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HL Distributed Modeling Research

 Space-Time Scale Sensitivity of the Sacramento Model to Radar-Gage Precipitation Inputs
    B.D. Finnerty, M.B. Smith,  V. Koren, D.J. Seo, G. Moglen, Journal of Hydrology,  203 (1997) 21-38.
     Abstract:  Runoff timing and volume biases are investigated when performing hydrologic forecasting at space-time scales different from those at      which the model parameters were calibrated.  Hydrologic model parameters are inherently tied to the space-time scales at which they were      calibrated.  The National Weather Service calibrates rainfall runoff models using 6-hour mean areal precipitation (MAP) inputs derived from gage      networks.  The space-time scale sensitivity of the Sacramento model runoff volume is analyzed using 1-hour, 4x4 sq. km. next generation weather      radar (NEXRAD) precipitation estimates to derive input MAPs at various scales ranging from 4 x 4 sq. km. up to 256 x 256 sq. km. Results show      surface runoff, interflow, and supplemental baseflow runoff components are the most sensitive to the space-time scales analyzed.  Water balance      components of evapotranspiration and total channel inflow are also sensitive.


Comparing Mean Areal Precipitation Estimates from NEXRAD and Rain Gauge Networks
D. Johnson, M.B. Smith, V. Koren, and B. Finnerty, Journal of Hydrologic Engineering, Vol. 4, No. 2 April, 1999,  117-124.
Abstract:   Mean areal precipitation values (MAPX) derived from next generation weather radar (NEXRAD) Stage III data are compared with mean areal precipitation (MAP)  values derived from a precipitation gauge network.  The gauge derived MAPs are computed using Thiessen polygon weighting, whereas the radar-based MAPXs utilize the gridded Stage III radar precipitation products that have been conditioned with gauge measurements and have been merged with overlapping radar fields.  We compare over 4,000 pairs of MAPX and MAP estimates over a 3-year time period for each of the eight basins in the southern plains region of the United States.  Over the long term, mean areal estimates derived from NEXRAD generally are 5-10% below gauge-derived estimates.  In the smallest basin, the long-term MAPX mean was greater than the MAP.  For storm events, a slight tendency for NEXRAD to measure fewer yet more intense intervals of precipitation is identified.  Comparison of hydrologic simulations using the two forcings indicates that significant differences in runoff volume can result.  This work is aimed at providing insight into the use of a data product that is becoming increasingly available for public use.  It also is aimed at investigation the use of radar data in hydrologic models that have been calibrated using gauge-based precipitation estimates.


Semi-Distributed and Lumped Modeling Approaches: Case Study of NEXRAD Data Application to Large Headwater Basins in the Arkansas River Basin
M.B. Smith, V. Koren, Z. Zhang, D. Wang.  1999, Spring Meeting of the AGU, Boston.
     Abstract: In response to the nationwide implementation of the WSR-88D weather radar platforms, the Hydrologic Research Lab of the National      Weather Service (NWS) has developed a two phased plan to address the question: ‘How can the NWS most effectively utilize the radar      precipitation estimates to improve its river forecasts’? Inherent in this question is the suitability of current NWS models as well as the applicability of      distributed parameter hydrologic models for NWS streamflow forecast generation.      In Phase I, research has addressed the use of NEXRAD data with existing NWS hydrologic models, primarily the Sacramento Soil Moisture  Accounting (SAC-SMA) model. Modeling tests in this phase have involved the SAC-SMA applied to basins in a lumped and semi-distributed  format.  Results of continuous simulations with 4 basins over a multi-year period have shown that the SAC-SMA applied in a lumped mode at an  hourly time step provides satisfactory agreement with observed streamflow records.  In the semi-distributed simulations, each basin was disaggregated into between 5 and 8  sub-basins in an effort to capture the spatial variability of  precipitation.  Calibration of the sub-basin SAC-SMA parameters was accomplished by uniformly adjusting parameters in all sub-basins.  In such a  mode, the modeling scenario was one of distributed inputs, not distributed model parameters. Surprisingly, the semi-distributed approach did not lead ot significant  improvement over the lumped approach.  In some cases, hydrograph timing was improved compared to the lumped simulations.  However, overall goodness-of-fit statistics showed a slight degradation of simulation accuracy compared to the lumped simulations for several  basins. Examination of the simulations indicates that the method of uniformly calibrating the sub-basins model parameters is flawed and can lead  to  large simulation errors, which may unduly bias the statistics.
     In Phase 2, we plan to investigate distributed modeling approaches.  These plans will be presented along with the findings from Phase 1.


Scale Dependencies of Hydrologic Models to Spatial Variability of Precipitation
V.I Koren, B.D. Finnerty, J.C. Schaake, M.B. Smith, D.J. Seo, Q.Y Duan, Journal of Hydrology 217(1999) 285-302.
Abstract:  this study is focused on analyses of the scale dependency of lumped hydrological models with different formulations of the infiltration process.  Three lumped hydrological models of differing complexity were used in the study: The SAC-SMA model, the Oregon State University (OSU) model, and the simple water balance (SWB) model.  High Resolution (4x4 km) rainfall estimates from the next generation radar (NEXRAD) Stage III in the Arkansas-Red river basin were used in the study.  These gridded precipitation estimates are a multi-sensor product which combines the spatial resolution of the radar data with the ground truth estimates of the gage data.  Results were generated from each model using different resolutions of spatial averaging of hourly rainfall.  Although all selected models were scale dependent, the level of dependency varied significantly with different formulations of the rainfall-runoff partitioning mechanism.  Infiltration-excess type models were the most sensitive.  Saturation-excess type models were less scale dependent.  Probabilistic averaging of the point processes reduces scale dependency, however, its effectiveness varies depending on the scale and the spatial structure of rainfall.


The Potential for Improving Lumped Parameter Models using Remotely Sensed Data
V. Koren Paper J1.10, 13th Conference on Weather Analysis and Forecasting, August 2-6, 1993, Vienna, Virginia, pp 397-400.
Lumped parameter models are based upon using averaged input informatin for an entire drainage basin.. Preipitation is the most important input data in flash food forecasting.  Simulation errors in estimating runoff may be significant if the precipitation varies spatially and temporally within the basin.  Excess precipitation may be considerably underestimated in these cases.  To reduce errors in simulated runoff, the model parameters that control infiltration must be shifted from their 'actual' values, making analyses of their physical reliability difficult.
Breaking a basin into anumber of sub-basins is commonly used to take into account variations of input data and basin characteristics.  It is often necessary to use a large number of sub-basins and to calibrate parameters each of them using a limited number of outlets with observed runoff.  An additional difficulty is that the number and location of sub-basins may also vary from one individual storm to another.
Remote sources of data, such as radar or satellites, provide estimates of precipitation values with high resolution in space and time.  However, it is difficult to use this data in lumped parameter models.  This data appears to be surperfluous for them.  There are at least three factors which can increase the accuracy of hydrograph simulations by lumped paramtere models (Koren, 1991): (a) better estimates of mean areal precipitation totals, (b) a reduction of time steps, and (c) use of variability characteristics of precipitation.
It is easy to make use of the first two factors inlumped models.  Benefits derived from runoff simulations will depend on the quality ofradar calibration.  Borovikov (1969) stated that the accuracy of mean areal precipitation estimates for basins of less than 5000 sq. km. made by radars calibrated using only historical data were 35-45% higher than the accuracy levels when mean values were estimated by rain gages, specifically if there was one gage per 1000 -20000 sq. km.  For operationally on- line calibrated radars, the advantage was 50-60% (Berjulov, 1975)  One can expect that the accuracy of precipitation estimates by radars such as the NWS WSR-88D , when utilizing lumped parameter models, will increase significantly.
This paper will present a lumped parameter model using distribution functions of precipitation.


Reformulation of the SAC-SMA Model to Account for the Spatial Variability of Rainfall
V. Koren, M.B. Smith, D.J. Seo, B.D. Finnerty  (HRL internal publication)
Abstract:  Sensitivity analyses using high resolution radar precipitation estimates pointed out that the Sacramento Soil Moisture Accounting Model (SAC-SMa) is very sensitive to spatial scale.  The fast runoff components, especially surface runoff, may be underestimated significantly if the model is calibrated at one scale and is applied to another scale.  Rainfall spatial variability is a main factor of this dependency,  If there are high resolution measurements of rainfall, such as radar, then probabilistic averaging can be used to account for the spatial variation of rainfall (Koren, 1993).


Use of Soil Property Data in the Derivation of Conceptual Rainfall-Runoff Model Parameters
V. I. Koren, M. Smith, D. Wang, Z. Zhang.  80th Annual Meeting of the AMS, Long Beach, Ca. January 
Abstract:  Parameters for conceptual models such as the Sacramento Soil Moisture Accounting model (SAC-SMA, the NWS operational model) can be derived from observed hydrograph analysis, but are not readily derived from physical basin characteristics.  While soil property data are available now through the entire country as high resolution gridded files (e.g., STATSGO), they are used mostly as a qualitative information.  It restricts significantly application of these models (e.g., ungaged basins, semi-distributed versions, etc.).
   The basic physics of the SAC-SMA model is a two soil layer structure.  Each layer consists of tension and free water storages that interact in generating soil moisture states and five runoff components.  Most of the 16 parameters of the model have to be calibrated using historical rainfall/runoff data.  Initial model parameters are usually estimated based on hydrograph analysis at a river basin outlet.  This study is focused on developing a procedure to derive the SAC-SMA model parameters based on soil texture data.  To quantify relationships of model parameters with soil properties, the assumption was made that the SAC-SMA tension water storages relate to an available soil water, and that free water storages relate to gravitational soil water.  Porosity, field capacity, and wilting point derived from STATSGO dominant soil texture for eleven standard layers were used in estimating available and gravitational water storages.  SCS runoff curve numbers and saturated hydraulic conductivity of different soils were also used.  Analytical relationships were derived for 11 SAC-SMA model parameters.  Preliminary tests on a few basins in different regions suggest that most parameters derived from soil properties agreed reasonably well with calibrated parameters for those basins.  Accuracy statistics of hydrographs simulated using calibrated and derived parameters were also close.  Although calibrated parameter simulations usually give higher accuracy, the gain is not significant.  It means that parameters derived from soils data are very reasonable, and  can be improved by using calibration if observed historical data are available.


Strategy for Utilizing Radar-Based Precipitation Estimates for River Forecasting
S. Lindsey, ASCE International Symposium on Engineering Hydrology, San Francisco, Ca., July 25-30, 1993, 940-945
Abstract:  The rainfall-runoff models that are used by the National Weather Service (NWS) for river forecasting are generally applied on a lumped basis to each headwater or local area above a forecast point.  A 6-hour time interval generally is used for computations.  A few basins are sub-divided because of their large size or a significant range in elevation.  Spatial and temporal resolution of rainfall-runoff computations are controlled by the characteristics of the available data networks.
    The commissioning of NEXRAD (WSR-88D) radars, during the period from 1991 through  1996, makes available high resolution precipitation estimates at a 1-hour time interval.  The quality of these estimates creates a large discrepancy between the precipitation information available and the manner in which precipitation is currently utilized by the models.  Research is under way to determine how best to use the high resolution precipitation estimates at the NWS River Forecast Centers (RFCs).
    Techniques to incorporate information on the spatial and temporal distribution of precipitation events within a large basin are under investigation.  In the first phase, a gridded approach will be taken to derive a unit hydrograph which accounts for ths spatial distribution of rainfall.  The second phase focuses on the comparison and testing of different approaches.  Modeling at the grid level as well as disaggregating current basins into ungaged subbasins will be examined.  The use of synthetic unit hydrographs derived from topographic and other data in a Geographic Information System (GIS) to model ungaged sub-basins is being explored to obtain response functions.
    The impact of these approaches will be evaluated on basins in the Arkansas and Red River drainages.  The necessary data are being collected for a number of forecast points.  The techniques will be evaluated with the aim of developing preliminary guideline for the use of high resolution precipitation estimates for operational river forecasting.


Distributed Modeling:  Phase 1 Results NWS Technical Report 44 (200+ pages)  February 1, 1999 (
Table of Contents

  1. Introduction
  2. The Sensitivity of the Sacramento Model to Precipitation Forcing of Various Spatial and Temporal Scales
  3. Comparison of Mean Areal Precipitation Estimates from NEXRAD Stage III and Rain Gage Networks.
  4. Numerical Experiments on the Sensitivity of Runoff Values to Level of Basin Dissaggregation
  5. Semi-Distributed vs Lumped Modeling Tests
  6. Case Study in Upscaling and Downscaling of SAC-SMA Parameters
  7. Major Conclusions
  8. Recommendations

  9. Appendices



Statistical Comparison of Mean Areal Precipitation Estimates from WSR-88D, Operational, and Historical Gage Networks.
D. Wang, M.B. Smith, Z. Zhang, S. Reed, V.I. Koren 15th Annual Conference on Hydrology, 80th meeting of the AMS, Long Beach, Ca., January 10-14, 2000

ABSTRACT:  The mean areal precipitation (MAP) estimates derived using precipitation data from the River Forecast Center's (RFC) Weather Surveilance Radar 1988-Doppler (WSR-88D),  RFC's operational gage network and National Climatic Data Center's (NCDC) cooperative observer gage network are statistically compared over eight basins in Arkansas-Red River Basin.  6-hr radar-based MAP (MAPX), Operational MAP (MAPO) and historic MAP (MAPH) estimates in the period from June 1, 1993 to May 31, 1998 are used.  The MAPX values are derived from the gridded hourly NEXRAD stage III precipitation estimates before June 15, 1996 and there after from a mixed use of  the stage III and P1 processing algorithms, whereas the MAPO and MAPHvalues are computed by a calibration preprocessor using a Thiessen polygon weighting method.  In terms of long-term averages, MAPX are in very good agreement with MAPO and MAPH.  The overall average ratios of MAPX to MAPO and MAPH values over the eight basins are 0.985 and1.011, respectively.  However, the MAPX values are strongly dependent on the processing algorithms.  Underestimation in a range of 3~6% was found for MAPX values in comparison to MAPO estimates before June 15, 1996 while  overestimation was noted for MAPX values after June 15, 1996.  When radar and gages predicted the same amount of precipitation, the radar estimates tended to be more intense and less spread out.  Effects of the three MAP estimates on SAC-SMA runoff output are also studied.  Statistical analysis of three simulations vs runoff observation reveals that percent bias of MAPX simulation is -9.98%, while MAPO and MAPH simulations are -14.72% and -17.73, respectively.

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