Albert, J.: Bayesian Computation with R, 2nd Edn., Springer-Verlag, New York, 2009. a
Alfieri, L., Pappenberger, F., Wetterhall, F., Haiden, T., Richardson, D.,
and Salamon, P.: Evaluation of ensemble streamflow predictions in Europe, J.
Hydrol., 517, 913–922, 2014. a
Arango, C. and Ruiz, J. F.: Implementación del modelo WRF para la sabana
de Bogotá, Tech. rep., Instituto de Hidrología, Meteorología y
Estudios Ambientales, Bogotá, available at:
http://www.ideam.gov.co/documents/21021/21132/Modelo_WRF_Bogota.pdf/f1d34638-e9f8-4689-b5f4-31957c231c46
(last access: 20 November 2015), 2011. a, b, c
Bernal, G., Rosero, M., Cadena, M., Montealegre, J., and Sanabria, F.:
Estudio de la Caracterización Climática de Bogotá y cuenca alta
del Río Tunjuelo, Tech. rep., Instituto de Hidrología,
Meteorología y Estudios Ambientales IDEAM – Fondo de Prevención y
Atención de Emergencias FOPAE, Bogotá, 2007. a
Beven, K. J. and Kirkby, M. J.: A physically based, variable contributing
area model of basin hydrology/Un modèle à base physique de zone
d'appel variable de l'hydrologie du bassin versant, Hydrol. Sci. B., 24,
43–69, https://doi.org/10.1080/02626667909491834, 1979. a
Bogner, K., Pappenberger, F., and Cloke, H. L.: Technical Note: The normal
quantile transformation and its application in a flood forecasting system,
Hydrol. Earth Syst. Sci., 16, 1085–1094, https://doi.org/10.5194/hess-16-1085-2012, 2012. a
Bremnes, J. B.: Probabilistic forecasts of precipitation in terms of
quantiles using NWP model output, Mon. Weather Rev., 132, 338–347, 2004. a, b, c, d
Buytaert, W. and Beven, K.: Models as multiple working hypotheses:
hydrological simulation of tropical alpine wetlands, Hydrol. Process., 25,
1784–1799, https://doi.org/10.1002/hyp.7936, 2011. a
Buytaert, W., Wyseure, G., De Bièvre, B., and Deckers, J.: The effect
of land-use changes on the hydrological behaviour of Histic Andosols in south
Ecuador, Hydrol. Process., 19, 3985–3997, https://doi.org/10.1002/hyp.5867, 2005. a
Buytaert, W., Célleri, R., De Bièvre, B., Cisneros, F.,
Wyseure, G., Deckers, J., and Hofstede, R.: Human impact on the hydrology of
the Andean páramos, Earth-Sci. Rev., 79, 53–72, https://doi.org/10.1016/j.earscirev.2006.06.002, 2006. a
Calvetti, L., José, A., and Filho, P.: Ensemble hydrometeorological
forecasts using WRF hourly QPF and TopModel for a middle watershed, Adv.
Meteorol., 2014, 484120, https://doi.org/10.1155/2014/484120, 2014. a
Chen, J., Brissette, F. P., Chaumont, D., and Braun, M.: Finding appropriate
bias correction methods in downscaling precipitation for hydrologic impact
studies over North America, Water Resour. Res., 49, 4187–4205, https://doi.org/10.1002/wrcr.20331, 2013. a
Clark, M., Gangopadhyay, S., Hay, L., Rajagopalan, B., and Wilby, R.: The
Schaake Shuffle: a method for reconstructing space–time variability in
forecasted precipitation and temperature fields, J. Hydrometeorol., 5, 243–262, 2004. a, b
Cloke, H. L. and Pappenberger, F.: Ensemble flood forecasting: a review, J.
Hydrol., 375, 613–626, https://doi.org/10.1016/j.jhydrol.2009.06.005, 2009. a
Colman, B., Cook, K., and Snyder, B.: Numerical weather prediction and
weather forecasting in complex terrain, in: Mountain Weather Research and
Forecasting Recent Progress and Current Challenges, chap. 11, edited by:
Chow, F. K., De Wekker, S. F., and Snyder, B. J., Springer, Dordrecht, 2013. a, b
Cortinas, J. V. J., Brill, K. F., and Baldwin, M. E.: Probabilistic forecasts
of precipitation type, in: 16th Conference on Probability and Statistics in
the Atmospheric Sciences, 12–17 January 2002, Orlando, Florida, 2002. a
Cuo, L., Pagano, T. C., and Wang, Q. J.: A review of quantitative
precipitation forecasts and their use in short- to medium-range streamflow
forecasting, J. Hydrometeorol., 12, 713–728, https://doi.org/10.1175/2011JHM1347.1, 2011. a, b, c
Demeritt, D., Cloke, H., Pappenberger, F., Thielen, J., Bartholmes, J., and
Ramos, M. H.: Ensemble predictions and perceptions of risk, uncertainty, and
error in flood forecasting, Environ. Hazards, 7, 115–127, 2007. a, b, c
Demeritt, D., Nobert, Ś., Cloke, H., and Pappenberg, F.: Challenges in
communicating and using ensembles in operational flood forecasting, Meteorol.
Appl., 17, 209–222, 2010. a
Di, Z., Qingyun, D., Wei, G., Chen, W., Yanjun, G., Jiping, Q.,
Jianduo, L., Chiyuan, M., Aizhong, Y., and Charles, T.: Assessing WRF
model parameter sensitivity: A case study with 5 day summer
precipitation forecasting in the Greater Beijing Area,
Geophys. Res. Lett., 42, 579–587, https://doi.org/10.1002/2014GL061623, 2014. a
Duan, Q.-Y. Y., Sorooshian, S., and Gupta, V.: Effective and efficient global
optimization for conceptual rainfall–runoff models, Water Resour. Res., 28,
1015–1031, https://doi.org/10.1029/91WR02985, 1992. a
Ehret, U., Zehe, E., Wulfmeyer, V., Warrach-Sagi, K., and Liebert, J.: HESS
Opinions “Should we apply bias correction to global and regional climate
model data?”, Hydrol. Earth Syst. Sci., 16, 3391–3404, https://doi.org/10.5194/hess-16-3391-2012, 2012. a
Fan, F. M., Collischonn, W., Meller, A., and Botelho, L. C. M.: Ensemble
streamflow forecasting experiments in a tropical basin: the São
Francisco river case study, J. Hydrol., 519, 2906–2919, https://doi.org/10.1016/j.jhydrol.2014.04.038, 2014. a
Gupta, H. V., Kling, H., Yilmaz, K. K., and Martinez, G. F.: Decomposition of
the mean squared error and NSE performance criteria: Implications for improving
hydrological modelling, J. Hydrol., 377, 80–91, https://doi.org/10.1016/j.jhydrol.2009.08.003, 2009. a
Habets, F., LeMoigne, P., and Noilhan, J.: On the utility of operational
precipitation forecasts to served as input for streamflow forecasting, J.
Hydrol., 293, 270–288, 2004. a
Haerter, J. O., Hagemann, S., Moseley, C., and Piani, C.: Climate model bias
correction and the role of timescales, Hydrol. Earth Syst. Sci., 15, 1065–1079,
https://doi.org/10.5194/hess-15-1065-2011, 2011. a, b
Hao, L. and Naiman, D.: Quantile Regression, SAGE Publications, Thousand Oaks, California, 2007. a
Hersbach, H.: Decomposition of the continuous ranked probability score for
ensemble prediction systems, Weather Forecast., 15, 559–570,
https://doi.org/10.1175/1520-0434(2000)015<0559:DOTCRP>2.0.CO;2, 2000. a, b
Jankov, I., Gallus, W. A., Segal, M., Shaw, B., and Koch, S. E.: The impact
of different WRF model physical parameterizations and their interactions on
warm season MCS rainfall, Weather Forecast., 20, 1048–1060, https://doi.org/10.1175/WAF888.1, 2005. a, b
Jolliffe, I. and Stephenson, D.: Forecast Verification: A Practitioner's
Guide in Atmospheric Science, John Wiley & Sons, Ltd, West Sussex, England, 2012. a, b, c
Kleiber, W., Katz, R. W., and Rajagopalan, B.: Daily spatiotemporal
precipitation simulation using latent and transformed Gaussian processes,
Water Resour. Res., 48, 1–17, 2012. a
Koenker, R. and Machado, J. A. F.: Goodness of fit and related inference
processes for quantile regression. (Statistical Data Included), J. Am. Stat.
Assoc., 94, 1–22, 1999. a, b
Kryza, M., Werner, M., Wałaszek, K., and Dore, A. J.: Application and
evaluation of the WRF model for high-resolution forecasting of rainfall – a
case study of SW Poland, Meteorol. Z., 22, 595–601, 2013. a, b
Laing, A. and Evans, J.-L.: Introduction to Tropical Meteorology, The Comet
Program, Boulder, USA, 2010. a
Leutbecher, M. and Palmer, T. N.: Ensemble forecasting, J. Comput. Phys.,
227, 3515–3539, 2008. a
Liu, J., Wang, J., Pan, S., Tang, K., Li, C., and Han, D.: A real-time flood
forecasting system with dual updating of the NWP rainfall and the river flow,
Nat. Hazards, 77, 1161–1182, https://doi.org/10.1007/s11069-015-1643-8, 2015. a, b, c
López López, P., Verkade, J. S., Weerts, A. H., and Solomatine, D.
P.: Alternative configurations of quantile regression for estimating
predictive uncertainty in water level forecasts for the upper Severn River: a
comparison, Hydrol. Earth Syst. Sci., 18, 3411–3428, https://doi.org/10.5194/hess-18-3411-2014, 2014. a, b, c
Mourre, L., Condom, T., Junquas, C., Lebel, T., Sicart, J., Figueroa, R.,
and Cochachin, A.: Spatio-temporal assessment of WRF, TRMM and in situ
precipitation data in a tropical mountain environment (Cordillera Blanca,
Peru), Hydrol. Earth Syst. Sci., 20, 125–141, https://doi.org/10.5194/hess-20-125-2016, 2016. a
Muggeo, V. M. R., Sciandra, M., Tomasello, A., and Calvo, S.: Estimating
growth charts via nonparametric quantile regression: a practical framework
with application in ecology, Environ. Ecol. Stat., 20, 519–531, 2013. a
Ochoa, A., Pineda, L., Crespo, P., and Willems, P.: Evaluation of TRMM 3B42
precipitation estimates and WRF retrospective precipitation simulation over
the Pacific–Andean region of Ecuador and Peru, Hydrol. Earth Syst. Sci., 18,
3179–3193, https://doi.org/10.5194/hess-18-3179-2014, 2014. a, b
Rama Rao, Y. V., Alves, L., Seulall, B., Mitchell, Z., Samaroo, K., and
Cummings, G.: Evaluation of the weather research and forecasting (WRF) model
over Guyana, Nat. Hazards, 61, 1243–1261, 2012. a
R Development Core Team: A language and environment for statistical
computing, Tech. rep., R Foundation for Statistical Computing, Vienna,
Austria, available at: http://www.R-project.org (last access: 10 April 2015), 2010. a
Remesan, R., Bellerby, T., Holman, I., and Frostick, L.: WRF model
sensitivity to choice of parameterization: a study of the “York Flood 1999”,
Theor. Appl. Climatol., 122, 229–247, https://doi.org/10.1007/s00704-014-1282-0, 2014. a
Rene, J.-R., Madsen, H., and Mark, O.: Probabilistic forecasting for urban
water management: a case study, in: 9th International Conference on Urban
Drainage Modelling, 4–6 September 2012, Belgrade, Serbia, 1–11, 2012. a
Reyes, O.: Utilización de modelos hidrológicos para la
determinación de cuencas en ecosistemas de páramo, Revista Ambiental
Agua, Aire y Suelo, 56–65, available at:
http://revistas.unipamplona.edu.co/ojs_viceinves/index.php/RA/article/view/432
(last access: 22 August 2015), 2014. a
Roberts, N. M., Cole, S. J., Forbes, R. M., Moore, R. J., and Boswellc, D.:
Use of high-resolution NWP rainfall and river flow forecasts for advance
warning of the Carlisle flood, north-west England, Meteorol. Appl., 16, 23–34, 2009. a
Robertson, D. E., Shrestha, D. L., and Wang, Q. J.: Post-processing rainfall
forecasts from numerical weather prediction models for short-term streamflow
forecasting, Hydrol. Earth Syst. Sci., 17, 3587–3603, https://doi.org/10.5194/hess-17-3587-2013, 2013. a, b, c, d
Rogelis, M. C.: Sistema de alerta temprana del río Tunjuelo 2006,
Tech. rep., Fondo de Prevención y Atención de Emergencias, Bogotá, 2006. a, b
Rogelis, M. C.: Operational flood forecasting, warning and response for
multi-scale flood risks in developing cities, PhD thesis, Delft University of
Technology and of the Academic Board of the UNESCO-IHE Institute for Water
Education, Leiden, the Netherlands, 2016. a
Rossa, A., Liechti, K., Zappa, M., Bruen, M., Germann, U., Haase, G.,
Keil, C., and Krahe, P.: The COST 731 Action: a review on uncertainty
propagation in advanced hydro-meteorological forecast systems, Atmos. Res.,
100, 150–167, https://doi.org/10.1016/j.atmosres.2010.11.016, 2011.
a
Ruiz, J. F.: ¿Como Interpretar Los Modelos De Pronóstico Del Estado Del
Tiempo?, Tech. rep., Instituto de Hidrología, Meteorología y
Estudios Ambientales, Bogotá, available at: http://bart.ideam.gov.co/wrfideam/GUIA_MODELOS.pdf
(last access: 15 September 2014), 2010. a
Scardovi, E.: Rainfall spatial predictions: a two-part model and its
assessment, PhD thesis, University of Bologna, Bologna, available at:
http://amsdottorato.unibo.it/6744/1/scardovi_elena_tesi.pdf (last access:
10 January 2016), 2015. a
Sevink, J.: Páramo Andino Project Hydrology workshop in Merida, Venezuela, 2011. a
Teng, J., Potter, N. J., Chiew, F. H. S., Zhang, L., Wang, B., Vaze, J., and
Evans, J. P.: How does bias correction of regional climate model precipitation
affect modelled runoff?, Hydrol. Earth Syst. Sci., 19, 711–728, https://doi.org/10.5194/hess-19-711-2015, 2015. a
Theis, S. E., Hense, A., and Damrath, U.: Probabilistic precipitation
forecasts from a deterministic model: a pragmatic approach, Meteorol. Appl.,
12, 257–268, https://doi.org/10.1017/S1350482705001763, 2005. a
Verkade, J. S. and Werner, M. G. F.: Estimating the benefits of single value
and probability forecasting for flood warning, Hydrol. Earth Syst. Sci., 15,
3751–3765, https://doi.org/10.5194/hess-15-3751-2011, 2011. a
Verkade, J. S., Brown, J. D., Reggiani, P., and Weerts, A. H.:
Post-processing ECMWF precipitation and temperature ensemble reforecasts for
operational hydrologic forecasting at various spatial scales, J. Hydrol.,
501, 73–91, https://doi.org/10.1016/j.jhydrol.2013.07.039, 2013. a
Vincendon, B., Ducrocq, V., Nuissier, O., and Vié, B.: Perturbation of
convection-permitting NWP forecasts for flash-flood ensemble forecasting,
Nat. Hazards Earth Syst. Sci., 11, 1529–1544, https://doi.org/10.5194/nhess-11-1529-2011, 2011. a, b
Wilks, D. S.: Forecast Verification, in: vol. 100, 2nd Edn., Elsevier, San Diego,
California, USA, 2011. a, b, c, d, e, f, g
Xuan, Y., Cluckie, I. D., and Wang, Y.: Uncertainty analysis of hydrological
ensemble forecasts in a distributed model utilising short-range rainfall
prediction, Hydrol. Earth Syst. Sci., 13, 293–303, https://doi.org/10.5194/hess-13-293-2009, 2009. a
Yang, W., Andréasson, J., Phil Graham, L., Olsson, J., Rosberg, J., and
Wetterhall, F.: Distribution-based scaling to improve usability of regional
climate model projections for hydrological climate change impacts studies,
Hydrol. Res., 41, 211–229, 2010. a, b
Yuan, X. and Wood, E. F.: Downscaling precipitation or bias-correcting
streamflow? Some implications for coupled general circulation model
(CGCM)-based ensemble seasonal hydrologic forecast, Water Resour. Res., 48, 1–7, 2012. a
Yucel, I., Onen, a., Yilmaz, K. K., and Gochis, D. J.: Calibration and
evaluation of a flood forecasting system: Utility of numerical weather
prediction model, data assimilation and satellite-based rainfall, J. Hydrol.,
523, 49–66, https://doi.org/10.1016/j.jhydrol.2015.01.042, 2015. a