Quantifying shape and multiscale structure of meanders with wavelets

features in the landscape, but the processes shaping them, act on a wide range of spatial and temporal scales. This results in meanders that curve at several spatial scales with smaller scale curves embedded in larger scale curves. Here, we show how to quantify the multi-scale structure of meanders from the valley scale until the sub-meander scale based on continuous wavelet transforms of the planform curvature. The zero crossings and maximum lines of the wavelet transform capture the main characteristics of the meander shape and their structure is quantified in a scale-space tree (Figure 1). The tree is used to identify meander wavelength and how meanders are embedded in larger scale features. The submeander structure determines meander shape, which is quantified with two parameters: skewness and fattening. The method is applied to the Mahakam River planform, which features very sharp, angular bends. Strong negative fattening is found for this river which corresponds to angular non-harmonic meanders which are characterized by strong flow recirculation and deep scouring

Geostatistical simulation of two-dimensional fields of raindrop size distributions at the meso-¿ scale

The large variability of the raindrop size distribution (DSD) in space and time must be taken into account to improve remote sensing of precipitation. The ability to simulate a large number of 2-D fields of DSDs sharing the same statistical properties provides a very useful simulation framework that nicely complements experimental approaches based on DSD ground measurements. These simulations can be used to investigate radar beam propagation through rain and to evaluate different radar retrieval techniques. The proposed approach uses geostatistical methods to provide structural analysis and stochastic simulation of DSD fields. First, the DSD is assumed to follow a Gamma distribution with three parameters. As a consequence, 2-D fields of DSDs can be described as a multivariate random function. The parameters are normalized using a Gaussian anamorphosis and simulated by taking advantage of fast Gaussian simulation algorithms. Variograms are used to characterize the spatial structure of the DSD fields. The generated fields have identical spatial structure and are consistent with the observations. Because intermittency cannot be simulated using this technique, the size of the simulation domain is limited to the meso-¿ scale (2-20 km). To assess the proposed approach, the method is applied to data collected during intense Mediterranean rainfall. Taylor's hypothesis is invoked to convert time series into 1-D range profiles. The anisotropy of the fields is derived from radar measurements. Simulated and measured reflectivity fields are in good agreement with respect to the mean, the standard deviation, and the spatial structure, demonstrating the promising potential of the proposed stochastic model of DSD field

Soil moisture storage and hillslope stability

International audienceRecently, we presented a steady-state analytical hillslope stability model to study rain-induced shallow landslides. This model is based on kinematic wave dynamics of saturated subsurface storage and the infinite slope stability assumption. Here we apply the model to investigate the effect of neglecting the unsaturated storage on the assessment of slope stability in the steady-state hydrology. For that purpose we extend the hydrological model to compute the soil pore pressure distribution over the entire flow domain. We also apply this model for hillslopes with non-constant soil depth to compare the stability of different hillslopes and to find the critical slip surface in hillslopes with different geometric characteristics. In order to do this, we incorporate more complex approaches to compute slope stability (Janbu's non-circular method and Bishop's simplified method) in the steady-state analytical hillslope stability model. We compare the safety factor (FS) derived from the infinite slope stability method and the more complex approach for two cases: with and without the soil moisture profile in the unsaturated zone. We apply this extended hillslope stability model to nine characteristic hillslope types with three different profile curvatures (concave, straight, convex) and three different plan shapes (convergent, parallel, divergent). Overall, we find that unsaturated zone storage does not play a critical role in determining the factor of safety for shallow and deep landslides. As a result, the effect of the unsaturated zone storage on slope stability can be neglected in the steady-state hydrology and one can assume the same bulk specific weight below and above the water table. We find that steep slopes with concave profile and convergent plan shape have the least stability. We also demonstrate that in hillslopes with non-constant soil depth (possible deep landslides), the ones with convex profiles and convergent plan shapes have slip surfaces with the minimum safety factor near the outlet region. In general, when plan shape changes from divergent to convergent, stability decreases for all length profiles. Finally, we show that the applied slope stability methods and steady-state hydrology model based on the relative saturated storage can be used safely to investigate the relation between hillslope geometry and hillslope stability

Simulating Rhine river discharges using a land surface model

The Rhine basin will shift from a combined rainfall and snow melt regime to a more rainfall dominated regime, resulting in increased flood risk in winter and a higher probability of extensive droughts in summer. To be prepared for these projected changes, a thorough understanding of the impacts of climate change to the hydrological regime is necessary. A model has been presented at CAIWA conference at Basel, 12-15 November 2007

Mixtures of Gaussians for uncertainty description in bivariate latent heat flux proxies

This paper proposes a new probabilistic approach for describing uncertainty in the ensembles of latent heat flux proxies. The proxies are obtained from hourly Bowen ratio and satellite-derived measurements, respectively, at several locations in the southern Great Plains region in the United States. The novelty of the presented approach is that the proxies are not considered separately, but as bivariate samples from an underlying probability density function. To describe the latter, the use of Gaussian mixture density models¿a class of nonparametric, data-adaptive probability density functions¿is proposed. In this way any subjective assumptions (e.g., Gaussianity) on the form of bivariate latent heat flux ensembles are avoided. This makes the estimated mixtures potentially useful in nonlinear interpolation and nonlinear probabilistic data assimilation of noisy latent heat flux measurements. The results in this study show that both of these applications are feasible through regionalization of estimated mixture densities. The regionalization scheme investigated here utilizes land cover and vegetation fraction as discriminatory variables