Multimodel uncertainty changes in simulated river flows induced by human impact parameterizations

Human impacts increasingly affect the global hydrological cycle and indeed dominate hydrological changes in some regions. Hydrologists have sought to identify the human-impact-induced hydrological variations via parameterizing anthropogenic water uses in global hydrological models (GHMs). The consequently increased model complexity is likely to introduce additional uncertainty among GHMs. Here, using four GHMs, between-model uncertainties are quantified in terms of the ratio of signal to noise (SNR) for average river flow during 1971–2000 simulated in two experiments, with representation of human impacts (VARSOC) and without (NOSOC). It is the first quantitative investigation of between-model uncertainty resulted from the inclusion of human impact parameterizations. Results show that the between-model uncertainties in terms of SNRs in the VARSOC annual flow are larger (about 2% for global and varied magnitude for different basins) than those in the NOSOC, which are particularly significant in most areas of Asia and northern areas to the Mediterranean Sea. The SNR differences are mostly negative (-20% to 5%, indicating higher uncertainty) for basin-averaged annual flow. The VARSOC high flow shows slightly lower uncertainties than NOSOC simulations, with SNR differences mostly ranging from -20% to 20%. The uncertainty differences between the two experiments are significantly related to the fraction of irrigation areas of basins. The large additional uncertainties in VARSOC simulations introduced by the inclusion of parameterizations of human impacts raise the urgent need of GHMs development regarding a better understanding of human impacts. Differences in the parameterizations of irrigation, reservoir regulation and water withdrawals are discussed towards potential directions of improvements for future GHM development. We also discuss the advantages of statistical approaches to reduce the between-model uncertainties, and the importance of calibration of GHMs for not only better performances of historical simulations but also more robust and confidential future projections of hydrological changes under a changing environment

Intercomparison of global river discharge simulations focusing on dam operation --- Part II: Multiple models analysis in two case-study river basins, Missouri-Mississippi and Green-Colorado

We performed a twofold intercomparison of river discharge regulated by dams under multiple meteorological forcings among multiple global hydrological models for a historical period by simulation. Paper II provides an intercomparison of river discharge simulated by five hydrological models under four meteorological forcings. This is the first global multimodel intercomparison study on dam-regulated river flow. Although the simulations were conducted globally, the Missouri-Mississippi and Green-Colorado Rivers were chosen as case-study sites in this study. The hydrological models incorporate generic schemes of dam operation, not specific to a certain dam. We examined river discharge on a longitudinal section of river channels to investigate the effects of dams on simulated discharge, especially at the seasonal time scale. We found that the magnitude of dam regulation differed considerably among the hydrological models. The difference was attributable not only to dam operation schemes but also to the magnitude of simulated river discharge flowing into dams. That is, although a similar algorithm of dam operation schemes was incorporated in different hydrological models, the magnitude of dam regulation substantially differed among the models. Intermodel discrepancies tended to decrease toward the lower reaches of these river basins, which means model dependence is less significant toward lower reaches. These case-study results imply that, intermodel comparisons of river discharge should be made at different locations along the river’s course to critically examine the performance of hydrological models because the performance can vary with the locations

Restructuring of the Gut Microbiome by Intermittent Fasting Prevents Retinopathy and Prolongs Survival in db/db Mice

Intermittent fasting (IF) protects against the development of metabolic diseases and cancer, but whether it can prevent diabetic microvascular complications is not known. In db/db mice, we examined the impact of long-term IF on diabetic retinopathy (DR). Despite no change in glycated hemoglobin, db/db mice on the IF regimen displayed significantly longer survival and a reduction in DR end points, including acellular capillaries and leukocyte infiltration. We hypothesized that IF-mediated changes in the gut microbiota would produce beneficial metabolites and prevent the development of DR. Microbiome analysis revealed increased levels of Firmicutes and decreased Bacteroidetes and Verrucomicrobia. Compared with db/db mice on ad libitum feeding, changes in the microbiome of the db/db mice on IF were associated with increases in gut mucin, goblet cell number, villi length, and reductions in plasma peptidoglycan. Consistent with the known modulatory effects of Firmicutes on bile acid (BA) metabolism, measurement of BAs demonstrated a significant increase of tauroursodeoxycholate (TUDCA), a neuroprotective BA, in db/db on IF but not in db/db on AL feeding. TGR5, the TUDCA receptor, was found in the retinal primary ganglion cells. Expression of TGR5 did not change with IF or diabetes. However, IF reduced retinal TNF-α mRNA, which is a downstream target of TGR5 activation. Pharmacological activation of TGR5 using INT-767 prevented DR in a second diabetic mouse model. These findings support the concept that IF prevents DR by restructuring the microbiota toward species producing TUDCA and subsequent retinal protection by TGR5 activation

Multi-model evaluation of catchment- and global-scale hydrological model simulations of drought characteristics across eight large river catchments

Although global- and catchment-scale hydrological models are often shown to accurately simulate long-term runoff time-series, far less is known about their suitability for capturing hydrological extremes, such as droughts. Here we evaluated simulations of hydrological droughts from nine catchment scale hydrological models (CHMs) and eight global scale hydrological models (GHMs) for eight large catchments: Upper Amazon, Lena, Upper Mississippi, Upper Niger, Rhine, Tagus, Upper Yangtze and Upper Yellow. The simulations were conducted within the framework of phase 2a of the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP2a). We evaluated the ability of the CHMs, GHMs and their respective ensemble means (Ens-CHM and Ens-GHM) to simulate observed hydrological droughts of at least one month duration, over 31 years (1971–2001). Hydrological drought events were identified from runoff-deficits and the Standardised Runoff Index (SRI). In all catchments, the CHMs performed relatively better than the GHMs, for simulating monthly runoff-deficits. The number of drought events identified under different drought categories (i.e. SRI values of -1 to -1.49, -1.5 to -1.99, and ≤-2) varied significantly between models. All the models, as well as the two ensemble means, have limited abilities to accurately simulate drought events in all eight catchments, in terms of their occurrence and magnitude. Overall, there are opportunities to improve both CHMs and GHMs for better characterisation of hydrological droughts

Caenorhabditis elegans Genomic Response to Soil Bacteria Predicts Environment-Specific Genetic Effects on Life History Traits

With the post-genomic era came a dramatic increase in high-throughput technologies, of which transcriptional profiling by microarrays was one of the most popular. One application of this technology is to identify genes that are differentially expressed in response to different environmental conditions. These experiments are constructed under the assumption that the differentially expressed genes are functionally important in the environment where they are induced. However, whether differential expression is predictive of functional importance has yet to be tested. Here we have addressed this expectation by employing Caenorhabditis elegans as a model for the interaction of native soil nematode taxa and soil bacteria. Using transcriptional profiling, we identified candidate genes regulated in response to different bacteria isolated in association with grassland nematodes or from grassland soils. Many of the regulated candidate genes are predicted to affect metabolism and innate immunity suggesting similar genes could influence nematode community dynamics in natural systems. Using mutations that inactivate 21 of the identified genes, we showed that most contribute to lifespan and/or fitness in a given bacterial environment. Although these bacteria may not be natural food sources for C. elegans, we show that changes in food source, as can occur in environmental disturbance, can have a large effect on gene expression, with important consequences for fitness. Moreover, we used regression analysis to demonstrate that for many genes the degree of differential gene expression between two bacterial environments predicted the magnitude of the effect of the loss of gene function on life history traits in those environments

Porcine Deltacoronaviruses: Origin, Evolution, Cross-Species Transmission and Zoonotic Potential

Porcine deltacoronavirus (PDCoV) is an emerging enteropathogenic coronavirus of swine that causes acute diarrhoea, vomiting, dehydration and mortality in seronegative neonatal piglets. PDCoV was first reported in Hong Kong in 2012 and its etiological features were first characterized in the United States in 2014. Currently, PDCoV is a concern due to its broad host range, including humans. Chickens, turkey poults, and gnotobiotic calves can be experimentally infected by PDCoV. Therefore, as discussed in this review, a comprehensive understanding of the origin, evolution, cross-species transmission and zoonotic potential of epidemic PDCoV strains is urgently needed