Une méthode pour anticiper les mises en alerte de crues sur la rivière Thoré (France)

Cet article présente les conclusions d'une recherche visant l'amélioration de l'annonce des crues, et son application à la rivière Thoré, dans le contexte du système d'alerte français. On y exploite les informations météorologiques contenues couramment dans les bulletins d'alerte aux précipitations [BAP] émis par Météo-France, dans le but d'aider les prévisionnistes du Service d'annonce de crues [SAC] à anticiper l'atteinte de la cote d'alerte sur une rivière. Le travail présenté fait partie d'une approche visant à munir les SAC d'outils prévisionnels fonctionnant en temps réel et aptes à prévenir d'une mise en alerte probable. L'approche préconisée conduit à une utilisation directe des informations contenues dans les BAP reçus des services de météorologie dans le processus de surveillance des crues. C'est au moyen de courbes d'intensité-durée-temps d'alerte [IDTA], préalablement établies pour des prévisions de pluies uniformément réparties, et de courbes d'intensité-superficie-temps d'alerte [ISTA] pour les prévisions relatives à des cellules orageuses localisées, que l'approche proposée est développée.This work was designed to contribute to the improvement of flood forecasting, in the context of the French alert system. We propose that the meteorological information contained in the French weather forecast bulletin (Bulletins d'alerte aux précipitations; BAP), produced by Météo-France (French meteorological organization), should be utilized in order to aid the forecasters of the French flood forecasting agencies (Services d'annonce de crues; SAC) to anticipate the timing of an alert associated with an increase in the water level of a river. The goal was to develop an approach to provide the SAC with a real-time operational forecasting tool in order to improve the evaluation of a probable Flood Alert decision. This approach integrates the information contained in the BAP received from Météo-France into the existing flood control process with the use of Duration-Intensity-Warning Time (durée-intensité-temps d'alerte, IDTA) curves for uniform rainfall forecasting, and Intensity-Area-Warning Time curves (intensité-superficie-temps d'alerte, ISTA) for localized storm cells.The rainfall parameters considered were the intensity (I, mm/h), the duration (D, h), and the area of the watershed affected by the rainfall (S, km2). These parameters are related by the equation V=I x D x S, where V is the volume of rain (hm3). The parameter directly related to the Flood Alert decision is the warning time (Talerte), measured in hours. It is defined as the time from the beginning of the rainfall to the time when the flow at the watershed outlet reaches the alert flow (Qalerte in m3 /s), regardless of the maximum discharge (Qmax). Although Qmax may be an important indicator of the magnitude of the upcoming event, the chief concern is the Flood Alert decision, and therefore, the time to alert parameter (Talert) is of primary importance.The proposed approach involves creating a graphical connection of a series of rainfall intensity values (I) as a function of a range of rainfall (D) duration values with time to alert (Talert) curves, which represent the I-D couples. As a result, a SAC forecast agent that receives a BAP indicating the quantitative precipitation forecast in a precise region for a defined period will be able to evaluate the time after the start of the rainfall that the alert flow (Qalert) will be reached, simply by referring to the IDTA and/or ISTA curves. If an alert is foreseen within a certain delay, the flood forecast agent can wait to receive improved forecasts before making the decision whether to start the flood alert procedures or not.The construction of the IDTA and ISTA curves requires numerous simulations in order to cover a wide variety of intensity-duration and intensity-area of rainfall couples for which the alert flow (Qalert) will be reached at the watershed outlet, and therefore the time corresponding to this discharge can be estimated. The simulations were performed through the use of a combination of a deterministic distributed parameter hydrological model and a hydraulic one-dimensional hydrograph transfer model. The neuronal models of the Generalized Regression Network (GRNN) type were also used. This allowed for the extraction of and/or interpolation between values in the database containing the parameters intensity, duration, area, and the hydrographs that resulted from the simulations done with the first two models. The interest in using the GRNN model is to cover a large range of values for all of the parameters considered, without having to simulate all cases, therefore reducing the potential computation time.We developed this forecasting approach on the basis of a specific case related to the extreme flooding that occurred in southern France on November 1999. More precisely, our case study concerns the mountainous region in the upstream area of the Thoré watershed, in the Tarn Department. The simulation scenarios were 1) uniformly distributed rainfall on a watershed of 208 km2 ; 2) storm cells of 9, 36, 64 and 144 km2 located in the watershed center; and 3) a storm cell located in various zones of the watershed.The main observation of the simulation results was that the Talert was constant for a rainfall of intensity I, as long as the duration was longer than the Talert (i.e., provided it was still raining after Qalert was attained at the outlet). On the other hand, if the rain stops before Qalert is attained, Talert is delayed. Talert increases as a function of the duration of the rainfall, for a constant I. This is true for both uniform and localized rainfall.The IDTA and ISTA curves were developed on the basis of several simplifying hypotheses and should be improved in order to increase their precision and flexibility. Therefore this approach can be amended by taking into account the following factors:- infiltration (when the laws defining it are established);- the initial conditions: since the results of the simulations for this study are valid for constant initial conditions of Qini=20 m3/s, it would be pertinent to include a correction factor to adjust the results (Talert) for the real initial conditions such as the actual Qini and the actual soil humidity;- the spatial variability of the storm cells; and- the combination of uniformly distributed rainfall and localized storm cells.Nevertheless, we evaluated the use of the forecasting approach with the IDTA and ISTA curves referring to the November 1999 events. The contribution of these curves in the Flood Alert decision process was assessed with a fictitious scenario defined by the issued BAP related to this event. Understanding the simplifying hypotheses discussed above, we conclude that the flood alert on the Thoré River watershed could have been advanced up to seven hours and thirty minutes from the actual time it was issued. In a fast or flash flood event, this range of anticipation could have a considerable impact

Noisy Monte Carlo: Convergence of Markov chains with approximate transition kernels

Monte Carlo algorithms often aim to draw from a distribution $\pi$ by simulating a Markov chain with transition kernel $P$ such that $\pi$ is invariant under $P$. However, there are many situations for which it is impractical or impossible to draw from the transition kernel $P$. For instance, this is the case with massive datasets, where is it prohibitively expensive to calculate the likelihood and is also the case for intractable likelihood models arising from, for example, Gibbs random fields, such as those found in spatial statistics and network analysis. A natural approach in these cases is to replace $P$ by an approximation $\hat{P}$. Using theory from the stability of Markov chains we explore a variety of situations where it is possible to quantify how 'close' the chain given by the transition kernel $\hat{P}$ is to the chain given by $P$. We apply these results to several examples from spatial statistics and network analysis.Comment: This version: results extended to non-uniformly ergodic Markov chain

Nitrate reducing bacterial activity in concrete cells of nuclear waste disposal

Leaching experiments of solid matrices (bitumen and cement pastes) have been first implemented to define the physicochemical conditions that microorganisms are likely to meet at the bitumen-concrete interface (see the paper of Bertron et al.). Of course, as might be suspected, the cement matrix imposes highly alkaline pH conditions (10 < pH < 11). The screening of a range of anaerobic denitrifying bacterial strains led us to select Halomonas desiderata as a model bacterium capable of catalyzing the reaction of nitrate reduction in these extreme conditions of pH. The denitrifying activity of Halomonas desiderata was quantified in batch bioreactor in the presence of solid matrices and / or leachate from bitumen and cement matrices. Denitrification was relatively fast in the presence of cement matrix (< 100 hours) and 2 to 3 times slower in the presence of bituminous matrix. Overall, the presence of solid cement promoted the kinetics of denitrification. The observation of solid surfaces at the end of the experiment revealed the presence of a biofilm of Halomonas desiderata on the cement paste surface. These attached bacteria showed a denitrifying activity comparable to planktonic bacterial culture. On the other side, no colonization of bitumen could be highlighted as either by SEM or epifluorescence microscopy. Now, we are currently developing a continuous experimental bioreactor which should allow us a more rational understanding of the bitumen-cement-microbe interactions

Rank-based model selection for multiple ions quantum tomography

The statistical analysis of measurement data has become a key component of many quantum engineering experiments. As standard full state tomography becomes unfeasible for large dimensional quantum systems, one needs to exploit prior information and the "sparsity" properties of the experimental state in order to reduce the dimensionality of the estimation problem. In this paper we propose model selection as a general principle for finding the simplest, or most parsimonious explanation of the data, by fitting different models and choosing the estimator with the best trade-off between likelihood fit and model complexity. We apply two well established model selection methods -- the Akaike information criterion (AIC) and the Bayesian information criterion (BIC) -- to models consising of states of fixed rank and datasets such as are currently produced in multiple ions experiments. We test the performance of AIC and BIC on randomly chosen low rank states of 4 ions, and study the dependence of the selected rank with the number of measurement repetitions for one ion states. We then apply the methods to real data from a 4 ions experiment aimed at creating a Smolin state of rank 4. The two methods indicate that the optimal model for describing the data lies between ranks 6 and 9, and the Pearson $\chi^{2}$ test is applied to validate this conclusion. Additionally we find that the mean square error of the maximum likelihood estimator for pure states is close to that of the optimal over all possible measurements.Comment: 24 pages, 6 figures, 3 table

Ca2+/Calmodulin-Dependent Protein Kinase Kinase Is Not Involved in Hypothalamic AMP-Activated Protein Kinase Activation by Neuroglucopenia

Hypoglycemia and neuroglucopenia stimulate AMP-activated protein kinase (AMPK) activity in the hypothalamus and this plays an important role in the counterregulatory responses, i.e. increased food intake and secretion of glucagon, corticosterone and catecholamines. Several upstream kinases that activate AMPK have been identified including Ca2+/Calmodulin-dependent protein kinase kinase (CaMKK), which is highly expressed in neurons. However, the involvement of CaMKK in neuroglucopenia-induced activation of AMPK in the hypothalamus has not been tested. To determine whether neuroglucopenia-induced AMPK activation is mediated by CaMKK, we tested whether STO-609 (STO), a CaMKK inhibitor, would block the effects of 2-deoxy-D-glucose (2DG)-induced neuroglucopenia both ex vivo on brain sections and in vivo. Preincubation of rat brain sections with STO blocked KCl-induced α1 and α2-AMPK activation but did not affect AMPK activation by 2DG in the medio-basal hypothalamus. To confirm these findings in vivo, STO was pre-administrated intracerebroventricularly (ICV) in rats 30 min before 2DG ICV injection (40 µmol) to induce neuroglucopenia. 2DG-induced neuroglucopenia lead to a significant increase in glycemia and food intake compared to saline-injected control rats. ICV pre-administration of STO (5, 20 or 50 nmol) did not affect 2DG-induced hyperglycemia and food intake. Importantly, activation of hypothalamic α1 and α2-AMPK by 2DG was not affected by ICV pre-administration of STO. In conclusion, activation of hypothalamic AMPK by 2DG-induced neuroglucopenia is not mediated by CaMKK

Pre- and early-postnatal nutrition modify gene and protein expressions of muscle energy metabolism markers and phospholipid fatty acid composition in a muscle type specific manner in sheep.

We previously reported that undernutrition in late fetal life reduced whole-body insulin sensitivity in adult sheep, irrespective of dietary exposure in early postnatal life. Skeletal muscle may play an important role in control of insulin action. We therefore studied a range of putative key muscle determinants of insulin signalling in two types of skeletal muscles (longissimus dorsi (LD) and biceps femoris (BF)) and in the cardiac muscle (ventriculus sinister cordis (VSC)) of sheep from the same experiment. Twin-bearing ewes were fed either 100% (NORM) or 50% (LOW) of their energy and protein requirements during the last trimester of gestation. From day-3 postpartum to 6-months of age (around puberty), twin offspring received a high-carbohydrate-high-fat (HCHF) or a moderate-conventional (CONV) diet, whereafter all males were slaughtered. Females were subsequently raised on a moderate diet and slaughtered at 2-years of age (young adults). The only long-term consequences of fetal undernutrition observed in adult offspring were lower expressions of the insulin responsive glucose transporter 4 (GLUT4) protein and peroxisome proliferator-activated receptor gamma, coactivator 1α (PGC1α) mRNA in BF, but increased PGC1α expression in VSC. Interestingly, the HCHF diet in early postnatal life was associated with somewhat paradoxically increased expressions in LD of a range of genes (but not proteins) related to glucose uptake, insulin signalling and fatty acid oxidation. Except for fatty acid oxidation genes, these changes persisted into adulthood. No persistent expression changes were observed in BF and VSC. The HCHF diet increased phospholipid ratios of n-6/n-3 polyunsaturated fatty acids in all muscles, even in adults fed identical diets for 1½ years. In conclusion, early postnatal, but not late gestation, nutrition had long-term consequences for a number of determinants of insulin action and metabolism in LD. Tissues other than muscle may account for reduced whole body insulin sensitivity in adult LOW sheep

Lack of Wdr13 Gene in Mice Leads to Enhanced Pancreatic Beta Cell Proliferation, Hyperinsulinemia and Mild Obesity

WD-repeat proteins are very diverse, yet these are structurally related proteins that participate in a wide range of cellular functions. WDR13, a member of this family, is conserved from fishes to humans and localizes into the nucleus. To understand the in vivo function(s) of Wdr13 gene, we have created and characterized a mutant mouse strain lacking this gene. The mutant mice had higher serum insulin levels and increased pancreatic islet mass as a result of enhanced beta cell proliferation. While a known cell cycle inhibitor, p21, was downregulated in the mutant islets, over expression of WDR13 in the pancreatic beta cell line (MIN6) resulted in upregulation of p21, accompanied by retardation of cell proliferation. We suggest that WDR13 is a novel negative regulator of the pancreatic beta cell proliferation. Given the higher insulin levels and better glucose clearance in Wdr13 gene deficient mice, we propose that this protein may be a potential candidate drug target for ameliorating impaired glucose metabolism in diabetes

Metformin Prevents Nigrostriatal Dopamine Degeneration Independent of AMPK Activation in Dopamine Neurons

Metformin is a widely prescribed drug used to treat type-2 diabetes, although recent studies show it has wide ranging effects to treat other diseases. Animal and retrospective human studies indicate that Metformin treatment is neuroprotective in Parkinson’s Disease (PD), although the neuroprotective mechanism is unknown, numerous studies suggest the beneficial effects on glucose homeostasis may be through AMPK activation. In this study we tested whether or not AMPK activation in dopamine neurons was required for the neuroprotective effects of Metformin in PD. We generated transgenic mice in which AMPK activity in dopamine neurons was ablated by removing AMPK beta 1 and beta 2 subunits from dopamine transporter expressing neurons. These AMPK WT and KO mice were then chronically exposed to Metformin in the drinking water then exposed to MPTP, the mouse model of PD. Chronic Metformin treatment significantly attenuated the MPTP-induced loss of Tyrosine Hydroxylase (TH) neuronal number and volume and TH protein concentration in the nigrostriatal pathway. Additionally, Metformin treatment prevented the MPTP-induced elevation of the DOPAC:DA ratio regardless of genotype. Metformin also prevented MPTP induced gliosis in the Substantia Nigra. These neuroprotective actions were independent of genotype and occurred in both AMPK WT and AMPK KO mice. Overall, our studies suggest that Metformin’s neuroprotective effects are not due to AMPK activation in dopaminergic neurons and that more research is required to determine how metformin acts to restrict the development of PD

Bayesian Computation with Intractable Likelihoods

This article surveys computational methods for posterior inference with intractable likelihoods, that is where the likelihood function is unavailable in closed form, or where evaluation of the likelihood is infeasible. We review recent developments in pseudo-marginal methods, approximate Bayesian computation (ABC), the exchange algorithm, thermodynamic integration, and composite likelihood, paying particular attention to advancements in scalability for large datasets. We also mention R and MATLAB source code for implementations of these algorithms, where they are available.Comment: arXiv admin note: text overlap with arXiv:1503.0806