A test of TOPMODEL’s ability to predict spatially distributed groundwater levels

TOPMODEL was calibrated to a small catchment using precipitation and runo data. Acceptable fits of simulated and observed runo were obtained during both the calibration and validation periods. Predictions of groundwater levels using this calibration did not agree well with observations at the 37 points within the catchment where groundwater levels were measured, including three locations with continuous recordings. Groundwater level observations at one single point in time, however, suced to calibrate new topographic–soil indices that improved the prediction of the local ground-water levels at the observed tubes. This suggests that spatially distributed calibration data are necessary to exploit reliably TOPMODEL’s ability to predict spatially distributed hydrology. The mean or recalibrated transmissivity values at these 37 points diered from the catchment mean as determined by the precipitation–runo calibration. Thus, while ground-water information can help in predicting groundwater levels at specific locations, increasing the number of local groundwater level measurements is not sucient to improve the spatially distributed representation of subsurface flow by TOPMODEL for the catchment as a whole, as long as the groundwater information is not integrated in th