I'm currently busy with my Master's Thesis: Analyse the Propagation of Uncertainty from DEMs used for Hydrological Modelling. I need to let everyone know upfront that I've got very little knowledge in geostatistics.
My masters closely following the approach used by Christian Venzin based on his Masters Thesis: "Analyzing the Impact of High Resolution DEM Uncertainty on Hydrological Models Using a Parallel Computing Approach" , where Christian Venzin used a Process Convolution method. Christian Venzin concludes that due to the limitations with Process Convolution the next logical step is to use a Conditional Sequential Gaussian Simulation based on the paper written by Tomislav Hengl: "On the uncertainty of stream networks derived from elevation data: the error propogation approach".
I recently found out that Conditional Sequential Gaussian Simulation is not available within Geostatistical Analyst. I'm looking for advice from the community that understands Geostatistics and in particular Gaussian Simulations. I need to develop a similar approach to C.Venzin and T.Hengl using the available tools within Geostatistical Analyst to derive multiple realizations of a DEM (i.e. SRTM 30m and SRTM 90m DSM) and control points as true values. I then want to use the results from the simulations to derive watersheds and stream networks to derive probability distributions to quantify and visualise the uncertainty within the derived results as done by C.Venzin and T.Hengl.
Any advice in developing a geostatistical simulation approach using geostatistical analyst extension to derive similar results as C.Venzin and T.Hengl will truly be appreciated. Please provide explanations for the choice of the methods you propose so that I may better understand geostatistical uncertainty modelling.