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"abstract": "Bij het meten en interpreteren van grondwaterstanden spelen drie vragen: 1) Hoe kunnen we een grondwaterstand meten op één bepaalde locatie?; 2) Hoe kunnen we een tijdreeks van grondwaterstanden op een locatie in een samenvattende maat karakteriseren?; 3) Hoe kunnen we deze maat ruimtelijk interpoleren en opschalen naar een groter gebied? Dit rapport beschrijft de methoden en geeft aan wat voor soort fouten kunnen optreden en wat de grootteorde van deze fouten is. De studie wijst als foutenbronnen aan: 1) het hanteren van uiteenlopende definities voor de doelvariabele (grondwaterstandsdiepte) en doelparameters zoals gemiddeld hoogste en laagste grondwaterstanden; 2) fouten in het meten van de grondwaterstand op locaties, en 3) fouten door het gebruik van modellen of expertkennis bij het aggregeren van grondwaterstanden in tijd en ruimte. Een onafhankelijke validatiestudie waarbij de objectiviteit van het resultaat niet ter discussie staat verdient aanbeveling.",
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"collection-number": "2345",
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"collection-title": "Alterra-rapport",
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"event-place": "Netherlands",
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"ISBN": "ISSN 1566-7197",
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"language": "Nederlands",
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"publisher": "Wageningen University & Research",
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"publisher-place": "Netherlands",
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"title": "Meten en interpreteren van grondwaterstanden : analyse van methodieken en nauwkeurigheid",
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"URL": "https://edepot.wur.nl/215081",
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"author": [
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{
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"family": "Ritzema",
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"given": "H.P."
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},
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{
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"family": "Heuvelink",
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"given": "G.B.M."
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},
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{
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"family": "Heinen",
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"given": "M."
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},
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{
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"family": "Bogaart",
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"given": "P.W."
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},
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{
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"family": "Bolt",
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"given": "F.J.E.",
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"non-dropping-particle": "van der"
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},
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{
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"family": "Hack-ten Broeke",
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"given": "M.J.D."
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},
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{
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"family": "Hoogland",
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"given": "T."
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},
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{
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"family": "Knotters",
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"given": "M."
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},
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{
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"family": "Massop",
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"given": "H.T.L."
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},
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{
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"family": "Vroon",
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"given": "H.R.J."
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}
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],
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"issued": {
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"date-parts": [
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[
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"2012"
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]
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]
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}
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},
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{
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"id": "Cressie1993",
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"type": "book",
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"publisher": "John Wiley & Sons",
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"title": "Statistics for spatial data",
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"author": [
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{
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"family": "Cressie",
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"given": "Noel"
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}
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],
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"issued": {
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"date-parts": [
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[
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"1993"
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]
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]
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}
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},
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{
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"id": "Matheron1962",
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"type": "book",
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"note": "number: 14",
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"publisher": "Memoires du Bureau de Recherches Geologiques et Minieres, Editions Technip, Paris",
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"title": "Traité de géostatistique appliquée",
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"author": [
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{
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"family": "Matheron",
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"given": "Georges"
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}
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],
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"issued": {
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"date-parts": [
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[
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"1962"
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]
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]
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}
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},
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{
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"id": "Lindgren2011",
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"type": "article-journal",
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"abstract": "Summary. Continuously indexed Gaussian fields (GFs) are the most important ingredient in spatial statistical modelling and geostatistics. The specification through the covariance function gives an intuitive interpretation of the field properties. On the computational side, GFs are hampered with the big n problem, since the cost of factorizing dense matrices is cubic in the dimension. Although computational power today is at an all time high, this fact seems still to be a computational bottleneck in many applications. Along with GFs, there is the class of Gaussian Markov random fields (GMRFs) which are discretely indexed. The Markov property makes the precision matrix involved sparse, which enables the use of numerical algorithms for sparse matrices, that for fields in only use the square root of the time required by general algorithms. The specification of a GMRF is through its full conditional distributions but its marginal properties are not transparent in such a parameterization. We show that, using an approximate stochastic weak solution to (linear) stochastic partial differential equations, we can, for some GFs in the Matérn class, provide an explicit link, for any triangulation of , between GFs and GMRFs, formulated as a basis function representation. The consequence is that we can take the best from the two worlds and do the modelling by using GFs but do the computations by using GMRFs. Perhaps more importantly, our approach generalizes to other covariance functions generated by SPDEs, including oscillating and non-stationary GFs, as well as GFs on manifolds. We illustrate our approach by analysing global temperature data with a non-stationary model defined on a sphere.",
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"container-title": "Journal of the Royal Statistical Society: Series B (Statistical Methodology)",
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"DOI": "10.1111/j.1467-9868.2011.00777.x",
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"issue": "4",
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"page": "423-498",
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"title": "An explicit link between Gaussian fields and Gaussian Markov random fields: the stochastic partial differential equation approach",
"abstract": "L-BFGS-B is a limited-memory algorithm for solving large nonlinear optimization problems subject to simple bounds on the variables. It is intended for problems in which information on the Hessian matrix is difficult to obtain, or for large dense problems. L-BFGS-B can also be used for unconstrained problems and in this case performs similarly to its predessor, algorithm L-BFGS (Harwell routine VA15). The algorithm is implemented in Fortran 77.",
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"container-title": "ACM Transactions on Mathematical Software",
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"DOI": "10.1145/279232.279236",
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"ISSN": "0098-3500, 1557-7295",
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"issue": "4",
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"journalAbbreviation": "ACM Trans. Math. Softw.",
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"language": "en",
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"page": "550-560",
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"source": "DOI.org (Crossref)",
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"title": "Algorithm 778: L-BFGS-B: Fortran subroutines for large-scale bound-constrained optimization",
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