TY - JOUR
T1 - Overall distributed model intercomparison project results
AU - DMIP Participants
AU - Reed, Seann
AU - Koren, Victor
AU - Smith, Michael
AU - Zhang, Ziya
AU - Moreda, Fekadu
AU - Seo, Dong Jun
AU - Arnold, Jeff
AU - Bandaragoda, Christina
AU - Bingeman, Allyson
AU - Bras, Rafael
AU - Butts, Michael
AU - Carpenter, Theresa
AU - Cui, Zhengtao
AU - Diluzio, Mauro
AU - Georgakakos, Konstantine
AU - Gaur, Anubhav
AU - Guo, Jianzhong
AU - Gupta, Hoshin
AU - Hogue, Terri
AU - Ivanov, Valeri
AU - Khodatalab, Newsha
AU - Lan, Li
AU - Liang, Xu
AU - Lohmann, Dag
AU - Mitchell, Ken
AU - Peters-Lidard, Christa
AU - Rodriguez, Erasmo
AU - Seglenieks, Frank
AU - Shamir, Eylon
AU - Tarboton, David
AU - Vieux, Baxter
AU - Vivoni, Enrique
AU - Woods, Ross
PY - 2004/10/1
Y1 - 2004/10/1
N2 - This paper summarizes results from the Distributed Model Intercomparison Project (DMIP) study. DMIP simulations from twelve different models are compared with both observed streamflow and lumped model simulations. The lumped model simulations were produced using the same techniques used at National Weather Service River Forecast Centers (NWS-RFCs) for historical calibrations and serve as a useful benchmark for comparison. The differences between uncalibrated and calibrated model performance are also assessed. Overall statistics are used to compare simulated and observed flows during all time steps, flood event statistics are calculated for selected storm events, and improvement statistics are used to measure the gains from distributed models relative to the lumped models and calibrated models relative to uncalibrated models. Although calibration strategies for distributed models are not as well defined as strategies for lumped models, the DMIP results show that some calibration efforts applied to distributed models significantly improve simulation results. Although for the majority of basin-distributed model combinations, the lumped model showed better overall performance than distributed models, some distributed models showed comparable results to lumped models in many basins and clear improvements in one or more basins. Noteworthy improvements in predicting flood peaks were demonstrated in a basin distinguishable from other basins studied in its shape, orientation, and soil characteristics. Greater uncertainties inherent to modeling small basins in general and distinguishable inter-model performance on the smallest basin (65 km2) in the study point to the need for more studies with nested basins of various sizes. This will improve our understanding of the applicability and reliability of distributed models at various scales.
AB - This paper summarizes results from the Distributed Model Intercomparison Project (DMIP) study. DMIP simulations from twelve different models are compared with both observed streamflow and lumped model simulations. The lumped model simulations were produced using the same techniques used at National Weather Service River Forecast Centers (NWS-RFCs) for historical calibrations and serve as a useful benchmark for comparison. The differences between uncalibrated and calibrated model performance are also assessed. Overall statistics are used to compare simulated and observed flows during all time steps, flood event statistics are calculated for selected storm events, and improvement statistics are used to measure the gains from distributed models relative to the lumped models and calibrated models relative to uncalibrated models. Although calibration strategies for distributed models are not as well defined as strategies for lumped models, the DMIP results show that some calibration efforts applied to distributed models significantly improve simulation results. Although for the majority of basin-distributed model combinations, the lumped model showed better overall performance than distributed models, some distributed models showed comparable results to lumped models in many basins and clear improvements in one or more basins. Noteworthy improvements in predicting flood peaks were demonstrated in a basin distinguishable from other basins studied in its shape, orientation, and soil characteristics. Greater uncertainties inherent to modeling small basins in general and distinguishable inter-model performance on the smallest basin (65 km2) in the study point to the need for more studies with nested basins of various sizes. This will improve our understanding of the applicability and reliability of distributed models at various scales.
KW - Distributed hydrologic modeling
KW - Hydrologic simulation
KW - Model intercomparison
KW - Radar precipitation
KW - Rainfall-runoff
UR - http://www.scopus.com/inward/record.url?scp=4143112490&partnerID=8YFLogxK
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U2 - 10.1016/j.jhydrol.2004.03.031
DO - 10.1016/j.jhydrol.2004.03.031
M3 - Article
AN - SCOPUS:4143112490
SN - 0022-1694
VL - 298
SP - 27
EP - 60
JO - Journal of Hydrology
JF - Journal of Hydrology
IS - 1-4
ER -