Corneal Transduction by Intra-Stromal Injection of AAV Vectors In Vivo in the Mouse and Ex Vivo in Human Explants

The cornea is a transparent, avascular tissue that acts as the major refractive surface of the eye. Corneal transparency, assured by the inner stroma, is vital for this role. Disruption in stromal transparency can occur in some inherited or acquired diseases. As a consequence, light entering the eye is blocked or distorted, leading to decreased visual acuity. Possible treatment for restoring transparency could be via viral-based gene therapy. The stroma is particularly amenable to this strategy due to its immunoprivileged nature and low turnover rate. We assayed the potential of AAV vectors to transduce keratocytes following intra-stromal injection in vivo in the mouse cornea and ex vivo in human explants. In murine and human corneas, we transduced the entire stroma using a single injection, preferentially targeted keratocytes and achieved long-term gene transfer (up to 17 months in vivo in mice). Of the serotypes tested, AAV2/8 was the most promising for gene transfer in both mouse and man. Furthermore, transgene expression could be transiently increased following aggression to the cornea

Performance and Uncertainty Evaluation of Snow Models on Snowmelt Flow Simulations over a Nordic Catchment (Mistassibi, Canada)

An analysis of hydrological response to a multi-model approach based on an ensemble of seven snow models (SM; degree-day and mixed degree-day/energy balance models) coupled with three hydrological models (HM) is presented for a snowmelt-dominated basin in Canada. The present study aims to compare the performance and the reliability of different types of SM-HM combinations at simulating snowmelt flows over the 1961–2000 historical period. The multi-model approach also allows evaluating the uncertainties associated with the structure of the SM-HM ensemble to better predict river flows in Nordic environments. The 20-year calibration shows a satisfactory performance of the ensemble of 21 SM-HM combinations at simulating daily discharges and snow water equivalents (SWEs), with low streamflow volume biases. The validation of the ensemble of 21 SM-HM combinations is conducted over a 20-year period. Performances are similar to the calibration in simulating the daily discharges and SWEs, again with low model biases for streamflow. The spring-snowmelt-generated peak flow is captured only in timing by the ensemble of 21 SM-HM combinations. The results of specific hydrologic indicators show that the uncertainty related to the choice of the given HM in the SM-HM combinations cannot be neglected in a more quantitative manner in simulating snowmelt flows. The selection of the SM plays a larger role than the choice of the SM approach (degree-day versus mixed degree-day/energy balance) in simulating spring flows. Overall, the snow models provide a low degree of uncertainty to the total uncertainty in hydrological modeling for snow hydrology studies

NAC2H:The North-American Climate Change and hydroclimatology dataset

This dataset contains hydrometeorological and hydroclimatological data for 3540 watersheds in North America. The dataset contains four main parts: (a) Observed hydrometeorological data including daily streamflow observations, precipitation, minimum temperature and maximum temperature; (b) 20 bias-corrected climate model projections for 2 Representative Concentration Pathway (RCP) scenarios and 5 bias-correction methods; (c) hydrological model calibration parameters and simulated streamflow for 4 hydrological models, 2 objective functions and 10 calibration parameter sets for the reference period; and (d) hydrological simulations for each of the combinations of the abovementioned elements of the climate change impact study chain, for a total of 16000 combinations. The dataset also contains simulations and bias-corrected climate for 30-year horizons corresponding to 1.5°C and 2°C temperature increases for a subset of the climate models, for an additional 8000 combinations. All simulations in the reference period are also provided. 51 pre-computed hydrological indices are made available for each simulation. Overall, 2.89 trillion years of simulations are classified, analyzed, compressed and made available for all researchers. This dataset can be used to evaluate the uncertainty of various components in the impact study chain, to establish relationships between catchment properties and hydrological response to climate change and to evaluate the spatial distribution of hydrological change according to a multitude of hydrological indices. Please see the associated publication in Water Resources Research: Arsenault, R., Brissette, F., Chen, J., Guo, Q. and Dallaire, G., 2020. NAC2H: The North American Climate Change and Hydroclimatology Data Set. Water Resources Research, 56(8), p.e2020WR027097. https://doi.org/10.1029/2020WR02709

Impact of parameter set dimensionality and calibration procedures on streamflow prediction at ungauged catchments

Spatial proximity, physical similarity and multiple linear regression are implemented on 266 snowmelt dominated catchments located in Québec, Canada. This paper evaluates: (1) the impact of the parameter set dimensionality by comparing 6, 9 and 15 free parameters structures of the GR4J hydrological model coupled to the CemaNeige snow model and; (2) the impact of the parameter set calibration method by comparing SCE-UA, CMAES and a uniform random sampling procedure. Results show that physical similarity performs better than spatial proximity and that both methods outperform multiple linear regression. Among 12 catchment descriptors, the percentage of water and geographical coordinates are the most relevant for this region. Results show that 9 free parameters are globally sufficient to regionalize the snow covered catchments but that 15 free parameters are necessary for lower quality time-series or catchments dominated by arctic or subarctic climates, high water storage capacity or low annual precipitation. Compared to complex models, parsimonious models are more robust in regionalization but their lower performance in model calibration results in lower performance in regionalization. Results show a relationship between the robustness of the parameter sets generated by the calibration procedures and their dispersion within the parameter space. Uniform random sampling is the most robust calibration method but shows an overall performance that is similar to both optimization algorithms because of its weaker performance in model calibration

A daily stochastic weather generator for preserving low-frequency of climate variability

s u m m a r y Weather generators are computer models that produce time series of meteorological data that have similar statistical properties as that of observed data. The past decade has seen a sharp and renewed increase in interest in weather generators, linked to their potential use in climate change studies. One appealing property of weather generators is their ability to rapidly produce time series of unlimited length, thus permitting impact studies of rare occurrences of meteorological variables. However, one problem with daily weather generators is that they underestimate monthly and inter-annual variances because they do not take into account the low-frequency component of climate variability. This research aims to present an approach for correcting the low-frequency variability of weather variables for weather generator and to assess its ability to reproduce key statistical parameters at the daily, monthly and yearly scales. The approach is applied to precipitation which is usually the variable displaying the largest inter-annual variability. The daily stochastic precipitation model is a Richardson-based weather generator that uses a first-order two-state Markov chain for precipitation occurrence and a gamma distribution for precipitation amounts. Low-frequency variability was modeled based on observed power spectra of monthly and annual time series. Generation of synthetic monthly and yearly precipitation data was achieved by assigning random phases for each spectral component. This preserved the power spectra, variances and the autocorrelation functions at the monthly and annual scales. The link to daily parameters was established through linear functions. The quality of these corrections was assessed through direct and indirect validation tests, with the direct validation focusing on comparing the means, standard deviations and autocorrelations of different weather series. The results showed that standard deviations of both monthly and annual precipitations were produced almost exactly. The proposed method also preserved the autocorrelation of annual precipitation. The indirect validation involved modelling the discharge of a river basin using a hydrological model driven by different precipitation series. The results showed that the corrected weather series significantly improved the variability of simulated flow discharges at the monthly and annual scales compared to those simulated using the data generated by the standard weather generator

Hydrological response to dynamical downscaling of climate model outputs: A case study for western and eastern snowmelt-dominated Canada catchments

Study region: An analysis of hydrological response to a dynamically downscaled multi-member multi-model global climate model (GCM) ensemble of simulations based on the Canadian Regional Climate Model (CRCM) is presented for three snowmelt-dominated basins in Canada. The basins are situated in the western mountainous (British Columbia) and eastern level (Quebec) regions in Canada, providing comprehensive experiments to validate the CRCM over various topographic features. Study focus: The evaluation of the CRCM as a tool to improve GCM simulations of catchment scale hydrology is investigated within the bounds of uncertainty associated with RCM simulations. Daily climate variables were extracted from a 30-year CRCM and GCM ensemble simulations. The hydrological response was assessed through the comparison of catchment water components simulated by SWAT. New hydrological insights for the region: Results show that the CRCM captures the primary features of observed climate, but there are significant biases. Most noteworthy are a positive bias in precipitation and a negative bias in temperature over the BC basin. When looking at the hydrological modeling results, the benefit of using the RCM versus GCMs emerged distinctly for the mountainous BC basin where the RCM is preferred over the GCMs. The sensitivity experiments show that uncertainty in the GCM/RCM’s internal variability must be assessed to provide suitable regional hydrological responses to climate change