Comparison between statistical and dynamical downscaling of rainfall over the Gwadar‐Ormara basin, Pakistan

Abstract This paper evaluated and compared the performance of a statistical downscaling method and a dynamical downscaling method to simulate the spatial–temporal rainfall distribution. Outputs from RegCM4 Regional Climate Model (RCM) and the CanESM2 Atmosphere–Ocean General Circulation Model (AOGCM) were selected for the data scarce Gwadar‐Ormara basin, Pakistan. The evaluation was based on the climatological average and standard deviation for historic (1971–2000) and future (2041–2070) time periods under Representative Concentration Pathways (RCP) 4.5 and 8.5 scenarios. The performance evaluation showed that statistical downscaling is preferred to simulate and project rainfall patterns in the study area. Additionally, the Statistical DownScaling Model (SDSM) showed low R2 values in calibration and validation of the simulations with respect to observed data for the historic period. Overall, SDSM generated satisfactory results in simulating the monthly rainfall cycle of the entire basin. In this study, RegCM4 showed large rainfall errors and missed one rainfall season in the historic period. This study also explored whether the grid‐based rainfall time series of the Asian Precipitation—Highly Resolved Observational Daily Integration Towards Evaluation (APHRODITE) dataset could be used to enlarge and complement the sample of in situ observed rainfall time series. A spatial correlogram was used for observed and APHRODITE rainfall data to assess the consistency between the two data sources, which resulted in rejecting APHRODITE data. For the future time period (2041–2070) under RCPs 4.5 and 8.5 scenarios, rainfall projections did not show significant difference for both downscaling approaches. This may relate to the driving model (CanESM2 AOGCM) and not necessarily suggests poor performance of downscaling; either statistical or dynamical. Hence, the study recommends evaluating a multi‐model ensemble including other GCMs and RCMs for the same area of study

Evolution of surface gravity waves over a submarine canyon

The effects of a submarine canyon on the propagation of ocean surface waves are examined with a three-dimensional coupled-mode model for wave propagation over steep topography. Whereas the classical geometrical optics approximation predicts an abrupt transition from complete transmission at small incidence angles to no transmission at large angles, the full model predicts a more gradual transition with partial reflection/transmission that is sensitive to the canyon geometry and controlled by evanescent modes for small incidence angles and relatively short waves. Model results for large incidence angles are compared with data from directional wave buoys deployed around the rim and over Scripps Canyon, near San Diego, California, during the Nearshore Canyon Experiment (NCEX). Wave heights are observed to decay across the canyon by about a factor 5 over a distance shorter than a wavelength. Yet, a spectral refraction model predicts an even larger reduction by about a factor 10, because low frequency components cannot cross the canyon in the geometrical optics approximation. The coupled-mode model yields accurate results over and behind the canyon. These results show that although most of the wave energy is refractively trapped on the offshore rim of the canyon, a small fraction of the wave energy 'tunnels' across the canyon. Simplifications of the model that reduce it to the standard and modified mild slope equations also yield good results, indicating that evanescent modes and high order bottom slope effects are of minor importance for the energy transformation of waves propagating across depth contours at large oblique angles

Passive sampling and benchmarking to rank HOC levels in the aquatic environment

The identification and prioritisation of water bodies presenting elevated levels of anthropogenic chemicals is a key aspect of environmental monitoring programmes. Albeit this is challenging owing to geographical scales, choice of indicator aquatic species used for chemical monitoring, and inherent need for an understanding of contaminant fate and distribution in the environment. Here, we propose an innovative methodology for identifying and ranking water bodies according to their levels of hydrophobic organic contaminants (HOCs) in water. This is based on a unique passive sampling dataset acquired over a 10-year period with silicone rubber exposures in surface water bodies across Europe. We show with these data that, far from point sources of contamination, levels of hexachlorobenzene (HCB) and pentachlorobenzene (PeCB) in water approach equilibrium with atmospheric concentrations near the air/water surface. This results in a relatively constant ratio of their concentrations in the water phase. This, in turn, allows us to (i) identify sites of contamination with either of the two chemicals when the HCB/PeCB ratio deviates from theory and (ii) define benchmark levels of other HOCs in surface water against those of HCB and/or PeCB. For two polychlorinated biphenyls (congener 28 and 52) used as model chemicals, differences in contamination levels between the more contaminated and pristine sites are wider than differences in HCB and PeCB concentrations endorsing the benchmarking procedure

Reflections on psychological resilience:a comparison of three conceptually different operationalizations in predicting mental health

BACKGROUND: Psychological resilience refers to the ability to maintain mental health or recover quickly after stress. Despite the popularity of resilience research, there is no consensus understanding or operationalization of resilience. OBJECTIVE: We plan to compare three indicators of resilience that each involve a different operationalization of the construct: a) General resilience or one’s self-reported general ability to overcome adversities; b) Daily resilience as momentarily experienced ability to overcome adversities; and c) Recovery speed evident in the pattern of negative affect recovery after small adversities in daily life. These three indicators are constructed per person to investigate their cross-sectional associations, stability over time, and predictive validity regarding mental health. METHODS: Data will be derived from the prospective MIRORR study that comprises 96 individuals at different levels of psychosis risk and contains both single-time assessed questionnaires and 90-days intensive longitudinal data collection at baseline (T0) and three yearly follow-up waves (T1–T3). General resilience is assessed using the Brief Resilience Scale (BRS) at baseline. Daily resilience is measured by averaging daily resilience scores across 90 days. For recovery speed, vector-autoregressive models with consecutive impulse response simulations will be applied to diary data on negative affect and daily stressors to calculate pattern of affect recovery. These indicators will be correlated concurrently (at T0) to assess their overlap and prospectively (between T0 and T1) to estimate their stability. Their predictive potential will be assessed by regression analysis with mental health (SCL-90) as an outcome, resilience indicators as predictors, and stressful life events as a moderator. CONCLUSION: The comparison of different conceptualizations of psychological resilience can increase our understanding of its multifaceted nature and, in future, help improve diagnostic, prevention and intervention strategies aimed at increasing psychological resilience

Cortisol dynamics in depression:Application of a continuous-time process model

Background: The temporal dynamics of cortisol may be altered in depression. Optimally studying these dynamics in daily life requires specific analytical methods. We used a continuous-time multilevel process model to study set point (rhythm-corrected mean), variability around this set point, and regulation strength (speed with which cortisol levels regulate back to the set point after any perturbation). We examined the generalizability of the parameters across two data sets with different sampling and assay methods, and the hypothesis that regulation strength, but not set point or variability thereof, would be altered in depressed, compared to non-depressed individuals. Methods: The first data set is a general population sample of female twins (n = 523), of which 21 were depressed, with saliva samples collected 10 times a day for 5 days. The second data set consists of pair-matched clinically depressed and non-depressed individuals (n = 30), who collected saliva samples 3 times a day for 30 days. Set point, regulation strength and variability were examined using a Bayesian multilevel Ornstein-Uhlenbeck (OU) process model. They were first compared between samples, and thereafter assessed within samples in relation to depression. Results: Set point and variability of salivary cortisol were twice as high in the female twin sample, compared to the pair-matched sample. The ratio between set point and variability, as well as regulation strength, which are relative measures and therefore less affected by the specific assay method, were similar across samples. The average regulation strength was high; after an increase in cortisol, cortisol levels would decrease by 63 % after 10 min, and by 95 % after 30 min, but depressed individuals of the pair-matched sample displayed an even faster regulation strength. Conclusions: The relative parameters of the two data sets. The results suggest that regulation strength is increased in depressed individuals. We recommend the presented methodology for future studies and call for replications with more diverse depressed populations

Dynamic symptom networks across different at-risk stages for psychosis:An individual and transdiagnostic perspective

The clinical staging model distinguishes different stages of mental illness. Early stages, are suggested to be more mild, diffuse and volatile in terms of expression of psychopathology than later stages. This study aimed to compare individual transdiagnostic symptom networks based on intensive longitudinal data between individuals in different early clinical stages for psychosis. It was hypothesized that with increasing clinical stage (i) density of symptom networks would increase and (ii) psychotic experiences would be more central in the symptom networks. Data came from a 90-day diary study, resulting in 8640 observations within N = 96 individuals, divided over four subgroups representing different early clinical stages (n1 = 25, n2 = 27, n3 = 24, n4 = 20). Sparse Time Series Chain Graphical Models were used to create individual contemporaneous and temporal symptom networks based on 10 items concerning symptoms of depression, anxiety, psychosis, non-specific and vulnerability domains. Network density and symptom centrality (strength) were calculated individually and compared between and within the four subgroups. Level of psychopathology increased with clinical stage. The symptom networks showed large between-individual variation, but neither network density not psychotic symptom strength differed between the subgroups in the contemporaneous (pdensity = 0.59, pstrength > 0.51) and temporal (pdensity = 0.75, pstrength > 0.35) networks. No support was found for our hypothesis that higher clinical stage comes with higher symptom network density or a more central role for psychotic symptoms. Based on the high inter-individual variability, our results highlight the importance of individualized assessment of symptom networks