Assimilation of water temperature and discharge data for ensemble water temperature forecasting.

Abstract Recent work demonstrated the value of water temperature forecasts to improve water resources allocation and highlighted the importance of quantifying their uncertainty adequately. In this study, we perform a multisite cascading ensemble assimilation of discharge and water temperature on the Nechako River (Canada) using particle filters. Hydrological and thermal initial conditions were provided to a rainfall-runoff model, coupled to a thermal module, using ensemble meteorological forecasts as inputs to produce 5 day ensemble thermal forecasts. Results show good performances of the particle filters with improvements of the accuracy of initial conditions by more than 65% compared to simulations without data assimilation for both the hydrological and the thermal component. All thermal forecasts returned continuous ranked probability scores under 0.8 °C when using a set of 40 initial conditions and meteorological forecasts comprising 20 members. A greater contribution of the initial conditions to the total uncertainty of the system for 1-dayforecasts is observed (mean ensemble spread = 1.1 °C) compared to meteorological forcings (mean ensemble spread = 0.6 °C). The inclusion of meteorological uncertainty is critical to maintain reliable forecasts and proper ensemble spread for lead times of 2 days and more. This work demonstrates the ability of the particle filters to properly update the initial conditions of a coupled hydrological and thermal model and offers insights regarding the contribution of two major sources of uncertainty to the overall uncertainty in thermal forecasts.

[1]  B. Rajagopalan,et al.  A DECISION SUPPORT SYSTEM TO MANAGE SUMMER STREAM TEMPERATURES 1 , 2006 .

[2]  F. Anctil,et al.  Can a multi-model approach improve hydrological ensemble forecasting? A study on 29 French catchments using 16 hydrological model structures , 2011 .

[3]  Matthias Schmid,et al.  Developing and testing temperature models for regulated systems: A case study on the Upper Delaware River , 2014 .

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

[5]  Denis Tremblay,et al.  A comparison between ensemble and deterministic hydrological forecasts in an operational context , 2011 .

[6]  Heinz G. Stefan,et al.  A nonlinear regression model for weekly stream temperatures , 1998 .

[7]  Taha B. M. J. Ouarda,et al.  Predicting river water temperatures using stochastic models: case study of the Moisie River (Québec, Canada) , 2007 .

[8]  Göran Kauermann,et al.  Functional hourly forecasting of water temperature , 2010 .

[9]  Richard M. Adams,et al.  Using Instream Water Temperature Forecasts for Fisheries Management: An Application in the Pacific Northwest 1 , 2011 .

[10]  E. Rivot,et al.  A Hierarchical Bayesian Model to Quantify Uncertainty of Stream Water Temperature Forecasts , 2014, PloS one.

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

[12]  G. Sahoo,et al.  Forecasting stream water temperature using regression analysis, artificial neural network, and chaotic non-linear dynamic models , 2009 .

[13]  Dong-Jun Seo,et al.  Real-Time Variational Assimilation of Hydrologic and Hydrometeorological Data into Operational Hydrologic Forecasting , 2003 .

[14]  Bernard Bobée,et al.  A Review of Statistical Water Temperature Models , 2007 .

[15]  Tait,et al.  Interpolation of daily solar radiation for New Zealand using a satellite data-derived cloud cover surface , 2009 .

[16]  D. Hannah,et al.  Recent advances in stream and river temperature research , 2008 .

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

[18]  J. Tukey.  Comparing individual means in the analysis of variance. , 1949, Biometrics.

[19]  M. Boucher,et al.  Water Temperature Ensemble Forecasts: Implementation Using the CEQUEAU Model on Two Contrasted River Systems , 2017 .

[20]  Thomas K. Peucker,et al.  2. Algorithms for the Reduction of the Number of Points Required to Represent a Digitized Line or its Caricature , 2011 .

[21]  Hirofumi Hashimoto,et al.  River Temperature Forecasting: A Coupled-Modeling Framework for Management of River Habitat , 2012, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[22]  Martyn P. Clark,et al.  HEPEX: The Hydrological Ensemble Prediction Experiment , 2007 .

[23]  G. Katul,et al.  Soil moisture and vegetation controls on evapotranspiration in a heterogeneous Mediterranean ecosystem on Sardinia, Italy , 2006 .

[24]  W. Kruskal,et al.  Use of Ranks in One-Criterion Variance Analysis , 1952 .

[25]  Y. Kamarianakis,et al.  Water temperature forecasting for Spanish rivers by means of nonlinear mixed models , 2016 .

[26]  Peter Jan,et al.  Particle Filtering in Geophysical Systems , 2009 .

[27]  Balaji Rajagopalan,et al.  Forecasting river temperatures in real time using a stochastic dynamics approach , 2013 .

[28]  Florian Pappenberger,et al.  Ensemble flood forecasting: a review. , 2009 .

[29]  D. Richardson.  Skill and relative economic value of the ECMWF ensemble prediction system , 2000 .

[30]  S. Schymanski,et al.  An optimality‐based model of the dynamic feedbacks between natural vegetation and the water balance , 2009 .

[31]  Youlong Xia,et al.  Interpolation of Daily Global Solar Radiation with Thin Plate Smoothing Splines , 2000 .

[32]  G. Blöschl,et al.  Soil moisture updating by Ensemble Kalman Filtering in real-time flood forecasting , 2008 .

[33]  J. Stanford,et al.  Thermal Responses in the Evolutionary Ecology of Aquatic Insects , 1982 .

[34]  John Yearsley,et al.  A semi‐Lagrangian water temperature model for advection‐dominated river systems , 2009 .

[35]  N. Papadakis,et al.  Data assimilation with the weighted ensemble Kalman filter , 2010 .

[36]  D. Caissie,et al.  River temperature forecasting: case study for Little Southwest Miramichi River (New Brunswick, Canada) , 2017 .

[37]  Peter Salamon,et al.  Disentangling uncertainties in distributed hydrological modeling using multiplicative error models and sequential data assimilation , 2010 .

[38]  Dong-Jun Seo,et al.  Improving water quality forecasting via data assimilation – Application of maximum likelihood ensemble filter to HSPF , 2014 .

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

[40]  Paul R. Houser,et al.  Reducing Water Imbalance in Land Data Assimilation: Ensemble Filtering without Perturbed Observations , 2012 .

[41]  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 .

[42]  Heinz G. Stefan,et al.  Stream temperature dynamics: Measurements and modeling , 1993 .

[43]  G. Blöschl,et al.  Real time flood forecasting in the Upper Danube basin , 2016 .

[44]  A. Raftery,et al.  Strictly Proper Scoring Rules, Prediction, and Estimation , 2007 .

[45]  François Anctil,et al.  Accounting for three sources of uncertainty in ensemble hydrological forecasting , 2015 .

[46]  Y. Hong,et al.  Hydrological data assimilation with the Ensemble Square-Root-Filter: Use of streamflow observations to update model states for real-time flash flood forecasting , 2013 .

[47]  R. L. Winkler,et al.  Scoring Rules for Continuous Probability Distributions , 1976 .

[48]  L. Feyen,et al.  Assessing parameter, precipitation, and predictive uncertainty in a distributed hydrological model using sequential data assimilation with the particle filter , 2009 .

[49]  G. Lannoy,et al.  The importance of parameter resampling for soil moisture data assimilation into hydrologic models using the particle filter , 2011 .

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

[51]  Peter J. Webster,et al.  A 1–10-Day Ensemble Forecasting Scheme for the Major River Basins of Bangladesh: Forecasting Severe Floods of 2003–07* , 2010 .

[52]  Guohe Huang,et al.  Development of Integrated Approaches for Hydrological Data Assimilation through Combination of Ensemble Kalman Filter and Particle Filter Methods , 2017 .

[53]  Anthony J. Jakeman,et al.  Assessing the impact of land use change on hydrology by ensemble modelling(LUCHEM) II: ensemble combinations and predictions , 2009 .

[54]  A. Farrell,et al.  High river temperature reduces survival of sockeye salmon (Oncorhynchus nerka) approaching spawning grounds and exacerbates female mortality , 2012 .

[55]  Dmitri Kavetski,et al.  A unified approach for process‐based hydrologic modeling: 2. Model implementation and case studies , 2015 .

[56]  Youmin Tang,et al.  A modified ensemble Kalman particle filter for non‐Gaussian systems with nonlinear measurement functions , 2015 .

[57]  N. K. Goel,et al.  Improving real time flood forecasting using fuzzy inference system , 2014 .

[58]  Roberto Buizza,et al.  TIGGE: Preliminary results on comparing and combining ensembles , 2008 .

[59]  Kristan M. Boudreau.  NECHAKO WATERSHED COUNCIL REPORT: ASSESSMENT OF POTENTIAL FLOW REGIMES FOR THE NECHAKO WATERSHED , 2005 .

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

[61]  Agnès Ducharne,et al.  Importance of stream temperature to climate change impact on water quality , 2007 .

[62]  G. Poole,et al.  An Ecological Perspective on In-Stream Temperature: Natural Heat Dynamics and Mechanisms of Human-CausedThermal Degradation , 2001, Environmental management.

[63]  E. Steel,et al.  Beyond the mean: The role of variability in predicting ecological effects of stream temperature on salmon , 2012 .

[64]  Forecasting Fraser River flows and temperatures during upstream salmon migration , 2005 .

[65]  Zhuoqi Chen,et al.  Validation of China-wide interpolated daily climate variables from 1960 to 2011 , 2015, Theoretical and Applied Climatology.

[66]  G. Brier.  VERIFICATION OF FORECASTS EXPRESSED IN TERMS OF PROBABILITY , 1950 .

[67]  Lucy Marshall,et al.  Detecting non-stationary hydrologic model parameters in a paired catchment system using data assimilation , 2016 .

[68]  Y. Tachikawa,et al.  Applying sequential Monte Carlo methods into a distributed hydrologic model: lagged particle filtering approach with regularization , 2011 .

[69]  Peter R. Oke,et al.  A deterministic formulation of the ensemble Kalman filter: an alternative to ensemble square root filters , 2008 .

[70]  F. Massey.  The Kolmogorov-Smirnov Test for Goodness of Fit , 1951 .

[71]  A. Weerts,et al.  Particle filtering and ensemble Kalman filtering for state updating with hydrological conceptual rainfall‐runoff models , 2006 .

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

[73]  Emmanuel Roulin,et al.  Post‐processing of medium‐range probabilistic hydrological forecasting: impact of forcing, initial conditions and model errors , 2015 .

[74]  Denis Tremblay,et al.  Hydro-economic assessment of hydrological forecasting systems , 2012 .

[75]  D. Patterson,et al.  Evaluation of Statistical River Temperature Forecast Models for Fisheries Management , 2014 .

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

[77]  D. Patterson,et al.  The Efficacy of Reservoir Flow Regulation for Cooling Migration Temperature for Sockeye Salmon in the Nechako River Watershed of British Columbia , 2012 .

[78]  J. Olden,et al.  Incorporating thermal regimes into environmental flows assessments: modifying dam operations to restore freshwater ecosystem integrity , 2010 .