Local sensitivity analysis for compositional data with application to soil texture in hydrologic modelling

Compositional data, such as soil texture, are hard to deal with in the geosciences as standard statistical methods are often inappropriate to analyse this type of data. Especially in sensitivity analysis, the closed character of the data is often ignored. To that end, we developed a method to assess the local sensitivity of a model output with resect to a compositional model input. We adapted the finite difference technique such that the different parts of the input are perturbed simultaneously while the closed character of the data is preserved. This method was applied to a hydrologic model and the sensitivity of the simulated soil moisture content to local changes in soil texture was assessed. Based on a high number of model runs, in which the soil texture was varied across the entire texture triangle, we identified zones of high sensitivity in the texture triangle. In such zones, the model output uncertainty induced by the discrepancy between the scale of measurement and the scale of model application, is advised to be reduced through additional data collection. Furthermore, the sensitivity analysis provided more insight into the hydrologic model behaviour as it revealed how the model sensitivity is related to the shape of the soil moisture retention curve

Aqueous ZrO 2 and YSZ colloidal systems through microwave assisted hydrothermal synthesis

In this paper, the formation of ZrO2 and yttria-stabilised-zirconia (YSZ) aqueous colloidal systems via microwave assisted hydrothermal synthesis is studied. Microwave synthesis allows a fast screening of the influence of different parameters such as time and temperature. The temperature varied from 140 degrees C up to 180 degrees C and the used reaction time varied from 5 min up to 1 h. The synthesised zirconia nanoparticles have a particle size of 50 nm confirmed by TEM. A H-1 NMR (nuclear magnetic resonance) study helped to understand the stabilization mechanism of the synthesised particles. By the addition of ytrrium ions into the zirconia colloidal solution, YSZ could be formed via an additional thermal treatment. Hereby, the samples are heated up to 400 degrees C for 1 h. YSZ colloidal solutions are synthesised by making use of complexing agents such as nitrilotriacetic acid, ethylenediaminetetraacetic acid and citric acid to control the hydrolysis and condensation of both ions to avoid non-stoichiometric phases. The ratio of Zr/Y in the particles is quantified by XRF. The amorphous structure of those particles necessitates an additional thermal treatment up to 600 degrees C during 1 h in order to obtain crystalline YSZ

Effective drought communication : using the past to assess the present and anticipate the future

Especially during drought events, it is important that water gets properly allocated and is not misused or wasted. For an effective drought management, it is thus of utmost importance to raise the awareness of water managers as well as the general public about the drought’s severity. In this paper, we provide two possible sources of information that can be used to communicate about drought events. To illustrate our approach, we make use of drought events that were identified in preceding work as connected components in space and time through the use of operators from the field of mathematical morphology and summarized in terms of characteristics such as affected area, duration and intensity. We demonstrate how these drought characteristics can be used to query for historical drought events that are most similar to an ongoing event, such that lessons learnt from the management of past events can be incorporated in the management of the ongoing event. Further, we also demonstrate how a probabilistic model describing the dependence structure between the drought characteristics is identified and how such model can serve as a basis to estimate the severity of the event. Both approaches provide information that can be used to communicate to laymen about the severity of the ongoing drought, which will help them to anticipate the future

A coupled stochastic rainfall-evapotranspiration model for hydrological impact analysis

A hydrological impact analysis concerns the study of the consequences of certain scenarios on one or more variables or fluxes in the hydrological cycle. In such an exercise, discharge is often considered, as floods originating from extremely high discharges often cause damage. Investigating the impact of extreme discharges generally requires long time series of precipitation and evapotranspiration to be used to force a rainfall-runoff model. However, such kinds of data may not be available and one should resort to stochastically generated time series, even though the impact of using such data on the overall discharge, and especially on the extreme discharge events, is not well studied. In this paper, stochastically generated rainfall and corresponding evapotranspiration time series, generated by means of vine copulas, are used to force a simple conceptual hydrological model. The results obtained are comparable to the modelled discharge using observed forcing data. Yet, uncertainties in the modelled discharge increase with an increasing number of stochastically generated time series used. Notwithstanding this finding, it can be concluded that using a coupled stochastic rainfall-evapotranspiration model has great potential for hydrological impact analysis

Stochastic simulation of precipitation-consistent daily reference evapotranspiration using vine copulas

Evapotranspiration is an important process in the water cycle that represents a considerable amount of moisture lost to the atmosphere through evaporation from the soil and wet surfaces, and transpiration from plants. Therefore, several water management methods, such as irrigation scheduling and hydrological impact analysis, rely on an accurate estimation of evapotranspiration rates. Often, daily reference evapotranspiration is modelled based on the Penman, Priestley–Taylor or Hargraeves equation. However, each of these models requires extensive input data, such as daily mean temperature, wind speed, relative humidity and solar radiation. Yet, in design studies, such data may be unavailable and therefore, another approach may be needed that is based on stochastically generated time series. More specifically, when rainfall-runoff models are used, these evapotranspiration data need to be consistent with the accompanying (stochastically generated) precipitation time series data. In this paper, such an approach is presented in which the statistical dependence between evapotranspiration, precipitation and temperature is described by three- and four-dimensional vine copulas. Based on a case study of 72 years of evapotranspiration, temperature and precipitation data, observed in Uccle, Belgium, it is shown that canonical vine copulas (C-vines) perform very well in preserving the dependences between variables