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Record W7009737095

Facilitating and enabling large-scale, hyper-resolution, groundwater modeling with distributed-memory parallel computing

2024· dissertation· en· W7009737095 on OpenAlexaboutno aff

Bibliographic record

VenueUtrecht University Repository (Utrecht University) · 2024
Typedissertation
Languageen
FieldEngineering
TopicUAV Applications and Optimization
Canadian institutionsnot available
Fundersnot available
KeywordsGroundwaterGroundwater flowGridGroundwater modelMODFLOWUnstructured gridWater resourcesScale (ratio)
DOInot available

Abstract

fetched live from OpenAlex

Water managers and policymakers worldwide face the major challenge of securing the availability of fresh groundwater under excessive groundwater extraction and climate change. To this end, they need future projections of groundwater resources computed with numerical groundwater models. These models must have a sufficiently high spatial resolution (i.e., sufficiently small grid cell size) to capture the relevant physical processes. This has raised the call for models with grid cells that are "hyper resolution", i.e., with sizes that are less than or equal to 1 km. However, applying hyper-resolution numerical groundwater models at larger scales typically results in long runtimes and large memory requirements. To solve this problem, this research investigated the potential of distributed-memory parallel computing. MODFLOW, the world’s most widely used groundwater simulation code, was fully parallelized including the linear Krylov solvers applying the additive Schwarz preconditioner. Experiments were conducted on the Dutch national computer cluster, up to a (relatively small) maximum of 1024 processor cores. Two real-world existing groundwater models were facilitated, that still use structured grids, as well as two new applications were enabled that use more flexible (quad-based) unstructured grids. For facilitating existing models, the (quantitative) integrated National Hydrological Model of the Netherlands (NHM) was considered and the Sand Engine model, a (qualitative) 3D variable-density groundwater flow and salt transport model. Orthogonal recursive bisection partitioning was applied, and strong parallel scaling was evaluated. Large, obtained speedups with a relatively low number of cores (speedup of 22 and 86 with 64 and 256 cores, respectively), show that the applied parallelization could significantly increase the practical applicability of these existing groundwater models. As a first new enabled application, GLOBGM was developed, the world's first time-dependent global groundwater model with a resolution of 30 arc seconds (~ 1 km at the Equator). The METIS graph partitioner was applied in both a straightforward and (hydrological) area-based manner. Three continental-scale groundwater models and one for the remaining islands were derived, for a total of 278 million grid cells. Necessarily, parallel pre-processing of input data was applied. With a relatively low number of cores (382 cores in total), 58 years could be computed in parallel in one night (corresponding to a speedup of 138 with 224 cores for the largest Afro-Eurasia model). This demonstrated that GLOBGM could also be used by modelers who lack access to very large computer clusters. As a second new enabled application, a multi-resolution groundwater model was explored for the Netherlands to incorporate regional-scale models in the NHM. Again, area-based METIS partitioning was applied, but now for a smaller set with a larger variation in size. For evaluating weak parallel scaling, nationwide grid refinements up to a regional-scale resolution of 12.5 m were considered, resulting in a model with more than one billion grid cells. With a relatively low number of cores (up to 469 with a speedup of 326), 8 years could be computed in parallel in 2 days. This shows that these very large groundwater models are already within reach with today's computers.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.361
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.005
GPT teacher head0.165
Teacher spread0.160 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designSimulation or modeling
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2024
Admission routes1
Has abstractyes

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