The distributed model intercomparison project – Phase 2: Experiment design and summary results of the western basin experiments

Abstract

Results indicate that in the two study basins, no single model performed best in all cases. In addition, no distributed model was able to consistently outperform the lumped model benchmark. However, one or more distributed models were able to outperform the lumped model benchmark in many of the analyses. Several calibrated distributed models achieved higher correlation and lower bias than the calibrated lumped benchmark in the calibration, validation, and combined periods. Evaluating a number of specific precipitation-runoff events, one calibrated distributed model was able to perform at a level equal to or better than the calibrated lumped model benchmark in terms of event-averaged peak and runoff volume error. However, three distributed models were able to provide improved peak timing compared to the lumped benchmark. Taken together, calibrated distributed models provided specific improvements over the lumped benchmark in 24% of the model-basin pairs for peak flow, 12% of the model-basin pairs for event runoff volume, and 41% of the model-basin pairs for peak timing. Model calibration improved the performance statistics of nearly all models (lumped and distributed). Analysis of several precipitation/runoff events indicates that distributed models may more accurately model the dynamics of the rain/snow line (and resulting hydrologic conditions) compared to the lumped benchmark model. Analysis of SWE simulations shows that better results were achieved at higher elevation observation sites. Although the performance of distributed models was mixed compared to the lumped benchmark, all calibrated models performed well compared to results in the DMIP 2 Oklahoma basins in terms of run period correlation and %Bias, and event-averaged peak and runoff error. This finding is noteworthy considering that these Sierra Nevada basins have complications such as orographicallyenhanced precipitation, snow accumulation and melt, rain on snow events, and highly variable topography. Looking at these findings and those from the previous DMIP experiments, it is clear that at this point in their evolution, distributed models have the potential to provide valuable information on specific flood events that could complement lumped model simulations.

Assessment of Different Modelling Studies on the Spatial Hydrological Processes in an Arid Alpine Catchment

“…In the phase2 of the Distributed Model Intercomparison Project (DMIP2) (Smith et al 2012(Smith et al , 2013, several distributed models was mixed compared to lumped benchmark, results indicated there was none single model can perform best in all cases. DANUBIA component (Barthel et al 2012) comprised of 17 model components and discussed the integrated simulation of global change influence on agriculture and groundwater.…”

Challenges of Operational River Forecasting

“…While more complex hydrologic models have been developed-fully spatially distributed physically based models were already developed in the 1990s-they are computationally demanding and by some accounts do not demonstrate dramatic improvements in streamflow forecasting skill compared to simpler models (Reed et al 2004;Smith et al 2013). One difficulty for physically based models may be that they attempt to apply physically oriented or empirical laws relevant at fine scales (e.g., soil column infiltration dynamics measurable on the order of 0.1 m) to simulate coarse catchment behavior (e.g., on the order of 1-100 km; Savenije 2009).…”

RRAWFLOW: Rainfall-Response Aquifer and Watershed Flow Model (v1.15)

“…In another comparison, Smith et al (2013) concluded that distributed models provided improvements over lumped models in 12-24 % of the cases tested, depending on the criteria of evaluation. The mixed results of these comparisons indicate that lumped models are a good choice when the objectives do not require a distributed model.…”

“…Although this preliminary version was applied primarily, but not exclusively, to karst, RRAWFLOW is suitable for aquifers and watersheds of any type, and non-karst systems generally are easier to model than karst systems. Convolution, as used in RRAWFLOW, has been applied extensively to non-karst surface-water and groundwater systems (e.g., Nash, 1959;Blank et al, 1971;Delleur and Rao, 1971;Dooge, 1973;Neuman and de Marsily, 1976;Maloszewski and Zuber, 1982;Besbes and de Marsily, 1984;Beven, 1989;Jakeman and Hornberger, 1993;von Asmuth et al, 2002;Reed et al, 2004;von Asmuth and Knotters, 2004;Olsthoorn, 2008;Jurgens et al, 2012;Smith et al, 2013).…”