Assessing the value of seasonal hydrological forecasts for improving water resource management:insights from a pilot application in the UK

Improved skill of long-range weather forecasts have 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 optimisation 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 ten 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 other factors such as bias correction, the decision maker priorities, hydrological conditions and level of uncertainty consideration. We find that some of these factors control the forecast value much more strongly than the forecast skill. For the (realistic) scenario where the decision-maker prioritises water resource availability over energy cost reductions, we identify clear operational benefits from using seasonal forecasts, provided that forecast uncertainty is explicitly considered. However, when comparing the use of ECMWF-SEAS5 products to ensemble streamflow predictions (ESP), which are 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

Towards improving subgrid surface flow hydrograph estimation using the relative surface connection function as a connectivity indicator

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 function (RSCf) was proposed by Antoine et al. (2009) as a functional indicator of overland flow connectivity. This function performs better compared to previous connectivity indicators and it can be potentially integrated in hillslope or watershed models as a descriptive function of subgrid overland flow dynamics. Nevertheless, several issues remained to be addressed, which was the subject of the present research. First, it was as yet unknown how changes in scale affect the RSCf and whether it can be extrapolated to other scales. We found that both scale effects and border effects affect the RSCf at the plot scale and a minimal scale to study overland flow connectivity was identified. The RSCf showed a great potential to be extrapolated to larger scales. Secondly, it was also unknown how the RSCf is affected by surface roughness and slope and whether these effects can be predicted. Results showed that the characteristic parameters of the RSCf are greatly influenced by surface roughness and slope. Based on a simple rectangular conceptualization of surface roughness, predictive equations relating the RSCf in function of slope and roughness were established. Finally, it was still unknown how the RSCf evolves after erosion processes and to what extent changes in the RSCf reflect changes in overland flow generation. The RSCf showed important changes in terms of both maximum depression storage and shape. These changes were found to be highly correlated to the delay and the rate of increase of the hydrograph, flow velocities continuity and erosion energy.(AGRO - Sciences agronomiques et ingénierie biologique) -- UCL, 201

How do slope and surface roughness affect plot-scale overland flow connectivity?

Surface micro-topography and slope drive the hydrological response of plots through the gradual filling of depressions as well as the establishment of hydraulic connections between overflowing depressions. Therefore, quantifying and understanding the effects of surface roughness and slope on plot-scale Overland flow connectivity is crucial to improve current hydrological modeling and runoff prediction. This study aimed at establishing predictive equations relating structural and functional connectivity indicators in function of slope and roughness. The Relative Surface Connection function (RSCf) was used as a functional connectivity indicator was applied. Three characteristic parameters were defined to characterize the RSCf: the surface initially connected to the outlet, the connectivity threshold and the maximum depression storage (DSmax). Gaussian surface elevation fields (6 m 6 m) were generated for a range of slopes and roughnesses (sill r and range R of the variogram). A full factorial of 6 slopes (0–15%), 6 values of R (50–400 mm) and 6 values of r (2–40 mm) was considered, and the RSCf calculated for 10 realizations of each combination. Results showed that the characteristic parameters of the RSCf are greatly influenced by R, r and slope. At low slopes and high ratios of r/2R, the characteristic parameters of the RSCf appear linked to a single component of the surface roughness (R or r). On the contrary, both R and r are needed to predict the RSCf at high slopes and low ratios of r/2R. A simple conceptualization of surface depressions as rectangles, whose shape was determined by R and r, allowed deriving simple mathematical expressions to estimate the characteristic parameters of the RSCf in function of R, r and slope. In the case of DSmax, the proposed equation performed better than previous empirical expressions found in the literature which do not account for the horizontal component of the surface roughness. The proposed expressions allow estimating the characteristic points of the RSCf with reasonable accuracy and could therefore prove useful for integrating plot-scale overland flow connectivity into hydrological models whenever the RSCf presents a well-defined connectivity threshold

Effect of Soil Roughness on Overland Flow Connectivity at Different Slope Scenarios

Runoff generation, which involves the gradual depression filling and connection of overflowing depressions, is affected by surface roughness and slope. Therefore, quantifying and understanding the effects of surface roughness and slope on overland flow connectivity at the sub-grid scale can potentially improve current hydrological modeling and runoff prediction. However, little work has been conducted on quantifying these effects. This study examines the role of surface roughness on overland flow connectivity at the plot scale at different slopes. For this purpose, standard multi-Gaussian synthetic fields (6 × 6 m) with contrasting surface roughnesses, as defined by the parameters of the variogram (sill and range) of surface elevation, were used. In order to quantify the effects of soil roughness and slope on overland flow connectivity a functional connectivity indicator, so-called the Relative Surface Connection function (Antoine et al., 2009), was applied. This indicator, that represents the ratio of area connected to the outflow boundary (C) in function of the depression storage (DS), is able to capture runoff-relevant connectivity properties. Three parameters characterizing the connectivity function were used to quantify the effects of roughness and slope. These parameters are: C at DS = 0 (CDS=0), connectivity threshold (CT) and maximum depression storage (MDS). Results showed that variations on soil roughness and slope greatly affect the three parameters showing in some cases a clear relationship between structural connectivity and functional connectivity, such as between the ratio sill/range and MDS and between CDS=0 and range. This relationship, described by mathematical expressions, not only allows the quantification and comparison of the effects of soil roughness and slope in overland flow connectivity but also the prediction of these effects by the study of the variogram

Scale effect on overland flow connectivity at the interill scale

The relative surface connection function (RSC) 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 outlet (C) 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 path’ connectivity. It has been shown that this function could help improve the modeling of the hydrogram 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 x 3 m) and three synthetic fields (6 x 6 m) with contrasting hydrological responses was used, and the RSC function was calculated at different scales by changing the length (L) or width (1) of the field. Border effects were observed for the smaller scales. In most of cases, for L or I smaller than 750mm, increasing L or 1, resulted in a strong increase or decrease of the maximum depression storage, respectively. There was no scale effect on the RSC function when changing 1. On the contrary, a remarkable scale effect was observed in the RSC function when changing L. In general, for a given degree of fihling of the depression storage, C decreased as L increased. This change in C was inversely proportional to the change in L. This observation applied only up to approx. 50-70% (depending on the hydrological response of the field) of fihling of depression storage, after which no correlation was found between C and L. The results of this study help identify the critical scale to study overland flow connectivity. At scales larger than the critical scale, the RSC function showed a great potential to be extrapolated to other scales. _____________________

Skill of seasonal flow forecasts at catchment scale: an assessment across South Korea

Recent advancements in numerical weather predictions have improved forecasting performance at longer lead times. Seasonal weather forecasts, providing predictions of weather variables for the next several months, have gained significant attention from researchers due to their potential benefits for water resources management. Many efforts have been made to generate Seasonal Flow Forecasts (SFFs) by combining seasonal weather forecasts and hydrological models. However, producing SFFs with good skill at a finer catchment scale remains challenging, hindering their practical application and adoption by water managers. Consequently, water management decisions, both in South Korea and numerous other countries, continue to rely on worst-case scenarios and the conventional Ensemble Streamflow Prediction (ESP) method.This study investigates the potential of SFFs in South Korea at the catchment scale, examining 12 reservoir catchments of varying sizes (ranging from 59 to 6648 km2) over the last decade (2011-2020). Seasonal weather forecasts data (including precipitation, temperature and evapotranspiration) from the European Centre for Medium-Range Weather Forecasts (ECMWF system5) is used to drive the Tank model (conceptual hydrological model) for generating the flow ensemble forecasts. We assess the contribution of each weather variable to the performance of flow forecasting by isolating individual variables. In addition, we quantitatively evaluate the overall skill of SFFs, representing the probability of outperforming the benchmark (ESP), using the Continuous Ranked Probability Skill Score (CRPSS). Our results highlight that precipitation is the most important variable in determining the performance of SFFs, and temperature also plays a key role during the dry season in snow-affected catchments. Given the coarse resolution of seasonal weather forecasts, a linear scaling method to adjust the forecasts is applied, and it is found that bias correction is highly effective in enhancing the overall skill. Furthermore, bias corrected SFFs have skill with respect to ESP up to 3 months ahead, this being particularly evident during abnormally dry years. To facilitate future applications in other regions, the code developed for this analysis has been made available as an open-source Python package

Scale effect on Overland flow connectivity, at the interill scale

The relative surface connection function (RSC) 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 outlet (C) 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 hydrogram 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 x 3 m) and three synthetic fields (6 x 6 m) with contrasting hydrological responses was used, and the RSC function was calculated at different scales by changing the length (L) or width (l) of the field. Border effects were observed for the smaller scales. In most of cases, for L or l smaller than 750mm, increasing L or l, resulted in a strong increase or decrease of the maximum depression storage, respectively. There was no scale effect on the RSC function when changing l. On the contrary, a remarkable scale effect was observed in the RSC function when changing L. In general, for a given degree of filling of the depression storage, C decreased as L increased. This change in C was inversely proportional to the change in L. 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 C and L. The results of this study help identify the critical scale to study overland flow connectivity. At scales larger than the critical scale, the RSC function showed a great potential to be extrapolated to other scales

Effect of sheet and rill erosion on overland flow connectivity in bare agricultural plots

Rill erosion processes generate preferential flow paths that may increase the degree of connectivity of the soil surface and hence strongly modify its hydrological response. However, few studies have tried to quantify the effect of rill development on overland flow connectivity. For this purpose, changes in surface microtopography were monitored on three bare agricultural plots (3 m wide x 10 m long and 11% of slope) in Louvain-la-Neuve (Belgium) under natural rainfall conditions