Geostatistics for Water Resources and Environmental Science

This course addresses the relationship of geostatistics with water resources and environmental science. It provides overview of the geostatistical techniques in space/time applications using real data.

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For whom?

Hydrologists, geoscientists, ecologists, MSc, PhD students with an interest in capacity building in spatiotemporal data analysis.


Preferable knowledge of basic statistics, matrix algebra.

Dates, Fee, ECTS

Start: 19 September 2022
End: 30 September 2022
Deadline IHE application: 18 August 2022 - 23.59 (CET)
Course fee: € 2000

VAT is not included in the course fee

Learning objectives

Upon completion, the participant should be able to:
  1. Perform proper and efficient sample statistical assessment and to statistically characterize spatially referenced data
  2. Know the advantages and disadvantages of stochastic and deterministic geostatistical techniques and to appropriately select and apply the right geostatistical approaches
  3. Apply effective quantitative analysis of spatial and spatio-temporal data
  4. Work with real hydrological data and produce efficient and useful maps
  5. Work comfortably in R programming environment for statistics
  6. Should be able to choose and describe adequate spatial and spatiotemporal continuity models (variograms) for different applications

Course content

The first part of the course is on statistical and geostatistical inference of data with known locations presenting interpolation methods. The theory of spatial variability is explained presenting the structure of spatial variability-dependence with covariance functions and variograms. Data normalization techniques and trend analysis are presented for appropriate geostatistical applications. Comparison of geostatistical methodologies is applied, optimal sampling is discussed and attention is given to multivariate geostatistics.

The second part of the course considers modelling spatiotemporal phenomena. The extension from pure spatial to spatiotemporal approaches is not trivial. Spatiotemporal covariance models (separable, nonseparable) are presented and explored on real datasets. The practical part deals with the exploration of different spatiotemporal variogram models and space-time kriging approaches applied to an own data set or a provided example.

Finally, the third part of the course presents simulation techniques combined with estimations of uncertainty.


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