Quantifying the Effect of GRACE Terrestrial Water Storage Anomaly in the Simulation of Extreme Flows

AbstractThe conventional hydrological modeling framework with a traditional streamflow-alone calibration approach is often challenged with the difficulty of accurately simulating the extreme flow c...

[1]  C. Dhanya,et al.  The potential of GRACE in assessing the flood potential of Peninsular Indian River basins , 2020 .

[2]  J. Reager,et al.  Estimation of hydrological drought recovery based on GRACE water storage deficit , 2019 .

[3]  A. P. Dimri,et al.  On the Recent Floods in India , 2019, Current Science.

[4]  H. Madsen,et al.  Real-time simulation of surface water and groundwater with data assimilation , 2019, Advances in Water Resources.

[5]  A. Kawamura,et al.  Improving Urban Runoff in Multi-Basin Hydrological Simulation by the HYPE Model Using EEA Urban Atlas: A Case Study in the Sege River Basin, Sweden , 2019, Hydrology.

[6]  Anil Kumar Singh,et al.  Monitoring groundwater fluctuations over India during Indian Summer Monsoon (ISM) and Northeast monsoon using GRACE satellite: Impact on agriculture , 2019, Quaternary International.

[7]  Wen Wang,et al.  Impacts of Antecedent Soil moisture on the Rainfall–Runoff Transformation Process Based on High-Resolution Observations in Soil Tank Experiments , 2019, Water.

[8]  J. Reager,et al.  Using GRACE in a streamflow recession to determine drainable water storage in the Mississippi River basin , 2019, Hydrology and Earth System Sciences.

[9]  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.

[10]  I. Zin,et al.  How does initial soil moisture influence the hydrological response? A case study from southern France , 2018, Hydrology and Earth System Sciences.

[11]  X. Lei,et al.  Multi-objective calibration of MIKE SHE with SMAP soil moisture datasets , 2018, Hydrology Research.

[12]  Berit Arheimer,et al.  A comparison of hydrological climate services at different scales by users and scientists , 2018, Climate Services.

[13]  Q. J. Wang,et al.  On the importance of soil moisture in calibration of rainfall–runoff models: two case studies , 2018, Hydrological Sciences Journal.

[14]  Xiaomang Liu,et al.  Improving hydrological simulations by incorporating GRACE data for model calibration , 2018 .

[15]  Z. Duan,et al.  Understanding the impacts of catchment characteristics on the shape of the storage capacity curve and its influence on flood flows , 2018 .

[16]  Zhangli Sun,et al.  Assessing Terrestrial Water Storage and Flood Potential Using GRACE Data in the Yangtze River Basin, China , 2017, Remote. Sens..

[17]  Saman Razavi,et al.  Enhanced identification of a hydrologic model using streamflow and satellite water storage data: A multicriteria sensitivity analysis and optimization approach , 2017 .

[18]  Anil Kumar Singh,et al.  Estimation of quantitative measures of total water storage variation from GRACE and GLDAS-NOAH satellites using geospatial technology , 2017 .

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

[20]  Lei Zou,et al.  Implementation of evapotranspiration data assimilation with catchment scale distributed hydrological model via an ensemble Kalman Filter , 2017 .

[21]  Yiming Hu,et al.  Investigating the impact of surface soil moisture assimilation on state and parameter estimation in SWAT model based on the ensemble Kalman filter in upper Huai River basin , 2017 .

[22]  Thorsten Wagener,et al.  Dynamics of water fluxes and storages in an Alpine karst catchment under current and potential future climate conditions , 2017, Hydrology and Earth System Sciences.

[23]  Bhagu R. Chahar,et al.  GIS-based SWMM model for simulating the catchment response to flood events , 2017 .

[24]  Y. Hundecha,et al.  Analysis of hydrological extremes at different hydro-climatic regimes under present and future conditions , 2017, Climatic Change.

[25]  Bhagu R. Chahar,et al.  A regional scale performance evaluation of SMOS and ESA-CCI soil moisture products over India with simulated soil moisture from MERRA-Land , 2016 .

[26]  Charles Onyutha,et al.  Influence of Hydrological Model Selection on Simulation of Moderate and Extreme Flow Events: A Case Study of the Blue Nile Basin , 2016 .

[27]  Berit Arheimer,et al.  Large-scale hydrological modelling by using modified PUB recommendations: the India-HYPE case , 2015 .

[28]  Bailing Li,et al.  Assimilation of GRACE Terrestrial Water Storage Observations into a Land Surface Model for the Assessment of Regional Flood Potential , 2015, Remote. Sens..

[29]  D. Hughes,et al.  Surface water–groundwater interactions in catchment scale water resources assessments—understanding and hypothesis testing with a hydrological model , 2015 .

[30]  Indrajeet Chaubey,et al.  Impact of the numbers of observations and calibration parameters on equifinality, model performance, and output and parameter uncertainty , 2015 .

[31]  Henrik Madsen,et al.  Data assimilation in integrated hydrological modeling using ensemble Kalman filtering: evaluating the effect of ensemble size and localization on filter performance , 2015 .

[32]  P. Willems,et al.  Empirical statistical characterization and regionalization of amplitude–duration–frequency curves for extreme peak flows in the Lake Victoria Basin, East Africa , 2015 .

[33]  G. Dalla Fontana,et al.  Seasonal changes in runoff generation in a small forested mountain catchment , 2015 .

[34]  L. Brocca,et al.  Potential of soil moisture observations in flood modelling : estimating initial conditions and correcting rainfall , 2014 .

[35]  B. Boudevillain,et al.  Multi-scale hydrometeorological observation and modelling for flash flood understanding , 2014 .

[36]  Chandranath Chatterjee,et al.  Are recent frequent high floods in Mahanadi basin in eastern India due to increase in extreme rainfalls , 2014 .

[37]  Brian F. Thomas,et al.  River basin flood potential inferred using GRACE gravity observations at several months lead time , 2014 .

[38]  G. Blöschl,et al.  The June 2013 flood in the Upper Danube Basin, and comparisons with the 2002, 1954 and 1899 floods , 2013 .

[39]  P. Willems,et al.  Uncertainties in Flow-Duration-Frequency Relationships of High and Low Flow Extremes in Lake Victoria Basin , 2013 .

[40]  Ashwani Kumar,et al.  Streamflow trends in the Mahanadi River basin (India): Linkages to tropical climate variability , 2013 .

[41]  M. Rodell,et al.  Water in the Balance , 2013, Science.

[42]  J. Kusche,et al.  Calibration/Data Assimilation Approach for Integrating GRACE Data into the WaterGAP Global Hydrology Model (WGHM) Using an Ensemble Kalman Filter: First Results , 2013, Surveys in Geophysics.

[43]  Claudia Ringler,et al.  Calibration and evaluation of a semi-distributed watershed model of Sub-Saharan Africa using GRACE data , 2012 .

[44]  Dennis P. Lettenmaier,et al.  Multi-criteria parameter estimation for the unified land model , 2012 .

[45]  Göran Lindström,et al.  Water and nutrient simulations using the HYPE model for Sweden vs. the Baltic Sea basin - influence of input-data quality and scale , 2012 .

[46]  Murugesu Sivapalan,et al.  Comparative diagnostic analysis of runoff generation processes in Oklahoma DMIP2 basins: The Blue River and the Illinois River , 2012 .

[47]  Kuolin Hsu,et al.  From lumped to distributed via semi-distributed: Calibration strategies for semi-distributed hydrologic models , 2012 .

[48]  Venkatesh Merwade,et al.  Implementation of surface soil moisture data assimilation with watershed scale distributed hydrological model , 2012 .

[49]  Yves Tramblay,et al.  Assessment of initial soil moisture conditions for event-based rainfall–runoff modelling , 2010 .

[50]  B. Arheimer,et al.  Development and testing of the HYPE (Hydrological Predictions for the Environment) water quality model for different spatial scales , 2010 .

[51]  Anuj Srivastava,et al.  Development of a high resolution daily gridded temperature data set (1969–2005) for the Indian region , 2009 .

[52]  F. De Smedt,et al.  Hydrological Modeling of Snow Accumulation and Melting on River Basin Scale , 2009 .

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

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

[55]  E. Todini.  Hydrological catchment modelling: past, present and future , 2007 .

[56]  Günter Blöschl,et al.  Spatio-temporal variability of event runoff coefficients , 2006 .

[57]  Cajo J. F. ter Braak,et al.  A Markov Chain Monte Carlo version of the genetic algorithm Differential Evolution: easy Bayesian computing for real parameter spaces , 2006, Stat. Comput..

[58]  P. Döll,et al.  Development and validation of a global database of lakes, reservoirs and wetlands , 2004 .

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

[60]  E. Todini,et al.  Towards a comprehensive physically-based rainfall-runoff model , 2002 .

[61]  Keith Beven,et al.  Dalton Medal Lecture: How far can we go in distributed hydrological modelling? , 2001 .

[62]  Soroosh Sorooshian,et al.  A framework for development and application of hydrological models , 2001, Hydrology and Earth System Sciences.

[63]  J. Seibert.  Multi-criteria calibration of a conceptual runoff model using a genetic algorithm , 2000 .

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

[65]  C. Cunnane,et al.  A particular comparison of annual maxima and partial duration series methods of flood frequency prediction , 1973 .

[66]  J. Nash,et al.  River flow forecasting through conceptual models part I — A discussion of principles☆ , 1970 .

[67]  D. Shepard.  A two-dimensional interpolation function for irregularly-spaced data , 1968, ACM National Conference.

[68]  A. Jenkinson.  The frequency distribution of the annual maximum (or minimum) values of meteorological elements , 1955 .

[69]  Naresh Pai,et al.  Hydrologic and Water Quality Models: Performance Measures and Evaluation Criteria , 2015 .

[70]  H. Ishidaira,et al.  CALIBRATING A HYDROLOGIC MODEL BY STEP-WISE METHOD USING GRACE TWS AND DISCHARGE DATA , 2015 .

[71]  O. P. Sreejith,et al.  Development of a new high spatial resolution (0.25° × 0.25°) long period (1901-2010) daily gridded rainfall data set over India and its comparison with existing data sets over the region , 2014 .

[72]  Lars Marklund,et al.  Multi-Variable Evaluation of An Integrated Model System Covering Sweden (S-HYPE) , 2011 .

[73]  K. Loague.  Rainfall-Runoff Modelling , 2010 .

[74]  Soroosh Sorooshian,et al.  Model Calibration in Watershed Hydrology , 2009 .

[75]  Thomas R. Loveland,et al.  The global land-cover characteristics database : The users' perspective , 1999 .

[76]  J. Pickands.  Statistical Inference Using Extreme Order Statistics , 1975 .