@article{SavoyHesse2019,
  author    = {Savoy, Heather and Heße, Falk},
  title     = {Dimension reduction for integrating data series in Bayesian inversion of geostatistical models},
  series = {Stochastic environmental research and risk assessment},
  volume    = {33},
  journal   = {Stochastic environmental research and risk assessment},
  number    = {7},
  publisher = {Springer},
  address   = {New York},
  issn      = {1436-3240},
  doi       = {10.1007/s00477-019-01697-9},
  pages     = {1327 -- 1344},
  year      = {2019},
  abstract  = {This study explores methods with which multidimensional data, e.g. time series, can be effectively incorporated into a Bayesian framework for inferring geostatistical parameters. Such series are difficult to use directly in the likelihood estimation procedure due to their high dimensionality; thus, a dimension reduction approach is taken to utilize these measurements in the inference. Two synthetic scenarios from hydrology are explored in which pumping drawdown and concentration breakthrough curves are used to infer the global mean of a log-normally distributed hydraulic conductivity field. Both cases pursue the use of a parametric model to represent the shape of the observed time series with physically-interpretable parameters (e.g. the time and magnitude of a concentration peak), which is compared to subsets of the observations with similar dimensionality. The results from both scenarios highlight the effectiveness for the shape-matching models to reduce dimensionality from 100+ dimensions down to less than five. The models outperform the alternative subset method, especially when the observations are noisy. This approach to incorporating time series observations in the Bayesian framework for inferring geostatistical parameters allows for high-dimensional observations to be faithfully represented in lower-dimensional space for the non-parametric likelihood estimation procedure, which increases the applicability of the framework to more observation types. Although the scenarios are both from hydrogeology, the methodology is general in that no assumptions are made about the subject domain. Any application that requires the inference of geostatistical parameters using series in either time of space can use the approach described in this paper.},
  language  = {en}
}
@article{SchernthannerAscheGonschoreketal.2017,
  author    = {Schernthanner, Harald and Asche, Hartmut and Gonschorek, Julia and Scheele, Lasse},
  title     = {Spatial Modeling and Geovisualization of Rental Prices for Real Estate portals},
  series = {International journal of agricultural and environmental information systems : an official publication of the Information Resources Management Association},
  volume    = {8},
  journal   = {International journal of agricultural and environmental information systems : an official publication of the Information Resources Management Association},
  publisher = {IGI Global},
  address   = {Hershey},
  issn      = {1947-3192},
  doi       = {10.4018/IJAEIS.2017040106},
  pages     = {78 -- 91},
  year      = {2017},
  language  = {en}
}
@article{VossZimmermannZimmermann2016,
  author    = {Voss, Sebastian and Zimmermann, Beate and Zimmermann, Alexander},
  title     = {Detecting spatial structures in throughfall data: The effect of extent, sample size, sampling design, and variogram estimation method},
  series = {Journal of hydrology},
  volume    = {540},
  journal   = {Journal of hydrology},
  publisher = {Elsevier},
  address   = {Amsterdam},
  issn      = {0022-1694},
  doi       = {10.1016/j.jhydrol.2016.06.042},
  pages     = {527 -- 537},
  year      = {2016},
  abstract  = {In the last decades, an increasing number of studies analyzed spatial patterns in throughfall by means of variograms. The estimation of the variogram from sample data requires an appropriate sampling scheme: most importantly, a large sample and a layout of sampling locations that often has to serve both variogram estimation and geostatistical prediction. While some recommendations on these aspects exist, they focus on Gaussian data and high ratios of the variogram range to the extent of the study area. However, many hydrological data, and throughfall data in particular, do not follow a Gaussian distribution. In this study, we examined the effect of extent, sample size, sampling design, and calculation method on variogram estimation of throughfall data. For our investigation, we first generated non Gaussian random fields based on throughfall data with large outliers. Subsequently, we sampled the fields with three extents (plots with edge lengths of 25 m, 50 m, and 100 m), four common sampling designs (two grid-based layouts, transect and random sampling) and five sample sizes (50, 100, 150, 200, 400). We then estimated the variogram parameters by method-of-moments (non-robust and robust estimators) and residual maximum likelihood. Our key findings are threefold. First, the choice of the extent has a substantial influence on the estimation of the variogram. A comparatively small ratio of the extent to the correlation length is beneficial for variogram estimation. Second, a combination of a minimum sample size of 150, a design that ensures the sampling of small distances and variogram estimation by residual maximum likelihood offers a good compromise between accuracy and efficiency. Third, studies relying on method-of-moments based variogram estimation may have to employ at least 200 sampling points for reliable variogram estimates. These suggested sample sizes exceed the number recommended by studies dealing with Gaussian data by up to 100 \%. Given that most previous through fall studies relied on method-of-moments variogram estimation and sample sizes <<200, currently available data are prone to large uncertainties. (C) 2016 Elsevier B.V. All rights reserved.},
  language  = {en}
}
@article{StrightBernhardtBoucher2013,
  author    = {Stright, Lisa and Bernhardt, Anne and Boucher, Alexandre},
  title     = {DFTopoSim modeling topographically-controlled deposition of subseismic scale sandstone packages within a mass transport dominated deep-water channel belt},
  series = {Mathematical geosciences : the official journal of the International Association for Mathematical Geosciences},
  volume    = {45},
  journal   = {Mathematical geosciences : the official journal of the International Association for Mathematical Geosciences},
  number    = {3},
  publisher = {Springer},
  address   = {Heidelberg},
  issn      = {1874-8961},
  doi       = {10.1007/s11004-013-9444-7},
  pages     = {277 -- 296},
  year      = {2013},
  abstract  = {Facies bodies in geostatistical models of deep-water depositional environments generally represent channel-levee-overbank-lobe morphologies. Such models adequately capture one set of the erosional and depositional processes resulting from turbidity currents traveling downslope to the ocean basin floor. However, depositional morphologies diverge from the straight forward channel-levee-overbank-lobe paradigm when the topography of the slope or the shape of the basin impacts the timing and magnitude of turbidity current deposition. Subaqueous mass-transport-deposits (MTDs) present the need for an exception to the channel-levee-overbank-lobe archetype. Irregular surface topography of subaqueous MTDs can play a primary role in controlling sand deposition from turbidity currents. MTD topography creates mini-basins in which sand accumulates in irregularly-shaped deposits. These accumulations are difficult to laterally correlate using well-log data due to their variable and unpredictable shape and size. Prediction is further complicated because sandstone bodies typical of this setting are difficult to resolve in seismic-reflection data. An event-based model is presented, called DFTopoSim, which simulates debris flows and turbidity currents. The accommodation space on top of and between debris flow lobes is filled in by sand from turbidity currents. When applied to a subsurface case in the Molasse Basin of Upper Austria, DFTopoSim predicts sand packages consistent with observations from core, well, and seismic data and the interpretation of the sedimentologic processes. DFTopoSim expands the set of available geostatistical deep-water depositional models beyond the standard channel-levee-overbank-lobe model.},
  language  = {en}
}
@article{BaroniOrtuaniFacchietal.2013,
  author    = {Baroni, Gabriele and Ortuani, B. and Facchi, A. and Gandolfi, C.},
  title     = {The role of vegetation and soil properties on the spatio-temporal variability of the surface soil moisture in a maize-cropped field},
  series = {Journal of hydrology},
  volume    = {489},
  journal   = {Journal of hydrology},
  number    = {7},
  publisher = {Elsevier},
  address   = {Amsterdam},
  issn      = {0022-1694},
  doi       = {10.1016/j.jhydrol.2013.03.007},
  pages     = {148 -- 159},
  year      = {2013},
  abstract  = {Soil moisture dynamics are affected by complex interactions among several factors. Understanding the relative importance of these factors is still an important challenge in the study of water fluxes and solute transport in unsaturated media. In this study, the spatio-temporal variability of surface soil moisture was investigated in a 10 ha flat cropped field located in northern Italy. Soil moisture was measured on a regular 50 x 50 m grid on seven dates during the growing season. For each measurement campaign, the spatial variability of the soil moisture was compared with the spatial variability of the soil texture and crop properties. In particular, to better understand the role of the vegetation, the spatio-temporal variability of two different parameters - leaf area index and crop height - was monitored on eight dates at different crop development stages. Statistical and geostatistical analysis was then applied to explore the interactions between these variables. In agreement with other studies, the results show that the soil moisture variability changes according to the average value within the field, with the standard deviation reaching a maximum value under intermediate mean soil moisture conditions and the coefficient of variation decreasing exponentially with increasing mean soil moisture. The controls of soil moisture variability change according to the average soil moisture within the field. Under wet conditions, the spatial distribution of the soil moisture reflects the variability of the soil texture. Under dry conditions, the spatial distribution of the soil moisture is affected mostly by the spatial variability of the vegetation. The interaction between these two factors is more important under intermediate soil moisture conditions. These results confirm the importance of considering the average soil moisture conditions within a field when investigating the controls affecting the spatial variability of soil moisture. This study highlights the importance of considering the spatio-temporal variability of the vegetation in investigating soil moisture dynamics, especially under intermediate and dry soil moisture conditions. The results of this study have important implications in different hydrological applications, such as for sampling design, ranking stability application, indirect measurements of soil properties and model parameterisation.},
  language  = {en}
}