Multiyear time-lapse ERT to study short- and long-term landslide hydrological dynamics

The geology of the “Vence” landslide (0.8 million m³, south-eastern France) explains the complex hydrology of the site which plays a key role in the destabilization of the slope (water circulation within the sliding mass, fluid exchanges between superficial layers and deep karstic aquifer through faults). To understand fluid circulations within the unstable slope, a 9.5-year multi-parametric survey was set up. The survey combines electrical resistivity tomography (daily acquisition), rainfall records since 2006 and boreholes monitoring groundwater level since 2009. The objective of this work is to present an automated clustering analysis applied to the ERT data enabled to locate geological units displaying distinct hydrogeological behaviours. Clustering analysis, based on a hierarchical ascendant classification (HAC), helped to simplify the ERT section isolating three groups of apparent resistivity values. Comparing the variations of these clusters’ behaviours in time to the variations of the groundwater levels on site, we identified hydrogeological units. The role of the faults cutting the substratum is thereby highlighted. It is the simultaneous analysis of such a large real dataset that allowed obtaining robust results characteristic of the long-term behaviour of the natural hydrogeological system. This type of qualitative information on the variability of the slope hydrogeological behaviour both spatially and temporally is crucial to help improving the conversion of resistivity data into hydrologic quantities. Indeed, the definition of petrophysical models to convert ERT measurements into hydrological measurements should be site-specific and take into account the spatial and temporal variability of the medium. In this work, we show a method that can also help to focus on the areas in depth that have different levels of permeability and observe how the saturation degree evolves in time. This can be used to optimize the location of additional instrumentation (such as temperature probes and chemical sampling) and, thus, help in the prevention of the risk in such problematic areas.     

Improving flood forecasting using an input correction method in urban models in poorly gauged areas

ABSTRACT

Poorly monitored catchments could pose a challenge in the provision of accurate flood predictions by hydrological models, especially in urbanized areas subject to heavy rainfall events. Data assimilation techniques have been widely used in hydraulic and hydrological models for model updating (typically updating model states) to provide a more reliable prediction. However, in the case of nonlinear systems, such procedures are quite complex and time-consuming, making them unsuitable for real-time forecasting. In this study, we present a data assimilation procedure, which corrects the uncertain inputs (rainfall), rather than states, of an urban catchment model by assimilating water-level data. Five rainfall correction methods are proposed and their effectiveness is explored under different scenarios for assimilating data from one or multiple sensors. The methodology is adopted in the city of São Carlos, Brazil. The results show a significant improvement in the simulation accuracy.     

Drought 2002 in Colorado: An Unprecedented Drought or a Routine Drought?

The 2002 drought in Colorado was reported by the media and by public figures, and even by a national drought-monitoring agency, as an exceptionally severe drought. In this paper we examine evidence for this claim. Our study shows that, while the impacts of water shortages were exceptional everywhere, the observed precipitation deficit was less than extreme over a good fraction of the state. A likely explanation of this discrepancy is the imbalance between water supply and water demand over time. For a given level of water supply, water shortages become intensified as water demands increase over time. The sobering conclusion is that Colorado is more vulnerable to drought today than under similar precipitation deficits in the past.     

Climate and irrigation scenario analyses using three-dimensional numerical modelling: a case study of the Nasia sub-basin in the White Volta Basin,Ghana

A groundwater resource characterisation and assessment model was developed for Nasia river sub-basin in the White Volta Basin, Ghana. The model is useful to policymakers for planning and sustainable management of groundwater resources in the basin for domestic and irrigation purposes. A conceptual model was constructed that characterized boundary conditions and hydrostratigraphy, and estimated recharge rates and hydraulic and storage parameters. From current understanding of the hydrogeological dynamics, three hydrostratigraphic layers were delineated. The conceptual model was converted to a three-dimensional steady-state groundwater flow model using MODFLOW. Recharge rates estimated from the base model indicate a minimum of 1.1% and maximum of 6.2% of the total rainfall. The hydraulic conductivity ranged between 0.20 and 15 m/day. Four possible scenarios were simulated: (1) increased population, (2) climate variations (reduced recharge), (3) increased abstraction for irrigation, and (4) worst-case scenario which is a combination of the first three scenarios. Results from scenarios 1 and 2 indicated that, under such conditions, the groundwater resources could be sustained and no significant effect on any of the water budget indicators was observed. For scenario 3, there was significant drop in hydraulic head in the central portions of the study area. The scenario 4 simulation indicated that there was significant reduction in groundwater levels and groundwater discharge into streams under these stressors. Such reduction can affect stream levels in the basin and, subsequently, the ecosystem. These findings are valid within the limits of uncertainty in the hydrogeological data that were used in this study.

     

Variations of trace elements and rare earth elements (REEs) treated by two different methods for snow-pit samples on the Qinghai-Tibetan Plateau and their implications

Although previous investigations of the trace elements in snow and ice from the Qinghai-Tibetan Plateau obtained interesting information about pollution from human activities on the plateau, most were based on traditional acidification methods. To emphasize the influence of the different sample-preparation methods on the records of trace elements and rare earth elements, snow samples were collected from glaciers on the Qinghai-Tibetan Plateau in China and prepared using two methods: traditional acidification and total digestion. Concentrations of 18 trace elements (Al, Ti, Fe, Rb, Sr, Ba, V, Cr, Mn, Li, Cu, Co, Mo, Cs, Sb, Pb, Tl, and U), along with 14 rare earth elements (REEs: La, Ce, Pr, Nd, Sm, Eu, Gd, Tb, Dy, Ho, Er, Tm, Yb, and Lu), Y, and Th in the snow samples, were measured using inductively coupled plasma-sector field mass spectrometry (ICP-SFMS). The results showed that the mass fraction of the trace elements (defined as ratio of concentration in the acid-leachable fraction to that in the digested sample) such as Mo, Ti, Al, Rb, and V, varied from 0.06 to 0.5. The mass fraction of other trace elements varied from about 0.6 to more than 0.9; those of the REEs, Y, and Th varied from 0.34 to 0.75. Lower mass fractions will lead to an overestimated contribution of other sources, especially human activities, and the underestimated fluxes of these trace elements (especially REEs, Y, and Th, as well as dust) if the REEs are used as the proxy for the crust dust. The two sample-preparation methods exhibited different REE normalized distribution patterns, REE ratios, and provenance-tracing results. The REE normalized distribution patterns and proxies in the digested samples are more reliable and integrated than those found in traditional acidification method for dust-provenance tracing.     

An algorithm for soil moisture estimation using GPS-interferometric reflectometry for bare and vegetated soil

Ground-reflected global positioning system signals measured by a geodetic-quality GPS system can be used to infer temporal changes in near-surface soil moisture for the area surrounding the antenna. This technique, known as GPS-interferometric reflectometry, analyzes changes in the interference pattern of the direct and reflected signals, which are recorded in signal-to-noise ratio (SNR) data, as interferograms. Temporal fluctuations in the phase of the interferogram are indicative of changes in near-surface volumetric soil moisture content. However, SNR phase is also highly sensitive to changes in overlying vegetation, and thus, the effects of seasonal vegetation changes on the ground-reflected signal must be considered. Here a method is described for determining whether SNR data are significantly corrupted by vegetation and for correcting these effects. Absolute soil moisture content must be determined for each site using ancillary data for the residual moisture content. Accounting for vegetation effects significantly improves the agreement between GPS-derived soil moisture and in situ measurements.     

Climatic forcing on hydrography of a Mediterranean bay (Alfacs Bay)

Time series of meteorological and hydrographic variables were analyzed using Huang's Empirical Mode Decomposition (EMD) to ascertain the relationships among climatic forcings and the hydrographic behavior in an estuarine bay. The EMD method allowed us to separate the different characteristic oscillation patterns (or modes) of a 14 year-long time series of weekly hydrographic (water temperature and salinity) and meteorological (air temperature, pressure, wind and precipitation) data from Alfacs Bay (Ebre delta, NW Mediterranean). In order to explore the relations between couples of oscillation modes from different series, we developed a correlation index based on the phase differences between these modes. Common characteristic modes in the studied series are a seasonal pattern and an inter-annual oscillation. The comparison between series of meteorological and hydrographic variables shows significant correlations of two modes (of 1 year and 2–3 year periods, respectively) of water temperature with the corresponding two modes of air temperature and air pressure. There were also significant positive correlations between wind speed and water temperature. The use of EMD allowed to discover a strong connection between stratification and the use of irrigation channels in the bay; in addition, with the help of this method we can propose a common meteorological forcing mechanism for the observed patterns of variability. Those findings would have been impossible to guess by use of classical Fourier methods, and gives a demonstration of the power of EMD in climatic series analysis.