NEE), latent heat flux (LE) and environmental factors in a winter wheat cropping system located at the North China Plain. The results showed that the NEE, LE and environmental factors can be decomposed into 12 significant quasi-period oscillations on various time-scales i.e. hourly, diurnal, weekly and seasonal timescales. Variance of NEE in diurnal, hourly, seasonal, weekly scale was 58.9, 29.6, 4.7, 0.6%, respectively. Variance of LE in diurnal, hourly, seasonal, weekly scale was 55.2, 15.5, 5.1, 1.8%, respectively. The largest of variance contribution is at diurnal time-scale from net radiation (Rn), wind speed (μ) and vapor pressure deficit (VPD) due to daily rhythms in solar radiation. The soil water content varied significantly at a relatively longer time-scale i.e. weekly and seasonal scale. Large variance contribution of ambient temperature (T) (63.4%) and VPD (33.6%) is in trend term due to the significant increasing seasonal trend from winter to summer. The correlation analysis indicated that NEE and LE was correlated highly with net radiation (Rn) at all time-scale, as well as with VPD, ambient temperature (T), and wind speed (μ) in diurnal scale and with soil water in seasonal time-scales. This implied that solar radiation contributed the main variation of carbon and water in short time-scale...
Li, Y, Shi, H, Zhou, L, Eamus, D, Huete, A, Li, L, Cleverly, J, Hu, Z, Harahap, M, Yu, Q, He, L & Wang, S 2018, 'Disentangling Climate and LAI Effects on Seasonal Variability in Water Use Efficiency Across Terrestrial Ecosystems in China', Journal of Geophysical Research: Biogeosciences, vol. 123, no. 8, pp. 2429-2443.View/Download from: UTS OPUS or Publisher's site
©2018. American Geophysical Union. All Rights Reserved. Water use efficiency (WUE), the ratio of gross primary productivity (GPP) over evapotranspiration (ET), is a critical ecosystem function. However, it is difficult to distinguish the individual effects of climatic variables and leaf area index (LAI) on WUE, mainly due to the high collinearity among these factors. Here we proposed a partial least squares regression-based sensitivity algorithm to confront the issue, which was first verified at seven ChinaFlux sites and then applied across China. The results showed that across all biomes in China, monthly GPP (0.42–0.65), ET (0.33–0.56), and WUE (0.01–0.31) showed positive sensitivities to air temperature, particularly in croplands in northeast China and forests in southwest China. Radiation exerted stronger effects on ET (0.55–0.78) than GPP (0.19–0.65), resulting in negative responses (−0.44 to 0.04) of WUE to increased radiation among most biomes. Increasing precipitation stimulated both GPP (0.06–0.17) and ET (0.05–0.12) at the biome level, but spatially negative effects of excessive precipitation were also found in some grasslands. Both monthly GPP (−0.01 to 0.29) and ET (0.02–0.12) showed weak or moderate responses to vapor pressure deficit among biomes, resulting in weak response of monthly WUE to vapor pressure deficit (−0.04 to 0.08). LAI showed positive effects on GPP (0.18–0.60), ET (0–0.23), and WUE (0.13–0.42) across biomes, particularly on WUE in grasslands (0.42 ± 0.30). Our results highlighted the importance of LAI in influencing WUE against climatic variables. Furthermore, the sensitivity algorithm can be used to inform the design of manipulative experiments and compare with factorial simulations for discerning effects of various variables on ecosystem functions.
Razavy, S, Gadau, M, Zhang, SP, Wang, FC, Bangrazi, S, Berle, C, Harahap, M, Li, T, Li, WH & Zaslawski, C 2017, 'Investigation of the Phenomenon of Propagated Sensation along the Channels in the Upper Limb Following Administration of Acupuncture and Mock Laser', JAMS Journal of Acupuncture and Meridian Studies, vol. 10, no. 5, pp. 307-316.View/Download from: UTS OPUS or Publisher's site
© 2017 Background Similar to De Qi psychophysical responses, propagated sensation along the channels (PSC) is considered an important phenomenon in traditional Chinese acupuncture. In acupuncture clinical trials, different acupuncture manipulation techniques are used to enhance the propagation of sensation along the channels to facilitate an optimum therapeutic result. Aim To examine and compare the PSC reported by participants in a clinical trial following the administration of acupuncture and inactive mock laser. Methods The study was embedded in a two-arm parallel design multicenter, randomized clinical trial, the Tennis Elbow Acupuncture—International Study—China, Hong Kong, Australia, Italy (TEA IS CHAI). Needle sensations were measured using a validated instrument, the Massachusetts General Hospital Acupuncture Sensation Spreading Scale. Ninety-six participants with lateral elbow pain were randomly allocated into two groups in a 1:1 ratio; the acupuncture treatment group (n = 47) and the mock laser control group (n = 49). Participants in both groups received the intervention at two acupoints, LI10 and LI11, consisting of 2 minutes of either standardized needle manipulation or mock laser at each acupoint with a rest period between each intervention period. Data were collected immediately following the interventions at the first and the ninth session within the clinical trial. Results Although participants in both groups perceived PSC radiating to similar sites along the upper limb, the frequency of the reported radiation sites among the two intervention groups for both radiation up the limb (p < 0.05) and radiation down the limb (p < 0.001) were statistically significantly different. Among the radiating sensation sites recorded within the two study groups, the sensations were reported as radiating a greater distance down the forearm to the wrist compared to up the arm. Evaluation of PSC across the four study sites revealed a statistically significant differe...
Razavy, S, Gadau, M, Zhang, SP, Wang, FC, Bangrazi, S, Berle, C, Harahap, M, Li, T, Li, WH & Zaslawski, C 2017, 'Psychophysical responses in patients receiving a mock laser within context of an acupuncture clinical trial: an interoceptive perspective', BMC COMPLEMENTARY AND ALTERNATIVE MEDICINE, vol. 17.View/Download from: UTS OPUS or Publisher's site