A robust multimodel framework for ensemble seasonal hydroclimatic forecasts

We provide a framework for careful analysis of the different methodological choices we make when constructing multimodel ensemble seasonal forecasts of hydroclimatic variables. Specifically, we focus on three common modeling decisions: (i) number of models, (ii) multimodel combination approach, and (iii) lead time for prediction. The analysis scheme includes a multimodel ensemble forecasting algorithm based on nonparametric regression, a set of alternatives for the options previously pointed, and a selection of probabilistic verification methods for ensemble forecast evaluation. The usefulness of this framework is tested through an example application aimed to generate spring/summer streamflow forecasts at multiple locations in Central Chile. Results demonstrate the high impact that subjectivity in decision-making may have on the quality of ensemble seasonal hydroclimatic forecasts. In particular, we note that the probabilistic verification criteria may lead to different choices regarding the number of models or the multimodel combination method. We also illustrate how this objective analysis scheme may lead to results that are extremely relevant for the case study presented here, such as skillful seasonal streamflow predictions for very dry conditions.

[1]  A. Raftery,et al.  Using Bayesian Model Averaging to Calibrate Forecast Ensembles , 2005 .

[2]  Guohua Pan,et al.  Local Regression and Likelihood , 1999, Technometrics.

[3]  Ricardo Villalba,et al.  Multicentury tree ring reconstruction of annual streamflow for the Maule River watershed in south central Chile , 2011 .

[4]  Michael D. Dettinger,et al.  Primary Modes and Predictability of Year-to-Year Snowpack Variations in the Western United States from Teleconnections with Pacific Ocean Climate , 2002 .

[5]  H. Kwon,et al.  Accounting for Uncertainty Propagation: A Streamflow Forecasting Framework using Multiple Climate and Hydrological Models , 2009 .

[6]  Ricardo Villalba,et al.  Snowpack Variations in the Central Andes of Argentina and Chile, 1951–2005: Large-Scale Atmospheric Influences and Implications for Water Resources in the Region , 2006 .

[7]  D. Rubin,et al.  Maximum likelihood from incomplete data via the EM - algorithm plus discussions on the paper , 1977 .

[8]  Upmanu Lall,et al.  Seasonal to interannual ensemble streamflow forecasts for Ceara, Brazil: Applications of a multivariate, semiparametric algorithm , 2003 .

[9]  B. Rajagopalan,et al.  A multisite seasonal ensemble streamflow forecasting technique , 2010 .

[10]  M. Schmeits,et al.  A Comparison between Raw Ensemble Output, (Modified) Bayesian Model Averaging, and Extended Logistic Regression Using ECMWF Ensemble Precipitation Reforecasts , 2010 .

[11]  J. Quintana,et al.  Changes in the rainfall regime along the extratropical west coast of South America (Chile): 30-43º S , 2012 .

[12]  A. Sankarasubramanian,et al.  Improved categorical winter precipitation forecasts through multimodel combinations of coupled GCMs , 2010 .

[13]  S. Sorooshian,et al.  Evaluation of Official Western U.S. Seasonal Water Supply Outlooks, 1922–2002 , 2004 .

[14]  Balaji Rajagopalan,et al.  A technique for incorporating large‐scale climate information in basin‐scale ensemble streamflow forecasts , 2005 .

[15]  D. Stensrud,et al.  Reliable Probabilistic Quantitative Precipitation Forecasts from a Short-Range Ensemble Forecasting System , 2007 .

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

[17]  Jorge F. Carrasco,et al.  Changes of the 0°C isotherm and the equilibrium line altitude in central Chile during the last quarter of the 20th century : Glacier shrinkage in the Andes and consequences for water resources , 2005 .

[18]  Balaji Rajagopalan,et al.  Categorical Climate Forecasts through Regularization and Optimal Combination of Multiple GCM Ensembles , 2002 .

[19]  A. Sankarasubramanian,et al.  Improving the Prediction of Winter Precipitation and Temperature over the Continental United States: Role of the ENSO State in Developing Multimodel Combinations , 2010 .

[20]  J. M. Bates,et al.  The Combination of Forecasts , 1969 .

[21]  Bruce A. Robinson,et al.  Treatment of uncertainty using ensemble methods: Comparison of sequential data assimilation and Bayesian model averaging , 2007 .

[22]  T. Hamill.  Interpretation of Rank Histograms for Verifying Ensemble Forecasts , 2001 .

[23]  Francis H. S. Chiew,et al.  Use of seasonal streamflow forecasts in water resources management , 2003 .

[24]  D. J. Reid.  Combining Three Estimates of Gross Domestic Product , 1968 .

[25]  Aldo Montecinos,et al.  Seasonality of the ENSO-Related Rainfall Variability in Central Chile and Associated Circulation Anomalies , 2003 .

[26]  H. Akaike.  A new look at the statistical model identification , 1974 .

[27]  R. Buizza,et al.  The Skill of Probabilistic Precipitation Forecasts under Observational Uncertainties within the Generalized Likelihood Uncertainty Estimation Framework for Hydrological Applications , 2009 .

[28]  J. McPhee,et al.  Stable water isotope variation in a Central Andean watershed dominated by glacier and snowmelt , 2012 .

[29]  Stefania Tamea,et al.  Verification tools for probabilistic forecasts of continuous hydrological variables , 2006 .

[30]  Pablo A. Mendoza,et al.  Uncertainty in flood forecasting: A distributed modeling approach in a sparse data catchment , 2012 .

[31]  J. McPhee,et al.  Patterns of spatial and temporal variability in streamflow records in south central Chile in the period 1952–2003 , 2009 .

[32]  T. Barnett,et al.  Long-Lead Seasonal ForecastsWhere Do We Stand? , 1994 .

[33]  R. Clemen.  Combining forecasts: A review and annotated bibliography , 1989 .

[34]  Naresh Devineni,et al.  Multimodel ensembles of streamflow forecasts: Role of predictor state in developing optimal combinations , 2007, Water resources research.

[35]  Tilmann Gneiting,et al.  Calibrating Multimodel Forecast Ensembles with Exchangeable and Missing Members Using Bayesian Model Averaging , 2010 .

[36]  M. Clark,et al.  A multimodel ensemble forecast framework: Application to spring seasonal flows in the Gunnison River Basin , 2006 .

[37]  A. Sankarasubramanian,et al.  Flood quantiles in a changing climate: Seasonal forecasts and causal relations , 2003 .

[38]  Balaji Rajagopalan,et al.  Local polynomial method for ensemble forecast of time series , 2005 .

[39]  Francis H. S. Chiew,et al.  Seasonal streamflow forecasting in eastern Australia and the El Niño–Southern Oscillation , 1998 .

[40]  A. Barnston,et al.  Long-Lead Seasonal Forecast Skill in Far Eastern Asia Using Canonical Correlation Analysis , 2001 .

[41]  B. Rajagopalan,et al.  Interannual Variability and Ensemble Forecast of Upper Blue Nile Basin Kiremt Season Precipitation , 2006 .

[42]  Renate Hagedorn,et al.  The rationale behind the success of multi-model ensembles in seasonal forecasting — I. Basic concept , 2005 .

[43]  T. Palmer,et al.  ENSEMBLES: A new multi‐model ensemble for seasonal‐to‐annual predictions—Skill and progress beyond DEMETER in forecasting tropical Pacific SSTs , 2009 .

[44]  J. M. Sloughter,et al.  Probabilistic Quantitative Precipitation Forecasting Using Bayesian Model Averaging , 2007 .

[45]  Dong-Jun Seo,et al.  Towards the characterization of streamflow simulation uncertainty through multimodel ensembles , 2004 .

[46]  C. M. Kishtawal,et al.  Multimodel Ensemble Forecasts for Weather and Seasonal Climate , 2000 .

[47]  M. Falvey,et al.  Regional cooling in a warming world: Recent temperature trends in the southeast Pacific and along the west coast of subtropical South America (1979–2006) , 2009 .

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

[49]  R. Reynolds,et al.  The NCEP/NCAR 40-Year Reanalysis Project , 1996, Renewable Energy.

[50]  Ashish Sharma,et al.  Seasonal to interannual rainfall probabilistic forecasts for improved water supply management: Part 3 — A nonparametric probabilistic forecast model , 2000 .

[51]  B. Rajagopalan,et al.  Using Parametric and Nonparametric Methods to Model Total Organic Carbon, Alkalinity, and pH after Conventional Surface Water Treatment , 2009 .

[52]  Balaji Rajagopalan,et al.  Statistical Nonparametric Model for Natural Salt Estimation , 2005 .

[53]  George Kuczera,et al.  Critical evaluation of parameter consistency and predictive uncertainty in hydrological modeling: A case study using Bayesian total error analysis , 2009 .

[54]  O. Ndiaye,et al.  Predictability of Seasonal Sahel Rainfall Using GCMs and Lead-Time Improvements Through the Use of a Coupled Model , 2011 .

[55]  X. Vargas,et al.  Climatic sensitivity of streamflow timing in the extratropical western Andes Cordillera , 2011 .

[56]  M. Clark,et al.  Probabilistic Quantitative Precipitation Estimation in Complex Terrain , 2005 .

[57]  A. Barnston,et al.  Long-Lead Seasonal Temperature Prediction Using Optimal Climate Normals , 1996 .

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

[59]  W. M. Gray,et al.  The Extremely Active 1995 Atlantic Hurricane Season: Environmental Conditions and Verification of Seasonal Forecasts , 1998 .