Details

Autor(en) / Beteiligte

• Francés, Alain P.
• Lubczynski, Maciek W.

Titel

Topsoil thickness prediction at the catchment scale by integration of invasive sampling, surface geophysics, remote sensing and statistical modeling

Ist Teil von

• Journal of hydrology (Amsterdam), 2011-07, Vol.405 (1), p.31-47

Ort / Verlag

Kidlington: Elsevier B.V

Erscheinungsjahr

2011

Quelle

Alma/SFX Local Collection

Beschreibungen/Notizen

• ► We present a method to predict the topsoil thickness at the catchment scale. ► Geophysics, remote sensing and statistics limit the costly invasive sampling. ► Prediction with geophysical data is as good as the prediction with invasive sampling. ► Results are suitable for hydrological studies over large areas. Topsoil thickness is a critical input in hydrological modeling because it controls, in conjunction with soil hydraulic properties, the partitioning of water fluxes between the atmosphere and the subsurface. To parameterize a distributed hydrological model that computes groundwater recharge, we developed a data-integration method to predict the clayey topsoil thickness (CTT) that we applied in a small catchment in Portugal (∼19 km 2). The prediction method is based on the integration of: (i) invasive sampling used as a CTT reference dataset (61 invasive measurements); (ii) surface geophysics applied to complement the time-consuming invasive sampling; (iii) remote sensing (RS) image processing (high resolution QuickBird image, aerial photographs and ASTER GDEM) used to derive soils classes and terrain parameters; (iv) geostatistical mixed linear model (MLM) applied to integrate the CTT variability at the catchment scale using geophysical and RS derived auxiliary variables. The selection of the appropriate statistical model derived from the MLM was based on the verification of model assumptions using diagnostic tools. We first converted 436 Geonics™ EM-31 field measurements of soil apparent electrical conductivity (EC a ) into CTT. This was achieved by building MLM based calibration models that integrated 25 invasive CTT measurements paired with corresponding EC a and RS-derived auxiliary variables. Next, we predicted the CTT at the catchment scale by applying the MLM approach and integrating the RS-derived auxiliary variables with: (i) the 436 CTT values derived from surface geophysical dataset; (ii) the 61 CTT values from the reference invasive dataset. The two maps had similar CTT patterns which depicted the spatial variability of the CTT over the geomorphologic catchment features. The prediction map derived from the geophysical dataset resulted in slightly lower CTT values than the reference map (median of 0.87 m against 1.11 m) and a comparable accuracy (RMSE of 0.76 m against 0.88 m). As these differences will be minimized during the calibration process of the hydrological model, the presented methodology is considered suitable for hydrological and environmental studies, in which catchments often need to be investigated over large areas.