Ms. Seyfried et Bp. Wilcox, SCALE AND THE NATURE OF SPATIAL VARIABILITY - FIELD EXAMPLES HAVING IMPLICATIONS FOR HYDROLOGIC MODELING, Water resources research, 31(1), 1995, pp. 173-184
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.