SCALE AND THE NATURE OF SPATIAL VARIABILITY - FIELD EXAMPLES HAVING IMPLICATIONS FOR HYDROLOGIC MODELING

In this paper we examine how the nature of spatial variability affects hydrologic response over a range of scales using five field studies a s examples. The nature of variability was characterized as either stoc hastic, when random, or deterministic, when due to known, nonrandom so urces. We have emphasized how that characterization may change with th e scale of hydrologic model. The five field examples, along with corre sponding sources of variability, were (1) infiltration and surface run off affected by shrub canopy, (2) groundwater recharge affected by soi l depth, (3) groundwater recharge and streamflow affected by small-sca le topography, (4) frozen soil runoff affected by elevation, and (5) s nowfall distribution affected by large-scale topography. In each examp le there was a scale, the deterministic length scale, over which the h ydrologic response was strongly dependent upon the specific, location- dependent ecosystem properties. Smaller-scale variability may be repre sented as either stochastic or homogeneous with nonspatial data. In ad dition, changes in scale or location sometimes resulted in the introdu ction of larger-scale sources of variability that subsume smaller-scal e sources. Thus recognition of the nature and sources of variability c an reduce data requirements by focusing on important sources of variab ility and using nonspatial data to characterize variability at scales smaller than the deterministic length scale. All the sources of variab ility described are present in the same watershed and affect hydrologi c response simultaneously. Physically based models should therefore ut ilize both spatial and stochastic data where scale appropriate. Other implications for physically based modeling are that modeling algorithm s should reflect larger-scale variability which generally has greater impact and that model and measurement grids should be consistent with the nature of variability.