Dynamical process upscaling for deriving catchment scale state variables and constitutive relations for meso-scale process models

Abstract

Abstract. In this study we propose an uspcaling approach to derive time series of (a) REW scale state variables, and (b) effective REW scale soil hydraulic functions to test and parameterise models based on the REW approach. To this end we employed a physically based hydrological model, that represents the typical patterns and structures in the study catchment, and has previously been shown to reproduce observed runoff response and state dynamics well. This landscapeand process-compatible model is used to simulate numerical drainage and wetting experiments. The effective soil water retention curve and soil hydraulic conductivity curve are derived using the spatially averaged saturation and capillary pressure as well as averaged fluxes. When driven with observed boundary conditions during a one year simulation the model is used to estimate how the spatial pattern of soil moisture evolved during this period in the catchment. The time series of the volume integrated soil moisture is deemed as best estimate for the average catchment scale soil moisture. The approach is applied to the extensively monitored Weiherbach catchment in Germany. A sensitivity analysis showed that catchment scale model structures different from the landscape-and process compatible one yielded different times series of average catchment scale soil moisture and where not able to reproduce the observed rainfall runoff response. Hence, subscale typical heterogeneity leaves a clear fingerprint in the time series of average catchment scale saturation. In case of the Weiherbach catchment local scale heterogeneity of ks could be neglected and a simple representation of the typical hillslope scale patterns of soil types and macroporosity was sufficient for obtaining effective REW scale soil hydraulic functions. Both the effective soil hydraulic functions and time series of catchment scale saturation turned out to be useful to parameterise and test the CREW model, which is based on the REW approach and was Correspondence to: E. Zehe

Threshold behaviour in hydrological systems as (human) geo-ecosystems: manifestations, controls, implications

“…We suggest that the REW approach (Reggiani et al, 1998(Reggiani et al, , 1999(Reggiani et al, , 2000Reggiani and Rientjes, 2005) already meets the second requirement (Lee et al, 2005(Lee et al, , 2007Zehe et al, 2005bZehe et al, , 2006Zhang et al, , 2006Beven, 2006), however, it fails to meet the first. Furthermore, it is a suitable framework to reunify the different fields of hydrological research as it postulates the existence of functional units with homogeneous hydrological response/behaviour in the landscape (Beven, 2006;.…”

“…In a modelling study Zehe et al ( , 2006 showed that a reduced population of earthworms that goes along with a reduction of apparent connected preferential pathways would thus reduce the intensity threshold for overland flow production and increase connectivity of surface flow paths that control overland and thus flood response. A positive effect of an apparently lower number of earthworms and thus worm burrows would be a lower susceptibility for rapid transport of agrochemicals and thus a possible reduction of related environmental risk.…”

HESS Opinions: The complementary merits of competing modelling philosophies in hydrology

“…In contrast to what the terms imply, different applications of fully distributed models span several magnitudes of grid sizes from centimetres to kilometres (e.g. Butts et al, 2004;Kollet and Maxwell, 2006;Zehe et al, 2006;Kumar et al, 2013), and thus do not necessarily describe the system at a higher spatial resolution than so-called semi-distributed models, as the applied grid cells can often be larger than sub-catchments and/or hydrological response units (e.g. Nijzink et al, 2016a).…”

“…Zehe et al, 2007) caused by the combined effects of limited observation accuracy and representativeness.…”

“…For the sub-surface a common continuum-based model is a three-dimensional implementation of Richard's equation (e.g. Zehe and Blöschl, 2004;Kollet and Maxwell, 2006;Zehe et al, 2006;Sudicky et al, 2008). A distinguishing feature of continuum-based models is that model fluxes are computed based on spatial gradients in model state variables, e.g.…”