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Modelling of spatial controls on denitrification at the landscape scale
Author(s) -
Whelan M. J.,
Gandolfi C.
Publication year - 2002
Publication title -
hydrological processes
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.222
H-Index - 161
eISSN - 1099-1085
pISSN - 0885-6087
DOI - 10.1002/hyp.354
Subject(s) - denitrification , environmental science , spatial variability , hydrology (agriculture) , spatial distribution , soil science , nitrate , spatial ecology , soil water , soil carbon , precipitation , atmospheric sciences , nitrogen , ecology , geology , mathematics , geography , meteorology , chemistry , statistics , geotechnical engineering , organic chemistry , biology
A simple model for estimating likely spatial patterns in landscape‐scale denitrification rates is described. In the absence of limiting nitrate concentration, denitrification is assumed to be controlled principally by the soil water regime and the amount of available soil carbon. A formulation of TOPMODEL is used to estimate the spatial distribution of water table depths. Soil carbon concentration is assumed to decrease exponentially with depth. The spatial distribution of carbon concentrations at the soil surface is assumed to be imperfectly correlated with the topographic index used in TOPMODEL. Monte Carlo simulation techniques were used to introduce a stochastic element to the spatial distribution of soil carbon. This allowed estimates of the uncertainty in model outputs, resulting from uncertainties in the distribution and variability of soil carbon to be made. The model predicted spatial and temporal patterns of nitrate‐non‐limiting denitrification for a 15 year period in the Slapton Wood catchment in southwest England. Predicted denitrification followed a slight seasonal pattern with a winter maximum. Total annual denitrification losses tended to be positively correlated with total annual precipitation in the catchment. Highest rates tended to be predicted near to the stream. The modelling approach provides a means of assessing the proximity of local‐scale field measurements to probable landscape‐scale denitrification fluxes. Combining a deterministic model core with a stochastic generation of model parameters or state variables provides an attractive way of embracing variability and uncertainty whilst maintaining a conceptual description of the system dynamics. Copyright © 2002 John Wiley & Sons, Ltd.