References

NHESS

Natural Hazards and Earth System Sciences

NHESS

Nat. Hazards Earth Syst. Sci.

1684-9981

Copernicus Publications

Göttingen, Germany

10.5194/nhess-10-353-2010

Use of past precipitation data for regionalisation of hourly rainfall in the low mountain ranges of Saxony, Germany

Pluntke

¹ Jatho

¹ Kurbjuhn

¹ Dietrich

² Bernhofer

Institute of Hydrology and Meteorology, Department of Meteorology, Technische Universität Dresden, Dresden, Germany

Institute of Water Resources Management, Hydrology and Agricultural Hydraulic Engineering, Leibniz Universität Hannover, Hannover, Germany

23 02 2010

10 2 353 370

2010

This work is licensed under the Creative Commons Attribution 3.0 Unported License. To view a copy of this licence, visit https://creativecommons.org/licenses/by/3.0/

This article is available from https://nhess.copernicus.org/articles/10/353/2010/nhess-10-353-2010.html

The full text article is available as a PDF file from https://nhess.copernicus.org/articles/10/353/2010/nhess-10-353-2010.pdf

Within the context of flood forecasting we deal with the improvement of regionalisation methods for the generation of highly resolved (1 h, 1×1km2) precipitation fields, which can be used as input for rainfall-runoff models or for verification of weather forecasts. Although radar observations of precipitation are available in many regions, it might be necessary to apply regionalisation methods near real-time for the cases that radar is not available or observations are of low quality. The aim of this paper is to investigate whether past precipitation information can be used to improve regionalisation of rainfall. Within a case study we determined typical precipitation Background-Fields (BGF) for the mountainous and hilly regions of Saxony using hourly and daily rain gauge data. Additionally, calibrated radar data served as past information for the BGF generation. For regionalisation of precipitation we used de-trended kriging and compared the results with another kriging based regionalisation method and with Inverse Distance Weighting (IDW). The performance of the methods was assessed by applying cross-validation, by inspection and by evaluation with rainfall-runoff simulations. The regionalisation of rainfall yielded better results in case of advective events than in case of convective events. The performance of the applied regionalisation methods showed no significant disagreement for different precipitation types. Cross-validation results were rather similar in most cases. Subjectively judged, the BGF-method reproduced best the structures of rain cells. Precipitation input derived from radar or kriging resulted in a better matching between observed and simulated flood hydrographs. Simple techniques like IDW also deliver satisfying results in some occasions. Implementation of past radar data into the BGF-method rendered no improvement, because of data shortages. Thus, no method proved to outperform the others generally. The decision, which method is appropriate for an event, should be made objectively using cross-validation, but also subjectively, using the expert knowledge of the forecaster.

References 1

Ahrens, B.: Distance in spatial interpolation of daily rain gauge data, Hydrol. Earth Syst. Sci., 10, 197–208, 2006

Allamano, P., Claps, P., Laio, F., and Thea, C.: A data-based assessment of the dependence of short-duration precipitation on elevation, Phys. Chem. Earth, 34(10–12), 635–641, 2009.

Alpuim, T. and Barbosa, S.: The Kalman filter in the estimation of area precipitation, Environmetrics, 10(4), 377–394, 1999.

Atkinson, P. M. and Lloyd, C. D.: Mapping Precipitation in Switzerland with Ordinary and Indicator Kriging, Journal of Geographic Information and Decision Analysis (GIDA), 2(2), 65–76, 1998.

Bàrdossy, A., and Das, T.: Influence of rainfall observation network on model calibration and application, Hydrol. Earth Syst. Sci., 12, 77–89, 2008.

Barlo, R., Bartholomew, D., Brenner, J., and Brunk, H.: Statistical Inference under Order Restrictions, John Wiley and Sons, New York, USA, 1972.

Bartels, H.: Projekt RADOLAN. Routineverfahren zur Online-Aneichung der Radarniederschlagsdaten mit Hilfe von automatischen Bodenniederschlagsstationen (Ombrometer), Projekt-Abschlussbericht, available at: <a href="http://www.dwd.de">http://www.dwd.de</a>, (search for: Radolan) (last access: 10 February 2010), 2004.

Becker, A., Klöcking, B., Lahmer, W., and Pfützner, B.: The hydrological modelling system ARC/EGMO, in: Mathematical models of large watershed hydrology, edited by: Singh, V. P. and Frevert, D. K., Water Resour. Publ., Littleton, Colorado, USA, 2002.

Berne, A., Delrieu, G., Creutin, J. D., and Obled, C.: Temporal and spatial resolution of rainfall measurements required for urban hydrology, J. Hydrol., 299, 166–179, 2004.

Bernhofer, C., Goldberg, V., Franke, J., Häntzschel, J., Harmansa, S., Pluntke, T., Geidel, K., Surke, M., Prasse, H., Freydank, E., Hänsel, S., Mellentin, U., and Küchler, W.: Sachsen im Klimawandel – Eine Analyse. Sächsisches Staatsministerium für Umwelt und Landwirtschaft, Eigenverlag, Dresden, Germany, 2008 (in German).

Bissolli, P. and Dittmann, E.: Objektive Wetterlagenklassen, Klimastatusbericht des DWD 2002, Germany, 2003.

Bliefernicht, J., Bàrdossy, A., and Ebert, C.: Stochastic simulation of hourly precipitation fields for extreme events on the rivers Freiberger Mulde, Oberer Main, and Fränkische Saale, Hydrol. Wasserbewirts., 52(4), 168–172, 2008.

Brandes, E. A.: Optimizing rainfall estimates with the aid of RADAR, J. Appl. Meteorol., 14, 1339–1345, 1975.

Casper, M. C., Herbst, M., Grundmann, J., Buchholz, O., and Bliefernicht, J.: Influence of rainfall variability on the simulation of extreme runoff in small catchments, Hydrol. Wasserbewirts., 53(3), 134–139, 2009.

Chilès, J. P. and Delfiner, P.: Geostatistics: modelling spatial uncertainty, John Wiley and Sons, New York, USA, 1999.

Cressie, N.: Statistics for spatial data, John Wiley and Sons, New York, USA, 1991.

Creutin, J. D. and Obled, C.: Objective analyses and mapping techniques for rainfall fields: an objective comparison, Water Resour. Res., 18(2), 413–431, 1982.

Dietrich, J., Trepte, S., Wang, Y., Schumann, A. H., Vo{ß}, F., Hesser, F. B., and Denhard, M.: Combination of different types of ensembles for the adaptive simulation of probabilistic flood forecasts: hindcasts for the Mulde 2002 extreme event, Nonlin. Processes Geophys., 15, 275–286, 2008.

Dirks, K. N., Hay, J. E., Stow, C. D., and Harris, D.: High-resolution studies of rainfall on Norfolk Island, Part II: Interpolation of rainfall data, J. Hydrol., 208(3–4), 187–193, 1998.

Dorninger, M., Schneider, S., and Steinacker, R.: On the interpolation of precipitation data over complex terrain, Meteorol. Atmos. Phys., 101, 175–189, 2008.

Ehret, U.: Rainfall and Flood Nowcasting in Small Catchments using Weather Radar, Mitteilungen Instit. Wasserbau, Universität Stuttgart, Germany, 2003.

Eischeid, J. K., Pasteris, P. A., Diaz, H. F., Plantico, M. S., and Lott, N. J.: Creating a serially complete, national daily time series of temperature and precipitation for the Western United States, J. Appl. Meteorol., 39, 1580–1591, 2000.

Flemming, G.: Angewandte Klimatologie von Sachsen – Basis und Zustandsklima von Sachsen. Tharandter Klimaprotokolle, 4, Technische Universität Dresden, Germany, 2001 (in German).

Genton, M. G.: Highly Robust Variogram Estimation, Math. Geol., 30(2), 213–221,1998.

Goovaerts, P.: Geostatistical approaches for incorporating elevation into the spatial interpolation of rainfall, J. Hydrol., 228, 113–129, 2000.

Grijsen, J. G., Snoeker, X. C., Vermeulen, C. J. M., Mohamed El Amin Moh. Nur, and Yasir, A. M.: An Information System for Flood Early Warning, in: Floods and Flood Management, edited by: Saul, A. J., Kluwer Academic Publishers, 263–289, 1992.

Haberlandt, U.: Geostatistical interpolation of hourly precipitation from rain gauges and radar for a large-scale extreme rainfall event, J. Hydrol., 332(1–2), 144–157, 2007.

Hess, P. and Brezowsky, H.: Katalog der Gro{ß}wetterlagen Europas 1881–1976. Berichte des Deutschen Wetterdienstes, 113, Eigenverlag Deutscher Wetterdienst, Offenbach a. M., Germany, 1977 (in German).

Hinterding, A.: Entwicklung hybrider Interpolationsverfahren für den automatisierten Betrieb am Beispiel meteorologischer Grö{ß}en, Inst. f. Geoinformatik, Westfälische Wilhelms-Universität Münster, IfGIprints, 19, Münster, Germany, 2003 (in German).

Jensen, N. E. and Pedersen, L.: Spatial variability of rainfall: Variations within a single radar pixel, Atmos. Res., 77, 269–277, 2005.

Jatho, N., Pluntke, T., Kurbjuhn, C., and Bernhofer, C.: An approach to combine radar and gauge based rainfall data under consideration of their qualities in low mountain ranges of Saxony, Nat. Hazards Earth Syst. Sci., in review, 2010.

Kneis, D. and Heistermann, M.: Bewertung der Güte einer radarbasierten Niederschlagsschätzung am Beispiel eines kleinen Einzugsgebiets, Hydrol. Wasserbewirts., 53(3), 160–171, 2009 (in German).

Kuczera, G., Kavetski, D., Franks, S., and Thyer, M.: Towards a Bayesian total error analysis of conceptual rainfall-runoff models: Characterising model error using storm-dependent parameters, J. Hydrol., 331, 161–177, 2006.

Matheron, G.: Traite de geostatistique appliquee, Tome I: Memoires du Bureau de Recherches Geologiques et Minieres, 14, Editions Technip, Paris, 1962 (in French).

Merz, R., Blöschl, G., and Parajka, J.: Raum-zeitliche Variabilität von Ereignisabflussbeiwerten in Österreich, Hydrol. Wasserbewirts., 50(1), 2–11, 2006 (in German).

Nash, J. E. and Sutcliffe, J. V.: River flow forecasting through conceptual models, part I – A discussion of principles, J. Hydrol., 10(3), 282–290, 1970.

Paulat, M., Frei, C., Hagen, M., and Wernli, H.: A gridded dataset of hourly precipitation in Germany: Its construction, climatology and application, Meteorol. Z., 17(6), 719–732, 2008.

Pessoa, M. L., Rafael, L. B., and Earle, R. W.: Use of weather radar for flood forecasting in the Sieve river basin: a sensitivity analysis, J. Appl. Meteorol., 32, 462–475, 1993.

Richter, D.: Ergebnisse methodischer Untersuchungen zur Korrektur des systematischen Messfehlers des Hellmann-Niederschlagsmessers, Berichte des Deutschen Wetterdienstes 194, Eigenverlag Deutscher Wetterdienst, Offenbach a. M., Germany, 1995 (in German).

Ruelland, D., Ardoin-Bardin, S., Billen, G., and Servat, E.: Sensitivity of a lumped and semi-distributed hydrological model to several methods of rainfall interpolation on a large basin in West Africa, J. Hydrol., 361, 96–117, 2008.

Seo, D. J., Krajewski, W. F., and Bowles, D. S: Stochastic interpolation of rainfall data from raingauges and radar using co-kriging: 1. Design of experiments, Water Resour. Res., 26(3), 469–477, 1990a.

Seo, D. J., Krajewski, W. F., and Bowles, D. S: Stochastic interpolation of rainfall data from raingauges and radar using co-kriging: 2. Results, Water Resour. Res., 26(5), 915–924, 1990b.

Sun, X., Mein, R. G., Keenan, T. D., and Elliott, J. F.: Flood estimation using radar and raingauge data, J. Hydrol., 239(1–4), 4–48, 2000.

Tabios, G. Q. and Salas, J. D.: A comparative-analysis of techniques for spatial interpolation of precipitation, Water Resour. Bull., 21(3), 365–380, 1985.

Todini, E.: A Bayesian technique for conditioning radar precipitation estimates to rain-gauge measurements, Hydrol. Earth Syst. Sci., 5, 187–199, 2001.

Wackernagel, H.: Multivariate geostatistics: an introduction with applications, Springer, Heidelberg, Germany, 1995.