Comparative predictions of discharge from an artificial catchment (Chicken Creek) using sparse data

Abstract

Abstract. We used ten conceptually different models to predict discharge from the artificial Chicken Creek catchment in North-East Germany. Soil textural and topography data were given to the modellers, but discharge data were withheld. We compare the predictions with the measurements from the 6 ha catchment and discuss the conceptualization and parameterization of the models. The predictions vary in a wide range, e.g. the predicted actual evapotranspiration ranged from 88 to 579 mm/y and the discharge from 19 to 346 mm/y. All model simulations revealed systematic deviations between observations of major components of the hydrological cycle (not known to the modellers) and the simulation results. Discharge was predicted mainly as subsurface discharge with little direct runoff. In reality, surface runoff was a major flow component despite the fairly coarse soil texture. The actual evapotranspiration (AET) was systematically overestimated by nine of ten models as was the ratio between actual and potential ET. Overall, none of the model simulations came close to the correct water balance during the entire 3-year study period. The comparison indicated that the personal judgement of the modellers was a major source of the differences between the model results. The most important parameters to be guessed were the soil parameters and the initial soil water content while plant parameterization had in this particular case of a sparse vegetation only a minor influence on the results.

Field-scale spatial variability of saturated hydraulic conductivity on a recently constructed artificial ecosystem

“…Even, sandbank island sediments consisting of quartz sand had lower K s values (2.4-3.8 m day − 1 ) Klaassen et al, 2008) than observed in the present study. Moreover, the observed K s was even much higher than that of other artificial ecosystems such as engineered covers (8.6 × 10 − 7 -8.6 × 10 − 1 m day − 1 ) (Bohnhoff et al, 2009;Ogorzalek et al, 2008) and the artificial Chicken Creek catchment (0.2-2.3 m day − 1 ) (Gerwin et al, 2009;Holländer et al, 2009;Mazur et al, 2011). However, most of these studies (e.g.…”

“…Discrepancies between measured and predicted K s values have been reported on both natural soils (e.g. Sobieraj et al, 2001;Tietje and Hennings, 1996), artificial ecosystems (Holländer et al, 2009) and well-sorted marine sands (Wilson et al, 2008). The poor performance of the PTFs observed in the present study may be due to a number of reasons; first, the PTFs were developed based on databases for natural soils, most of which tend to be poorly sorted.…”

“…Bohnhoff et al, 2009;Ogorzalek et al, 2008). Comparative hydrological prediction of the artificial Chicken Creek using ten different models clearly demonstrated the impact of hydraulic properties and their estimation on water balance components (Holländer et al, 2009).…”

“…Botros et al, 2009;Gόmez et al, 2005;Sobieraj et al, 2002), there has been no systematic field evaluation of K s and its spatial variability for artificially constructed ecosystems such as engineered covers. The few studies conducted on mine waste disposal facilities or artificial catchments are based on indirect methods involving analysis of qualitative data on soil structure (Breshears et al, 2005;Buczko et al, 2001), while results of spatial analysis are often not reported probably due to small sample sizes that restrict such analysis (Gerwin et al, 2009;Holländer et al, 2009;Mazur et al, 2011;Wehr et al, 2005). Notable exceptions are two studies conducted in Germany, where geostatistical analysis of hydraulic parameters derived from pedotransfer functions developed for natural soils showed that particle segregation during transportation and dumping, and amelioration practices contributed to spatial variability on lignite mine spoils (Buczko et al, 2001;Buczko and Gerke, 2005).…”

“…Hydraulic properties, particularly saturated hydraulic conductivity K s and soil moisture retention, exert a strong influence on soil moisture storage, deep drainage, runoff and infiltration. Therefore, accurate knowledge of K s and its spatial variability is crucial for understanding the hydrology of artificially constructed ecosystems such as engineered covers, and provides key inputs for most water balance and solute transport models (Swanson et al, 2003;Holländer et al, 2009. The methods for measuring K s and its spatial structure are wellknown, and substantial literature exists on the topic particularly on natural soils Sobieraj et al, 2002;Gόmez et al, 2005). However, most existing methods for measuring K s are timeconsuming and labour-intensive, thus constraining the acquisition of large K s datasets required for spatial analysis.…”