OHD/HL/DMIP - Introduction

Introduction
 

1.   Background

        Numerous 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. [1979] 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 [1982] 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. [1991] found a higher sensitivity to the temporal resolution of precipitation than to the spatial resolution. Ogden and Julien [1994] performed tests that identified when spatial and temporal variability of precipitation was dominant. Troutman [1983], Ogden and Julien [1994] 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. [1994] 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. [1998] and Winchell et al. [1997] 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. [1999] 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. [1993] 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 [1989] 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. [1996] 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 [1997] 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 [1994] 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 approach.

    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, and applications.

    3.     Science Questions

        Among the science questions that DMIP will address are:

    A.      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 be identified?

    B.      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?

    C.      What level of complexity is required in distributed models to improve basin outlet simulations?

    D.      What is the potential for distributed models set up for basin outlet simulations to generate hydrographs at interior locations for flash flood forecasting?

    E.     Which approaches work well for handling sub-grid heterogeneity of hydrologic variables?

    4.     Operational Questions

      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 and updates.

    C.  Parameterization and calibration requirements.

    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?

    5.     References

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, 285-302, 1999.

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, 1994.

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, 1993

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), 462-475, 1993.

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, 2655-2670, 1998.

Wood, E. F., M. Sivapalan, and K. Beven, Similarity and scale in catchment storm response, Review of Geophysics, 28(1), 1-18, 1990.