31. Beven K, Freer J (2001) Equifinality, data assimilation, and uncertainty estimation in
mechanistic modelling of complex environmental systems using the GLUE methodology.
J Hydrol 249:11–29
32. Mantovan P, Todini E (2006) Hydrological forecasting uncertainty assessment: incoherence of
the GLUE methodology. J Hydrol 330(1–2):368–381. https://doi.org/10.1016/j.jhydrol.2006.
04.046
33. Pappenberger F, Beven KJ (2006) Ignorance is bliss: or seven reasons not to use uncertainty
analysis. Water Resour Res 42(5):1–8. https://doi.org/10.1029/2005WR004820
34. Liu Y, Weerts AH, Clark M, Hendricks Franssen HJ, Kumar S, Moradkhani H, Seo DJ,
Schwanenberg D, Smith P, Van Dijk AIJM, Van Velzen N, He M, Lee H, Noh SJ,
Rakovec O, Restrepo P (2012) Advancing data assimilation in operational hydrologic forecasting: progresses, challenges, and emerging opportunities. Hydrol Earth Syst Sci 16
(10):3863–3887. https://doi.org/10.5194/hess-16-3863-2012
35. Seo DJ, Cajina L, Corby R, Howieson T (2009) Automatic state updating for operational
streamflow forecasting via variational data assimilation. J Hydrol 367(3–4):255–275. https://
doi.org/10.1016/j.jhydrol.2009.01.019
36. Welles E, Sorooshian S, Carter G, Olsen B (2007) Hydrologic verification: a call for action and
collaboration. Bull Am Meteorol Soc 88:503–511
37. Yarvis M, Kushalnagar N, Singh H, Rangarajan A, Liu Y, Singh S (2005) Exploiting heterogeneity in sensor networks. Proceedings IEEE INFOCOM 2005. 24th annual joint conference
of the IEEE computer and communications societies, vol 2, pp 878–890
38. Bonney R, Shirk JL, Phillips TB, Wiggins A, Ballard HL, Miller-Rushing AJ, Parrish JK (2014)
Next steps for citizen science. Science 343(6178):1436–1437. https://doi.org/10.1126/science.
1251554
39. Buytaert W, Zulkafli Z, Grainger S, Acosta L, Alemie TC, Bastiaensen J, De BiÃvre B,
Bhusal J, Clark J, Dewulf A, Foggin M, Hannah DM, Hergarten C, Isaeva A,
Karpouzoglou T, Pandeya B, Paudel D, Sharma K, Steenhuis T, Tilahun S, Van Hecken G,
Zhumanova M (2014) Citizen science in hydrology and water resources: opportunities for
knowledge generation, ecosystem service management, and sustainable development. Front
Earth Sci 2:1–21. https://doi.org/10.3389/feart.2014.00026
40. Clark MP, Rupp DE, Woods RA, Zheng X, Ibbitt RP, Slater AG, Schmidt J, Uddstrom MJ
(2008) Hydrological data assimilation with the ensemble Kalman filter: use of streamflow
observations to update states in a distributed hydrological model. Adv Water Resour 31
(10):1309–1324. https://doi.org/10.1016/j.advwatres.2008.06.005
41. Mazzoleni M, Alfonso L, Solomatine D (2016) Influence of spatial distribution of sensors and
observation accuracy on the assimilation of distributed streamflow data in hydrological modelling. Hydrol Sci J 62(3):389–407. https://doi.org/10.1080/02626667.2016.1247211
42. Mazzoleni M, Noh SJ, Lee H, Liu Y, Seo DJ, Amaranto A, Alfonso L, Solomatine DP (2018)
Real-time assimilation of streamflow observations into a hydrological routing model: effects of
model structures and updating methods. Hydrol Sci J 63:386–407
43. Rakovec O, Weerts AH, Hazenberg P, Torfs PJJF, Uijlenhoet R (2012) State updating of a
distributed hydrological model with ensemble Kalman filtering: effects of updating frequency
and observation network density on forecast accuracy. Hydrol Earth Syst Sci 16(9):3435–3449.
https://doi.org/10.5194/hess-16-3435-2012
44. Alfonso L, Lobbrecht A, Price R (2010) Using mobile phones to validate models of extreme
events. 9th international conference on hydroinformatics, Tianjin, China, pp 1447–1454
45. de Vos L, Leijnse H, Overeem A, Uijlenhoet R (2017) The potential of urban rainfall monitoring with crowdsourced automatic weather stations in Amsterdam. Hydrol Earth Syst Sci
21:765–777. https://doi.org/10.5194/hess-21-765-2017
46. Etter S, Strobl B, Seibert J, van Meerveld I (2018) Value of uncertain streamflow observations
for hydrological modelling. Hydrol Earth Syst Sci Discuss 22(10):5243–5257. https://doi.org/
10.5194/hess-2018-355
Exploring Assimilation of Crowdsourcing Observations into Flood Models
231
mechanistic modelling of complex environmental systems using the GLUE methodology.
J Hydrol 249:11–29
32. Mantovan P, Todini E (2006) Hydrological forecasting uncertainty assessment: incoherence of
the GLUE methodology. J Hydrol 330(1–2):368–381. https://doi.org/10.1016/j.jhydrol.2006.
04.046
33. Pappenberger F, Beven KJ (2006) Ignorance is bliss: or seven reasons not to use uncertainty
analysis. Water Resour Res 42(5):1–8. https://doi.org/10.1029/2005WR004820
34. Liu Y, Weerts AH, Clark M, Hendricks Franssen HJ, Kumar S, Moradkhani H, Seo DJ,
Schwanenberg D, Smith P, Van Dijk AIJM, Van Velzen N, He M, Lee H, Noh SJ,
Rakovec O, Restrepo P (2012) Advancing data assimilation in operational hydrologic forecasting: progresses, challenges, and emerging opportunities. Hydrol Earth Syst Sci 16
(10):3863–3887. https://doi.org/10.5194/hess-16-3863-2012
35. Seo DJ, Cajina L, Corby R, Howieson T (2009) Automatic state updating for operational
streamflow forecasting via variational data assimilation. J Hydrol 367(3–4):255–275. https://
doi.org/10.1016/j.jhydrol.2009.01.019
36. Welles E, Sorooshian S, Carter G, Olsen B (2007) Hydrologic verification: a call for action and
collaboration. Bull Am Meteorol Soc 88:503–511
37. Yarvis M, Kushalnagar N, Singh H, Rangarajan A, Liu Y, Singh S (2005) Exploiting heterogeneity in sensor networks. Proceedings IEEE INFOCOM 2005. 24th annual joint conference
of the IEEE computer and communications societies, vol 2, pp 878–890
38. Bonney R, Shirk JL, Phillips TB, Wiggins A, Ballard HL, Miller-Rushing AJ, Parrish JK (2014)
Next steps for citizen science. Science 343(6178):1436–1437. https://doi.org/10.1126/science.
1251554
39. Buytaert W, Zulkafli Z, Grainger S, Acosta L, Alemie TC, Bastiaensen J, De BiÃvre B,
Bhusal J, Clark J, Dewulf A, Foggin M, Hannah DM, Hergarten C, Isaeva A,
Karpouzoglou T, Pandeya B, Paudel D, Sharma K, Steenhuis T, Tilahun S, Van Hecken G,
Zhumanova M (2014) Citizen science in hydrology and water resources: opportunities for
knowledge generation, ecosystem service management, and sustainable development. Front
Earth Sci 2:1–21. https://doi.org/10.3389/feart.2014.00026
40. Clark MP, Rupp DE, Woods RA, Zheng X, Ibbitt RP, Slater AG, Schmidt J, Uddstrom MJ
(2008) Hydrological data assimilation with the ensemble Kalman filter: use of streamflow
observations to update states in a distributed hydrological model. Adv Water Resour 31
(10):1309–1324. https://doi.org/10.1016/j.advwatres.2008.06.005
41. Mazzoleni M, Alfonso L, Solomatine D (2016) Influence of spatial distribution of sensors and
observation accuracy on the assimilation of distributed streamflow data in hydrological modelling. Hydrol Sci J 62(3):389–407. https://doi.org/10.1080/02626667.2016.1247211
42. Mazzoleni M, Noh SJ, Lee H, Liu Y, Seo DJ, Amaranto A, Alfonso L, Solomatine DP (2018)
Real-time assimilation of streamflow observations into a hydrological routing model: effects of
model structures and updating methods. Hydrol Sci J 63:386–407
43. Rakovec O, Weerts AH, Hazenberg P, Torfs PJJF, Uijlenhoet R (2012) State updating of a
distributed hydrological model with ensemble Kalman filtering: effects of updating frequency
and observation network density on forecast accuracy. Hydrol Earth Syst Sci 16(9):3435–3449.
https://doi.org/10.5194/hess-16-3435-2012
44. Alfonso L, Lobbrecht A, Price R (2010) Using mobile phones to validate models of extreme
events. 9th international conference on hydroinformatics, Tianjin, China, pp 1447–1454
45. de Vos L, Leijnse H, Overeem A, Uijlenhoet R (2017) The potential of urban rainfall monitoring with crowdsourced automatic weather stations in Amsterdam. Hydrol Earth Syst Sci
21:765–777. https://doi.org/10.5194/hess-21-765-2017
46. Etter S, Strobl B, Seibert J, van Meerveld I (2018) Value of uncertain streamflow observations
for hydrological modelling. Hydrol Earth Syst Sci Discuss 22(10):5243–5257. https://doi.org/
10.5194/hess-2018-355
Exploring Assimilation of Crowdsourcing Observations into Flood Models
231