Assimilation de données d'humidité des sols pour la prévision de crues : comparaison d'un modèle pluie-débit conceptuel et d'un modèle intégrant une interface sol-végétation-atmosphère

Le but de cet article est de présenter une méthodologie de mise à jour des paramètres de modèles pluie-débit en période de crue. Elle a été mise au point afin d'améliorer un des aspects de la gestion des réservoirs dans un contexte opérationnel de protection contre les crues: la réduction des incertitudes sur la prévision des débits. L'originalité de la méthode proposée réside dans le fait que l'on utilise non seulement une information sur les débits mais aussi une information sur l'humidité du sol. L'objectif de l'étude est d'évaluer l'intérêt de l'introduction de cette information supplémentaire. Pour cela, les données d'humidité du sol sont introduites au sein du modèle par l'intermédiaire d'une relation de passage établie entre l'humidité mesurée in situ et l'humidité calculée implicitement ou explicitement par les modèles. Cette méthodologie a été testée dans le cadre du projet européen AIMWATER sur quatre sous-bassins de la Seine en amont de Paris (France). Deux modèles pluie-débit sont utilisés dans cette étude, un modèle conceptuel semi-emprique et un modèle conceptuel couplé à un schéma de surface simulant une interface sol-végétation-atmosphère et permettant de calculer l'évolution de l'humidité du sol à différentes profondeurs. Cette approche comparative étudie l'intérêt d'un tel modèle couplé par rapport au modèle conceptuel semi-empirique sans représentation explicite des phénomènes se produisant à l'interface sol-végétation-atmosphère.Improving the accuracy of rainfall-runoff models and in particular their performances in flood prediction is a key point of continental hydrology. Methods have been developed to improve flood prediction in hydrology based on a better compliance of the model with current observations prior to its use in forecasting mode. This operation has been termed updating in hydrology and assimilation in meteorology. The fundamental idea is that if model predictions diverge from observations at a given time, there is little chance that future estimations will approach correct values. The improvement then comes from a correction of the trajectory of the model based on observations during the period preceding the day when a prediction into the immediate or long-term future is desired. This can be dealt with by a correction of model parameters, which is usually called "parameter updating".The inability of rainfall-runoff models to produce correct streamflow values generally translates into parameter uncertainty. Parameter calibration is the means used by a model structure to adjust to a given set of data. Therefore, a parameter updating methodology seems to be a natural way to amend errors in streamflow values. In this paper, a specific methodology of parameter updating is presented. The main feature of this method is that it does not carry out updating by reference only to recent streamflow observations, as classic procedures do, but also to soil moisture measurements, which can be retrieved daily from TDR probes. Indeed, it appears that the integration of soil moisture data allows better control of the evolution of the model and improves its performances, in particular in terms of forecasting.The aim of the research was to assess the usefulness of this additional soil moisture information. To this end, an approach has been suggested that gradually introduces additional information thanks to a constraint relationship between observed and modelled soil moisture. In fact, soil moisture can be calculated implicitly or explicitly by the model when extracting step-by-step the values of the model's store contents. This methodology was put forward for use in the European AIMWATER project on four catchments within the Seine River basin upstream of Paris (France). The other issue addressed in this paper was whether or not it is necessary to use a model that simulates explicitly the evolution of soil moisture at different depths. One can argue that if the model employed does not feature a store that can be identified closely to the observed soil moisture, there would be no possibility of benefiting from such measurements. On the other hand, it can be argued that if soil moisture is a model output, all the information drawn from soil moisture observations will be directed at improving this specific output at the expense of improving streamflow values. To answer this issue, two models were tested. The first model, GR4j, has no explicit counterpart for soil moisture measurements. The second one, GRHum, has been especially developed to introduce a two-layer soil reservoir that simulates the surface and sub-surface soil moisture.Since the aim of the present research was to analyse different ways of accounting for soil moisture, and to identify the one that offers the best prospects, several tests were carried out, using different relationships between observed and modelled soil moisture. Indeed, TDR probes give point measurements of soil moisture at several depths and several store contents can be taken into account in a constraint relationship.First, for both GR4j and GRHum models, tests showed that performances for flood forecasting are significantly improved when assimilating in situ measurements of soil moisture at a daily time-step, especially for the basins where poor simulations are obtained. It is also noteworthy that performances are very dependent on the items taken into account in a constraint relationship.Secondly, the GRHum model did not appear to be more efficient than the GR4j model when assimilating both streamflow and soil moisture data. However, the GRHum model gave the best results when assimilating only streamflow data, and superficial soil moisture seemed to fit the GRHum better than the GR4j model.Finally, although the tests required perfect foreknowledge of rainfall, the results of the research are encouraging from an operational point of view. Another interesting perspective is provided by the Earth Observation data. Indeed, previous studies have shown that soil moisture can be derived from EO data using, for example, microwave spaceborne Synthetic Aperture Radar (SAR) images (QUESNEY et al., 2000). This type of catchment-scale data could be more relevant than a local measure given by TDR probes (PAUWELS et al., 2002)

Activation of Type 1 Cannabinoid Receptor (CB1R) promotes neurogenesis in murine subventricular zone cell cultures

The endocannabinoid system has been implicated in the modulation of adult neurogenesis. Here, we describe the effect of type 1 cannabinoid receptor (CB1R) activation on self-renewal, proliferation and neuronal differentiation in mouse neonatal subventricular zone (SVZ) stem/progenitor cell cultures. Expression of CB1R was detected in SVZ-derived immature cells (Nestin-positive), neurons and astrocytes. Stimulation of the CB1R by (R)-(+)-Methanandamide (R-m-AEA) increased self-renewal of SVZ cells, as assessed by counting the number of secondary neurospheres and the number of Sox2+/+ cell pairs, an effect blocked by Notch pathway inhibition. Moreover, R-m-AEA treatment for 48 h, increased proliferation as assessed by BrdU incorporation assay, an effect mediated by activation of MAPK-ERK and AKT pathways. Surprisingly, stimulation of CB1R by R-m-AEA also promoted neuronal differentiation (without affecting glial differentiation), at 7 days, as shown by counting the number of NeuN-positive neurons in the cultures. Moreover, by monitoring intracellular calcium concentrations ([Ca2+](i)) in single cells following KCl and histamine stimuli, a method that allows the functional evaluation of neuronal differentiation, we observed an increase in neuronal-like cells. This proneurogenic effect was blocked when SVZ cells were co-incubated with R-m-AEA and the CB1R antagonist AM 251, for 7 days, thus indicating that this effect involves CB1R activation. In accordance with an effect on neuronal differentiation and maturation, R-m-AEA also increased neurite growth, as evaluated by quantifying and measuring the number of MAP2-positive processes. Taken together, these results demonstrate that CB1R activation induces proliferation, self-renewal and neuronal differentiation from mouse neonatal SVZ cell cultures.Fundacao para a Ciencia e a Tecnologia - Portugal [POCTI/SAU-NEU/68465/2006, PTDC/SAU-NEU/104415/2008, PTDC/SAU-NEU/101783/2008, POCTI/SAU-NEU/110838/2009]; Fundacao Calouste Gulbenkian [96542]; Fundacao para a Ciencia e Tecnologiainfo:eu-repo/semantics/publishedVersio

Geographical patterns in blood lead in relation to industrial emissions and traffic in Swedish children, 1978–2007

<p>Abstract</p> <p>Background</p> <p>Blood lead concentrations (B-Pb) were measured in 3 879 Swedish school children during the period 1978–2007. The objective was to study the effect of the proximity to lead sources based on the children's home and school location.</p> <p>Methods</p> <p>The children's home address and school location were geocoded and their proximity to a lead smelter and major roads was calculated using geographical information system (GIS) software. All the statistical analyses were carried out using means of generalized log-linear modelling, with natural-logarithm-transformed B-Pb, adjusted for sex, school year, lead-exposing hobby, country of birth and, in the periods 1988–1994 and 1995–2007, parents' smoking habits.</p> <p>Results</p> <p>The GIS analysis revealed that although the emission from the smelter and children's B-Pb levels had decreased considerably since 1978, proximity to the lead smelter continued to affect levels of B-Pb, even in recent years (geometric mean: near smelter: 22.90 μg/l; far from smelter 19.75 μg/l; p = 0.001). The analysis also revealed that proximity to major roads noticeably affected the children's B-Pb levels during the period 1978–1987 (geometric mean near major roads: 44.26 μg/l; far from roads: 38.32 μg/l; p = 0.056), due to the considerable amount of lead in petrol. This effect was, however, not visible after 1987 due to prohibition of lead in petrol.</p> <p>Conclusion</p> <p>The results show that proximity to the lead smelter still has an impact on the children's B-Pb levels. This is alarming since it could imply that living or working in the vicinity of a former lead source could pose a threat years after reduction of the emission. The analysis also revealed that urban children exposed to lead from traffic were only affected during the early period, when there were considerable amounts of lead in petrol, and that the prohibition of lead in petrol in later years led to reduced levels of lead in the blood of urban children.</p

Long-term exposure to air pollution and hospital admissions for ischemic stroke. A register-based case-control study using modelled NOx as exposure proxy

Background: Long-term exposure to air pollution is a hypothesized risk factor for ischemic stroke. In a large case-control study with a complete study base, we investigated whether hospital admissions for ischemic stroke were associated with residential concentrations of outdoor NOx, as a proxy for exposure to air pollution, in the region of Scania, Southern Sweden. Methods: We used a two-phase case-control study design, including as first-phase controls all individuals born between 1923 and 1965 and residing in Scania in 2002 (N=556 912). We defined first-phase cases as first-time ischemic stroke patients residing in Scania and registered in the Swedish stroke register between 2001 and 2005 (N=4 904) and second-phase cases as cases for whom we had information on smoking status, diabetes, and medication for hypertension (N=4 375). For the controls, information on these covariables was collected from a public health survey, resulting in 4 716 second-phase controls. With a geographical information system and an emission database, individual residential outdoor annual mean NOx concentration was modelled. The data were analyzed with logistic regression. Results: We found no evident association between NOx and ischemic stroke. For example, the odds ratio for ischemic stroke associated with the NOx category 20-30 mu g/m(3) compared to the reference category of <10 mu g/m(3) was 0.95 (95% CI 0.86-1.06). Conclusion: In this study area, with generally low levels of air pollution, using a complete study base, high-quality ascertainment of cases, and individually modelled exposure, we did not observe any clear association between NOx and ischemic stroke hospital admissions

Efficiency of two-phase methods with focus on a planned population-based case-control study on air pollution and stroke

<p>Abstract</p> <p>Background</p> <p>We plan to conduct a case-control study to investigate whether exposure to nitrogen dioxide (NO₂) increases the risk of stroke. In case-control studies, selective participation can lead to bias and loss of efficiency. A two-phase design can reduce bias and improve efficiency by combining information on the non-participating subjects with information from the participating subjects. In our planned study, we will have access to individual disease status and data on NO₂exposure on group (area) level for a large population sample of Scania, southern Sweden. A smaller sub-sample will be selected to the second phase for individual-level assessment on exposure and covariables. In this paper, we simulate a case-control study based on our planned study. We develop a two-phase method for this study and compare the performance of our method with the performance of other two-phase methods.</p> <p>Methods</p> <p>A two-phase case-control study was simulated with a varying number of first- and second-phase subjects. Estimation methods: <it>Method 1</it>: Effect estimation with second-phase data only. <it>Method 2</it>: Effect estimation by adjusting the first-phase estimate with the difference between the adjusted and unadjusted second-phase estimate. The first-phase estimate is based on individual disease status and residential address for all study subjects that are linked to register data on NO₂-exposure for each geographical area. <it>Method 3</it>: Effect estimation by using the expectation-maximization (EM) algorithm without taking area-level register data on exposure into account. <it>Method 4</it>: Effect estimation by using the EM algorithm and incorporating group-level register data on NO₂-exposure.</p> <p>Results</p> <p>The simulated scenarios were such that, unbiased or marginally biased (< 7%) odds ratio (OR) estimates were obtained with all methods. The efficiencies of method 4, are generally higher than those of methods 1 and 2. The standard errors in method 4 decreased further when the case/control ratio is above one in the second phase. For all methods, the standard errors do not become substantially reduced when the number of first-phase controls is increased.</p> <p>Conclusion</p> <p>In the setting described here, method 4 had the best performance in order to improve efficiency, while adjusting for varying participation rates across areas.</p

Air quality and error quantity: pollution and performance in a high-skilled, quality-focused occupation

We provide the first evidence that short-term exposure to air pollution affects the work performance of a group of highly-skilled, quality-focused employees. We repeatedly observe the decision-making of individual professional baseball umpires, quasi-randomly assigned to varying air quality across time and space. Unique characteristics of this setting combined with high-frequency data disentangle effects of multiple pollutants and identify previously under-explored acute effects. We find a 1 ppm increase in 3-hour CO causes an 11.5% increase in the propensity of umpires to make incorrect calls and a 10 mg/m3 increase in 12-hour PM2.5 causes a 2.6% increase. We control carefully for a variety of potential confounders and results are supported by robustness and falsification checks