[1] This study reports on two strategies for accelerating posterior inference of a highly parameterized and CPU-demanding groundwater flow model. Our method builds on previous stochastic collocation approaches, e.g., Marzouk and Xiu (2009) and Marzouk and Najm (2009), and uses generalized polynomial chaos (gPC) theory and dimensionality reduction to emulate the output of a large-scale groundwater flow model. The resulting surrogate model is CPU efficient and serves to explore the posterior distribution at a much lower computational cost using two-stage MCMC simulation. The case study reported in this paper demonstrates a two to five times speed-up in sampling efficiency.
Hydrogeological conceptual models are collections of hypotheses describing the understanding of groundwater systems and they are considered one of the major sources of uncertainty in groundwater flow and transport modelling. A common method for characterizing the conceptual uncertainty is the multi-model approach, where alternative plausible conceptual models are developed and evaluated. This review aims to give an overview of how multiple alternative models have been developed, tested and used for predictions in the multi-model approach in international literature and to identify the remaining challenges. The review shows that only a few guidelines for developing the multiple conceptual models exist, and these are rarely followed. The challenge of generating a mutually exclusive and collectively exhaustive range of plausible models is yet to be solved. Regarding conceptual model testing, the reviewed studies show that a challenge remains in finding data that is both suitable to discriminate between conceptual models and relevant to the model objective. We argue that there is a need for a systematic approach to conceptual model building where all aspects of conceptualization relevant to the study objective are covered. For each conceptual issue identified, alternative models representing hypotheses that are mutually exclusive should be defined. Using a systematic, hypothesis based approach increases the transparency in the modelling workflow and therefore the confidence in the final model predictions, while also anticipating conceptual surprises. While the focus of this review is on hydrogeological applications, the concepts and challenges concerning model building and testing are applicable to spatio-temporal dynamical environmental systems models in general.
Abstract. In this paper, we present and analyze a novel global database of
soil infiltration measurements, the Soil Water Infiltration Global (SWIG)
database. In total, 5023 infiltration curves were collected across all
continents in the SWIG database. These data were either provided and quality
checked by the scientists who performed the experiments or they were
digitized from published articles. Data from 54 different countries were
included in the database with major contributions from Iran, China, and the USA.
In addition to its extensive geographical coverage, the collected
infiltration curves cover research from 1976 to late 2017. Basic information
on measurement location and method, soil properties, and land use was
gathered along with the infiltration data, making the database valuable for
the development of pedotransfer functions (PTFs) for estimating soil hydraulic
properties, for the evaluation of infiltration measurement methods, and for
developing and validating infiltration models. Soil textural information
(clay, silt, and sand content) is available for 3842 out of 5023 infiltration
measurements (∼ 76%) covering nearly all soil USDA textural classes
except for the sandy clay and silt classes. Information on land use is
available for 76 % of the experimental sites with agricultural land use as
the dominant type (∼ 40%). We are convinced that the SWIG database
will allow for a better parameterization of the infiltration process in land
surface models and for testing infiltration models. All collected data and
related soil characteristics are provided online in
*.xlsx and *.csv formats for reference, and we add a disclaimer that the
database is for public domain use only and can be copied freely by
referencing it. Supplementary data are available at
https://doi.org/10.1594/PANGAEA.885492 (Rahmati et al., 2018). Data
quality assessment is strongly advised prior to any use of this database.
Finally, we would like to encourage scientists to extend and update the SWIG database
by uploading new data to it.
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