Tytuł artykułu
Autorzy
Treść / Zawartość
Pełne teksty:
Identyfikatory
Warianty tytułu
Języki publikacji
Abstrakty
Contemporary models of urban hydrology and especially hydrodynamic models of stormwater systems demand on supply with high resolution spatial and temporal precipitation information. A plausible solution of precipitation information acquiring over urban catchments is seen in coupling of radar signals and raingauge networks measurements. Suitability of this approach is tested in the case of standard C-band weather radar and the dense network of fast-response raingauges. Rainfall rate estimated based on dependence on the reflectivity factor (Z-R relationship) in single pixels of the radar image are compared to rainfall rates measured by 25 raingauges located in Warsaw, Poland. In the analyzed period, 23 precipitation days with rain from convective clouds and cloud systems are detected. The main conclusion is that despite the fact that adopted Z-R relationship holds well in statistical sense (i.e. the whole period long empirical probability distribution functions (PDFs) of estimated and measured rainfall rates are in good agreement), instantaneous measurements and estimates as well as short-term (one day) PDFs differ remarkably. These differences are not systematic, they vary from the raingauge to raingauge and from day to day. Moreover, the most remarkable differences are associated with the highest rainrates which should be carefully considered prior radar data use as input for urban hydrology modelling.
Czasopismo
Rocznik
Tom
Strony
159--170
Opis fizyczny
Bibliogr. 17 poz., rys.
Twórcy
autor
- Interdisciplinary Centre for Mathematical and Computational Modelling, University of Warsaw, Warsaw, Poland
autor
- Institute of Environment Protection Engineering, Wroclaw University of Technology, Wrocław, Poland
autor
- Institute of Geophysics, Faculty of Physics, University of Warsaw, Warsaw, Poland
Bibliografia
- [1] FLETCHER T.D., ANDRIEU H., HAMEL P., Understanding, management and modelling of urban hydrology and its consequences for receiving waters. A state of the art, Adv. Water Resour., 2013, 51, 261.
- [2] NIEMCZYNOWICZ J., Urban hydrology and water management: present and future challenges, Urban Water, 1999, 1, 1.
- [3] SCHMITT T.G., ATV-DVWK Kommentar, ATV-A 118 Hydraulische Berechnung von Entwässerungssystemen, DWA, Hennef 2000.
- [4] QUIRMBACH M., SCHULTZ G.A., Comparison of rain gauge and radar data as input to an urban rainfall- runoff model, Water Sci. Technol., 2002, 45, 27.
- [5] EINFALT T., ARNBJERG-NIELSEN K., GOLZ C., JENSEN N.E., QUIRMBACH M., VAES G., VIEUX B., Towards a roadmap for use of radar rainfall data in urban drainage, J. Hydrol., 2004, 299, 186.
- [6] DELRIEU G., CREUTIN J.D., Weather radar and urban hydrology. Advantages and limitations of X-band light configuration systems, Atmos. Res., 1991, 27, 159.
- [7] THORNDAHL S., RASMUSSEN M.R., Marine X-band weather radar data calibration, Atmos. Res., 2011, 103, 33.
- [8] RENDON S., VIEUX B., PATHAK C., Continuous forecasting and evaluation of derived Z–R relationships in a sparse rain gauge network using NEXRAD, J. Hydrol. Eng., 2013, 18 (2), 175.
- [9] CUNHA L.K., SMITH J.A., BAECK L.M., KRAJEWSKI W.F., An early performance evaluation of the nexrad dual-polarization radar rainfall estimates for urban flood applications, Weather Forecasting, 2013, 28, 1478.
- [10] Appendix B. Report on Approaches to UWWTD Compliance in Relation to CSO’s in major cities across the EU, Thames Tunnel Needs Report, 2010.
- [11] SAUVAGEOT H., Radar meteorology, Artech House Publishers, Norwood, MA, 1992.
- [12] CAMPOS E., ZAWADZKI I., Instrumental uncertainties in Z–R relations, J. Applied Meteorol., 2000, 39, 1088.
- [13] RUPP D.E., LICZNAR P., ADAMOWSKI W., LEŚNIEWSKI M., Multiplicative cascade models for fine spatial downscaling of rainfall: parameterization with rain gauge data, Hydrol. Earth Syst. Sci., 2012, 16, 671.
- [14] JOSS J., WALDVOGEL A., A method to improve the accuracy of radar-measured amounts of precipitation, Prepr., Radar Meteorol. Conf., 1970, 14, 237.
- [15] GRAMACY R., tgp: An R package for Bayesian Nonstationary, Semiparametric Nonlinear Regression and design by treed Gaussian process models, J. Stat. Soft., 2007, 19 (9), 1.
- [16] MACKAY G.J.C., Bayesian computation, Neural Comput., 1992, 4, 415.
- [17] MATHERTON G., Principles of geostatics, Econ. Geol., 1963, 58, 1246
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-2a6337df-18e9-49d1-8b6b-84b76d46ef02