No abstract

Soil surface roughness not only delays overland flow generation but also strongly affects the spatial distribution and concentration of overland flow. Previous studies generally aimed at predicting the delay in overland flow generation by means of a single parameter characterizing soil roughness. However, little work has been done to find a link between soil roughness and overland flow dynamics. This is made difficult because soil roughness and hence overland flow characteristics evolve differently depending on whether diffuse or concentrated erosion dominates. The present study examined whether the concept of connectivity can be used to link roughness characteristics to overland flow dynamics. For this purpose, soil roughness of three 30-m 2 tilled plots exposed to natural rainfall was monitored for two years. Soil micro-topography was characterized by means of photogrammetry on a monthly basis. Soil roughness was characterized by the variogram, the surface stream network was characterized by network-based indices and overland flow connectivity was characterized by Relative Surface Connection function (RSCf) functional connectivity indicator. Overland flow hydrographs were generated by means of a physically-based overland flow model based on 1-cm resolution digital elevation models. The development of eroded flow paths at the soil surface not only reduced the delay in overland flow generation but also resulted in a higher continuity of high flow velocity paths, an increase in erosive energy and a higher rate of increase of the overland flow hydrograph. Overland flow dynamics were found to be highly correlated to the RSCf characteristic points. By providing information regarding overland flow dynamics, the RSCf may thus serve as a quantitative link between soil roughness and overland flow generation in order to improve the overland flow hydrograph prediction. Elevation at the grid location (i, j) (mm) z(x)Elevation of the point x (mm)

A major challenge in present-day hydrological sciences is to enhance the performance of existing distributed hydrological models through a better description of subgrid processes, in particular the subgrid connectivity of flow paths. The Relative Surface Connection (RSC) function was proposed by Antoine et al. (2009) as a functional indicator of runoff flow connectivity. For a given area, it expresses the percentage of the surface connected to the outflow boundary (<i>C</i>) as a function of the degree of filling of the depression storage. This function explicitly integrates the flow network at the soil surface and hence provides essential information regarding the flow paths' connectivity. It has been shown that this function could help improve the modeling of the hydrograph at the square meter scale, yet it is unknown how the scale affects the RSC function, and whether and how it can be extrapolated to other scales. The main objective of this research is to study the scale effect on overland flow connectivity (RSC function). For this purpose, digital elevation data of a real field (9 &times; 3 m) and three synthetic fields (6 &times; 6 m) with contrasting hydrological responses were used, and the RSC function was calculated at different scales by changing the length (<i>l</i>) or width (<i>w</i>) of the field. To different extents depending on the microtopography, border effects were observed for the smaller scales when decreasing <i>l</i> or <i>w</i>, which resulted in a strong decrease or increase of the maximum depression storage, respectively. There was no scale effect on the RSC function when changing <i>w</i>, but a remarkable scale effect was observed in the RSC function when changing <i>l</i>. In general, for a given degree of filling of the depression storage, <i>C</i> decreased as <i>l</i> increased, the change in <i>C</i> being inversely proportional to the change in <i>l</i>. However, this observation applied only up to approx. 50–70% (depending on the hydrological response of the field) of filling of depression storage, after which no correlation was found between <i>C</i> and <i>l</i>. The results of this study help identify the minimal scale to study overland flow connectivity. At scales larger than the minimal scale, the RSC function showed a great potential to be extrapolated to other scales

Abstract. Improved skill of long-range weather forecasts has motivated an increasing effort towards developing seasonal hydrological forecasting systems across Europe. Among other purposes, such forecasting systems are expected to support better water management decisions. In this paper we evaluate the potential use of a real-time optimization system (RTOS) informed by seasonal forecasts in a water supply system in the UK. For this purpose, we simulate the performances of the RTOS fed by ECMWF seasonal forecasting systems (SEAS5) over the past 10 years, and we compare them to a benchmark operation that mimics the common practices for reservoir operation in the UK. We also attempt to link the improvement of system performances, i.e. the forecast value, to the forecast skill (measured by the mean error and the continuous ranked probability skill score) as well as to the bias correction of the meteorological forcing, the decision maker priorities, the hydrological conditions and the forecast ensemble size. We find that in particular the decision maker priorities and the hydrological conditions exert a strong influence on the forecast skill–value relationship. For the (realistic) scenario where the decision maker prioritizes the water resource availability over energy cost reductions, we identify clear operational benefits from using seasonal forecasts, provided that forecast uncertainty is explicitly considered by optimizing against an ensemble of 25 equiprobable forecasts. These operational benefits are also observed when the ensemble size is reduced up to a certain limit. However, when comparing the use of ECMWF-SEAS5 products to ensemble streamflow prediction (ESP), which is more easily derived from historical weather data, we find that ESP remains a hard-to-beat reference, not only in terms of skill but also in terms of value.

Agricultural land management requires strategies to reduce impacts on soil and water resources while maintaining food production. Models that capture the effects of agricultural and conservation practices on soil erosion and sediment delivery can help to address this challenge. Historic records of climatic variability and agricultural change over the last century also offer valuable information for establishing extended baselines against which to evaluate management scenarios. Here, we present an approach that combines centennial-scale reconstructions of climate and agricultural land cover with modelling across four lake catchments in the UK where radiometric dating provides a record of lake sedimentation. We compare simulations using MMF-TWI, a catchment-scale model developed for humid agricultural landscapes that incorporates representation of seasonal variability in vegetation cover, soil water balance, runoff and sediment contributing areas. MMF-TWI produced mean annual sediment exports within 9-20% of sediment core-based records without calibration and using guide parameter values to represent vegetation cover. Simulations of land management scenarios compare upland afforestation and lowland field-scale conservation measures to reconstructed historic baselines. Oak woodland versus conifer afforestation showed similar reductions in mean annual surface runoff (8-16%) compared to current moorland vegetation but a larger reduction in sediment exports (26-46 versus 4-30%). Riparian woodland buffers reduced upland sediment yields by 15-41%, depending on understorey cover levels, but had only minor effect on surface runoff. Planting of winter cover crops in the lowland arable catchment halved historic sediment exports. Permanent grass margins applied to sets of arable fields across 15% or more of the catchment led to further significant reduction in exports. Our findings show the potential for reducing sediment delivery at the catchment scale with land management interventions. We also demonstrate how MMF-TWI can support hydrologically-informed decision making to better target conservation measures in humid agricultural environments.