Abstract
Verification studies and systems often focus solely on the exercise of verifying forecasts and not on the application of verification information. This chapter discusses the potential for application of hydrological forecast verification information to improve decision-making in and around the forecast process. Decision-makers include model developers and system designers, forecasters, forecast consumers, and forecast administrators. Each of these has an important role in decisions about forecasts and/or the application of forecasts that may be improved through use of forecast verification. For each, we describe the role, the actions that could be taken to improve forecasts or their application, the context and constraints of those actions, and needs for verification information. Consistent with other studies and assessments on forecast verification, we identify the need for a routine forecast verification system to archive data, plan for operations, measure forecast performance, and group forecasts according to application. Further, we call on forecast agencies and forecast consumers to use forecast verification as a routine part of their operations in order to continually improve services and to engage others to use forecast verification to improve decision-making.
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References
American Geophysical Union, AGU statement: investigation of scientists and officials in L’Aquila, Italy, is unfounded. EOS Trans. Am. Geophys. Union 91(28), 248 (2010). https://doi.org/10.1029/2010EO280005
C. Baldwin, M. Waage, R. Steger, J. Garbrecht, T. Piechota et al., Acclimatizing water managers to climate forecasts through decision experiments, in Climate Variations, Climate Change, and Water Resources Engineering (ASCE, Reston, 2006), pp. 115–131
N.D. Bennett, B.F.W. Croke, G. Guariso, J.H.A. Guillaume, S.H. Hamilton, A.J. Jakeman, S. Marsili-Libelli, L.T.H. Newham, J.P. Norton, C. Perrin, S.A. Pierce, B. Robson, R. Seppelt, A.A. Voinov, B.D. Fath, V. Andreassian, Characterising performance of environmental models. Environ. Model. Softw. 40, 1–20 (2013). https://doi.org/10.1016/j.envsoft.2012.09.011
A.A. Bradley, S.S. Schwartz, Summary verification measures and their interpretation for ensemble forecasts. Mon. Weather Rev. 139(9), 3075–3089 (2011). https://doi.org/10.1175/2010MWR3305.1
A.A. Bradley, S.S. Schwartz, T. Hashino, Distributions-oriented verification of ensemble streamflow predictions. J. Hydrometeorol. 5(3), 532–545 (2004)
G.W. Brier, R.A. Allen, Verification of weather forecasts. Compend, in Compendium of Meteorology (1951), pp. 841–848
A.C. Bryant, T.H. Painter, Radiative forcing by desert dust in the Colorado River Basin from 2000 to 2009 inferred from MODIS data, in AGU Fall Meeting Abstracts, vol. 1 (2009), p. 0501. Online Available from http://adsabs.harvard.edu/abs/2009AGUFM.C33B0501B. Accessed 27 Jan 2015
Bureau of Meteorology, Verification in the Bureau. Framework Report (Bureau of Meteorology, Melbourne, 2015)
L. Cuo, T.C. Pagano, Q.J. Wang, A review of quantitative precipitation forecasts and their use in short-to medium-range streamflow forecasting. J. Hydrometeorol. 12(5), 713–728 (2011)
J. Demargne, M. Mullusky, K. Werner, T. Adams, S. Lindsey, N. Schwein, W. Marosi, E. Welles, Application of forecast verification science to operational river forecasting in the US National Weather Service. Bull. Am. Meteorol. Soc. 90(6), 779–784 (2009)
Q. Duan, N.K. Ajami, X. Gao, S. Sorooshian, Multi-model ensemble hydrologic prediction using Bayesian model averaging. Adv. Water Resour. 30(5), 1371–1386 (2007)
B.A. Faber, J.R. Stedinger, Reservoir optimization using sampling SDP with ensemble streamflow prediction (ESP) forecasts. J. Hydrol. 249(1–4), 113–133 (2001). https://doi.org/10.1016/S0022-1694(01)00419-X
M.H. Glantz, Consequences and responsibilities in drought forecasting: the case of Yakima, 1977. Water Resour. Res. 18(1), 3–13 (1982)
N. Gordon, J. Shaykewich, Guidelines on Performance Assessment of Public Weather Services (World Meteorological Organization, Geneva, 2000). Online Available from www.wmo.int/pages/prog/hwrp/documents/FFI/expert/Guidelines_on_Performance_Assessment_of_Public_Weather_Services.pdf
A. Hamlet, D. Huppert, D. Lettenmaier, Economic value of long-lead streamflow forecasts for Columbia River Hydropower. J. Water Resour. Plan. Manag. 128(2), 91–101 (2002). https://doi.org/10.1061/(ASCE)0733-9496(2002)128:2(91)
H. Hartmann, R. Bales, S. Sorooshian, Weather, Climate, and Hydrologic Forecasting for the Southwest U.S. (The University of Arizona, Tucson, 1999). Online Available from http://www.climas.arizona.edu/publication/report/weather-climate-and-hydrologic-forecasting-southwest-us
H.C. Hartmann, R. Bales, S. Sorooshian, Weather, climate, and hydrologic forecasting for the US Southwest: a survey. Clim. Res. 21(3), 239–258 (2002)
I.T. Jolliffe, D.B. Stephenson, Forecast Verification: A Practitioner’s Guide in Atmospheric Science (Wiley, 2012). Online Available from http://books.google.nl/books?hl=en&lr=&id=DCxsKQeaBH8C&oi=fnd&pg=PT8&dq=jolliffe+stephenson&ots=3Ojk_X1AOy&sig=8hGKrwljwaUgKxxYwxzmafhUB8k. Accessed 27 Jan 2015
D. Kahneman, G. Klein, Conditions for intuitive expertise: a failure to disagree. Am. Psychol. 64(6), 515 (2009)
D. Kavetski, M.P. Clark, Numerical troubles in conceptual hydrology: approximations, absurdities and impact on hypothesis testing. Hydrol. Process. 25(4), 661–670 (2011)
Y. Liu, A.H. Weerts, M. Clark, H.-J. Hendricks Franssen, S. Kumar, H. Moradkhani, D.-J. Seo, D. Schwanenberg, P. Smith, A. Van Dijk et al., Advancing data assimilation in operational hydrologic forecasting: progresses, challenges, and emerging opportunities. Hydrol. Earth Syst. Sci. 16(10), 3863–3887 (2012)
B.M. Muir, Trust in automation: Part I. Theoretical issues in the study of trust and human intervention in automated systems. Ergonomics 37(11), 1905–1922 (1994)
B.M. Muir, N. Moray, Trust in automation. Part II. Experimental studies of trust and human intervention in a process control simulation. Ergonomics 39(3), 429–460 (1996)
A.H. Murphy, What is a good forecast? An essay on the nature of goodness in weather forecasting. Weather Forecast. 8(2), 281–293 (1993)
A.H. Murphy, Forecast verification, in Economic Value of Weather and Climate Forecasts (Cambridge University Press, Cambridge, UK/New York/Melbourne, 1997)
N. Nicholls, Cognitive illusions, heuristics, and climate prediction. Bull. Am. Meteorol. Soc. 80(7), 1385–1397 (1999). https://doi.org/10.1175/1520-0477(1999)080<1385:CIHACP>2.0.CO;2
K. O’Grady, L. Shabman, Communicating the probability of Great Lakes water levels and storms, in Proceedings of Great Lakes Water Level Forecast and Statistics Symposium, Windsor (1990) pp. 197–204
T.C. Pagano, Evaluation of Mekong River commission operational flood forecasts, 2000–2012. Hydrol. Earth Syst. Sci. 18(7), 2645–2656 (2014)
T.C. Pagano, D. Garen, S. Sorooshian, Evaluation of official western US seasonal water supply outlooks, 1922–2002. J. Hydrometeorol. 5(5), 896–909 (2004)
T.C. Pagano, H. Hapuarachchi, Q.J. Wang, Continuous Soil Moisture Accounting and Routing Modelling to Support Short Lead-Time Streamflow Forecasting (CSIRO Water for a Healthy Country National Research Flagship, Melbourne, 2009)
T.C. Pagano, A.W. Wood, M.-H. Ramos, H.L. Cloke, F. Pappenberger, M.P. Clark, M. Cranston, D. Kavetski, T. Mathevet, S. Sorooshian, J.S. Verkade, Challenges of operational river forecasting. J. Hydrometeorol. (2014). Online Available from http://journals.ametsoc.org/doi/abs/10.1175/JHM-D-13-0188.1. Accessed 27 Jan 2015
F. Pappenberger, J. Thielen, M. Del Medico, The impact of weather forecast improvements on large scale hydrology: analysing a decade of forecasts of the European Flood Alert System. Hydrol. Process. 25(7), 1091–1113 (2011)
F. Pappenberger, M.H. Ramos, H.L. Cloke, F. Wetterhall, L. Alfieri, K. Bogner, A. Mueller, P. Salamon, How do I know if my forecasts are better? Using benchmarks in hydrological ensemble prediction. J. Hydrol. 522, 697–713 (2015a). https://doi.org/10.1016/j.jhydrol.2015.01.024
F. Pappenberger, H.L. Cloke, D.J. Parker, F. Wetterhall, D.S. Richardson, J. Thielen, The monetary benefit of early flood warnings in Europe. Environ. Sci. Pol. 51, 278–291 (2015b). https://doi.org/10.1016/j.envsci.2015.04.016
R. Parasuraman, V. Riley, Humans and automation: use, misuse, disuse, abuse. Hum. Factors J. Hum. Factors Ergon. Soc. 39(2), 230–253 (1997)
L. Paskus, Why the Silvery Minnow Matters, AlterNet (2003). Online Available from http://www.alternet.org/story/17152/why_the_silvery_minnow_matters. Accessed 27 Jan 2015
C. Perrin, C. Michel, V. Andréassian, Improvement of a parsimonious model for streamflow simulation. J. Hydrol. 279(1), 275–289 (2003)
R.A. Pielke Jr., Who decides? Forecasts and responsibilities in the 1997 Red River flood. Appl. Behav. Sci. Rev. 7(2), 83–101 (1999)
M. Pocernich, Appendix: verification software, in Forecast Verification: A Practitioner’s Guide in Atmospheric Science, 2nd edn. (Wiley, Chichester, 2012), pp. 231–240. Online Available from http://onlinelibrary.wiley.com/doi/10.1002/9781119960003.app1/summary. Accessed 27 Jan 2015
S. Rayner, D. Lach, H. Ingram, Weather forecasts are for wimps: why water resource managers do not use climate forecasts. Clim. Chang. 69(2–3), 197–227 (2005)
S.L. Rhodes, D. Ely, J.A. Dracup, Climate and the Colorado River: the limits of management. Bull. Am. Meteorol. Soc. 65(7), 682–691 (1984)
D.E. Robertson, D.L. Shrestha, Q.J. Wang, Post processing rainfall forecasts from numerical weather prediction models for short term streamflow forecasting. Hydrol. Earth Syst. Sci. Discuss. 10(5), 6765–6806 (2013). https://doi.org/10.5194/hessd-10-6765-2013
A. Rossa, K. Liechti, M. Zappa, M. Bruen, U. Germann, G. Haase, C. Keil, P. Krahe, The COST 731 action: a review on uncertainty propagation in advanced hydro-meteorological forecast systems. Atmos. Res. 100(2–3), 150–167 (2011). https://doi.org/10.1016/j.atmosres.2010.11.016
D. Sarewitz, R.A. Pielke, R. Byerly, Prediction: Science, Decision Making, and the Future of Nature (Island Press, 2000). Online Available from http://books.google.nl/books?hl=en&lr=&id=O0nxEU-deAUC&oi=fnd&pg=PR11&dq=sarewitz+pielke+prediction&ots=F3r_mNYv9p&sig=78GiOAFyglce8xbodoqOVanjRZA. Accessed 27 Jan 2015
L.J. Skitka, K.L. Mosier, M. Burdick, Does automation bias decision-making? Int. J. Hum. Comput. Stud. 51(5), 991–1006 (1999)
H.R. Stanski, L.J. Wilson, W.R. Burrows, Survey of Common Verification Methods in Meteorology (World Meteorological Organization, Geneva, 1989). Online Available from http://www.eumetcal.org/resources/ukmeteocal/verificationSAV/www/english/msg/library/SWB_Chapter1.pdf. Accessed 27 Jan 2015
J.S. Verkade, M.G.F. Werner, Estimating the benefits of single value and probability forecasting for flood warning. Hydrol. Earth Syst. Sci. 15(12), 3751–3765 (2011). https://doi.org/10.5194/hess-15-3751-2011
R.Y. Wang, D.M. Strong, Beyond accuracy: what data quality means to data consumers. J. Manag. Inf. Syst. 12, 5–33 (1996)
A.H. Weerts, H.C. Winsemius, J.S. Verkade, Estimation of predictive hydrological uncertainty using quantile regression: examples from the National Flood Forecasting System (England and Wales). Hydrol. Earth Syst. Sci. 15(1), 255–265 (2011). https://doi.org/10.5194/hess-15-255-2011
E. Welles, Verification of River Stage Forecasts (2005). Online Available from http://arizona.openrepository.com/arizona/handle/10150/195133. Accessed 27 Jan 2015
E. Welles, S. Sorooshian, Scientific verification of deterministic river stage forecasts. J. Hydrometeorol. 10(2), 507–520 (2009)
E. Welles, S. Sorooshian, G. Carter, B. Olsen, Hydrologic verification: a call for action and collaboration. Bull. Am. Meteorol. Soc. 88(4), 503–511 (2007)
F. Wetterhall, F. Pappenberger, H.L. Cloke, J. Thielen-del Pozo, S. Balabanova, J. Daňhelka, A. Vogelbacher, P. Salamon, I. Carrasco, A.J. Cabrera-Tordera et al., Forecasters priorities for improving probabilistic flood forecasts. Hydrol. Earth Syst. Sci. Discuss. 10(2), 2215–2242 (2013)
D.S. Wilks, Statistical Methods in the Atmospheric Sciences (Academic, 2011). Online Available from http://books.google.nl/books?hl=en&lr=&id=IJuCVtQ0ySIC&oi=fnd&pg=PP2&dq=wilks+statistical&ots=anHlqQBLNU&sig=w7ZsTvkiX5BaOjYRzngpdtC9l1M. Accessed 27 Jan2015
World Meteorological Organization, Guide to the Implementation of a Quality Management System for National Meteorological and Hydrological Services (World Meteorological Organization, Geneva, 2013). Online Available from http://www.wmo.int/pages/prog/hwrp/qmf-h/documents/ext/wmo_1100_en.pdf
WWRP/WGNE Joint Working Group on Forecast Verification Research, Forecast Verification: Issues, Method and FAQ (2015). Online Available from http://www.cawcr.gov.au/projects/verification/. Accessed 27 Jan 2015
M. Zappa, K.J. Beven, M. Bruen, A.S. Cofino, K. Kok, E. Martin, P. Nurmi, B. Orfila, E. Roulin, K. Schröter et al., Propagation of uncertainty from observing systems and NWP into hydrological models: COST-731 Working Group 2. Atmos. Sci. Lett. 11(2), 83–91 (2010)
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Werner, K., Verkade, J.S., Pagano, T.C. (2019). Application of Hydrological Forecast Verification Information. In: Duan, Q., Pappenberger, F., Wood, A., Cloke, H., Schaake, J. (eds) Handbook of Hydrometeorological Ensemble Forecasting. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-39925-1_7
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