EVALUATION OF THE NWS DISTRIBUTED HYDROLOGIC MODEL OVER THE TRINITY RIVER BASIN IN TEXAS

Transcription

1 1 EVALUATION OF THE NWS DISTRIBUTED HYDROLOGIC MODEL OVER THE TRINITY RIVER BASIN IN TEXAS Arezoo Rafieei Nasab 1, Dong-Jun Seo 1, Robert Corby 2 and Paul McKee 2 1- Department of Civil Engineering, The University of Texas at Arlington, Arlington, TX 2- West Gulf River Forecast Center, National Weather Service, Fort Worth, TX

2 2 In this presentation Motivation NWS distributed hydrologic model, RDHM Study area Comparison between RDHM and operational lumped models Comparison of RDHM-simulated soil moisture with insitu observation Conclusions and plans

3 3 Motivation River Services (600 miles per forecast point) Water Resource Services (6 square mile forecast basins) River Conditions From Carter (2006) Soil Conditions Predicting Floods to Droughts on High-Resolution Grid Provide accurate and dynamically-consistent water (and energy) balance components on a fine grid

4 4 Precipitation

5 5 Near-surface soil moisture

6 6 Surface runoff

7 7 Subsurface runoff

8 8 Surface soil temperature

9 9 Potential evaporation

10 10 Evaporation

11 11 Streamflow

12 12 Questions How do uncalibrated operational distributed models compare with fine-tuned operational lumped models for streamflow simulation? Calibrated distributed models perform comparably to calibrated lumped models for outlet flow simulation (Distributed Model Intercomparison Project (DMIP)) How skillful are uncalibrated operational distributed models for simulating soil moisture? Given the many challenges in distributed modeling, how to produce high-quality high-resolution water resources information?

13 13 Study Area - Trinity River Basin (40,380 km 2 )

14 14 Distributed models used RDHM (Koren et al. 2004) Gridded SAC-HT A priori parameters based on STATSGO/SSURGO (Koren et al. 2000) Kinematic-wave routing Hillslope, channel A priori parameters based on DEM, top width, flow cross section and flow (Reed et al. 2010)

15 15 Hillslope routing: kinematic wave

16 16 Channel routing: kinematic wave (~4x4 km 2 )

17 17 Comparison between lumped and distributed models To produce high-resolution water resources information, it is necessary to use distributed models (RDHM in this study) As a reference, assess the quality of RDHM simulation relative to that of lumped models (SAC-UHG in this study) for headwater basins DMIP suggests that the quality of calibrated RDHM simulation for outlet flow is comparable to that of calibrated lumped simulation How does uncalibrated RDHM simulation compare with calibrated lumped simulation (i.e. using the a priori parameters, Koren et al. 2000, Reed et al. 2010)? Calibration of distributed models is very expensive

18 18 Lumped model simulation SAC-UHG SAC-SMA for rainfall-runoff UHG for routing Obtained operational SAC-UHG parameters from WGRFC (valid for 6-hr timestep) Derived SAC-UHG parameters valid for 1-hr timestep via AB_OPT (adjoint-based optimizer, Seo et al. 2009) Estimates PXADJ, PEADJ Derives 1-hr UHG Adjusts SAC parameters for 1-hr timestep (SLS, Kuzmin et al. 2008)

19 19 Results RDHM simulation period: 1996 ~ 2011 AB_OPT calibration period: varies from basin to basin, mostly 1996 ~ 2012 AB_OPT-aided lumped model simulation represents dependent validation whereas RDHM simulation does not A very high bar for RDHM

20 20 Study basins DCJT2 JAKT2 BRPT2 SGET2 GLLT2 MCKT2 DWCT2 Basin Name Area (mi 2 ) T p (hrs) PXADJ PEADJ JAKT2 BRPT2 DCJT2 SGET2 GLLT2 DWCT2 MCKT2 MDST2 LIVT2 RYET MDST2 RYET2 LIVT2

21 21 Lumped vs. Distributed 100 % Bias 35 RMSE (CMS) AB_OPT RDHM AB_OPT RDHM Correlation Coefficient AB_OPT RDHM Nash-Sutcliffe Efficiency AB_OPT RDHM

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24 24 Lumped vs. Distributed 100 % Bias 35 RMSE (CMS) AB_OPT RDHM AB_OPT RDHM Correlation Coefficient AB_OPT RDHM Nash-Sutcliffe Efficiency AB_OPT RDHM

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26 26 DCJT2 JAKT2 BRPT2 SGET2 GLLT2 MCKT2 DWCT2 LIVT2 MDST2 RYET2

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28 28 Soil moisture comparison A SCAN soil moisture site exists in the Trinity River Basin

29 ρ=0.62

30 ρ= Reasonably good correlation near the surface, but the error bound is very large - Microscale variability of in-situ soil moisture accounts for a significant part of the error bound

31 ρ=0.59

32 ρ=0.51

33 Spatial variability of simulated soil moisture between two adjacent RDHM grid boxes suggests potentially very large microscale variability

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37 37 Conclusions and plans Uncalibrated RDHM produced reasonable simulations of outlet flow for 8 of the 10 headwater basins of the Trinity River Basin Grossly oversimulated for JAKT2 (under investigation) Did very well for RYET2, a slowly responding basin with complex UHG Uncalibrated RDHM produced soil moisture content that correlates reasonably well with near-surface in-situ observations Error bound is large In addition to microscale variability of in-situ soil moisture, inter-grid box variability of simulated soil moisture may be a large contributing factor to the large error bound Plans calibration/parameter optimization of distributed models SAC SLS (Kuzmin et al. 2008) Routing using data assimilation (DA) techniques Real-time assimilation of streamflow and in-situ soil moisture data Lee et al. (2011)

38 38 Parameter optimization of distributed routing model

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41 Jan 10, 2013 AMS Annual Meeting Real-time assimilation of streamflow and soil moisture data into distributed hydrologic models 41 Forecast time-n Forecast time-(n-1) Forecast time-(n-2) Assimilation window forecast horizon Forecast time-2 Forecast time-1 Forecast time present Lee et al. (2011)

42 42 THANK YOU For more information, contact