Soil Moisture Initialization Error and Subgrid Variability of Precipitation in Seasonal Streamflow Forecasting

AbstractOffline simulations over the conterminous United States (CONUS) with a land surface model are used to address two issues relevant to the forecasting of large-scale seasonal streamflow: (i) the extent to which errors in soil moisture initialization degrade streamflow forecasts, and (ii) the extent to which a realistic increase in the spatial resolution of forecasted precipitation would improve streamflow forecasts. The addition of error to a soil moisture initialization field is found to lead to a nearly proportional reduction in large-scale seasonal streamflow forecast skill. The linearity of the response allows the determination of a lower bound for the increase in streamflow forecast skill achievable through improved soil moisture estimation, for example, through the assimilation of satellite-based soil moisture measurements. An increase in the resolution of precipitation is found to have an impact on large-scale seasonal streamflow forecasts only when evaporation variance is significant relativ...

[1]  R. Koster,et al.  Comparison and assimilation of global soil moisture retrievals from the Advanced Microwave Scanning Radiometer for the Earth Observing System (AMSR‐E) and the Scanning Multichannel Microwave Radiometer (SMMR) , 2007 .

[2]  Yann Kerr,et al.  The SMOS Mission: New Tool for Monitoring Key Elements ofthe Global Water Cycle , 2010, Proceedings of the IEEE.

[3]  S. Schubert,et al.  MERRA: NASA’s Modern-Era Retrospective Analysis for Research and Applications , 2011 .

[4]  Randal D. Koster,et al.  A Simple Framework for Examining the Interannual Variability of Land Surface Moisture Fluxes , 1999 .

[5]  Wade T. Crow,et al.  The Contributions of Precipitation and Soil Moisture Observations to the Skill of Soil Moisture Estimates in a Land Data Assimilation System , 2011 .

[6]  Dennis P. Lettenmaier,et al.  Soil Moisture, Snow, and Seasonal Streamflow Forecasts in the United States , 2012 .

[7]  M. Budyko,et al.  Climate and life , 1975 .

[8]  Rolf H. Reichle,et al.  Assimilation of passive and active microwave soil moisture retrievals , 2012 .

[9]  R. Koster,et al.  Assessment and Enhancement of MERRA Land Surface Hydrology Estimates , 2011 .

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

[11]  D. Lettenmaier,et al.  An ensemble approach for attribution of hydrologic prediction uncertainty , 2008 .

[12]  Dennis P. Lettenmaier,et al.  Skill in streamflow forecasts derived from large-scale estimates of soil moisture and snow , 2010 .

[13]  Lifeng Luo,et al.  Use of Bayesian Merging Techniques in a Multimodel Seasonal Hydrologic Ensemble Prediction System for the Eastern United States , 2008 .

[14]  Dennis P. Lettenmaier,et al.  EFFECTS OF CLIMATE CHANGE ON HYDROLOGY AND WATER RESOURCES IN THE COLUMBIA RIVER BASIN 1 , 1999 .

[15]  E. Wood,et al.  Development of a 50-Year High-Resolution Global Dataset of Meteorological Forcings for Land Surface Modeling , 2006 .

[16]  E. Wood,et al.  Bayesian merging of multiple climate model forecasts for seasonal hydrological predictions , 2007 .

[17]  D. Mocko,et al.  Simulation of high-latitude hydrological processes in the Torne-Kalix basin: PILPS phase 2(e) - 1: Experiment description and summary intercomparisons , 2003 .

[18]  Enli Wang,et al.  Monthly and seasonal streamflow forecasts using rainfall‐runoff modeling and historical weather data , 2011 .

[19]  R. Koster,et al.  The Rhône-Aggregation Land Surface Scheme Intercomparison Project: An Overview , 2002 .

[20]  Randal D. Koster,et al.  The role of soil moisture initialization in subseasonal and seasonal streamflow prediction – A case study in Sri Lanka , 2008 .

[21]  Paul W. Stackhouse,et al.  Revisiting a hydrological analysis framework with International Satellite Land Surface Climatology Project Initiative 2 rainfall, net radiation, and runoff fields , 2006 .

[22]  Jiancheng Shi,et al.  The Soil Moisture Active Passive (SMAP) Mission , 2010, Proceedings of the IEEE.

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

[24]  E. Wood,et al.  WRF ensemble downscaling seasonal forecasts of China winter precipitation during 1982–2008 , 2012, Climate Dynamics.

[25]  Dennis P. Lettenmaier,et al.  A TEST BED FOR NEW SEASONAL HYDROLOGIC FORECASTING APPROACHES IN THE WESTERN UNITED STATES , 2006 .

[26]  R. Koster,et al.  Assessing the Impact of Horizontal Error Correlations in Background Fields on Soil Moisture Estimation , 2003 .

[27]  Wade T. Crow,et al.  Performance Metrics for Soil Moisture Retrievals and Application Requirements , 2009 .

[28]  Dennis P. Lettenmaier,et al.  Economic Value of Long-Lead Streamflow Forecasts for Columbia River Hydropower , 2002 .

[29]  A. Georgakakos,et al.  Assessment of Folsom Lake response to historical and potential future climate scenarios: 2. Reservoir management , 2001 .

[30]  R. Koster,et al.  A catchment-based approach to modeling land surface processes in a general circulation model , 2000 .

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

[32]  D. Garen.  Improved Techniques in Regression‐Based Streamflow Volume Forecasting , 1992 .

[33]  D. Lettenmaier,et al.  Twentieth-Century Drought in the Conterminous United States , 2005 .

[34]  E. Wood,et al.  The role of initial conditions and forcing uncertainties in seasonal hydrologic forecasting , 2009 .

[35]  R. Koster,et al.  Land Surface Controls on Hydroclimatic Means and Variability , 2012 .

[36]  M. Ek,et al.  Comparative analysis of relationships between NLDAS‐2 forcings and model outputs , 2012 .

[37]  T. Hamill,et al.  Summary of recommendations of the first workshop on Postprocessing and Downscaling Atmospheric Forecasts for Hydrologic Applications held at Météo‐France, Toulouse, France, 15–18 June 2009 , 2010 .

[38]  Praveen Kumar,et al.  A catchment‐based approach to modeling land surface processes in a general circulation model: 1. Model structure , 2000 .

[39]  Marc F. P. Bierkens,et al.  Seasonal Predictability of European Discharge: NAO and Hydrological Response Time , 2009 .