Capturing the essential spatial variability in distributed hydrological modelling: Infiltration parameters

Abstract

AbstractSelecting the correct resolution in distributed hydrological modelling at the watershed scale is essential in reducing scale‐related errors. The work presented herein uses information content (entropy) to identify the resolution which captures the essential variability, at the watershed scale, of the infiltration parameters in the Green and Ampt infiltration equation. A soil map of the Little Washita watershed in south‐west Oklahoma, USA was used to investigate the effects of grid cell resolution on the distributed modelling of infiltration. Soil‐derived parameters and infiltration exhibit decreased entropy as resolutions become coarser. This is reflected in a decrease in the maximum entropy value for the reclassified/derived parameters vis a vis the original data. Moreover, the entropy curve, when plotted against resolution, shows two distinct segments: a constant section where no entropy was lost with decreasing resolution and another part which is characterized by a sharp decrease in entropy after a critical resolution of 1209 m is reached. This methodology offers a technique for assessing the largest cell size that captures the spatial variability of infiltration parameters for a particular basin. A geographical information system (GIS) based rainfall‐runoff model is used to simulate storm hydrographs using infiltration parameter maps at different resolutions as inputs. Model results up to the critical resolution are reproducible and errors are small. However, at resolutions beyond the critical resolution the results are erratic with large errors. A major finding of this study is that a large resolution (1209 m for this basin) yields reproducible model results. When modelling a river basin using a distributed model, the resolution (grid cell size) can drastically affect the model results and calibration. The error structure attributable to grid cell resolution using entropy as a spatial variability measure is shown.