ALBERT

Description: Improving the accuracy of pedotransfer functions (PTFs) requires studying how prediction uncertainty can be apportioned to different sources of uncertainty in inputs. In this study, the question addressed was as follows: which variable input is the main or best complementary predictor of water retention, and at which water potential? Two approaches were adopted to generate PTFs: multiple linear regressions (MLRs) for point PTFs and multiple nonlinear regressions (MNLRs) for parametric PTFs. Reliability tests showed that point PTFs provided better estimates than parametric PTFs (root mean square error, RMSE: 0.0414 and 0.0444 cm3 cm−3, and 0.0613 and 0.0605 cm3 cm−3 at −33 and −1500 kPa, respectively). The local parametric PTFs provided better estimates than Rosetta PTFs at −33 kPa. No significant difference in accuracy, however, was found between the parametric PTFs and Rosetta H2 at −1500 kPa with RMSE values of 0.0605 cm3 cm−3 and 0.0636 cm3 cm−3, respectively. The results of global sensitivity analyses (GSAs) showed that the mathematical formalism of PTFs and their input variables reacted differently in terms of point pressure and texture. The point and parametric PTFs were sensitive mainly to the sand fraction in the fine- and medium-textural classes. The use of clay percentage (C %) and bulk density (BD) as inputs in the medium-textural class improved the estimation of PTFs at −33 kPa.

Description: Determining soil hydraulic properties is of major concern in various fields of study. Although stony soils are widespread across the globe, most studies deal with gravel-free soils, so that the literature describing the impact of stones on the hydraulic conductivity of a soil is still rather scarce. Most frequently, models characterizing the saturated hydraulic conductivity of stony soils assume that the only effect of rock fragments is to reduce the volume available for water flow, and therefore they predict a decrease in hydraulic conductivity with an increasing stoniness. The objective of this study is to assess the effect of rock fragments on the saturated and unsaturated hydraulic conductivity. This was done by means of laboratory experiments and numerical simulations involving different amounts and types of coarse fragments. We compared our results with values predicted by the aforementioned predictive models. Our study suggests that it might be ill-founded to consider that stones only reduce the volume available for water flow. We pointed out several factors of the saturated hydraulic conductivity of stony soils that are not considered by these models. On the one hand, the shape and the size of inclusions may substantially affect the hydraulic conductivity. On the other hand, laboratory experiments show that an increasing stone content can counteract and even overcome the effect of a reduced volume in some cases: we observed an increase in saturated hydraulic conductivity with volume of inclusions. These differences are mainly important near to saturation. However, comparison of results from predictive models and our experiments in unsaturated conditions shows that models and data agree on a decrease in hydraulic conductivity with stone content, even though the experimental conditions did not allow testing for stone contents higher than 20 %.

Description: Determining soil hydraulic properties is of major concern in various fields of study. Though stony soils are widespread across the globe, most studies deal with gravel-free soils so that the literature describing the impact of stones on soil's hydraulic conductivity is still rather scarce. Most frequently, models characterizing the saturated hydraulic conductivity of stony soils assume that the only effect of rock fragments is to reduce the volume available for water flow and therefore they predict a decrease in hydraulic conductivity with an increasing stoniness. The objective of this study is to assess the effect of rock fragments on the saturated and unsaturated hydraulic conductivity. This was done by means of laboratory and numerical experiments involving different amounts and types of coarse fragments. We compared our results with values predicted by the aforementioned models. Our study suggests that considering that stones only reduce the volume available for water flow might be ill-founded. We pointed out several drivers of the saturated hydraulic conductivity of stony soils, not considered by these models. On the one hand, the shape and the size of inclusions may substantially affect the hydraulic conductivity. On the other hand, the presence of rock fragments can counteract and even overcome the effect of a reduced volume in some cases. We attribute this to the creation of voids at the fine earth-stone interface. Nevertheless, these differences are mainly important near to saturation. However, we come up with a more nuanced view regarding the validity of the models under unsaturated conditions. Indeed, under unsaturated conditions, the models seem to represent the hydraulic behaviour of stones reasonably well.

Description: This study presents modelling work of extreme discharge response to rainfall inputs interpolated by geostatistical approaches. Multivariate geostatistics are used by incorporating elevation as external data to improve the rainfall prediction. Thirty year daily rainfall in the Ourthe and Ambleve nested catchments, located in the Ardennes hilly landscape in the Walloon region, Belgium are interpolated and then used as inputs for a distributed physically-based hydrological model (EPIC-GRID). The effect of different raingage densities and particularly the effect of the raingage positions for very sparse raingage data used for rainfall interpolation, on extreme flow is analysed. We propose an index that can illustrate the quality of the raingage distribution with respect to the calculation of extreme discharge. In high elevation sub-catchment, we found that the multivariate geostatistics can significantly improve the rainfall prediction to produce very good simulated peak discharge. In the low elevation sub-catchment and the low raingage density, our results indicated that the Universal Kriging (UNK) is not appropriate. The IDW, Ordinary Kriging (ORK) and Ordinary Cokriging (OCK) methods provide generally good performance. The Thiessen polygon (THI) and Kriging with External Drift (KED) provide good performance for the whole catchment but less good for sub-catchments. The position of the raingages is the key factor for rainfall interpolation, particularly in the data-scarce region. UNK and KED methods are the most sensitive.

Description: Spatial interpolation of precipitation data is of great importance for hydrological modelling. Geostatistical methods (kriging) are widely applied in spatial interpolation from point measurement to continuous surfaces. The first step in kriging computation is the semi-variogram modelling which usually used only one variogram model for all-moment data. The objective of this paper was to develop different algorithms of spatial interpolation for daily rainfall on 1 km2 regular grids in the catchment area and to compare the results of geostatistical and deterministic approaches. This study leaned on 30-yr daily rainfall data of 70 raingages in the hilly landscape of the Ourthe and Ambleve catchments in Belgium (2908 km2). This area lies between 35 and 693 m in elevation and consists of river networks, which are tributaries of the Meuse River. For geostatistical algorithms, seven semi-variogram models (logarithmic, power, exponential, Gaussian, rational quadratic, spherical and penta-spherical) were fitted to daily sample semi-variogram on a daily basis. These seven variogram models were also adopted to avoid negative interpolated rainfall. The elevation, extracted from a digital elevation model, was incorporated into multivariate geostatistics. Seven validation raingages and cross validation were used to compare the interpolation performance of these algorithms applied to different densities of raingages. We found that between the seven variogram models used, the Gaussian model was the most frequently best fit. Using seven variogram models can avoid negative daily rainfall in ordinary kriging. The negative estimates of kriging were observed for convective more than stratiform rain. The performance of the different methods varied slightly according to the density of raingages, particularly between 8 and 70 raingages but it was much different for interpolation using 4 raingages. Spatial interpolation with the geostatistical and Inverse Distance Weighting (IDW) algorithms outperformed considerably the interpolation with the Thiessen polygon, commonly used in various hydrological models. Integrating elevation into Kriging with an External Drift (KED) and Ordinary Cokriging (OCK) did not improve the interpolation accuracy for daily rainfall. Ordinary Kriging (ORK) and IDW were considered to be the best methods, as they provided smallest RMSE value for nearly all cases. Care should be taken in applying UNK and KED when interpolating daily rainfall with very few neighbourhood sample points. These recommendations complement the results reported in the literature. ORK, UNK and KED using only spherical model offered a slightly better result whereas OCK using seven variogram models achieved better result.

Description: Agricultural management practices influence soil structure, but the characterization of these modifications and consequences are still not completely understood. In this study, we combine X-ray microtomography with retention and hydraulic conductivity measurements in the context of tillage simplification. First, this association is used to validate microtomography information with a quick scan method. Secondly, X-ray microtomography is used to increase our knowledge of soil structural differences. Notably, we show a good match for retention and conductivity functions between macroscopic measurements and microtomographic information. Microtomography refines the shape of the retention function, highlighting the presence of a secondary pore system in our soils. Analysis of structural parameters for these pores appears to be of interest and offers additional clues for soil structure differentiation, through – among others – connectivity and tortuosity parameters. These elements make microtomography a highly competitive instrument for routine soil characterization.

Description: The Meuse is an important rain-fed river in North-Western Europe. Nine million people live in its catchment, split over five countries. Projected changes in precipitation and temperature characteristics due to climate change would have a significant impact on the Meuse River and its tributaries. In this study, we focused on the impacts of climate change on the hydrology of two sub-catchments of the Meuse in Belgium, the Lesse and the Vesdre, placing the emphasis on the water-soil-plant continuum in order to highlight the effects of climate change on plant growth, and water uptake on the hydrology of two sub-catchments. These effects were studied using two climate scenarios and a physically based distributed model, which reflects the water-soil-plant continuum. Our results show that the vegetation will evapotranspirate between 10 and 17 % less at the end of the century because of water scarcity in summer, even if the root development is better under climate change conditions. In the low scenario, the mean minimal 7 days discharge value could decrease between 19 and 24 % for a two year return period, and between 20 and 35 % for a fifty year return period. It will lead to rare but severe drought in rivers, with potentially huge consequences on water quality.

Description: Well-integrated water management can notably require estimating low flows at any point of a river. Depending on the management practice, it can be needed for various return periods. This is seldom addressed in the literature. This paper shows the development of a full analysis chain including quality analysis of gauging stations, low-flow frequency analysis, and building of a global model to assess low-flow indices on the basis of catchment physical parameters. The most common distributions that fit low-flow data in Wallonia were two-parameter lognormal and gamma. The recession coefficient and percolation were the most explanatory variables, regardless of the return period. The determination coefficients of the models ranged from 0.51 to 0.67 for calibration and from 0.61 to 0.80 for validation. The regression coefficients were found to be linked to the return period. This was used to design a complete equation that gives the low-flow index based on physical parameters and the desired return period (in a 5 to 50 yr range). The interest of regionalisation and the development of regional models are also discussed. Four homogeneous regions are identified, but to date the global model remains more robust due to the limited number of 20-yr-long gauging stations. This should be reconsidered in the future when enough data will be available.

Description: Spatial interpolation of precipitation data is of great importance for hydrological modelling. Geostatistical methods (krigings) are widely used in spatial interpolation from point measurement to continuous surfaces. However, the majority of existing geostatistical algorithms are available only for single-moment data. The first step in kriging computation is the semi-variogram modelling which usually uses only one variogram model for all-moment data. The objective of this paper was to develop different algorithms of spatial interpolation for daily rainfall on 1 km2 regular grids in the catchment area and to compare the results of geostatistical and deterministic approaches. In this study, we used daily rainfall data from 70 raingages in the hilly landscape of the Ourthe and Ambleve catchments in Belgium (2908 km2). This area lies between 35 and 693 m in elevation and consists of river networks, which are tributaries of the Meuse River. For geostatistical algorithms, Cressie's Approximate Weighted Least Squares method was used to fit seven semi-variogram models (logarithmic, power, exponential, Gaussian, rational quadratic, spherical and penta-spherical) to daily sample semi-variogram on a daily basis. Seven selected raingages were used to compare the interpolation performance of these algorithms applied to many degenerated-raingage cases. Spatial interpolation with the geostatistical and Inverse Distance Weighting (IDW) algorithms outperformed considerably interpolation with the Thiessen polygon that is commonly used in various hydrological models. Kriging with an External Drift (KED) and Ordinary Cokriging (OCK) presented the highest Root Mean Square Error (RMSE) between the geostatistical and IDW methods. Ordinary Kriging (ORK) and IDW were considered to be the best methods, as they provided smallest RMSE value for nearly all cases.

Description: Being able to estimate low flows at any point of a river is really important nowadays for a good integrated management of rivers. Knowing the magnitude as well as the frequency of such extreme events becomes essential. In order to build a model of low flow calculation, usable in ungauged catchments and which takes also into account low flow frequency, we started with a low flow frequency analysis including a comparison of different distributions. Two-parameter Lognormal and Gamma were the most common distributions that fit low flow data in Wallonia. This was followed by a regionalisation of low flows using 25 different climatic and physical catchment variables, and the development of regression models that can be used to estimate the minimum 7-day average flow for different return periods, using catchment characteristics. The variables the most correlated to specific minimum 7-day average flows were the recession coefficient and percolation, regardless of the return period. The determination coefficients of the models ranged from 0.51 to 0.67 for calibration and from 0.61 to 0.80 for validation. Finally, regression coefficients were logarithmically linked to the return period. This enabled us to develop a single model per region and for the whole study area, in function of the return period. In conclusion, the method developed in this study allows us to estimate low flows in gauged and ungauged catchments of a given region for a given return period. The interest of regionalisation and development of regional models is also discussed.