References
Aires, F. (2014). Combining Datasets of Satellite-Retrieved Products.
Part I: Methodology and Water Budget Closure. Journal of
Hydrometeorology . https://doi.org/10.1175/jhm-d-13-0148.1
Alkama, R., Decharme, B., Douville, H., Becker, M., Cazenave, A.,
Sheffield, J., et al. (2010). Global evaluation of the ISBA-TRIP
continental hydrological system. Part I: Comparison to GRACE terrestrial
water storage estimates and in situ river discharges. Journal of
Hydrometeorology . https://doi.org/10.1175/2010JHM1211.1
Asadzadeh Jarihani, A., Callow, J. N., Johansen, K., & Gouweleeuw, B.
(2013). Evaluation of multiple satellite altimetry data for studying
inland water bodies and river floods. Journal of Hydrology .
https://doi.org/10.1016/j.jhydrol.2013.09.010
Di Baldassarre, G., & Montanari, A. (2009). Uncertainty in river
discharge observations: A quantitative analysis. Hydrology and
Earth System Sciences . https://doi.org/10.5194/hess-13-913-2009
Baroni, G., Schalge, B., Rakovec, O., Kumar, R., Schüler, L., Samaniego,
L., et al. (2019). A Comprehensive Distributed Hydrological Modeling
Intercomparison to Support Process Representation and Data Collection
Strategies. Water Resources Research .
https://doi.org/10.1029/2018WR023941
Bates, P. D., Horritt, M. S., & Fewtrell, T. J. (2010). A simple
inertial formulation of the shallow water equations for efficient
two-dimensional flood inundation modelling. Journal of Hydrology .
https://doi.org/10.1016/j.jhydrol.2010.03.027
Beven, K. (2006). A manifesto for the equifinality thesis. InJournal of Hydrology .
https://doi.org/10.1016/j.jhydrol.2005.07.007
Beven, K., & Binley, A. (1992). The future of distributed models: Model
calibration and uncertainty prediction. Hydrological Processes .
https://doi.org/10.1002/hyp.3360060305
Blöschl, G., Bierkens, M. F. P., Chambel, A., Cudennec, C., Destouni,
G., Fiori, A., et al. (2019). Twenty-three Unsolved Problems in
Hydrology (UPH)—A community perspective. Hydrological Sciences
Journal. https://doi.org/10.1080/02626667.2019.1620507
Brêda, J. P. L. F., Paiva, R. C. D., Bravo, J. M., Passaia, O. A., &
Moreira, D. M. (2019). Assimilation of Satellite Altimetry Data for
Effective River Bathymetry. Water Resources Research .
https://doi.org/10.1029/2018wr024010
Clark, M. P., Fan, Y., Lawrence, D. M., Adam, J. C., Bolster, D.,
Gochis, D. J., et al. (2015). Improving the representation of hydrologic
processes in Earth System Models. Water Resources Research .
https://doi.org/10.1002/2015WR017096
Collischonn, B., Collischonn, W., & Tucci, C. E. M. (2008). Daily
hydrological modeling in the Amazon basin using TRMM rainfall estimates.Journal of Hydrology .
https://doi.org/10.1016/j.jhydrol.2008.07.032
Collischonn, W., Allasia, D., da Silva, B. C., & Tucci, C. E. M.
(2007). The MGB-IPH model for large-scale rainfall-runoff modelling.Hydrological Sciences Journal .
https://doi.org/10.1623/hysj.52.5.878
Croke, B. F. W. (2009). Representing uncertainty in objective functions:
Extension to include the influence of serial correlation. In 18th
World IMACS Congress and MODSIM09 International Congress on Modelling
and Simulation: Interfacing Modelling and Simulation with Mathematical
and Computational Sciences, Proceedings .
Crow, W. T., Wood, E. F., & Pan, M. (2003). Multiobjective calibration
of land surface model evapotranspiration predictions using streamflow
observations and spaceborne surface radiometric temperature retrievals.Journal of Geophysical Research D: Atmospheres .
https://doi.org/10.1029/2002JD003292
Demirel, M. C., Mai, J., Mendiguren, G., Koch, J., Samaniego, L., &
Stisen, S. (2018). Combining satellite data and appropriate objective
functions for improved spatial pattern performance of a distributed
hydrologic model. Hydrology and Earth System Sciences.https://doi.org/10.5194/hess-22-1299-2018
Demirel, M. C., Özen, A., Orta, S., Toker, E., Demir, H. K.,
Ekmekcioglu, Ö., Taysi, H., Eruçar, S., Sag, A. B., Sari, Ö., Tuncer,
E., Hanci, H., Özcan, T. I., Erdem, H., Kosucu, M. M., Basakin, E. E.,
Ahmed, K., Anwar, A., Avcuoglu, M. B., Vanli, Ö., Stisen, S., & Booij,
M. J. (2019). Additional value of using satellite-based soil moisture
and two sources of groundwater data for hydrological model calibration.Water. https://doi.org/10.3390/w11102083
Duan, Q., Sorooshian, S., & Gupta, V. (1992). Effective and efficient
global optimization for conceptual rainfall‐runoff models. Water
Resources Research . https://doi.org/10.1029/91WR02985
Fan, F. M., Buarque, D. C., Pontes, P. R. M., & Collischonn, W. (2015).
Um mapa de Unidades de Resposta Hidrológica para a América do Sul.XXI Simpósio Brasileiro de Recursos Hídricos .
Fleischmann, A.S., Paiva, R.C.D., Collischonn, W., Siqueira, V.A.,
Paris, A., Moreira, D.M., Papa, F., Bitar, A.A., Parrens, M., Aires, F.
& Garambois, P.A. (2020). Trade‐offs between 1D and 2D regional river
hydrodynamic models. Water Resources Research.https://doi.org/10.1029/2019WR026812
Foglia, L., Hill, M. C., Mehl, S. W., & Burlando, P. (2009).
Sensitivity analysis, calibration, and testing of a distributed
hydrological model using error-based weighting and one objective
function. Water Resources Research .
https://doi.org/10.1029/2008WR007255
Franks, S. W., Gineste, P., Beven, K. J., & Merot, P. (1998). On
constraining the predictions of a distributed model: The incorporation
of fuzzy estimates of saturated areas into the calibration process.Water Resources Research . https://doi.org/10.1029/97WR03041
Gharari, S., Shafiei, M., Hrachowitz, M., Kumar, R., Fenicia, F., Gupta,
H. V., & Savenije, H. H. G. (2014). A constraint-based search algorithm
for parameter identification of environmental models. Hydrology
and Earth System Sciences . https://doi.org/10.5194/hess-18-4861-2014
Gomis-Cebolla, J., Jimenez, J. C., Sobrino, J. A., Corbari, C., &
Mancini, M. (2019). Intercomparison of remote-sensing based
evapotranspiration algorithms over amazonian forests.International Journal of Applied Earth Observation and
Geoinformation . https://doi.org/10.1016/j.jag.2019.04.009
Grimaldi, S., Schumann, G. J. P., Shokri, A., Walker, J. P., & Pauwels,
V. R. N. (2019). Challenges, Opportunities, and Pitfalls for Global
Coupled Hydrologic-Hydraulic Modeling of Floods. Water Resources
Research . https://doi.org/10.1029/2018WR024289
Gupta, H. V., Kling, H., Yilmaz, K. K., & Martinez, G. F. (2009).
Decomposition of the mean squared error and NSE performance criteria:
Implications for improving hydrological modelling. Journal of
Hydrology . https://doi.org/10.1016/j.jhydrol.2009.08.003
Haddeland, I., Skaugen, T., & Lettenmaier, D. P. (2006). Anthropogenic
impacts on continental surface water fluxes. Geophysical Research
Letters . https://doi.org/10.1029/2006GL026047
Hasler, N., & Avissar, R. (2007). What controls evapotranspiration in
the Amazon basin? Journal of Hydrometeorology .
https://doi.org/10.1175/JHM587.1
Herman, M. R., Nejadhashemi, A. P., Abouali, M., Hernandez-suarez, S.,
Daneshvar, F., Zhang, Z., et al. (2017). Evaluating the Role of
Evapotranspiration Remote Sensing Data in Improving Hydrological
Modeling Predictability. Journal of Hydrology .
https://doi.org/10.1016/j.jhydrol.2017.11.009
Hess, L. L., Melack, J. M., Novo, E. M. L. M., Barbosa, C. C. F., &
Gastil, M. (2003). Dual-season mapping of wetland inundation and
vegetation for the central Amazon basin. Remote Sensing of
Environment . https://doi.org/10.1016/j.rse.2003.04.001
Hodges, B. R. (2013). Challenges in continental river dynamics.Environmental Modelling and Software .
https://doi.org/10.1016/j.envsoft.2013.08.010
Holeman, J. N. (1968). The Sediment Yield of Major Rivers of the World.Water Resources Research . https://doi.org/10.1029/WR004i004p00737
Houser, P. R., Shuttleworth, W. J., Famiglietti, J. S., Gupta, H. V.,
Syed, K. H., & Goodrich, D. C. (1998). Integration of soil moisture
remote sensing and hydrologic modeling using data assimilation.Water Resources Research . https://doi.org/10.1029/1998WR900001
Hrachowitz, M., Savenije, H. H. G., Blöschl, G., McDonnell, J. J.,
Sivapalan, M., Pomeroy, J. W., et al. (2013). A decade of Predictions in
Ungauged Basins (PUB)-a review. Hydrological Sciences Journal .
https://doi.org/10.1080/02626667.2013.803183
Huffman, G. J., Adler, R. F., Bolvin, D. T., Gu, G., Nelkin, E. J.,
Bowman, K. P., et al. (2007). The TRMM Multisatellite Precipitation
Analysis (TMPA): Quasi-global, multiyear, combined-sensor precipitation
estimates at fine scales. Journal of Hydrometeorology .
https://doi.org/10.1175/JHM560.1Jiang, D., & Wang, K. (2019). The Role
of Satellite-Based Remote Sensing in Improving Simulated Streamflow: A
Review. Water . https://doi.org/10.3390/w11081615
Junk, W. J. (1997). General Aspects of Floodplain Ecology with Special
Reference to Amazonian Floodplains.
https://doi.org/10.1007/978-3-662-03416-3_1
Karthikeyan, L., Pan, M., Wanders, N., Kumar, D. N., & Wood, E. F.
(2017). Four decades of microwave satellite soil moisture observations:
Part 2. Product validation and inter-satellite comparisons.Advances in Water Resources .
https://doi.org/10.1016/j.advwatres.2017.09.010
Kerr, Y. H., Waldteufel, P., Wigneron, J. P., Martinuzzi, J. M., Font,
J., & Berger, M. (2001). Soil moisture retrieval from space: The Soil
Moisture and Ocean Salinity (SMOS) mission. IEEE Transactions on
Geoscience and Remote Sensing . https://doi.org/10.1109/36.942551
Kirchner, J. W. (2006). Getting the right answers for the right reasons:
Linking measurements, analyses, and models to advance the science of
hydrology. Water Resources Research .
https://doi.org/10.1029/2005WR004362
Kittel, C., Nielsen, K., Tøttrup, C., & Bauer-Gottwein, P. (2018).
Informing a hydrological model of the Ogooué with multi-mission remote
sensing data. Hydrology and Earth System Sciences .
https://doi.org/10.5194/hess-22-1453-2018
Koch, J., Demirel, M. C., & Stisen, S. (2018). The SPAtial EFficiency
metric (SPAEF): Multiple-component evaluation of spatial patterns for
optimization of hydrological models. Geoscientific Model
Development . https://doi.org/10.5194/gmd-11-1873-2018
Koppa, A., Gebremichael, M., & Yeh, W. W. G. (2019). Multivariate
calibration of large scale hydrologic models: The necessity and value of
a Pareto optimal approach. Advances in Water Resources .
https://doi.org/10.1016/j.advwatres.2019.06.005
Kottek, M., Grieser, J., Beck, C., Rudolf, B., & Rubel, F. (2006).
World map of the Köppen-Geiger climate classification updated.Meteorologische Zeitschrift .
https://doi.org/10.1127/0941-2948/2006/0130
Lambin, J., Morrow, R., Fu, L. L., Willis, J. K., Bonekamp, H.,
Lillibridge, J., et al. (2010). The OSTM/Jason-2 Mission. Marine
Geodesy . https://doi.org/10.1080/01490419.2010.491030
Lee, H., Jung, H. C., Yuan, T., Beighley, R. E., & Duan, J. (2014).
Controls of Terrestrial Water Storage Changes Over the Central Congo
Basin Determined by Integrating PALSAR ScanSAR, Envisat Altimetry, and
GRACE Data. In Remote Sensing of the Terrestrial Water Cycle .
https://doi.org/10.1002/9781118872086.ch7
Lettenmaier, D. P., Alsdorf, D., Dozier, J., Huffman, G. J., Pan, M., &
Wood, E. F. (2015). Inroads of remote sensing into hydrologic science
during the WRR era. Water Resources Research .
https://doi.org/10.1002/2015WR017616
Li, Y., Grimaldi, S., Pauwels, V. R. N., & Walker, J. P. (2018).
Hydrologic model calibration using remotely sensed soil moisture and
discharge measurements: The impact on predictions at gauged and ungauged
locations. Journal of Hydrology .
https://doi.org/10.1016/j.jhydrol.2018.01.013
Liang, X., Lettenmaier, D. P., Wood, E. F., & Burges, S. J. (1994). A
simple hydrologically based model of land surface water and energy
fluxes for general circulation models. Journal of Geophysical
Research . https://doi.org/10.1029/94jd00483
Lo, M. H., Famiglietti, J. S., Yeh, P. J. F., & Syed, T. H. (2010).
Improving parameter estimation and water table depth simulation in a
land surface model using GRACE water storage and estimated base flow
data. Water Resources Research .
https://doi.org/10.1029/2009WR007855
López, P. L., Sutanudjaja, E. H., Schellekens, J., Sterk, G., &
Bierkens, M. F. P. (2017). Calibration of a large-scale hydrological
model using satellite-based soil moisture and evapotranspiration
products. Hydrology and Earth System Sciences .
https://doi.org/10.5194/hess-21-3125-2017
Maeda, E. E., Ma, X., Wagner, F. H., Kim, H., Oki, T., Eamus, D., &
Huete, A. (2017). Evapotranspiration seasonality across the Amazon
Basin. Earth System Dynamics .
https://doi.org/10.5194/esd-8-439-2017
Manfreda, S., Mita, L., Dal Sasso, S. F., Samela, C., & Mancusi, L.
(2018). Exploiting the use of physical information for the calibration
of a lumped hydrological model. Hydrological Processes .
https://doi.org/10.1002/hyp.11501
Maurer, E. P., Adam, J. C., & Wood, A. W. (2009). Climate model based
consensus on the hydrologic impacts of climate change to the Rio Lempa
basin of Central America. Hydrology and Earth System Sciences .
https://doi.org/10.5194/hess-13-183-2009
Milzow, C., Krogh, P. E., & Bauer-Gottwein, P. (2011). Combining
satellite radar altimetry, SAR surface soil moisture and GRACE total
storage changes for hydrological model calibration in a large poorly
gauged catchment. Hydrology and Earth System Sciences .
https://doi.org/10.5194/hess-15-1729-2011
Mitchell, K. E., Lohmann, D., Houser, P. R., Wood, E. F., Schaake, J.
C., Robock, A., et al. (2004). The multi-institution North American Land
Data Assimilation System (NLDAS): Utilizing multiple GCIP products and
partners in a continental distributed hydrological modeling system.Journal of Geophysical Research D: Atmospheres .
https://doi.org/10.1029/2003JD003823
Montanari, A., & Koutsoyiannis, D. (2014). Modeling and mitigating
natural hazards: Stationarity is immortal! Water Resources
Research . https://doi.org/10.1002/2014WR016092
Motovilov, Y. G., Gottschalk, L., Engeland, K., & Rodhe, A. (1999).
Validation of a distributed hydrological model against spatial
observations. Agricultural and Forest Meteorology .
https://doi.org/10.1016/S0168-1923(99)00102-1
Mu, Q., Zhao, M., & Running, S. W. (2011). Improvements to a MODIS
global terrestrial evapotranspiration algorithm. Remote Sensing of
Environment . https://doi.org/10.1016/j.rse.2011.02.019
Naz, B. S., Frans, C. D., Clarke, G. K. C., Burns, P., & Lettenmaier,
D. P. (2014). Modeling the effect of glacier recession on streamflow
response using a coupled glacio-hydrological model. Hydrology and
Earth System Sciences . https://doi.org/10.5194/hess-18-787-2014
Neal, J.C., Odoni, N. A., Trigg, M.A., Freer, J. E., Garcia-Pintado, J.,
& Mason, D. C. (2015). Efficient incorporation of channel cross-section
geometry uncertainty into regional and global scale flood inundation
models. Journal of Hydrology .
https://doi.org/10.1016/j.jhydrol.2015.07.026
Neal, J., Schumann, G., & Bates, P. (2012). A subgrid channel model for
simulating river hydraulics and floodplain inundation over large and
data sparse areas. Water Resources Research .
https://doi.org/10.1029/2012WR012514
Nearing, G. S., Tian, Y., Gupta, H. V., Clark, M. P., Harrison, K. W.,
& Weijs, S. V. (2016). A philosophical basis for hydrological
uncertainty. Hydrological Sciences Journal .
https://doi.org/10.1080/02626667.2016.1183009
Nepstad, D. C., De Carvalho, C. R., Davidson, E. A., Jipp, P. H.,
Lefebvre, P. A., Negreiros, G. H., et al. (1994). The role of deep roots
in the hydrological and carbon cycles of Amazonian forests and pastures.Nature . https://doi.org/10.1038/372666a0
New, M., Hulme, M., & Jones, P. (2000). Representing twentieth-century
space-time climate variability. Part II: Development of 1901-96 monthly
grids of terrestrial surface climate. Journal of Climate .
https://doi.org/10.1175/1520-0442(2000)013<2217:RTCSTC>2.0.CO;2
Nijzink, R. C., Almeida, S., Pechlivanidis, I. G., Capell, R.,
Gustafssons, D., Arheimer, B., et al. (2018). Constraining Conceptual
Hydrological Models With Multiple Information Sources. Water
Resources Research . https://doi.org/10.1029/2017WR021895
O’Loughlin, F. E., Paiva, R. C. D., Durand, M., Alsdorf, D. E., &
Bates, P. D. (2016). A multi-sensor approach towards a global vegetation
corrected SRTM DEM product. Remote Sensing of Environment .
https://doi.org/10.1016/j.rse.2016.04.018
Paiva, R. C.D., Collischonn, W., Bonnet, M. P., De Gonçalves, L. G. G.,
Calmant, S., Getirana, A., & Santos Da Silva, J. (2013). Assimilating
in situ and radar altimetry data into a large-scale
hydrologic-hydrodynamic model for streamflow forecast in the Amazon.Hydrology and Earth System Sciences .
https://doi.org/10.5194/hess-17-2929-2013
Paiva, R. C.D., Collischonn, W., & Tucci, C. E. M. (2011). Large scale
hydrologic and hydrodynamic modeling using limited data and a GIS based
approach. Journal of Hydrology .
https://doi.org/10.1016/j.jhydrol.2011.06.007
Paiva, R. C. D., Buarque, D. C., Collischonn, W., Bonnet, M. P.,
Frappart, F., Calmant, S., & Bulhões Mendes, C. A. (2013). Large-scale
hydrologic and hydrodynamic modeling of the Amazon River basin.Water Resources Research . https://doi.org/10.1002/wrcr.20067
Pan, M., & Wood, E. F. (2006). Data assimilation for estimating the
terrestrial water budget using a constrained ensemble Kalman filter.Journal of Hydrometeorology . https://doi.org/10.1175/JHM495.1
Pan, S., Liu, L., Bai, Z., & Xu, Y. P. (2018). Integration of remote
sensing evapotranspiration into multi-objective calibration of
distributed hydrology-soil-vegetation model (DHSVM) in a humid region of
China. Water (Switzerland) . https://doi.org/10.3390/w10121841
Pan, S., Pan, N., Tian, H., Friedlingstein, P., Sitch, S., Shi, H.,
Arora, V.K., Haverd, V., Jain, A.K., Kato, E., Lienert, S., Lombardozzi,
D., Nabel, J.E.M.S., Ottlé, C., Poulter, B., Zaehle, S., Running, S.W.
(2020). Evaluation of global terrestrial evapotranspiration using
state-of-the-art approaches in remote sensing, machine learning and land
surface modeling. Hydrol. Earth Syst. Sci.https://doi.org/10.5194/hess-24-1485-2020
Pathiraja, S., Marshall, L., Sharma, A., & Moradkhani, H. (2016).
Hydrologic modeling in dynamic catchments: A data assimilation approach.Water Resources Research . https://doi.org/10.1002/2015WR017192
Pellet, V., Aires, F., Munier, S., Fernández Prieto, D., Jordá, G.,
Arnoud Dorigo, W., et al. (2019). Integrating multiple satellite
observations into a coherent dataset to monitor the full water cycle -
Application to the Mediterranean region. Hydrology and Earth
System Sciences . https://doi.org/10.5194/hess-23-465-2019
Peña-Arancibia, J. L., Zhang, Y., Pagendam, D. E., Viney, N. R., Lerat,
J., van Dijk, A. I. J. M., et al. (2015). Streamflow rating uncertainty:
Characterisation and impacts on model calibration and performance.Environmental Modelling and Software .
https://doi.org/10.1016/j.envsoft.2014.09.011
Poméon, T., Diekkrüger, B., & Kumar, R. (2018). Computationally
efficient multivariate calibration and validation of a grid-based
hydrologic model in sparsely gauged West African river basins.Water (Switzerland) . https://doi.org/10.3390/w10101418
Pontes, P. R. M., Fan, F. M., Fleischmann, A. S., de Paiva, R. C. D.,
Buarque, D. C., Siqueira, V. A., et al. (2017). MGB-IPH model for
hydrological and hydraulic simulation of large floodplain river systems
coupled with open source GIS. Environmental Modelling and
Software . https://doi.org/10.1016/j.envsoft.2017.03.029
Rajib, M. A., Merwade, V., & Yu, Z. (2016). Multi-objective calibration
of a hydrologic model using spatially distributed remotely
sensed/in-situ soil moisture. Journal of Hydrology .
https://doi.org/10.1016/j.jhydrol.2016.02.037
Rakovec, O., Kumar, R., Attinger, S., & Samaniego, L. (2016). Improving
the realism of hydrologic model functioning through multivariate
parameter estimation. Water Resources Research .
https://doi.org/10.1002/2016WR019430
Reichle, R. H., McLaughlin, D. B., & Entekhabi, D. (2002). Hydrologic
data assimilation with the ensemble Kalman filter. Monthly Weather
Review .
https://doi.org/10.1175/1520-0493(2002)130<0103:HDAWTE>2.0.CO;2
Rosenqvist, A., Shimada, M., Ito, N., & Watanabe, M. (2007). ALOS
PALSAR: A pathfinder mission for global-scale monitoring of the
environment. In IEEE Transactions on Geoscience and Remote
Sensing . https://doi.org/10.1109/TGRS.2007.901027
Samaniego, L., Kumar, R., & Attinger, S. (2010). Multiscale parameter
regionalization of a grid-based hydrologic model at the mesoscale.Water Resources Research . https://doi.org/10.1029/2008WR007327
Schattan, P., Schwaizer, G., Schöber, J., & Achleitner, S. (2020). The
complementary value of cosmic-ray neutron sensing and snow covered area
products for snow hydrological modelling. Remote Sensing of
Environment. https://doi.org/10.1016/j.rse.2019.111603
Schneider, R., Nygaard Godiksen, P., Villadsen, H., Madsen, H., &
Bauer-Gottwein, P. (2017). Application of CryoSat-2 altimetry data for
river analysis and modelling. Hydrology and Earth System
Sciences . https://doi.org/10.5194/hess-21-751-2017
Schumacher, M., Forootan, E., van Dijk, A. I. J. M., Müller Schmied, H.,
Crosbie, R. S., Kusche, J., & Döll, P. (2018). Improving drought
simulations within the Murray-Darling Basin by combined
calibration/assimilation of GRACE data into the WaterGAP Global
Hydrology Model. Remote Sensing of Environment .
https://doi.org/10.1016/j.rse.2017.10.029
Semenova, O., & Beven, K. (2015). Barriers to progress in distributed
hydrological modelling. Hydrological Processes .
https://doi.org/10.1002/hyp.10434
Shafii, M., & Tolson, B. A. (2015). Optimizing hydrological consistency
by incorporating hydrological signatures into model calibration
objectives. Water Resources Research .
https://doi.org/10.1002/2014WR016520
Silvestro, F., Gabellani, S., Rudari, R., Delogu, F., Laiolo, P., &
Boni, G. (2015). Uncertainty reduction and parameter estimation of a
distributed hydrological model with ground and remote-sensing data.Hydrology and Earth System Sciences .
https://doi.org/10.5194/hess-19-1727-2015
Siqueira, V., Fleischmann, A., Jardim, P., Fan, F., & Collischonn, W.
(2016). IPH-Hydro Tools: a GIS coupled tool for watershed topology
acquisition in an open-source environment. Revista Brasileira de
Recursos Hídricos . https://doi.org/10.21168/rbrh.v21n1.p274-287
Siqueira, V. A., Paiva, R. C. D., Fleischmann, A. S., Fan, F. M.,
Ruhoff, A. L., Pontes, P. R. M., et al. (2018). Toward continental
hydrologic-hydrodynamic modeling in South America. Hydrology and
Earth System Sciences . https://doi.org/10.5194/hess-22-4815-2018
Sivapalan, M., Takeuchi, K., Franks, S. W., Gupta, V. K., Karambiri, H.,
Lakshmi, V., et al. (2003). IAHS Decade on Predictions in Ungauged
Basins (PUB), 2003-2012: Shaping an exciting future for the hydrological
sciences. Hydrological Sciences Journal .
https://doi.org/10.1623/hysj.48.6.857.51421
Sun, W., Ishidaira, H., & Bastola, S. (2012). Calibration of
hydrological models in ungauged basins based on satellite radar
altimetry observations of river water level. Hydrological
Processes . https://doi.org/10.1002/hyp.8429
Sun, W., Fan, J., Wang, G., Ishidaira, H., Bastola, S., Yu, J., et al.
(2018). Calibrating a hydrological model in a regional river of the
Qinghai–Tibet plateau using river water width determined from high
spatial resolution satellite images. Remote Sensing of
Environment . https://doi.org/10.1016/j.rse.2018.05.020
Sun, W. C., Ishidaira, H., & Bastola, S. (2010). Towards improving
river discharge estimation in ungauged basins: Calibration of
rainfall-runoff models based on satellite observations of river flow
width at basin outlet. Hydrology and Earth System Sciences .
https://doi.org/10.5194/hess-14-2011-2010
Tapley, B. D., Bettadpur, S., Ries, J. C., Thompson, P. F., & Watkins,
M. M. (2004). GRACE measurements of mass variability in the Earth
system. Science . https://doi.org/10.1126/science.1099192
Tarpanelli, A., Brocca, L., Melone, F., & Moramarco, T. (2013).
Hydraulic modelling calibration in small rivers by using coarse
resolution synthetic aperture radar imagery. Hydrological
Processes . https://doi.org/10.1002/hyp.9550
Teutschbein, C., & Seibert, J. (2012). Bias correction of regional
climate model simulations for hydrological climate-change impact
studies: Review and evaluation of different methods. Journal of
Hydrology . https://doi.org/10.1016/j.jhydrol.2012.05.052
Vrugt, J. A., Diks, C. G. H., Gupta, H. V., Bouten, W., & Verstraten,
J. M. (2005). Improved treatment of uncertainty in hydrologic modeling:
Combining the strengths of global optimization and data assimilation.Water Resources Research . https://doi.org/10.1029/2004WR003059
Wagener, T., McIntyre, N., Lees, M. J., Wheater, H. S., & Gupta, H. V.
(2003). Towards reduced uncertainty in conceptual rainfall-runoff
modelling: Dynamic identifiability analysis. Hydrological
Processes . https://doi.org/10.1002/hyp.1135
Wambura, F. J., Dietrich, O., & Lischeid, G. (2018). Improving a
distributed hydrological model using evapotranspiration-related boundary
conditions as additional constraints in a data-scarce river basin.Hydrological Processes . https://doi.org/10.1002/hyp.11453
Werth, S., & Güntner, A. (2010). Calibration analysis for water storage
variability of the global hydrological model WGHM. Hydrology and
Earth System Sciences . https://doi.org/10.5194/hess-14-59-2010
Werth, S., Güntner, A., Petrovic, S., & Schmidt, R. (2009). Integration
of GRACE mass variations into a global hydrological model. Earth
and Planetary Science Letters .
https://doi.org/10.1016/j.epsl.2008.10.021
Willem Vervoort, R., Miechels, S. F., van Ogtrop, F. F., & Guillaume,
J. H. A. (2014). Remotely sensed evapotranspiration to calibrate a
lumped conceptual model: Pitfalls and opportunities. Journal of
Hydrology . https://doi.org/10.1016/j.jhydrol.2014.10.034
Winsemius, H. C., G. Savenije, H. H., & M. Bastiaanssen, W. G. (2008).
Constraining model parameters on remotely sensed evaporation:
Justification for distribution in ungauged basins? Hydrology and
Earth System Sciences . https://doi.org/10.5194/hess-12-1403-2008
Xu, C. Y., Widén, E., & Halldin, S. (2005). Modelling hydrological
consequences of climate change - Progress and challenges. Advances
in Atmospheric Sciences . https://doi.org/10.1007/BF02918679
Xu, X., Li, J., & Tolson, B. A. (2014). Progress in integrating remote
sensing data and hydrologic modeling. Progress in Physical
Geography . https://doi.org/10.1177/0309133314536583
Yamazaki, D., Kanae, S., Kim, H., & Oki, T. (2011). A physically based
description of floodplain inundation dynamics in a global river routing
model. Water Resources Research .
https://doi.org/10.1029/2010WR009726
Yapo, P. O., Gupta, H. V., & Sorooshian, S. (1998). Multi-objective
global optimization for hydrologic models. Journal of Hydrology .
https://doi.org/10.1016/S0022-1694(97)00107-8
Zajac, Z., Revilla-Romero, B., Salamon, P., Burek, P., Hirpa, F., &
Beck, H. (2017). The impact of lake and reservoir parameterization on
global streamflow simulation. Journal of Hydrology .
https://doi.org/10.1016/j.jhydrol.2017.03.022
Zink, M., Mai, J., Cuntz, M., & Samaniego, L. (2018). Conditioning a
Hydrologic Model Using Patterns of Remotely Sensed Land Surface
Temperature. Water Resources Research .
https://doi.org/10.1002/2017WR021346