studies have been conducted in the past two decades that point to
the sensitivity of runoff hydrographs to spatial and temporal variations
in precipitation. Many of these studies examined the effects of
raingauge sampling errors on the outflow hydrograph. In an early
and oft-quoted work, Wilson et al.  showed that the spatial
distribution of rainfall had a marked influence on the runoff hydrograph
from a small catchment. On the other hand, Beven and Hornberger
 stated that rainfall patterns have only a secondary effect
on runoff hydrographs, while a correct assessment of the global
volume of rainfall input in a variable pattern is more important
in simulating streamflow hydrographs. On a small watershed, Krajweski
et al.  found a higher sensitivity to the temporal resolution
of precipitation than to the spatial resolution. Ogden and Julien
 performed tests that identified when spatial and temporal
variability of precipitation was dominant. Troutman , Ogden
and Julien  and Shah et al. [1996a,b] also investigated the
effects of precipitation variability on hydrologic simulations.
It is interesting
to note that the majority of these and other studies were based
on synthetically generated precipitation and streamflow records.
Usually, comparisons were made against a 'reference' or 'truth'
hydrograph generated by running the hydrologic model at the finest
data resolution. Synthetically-generated data were often used due
to the lack of appropriately long periods of observed data. Moreover,
many of the studies emphasizing the importance of the spatial variability
of precipitation used models containing the Hortonian runoff generation
mechanism. It is now recognized that runoff results from a complex
variety of mechanisms and that, in some basins, a significant portion
of runoff hydrographs is derived from slower responding subsurface
runoff [Wood et al. 1990]. Obled et al.  commented that numerical
experiments in the literature were based on the use of models which
may be only 'a crude representation of reality.' Furthermore, they
argued that the actual processes at work in a basin may not be those
predicted by the model. Thus, the research in the literature may
have shown the sensitivity of a particular model to the spatial
variability of precipitation, and not the sensitivity of the actual
basin. The work of Obled et al. (1994) is significant in that they
were perhaps the first to examine the effects of the spatial variation
of rainfall using observed precipitation and streamflow data. In
addition, the model used in their studies focused on saturation
excess runoff as the main runoff generation mechanism. In simulations
against observed data, they were unable to prove the value of distributed
inputs as they had intended. A semi-distributed representation of
the basin did not lead to improved simulations compared to a lumped
basin modeling scenario. The authors reasoned that the runoff mechanism
may be responsible for the lack of improvement:
"If, on the other hand, the dominant
process involves either surface or subsurface contributing areas
of the Dunne type, then most of the water infiltrates and local
variations in input will be smoothed as the water is stored and
delayed within the soil.....this type of mechanism may be much less
sensitive to different rainfall patterns at the scale of small catchments."
Winchell et al.  and Winchell
et al.  extend this theme by noting that there has been a
bias towards the use of infiltration-excess runoff mechanisms as
opposed to the saturation excess type. Their work with both types
of runoff generation mechanisms found that saturation excess and
infiltration excess models respond differently to uncertainty in
precipitation. They suggest that generalizations concerning the
effects of rainfall variability on runoff generation cannot be made.
Koren et al.  came to a similar conclusion based on simulation
results from several different rainfall-runoff partitioning mechanisms.
Nonetheless, a large volume
of research continues to emerge that addresses the possibility of
improving lumped hydrologic simulations by using distributed and
semi-distributed modeling approaches which account for the spatial
variation of not only physiographic basin features but of precipitation
as well. Recently, the availability of high resolution precipitation
estimates from different weather radar platforms has intensified
this investigation. Most efforts have focused on event-based modeling,
and mixed and somewhat surprising results have been realized compared
to the numerical results discussed above.
Pessoa et al. 
found that adequately averaged gridded precipitation estimates from
radar were just as viable as fully distributed estimates for streamflow
simulation using a distributed model. Kouwen and Garland 
investigated the effects of radar data resolution and attempted
to develop guidelines for the proper resolution of input rainfall
data resolution. They noted that spatially coarser rainfall data
sometimes led to better hydrograph simulation due to the smoothing
of errors present in finer resolution rainfall information. In preliminary
testing limited to a single extreme event, Kenner et al. 
reported that a 5 sub-basin approach produced better hydrograph
agreement than a lumped representation of the basin. Sub-basin rainfall
hyetographs revealed spatially varied precipitation totals for the
event. Refsgaard  illustrated the concepts of parameterization,
calibration, and validation of distributed parameter model. Noting
that hydrologists often assume that a distributed model calibrated
to basin outlet information will adequately model interior processes,
he realized poor simulations of discharge and piezometric head at
3 interior gaging stations.
In contrast, Michaud
and Sorooshian  found that a complex distributed model calibrated
at the basin outlet was able to generate simulations at 8 internal
points that were at least as accurate as the outlet simulations.
These results underscore one of the main advantages of distributed
parameter hydrologic modeling: the ability to predict hydrologic
variables at interior points. They also concluded that a simple
distributed model proved to be just as accurate as a complex distributed
model given that both were calibrated, and noted that model complexity
does not necessarily lead to improved simulation accuracy.
It is a concern that few of
the studies have shown a direct comparison of distributed model
and lumped model results to observed streamflow data. The emergence
of high resolution data sets, GIS capabilities, and rapidly increasing
computer power have pushed distributed hydrologic models to the
forefront of research and development. While the utility of distributed
models to predict interior hydrologic processes is well known, few
studies have specifically addressed the improvement of distributed
models over lumped models for predicting basin outflow hydrographs.
As a consequence, the hypothesis that higher resolution data will
lead to more accurate hydrograph simulations remains largely untested.
A few years ago,
the Hydrology Laboratory (HL) (then the Hydrologic Research Laboratory
(HRL)) of the NWS began a major research effort to address the question:
'How can the NWS best utilize the NEXRAD data to improve its river
forecasts?' In Phase I of this research, modeling tests have involved
the existing NWS hydrologic models applied in a lumped and semi-distributed
format. The model used in these efforts was the Sacramento Soil
Moisture Accounting Model (SAC-SMA). In Phase 2, new models such
as gridded distributed models will be developed and examined. In
the Phase 1 semi-distributed simulations, several RFC-scale basin
were disaggregated into 5 to 8 sub-basins in an effort to capture
the spatial variability of precipitation and soil/vegetation properties
(Smith et al. 1999). Simulations from lumped and semi-distributed
approaches were compared to observed data for five basins (with
drainage areas ranging from 820 to 4200 sq. km.) using results from
continuous simulations over a period of 4-6 years. The analyses
suggest that the spatial rainfall averages derived from the NEXRAD
data can improve flood prediction in mid/large basins as compared
to gage-only averages. While the semi-distributed approach shows
potential for improved hydrograph simulation, parameterization problems
and noisy data can eliminate benefits when applied to mid/large
basins with significant damping effects (e. g., those caused by
deep, well drained soils). A more noticeable benefit from the semi-distributed
approach is achieved for basins with a fast response runoff (Smith
et al. 2000). However, optimal model parameters from the lumped
approach may be far from the optimal parameter set for the semi-distributed
It is clear that distributed modeling
is in the future. However, there is no clear pathway in the literature
toward the class of distributed models that will suit NWS's forecasting
needs. To address this problem and the other issues mentioned above,
HL is initiating the Distributed Model Intercomparison Project (DMIP).
The intention is to access broad scientific community experience
to help guide NWS/HL's distributed modeling research and applications.
Within DMIP, HL will make available data sets for a number of basins.
Participants will download the data sets and run their models to
generate simulations at specific locations. HL will generate statistics
comparing the simulations to observed streamflow as well as to simulations
from a calibrated lumped SAC-SMA model. Participants will be invited
to a workshop at HL to present their models and results. The workshop
will also provide opportunities to discuss further research and
publication of results.
2. DMIP Goals
A. To identify
and help develop models and modeling systems that best utilize NEXRAD
and other spatial data sets to improve RFC-scale river simulations
B. To help
guide NWS/HL's distributed hydrologic modeling research, science,
science questions that DMIP will address are:
What are the characteristics of a basin that is more likely to benefit
from distributed modeling (i.e., accounting for the spatial variability
of precipitation and model parameters)? Can these characteristics
What is the optimal choice of computational element size to capture
the essential spatial variability of precipitation in runoff generation
and of flow in routing runoff to stream channels?
What level of complexity is required in distributed models to improve
basin outlet simulations?
What is the potential for distributed models set up for basin outlet
simulations to generate hydrographs at interior locations for flash
approaches work well for handling sub-grid heterogeneity of hydrologic
Questions that need
to be addressed before a model can be implemented in the NWS River
Forecast System (NWSRFS) for operational use are:
A. Computational requirements.
B. Run-time modifications
C. Parameterization and calibration
D. Does ease of parameterization/calibration
of a physically-based distributed parameter model warrant its use,
even when it might not provide improvements over simpler (but harder
to calibrate) lumped conceptual models?
Beven, K. J., and G. M. Hornberger, Assessing the
effect of spatial pattern of precipitation in modeling stream flow
hydrographs, Water Resources Bulletin, 823-829, 1982.
Koren, V. I., B. D. Finnerty, J. C. Schaake, M. B.
Smith, D.-J. Seo, Q. Y. Duan, Scale dependencies of hydrology models
to spatial variability of precipitation, Journal of Hydrology, 217,
Krajewski, W. F., V. Lakshmi, K. P. Georgakakos, and
S. C. Jain, A monte -carlo study of rainfall sampling effect on
a distributed catchment model, Water Resources Research, Vol. 27,
No. 1, 119-128, 1991.
Michaud, J., and S. Sorooshian, Comparison of simple
versus complex distributed runoff models on a midsized semiarid
watershed, Water Resources Research, Vol. 30, No. 3. 593-605, March,
Obled, C. H., J. Wendling, and K. Beven, The sensitivity
of hydrological models to spatial rainfall patterns: an evaluation
using observed data, Journal of Hydrology, 159,305-333, 1994.
Ogden, F. L., and P. Y. Julien, Runoff sensitivity
to temporal and spatial rainfall variability at runoff plane and
small basin scales, Water Resources Research, Vol. 29, No. 8, 2589-2597,
Ogden, F. L, and P. Y. Julien, Runoff model sensitivity
to radar rainfall resolution, Journal of Hydrology, 158, 1-18, 1994.
Pessoa, M. L., R. L., Bras,. and E. R. Williams, Use
of weather radar for flood forecasting in the Sieve river basin:
a sensitivity analysis, Journal of Applied Meteorology, 32 (3),
Refsgaard, J. C., Parameterisation, calibration, and
validation of distributed hydrological models, Journal of Hydrology,
(198), 69-97, 1997.
Shah, S. M. S., P. E. O=Connell, and J. R. M. Hosking,
Modeling the effects of spatial variability in rainfall on catchment
response. 1. Formulation and calibration of a stochastic rainfall
field model, Journal Hydrology, 175, 66-88, 1996a.
Shah, S. M. S., P. E. O=Connell,, and J. R. M. Hosking,,
Modeling the effects of spatial variability in rainfall on catchment
response. 2. Experiments with distributed and lumped models, Journal
of Hydrology, 175, 89-111, 1996b.
Smith, M. B., V. I. Koren, Z. Zhang, D. Wang, S. Reed.,
2000, >Semi-distributed vs Lumped Model Simulations: Comparisons
Using Observed Data for RFC Scale Basins=, EOS, Transactions of
the American Geophysical Union, 2000 Spring Meeting, Vol., 81, No.
19. Abstract only.
Smith, M. B, V. Koren, D. Johnson, B. D. Finnerty,
and D.-J. Seo, Distributed Modeling: Phase 1 Results, NOAA Technical
Report NWS 44, 210 pp., National Weather Service Hydrologic Research
Lab, February 1999.
Troutman, B. M., Runoff Prediction Errors and Bias
in Parameter Estimation induced by Spatial Variability of Precipitation,
Water Resources Research, Vol 19. No. 3, 791-810, 1983.
Wilson, C. B., J. B. Valdes, and I. Rodriquez-Iturbe,
On the Influence of the spatial distribution of rainfall on storm
runoff, Water Resources Research, Vol 15(2), 321-328, 1979.
Winchell, M., H. V. Gupta, and S. Sorooshian, Effects
of radar-estimated precipitation uncertainty on different runoff
generation mechanisms, Rep. HWR No. 97-080, 285 pp., Department
of Hydrology and Water Resources, University of Arizona, 1997.
Winchell, M., H. V. Gupta, and S. Sorooshian, >On
the simulation of infiltration- and saturation- excess runoff using
radar-based rainfall estimates: Effects of algorithm uncertainty
and pixel aggregation=, Water Resources Research, Vol 34, No. 10,
Wood, E. F., M. Sivapalan, and K. Beven, Similarity
and scale in catchment storm response, Review of Geophysics, 28(1),