Improving streamflow predictions at ungauged locations with real-time updating: application of an EnKF-based state-parameter estimation strategy

The challenge of streamflow predictions at ungauged locations is primarily attributed to various uncertainties in hydrological modelling. Many studies have been devoted to addressing this issue. The similarity regionalization approach, a commonly used strategy, is usually limited by subjective selection of similarity measures. This paper presents an application of a partitioned update scheme based on the ensemble Kalman filter (EnKF) to reduce the prediction uncertainties. This scheme performs real-time updating for states and parameters of a distributed hydrological model by assimilating gauged streamflow. The streamflow predictions are constrained by the physical rainfall-runoff processes defined in the distributed hydrological model and by the correlation information transferred from gauged to ungauged basins. This scheme is successfully demonstrated in a nested basin with real-world hydrological data where the subbasins have immediate upstream and downstream neighbours. The results suggest that the assimilated observed data from downstream neighbours have more important roles in reducing the streamflow prediction errors at ungauged locations. The real-time updated model parameters remain stable with reasonable spreads after short-period assimilation, while their estimation trajectories have slow variations, which may be attributable to climate and land surface changes. Although this real-time updating scheme is intended for streamflow predictions in nested basins, it can be a valuable tool in separate basins to improve hydrological predictions by assimilating multi-source data sets, including ground-based and remote-sensing observations.

[1]  Xuesong Zhang,et al.  SWAT Ungauged: Hydrological Budget and Crop Yield Predictions in the Upper Mississippi River Basin , 2010 .

[2]  Oldrich Rakovec,et al.  State updating of a distributed hydrological model with Ensemble Kalman Filtering: Effects of updating frequency and observation network density on forecast accuracy , 2012 .

[3]  Murugesu Sivapalan,et al.  Comparative assessment of predictions in ungauged basins – Part 1: Runoff-hydrograph studies , 2013 .

[4]  Eric F. Wood,et al.  Inverse streamflow routing , 2013 .

[5]  Soroosh Sorooshian,et al.  Optimal use of the SCE-UA global optimization method for calibrating watershed models , 1994 .

[6]  Misgana K. Muleta,et al.  Sensitivity and uncertainty analysis coupled with automatic calibration for a distributed watershed model , 2005 .

[7]  Yuguo Chen,et al.  State and parameter estimation of hydrologic models using the constrained ensemble Kalman filter , 2009 .

[8]  Kuolin Hsu,et al.  Uncertainty assessment of hydrologic model states and parameters: Sequential data assimilation using the particle filter , 2005 .

[9]  M. Clark,et al.  Operational hydrological data assimilation with the recursive ensemble Kalman filter , 2013 .

[10]  M. Sivapalan.  Prediction in ungauged basins: a grand challenge for theoretical hydrology , 2003 .

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

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

[13]  Wade T. Crow,et al.  An adaptive ensemble Kalman filter for soil moisture data assimilation , 2007 .

[14]  R. Ibbitt,et al.  Hydrological data assimilation with the ensemble Kalman filter: Use of streamflow observations to update states in a distributed hydrological model , 2007 .

[15]  Wade T. Crow,et al.  Improving hydrologic predictions of a catchment model via assimilation of surface soil moisture , 2011 .

[16]  Haksu Lee,et al.  Variational assimilation of streamflow into operational distributed hydrologic models: effect of spatiotemporal scale of adjustment , 2012 .

[17]  Yuqiong Liu,et al.  Uncertainty in hydrologic modeling: Toward an integrated data assimilation framework , 2007 .

[18]  Jeffrey G. Arnold,et al.  Soil and Water Assessment Tool Theoretical Documentation Version 2009 , 2011 .

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

[20]  Xuesong Zhang,et al.  Multi-Site Calibration of the SWAT Model for Hydrologic Modeling , 2008 .

[21]  Peter A. Troch,et al.  Climate-vegetation-soil interactions and long-term hydrologic partitioning: signatures of catchment co-evolution , 2013, Hydrology and Earth System Sciences.

[22]  Jon C. Helton,et al.  Latin Hypercube Sampling and the Propagation of Uncertainty in Analyses of Complex Systems , 2002 .

[23]  John R. Williams,et al.  LARGE AREA HYDROLOGIC MODELING AND ASSESSMENT PART I: MODEL DEVELOPMENT 1 , 1998 .

[24]  R. Dickinson,et al.  The role of satellite remote sensing in climate change studies , 2013 .

[25]  Anthony J. Jakeman,et al.  Predicting the daily streamflow of ungauged catchments in S.E. Australia by regionalising the parameters of a lumped conceptual rainfall-runoff model , 1999 .

[26]  G. Evensen.  Data Assimilation: The Ensemble Kalman Filter , 2006 .

[27]  Jasper A. Vrugt,et al.  Hydrologic data assimilation using particle Markov chain Monte Carlo simulation: Theory, concepts and applications (online first) , 2012 .

[28]  S. Grunwald,et al.  A global sensitivity analysis tool for the parameters of multivariable catchment models , 2006 .

[29]  G. Evensen.  Sequential data assimilation with a nonlinear quasi‐geostrophic model using Monte Carlo methods to forecast error statistics , 1994 .

[30]  Wade T. Crow,et al.  Impact of Incorrect Model Error Assumptions on the Sequential Assimilation of Remotely Sensed Surface Soil Moisture , 2006 .

[31]  M. Vanclooster,et al.  Uncertainty analysis in model parameters regionalization: a case study involving the SWAT model in Mediterranean catchments (Southern France) , 2013 .

[32]  R. Srinivasan,et al.  A global sensitivity analysis tool for the parameters of multi-variable catchment models , 2006 .

[33]  Peter A. Vanrolleghem,et al.  Sensitivity analysis for hydrology and pesticide supply towards the river in SWAT , 2005 .

[34]  Wade T. Crow,et al.  Comparison of adaptive filtering techniques for land surface data assimilation , 2008 .

[35]  M. Borga,et al.  Flash flood warning based on rainfall thresholds and soil moisture conditions: An assessment for gauged and ungauged basins , 2008 .

[36]  Günter Blöschl,et al.  A spatially distributed flash flood forecasting model , 2008, Environ. Model. Softw..

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

[38]  H. Lee,et al.  Variational assimilation of streamflow into operational distributed hydrologic models: effect of spatiotemporal scale of adjustment , 2012 .

[39]  V. Ponce,et al.  Runoff Curve Number: Has It Reached Maturity? , 1996 .

[40]  Qingyun Duan,et al.  An integrated hydrologic Bayesian multimodel combination framework: Confronting input, parameter, and model structural uncertainty in hydrologic prediction , 2006 .

[41]  J. Arnold,et al.  SWAT2000: current capabilities and research opportunities in applied watershed modelling , 2005 .

[42]  Peter A. Troch,et al.  Catchment-scale hydrological modeling and data assimilation , 2003 .

[43]  Jeffrey G. Arnold,et al.  The Soil and Water Assessment Tool: Historical Development, Applications, and Future Research Directions , 2007 .

[44]  Xianhong Xie,et al.  Data assimilation for distributed hydrological catchment modeling via ensemble Kalman filter , 2010 .

[45]  Ming Ye,et al.  Towards a comprehensive assessment of model structural adequacy , 2012 .

[46]  Andrew W. Western,et al.  Assimilation of stream discharge for flood forecasting: The benefits of accounting for routing time lags , 2013 .

[47]  Xianhong Xie,et al.  Development and test of SWAT for modeling hydrological processes in irrigation districts with paddy rice , 2011 .

[48]  Geir Evensen,et al.  The Ensemble Kalman Filter: theoretical formulation and practical implementation , 2003 .

[49]  R. E. Rallison,et al.  Past, present, and future SCS runoff procedure , 1982 .

[50]  Günter Blöschl,et al.  Regionalisation of catchment model parameters , 2004 .

[51]  Xianhong Xie,et al.  A partitioned update scheme for state‐parameter estimation of distributed hydrologic models based on the ensemble Kalman filter , 2013 .

[52]  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 .

[53]  Cajo J. F. ter Braak,et al.  Treatment of input uncertainty in hydrologic modeling: Doing hydrology backward with Markov chain Monte Carlo simulation , 2008 .

[54]  Dongxiao Zhang,et al.  Investigation of flow and transport processes at the MADE site using ensemble Kalman filter , 2008 .

[55]  Xianhong Xie.  Simultaneous State-Parameter Estimation for Hydrologic Modeling Using Ensemble Kalman Filter , 2013 .

[56]  Marnik Vanclooster,et al.  Joint estimation of soil moisture profile and hydraulic parameters by ground‐penetrating radar data assimilation with maximum likelihood ensemble filter , 2014 .

[57]  Hamid Moradkhani,et al.  Examining the effectiveness and robustness of sequential data assimilation methods for quantification of uncertainty in hydrologic forecasting , 2012 .

[58]  Soroosh Sorooshian,et al.  Dual state-parameter estimation of hydrological models using ensemble Kalman filter , 2005 .