12342 Databaseret hydrologisk modellering

2020/2021

Kursusinformation
Data Driven Hydrological Modeling
Engelsk
5
Kandidat
Kurset udbydes som enkeltfag
F4B (fre 8-12)
Campus Lyngby
Lectures, report work, computer exercises
13-uger
F4B
Bedømmelse af øvelser og rapport(er)
Overall assessment based on reports, online quizzes held during lectures, and oral examination
Skriftlige hjælpemidler er tilladt
7-trins skala , intern bedømmelse
12341
12320/02402 , Introductory hydrology and basic statistics. Python will be used throughout the course to solve the assignments. Basic programming skills are assumed known.
Roland Löwe , Lyngby Campus, Bygning 115, Tlf. (+45) 4525 1694 , rolo@env.dtu.dk
Karsten Arnbjerg-Nielsen , Lyngby Campus, Bygning 115, Tlf. (+45) 4525 1450 , karn@env.dtu.dk
12 Institut for Vand og Miljøteknologi
I studieplanlæggeren
Kontakt underviseren for information om hvorvidt dette kursus giver den studerende mulighed for at lave eller forberede et projekt som kan deltage i DTUs studenterkonference om bæredygtighed, klimateknologi og miljø (GRØN DYST). Se mere på http://www.groendyst.dtu.dk
Overordnede kursusmål
General course objectives:
The objective of the course is to be able to describe hydrological processes by means of relatively simple models such as purely statistical and combined statistical and deterministic models and then apply them to problems typical in applied urban hydrology. The course is centred around realistic projects, where you will learn to manage large hydrological datasets and implement your own models in scripting languages. The projects range from extreme rainfall statistics to lumped flow modelling. It is assumed that you have knowledge of hydrological processes, statistical testing procedures and basic data analysis in a scripting language. The major novelty lies in combining these skills to model and analyse actual hydrological data.
Læringsmål
En studerende, der fuldt ud har opfyldt kursets mål, vil kunne:
  • Manage and analyze large real urban hydrological data sets by means of professional softwarescripting tools
  • Understand assumptions and limitations of statistical procedures applied in hydrology
  • Apply statistical methods to model hydrological processes for different time scales
  • Apply time series analysis methods and assess the overall model performance
  • Create implementations for parameter estimation in different hydrological model contexts.
  • Construct, implement and improve rainfall runoff models using scripting languages
  • Decompose a hydrological time series into deterministic and stochastic elements
  • Select a proper statistical model for prediction of hydrologic extremes in consideration of the prediction uncertainty
  • Communicate modelling results and limitations in different formats
Kursusindhold
Data analysis. Decomposition of runoff time series and description of their deterministic and stochastic components. Stochastic models for description of extreme hydrological events.
Sidst opdateret
07. maj, 2020