@article{fdi:010074252,
  title = {{S}ubsurface topography to enhance the prediction of the spatial distribution of soil wetness},
  author = {{C}haplot, {V}incent and {W}alter, {C}.},
  editor = {},
  language = {{ENG}},
  abstract = {{T}he estimation of the spatial distribution of soil wetness within a catchment is one of the most important issues in hydrological and erosion modelling. {S}o far, such models have been based on soil surface topographic information only. {H}owever, soil hydrology is also controlled by subsurface flow pathways that may not be explained only by surface terrain features. {T}his study examined how the topography of the lower limit of the soil cover could improve quantitative modelling for the spatial prediction of soil wetness. {T}he study was conducted in an agricultural catchment of the {A}rmorican {M}assif (western {F}rance) characterized by impermeable granitic saprolites. {T}wo digital elevation models ({DEM}s) with a 10-m grid mesh and with a 0.3 m vertical resolution were generated from field investigations throughout the catchment. {O}ne {DEM} was a numerical representation of the soil surface and the other described the topography of the boundary between the soil cover and the underlying impermeable saprolite. {S}oil wetness (theta) was surveyed systematically from 1996 to 1997 along a hillslope. {T}he sampling scheme consisted of 149 nodes of a 10-m grid where theta at 0-10 cm was estimated using time-domain reflectometry. {T}he value of theta at depths of 20-30, 50-60 and 110-120 cm was estimated for a subset of 112 data points using a gravimetric method. {F}or both surface and subsurface {DEM}s, soil wetness at all depths significantly correlated with the topographic attributes, namely the distance to the stream bank, the elevation above the stream bank ({E}), the downslope gradient, the revised compound topographic index ({CTI}), and the specific monodirectional and multidirectional catchment areas. {T}he best correlations were observed between theta(10) of winter 1996 and the physically based attributes {E} and {CTI} estimated by using the subsurface {DEM} (r = 0.83 and 0.86, respectively). {T}wo multiple non-linear regression models for theta(10) spatial prediction were generated using non-autocorrelated topographic attributes estimated from both surface and subsurface topography. {M}odel validation using a new set of 41 data points showed root mean square errors ({RMSE}) lower than 10% of the theta(10) range. {T}he model based on subsurface topography decreased {RMSE} by 43%. {P}rediction errors were not spatially distributed. {F}inally, theses results are discussed in respect of processes involved in hillslope hydrology.},
  keywords = {{FRANCE}},
  booktitle = {},
  journal = {{H}ydrological {P}rocesses},
  volume = {17},
  numero = {3},
  pages = {2567--2580},
  ISSN = {0885-6087},
  year = {2003},
  DOI = {10.1002/hyp.1273},
  URL = {https://www.documentation.ird.fr/hor/fdi:010074252},
}