On the contribution of remote sensing-based calibration to model multiple hydrological variables in tropical regions

The accuracy of hydrological model predictions is limited by uncertainties in model structure and parameterization, and observations used for calibration, validation and model forcing. Conventional...

[1]  John N. Holeman.  The Sediment Yield of Major Rivers of the World , 1968 .

[2]  Keith Beven,et al.  The future of distributed models: model calibration and uncertainty prediction. , 1992 .

[3]  S. Sorooshian,et al.  Effective and efficient global optimization for conceptual rainfall‐runoff models , 1992 .

[4]  E. Davidson,et al.  The role of deep roots in the hydrological and carbon cycles of Amazonian forests and pastures , 1994, Nature.

[5]  W. Junk.  General aspects of floodplain ecology with special reference to Amazonian floodplains , 1997 .

[6]  K. Beven,et al.  On constraining the predictions of a distributed model: The incorporation of fuzzy estimates of saturated areas into the calibration process , 1998 .

[7]  W. J. Shuttleworth,et al.  Integration of soil moisture remote sensing and hydrologic modeling using data assimilation , 1998 .

[8]  L. Gottschalk,et al.  Validation of a distributed hydrological model against spatial observations , 1999 .

[9]  P. Jones,et al.  Representing Twentieth-Century Space-Time Climate Variability. Part II: Development of 1901-96 Monthly Grids of Terrestrial Surface Climate , 2000 .

[10]  D. McLaughlin,et al.  Hydrologic Data Assimilation with the Ensemble Kalman Filter , 2002 .

[11]  Neil McIntyre,et al.  Towards reduced uncertainty in conceptual rainfall‐runoff modelling: dynamic identifiability analysis , 2003 .

[12]  C. Barbosa,et al.  Dual-season mapping of wetland inundation and vegetation for the central Amazon basin , 2003 .

[13]  W. Crow,et al.  Multiobjective calibration of land surface model evapotranspiration predictions using streamflow observations and spaceborne surface radiometric temperature retrievals , 2003 .

[14]  P. E. O'connell,et al.  IAHS Decade on Predictions in Ungauged Basins (PUB), 2003–2012: Shaping an exciting future for the hydrological sciences , 2003 .

[15]  J. D. Tarpley,et al.  The multi‐institution North American Land Data Assimilation System (NLDAS): Utilizing multiple GCIP products and partners in a continental distributed hydrological modeling system , 2004 .

[16]  M. Watkins,et al.  GRACE Measurements of Mass Variability in the Earth System , 2004, Science.

[17]  Chong-yu Xu,et al.  Modelling hydrological consequences of climate change—Progress and challenges , 2005 .

[18]  C. Diks,et al.  Improved treatment of uncertainty in hydrologic modeling: Combining the strengths of global optimization and data assimilation , 2005 .

[19]  D. Lettenmaier,et al.  Anthropogenic impacts on continental surface water fluxes , 2006 .

[20]  J. Kirchner.  Getting the right answers for the right reasons: Linking measurements, analyses, and models to advance the science of hydrology , 2006 .

[21]  B. Rudolf,et al.  World Map of the Köppen-Geiger climate classification updated , 2006 .

[22]  Keith Beven,et al.  A manifesto for the equifinality thesis , 2006 .

[23]  R. Avissar,et al.  What Controls Evapotranspiration in the Amazon Basin , 2007 .

[24]  Y. Hong,et al.  The TRMM Multisatellite Precipitation Analysis (TMPA): Quasi-Global, Multiyear, Combined-Sensor Precipitation Estimates at Fine Scales , 2007 .

[25]  Manabu Watanabe,et al.  ALOS PALSAR: A Pathfinder Mission for Global-Scale Monitoring of the Environment , 2007, IEEE Transactions on Geoscience and Remote Sensing.

[26]  W. Collischonn,et al.  The MGB-IPH model for large-scale rainfall—runoff modelling , 2007 .

[27]  T. McMahon,et al.  Updated world map of the Köppen-Geiger climate classification , 2007 .

[28]  W. Bastiaanssen,et al.  Constraining model parameters on remotely sensed evaporation: Justification for distribution in ungauged basins? , 2008 .

[29]  A. Wood,et al.  Climate model based consensus on the hydrologic impacts of climate change to the Rio Lempa basin of Central America , 2008 .

[30]  S. Attinger,et al.  Multiscale parameter regionalization of a grid‐based hydrologic model at the mesoscale , 2010 .

[31]  B. Croke.  Representing uncertainty in objective functions: extension to include the influence of serial correlation , 2008 .

[32]  A. Montanari,et al.  Uncertainty in river discharge observations: a quantitative analysis , 2009 .

[33]  S. Petrovic,et al.  Integration of GRACE mass variations into a global hydrological model , 2009 .

[34]  Mary C. Hill,et al.  Sensitivity analysis, calibration, and testing of a distributed hydrological model using error‐based weighting and one objective function , 2009 .

[35]  A. Güntner,et al.  Calibration analysis for water storage variability of the global hydrological model WGHM , 2009 .

[36]  A. Cazenave,et al.  Global Evaluation of the ISBA-TRIP Continental Hydrological System. Part I: Comparison to GRACE Terrestrial Water Storage Estimates and In Situ River Discharges , 2010 .

[37]  S. Bastola,et al.  Towards improving river discharge estimation in ungauged basins: calibration of rainfall-runoff models based on satellite observations of river flow width at basin outlet , 2010 .

[38]  J. Famiglietti,et al.  Improving parameter estimation and water table depth simulation in a land surface model using GRACE water storage and estimated base flow data , 2010 .

[39]  J. Willis,et al.  The OSTM/Jason-2 Mission , 2010 .

[40]  P. Bauer‐Gottwein,et al.  Combining satellite radar altimetry, SAR surface soil moisture and GRACE total storage changes for hydrological model calibration in a large poorly gauged catchment , 2011 .

[41]  S. Kanae,et al.  A physically based description of floodplain inundation dynamics in a global river routing model , 2011 .

[42]  Maosheng Zhao,et al.  Improvements to a MODIS global terrestrial evapotranspiration algorithm , 2011 .

[43]  S. Bastola,et al.  Calibration of hydrological models in ungauged basins based on satellite radar altimetry observations of river water level , 2012 .

[44]  S. Calmant,et al.  Assimilating in situ and radar altimetry data into a large-scale hydrologic-hydrodynamic model for streamflow forecast in the Amazon , 2012 .

[45]  P. Bates,et al.  A subgrid channel model for simulating river hydraulics and floodplain inundation over large and data sparse areas , 2012 .

[46]  S. Calmant,et al.  Large‐scale hydrologic and hydrodynamic modeling of the Amazon River basin , 2013 .

[47]  Ben R. Hodges,et al.  Challenges in Continental River Dynamics , 2013, Environ. Model. Softw..

[48]  D. Lettenmaier,et al.  Modeling the effect of glacier recession on streamflow response using a coupled glacio-hydrological model , 2013 .

[49]  J. McDonnell,et al.  A decade of Predictions in Ungauged Basins (PUB)—a review , 2013 .

[50]  L. Brocca,et al.  Hydraulic modelling calibration in small rivers by using coarse resolution synthetic aperture radar imagery , 2013 .

[51]  F. Silvestro,et al.  Uncertainty reduction and parameter estimation of a distributed hydrological model with ground and remote-sensing data , 2014 .

[52]  H. Gupta,et al.  A constraint-based search algorithm for parameter identification of environmental models , 2014 .

[53]  Hahn Chul Jung,et al.  Controls of Terrestrial Water Storage Changes Over the Central Congo Basin Determined by Integrating PALSAR ScanSAR, Envisat Altimetry, and GRACE Data , 2014 .

[54]  Jonathan Li,et al.  Progress in integrating remote sensing data and hydrologic modeling , 2014 .

[55]  Demetris Koutsoyiannis,et al.  Modeling and mitigating natural hazards: Stationarity is immortal! , 2014 .

[56]  Keith Beven,et al.  Barriers to progress in distributed hydrological modelling , 2015 .

[57]  Bryan A. Tolson,et al.  Optimizing hydrological consistency by incorporating hydrological signatures into model calibration objectives , 2015 .

[58]  Albert I. J. M. van Dijk,et al.  Streamflow rating uncertainty: Characterisation and impacts on model calibration and performance , 2015, Environ. Model. Softw..

[59]  D. Lawrence,et al.  Improving the representation of hydrologic processes in Earth System Models , 2015 .

[60]  J. Dozier,et al.  Inroads of remote sensing into hydrologic science during the WRR era , 2015 .

[61]  Peter Nygaard Godiksen,et al.  Application of CryoSat-2 altimetry data for river analysis and modelling , 2016 .

[62]  F. O'Loughlin,et al.  A multi-sensor approach towards a global vegetation corrected SRTM DEM product , 2016 .

[63]  M. Clark,et al.  A philosophical basis for hydrological uncertainty , 2016 .

[64]  H. Moradkhani,et al.  Hydrologic modeling in dynamic catchments: A data assimilation approach , 2016 .

[65]  Walter Collischonn,et al.  IPH-Hydro Tools: uma ferramenta open source para determinação de informações topológicas em bacias hidrográficas integrada a um ambiente SIG , 2016 .

[66]  S. Attinger,et al.  Improving the realism of hydrologic model functioning through multivariate parameter estimation , 2016 .

[67]  G. Sterk,et al.  Calibration of a large-scale hydrological model using satellite-based soil moisture and evapotranspiration products , 2017 .

[68]  Juliane Mai,et al.  Combining satellite data and appropriate objective functions for improved spatial pattern performance of a distributed hydrologic model , 2017 .

[69]  T. Oki,et al.  Evapotranspiration seasonality across the Amazon Basin , 2017 .

[70]  E. Wood,et al.  Four decades of microwave satellite soil moisture observations: Part 2. Product validation and inter-satellite comparisons , 2017 .

[71]  C. Tøttrup,et al.  Informing a hydrological model of the Ogooué with multi-mission remote sensing data , 2017 .

[72]  Walter Collischonn,et al.  MGB-IPH model for hydrological and hydraulic simulation of large floodplain river systems coupled with open source GIS , 2017, Environ. Model. Softw..

[73]  Yue‐Ping Xu,et al.  Integration of Remote Sensing Evapotranspiration into Multi-Objective Calibration of Distributed Hydrology–Soil–Vegetation Model (DHSVM) in a Humid Region of China , 2018, Water.

[74]  S. Manfreda,et al.  Exploiting the use of physical information for the calibration of a lumped hydrological model , 2018 .

[75]  Julian Koch,et al.  The SPAtial EFficiency metric (SPAEF): multiple-component evaluation of spatial patterns for optimization of hydrological models , 2018 .

[76]  M. Cuntz,et al.  Conditioning a Hydrologic Model Using Patterns of Remotely Sensed Land Surface Temperature , 2018 .

[77]  B. Diekkrüger,et al.  Computationally Efficient Multivariate Calibration and Validation of a Grid-Based Hydrologic Model in Sparsely Gauged West African River Basins , 2018, Water.

[78]  S. Calmant,et al.  Toward continental hydrologic–hydrodynamic modeling in South America , 2018, Hydrology and Earth System Sciences.

[79]  O. Dietrich,et al.  Improving a distributed hydrological model using evapotranspiration‐related boundary conditions as additional constraints in a data‐scarce river basin , 2018 .

[80]  Hubert H. G. Savenije,et al.  Constraining Conceptual Hydrological Models With Multiple Information Sources , 2018, Water Resources Research.

[81]  J. Kusche,et al.  Improving drought simulations within the Murray-Darling Basin by combined calibration/assimilation of GRACE data into the WaterGAP Global Hydrology Model , 2018 .

[82]  Zongxue Xu,et al.  Calibrating a hydrological model in a regional river of the Qinghai–Tibet plateau using river water width determined from high spatial resolution satellite images , 2018, Remote Sensing of Environment.

[83]  M. P. González-Dugo,et al.  Twenty-three unsolved problems in hydrology (UPH) – a community perspective , 2019, Hydrological Sciences Journal.

[84]  Demirel,et al.  Additional Value of Using Satellite-Based Soil Moisture and Two Sources of Groundwater Data for Hydrological Model Calibration , 2019, Water.

[85]  José A. Sobrino,et al.  Intercomparison of remote-sensing based evapotranspiration algorithms over amazonian forests , 2019, Int. J. Appl. Earth Obs. Geoinformation.

[86]  S. Attinger,et al.  A Comprehensive Distributed Hydrological Modeling Intercomparison to Support Process Representation and Data Collection Strategies , 2019, Water Resources Research.

[87]  F. Aires,et al.  Integrating multiple satellite observations into a coherent dataset to monitor the full water cycle – application to the Mediterranean region , 2018, Hydrology and Earth System Sciences.

[88]  G. Schumann,et al.  Challenges, Opportunities, and Pitfalls for Global Coupled Hydrologic‐Hydraulic Modeling of Floods , 2019, Water Resources Research.

[89]  Dejuan Jiang,et al.  The Role of Satellite-Based Remote Sensing in Improving Simulated Streamflow: A Review , 2019, Water.

[90]  R. Paiva,et al.  Assimilation of Satellite Altimetry Data for Effective River Bathymetry , 2019, Water Resources Research.

[91]  W. Yeh,et al.  Multivariate calibration of large scale hydrologic models: The necessity and value of a Pareto optimal approach , 2019, Advances in Water Resources.

[92]  Atul K. Jain,et al.  Evaluation of global terrestrial evapotranspiration using state-of-the-art approaches in remote sensing, machine learning and land surface modeling , 2019, Hydrology and Earth System Sciences.

[93]  A. Al Bitar,et al.  Trade‐Offs Between 1‐D and 2‐D Regional River Hydrodynamic Models , 2020, Water Resources Research.

[94]  S. Achleitner,et al.  The complementary value of cosmic-ray neutron sensing and snow covered area products for snow hydrological modelling , 2020 .