Adams, R. M., Houston, L. L., McCarl, B. A., Tiscareño, M. L., Matus, J.
G., and Weiher, R. F.: The benefits to Mexican agriculture of an
El Niño-southern oscillation (ENSO) early warning system, Agr. Forest
Meteorol., 115, 183–194, https://doi.org/10.1016/S0168-1923(02)00201-0, 2003.

Andreadis, K. M. and Lettenmaier, D. P.: Assimilating remotely sensed snow
observations into a macroscale hydrology model, Adv. Water Resour., 29, 872–886,
https://doi.org/10.1016/j.advwatres.2005.08.004, 2006.

Bannister, R. N.: A review of forecast error covariance statistics in atmospheric
variational data assimilation. II: Modelling the forecast error covariance
statistics, Q. J. Roy. Meteorol. Soc., 134, 1971–1996, https://doi.org/10.1002/qj.340, 2008.

Bannister, R. N.: A review of operational methods of variational and
ensemble-variational data assimilation, Q. J. Roy. Meteorol. Soc., 29, 1–29,
https://doi.org/10.1002/QJ.2982, 2016.

Beven, K.: A manifesto for the equifinality thesis, J. Hydrol., 320, 18–36,
https://doi.org/10.1016/j.jhydrol.2005.07.007, 2006.

Bröcker, J.: Evaluating raw ensembles with the continuous ranked probability
score, Q. J. R. Meteorol. Soc., 138, 1611–1617, https://doi.org/10.1002/qj.1891, 2012.

Buehner, M., Houtekamer, P. L., Charette, C., Mitchell, H. L. and He, B.:
Intercomparison of Variational Data Assimilation and the Ensemble Kalman Filter
for Global Deterministic NWP. Part II: One-Month Experiments with Real Observations,
Mon. Weather Rev., 138, 1567–1586, https://doi.org/10.1175/2009MWR3158.1, 2010.

Carpenter, J., Clifford, P., and Fearnhead, P.: Improved particle filter for
nonlinear problems, IEEE Proc. - Radar, Sonar Navig., 146, 2–7,
https://doi.org/10.1049/ip-rsn:19990255, 1999.

Clark, M. P., Rupp, D. E., Woods, R. a., Zheng, X., Ibbitt, R. P., Slater, A.
G., Schmidt, J., and Uddstrom, M. J.: Hydrological data assimilation with the
ensemble Kalman filter: Use of streamflow observations to update states in a
distributed hydrological model, Adv. Water Resour., 31, 1309–1324,
https://doi.org/10.1016/j.advwatres.2008.06.005, 2008.

Clark, M. P., Bierkens, M. F. P. P., Samaniego, L., Woods, R. A., Uijlenhoet,
R., Bennett, K. E., Pauwels, V. R. N. N., Cai, X., Wood, A. W.,
Peters-Lidard, C. D., Uijenhoet, R., Bennet, K. E., Pauwels, V. R. N. N.,
Cai, X., Wood, A. W., and Peters-Lidard, C. D.: The evolution of
process-based hydrologic models: Historical challenges and the collective
quest for physical realism, Hydrol. Earth Syst. Sci., 21, 3427–3440,
https://doi.org/10.5194/hess-21-3427-2017, 2017.

Cosgrove, B. A., Lohmann, D., Mitchell, K. E., Houser, P. R., Wood, E. F.,
Schaake, J. C., Robock, A., Marshall, C., Sheffield, J., Duan, Q., Luo, L.,
Higgins, R. W., Pinker, R. T., Tarpley, J. D., and Meng, J.: Real-time and
retrospective forcing in the North American Land Data Assimilation
System (NLDAS) project, J. Geophys. Res.-Atmos., 108, 1–12,
https://doi.org/10.1029/2002JD003118, 2003.

Crainic, T. G. and Toulouse, M.: Parallel Meta-heuristics, in: Handbook of
Metaheuristics, vol. 146, edited by: Gendreau, M. and Potvin, J.-Y., Springer US,
497–541, 2010.

Deb, K.: Multi-objective Optimization, in: Search Methodologies: Introductory
Tutorials in Optimization and Decision Support Techniques, edited by: Burke,
E. K. and Kendall, G., Springer US, 403–449, 2014.

Deb, K., Pratap, A., Agarwal, S., and Meyarivan, T.: A fast and elitist
multiobjective genetic algorithm: NSGA-II, IEEE Trans. Evol. Comput., 6, 182–197,
https://doi.org/10.1109/4235.996017, 2002.

Desroziers, G., Camino, J. T., and Berre, L.: 4DEnVar: Link with 4D state
formulation of variational assimilation and different possible implementations,
Q. J. Roy. Meteorol. Soc., 140, 2097–2110, https://doi.org/10.1002/qj.2325, 2014.

Dumedah, G. and Coulibaly, P.: Evolutionary assimilation of streamflow in
distributed hydrologic modeling using in-situ soil moisture data, Adv. Water
Resour., 53, 231–241, https://doi.org/10.1016/j.advwatres.2012.07.012, 2013.

Dumedah, G., Berg, A. A., and Wineberg, M.: An Integrated Framework for a Joint
Assimilation of Brightness Temperature and Soil Moisture Using the Nondominated
Sorting Genetic Algorithm II, J. Hydrometeorol., 12, 1596–1609, https://doi.org/10.1175/JHM-D-10-05029.1, 2011.

Duong, T. and Hazelton, M. L.: Cross-validation bandwidth matrices for
multivariate kernel density estimation, Scand. J. Stat., 32, 485–506,
https://doi.org/10.1111/j.1467-9469.2005.00445.x, 2005.

Efstratiadis, A. and Koutsoyiannis, D.: One decade of multi-objective calibration
approaches in hydrological modelling: a review, Hydrolog. Sci. J., 55, 58–78,
https://doi.org/10.1080/02626660903526292, 2010.

Errico, R. M.: What Is an Adjoint Model?, B. Am. Meteorol. Soc., 78, 2577–2591,
https://doi.org/10.1175/1520-0477(1997)078<2577:WIAAM>2.0.CO;2, 1997.

Evensen, G.: Data assimilation: the ensemble Kalman filter, Springer
Science & Business Media, 2009.

Evensen, G. and van Leeuwen, P. J.: An ensemble Kalman smoother for nonlinear
dynamics, Mon. Weather Rev., 128, 1852–1867, https://doi.org/10.1175/1520-0493(2000)128<1852:AEKSFN>2.0.CO;2, 2000.

Fisher, M.: Background error covariance modelling, Semin. Recent Dev. Data
Assim., 45–63, available at:
https://www.ecmwf.int/sites/default/files/elibrary/2003/9404-background-error-covariance-modelling.pdf
(last access: 29 October 2018), 2003.

Friedman, J., Hastie, T., and Tibshirani, R.: Sparse inverse covariance
estimation with the graphical lasso, Biostatistics, 9, 432–441,
https://doi.org/10.1093/biostatistics/kxm045, 2008.

Gauthier, P., Tanguay, M., Laroche, S., Pellerin, S., and Morneau, J.: Extension
of 3DVAR to 4DVAR: Implementation of 4DVAR at the Meteorological Service of Canada,
Mon. Weather Rev., 135, 2339–2354, https://doi.org/10.1175/MWR3394.1, 2007.

Ghil, M. and Malanotte-Rizzoli, P.: Data assimilation in meteorology and
oceanography, Adv. Geophys., 33, 141–266,
https://doi.org/10.1016/S0065-2687(08)60442-2, 1991.

Ghorbanidehno, H., Kokkinaki, A., Li, J. Y., Darve, E. and Kitanidis, P. K.:
Real-time data assimilation for large-scale systems: The spectral Kalman filter,
Adv. Water Resour., 86, 260–272, https://doi.org/10.1016/j.advwatres.2015.07.017, 2015.

GitHub: felherc, available at: https://github.com/felherc/, last
access: 1 October 2018.

Gordon, N. J., Salmond, D. J., and Smith, A. F. M.: Novel approach to
nonlinear/non-Gaussian Bayesian state estimation, IEEE Proc. F Radar Signal
Process., 140, 107–113, https://doi.org/10.1049/ip-f-2.1993.0015, 1993.

Guo, J., Liang, X., and Leung, L. R.: A new multiscale flow network generation
scheme for land surface models, Geophys. Res. Lett., 31, 1–4, https://doi.org/10.1029/2004GL021381, 2004.

Haario, H., Saksman, E., and Tamminen, J.: An Adaptive Metropolis Algorithm,
Bernoulli, 7, 223–242, https://doi.org/10.2307/3318737, 2001.

Hawkins, D. M.: The Problem of Overfitting, J. Chem. Inf. Comput. Sci., 44,
1–12, https://doi.org/10.1021/ci0342472, 2004.

Hazelton, M. L.: Variable kernel density estimation, Aust. N. Z. J. Stat.,
45, 271–284, https://doi.org/10.1111/1467-842X.00283, 2003.

Homer, C., Fry, J., and Barnes, C.: The National Land Cover Database,
US Geol. Surv. Fact Sheet, 3020, 1–4, available at:
http://pubs.usgs.gov/fs/2012/3020/ (last access: 22 October 2018),
2012.

Houser, P. R., Shuttleworth, W. J., Famiglietti, J. S., Gupta, H. V., Syed,
K. H., and Goodrich, D. C.: Integration of soil moisture remote sensing and
hydrologic modeling using data assimilation, Water Resour. Res., 34,
3405–3420, 1998.

Karafotias, G., Hoogendoorn, M., and Eiben, A. E.: Parameter Control in
Evolutionary Algorithms: Trends and Challenges, IEEE Trans. Evol. Comput., 2,
167–187, https://doi.org/10.1109/TEVC.2014.2308294, 2014.

Koster, R. D., Betts, A. K., Dirmeyer, P. A., Bierkens, M., Bennett, K. E.,
Déry, S. J., Evans, J. P., Fu, R., Hernández, F., Leung, L. R., Liang,
X., Masood, M., Savenije, H., Wang, G., and Yuan, X.: Hydroclimatic variability
and predictability: a survey of recent research, Hydrol. Earth Syst. Sci., 21,
3777–3798, https://doi.org/10.5194/hess-21-3777-2017, 2017.

Krause, P., Boyle, D. P., and Bäse, F.: Comparison of different efficiency
criteria for hydrological model assessment, Adv. Geosci., 5, 89–97,
https://doi.org/10.5194/adgeo-5-89-2005, 2005.

Krishnamoorthy, A. and Menon, D.: Matrix Inversion Using Cholesky
Decomposition, CoRR, 10–12, available at:
http://arxiv.org/abs/1111.4144 (last access: 1 October 2018), 2011.

Li, J. Y., Kokkinaki, A., Ghorbanidehno, H., Darve, E. F., and Kitanidis, P. K.:
The compressed state Kalman filter for nonlinear state estimation: Application
to large-scale reservoir monitoring, Water Resour. Res., 51, 9942–9963,
https://doi.org/10.1002/2015WR017203, 2015.

Liang, X. and Xie, Z.: A new surface runoff parameterization with subgrid-scale
soil heterogeneity for land surface models, Adv. Water Resour., 24, 1173–1193, 2001.

Liang, X. and Xie, Z.: Important factors in land–atmosphere interactions:
surface runoff generations and interactions between surface and groundwater,
Global Planet. Change, 38, 101–114, 2003.

Liang, X., Lettenmaier, D. P., Wood, E. F., and Burges, S. J.: A simple
hydrologically based model of land surface water and energy fluxes for general
circulation models, J. Geophys. Res., 99, 14415, https://doi.org/10.1029/94JD00483, 1994.

Liang, X., Lettenmaier, D. P., and Wood, E. F.: One-dimensional statistical
dynamic representation of subgrid spatial variability of precipitation in the
two-layer variable infiltration capacity model, J. Geophys. Res.-Atmos.,
101, 21403–21422, 1996a.

Liang, X., Wood, E. F., and Lettenmaier, D. P.: Surface soil moisture
parameterization of the VIC-2L model: Evaluation and modification, Global Planet.
Change, 13, 195–206, 1996b.

Liu, J. S. and Chen, R.: Sequential Monte Carlo Methods for Dynamic Systems, J.
Am. Stat. Assoc., 93, 1032–1044, https://doi.org/10.2307/2669847, 1998.

Liu, Y. and Gupta, H. V.: Uncertainty in hydrologic modeling: Toward an
integrated data assimilation framework, Water Resour. Res., 43, 1–18,
https://doi.org/10.1029/2006WR005756, 2007.

Lohmann, D., Rashke, E., Nijssen, B., and Lettenmaier, D. P.: Regional scale
hydrology: I. Formulation of the VIC-2L model coupled to a routing model,
Hydrolog. Sci. J., 43, 131–141, https://doi.org/10.1080/02626669809492107, 1998.

Lorenc, A. C., Bowler, N. E., Clayton, A. M., Pring, S. R., and Fairbairn, D.:
Comparison of Hybrid-4DEnVar and Hybrid-4DVar Data Assimilation Methods for
Global NWP, Mon. Weather Rev., 143, 212–229, https://doi.org/10.1175/MWR-D-14-00195.1, 2015.

Mahalanobis, P. C.: On the generalized distance in statistics, Proc. Natl.
Inst. Sci., 2, 49–55, 1936.

Miller, D. A. and White, R. A.: A Conterminous United States Multilayer Soil
Characteristics Dataset for Regional Climate and Hydrology Modeling, Earth
Interact., 2, 1–26,
https://doi.org/10.1175/1087-3562(1998)002<0002:CUSMS>2.0.CO;2,
1998.

Molteni, F., Buizza, R., Palmer, T. N., and Petroliagis, T.: The ECMWF ensemble
prediction system: Methodology and validation, Q. J. Roy. Meteorol. Soc., 122,
73–119, https://doi.org/10.1002/qj.49712252905, 1996.

Montgomery, D. C.: Design and analysis of experiments, 8th Edn., John Wiley & Sons,
2012.

Montgomery, D. C., Runger, G. C., and Hubele, N. F.: Engineering statistics,
John Wiley & Sons, USA, 2009.

Montzka, C., Pauwels, V. R. N., Franssen, H.-J. H., Han, X., and Vereecken, H.:
Multivariate and Multiscale Data Assimilation in Terrestrial Systems: A Review,
Sensors, 12, 16291–16333, https://doi.org/10.3390/s121216291, 2012.

Moradkhani, H., DeChant, C. M., and Sorooshian, S.: Evolution of ensemble
data assimilation for uncertainty quantification using the particle filter –
Markov chain Monte Carlo method, Water Resour. Res., 48, 1–13,
https://doi.org/10.1029/2012WR012144, 2012.

Ning, L., Carli, F. P., Ebtehaj, A. M., Foufoula-Georgiou, E., and Georgiou, T.
T.: Coping with model error in variational data assimilation using optimal mass
transport, Water Resour. Res., 50, 5817–5830, https://doi.org/10.1002/2013WR014966, 2014.

Noh, S. J., Tachikawa, Y., Shiiba, M., and Kim, S.: Applying sequential Monte
Carlo methods into a distributed hydrologic model: Lagged particle filtering
approach with regularization, Hydrol. Earth Syst. Sci., 15, 3237–3251,
https://doi.org/10.5194/hess-15-3237-2011, 2011.

Park, S., Hwang, J. P., Kim, E., and Kang, H. J.: A new evolutionary particle
filter for the prevention of sample impoverishment, IEEE Trans. Evol. Comput.,
13, 801–809, https://doi.org/10.1109/TEVC.2008.2011729, 2009.

Penning-Rowsell, E. C., Tunstall, S. M., Tapsell, S. M. and Parker, D. J.: The
benefits of flood warnings: Real but elusive, and politically significant, J.
Chart. Inst. Water Environ. Manage., 14, 7–14, https://doi.org/10.1111/j.1747-6593.2000.tb00219.x, 2000.

Pham, D. T.: Stochastic methods for sequential data assimilation in strongly
nonlinear systems, Mon. Weather Rev., 129, 1194–1207, https://doi.org/10.1175/1520-0493(2001)129<1194:SMFSDA>2.0.CO;2, 2001.

Rawlins, F., Ballard, S. P., Bovis, K. J., Clayton, A. M., Li, D., Inverarity,
G. W., Lorenc, A. C., and Payne, T. J.: The Met Office global four-dimensional
variational data assimilation scheme, Q. J. Roy. Meteorol. Soc., 133, 347–362,
https://doi.org/10.1002/qj.32, 2007.

Reichle, R. H., McLaughlin, D. B., and Entekhabi, D.: Variational data
assimilation of microwave radiobrightness observations for land surface hydrology
applications, IEEE T. Geosci. Remote, 39, 1708–1718, https://doi.org/10.1109/36.942549, 2001.

Rodríguez, E., Morris, C. S., and Belz, J. E.: A global assessment of the
SRTM performance, Photogramm. Eng. Remote Sens., 72, 249–260, 2006.

Rogers, E., DiMego, G., Black, T., Ek, M., Ferrier, B., Gayno, G., Janic, Z.,
Lin, Y., Pyle, M., Wong, V., and Wu, W.-S.: The NCEP North American Mesoscale
Modeling System: Recent Changes and Future Plans, in: 23rd Conf. Weather
Anal. Forecast. Conf. Numer. Weather Predict., available at:
http://ams.confex.com/ams/23WAF19NWP/techprogram/paper_154114.htm (last
access: 1 October 2018), 2009.

Seaber, P. R., Kapinos, F. P., and Knapp, G. L.: Hydrologic unit maps,
US Government Printing Office Washington, D.C., USA, 1987.

Sheather, S. J. and Jones, M. C.: A Reliable Data-Based Bandwidth Selection
Method for Kernel Density Estimation, J. R. Stat. Soc. Ser. B, 53, 683–690,
available at: http://www.jstor.org/stable/2345597 (last access:
1 October 2018), 1991.

Silverman, B. B. W.: Density estimation for statistics and data analysis,
CRC Press, USA, 1986.

Smith, A., Doucet, A., de Freitas, N., and Gordon, N.: Sequential Monte Carlo
methods in practice, Springer Science & Business Media, USA, 2013.

Snyder, C., Bengtsson, T., Bickel, P., and Anderson, J.: Obstacles to
High-Dimensional Particle Filtering, Mon. Weather Rev., 136, 4629–4640,
https://doi.org/10.1175/2008MWR2529.1, 2008.

Socha, K. and Dorigo, M.: Ant colony optimization for continuous domains, Eur.
J. Oper. Res., 185, 1155–1173, https://doi.org/10.1016/j.ejor.2006.06.046, 2008.

Terrell, G. R. and Scott, D. W.: Variable kernel density estimation, Ann.
Stat., 20, 1236–1265, https://doi.org/10.1214/aos/1176348768, 1992.

Trémolet, Y.: Accounting for an imperfect model in 4D-Var, Q. J. Roy.
Meteorol. Soc., 132, 2483–2504, https://doi.org/10.1256/qj.05.224, 2006.

van Leeuwen, P. J.: Particle Filtering in Geophysical Systems, Mon. Weather
Rev., 137, 4089–4114, https://doi.org/10.1175/2009MWR2835.1, 2009.

van Leeuwen, P. J.: Nonlinear Data Assimilation for high-dimensional systems,
in Nonlinear Data Assimilation, edited by: Van Leeuwen, J. P., Cheng, Y., and
Reich, S., Springer International Publishing, 1–73, 2015.

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.

Vrugt, J. A. and Robinson, B. A.: Improved evolutionary optimization from
genetically adaptive multimethod search, P. Natl. Acad. Sci. USA, 104, 708–711,
https://doi.org/10.1073/pnas.0610471104, 2007.

Wand, M. P. and Jones, M. C.: Kernel smoothing, CRC Press, New York, 1994.

Wen, Z., Liang, X., and Yang, S.: A new multiscale routing framework and its
evaluation for land surface modeling applications, Water Resour. Res., 48,
1–16, https://doi.org/10.1029/2011WR011337, 2012.

West, M.: Mixture models, Monte Carlo, Bayesian updating, and dynamic models,
Comput. Sci. Stat., 1–11, available at:
http://www.stat.duke.edu/~mw/MWextrapubs/West1993a.pdf (last access:
1 October 2018), 1993.

Wigmosta, M. S., Vail, L. W., and Lettenmaier, D. P.: A distributed
hydrology-vegetation model for complex terrain, Water Resour. Res., 30,
1665–1679, https://doi.org/10.1029/94WR00436, 1994.

Wigmosta, M. S., Nijssen, B., and Storck, P.: The distributed hydrology soil
vegetation model, Math. Model. Small Watershed Hydrol. Appl., 7–42,
available at:
http://ftp.hydro.washington.edu/pub/dhsvm/The-distributed-hydrology-soil-vegetation-model.pdf
(last access: 1 October 2018), 2002.

Yang, S.-C., Corazza, M., Carrassi, A., Kalnay, E., and Miyoshi, T.: Comparison
of Local Ensemble Transform Kalman Filter, 3DVAR, and 4DVAR in a Quasigeostrophic
Model, Mon. Weather Rev., 137, 693–709, https://doi.org/10.1175/2008MWR2396.1, 2009.

Zhang, F., Zhang, M., and Hansen, J.: Coupling ensemble Kalman filter with
four-dimensional variational data assimilation, Adv. Atmos. Sci., 26, 1–8,
https://doi.org/10.1007/s00376-009-0001-8, 2009.

Zhang, L., Nan, Z., Liang, X., Xu, Y., Hernández, F., and Li, L.: Application
of the MacCormack scheme to overland flow routing for high-spatial resolution
distributed hydrological model, J. Hydrol., 558, 421–431, https://doi.org/10.1016/j.jhydrol.2018.01.048, 2018.

Zhang, X., Tian, Y., Cheng, R., and Jin, Y.: An Efficient Approach to
Nondominated Sorting for Evolutionary Multiobjective Optimization, IEEE Trans.
Evol. Comput., 19, 201–213, https://doi.org/10.1109/TEVC.2014.2308305, 2015.

Zhu, Y., Toth, Z., Wobus, R., Richardson, D., and Mylne, K.: The economic value
of ensemble-based weather forecasts, B. Am. Meteorol. Soc., 83, 73–83,
https://doi.org/10.1175/1520-0477(2002)083<0073:TEVOEB>2.3.CO;2, 2002.

Ziervogel, G., Bithell, M., Washington, R., and Downing, T.: Agent-based social
simulation: A method for assessing the impact of seasonal climate forecast
applications among smallholder farmers, Agric. Syst., 83, 1–26,
https://doi.org/10.1016/j.agsy.2004.02.009, 2005.