Improving runoff prediction using agronomical information in a cropped, loess covered catchment

Abstract

Predicting runoff hot spots and hot‐moments within a headwater crop‐catchment is of the utmost importance to reduce adverse effects on aquatic ecosystems by adapting land use management to control runoff. Reliable predictions of runoff patterns during a crop growing season remain challenging. This is mainly due to the large spatial and temporal variations of topsoil hydraulic properties controlled by complex interactions between weather, growing vegetation, and cropping operations. This interaction can significantly modify runoff patterns and few process‐based models can integrate this evolution of topsoil properties during a crop growing season at the catchment scale. Therefore, the purpose of this study was to better constrain the event‐based hydrological model Limburg Soil Erosion Model by incorporating temporal constraints for input topsoil properties during a crop growing season (LISEM). The results of the temporal constraint strategy (TCS) were compared with a classical event per event calibration strategy (EES) using multi‐scale runoff information (from plot to catchment). The EES and TCS approaches were applied in a loess catchment of 47 ha located 30 km northeast of Strasbourg (Alsace, France). A slight decrease of the Nash–Sutcliffe efficiency criterion on runoff discharge for TCS compared to EES was counterbalanced by a clear improvement of the spatial runoff patterns within the catchment. This study showed that limited agronomical and climatic information added during the calibration step improved the spatial runoff predictions of an event‐based model. Reliable prediction of runoff source, connectivity, and dynamics can then be derived and discussed with stakeholders to identify runoff hot spots and hot‐moments for subsequent land use and crop management modifications.

Pesticide fate on catchment scale: conceptual modelling of stream CSIA data

“…The catchment characteristics have been previously described in Lefrancq et al (2017). Briefly, the mean annual temperature between 2005 and 2011 was 11.7 • C, and mean annual precipitation and potential evapotranspiration were 704 mm (± 151 mm) and 820 mm (± 28 mm), respectively (data from Meteo France station in Waltenheim sur Zorn at 7 km distance from the catchment).…”

“…Weekly grab water samples were collected from the drain outlet. Lefrancq et al (2017) provides a more detailed description of the monitoring setup.…”

Reply to Reviewer 1

“…The study was conducted in a 47 ha headwater catchment, located 30 km north of Strasbourg (Bas-Rhin, France). The catchment characteristics have been previously described in Lefrancq et al (2017). Briefly, the mean annual temperature between 2005 and 2011 was 11.7 • C, and mean annual precipitation and potential evapotranspiration were 704 mm (± 151 mm) and 820 mm (± 28 mm), respectively (data from Meteo France station in Waltenheim sur Zorn at 7 km distance from the catchment).…”

“…In addition to testing of different model setups, we used our conceptual model to assess a field-integrated enrichment factor (ε C ). We propose that travel-time distribution modelling inherently accounts for dispersion and flow-path mixing (Hrachowitz et al, 2016) and is thus able to mirror attenuation of potentially large isotope fractionation due to dispersion and mixing in open systems (Abe and Hunkeler, 2006;Lutz et al, 2013;van Breukelen and Prommer, 2008). In contrast, enrichment factors derived from field CSIA data via the Rayleigh equation (Eq.…”

“…The Rayleigh equation links the measured isotope fractionation effect, via the isotope fractionation factor, to the extent of degradation (Elsner and Imfeld, 2016;Mariotti et al, 1981;Rayleigh, 1896). CSIA data and associated isotope fraction-ation effects have more recently been simulated using reactive transport models to characterize groundwater pollution (Atteia et al, 2008;D'Affonseca et al, 2011;Pooley et al, 2009;Prommer et al, 2009;van Breukelen et al, 2005;Wanner et al, 2012) and pesticide pollution on hillslope scale via a virtual experimental approach (Lutz et al, 2013). These models demonstrated that the Rayleigh equation systematically underestimates the extent of degradation as occurred in the analysed samples (Abe and Hunkeler, 2006;van Breukelen and Prommer, 2008;van Breukelen and Rolle, 2012).…”