Geological Modelling

Spatial representation of the distribution of sediments and rocks in the subsurface

Remote Sensing

Analyze and interprete terristrial remote sensing data

Carbon Cycle

Net exchange of carbon between the terrestrial biosphere and the atmosphere

Ecological Modelling

Simulating and analyzing the long-term dynamics and stability properties of complex ecological systems

Climate Change

Improve our predictive understanding of carbon balance towards the resilience of environmental change

Current and recent research highlights

Image quilting (IQ) for conditional simulation of geological textures

I have applied several analytical techniques to better understand different intricate processes within extremely diversified geological features during my post graduate research. To start with, I have developed a recent multiple point statistics (MPS) method named image quilting (IQ) for various hydrogeological applications that lead to novel and important scientific outcomes [1]. The numerical tool allows the creation of multiple realizations of a study domain, with a spatial continuity based on a TI that contains the variability, connectivity, and structural properties deemed realistic. This is valid for a range of modelling applications including representation of geological structures, Earth surface features, and spatiotemporal applications. I have also established a general methodology to integrate different measurement scales during my PhD second project, allowing a better characterization of aquifers by making the best possible use of hydrogeological data [2]. I have presented a multiscale workflow to deal with hydraulic conductivity data, which is one of the most critical and at the same time one of the most uncertain parameters in many groundwater models. To achieve this particular goal, I have used IQ as a multivariate MPS algorithm, and simplified renormalization as an upscaling technique, but the workflow is general. The method is tested on a series of synthetic examples with different connectivity properties that acts as proof-of-concept case studies.

Stochastic modelling of infiltration water heterogeneity using Lidar data

In my PhD third project, I have presented an exhaustive characterization of Golgotha Cave, South-West Western Australia, based on an extensive remote sensing measurement campaign to better manage groundwater resources in carbonate areas and improve our understanding of speleothem archives [3]. I have offered the first quantitative analysis of the morphology and spatial distribution of stalactites covering the cave ceiling surface. By performing statistical and morphological analysis of karstic features based on remote sensing images at three large chambers in the same cave system, I have been able to identify and categorize different types of possible flow patterns in karstified limestone such as matrix flow and fracture flow. Moreover, I have demonstrated the nature of the relationship between flow types classified by the morphological analysis of karstic features and drip time series characteristics [4]. A spatial survey of automated cave drip monitoring in two large chambers of Golgotha Cave has been utilized to achieve this goal. I have quantified recharge into the cave based on the drip data, Lidar measurements and flow classification. Finally, I have been able to estimate the water balance to develop a simple model describing the ground surface extent from which flow is focused on the monitored cave ceiling area and the associated lateral flow within the Tamala limestone formation. Finally in my fifth paper from PhD, I have demonstrated a complete methodology for automatic cave drip water hydrology measurements [5], which will help better characterize karst drip water hydrogeology and understand the relationship between drip hydrology and surface climate at any cave site. I have showed that the analysis of the time series produced by cave drip loggers generates useful hydrogeological information that can be applied generally. The time series behavior integrates a variety of characteristics that combine the properties of epikarst (storage), fracture configuration, and recharge.

Quantifying and constraining the terrestrial carbon cycle via DA

My current work is focusing on reducing uncertainty in site, regional and global scale vegetation dynamics and carbon budget simulations. As atmospheric CO2 concentration data show, the ocean and land surface absorb approximately 50% of anthropogenic CO2 emissions; therefore, the land and ocean act as a global carbon sink. However, beyond the global sink magnitude, uncertainty remains about the sink location, inter-annual variability and future trajectory. Uncertainty in TBM vegetation biomass and carbon sink projections is high; some models even predict the land may switch from a sink to a source by the end of the century. There is an urgent need to address this issue – without accurate sink estimates, we cannot quantify the level of carbon emissions that will keep us within a 2°C rise in temperature. In addition, a poor representation of vegetation dynamics leads to inaccurate predictions of land-atmospheric feedbacks. To achieve the goal of reducing TBM uncertainty, I use Bayesian DA methods to optimize uncertain TBM parameters. TBM parameters are often poorly constrained, either because they are not physically based and therefore cannot be measured, or because they are based on limited experimental data covering only a few species which are then grouped into the broad PFTs used in TBMs. Parameter values are therefore a key source of uncertainty in TBM simulations. My work involves optimizing vegetation and carbon cycle related model parameters using a wide range of data streams. These include: i) satellite-derived measures of vegetation activity (NDVI/FAPAR/LAI); ii) digital camera derived imagery (GCC), iii) biomass observations; iv) solar-induced chlorophyll fluorescence (SIF) data; v) biometeorological measurements (net CO2 and latent heat fluxes); vi) chamber-based CH4 fluxes; and vii) atmospheric CO2 concentration data.


Model-Data assimilation to quantify the effects of climate change on plant physiological processes

In my former role as a plant ecology modeler, I have implemented a DA framework using a simple tree carbon balance model to an experiment where below ground sink strength was manipulated by restricting root volume [6]. The project aimed to infer which processes were affected under sink limitation, and to attribute the overall reduction in growth observed in the experiment, to the effects on component processes. The R project containing the source code to perform all the data processing and analysis to reproduce the figures and tables of the paper is freely available as a Git repository ( While working in this project, I have learnt R markdown script writing to weave together narrative text and code to produce elegantly formatted output. I have also applied the same DA technique to other manipulative experiments, to infer the effects of temperature warming and drought on plant carbon balance processes.


Ecosystem carbon budget for mature forest under carbon dioxide enrichment

I have worked in collaboration with other colleagues from Western Sydney University (WSU) where I use data from the Eucalyptus Free Air CO2 Enrichment (EucFACE) experiment, with the aim to construct an ecosystem carbon budget to provide a comprehensive field-based overview of how a mature forest responds to elevated CO2 [7]. We compiled measurements on all major carbon pools and fluxes collected over four years of experimental treatment (2013-2016), including individual and aggregated biomass and associated fluxes measured or inferred from plants, litter, soil, microbes, and insects, and constructed an ecosystem carbon budget under both ambient (aCO2) and eCO2 conditions (+150 ppm). In this entire carbon budget framework, I have contributed by estimating the total stem volume and surface area, which was directly inferred from the Terrestrial Laser Scanning (TLS) data through quantitative structure models (QSMs). I have run the simulation through several Matlab and C++ scripts, to accomplish the entire workflow by optimising the model parameters for EucFACE site. The data has also been used where we have evaluated alternative stomatal modeling approaches at a woodland site where Vapour pressure deficit (D) reaches high levels every summer [8]. First, we have tested leaf-scale models against in situ observations which showed a reduction in stomatal conductance (gs) and photosynthesis (A) with increasing D. We have then implemented the best model into a canopy scale model against whole-tree-scale sap flow data that showed a decrease in transpiration at high D. We have aimed to quantify how well the alternative gas exchange models captured the high D responses of both gs and A.


Different treatment strategies for highly polluted landfill leachate

The aim of my MSc research was to determine appropriate treatment technique for effective treatment of heavily polluted landfill leachate. We accomplished several treatment experiments: (i) aerobic biological treatment, (ii) chemical coagulation, (iii) advanced oxidation process (AOP) and (iv) several combined treatment strategies [9]. Efficiency of these treatment procedures were monitored by analysing Chemical Oxygen Demand (COD) and colour removal. Leachate used for this study was taken from Matuail landfill site at Dhaka city. Fenton treatment which is an advanced oxidation process was the most successful between these three separate treatment procedures. Among the combined treatment options performed, we found extended aeration followed by Fenton method was the most suitable one.


  1. Mahmud, K., Mariethoz, G., Tahmasebi, P., Caers, J. and Baker A. (2014) “Simulation of Earth Textures by Conditional Image Quilting” Water Resources Research, Vol. 50 (4), pp. 3088–3107, DOI: 10.1002/2013WR015069.

  2. Mahmud, K., Mariethoz, Baker A. and Sharma A. (2015) “Integrating Multiple Scales of Hydraulic Conductivity Measurements in Training Image-Based Stochastic Models” Water Resources Research, Vol. 51(1), pp. 465–480, DOI: 10.1002/2014WR016150.

  3. Mahmud K., Mariethoz G., Pauline C.T., Baker A. (2015). “Terrestrial LiDAR Survey and Morphological Analysis to Identify Infiltration Properties in the Tamala Limestone, Western Australia, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, doi: 10.1109/JSTARS.2015.2451088.

  4. Mahmud K., Mariethoz G., Baker A., Treble P.C., Markowska M. and McGuire L. (2016). “Estimation of deep infiltration in unsaturated limestone environments using cave LiDAR and drip count data” Hydrol. Earth Syst. Sci., 20, 359-373,

  5. Mahmud, K., Mariethoz G., Baker A. and Pauline C.T. (2018) “Hydrological characterization of cave drip waters in a porous limestone: Golgotha Cave, Western Australia” Hydrol. Earth Syst. Sci., 22, 977-988,

  6. Mahmud, K., Medlyn B., Duursma R.A., Campany C. and De Kauwe M. (2018) “Inferring the effects of sink strength on plant carbon balance from experimental measurements” Biogeosciences, 15, 4003-4018,

  7. Mingkai J., Medlyn B., Duursma R.A., Drake J.E., Anderson I., Barton C., Boer M., Carrillo Y., Collins L., Crous K.Y., De Kauwe M., Facey S.L., Gherlenda A., Gimeno T.E., Gomez L.C., Hasegawa S., MacDonald C., Mahmud K., Moore B., Moreno R.L.S., Nazaries L., Nevado J.P., Noh N.J., Pathare V., Pendall E., Powell J., Power S., Reich P., Renchon A., Riegler M., Rymer P., Tjoelker M., Wujeska-Klause A., Yang J., Zaehle S., and Ellsworth D.S. (2020) “The fate of carbon in a mature forest under carbon dioxide enrichment” Accepted in Nature.

  8. Yang J., Duursma R.A., De Kauwe M.G., Kumarathunge D., Mingkai J., Mahmud K., Gimeno T.E., Crous K.Y., Ellsworth D.S., Peters J., Choat B., Eamus D., Medlyn B. (2019) “Incorporating non-stomatal limitation improves the performance of leaf and canopy models at high vapour pressure deficit” Tree Physiology, Volume 39. Issue 12, pp. 1961-1974,

  9. Mahmud, K., Hossain, M.D. and Shams, S. (2012) “Different Treatment Strategies for Highly Polluted Landfill Leachate in Developing Countries”, Waste Management, Volume 32, Issue 11, pp. 2096–2105, DOI:10.1016/j.wasman.2011.10.026.