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Hyperresolution information and hyperresolution ignorance in modelling the hydrology of the land surface
There is a strong drive towards hyperresolution earth system models in order to resolve finer scales of motion in the atmosphere. The problem of obtaining more realistic representation of terrestrial fluxes of heat and water, however, is not just a problem of moving to hyperresolution grid scales. It is much more a question of a lack of knowledge about the parameterisation of processes at whatever grid scale is being used for a wider modelling problem. Hyperresolution grid scales cannot alone solve the problem of this hyperresolution ignorance. This paper discusses these issues in more detail with specific reference to land surface parameterisations and flood inundation models. The importance of making local hyperresolution model predictions available for evaluation by local stakeholders is stressed. It is expected that this will be a major driving force for improving model performance in the future.
Keith BEVEN, Hannah CLOKE, Florian PAPPENBERGER, Rob LAMB, Neil HUNTE
An assessment of rainfall-runoff modeling methodology
This study reports model performance calculations for three event-based rainfall-runoff models on both real and synthetic data sets. The models include a regression model, a unit hydrograph model and a quasi-physically based model. The real data sets are for small upland catchments from the Washita River Experimental Watershed, Oklahoma; the Mahantango Creek Experimental Watershed, Pennsylvania; and the Hubbard Brook Experimental Forest, New Hampshire. The synthetic data sets are generated with a stochastic-conceptual rainfall-runoff simulator. Model performance is assessed for a verification period that is carefully distinguished from the calibration period. Performance assessment was carried out both in forecasting mode and in prediction mode. The results on the real data sets show surprisingly poor forecasting efficiencies for all models on all data sets. The unit hydrograph model and the quasi-physically based model have little forecasting power; the regression model is marginally better. The performance of the models in prediction mode is better. The regression model and the unit hydrograph model showed acceptable predictive power, but the quasi-physically based model produced acceptable predictions on only one of the three catchments. The performance of the regression and unit hydrograph models, in both forecasting and prediction modes, for synthetic data is much better than for the real catchments. The performance of the quasi-physically based model on a synthetic data set is surprisingly poor. Supplemental data gathered from the Oklahoma catchment was used for a spatial variability analysis of steady-state infiltration rates. These data were then used to re-excite the quasi-physically based model; the new information resulted in improved model performance. The concept of space-time tradeoffs across the hydrologic data sets of competing models is introduced and tested. Results show the existence of space-time tradeoffs within model data sets but not across model data sets. It is the belief of the author that the primary barrier to successful application of physically based models in the field lies in the scale problems that are associated with the unmeasurable spatial variability of rainfall and soil hydraulic properties. The fact that simpler, less data intensive models provided as good or better predictions than a physically based model is food for thought. The model evaluation and space-time tradeoff experiments reported in this study are conceptually linked to data-worth analysis, network-design, and model-choice criteria for future studies.Graduate and Postdoctoral StudiesGraduat
Simulation of Organic Chemical Movement in Hawaii Soils with PRZM: 1. Preliminary Results for Ethylene Dibromide
Leaching of agricultural chemicals to groundwater is an
environmental issue of major concern in Hawaii. Fumigants used by the
pineapple industry are a possible source of this contamination. In this paper we
report the results of an initial evaluation of the Pesticide Root Zone Model
(PRZM) for highly structured Hawaiian soils. We use PRZM to predict the
transport of the soil fumigant ethylene dibromide (EDB) for two pineapple fields
and compare the simulated concentration profiles with field measurements.
Although preliminary, our results suggest that PRZM may be useful in the future
for pesticide screening and risk assessment in Hawaii. The work reported here
is part of a larger ongoing study concerned with development and application
of methodology for assessing potential groundwater contamination by pesticides