{"url":"/dataset/whpa","name":"WHPA","full_name":"Wellhead Protection Area prediction from breakthrough curves","description_markdown":"This dataset was created as part of the following study, which was published in the Journal of Hydrology: *A new framework for experimental design using Bayesian Evidential Learning: the case of wellhead protection area* [https://doi.org/10.1016/j.jhydrol.2021.126903](https://doi.org/10.1016/j.jhydrol.2021.126903).\r\nThe pre-print is available on arXiv: [https://arxiv.org/pdf/2105.05539.pdf](https://arxiv.org/pdf/2105.05539.pdf)\r\n\r\n**Files description**\r\nThis dataset contains 4148 simulation results, i.e., 4148 pairs of predictor/target.\r\n**bkt.npy** contains the breakthrough curves from all 6 injection wells recorded at the pumping well.\r\n**pz.npy** contains the 2D coordinates of the backtracked particles' end points, used to delineate the WHPA.\r\n\r\n**Introduction**\r\nThe Wellhead Protection Area (WHPA) is a zone around a pumping well where human activities are limited in order to preserve water resources, usually based on how long dangerous chemicals in the area will take to reach the pumping well (according to local regulation). The flow velocity in the subsurface around the well determines it, and it can be computed numerically using particle tracking or transport simulation, or in practice using tracer testing. A groundwater model is typically calibrated against field data before being used to calculate the WHPA. In highly populated places where land occupation is a big concern, the introduction of such zones could have a large socioeconomic impact.\r\n\r\n**WHPA prediction**\r\nDifferent tracers emerge from six data sources (injection wells) scattered across the pumping well. Their job is to inject individual tracers into the system in order to predict their transport and record their breakthrough curves (BCs) at the pumping well location. \r\nNumerous particles are artificially positioned around the pumping well, and their origins are traced backward in time to identify the associated WHPA.\r\n\r\nOur predictor and target will be generated using the USGS' open-source finite-difference code Modflow. To get different sets of predictors and targets, we will run different hydrologic models with one variable parameter, namely hydraulic conductivity in metres per day. To obtain a satisfactory heterogeneity in the hydraulic conductivity fields, which will control the shape and extent of our target, the PAs, we use sequential gaussian simulation based on arbitrarily defined variograms. The pumping well is located at the 1000, 500 metres mark and is surrounded by six injection wells.","description_withheld":null,"homepage":"https://www.kaggle.com/robustus/whpa-prediction","introduced_date":"2021-05-12","introduced_date_note":null,"introduced_by":{"paper":"/paper/a-new-framework-for-experimental-design-using","title":"A new framework for experimental design using Bayesian Evidential Learning: the case of wellhead protection area","first_author":"Robin Thibaut","url":null},"license":null,"modalities":[],"tasks":[],"languages":[{"name":"English","url":"/datasets/language/english"}],"variants":["WHPA"],"data_loaders":[],"num_papers_in_archive":1,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[],"papers_with_a_benchmark_row":[],"syntology_totals":{"read_at":"2026-09-24T18:15:14+00:00","papers_with_samples":0,"samples_harvested":0,"samples_ran":0,"samples_unverified":0,"pointer_only_for_licence":0,"papers_with_no_sample_that_ran":0,"note":"the per-paper counts above, summed; not a rate"},"papers_note":"The archive never published its papers-using-dataset list; these are papers with a leaderboard row on this dataset's benchmarks."}