Summary Bulk density was determined indirectly in peat samples by X‐ray computed tomography (X‐ray CT) and compared with density values obtained by standard laboratory methods. Five Histosols were collected in the same cut‐over peatland, representing various degrees of disturbance related to the process of peat extraction. Soil cores were fully imaged by X‐ray CT with a voxel size of about 0.25 mm. Each one of these five attenuation profiles was analysed and compared with direct density measurements. A linear relationship, to convert attenuation values into density values, is proposed to determine the variation in bulk density with a spatial resolution clearly greater than standard laboratory determinations. It is also shown that X‐ray‐based density values can be effectively used to characterize the structure of peat soils and the possible consequences of disturbances after drainage and peat mining. Under the accepted limitations of the method, X‐ray CT opens up new opportunities to determine the structural quality of peat and to monitor its modifications with time. This indirect diagnostic could be particularly useful to study peatlands' hydraulic systems or evaluate the effectiveness of restoration measures.
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Extreme flood simulation with synthetic extreme precipitation events raises unavoidable questions about the choice of initial conditions. State-of-the-art extreme flood estimation frameworks propose to address these questions with the help of semicontinuous modeling and reanalysis of simulated state variables. In this context, the present work proposes a new method for the selection of initial conditions for extreme flood simulation. The method is based on generating sets of initial conditions from the matrix of state variables corresponding to a long simulation run of the selected hydrological model. Two sets of initial conditions are obtained: a deterministic set composed of selected state variable quantiles and a stochastic set composed of state variable vectors randomly drawn from the complete state variable matrix. The extreme flood simulations corresponding to both sets are compared in detail, and the stochastic simulations are used in a sensitivity analysis to identify the dominant state variables and possible interactions. The aim hereby is to provide a tool to analyze the role of initial conditions and the importance to account for state variable interactions in extreme flood estimation. The proposed method is applied to probable maximum flood estimation for the Swiss Mattmark Dam catchment with a semilumped hydrological model. The obtained results for this case study show that for high flood peak quantiles, the initial soil saturation is dominating other state variables, and deterministic initial conditions are sufficient to generate extreme floods.
Abstract. In this paper, a case study on the estimations of extreme floods is described. The watershed chosen for the analysis is the catchment of the Limmernboden dam situated in Switzerland. Statistical methods and the simulation based "Probable Maximum Precipitation -Probable maximum Flood" (PMP-PMF) approach are applied for the estimation of the safety flood according to the Swiss flood directives. The results of both approaches are compared in order to determine the discrepancies between them. It can be outlined that the PMP-PMF method does not always overestimate the flood.
a b s t r a c tExtreme floods are commonly estimated with the help of design storms and hydrological models. In this paper, we propose a new method to take into account the relationship between precipitation intensity (P) and air temperature (T) to account for potential snow accumulation and melt processes during the elaboration of design storms. The proposed method is based on a detailed analysis of this P-T relationship in the Swiss Alps. The region, no upper precipitation intensity limit is detectable for increasing temperature. However, a relationship between the highest measured temperature before a precipitation event and the duration of the subsequent event could be identified. An explanation for this relationship is proposed here based on the temperature gradient measured before the precipitation events. The relevance of these results is discussed for an example of Probable Maximum Precipitation-Probable Maximum Flood (PMP-PMF) estimation for the high mountainous Mattmark dam catchment in the Swiss Alps.The proposed method to associate a critical air temperature to a PMP is easily transposable to similar alpine settings where meteorological soundings as well as ground temperature and precipitation measurements are available. In the future, the analyses presented here might be further refined by distinguishing between precipitation event types (frontal versus orographic).
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