Real-Time Variational Assimilation of Hydrologic and Hydrometeorological Data into Operational Hydrologic Forecasting

Variational assimilation (VAR) of hydrologic and hydrometeorological data into operational hydrologic forecasting is explored. The data assimilated are the hourly real-time observations of streamflow and precipitation, and climatological estimates of potential evaporation (PE). The hydrologic system considered is a single headwater basin for which soil moisture accounting and routing are carried out in a lumped fashion via the Sacramento model (SAC) and the unit hydrograph (UH), respectively. The control variables in the VAR formulation are the fast-varying SAC soil moisture states at the beginning of the assimilation window and the multiplicative adjustment factors to the estimates of mean areal precipitation (MAP) and mean areal potential evaporation (MAPE) for each hour in the assimilation window. In a separate application of VAR as a parameter estimation tool, the estimation of empirical UH is also explored by treating its ordinates as the control variables. To evaluate the assimilation procedure thus developed, streamflow was forecast with and without the aid of VAR for three basins in the southern plains under the assumption of perfectly forecast future mean areal precipitation (FMAP). The streamflow forecasts were then compared with each other and with those based on persistence and the state space-based state-updating procedure, the state-space Sacramento model (SS-SAC). The results indicate that the VAR procedure significantly improves the accuracy of the basic forecast at short lead times and compares favorably with SS-SAC.

[1]  Ionel M. Navon,et al.  Optimality of variational data assimilation and its relationship with the Kalman filter and smoother , 2001 .

[2]  Claude Lemaréchal,et al.  Some numerical experiments with variable-storage quasi-Newton algorithms , 1989, Math. Program..

[3]  A. Jazwinski Stochastic Processes and Filtering Theory , 1970 .

[4]  Dong-Jun Seo,et al.  Real-time estimation of mean field bias in radar rainfall data , 1999 .

[5]  David R. Maidment,et al.  Developing a spatially distributed unit hydrograph by using GIS , 1993 .

[6]  Xiaolei Zou,et al.  Examination of Numerical Results from Tangent Linear and Adjoint of Discontinuous Nonlinear Models , 2001 .

[7]  Jay P. Breidenbach,et al.  Real-time adjustment of range-dependent biases in WSR-88D rainfall estimates due to nonuniform vertical profile of reflectivity , 2000 .

[8]  Dong-Jun Seo,et al.  An Intercomparison Study of NEXRAD Precipitation Estimates , 1996 .

[9]  Don Johnstone,et al.  Elements of Applied Hydrology , 1949 .

[10]  Jens Christian Refsgaard,et al.  Validation and Intercomparison of Different Updating Procedures for Real-Time Forecasting , 1997 .

[11]  Fred C. Schweppe,et al.  Uncertain dynamic systems , 1973 .

[12]  Konstantine P. Georgakakos,et al.  On improved hydrologic forecasting — Results from a WMO real-time forecasting experiment , 1990 .

[13]  M. Zupanski A Preconditioning Algorithm for Four-Dimensional Variational Data Assimilation , 1996 .

[14]  Dong-Jun Seo,et al.  The WSR-88D rainfall algorithm , 1998 .

[15]  James C. I. Dooge,et al.  Linear Theory of Hydrologic Systems , 1973 .

[16]  V. Koren,et al.  Lumped and Semi-distributed Modeling using NEXRAD Stage-III Data: Results from Continuous Multi-year Simulations , 1999 .

[17]  Thomas Kaminski,et al.  Recipes for adjoint code construction , 1998, TOMS.

[18]  Marco Borga,et al.  Adaptive Use of a Conceptual Model for Real Time Flood Forecasting , 1997 .

[19]  D. Zupanski A General Weak Constraint Applicable to Operational 4DVAR Data Assimilation Systems , 1997 .

[20]  H. L. Penman Natural evaporation from open water, bare soil and grass , 1948, Proceedings of the Royal Society of London. Series A. Mathematical and Physical Sciences.

[21]  Jay P. Breidenbach,et al.  Real-Time Correction of Spatially Nonuniform Bias in Radar Rainfall Data Using Rain Gauge Measurements , 2002 .

[22]  Walter T. Sittner,et al.  Improvement of hydrologic simulation by utilizing observed discharge as an indirect input : (Computed Hydrograph Adjustment Technique-- CHAT) , 1979 .

[23]  William H. Press,et al.  Numerical recipes , 1990 .

[24]  W. Chao,et al.  Development of a four-dimensional variational analysis system using the adjoint method at GLA. I : dynamics , 1992 .

[25]  Real-time, statistically linearized, adaptive flood routing , 1982 .

[26]  A. Feldman,et al.  Evolution of Clark's Unit Graph Method to Spatially Distributed Runoff , 1998 .

[27]  W. Menke Geophysical data analysis : discrete inverse theory , 1984 .

[28]  Dong-Jun Seo,et al.  Characterization of the climatological variability of mean areal rainfall through fractional coverage , 1996 .