How can expert knowledge increase the realism of conceptual hydrological models? : a case study based on the concept of dominant runoff process in the Swiss Pre-Alps

Both modellers and experimentalists agree that using expert knowledge can improve the realism of conceptual hydrological models. However, their use of expert knowledge differs for each step in the modelling procedure, which involves hydrologically mapping the dominant runoff processes (DRPs) occurring on a given catchment, parameterising these processes within a model, and allocating its parameters. Modellers generally use very simplified mapping approaches, applying their knowledge in constraining the model by defining parameter and process relational rules. In contrast, experimentalists usually prefer to invest all their detailed and qualitative knowledge about processes in obtaining as realistic spatial distribution of DRPs as possible, and in defining narrow value ranges for each model parameter. Runoff simulations are affected by equifinality and numerous other uncertainty sources, which challenge the assumption that the more expert knowledge is used, the better will be the results obtained. To test for the extent to which expert knowledge can improve simulation results under uncertainty, we therefore applied a total of 60 modelling chain combinations forced by five rainfall datasets of increasing accuracy to four nested catchments in the Swiss Pre-Alps. These datasets include hourly precipitation data from automatic stations interpolated with Thiessen polygons and with the inverse distance weighting (IDW) method, as well as different spatial aggregations of Combiprecip, a combination between ground measurements and radar quantitative estimations of precipitation. To map the spatial distribution of the DRPs, three mapping approaches with different levels of involvement of expert knowledge were used to derive so-called process maps. Finally, both a typical modellers' top-down set-up relying on parameter and process constraints and an experimentalists' set-up based on bottom-up thinking and on field expertise were implemented using a newly developed process-based runoff generation module (RGM-PRO). To quantify the uncertainty originating from forcing data, process maps, model parameterisation, and parameter allocation strategy, an analysis of variance (ANOVA) was performed. The simulation results showed that (i) the modelling chains based on the most complex process maps performed slightly better than those based on less expert knowledge; (ii) the bottom-up set-up performed better than the top-down one when simulating short-duration events, but similarly to the top-down set-up when simulating long-duration events; (iii) the differences in performance arising from the different forcing data were due to compensation effects; and (iv) the bottom-up set-up can help identify uncertainty sources, but is prone to overconfidence problems, whereas the top-down set-up seems to accommodate uncertainties in the input data best. Overall, modellers' and experimentalists' concept of model realism differ. This means that the level of detail a model should have to accurately reproduce the DRPs expected must be agreed in advance

Structural distortions in the Euro interbank market: The role of ‘key players’ during the recent market turmoil

We study the frictions in the patterns of trades in the Euro money market. We characterize the structure of lending relations during the period of recent financial turmoil. We use network-topology method on data from overnight transactions in the Electronic Market for Interbank Deposits (e-Mid) to investigate on two main issues. First, we characterize the division of roles between borrowers and lenders in long-run relations by providing evidence on network formation at a yearly frequency. Second, we identify the ‘key players’ in the marketplace and study their behaviour. Key players are ‘locally-central banks’ within a network that lend (or borrow) large volumes to (from) several counterparties, while borrowing (or lending) small volumes from (to) a small number of institutions. Our results are twofold. We show that the aggregate trading patterns in e-Mid are characterized by largely asymmetric relations. This implies a clear division of roles between lenders and borrowers. Second, the key players do not exploit their position of network leaders by imposing opportunistic pricing policies. We find that only a fraction of the networks composed by big players are characterized by interest rates that are statistically different from the average market rate throughout the turmoil period

Drivers of demand and supply in the Euro interbank market: the role of “Key Players” during the recent turmoil

We study frictions in trading patterns in the Euro money market. We characterize the structure of lending relations during the period of recent financial turmoil. We use a network-topology method on data from overnight transactions in the ElectronicMarket for Interbank Deposits (e-MID) to investigate two main issues. First, we characterize the roles of borrowers and lenders in long-run relationships by providing evidence on network formation at a 3-month frequency. Second, we identify the “key players” in the marketplace and study their behavior. In our formalization, key players are “locally-central banks” within a network that lend (or borrow) large volumes to (from) several counterparties, while borrowing (or lending) small volumes from (to) a small number of institutions. Our results are twofold.We show that the aggregate trading patterns in e-MID are characterized by largely asymmetric relations. This implies a clear difference in the roles of lenders and borrowers, with market positions changing only gradually over time.We also find that the large net lenders exploit their positions as network leaders by imposing aggressive pricing policies on their counterparties

Mapping dominant runoff processes : an evaluation of different approaches using similarity measures and synthetic runoff simulations

The identification of landscapes with similar hydrological behaviour is useful for runoff and flood predictions in small ungauged catchments. An established method for landscape classification is based on the concept of dominant runoff process (DRP). The various DRP-mapping approaches differ with respect to the time and data required for mapping. Manual approaches based on expert knowledge are reliable but time-consuming, whereas automatic GIS-based approaches are easier to implement but rely on simplifications which restrict their application range. To what extent these simplifications are applicable in other catchments is unclear. More information is also needed on how the different complexities of automatic DRP-mapping approaches affect hydrological simulations. In this paper, three automatic approaches were used to map two catchments on the Swiss Plateau. The resulting maps were compared to reference maps obtained with manual mapping. Measures of agreement and association, a class comparison, and a deviation map were derived. The automatically derived DRP maps were used in synthetic runoff simulations with an adapted version of the PREVAH hydrological model, and simulation results compared with those from simulations using the reference maps. The DRP maps derived with the automatic approach with highest complexity and data requirement were the most similar to the reference maps, while those derived with simplified approaches without original soil information differed significantly in terms of both extent and distribution of the DRPs. The runoff simulations derived from the simpler DRP maps were more uncertain due to inaccuracies in the input data and their coarse resolution, but problems were also linked with the use of topography as a proxy for the storage capacity of soils. The perception of the intensity of the DRP classes also seems to vary among the different authors, and a standardised definition of DRPs is still lacking. Furthermore, we argue not to use expert knowledge for only model building and constraining, but also in the phase of landscape classification

One century of hydrological monitoring in two small catchments with different forest coverage

Long-term data on precipitation and runoff are essential to draw firm conclusions about the behavior and trends of hydrological catchments that may be influenced by land use and climate change. Here the longest continuous runoff records from small catchments (<1km2) in Switzerland (and possibly worldwide) are reported. The history of the hydrological monitoring in the Sperbel- and Rappengraben (Emmental) is summarized, and inherent uncertainties in the data arising from the operation of the gauges are described. The runoff stations operated safely for more than 90% of the summer months when most of the major flood events occurred. Nevertheless, the absolute values of peak runoff during the largest flood events are subject to considerable uncertainty. The observed differences in average, base, and peak runoff can only partly be attributed to the substantial differences in forest coverage. This treasure trove of data can be used in various ways, exemplified here with an analysis of the generalized extreme value distributions of the two catchments. These distributions, and hence flood return periods, have varied greatly in the course of one century, influenced by the occurrence of single extreme events. The data will be made publicly available for the further analysis of the mechanisms governing the runoff behavior of small catchments, as well as for testing stochastic and deterministic model

Exploring the Use of European Weather Regimes for Improving User-Relevant Hydrological Forecasts at the Subseasonal Scale in Switzerland

Across the globe, there has been an increasing interest in improving the predictability of subseasonal hydrometeorological forecasts, as they play a valuable role in medium- to long-term planning in many sectors, such as agriculture, navigation, hydropower, and emergency management. However, these forecasts still have very limited skill at the monthly time scale; hence, this study explores the possibilities for improving forecasts through different pre- and postprocessing techniques at the interface with a Precipitationn–Runoff–Evapotranspiration Hydrological Response Unit Model (PREVAH). Specifically, this research aims to assess the benefit of European weather regime (WR) data within a hybrid forecasting setup, a combination of a traditional hydrological model and a machine learning (ML) algorithm, to improve the performance of subseasonal hydrometeorological forecasts in Switzerland. The WR data contain information about the large-scale atmospheric circulation in the North Atlantic–European region, and thus allow the hydrological model to exploit potential flow-dependent predictability. Four hydrological variables are investigated: total runoff, baseflow, soil moisture, and snowmelt. The improvements in the forecasts achieved with the pre- and postprocessing techniques vary with catchments, lead times, and variables. Adding WR data has clear benefits, but these benefits are not consistent across the study area or among the variables. The usefulness of WR data is generally observed for longer lead times, e.g., beyond the third week. Furthermore, a multimodel approach is applied to determine the “best practice” for each catchment and improve forecast skill over the entire study area. This study highlights the potential and limitations of using WR information to improve subseasonal hydrometeorological forecasts in a hybrid forecasting system in an operational mode

Importance of maximum snow accumulation for summer low flows in humid catchments

Winter snow accumulation obviously has an effect on the following catchment runoff. The question is, however, how long this effect lasts and how important it is compared to rainfall inputs. Here we investigate the relative importance of snow accumulation on one critical aspect of runoff, namely the summer low flow. This is especially relevant as the expected increase of air temperature might result in decreased snow storage. A decrease of snow will affect soil and groundwater storages during spring and might cause low streamflow values in the subsequent warm season. To understand these potential climate change impacts, a better evaluation of the effects of inter-annual variations in snow accumulation on summer low flow under current conditions is central. The objective in this study was (1) to quantify how long snowmelt affects runoff after melt-out and (2) to estimate the sensitivity of catchments with different elevation ranges to changes in snowpack. To find suitable predictors of summer low flow we used long time series from 14 Alpine and pre-Alpine catchments in Switzerland and computed different variables quantifying winter and spring snow conditions. In general, the results indicated that maximum winter snow water equivalent (SWE) influenced summer low flow, but could expectedly only partly explain the observed inter-annual variations. On average, a decrease of maximum SWE by 10 % caused a decrease of minimum discharge in July by 6–9 % in catchments higher than 2000 m a.s.l. This effect was smaller in middle- and lower-elevation catchments with a decrease of minimum discharge by 2–5 % per 10 % decrease of maximum SWE. For higher- and middle-elevation catchments and years with below-average SWE maximum, the minimum discharge in July decreased to 70–90 % of its normal level. Additionally, a reduction in SWE resulted in earlier low-flow occurrence in some cases. One other important factor was the precipitation between maximum SWE and summer low flow. When only dry preceding conditions in this period were considered, the importance of maximum SWE as a predictor of low flows increased. We assessed the sensitivity of individual catchments to the change of maximum SWE using the non-parametric Theil–Sen approach as well as an elasticity index. Both sensitivity indicators increased with increasing mean catchment elevation, indicating a higher sensitivity of summer low flow to snow accumulation in Alpine catchments compared to lower-elevation pre-Alpine catchments

Multi-year time series of daily solute and isotope measurements from three Swiss pre-Alpine catchments

Time series analyses of solute concentrations in streamwater and precipitation are powerful tools for unraveling the interplay of hydrological and biogeochemical processes at the catchment scale. While such datasets are available for many sites around the world, they often lack the necessary temporal resolution or are limited in the number of solutes they encompass. Here we present a multi-year dataset encompassing daily records of major ions and a range of trace metals in both streamwater and precipitation in three catchments in the northern Swiss Pre-Alps. These time series capture the temporal variability observed in solute concentrations in response to storm events, snow melt, and dry summer conditions. This dataset additionally includes stable water isotope data as an extension of a publicly available isotope dataset collected concurrently at the same locations, and together these data can provide insights into a range of ecohydrological processes and enable a suite of analyses into hydrologic and biogeochemical catchment functioning