THE SENSITIVITY OF TWO DISTRIBUTED NON-POINT SOURCE POLLUTION MODELS TO THE SPATIAL ARRANGEMENT OF THE LANDSCAPE

Distributed hydrological models are becoming increasingly complex with respect to spatial phenomena, and with the widespread availability of spatial data from GIS, this trend is likely to increase. In all such models the spatial arrangement of phenomena, such as soil properties and land‐use categori...

Full description

Saved in:
Bibliographic Details
Published in:Hydrological processes 1997-03, Vol.11 (3), p.241-252
Main Authors: FISHER, PETER, ABRAHART, ROBERT J., HERBINGER, WERNER
Format: Article
Language:English
Subjects:
AgNPS
ANSWERS
Chemical wastes
distributed models
Earth sciences
Earth, ocean, space
Engineering and environment geology. Geothermics
Exact sciences and technology
Geographic information systems
Hydrology
Hydrology. Hydrogeology
Infiltration
Pollution, environment geology
Q1
randomization
Sediments
spatial sensitivity analysis
Citations: Items that this one cites
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Distributed hydrological models are becoming increasingly complex with respect to spatial phenomena, and with the widespread availability of spatial data from GIS, this trend is likely to increase. In all such models the spatial arrangement of phenomena, such as soil properties and land‐use categories is fundamental, and so the arrangement should have an influence on the model output. Testing for this influence we term spatial sensitivity analysis. Here, we report on the spatial sensitivity of two widely used models, AgNPS (agricultural non‐point source pollution model) and ANSWERS (areal nonpoint source watershed environment response simulation). The input spatial data were subjected to spatially random mixing to varying degrees, such that the organized landscape became disorganized. The chemical discharge from AgNPS, and the sediment and water discharge from ANSWERS, are examined. In both cases most outputs exhibited little or no sensitivity to the spatial distribution of most input data. Only infiltration‐related inputs produced large variations, but these changes were not in the sense that might have been predicted. Although the analytical methods used require further refinement, there must now be some doubt as to the validity of the models, and whether they repay their computational complexity. Furthermore, it is felt that spatial sensitivity analysis should become a fundamental part of the verification of all such models. © 1997 by John Wiley & Sons, Ltd.
ISSN:0885-6087
1099-1085
DOI:10.1002/(SICI)1099-1085(19970315)11:3<241::AID-HYP438>3.0.CO;2-T