969
Views
8
CrossRef citations to date
0
Altmetric
Original Articles

Verification of short-term runoff forecasts for a small Philippine basin (Marikina)

, , &
Pages 205-216 | Received 07 Nov 2014, Accepted 13 Apr 2016, Published online: 29 Sep 2016

References

  • Abon, C.C. , David, C.P.C. , and Pellejera, N.E.B. , 2011. Reconstructing the tropical storm Ketsana flood event in Marikina River, Philippines. Hydrology and Earth System Sciences , 15 (4), 1283–1289. doi:10.5194/hess-15-1283-2011
  • Abon, C.C. , et al., 2015. Evaluating the potential of radar-based rainfall estimates for streamflow and flood simulations in the Philippines. Geomatics, Natural Hazards and Risk . doi:10.1080/19475705.2015.1058862
  • Arnaud, P. , et al., 2011. Sensitivity of hydrological models to uncertainty in rainfall input. Hydrological Sciences Journal , 56 (3), 397–410. doi:10.1080/02626667.2011.563742
  • Beven, K. and Binley, A. , 1992. The future of distributed models: model calibration and uncertainty prediction. Hydrological Processes , 6 (3), 279–298. doi:10.1002/(ISSN)1099-1085
  • Bronstert, A. , et al., 2012. Potentials and constraints of different types of soil moisture observations for flood simulations in headwater catchments. Natural Hazards , 60, 879–914. doi:10.1007/s11069-011-9874-9
  • Brown, J.D. and Seo, D.-J. , 2013. Evaluation of a nonparametric post-processor for bias correction and uncertainty estimation of hydrologic predictions. Hydrological Processes , 27 (1), 83–105. doi:10.1002/hyp.v27.1
  • Bürger, G. , Reusser, D. , and Kneis, D. , 2009. Early flood warnings from empirical (expanded) downscaling of the full ECMWF ensemble prediction system. Water Resources Research , 45, W10443. doi:10.1029/2009WR007779
  • Carnell, R. , 2012. lhs: Latin Hypercube Samples. R package version 0.10 . Available from: http://CRAN.R-project.org/package=lhs
  • Crisologo, I. , et al., 2014. Polarimetric rainfall retrieval from a c-band weather radar in a tropical environment (The Philippines). Asia-Pacific Journal of Atmospheric Sciences , 50 (S1), 595–607. doi:10.1007/s13143-014-0049-y
  • De Bruin, H.A.R. , 1987. From Penman to Makkink. In: J.C. Hooghart , Ed. Evaporation and Weather: Proceedings and information . Vol. 39. The Hague: TNO Committee on Hydrological Research.
  • De Roo, A.P.J. , Wesseling, C.G. , and Van Deursen, W.P.A. , 2000. Physically based river basin modelling within a gis: the lisflood model. Hydrological Processes , 14 (11–12), 1981–1992. doi:10.1002/(ISSN)1099-1085
  • Demargne, J. , et al., 2014. The science of NOAA’s operational hydrologic ensemble forecast service. Bulletin American Meteorological Social , 95 (1), 79–98. doi:10.1175/BAMS-D-12-00081.1
  • Ehret, U. , et al., 2008. Radar-based flood forecasting in small catchments, exemplified by the Goldersbach catchment, Germany. International Journal of River Basin Management , 6 (4), 323–329. doi:10.1080/15715124.2008.9635359
  • Engeland, K. and Gottschalk, L. , 2002. Bayesian estimation of parameters in a regional hydrological model. Hydrology and Earth Systems Sciences , 6 (5), 883–898. doi:10.5194/hess-6-883-2002
  • Feddes, R.A. , 1987. Crop factors in relation to Makkink reference-crop evapotranspiration. In: J.C. Hooghart , Ed. Evaporation and Weather: proceedings and information . Vol. 39. The Hague: TNO Committee on Hydrological Research.
  • Frederick, R.H. , Myers, V.A. , and Auciello, E.P. , 1977. Storm depth-area relations from digitized radar returns. Water Resources Research , 13 (3), 675–679. doi:10.1029/WR013i003p00675
  • Golding, B.W. , 1998. Nimrod: a system for generating automated very short range forecasts. Meteorological Applications , 5 (1), 1–16. doi:10.1017/S1350482798000577
  • Goudenhoofdt, E. and Delobbe, L. , 2009. Evaluation of radar-gauge merging methods for quantitative precipitation estimates. Hydrology Earth Systems Sciences , 13, 195–203. doi:10.5194/hess-13-195-2009
  • Gourley, J.J. , et al., 2011. Hydrologic evaluation of rainfall estimates from radar, satellite, gauge, and combinations on Ft. Cobb Basin, Oklahoma. Journal of Hydrometeorology , 12, 973–988. doi:10.1175/2011JHM1287.1
  • Gourley, J.J. and Vieux, B.E. , 2005. A method for evaluating the accuracy of quantitative precipitation estimates from a hydrologic modeling perspective. Journal of Hydrometeorology , 6, 115–133. doi:10.1175/JHM408.1
  • Heistermann, M. , et al., 2015. The emergence of open source software for the weather radar community. Bulletin of the American Meteorological Society , 96, 117–128. doi:10.1175/BAMS-D-13-00240.1
  • Heistermann, M. , et al., 2013a. Brief communication “Using the new Philippine radar network to reconstruct the Habagat of august 2012 monsoon event around Metropolitan Manila”. Natural Hazards and Earth System Sciences , 13 (3), 653–657. doi:10.5194/nhess-13-653-2013
  • Heistermann, M. , Jacobi, S. , and Pfaff, T. , 2013b. Technical note: an open source library for processing weather radar data (wradlib). Hydrology and Earth System Sciences , 17 (2), 863–871. doi:10.5194/hess-17-863-2013
  • Heistermann, M. and Kneis, D. , 2011. Benchmarking quantitative precipitation estimation by conceptual rainfall–runoff modeling. Water Resources Research , 47, W06514. doi:10.1029/2010WR009153
  • Jolliffe, I.T. and Stephenson, D.B. , Eds., 2003. Forecast verification, a practitioner’s guide in atmospheric science . Chichester: Wiley.
  • Kitanidis, P.K. and Bras, R.L. , 1980. Real-time forecasting with a conceptual hydrologic model: 1. Analysis of uncertainty. Water Resources Research , 16 (6), 1025–1033. doi:10.1029/WR016i006p01025
  • Kitchen, M. and Blackall, R.M. , 1992. Representativeness errors in comparisons between radar and gauge measurements of rainfall. Journal of Hydrology , 134 (1–4), 13–33. doi:10.1016/0022-1694(92)90026-R
  • Kneis, D. , 2012a. Eco-Hydrological Simulation Environment (ECHSE) - Documentation of model engines . University of Potsdam, Institute of Earth and Environmental Sciences. Available from: http://echse.github.io/downloads/documentation/echse_engines_doc.pdf
  • Kneis, D. , 2012b. Eco-Hydrological Simulation Environment (ECHSE) - Documentation of Pre- and Post-Processors . University of Potsdam, Institute of Earth and Environmental Sciences. Available from: http://echse.github.io/downloads/documentation/echse_tools_doc.pdf
  • Kneis, D. , 2015. A lightweight framework for rapid development of object-based hydrological model engines. Environmental Modelling & Software , 68, 110–121. doi:10.1016/j.envsoft.2015.02.009
  • Kneis, D. , Bürger, G. , and Bronstert, A. , 2012. Evaluation of medium-range runoff forecasts for a 50 km2 watershed. Journal of Hydrology , 414-415, 341–353. doi:10.1016/j.jhydrol.2011.11.005
  • Kneis, D. , Chatterjee, C. , and Singh, R. , 2014. Evaluation of TRMM rainfall estimates over a large Indian river basin (Mahanadi). Hydrology and Earth System Sciences , 18, 2493–2502. doi:10.5194/hess-18-2493-2014
  • Kneis, D. and Heistermann, M. , 2009. Bewertung der Güte einer Radar-basierten Niederschlagsschätzung am Beispiel eines kleinen Einzugsgebiets (Quality assessment of radar-based precipitation estimates with the example of a small catchment; in German). Hydrologie und Wasserbewirtschaftung , 53 (3), 160–171.
  • Krause, P. , Boyle, D.P. , and Bäse, F. , 2005. Comparison of different efficiency criteria for hydrological model assessment. Advances in Geosciences , 5, 89–97. doi:10.5194/adgeo-5-89-2005
  • Krzysztofowicz, R. , 1999. Bayesian theory of probabilistic forecasting via deterministic hydrologic model. Water Resources Research , 35 (9), 2739–2750. doi:10.1029/1999WR900099
  • Krzysztofowicz, R. , 2001. The case for probabilistic forecasting in hydrology. Journal of Hydrology , 249, 2–9. doi:10.1016/S0022-1694(01)00420-6
  • Kuzmin, V. , Seo, D.-J. , and Koren, V. , 2008. Fast and efficient optimization of hydrologic model parameters using a priori estimates and stepwise line search. Journal of Hydrology , 353, 109–128. doi:10.1016/j.jhydrol.2008.02.001
  • Liu, Y. , et al., 2012. Advancing data assimilation in operational hydrologic forecasting: Progresses, challenges, and emerging opportunities. Hydrology and Earth Systems Sciences , 16, 3863–3887. doi:10.5194/hess-16-3863-2012
  • Liu, Z. , Martina, M.L.V. , and Todini, E. , 2005. Flood forecasting using a fully distributed model: Application of the TOPKAPI model to the Upper Xixian Catchment. Hydrology and Earth Systems Sciences , 9, 347–364. doi:10.5194/hess-9-347-2005
  • Ludwig, K. and Bremicker, M. , Eds., 2006. The water balance model LARSIM - design, content and application. Vol. 22 of Freiburger Schriften zur Hydrologie . Freiburg, Germany: University of Freiburg, Institute of Hydrology.
  • Montanari, A. and Brath, A. , 2004. A stochastic approach for assessing the uncertainty of rainfall–runoff simulations. Water Resources Research , 40 (1), W01106. doi:10.1029/2003WR002540
  • Nash, J.E. and Sutcliffe, J.V. , 1970. River flow forecasting through conceptual models part I - A discussion of principles. Journal of Hydrology , 10 (3), 282–290. doi:10.1016/0022-1694(70)90255-6
  • Pierce, C. , et al., 2004. The nowcasting of precipitation during Sydney 2000: an appraisal of the QPF algorithms. Weather and Forecasting , 19 (1), 7–21. doi:10.1175/1520-0434(2004)019<0007:TNOPDS>2.0.CO;2
  • Plate, E.J. , 2009. HESS Opinions “Classification of hydrological models for flood management“. Hydrology and Earth Systems Sciences , 13, 1939–1951. doi:10.5194/hess-13-1939-2009
  • Refsgaard, J.C. , 1997. Validation and intercomparison of different updating procedures for real-time forecasting. Nordic Hydrology , 28, 65–84.
  • Robinson, J.S. , Sivapalan, M. , and Snell, J.D. , 1995. On the relative roles of hillslope processes, channel routing, and network geomorphology in the hydrologic response of natural catchments. Water Resources Research , 31 (12), 3089–3101. doi:10.1029/95WR01948
  • Rodriguez-Iturbe, I. and Mejía, J.M. , 1974. On the transformation of point rainfall to areal rainfall. Water Resources Research , 10 (4), 729–735. doi:10.1029/WR010i004p00729
  • Sadegh, M. and Vrugt, J.A. , 2013. Bridging the gap between GLUE and formal statistical approaches: approximate Bayesian computation. Hydrology and Earth System Sciences , 17 (12), 4831–4850. Available from: http://www.hydrol-earth-syst-sci.net/17/4831/2013/
  • Schmitz, G.H. and Cullmann, J. , 2008. PAI-OFF: A new proposal for online flood forecasting in flash flood prone catchments. Journal of Hydrology , 360 (1–4), 1–14. doi:10.1016/j.jhydrol.2008.07.002
  • Sebastianelli, S. , et al., 2013. On precipitation measurements collected by a weather radar and a rain gauge network. Natural Hazards and Earth Systems Sciences , 13, 605–623. doi:10.5194/nhess-13-605-2013
  • Seo, B.-C. and Krajewski, W.F. , 2010. Scale dependence of radar rainfall uncertainty: initial evaluation of NEXRAD’s new super-resolution data for hydrologic applications. Journal of Hydrometeorology , 11, 1191–1198. doi:10.1175/2010JHM1265.1
  • Seo, D.-J. , Herr, H.D. , and Schaake, J.C. , 2006. A statistical post-processor for accounting of hydrologic uncertainty in short-range ensemble streamflow prediction. Hydrology and Earth Systems Sciences Discussions , 3, 1987–2035. doi:10.5194/hessd-3-1987-2006
  • Solomatine, D.P. and Ostfeld, A. , 2008. Data-driven modelling: some past experiences and new approaches. Journal of Hydroinformatics , 10 (1), 3–22. doi:10.2166/hydro.2008.015
  • Tarboton, D.G. and Luce, C.H. , 1996. Utah energy balance snow accumulation and melt model (UEB). Technical report. Utah State University and USDA Forest Service.
  • Todini, E. , 1996. The ARNO rainfall–runoff model. Journal of Hydrology , 175, 339–382. doi:10.1016/S0022-1694(96)80016-3
  • Todini, E. , 2008. A model conditional processor to assess predictive uncertainty in flood forecasting. International Journal of River Basin Management , 6 (2), 123–137. doi:10.1080/15715124.2008.9635342
  • Todini, E. , 2013. From HUP to MCP: analogies and extended performances. Journal of Hydrology , 477, 33–42. doi:10.1016/j.jhydrol.2012.10.037
  • Toth, E. , Brath, A. , and Montanari, A. , 2000. Comparison of short-term rainfall prediction models for real-time flood forecasting. Journal of Hydrology , 239, 132–147. doi:10.1016/S0022-1694(00)00344-9
  • Vieux, B. and Imgarten, J. , 2012. On the scale-dependent propagation of hydrologic uncertainty using high-resolution x-band radar rainfall estimates. Atmospheric Research , 103, 96–105. doi:10.1016/j.atmosres.2011.06.009
  • Weerts, A.H. , Winsemius, H.C. , and Verkade, J.S. , 2011. Estimation of predictive hydrological uncertainty using quantile regression: examples from the national flood forecasting system (England and Wales). Hydrology and Earth System Sciences , 15 (1), 255–265. doi:10.5194/hess-15-255-2011
  • Werner, M. and Cranston, M. , 2009. Understanding the value of radar rainfall Nowcasts in flood forecasting and warning in flashy catchments. Meteorological Applications , 16, 41–55. doi:10.1002/met.v16:1
  • Zhao, R.-J. , et al., 1980. The Xinanjiang model. In: Hydrological forecasting, Proceedings of the Oxford Symposium. Vol. 129 of IAHS-AISH Publ . Wallingford, UK: IAHS Press, 351–356.

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.