Assessment of SWE data assimilation for ensemble streamflow predictions

Summary An assessment of data assimilation (DA) for Ensemble Streamflow Prediction (ESP) using seasonal water supply hindcasting in the North Fork of the American River Basin (NFARB) and the National Weather Service (NWS) hydrologic forecast models is undertaken. Two parameter sets, one from the California Nevada River Forecast Center (RFC) and one from the Differential Evolution Adaptive Metropolis (DREAM) algorithm, are tested. For each parameter set, hindcasts are generated using initial conditions derived with and without the inclusion of a DA scheme that integrates snow water equivalent (SWE) observations. The DREAM-DA scenario uses an Integrated Uncertainty and Ensemble-based data Assimilation (ICEA) framework that also considers model and parameter uncertainty. Hindcasts are evaluated using deterministic and probabilistic forecast verification metrics. In general, the impact of DA on the skill of the seasonal water supply predictions is mixed. For deterministic (ensemble mean) predictions, the Percent Bias (PBias) is improved with integration of the DA. DREAM-DA and the RFC-DA have the lowest biases and the RFC-DA has the lowest Root Mean Squared Error (RMSE). However, the RFC and DREAM-DA have similar RMSE scores. For the probabilistic predictions, the RFC and DREAM have the highest Continuous Ranked Probability Skill Scores (CRPSS) and the RFC has the best discrimination for low flows. Reliability results are similar between the non-DA and DA tests and the DREAM and DREAM-DA have better reliability than the RFC and RFC-DA for forecast dates February 1 and later. Despite producing improved streamflow simulations in previous studies, the hindcast analysis suggests that the DA method tested may not result in obvious improvements in streamflow forecasts. We advocate that integration of hindcasting and probabilistic metrics provides more rigorous insight on model performance for forecasting applications, such as in this study.

[1]  G. Blöschl,et al.  The value of MODIS snow cover data in validating and calibrating conceptual hydrologic models , 2008 .

[2]  Pengfei Du,et al.  Recovery efficiency of aquifer storage and recovery (ASR) with mass transfer limitation , 2011 .

[3]  Arun Kumar,et al.  Long‐range experimental hydrologic forecasting for the eastern United States , 2002 .

[4]  C. M. DeChant,et al.  Improving the characterization of initial condition for ensemble streamflow prediction using data assimilation , 2011 .

[5]  H. Moradkhani,et al.  Assessing the uncertainties of hydrologic model selection in climate change impact studies , 2011 .

[6]  Minxue He,et al.  Corruption of parameter behavior and regionalization by model and forcing data errors: A Bayesian example using the SNOW17 model , 2011 .

[7]  Steven A. Margulis,et al.  An integrated uncertainty and ensemble-based data assimilation approach for improved operational streamflow predictions , 2011 .

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

[9]  H. Moradkhani,et al.  Snow water equivalent prediction using Bayesian data assimilation methods , 2011 .

[10]  M. Clark,et al.  Use of Medium-Range Numerical Weather Prediction Model Output to Produce Forecasts of Streamflow , 2004 .

[11]  Young-Oh Kim,et al.  Rainfall‐runoff models using artificial neural networks for ensemble streamflow prediction , 2005 .

[12]  Steven A. Margulis,et al.  Characterizing parameter sensitivity and uncertainty for a snow model across hydroclimatic regimes , 2011 .

[13]  Julie Demargne,et al.  Application of Forecast Verification Science to Operational River Forecasting in the U.S. National Weather Service , 2009 .

[14]  Dennis P. Lettenmaier,et al.  Columbia River Streamflow Forecasting Based on ENSO and PDO Climate Signals , 1999 .

[15]  H. Hersbach.  Decomposition of the Continuous Ranked Probability Score for Ensemble Prediction Systems , 2000 .

[16]  E. Sprokkereef,et al.  Verification of ensemble flow forecasts for the River Rhine , 2009 .

[17]  Giuliano Di Baldassarre,et al.  Data errors and hydrological modelling: The role of model structure to propagate observation uncertainty , 2013 .

[18]  Gerald N. Day,et al.  Extended Streamflow Forecasting Using NWSRFS , 1985 .

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

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

[21]  Konstantine P. Georgakakos,et al.  Estimating snow depletion curves for American River basins using distributed snow modeling , 2007 .

[22]  Soroosh Sorooshian,et al.  Evolution of ensemble data assimilation for uncertainty quantification using the particle filter‐Markov chain Monte Carlo method , 2012 .

[23]  J. Schaake,et al.  Correcting Errors in Streamflow Forecast Ensemble Mean and Spread , 2008 .

[24]  Keith Beven,et al.  Prophecy, reality and uncertainty in distributed hydrological modelling , 1993 .

[25]  Dmitri Kavetski,et al.  Rainfall uncertainty in hydrological modelling: An evaluation of multiplicative error models , 2011 .

[26]  M. Clark,et al.  Snow Data Assimilation via an Ensemble Kalman Filter , 2006 .

[27]  Soroosh Sorooshian,et al.  A 'User-Friendly' approach to parameter estimation in hydrologic models , 2006 .

[28]  Cajo J. F. ter Braak,et al.  Equifinality of formal (DREAM) and informal (GLUE) Bayesian approaches in hydrologic modeling? , 2009 .

[29]  Ludovic Oudin,et al.  Hydrological model parameter instability: A source of additional uncertainty in estimating the hydrological impacts of climate change? , 2013 .

[30]  Arjen Ysbert Hoekstra,et al.  Effect of different uncertainty sources on the skill of 10 day ensemble low flow forecasts for two hydrological models , 2013 .

[31]  George Kuczera,et al.  Understanding predictive uncertainty in hydrologic modeling: The challenge of identifying input and structural errors , 2010 .

[32]  A. H. Murphy,et al.  A General Framework for Forecast Verification , 1987 .

[33]  S. Sorooshian,et al.  Multimodel Combination Techniques for Analysis of Hydrological Simulations: Application to Distributed Model Intercomparison Project Results , 2006 .

[34]  Terri S. Hogue,et al.  Evaluating uncertainty estimates in hydrologic models: borrowing measures from the forecast verification community , 2011 .

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

[36]  Soroosh Sorooshian,et al.  HYDROLOGIC VERIFICATION A Call for Action and Collaboration , 2007 .

[37]  Lihua Xiong,et al.  An empirical method to improve the prediction limits of the GLUE methodology in rainfall–runoff modeling , 2008 .

[38]  S. Sorooshian,et al.  A multistep automatic calibration scheme for river forecasting models , 2000 .

[39]  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 .

[40]  Seong Jin Noh,et al.  Advancing data assimilation in operational hydrologic forecasting: progresses, challenges, and emerging opportunities , 2012 .

[41]  H. Moradkhani,et al.  Analyzing the uncertainty of suspended sediment load prediction using sequential data assimilation , 2012 .

[42]  Qingyun Duan,et al.  NOAA'S Advanced Hydrologic Prediction Service: Building Pathways for Better Science in Water Forecasting , 2005 .

[43]  Soroosh Sorooshian,et al.  Toward improved calibration of hydrologic models: Multiple and noncommensurable measures of information , 1998 .

[44]  Soroosh Sorooshian,et al.  Operational snow modeling: Addressing the challenges of an energy balance model for National Weather Service forecasts , 2008 .

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

[46]  Mingliang Li,et al.  Calibration of a distributed flood forecasting model with input uncertainty using a Bayesian framework , 2012 .

[47]  S. Sorooshian,et al.  Multi-model ensemble hydrologic prediction using Bayesian model averaging , 2007 .

[48]  A. H. Murphy,et al.  Diagnostic verification of probability forecasts , 1992 .

[49]  Dong Jun Seo,et al.  Automatic state updating for operational streamflow forecasting via variational data assimilation , 2009 .

[50]  W. Briggs.  Statistical Methods in the Atmospheric Sciences , 2007 .

[51]  K. Franz,et al.  Calibration of a distributed snow model using MODIS snow covered area data , 2013 .

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

[53]  Victor Koren,et al.  Use of soil moisture observations to improve parameter consistency in watershed calibration , 2008 .

[54]  L. Andreassen,et al.  Improving runoff modelling using satellite-derived snow covered area? , 2007 .

[55]  Soroosh Sorooshian,et al.  Snow Model Verification Using Ensemble Prediction and Operational Benchmarks , 2008 .

[56]  G. Hornberger,et al.  Approach to the preliminary analysis of environmental systems , 1981 .

[57]  Assimilation of streamflow discharge into a continuous flood forecasting model , 2011 .

[58]  Soroosh Sorooshian,et al.  Verification of National Weather Service Ensemble Streamflow Predictions for Water Supply Forecasting in the Colorado River Basin , 2003 .

[59]  George Kuczera,et al.  Monte Carlo assessment of parameter uncertainty in conceptual catchment models: the Metropolis algorithm , 1998 .

[60]  Hoshin Vijai Gupta,et al.  Model identification for hydrological forecasting under uncertainty , 2005 .

[61]  Tempei Hashino,et al.  Distributions-Oriented Verification of Ensemble Streamflow Predictions , 2004 .

[62]  Newsha K. Ajami,et al.  Addressing snow model uncertainty for hydrologic prediction , 2010 .

[63]  F. Pappenberger,et al.  The European Flood Alert System and the communication, perception, and use of ensemble predictions for operational flood risk management , 2013 .

[64]  Clifford F. Mass,et al.  Description and evaluation of a hydrometeorological forecast system for mountainous watersheds , 2002 .

[65]  Eric A. Anderson,et al.  National Weather Service river forecast system: snow accumulation and ablation model , 1973 .

[66]  Thomas H. Painter,et al.  Mountain hydrology of the western United States , 2006 .

[67]  D. Entekhabi,et al.  Land data assimilation and estimation of soil moisture using measurements from the Southern Great Plains 1997 Field Experiment , 2001 .