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Distributed hydrologic modeling under the changing climate and environment is hampered by the difficulties in the representation of small scale variability. Identifying and understanding the links between spatial patterns of hydrologic processes at various scales may shed light on the search for simpler and yet effective representations of variability. The main objective of this thesis is to gain useful insight for the spatial aggregation of hydrologic processes by analyzing hydrologic systems at multiple spatial scales and conducting diagnostic simulations across scales. We first identified patterns of hydrologic processes by 1) comparing the surface runoff hydrographs (area-averaged discharges) from 12 hillslopes (0.5˜3 ha) with spatial proximity in agricultural land in Iowa over 72 runoff events; and 2) analyzing˜1000 recession curves observed in the nested Iowa and Cedar River basins (7~17000km2) over the period of 1988-2012 using consistent methods. We then used recession as an example to run diagnostic simulations in the Cedar River basins to reproduce the observed spatial patterns of hydrologic responses. The success allows us to decipher the link between the spatial pattern of processes at the small scale and that at larger scales. Results from this thesis demonstrate the usefulness of the "multiscale-analysis" approach and show that accounting for both the organization and randomness of the processes variability at the small scale can shape the hydrologic response at larger scales.