@article{MuellerSchuelerZechetal.2022,
  author    = {M{\"u}ller, Sebastian and Sch{\"u}ler, Lennart and Zech, Alraune and Heße, Falk},
  title     = {GSTools v1.3: a toolbox for geostatistical modelling in Python},
  series = {Geoscientific model development : an interactive open access journal of the European Geosciences Union},
  volume    = {15},
  journal   = {Geoscientific model development : an interactive open access journal of the European Geosciences Union},
  number    = {7},
  publisher = {Copernicus},
  address   = {G{\"o}ttingen},
  issn      = {1991-959X},
  doi       = {10.5194/gmd-15-3161-2022},
  pages     = {3161 -- 3182},
  year      = {2022},
  abstract  = {Geostatistics as a subfield of statistics accounts for the spatial correlations encountered in many applications of, for example, earth sciences. Valuable information can be extracted from these correlations, also helping to address the often encountered burden of data scarcity. Despite the value of additional data, the use of geostatistics still falls short of its potential. This problem is often connected to the lack of user-friendly software hampering the use and application of geostatistics. We therefore present GSTools, a Python-based software suite for solving a wide range of geostatistical problems. We chose Python due to its unique balance between usability, flexibility, and efficiency and due to its adoption in the scientific community. GSTools provides methods for generating random fields; it can perform kriging, variogram estimation and much more. We demonstrate its abilities by virtue of a series of example applications detailing their use.},
  language  = {en}
}
@article{SchweppeThoberMuelleretal.2022,
  author    = {Schweppe, Robert and Thober, Stephan and M{\"u}ller, Sebastian and Kelbling, Matthias and Kumar, Rohini and Attinger, Sabine and Samaniego, Luis},
  title     = {MPR 1.0: a stand-alone multiscale parameter regionalization tool for improved parameter estimation of land surface models},
  series = {Geoscientific model development : an interactive open access journal of the European Geosciences Union},
  volume    = {15},
  journal   = {Geoscientific model development : an interactive open access journal of the European Geosciences Union},
  number    = {2},
  publisher = {Copernicus},
  address   = {G{\"o}ttingen},
  issn      = {1991-959X},
  doi       = {10.5194/gmd-15-859-2022},
  pages     = {859 -- 882},
  year      = {2022},
  abstract  = {Distributed environmental models such as land surface models (LSMs) require model parameters in each spatial modeling unit (e.g., grid cell), thereby leading to a high-dimensional parameter space. One approach to decrease the dimensionality of the parameter space in these models is to use regularization techniques. One such highly efficient technique is the multiscale parameter regionalization (MPR) framework that translates high-resolution predictor variables (e.g., soil textural properties) into model parameters (e.g., porosity) via transfer functions (TFs) and upscaling operators that are suitable for every modeled process. This framework yields seamless model parameters at multiple scales and locations in an effective manner. However, integration of MPR into existing modeling workflows has been hindered thus far by hard-coded configurations and non-modular software designs. For these reasons, we redesigned MPR as a model-agnostic, stand-alone tool. It is a useful software for creating graphs of NetCDF variables, wherein each node is a variable and the links consist of TFs and/or upscaling operators. In this study, we present and verify our tool against a previous version, which was implemented in the mesoscale hydrologic model (mHM; https://www.ufz.de/mhm, last access: 16 January 2022). By using this tool for the generation of continental-scale soil hydraulic parameters applicable to different models (Noah-MP and HTESSEL), we showcase its general functionality and flexibility. Further, using model parameters estimated by the MPR tool leads to significant changes in long-term estimates of evapotranspiration, as compared to their default parameterizations. For example, a change of up to 25 \% in long-term evapotranspiration flux is observed in Noah-MP and HTESSEL in the Mississippi River basin. We postulate that use of the stand-alone MPR tool will considerably increase the transparency and reproducibility of the parameter estimation process in distributed (environmental) models. It will also allow a rigorous uncertainty estimation related to the errors of the predictors (e.g., soil texture fields), transfer function and its parameters, and remapping (or upscaling) algorithms.},
  language  = {en}
}