The Value of Empirical Data for Estimating the Parameters of a Sociohydrological Flood Risk Model.

In this paper, empirical data are used to estimate the parameters of a sociohydrological flood risk model. The proposed model, which describes the interactions between floods, settlement density, awareness, preparedness, and flood loss, is based on the literature. Data for the case study of Dresden, Germany, over a period of 200 years, are used to estimate the model parameters through Bayesian inference. The credibility bounds of their estimates are small, even though the data are rather uncertain. A sensitivity analysis is performed to examine the value of the different data sources in estimating the model parameters. In general, the estimated parameters are less biased when using data at the end of the modeled period. Data about flood awareness are the most important to correctly estimate the parameters of this model and to correctly model the system dynamics. Using more data for other variables cannot compensate for the absence of awareness data. More generally, the absence of data mostly affects the estimation of the parameters that are directly related to the variable for which data are missing. This paper demonstrates that combining sociohydrological modeling and empirical data gives additional insights into the sociohydrological system, such as quantifying the forgetfulness of the society, which would otherwise not be easily achieved by sociohydrological models without data or by standard statistical analysis of empirical data.

Land use change impacts on floods at the catchment scale: Challenges and opportunities for future research.

Research gaps in understanding flood changes at the catchment scale caused by changes in forest management, agricultural practices, artificial drainage, and terracing are identified. Potential strategies in addressing these gaps are proposed, such as complex systems approaches to link processes across time scales, long-term experiments on physical-chemical-biological process interactions, and a focus on connectivity and patterns across spatial scales. It is suggested that these strategies will stimulate new research that coherently addresses the issues across hydrology, soil and agricultural sciences, forest engineering, forest ecology, and geomorphology.

Citalopram amplifies the influence of living conditions on mood in depressed patients enrolled in the STAR*D study.

Selective serotonin reuptake inhibitors (SSRIs), the most commonly prescribed antidepressant drugs, have a variable and incomplete efficacy. In order to better understand SSRI action, we explored the hypothesis that SSRIs do not affect mood per se but amplify the influence of the living conditions on mood. To this aim, we exploited the Sequenced Treatment Alternatives to Relieve Depression (STAR*D) data set, selected a subpopulation of 591 patients with an overlapping clinical history and analyzed treatment outcome according to dosage -20 or 40 mg per day of citalopram. We found that sociodemographic characteristics affected treatment response in the same direction in the two dose groups, but these effects reached statistical significance only in the 40 mg per day dose group. In the latter, higher improvement rate was associated with having a working employment status (P=0.0219), longer education (P=0.0053), high income (P=0.01) or a private insurance (P=0.0031), and the higher remission rate was associated with having a working employment status (P=0.0326) or longer education (P=0.0484). Moreover, the magnitude of the effect of the sociodemographic characteristics on mood, measured as the percent of patients showing a positive outcome when exposed to favorable living conditions, was much greater-up to 37-fold-in the 40 compared to the 20 mg per day dose group. Overall, our results indicate that citalopram amplifies the influence of the living conditions on mood in a dose-dependent manner. These findings provide a potential explanation for the variable efficacy of SSRIs and might lead to the development of personalized strategies aimed at enhancing their efficacy.

Fragmented patterns of flood change across the United States.

Trends in the peak magnitude, frequency, duration, and volume of frequent floods (floods occurring at an average of two events per year relative to a base period) across the United States show large changes; however, few trends are found to be statistically significant. The multidimensional behavior of flood change across the United States can be described by four distinct groups, with streamgages experiencing (1) minimal change, (2) increasing frequency, (3) decreasing frequency, or (4) increases in all flood properties. Yet group membership shows only weak geographic cohesion. Lack of geographic cohesion is further demonstrated by weak correlations between the temporal patterns of flood change and large-scale climate indices. These findings reveal a complex, fragmented pattern of flood change that, therefore, clouds the ability to make meaningful generalizations about flood change across the United States.