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

By Michael Smith, Dong-Jun Seo, V. Koren, S. Reed, Ziya Zhang, Q. Duan, F. Moreda, S. Cong. 2004

446 citations recorded by the source corpus.

Subfields: Global and Planetary Change, Water Science and Technology

View Paper: https://www.semanticscholar.org/paper/574c9af08fd74628957fd97c06644d218058cdbd

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Global and Planetary Change

In Water Science and Technology

Citations in the corpus

A spatial regularization approach to parameter estimation for a distributed watershed model (2008, methodology): “The approach is demonstrated by application to calibration of the University of Arizona version of the Hydrology Laboratory-Distributed Hydrologic Model (HL-DHM) developed by the National Weather Service for flood forecasting throughout the continental US [Smith et al., 2004; Reed et al., 2004].”
Surface‐subsurface model intercomparison: A first set of benchmark results to diagnose integrated hydrology and feedbacks (2014, background): “DMIP [Reed et al., 2004; Smith et al., 2004] tested distributed hydrologic models against one another and against simpler, lumped models over several real sites in North America.”
Physically based modeling in catchment hydrology at 50: Survey and outlook (2015, background): “Benchmarking is essential for establishing a standard set of test cases and for bringing together the modeling community to directly and openly assess competing formulations, parameterizations, and algorithms [Smith et al., 2004; Sebben et al., 2013].”
Reducing hydrologic model uncertainty in monthly streamflow predictions using multimodel combination (2012, methodology): “The abcd model was originally introduced by Thomas [1981] at Harvard University and was later compared with numerous monthly water balance models, leading to its recommendation by Alley [1984].”
Large-sample hydrology: a need to balance depth with breadth (2013, background): “Most recently, the Distributed Model Intercomparison Projects (DMIP-I and DMIP-II, see Smith et al., 2004, 2012) sponsored by the US National Weather Service facilitated a comparative assessment of spatially-distributed hydrological models (and associated parameter estimation 15 strategies) by…”
MAST-2D diffusive model for flood prediction on domains with triangular Delaunay unstructured meshes (2011, background): “) depends also on the available input data and on the capability of generating the required hydraulic information in an appropriate format and detail level [50].”
Stochasticity, nonlinearity and forecasting of streamflow processes (2006, background): “…of United States proposed the Distributed Model Intercomparison Project (DMIP) for comparing the performance of distributed rainfallrunoff models (Reed et al., 2004; Smith et al., 2004), of which the focus is the sensitivity of runoff hydrographs to spatial and temporal variations in precipitation.”
A process‐based diagnostic approach to model evaluation: Application to the NWS distributed hydrologic model (2008, methodology): “Snow is insignificant and streamflow is natural and uninfluenced by man-made structures [ Smith et al., 2004 ].”
Multi-model ensemble hydrologic prediction using Bayesian model averaging (2007, methodology): “Georgakakos et al. [17] recently used a multi-model combination approach to analyze the simulation results from multiple models that participated in the distributed model intercomparison project (DMIP) [35].”