Wpływ jakości danych na modelowanie stref zagrożenia powodziowego

• Autorzy: Hejmanowska B.

Warianty tytułu

EN

Influence of data quality on modeling of flood zones

• Języki publikacji: PL

Abstrakty

EN

The aim of INSPIRE programme is to assure an easy access to reliable spatial information. Spatial data should thus be reliable and the degree of its reliability should be known and information about it should be contained in the database. Reliability of data is proved by its quality, which should be taken into account when GIS systems are used to support decisions, for instance, in modeling of flood risk areas. Coordination-Information Centers (OKI) of flood protection in Regional Water Management Boards set up within the framework of a Word Bank project .Removal of flood effects. deal, among others with mapping of flood risks [http://oki.krakow.rzgw.gov.pl]. Areas of flood risks, also called flooding areas, and their reach, are outlined based on historical or hypothetical data (assuming determined probability of a given water level, e.g. for water 1%, or water level probable to appear once in 100 years). Two types of flood risks areas distinguished: direct risk areas and potential risk areas. Direct flood risk areas are adjacent to water flow and cover terrain flooded when the river overflows floodbanks. Potential flood risk areas are the areas in danger of floods when there is a damage of floodbanks. Spatial reach of the area is outlined as a result of GIS analysis of intersection of the surface of water table with Digital Terrain Model (DTM). The accuracy of DTM is varies from +/-0.2m for floodbanks to +/-2.5m in the area with diversified lie of the land (gradient higher than 6 degrees). The risk of flood is usually associated with probability of a certain water level. However, there is other kind of risk, connected with analytical side of outlining flood areas, including first of all the quality of source data. When data is complete and up to date, the main parameter featuring the quality of data is their accuracy. In this case accuracy of source data may be understood as the accuracy of DTM and the accuracy of outlining the level of water table (e.g. based on hydrological modeling). The risk connected with not taking into account the quality of source data in modeling flood areas may be calculated on the basis of a formula [Kapłan S., Garrick B.J, 1981 . .On the quantitative definition of risk., Risk Analysis 1981]: R = S ź P ź C where: S . scenario, P . probability of scenario S taking place, C . measure of effects of the scenario S. The key issue is the probability (P) of scenario (S) taking place. Let us assume, for instance, scenario S (e.g.. water 1%). On the basis of spatial distribution: inaccuracy of DTM and water table may generate a map of probability distribution for a given terrain to be flooded, instead of a flood line traditionally outlined. When analyzing objects at potential risk we may assign to them .measure of effects. caused by flood, e.g. financial measure. Then, in the result of spatial analysis a map of risk distribution may be generated, based on the above formula, connected with outlining a given flood area based on source data with accuracy determined in the beginning. The paper presents an example of modeling a flood area taking into account and neglecting inaccuracy of source data. On this basis, the risk connected with not taking into account the quality of source data in modeling of flood risk area may be analyzed.

Słowa kluczowe

PL

strefy zagrożenia powodziowego; analiza ryzyka; niedokładność DTM

EN

flood zone; risk analysis; DTM uncertainty

• Wydawca: Polskie Towarzystwo Informacji Przestrzennej
• Czasopismo: Roczniki Geomatyki
• Rocznik: 2006
• Tom: T. 4, z. 1
• Strony: 145--150
• Opis fizyczny: Bibliogr. 6 poz.

Twórcy

autor

Hejmanowska B.

• Akademia Górniczo-Hutnicza,

Bibliografia

• 1. Eastman J.R., 2001: Guide to GIS and Image Processing. Idrisi Manual Version 32.20.
• 2. Hejmanowska B., 2003: Data inaccuracy in geographic system . propagation of DTM and ortophotomaps in the spatial analysis. Geodezja 40, Prace Komisji Geodezji i Inżynierii Środowiska, PAN, Oddział w Krakowie.
• 3. Hejmanowska B., 2005: Wpływ jakości danych na ryzyko procesów decyzyjnych wspieranych analizami GIS. AGH Uczelniane Wydawnictw Naukowo-Dydaktyczne, Kraków 2005, ISN 0867-6631.
• 4. Kaplan S., Garrick B.J, 1981: On the quantitative definition of risk. Risk Analysis 1981.
• 5. Krause J., Uhrich S., Bormann H., Diekkrüger B., 2003: Uncertainty Analysis of a Floodrisk Mapping Procedure Applied in Urban Areas. Hydrology Division, Geographical Institute University of Bonn, http://www.giub.uni-bonn.de/hrg/Poster%20and%20Presentations/nizza02.pdf.
• 6. Nachlik E., Kostecki S., Gądek W., Stochmal R., 2000: Strefy zagrożenia powodziowego. Rodzaje stref, podstawy ich ustalania i doświadczenia praktyczne . Komponent B.4 projektu Likwidacji Skutków Powodzi finansowego w ramach kredytu Banku Światowego, Biuro Koordynacji Projektu Banku Światowego we Wrocławiu.