The distributed model intercomparison project (DMIP): Motivation and experiment design

Abstract The distributed model intercomparison project (DMIP) was formulated as a broad comparison of many distributed models amongst themselves and to a lumped model used for operational river forecasting in the US. DMIP was intended to provide guidance on research and implementation directions for the US National Weather Service as well as to address unresolved questions on the variability of rainfall and its effect on basin response. Twelve groups participated, including groups from Canada, China, Denmark, New Zealand, and the US. Numerous data sets including seven years of concurrent radar-rainfall and streamflow data were provided to participants through web access. Detailed modeling instructions specified calibration and verification periods and modeling points. Participating models were run in ‘simulation’ mode without a forecast component. DMIP proved to be a successful endeavour, providing the hydrologic research and forecasting communities with a wealth of results. This paper presents the background and motivations for DMIP and describes the major project elements.

[1]  Vijay P. Singh,et al.  The NWS River Forecast System - catchment modeling. , 1995 .

[2]  J. Faundeen,et al.  The 1 km AVHRR global land data set: first stages in implementation , 1994 .

[3]  A. Bradley,et al.  The Hydrometeorological Environment of Extreme Rainstorms in the Southern Plains of the United States , 1994 .

[4]  Dong-Jun Seo,et al.  Space-time scale sensitivity of the Sacramento model to radar-gage precipitation inputs , 1997 .

[5]  J. Refsgaard.  Parameterisation, calibration and validation of distributed hydrological models , 1997 .

[6]  Applying radar rainfall data in basin hydrological modelling , 2003 .

[7]  D. Seo,et al.  On the Relationship Between Catchment Scale and Climatological Variability of Surface‐Runoff Volume , 1996 .

[8]  Michael Smith,et al.  Hydrology laboratory research modeling system (HL-RMS) of the US national weather service , 2004 .

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

[10]  K. Beven,et al.  Similarity and scale in catchment storm response , 1990 .

[11]  Victor Koren,et al.  Use of Next Generation Weather Radar Data and Basin Disaggregation to Improve Continuous Hydrograph Simulations , 2004 .

[12]  Juan B. Valdés,et al.  On the influence of the spatial distribution of rainfall on storm runoff , 1979 .

[13]  Konstantine P. Georgakakos,et al.  Impacts of parametric and radar rainfall uncertainty on the ensemble streamflow simulations of a distributed hydrologic model , 2004 .

[14]  Keith Beven,et al.  The sensitivity of hydrological models to spatial rainfall patterns: an evaluation using observed data , 1994 .

[15]  Murugesu Sivapalan,et al.  Catchment-scale runoff generation model by aggregation and similarity analyses , 1995 .

[16]  R. Allan Freeze,et al.  A Comparison of Rainfall-Runoff Modeling Techniques on Small Upland Catchments , 1985 .

[17]  Soroosh Sorooshian,et al.  EFFECTS OF RADAR-ESTIMATED PRECIPITATION UNCERTAINTY ON DIFFERENT RUNOFF-GENERATION MECHANISMS , 1997 .

[18]  M. J. Hamlin,et al.  The significance of rainfall in the study of hydrological processes at basin scale , 1983 .

[19]  V. Singh,et al.  Computer Models of Watershed Hydrology , 1995 .

[20]  D. Maidment,et al.  Coordinate Transformations for Using NEXRAD Data in GIS-Based Hydrologic Modeling , 1999 .

[21]  Victor Koren,et al.  Distributed modeling phase 1 results , 1999 .

[22]  A. Calver,et al.  Effects of Spatially-Distributed Rainfall on Runoff for a Conceptual Catchment , 1991 .

[23]  Rafael L. Bras,et al.  Use of Weather Radar for Flood Forecasting in the Sieve River Basin: A Sensitivity Analysis , 1993 .

[24]  J. C. Peters,et al.  Runoff simulation using radar rainfall data , 1996 .

[25]  U. Shamir,et al.  The characteristic time scale for basin hydrological response using radar data , 2001 .

[26]  Pierre Y. Julien,et al.  Runoff sensitivity to temporal and spatial rainfall variability at runoff plane and small basin scales , 1993 .

[27]  Dong-Jun Seo,et al.  Scale dependencies of hydrologic models to spatial variability of precipitation , 1999 .

[28]  Fekadu Moreda,et al.  NUTRIENT LOADING ASSESSMENT IN THE ILLINOIS RIVER USING A SYNTHETIC APPROACH 1 , 2003 .

[29]  K. Georgakakos,et al.  On the parametric and NEXRAD-radar sensitivities of a distributed hydrologic model suitable for operational use , 2001 .

[30]  J. Nash,et al.  River flow forecasting through conceptual models part I — A discussion of principles☆ , 1970 .

[31]  Pierre Y. Julien,et al.  Runoff model sensitivity to radar rainfall resolution , 1994 .

[32]  Witold F. Krajewski,et al.  Evaluating NEXRAD Multisensor Precipitation Estimates for Operational Hydrologic Forecasting , 2000 .

[33]  Witold F. Krajewski,et al.  A Monte Carlo Study of rainfall sampling effect on a distributed catchment model , 1991 .

[34]  L. A. Wenzel,et al.  Organization of the River and Flood Program in the National Weather Service , 1995 .

[35]  N. Kouwen,et al.  RESOLUTION CONSIDERATIONS IN USING RADAR RAINFALL DATA FOR FLOOD FORECASTING , 1989 .

[36]  Richard H. McCuen,et al.  A proposed index for comparing hydrographs , 1975 .

[37]  Keith Loague.  R‐5 revisited: 2. Reevaluation of a quasi‐physically based rainfall‐runoff model with supplemental information , 1990 .

[38]  Douglas A. Miller,et al.  A Conterminous United States Multilayer Soil Characteristics Dataset for Regional Climate and Hydrology Modeling , 1998 .

[39]  Murugesu Sivapalan,et al.  Scale issues in hydrological modelling , 1995 .

[40]  V. Bell,et al.  A grid-based distributed flood forecasting model for use with weather radar data: Part 2. Case studies , 1998 .

[41]  Soroosh Sorooshian,et al.  Effect of rainfall‐sampling errors on simulations of desert flash floods , 1994 .

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

[43]  D. L. Fread,et al.  Modernization in the National Weather Service River and Flood Program , 1995 .

[44]  Dong-Jun Seo,et al.  Real-time estimation of rainfall fields using radar rainfall and rain gage data , 1998 .

[45]  B. Vieux,et al.  NEGOTIATING SCIENCE AND VALUES WITH STAKEHOLDERS IN THE ILLINOIS RIVER BASIN 1 , 2002 .

[46]  P. Naden.  Spatial variability in flood estimation for large catchments: the exploitation of channel network structure , 1992 .

[47]  Brent M. Troutman,et al.  Runoff prediction errors and bias in parameter estimation induced by spatial variability of precipitation , 1983 .

[48]  Soroosh Sorooshian,et al.  On the simulation of infiltration‐ and saturation‐excess runoff using radar‐based rainfall estimates: Effects of algorithm uncertainty and pixel aggregation , 1998 .

[49]  Arlen Feldman,et al.  Closure of "Evolution of Clark’s Unit Graph Method to Spatially Distributed Runoff" , 1999 .

[50]  Efffects of Areal and Time Distribution of Rainfall on Small Watershed Runoff Hyrdographs , 1971 .

[51]  K. Beven,et al.  ASSESSING THE EFFECT OF SPATIAL PATTERN OF PRECIPITATION IN MODELING STREAM FLOW HYDROGRAPHS , 1982 .

[52]  Paul O'Connell,et al.  Modelling the effects of spatial variability in rainfall on catchment response. 2. Experiments with distributed and lumped models , 1996 .

[53]  S. Sorooshian,et al.  Comparison of simple versus complex distributed runoff models on a midsized semiarid watershed , 1994 .

[54]  Victor Koren,et al.  Comparing Mean Areal Precipitation Estimates from NEXRAD and Rain Gauge Networks , 1999 .

[55]  T. Carroll,et al.  Operational Responsibilities of the National Weather Service River and Flood Program , 1995 .

[56]  Vijay P. Singh,et al.  Effect of spatial and temporal variability in rainfall and watershed characteristics on stream flow hydrograph , 1997 .

[57]  D. A. Woolhiser,et al.  Impact of small-scale spatial rainfall variability on runoff modeling , 1995 .

[58]  Soroosh Sorooshian,et al.  Toward improved streamflow forecasts: value of semidistributed modeling , 2001 .

[59]  Zong-Liang Yang,et al.  The Project for Intercomparison of Land-surface Parameterization Schemes (PILPS) Phase 2(c) Red–Arkansas River basin experiment:: 1. Experiment description and summary intercomparisons , 1998 .

[60]  A. Bradley,et al.  The SpaceTime Structure of Extreme Storm Rainfall in the Southern Plains , 1994 .

[61]  D. Seo,et al.  Overall distributed model intercomparison project results , 2004 .

[62]  D. Lettenmaier,et al.  A Long-Term Hydrologically Based Dataset of Land Surface Fluxes and States for the Conterminous United States* , 2002 .

[63]  J. Refsgaard,et al.  Operational Validation and Intercomparison of Different Types of Hydrological Models , 1996 .

[64]  Baxter E. Vieux,et al.  Distributed Hydrologic Modeling Using GIS , 2001 .

[65]  M. Seyfried,et al.  Scale and the Nature of Spatial Variability: Field Examples Having Implications for Hydrologic Modeling , 1995 .

[66]  Victor Koren,et al.  Combining physically-based and conceptual approaches in the development and parameterization of a distributed system , 2003 .

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

[68]  Jay P. Breidenbach,et al.  The WSR-88 D Rainfall Algorithm , 1998 .