Hybrid Model Approach To Water Monitoring Network Design

Hybrid modeling approach including regionalization method, entropy technique, and Bayesian multiobjective optimization algorithm is proposed for optimum water monitoring network design. For hydrometric network design, all the components of the hybrid model are used as follows. Robust regionalization method is used to generate streamflow at all possible locations of new stations, and dual entropy-multiobjective optimization methods are used to optimize the number and locations of the new stations. For precipitation (rainfall or snowfall) network design, only the dual entropy-multiobjective optimization modules are used to optimize the number and locations of new stations based on an initial grid points which can be from remote sensing database (e.g. SNODAS) or interpolated ground observations. In addition to joint entropy and total correlation, other constraints such as cost, flow signatures, water vulnerability indicators, can be added to further optimize the number and locations of new stations. The hybrid model can also be applied to classified physiographic units to design optimal minimum network that meets the World Meteorological Organization minimum network standards. Three applications are proposed to assess the effectiveness of the hybrid model. This includes the design of optimum hydrometric networks for the middle St-Lawrence River basin and the St-John River basin; the design of optimum rainfall network for St-John River basin and an optimum snow network for the Columbia River basin. For each case study, the hybrid model appears a robust tool for designing optimum networks. Optimal number of stations and locations are determined for each solution (or optimum network) that is part of the Pareto front. Furthermore the hybrid model appears a robust and flexible method that can be further extended to include groundwater and water quality networks design

Temporal neural networks for downscaling climate variability and extremes

This paper presents an application of temporal neural networks for downscaling global climate models (GCMs) output. Because of computational constraints, GCMs are usually run at coarse grid resolution (in the order of 100s of kilometres) and as a result they are inherently unable to present local sub-grid scale features and dynamics. Consequently, outputs from these models cannot be used directly in many climate change impact studies. This research explored the issues of 'downscaling' the outputs of GCMs using a temporal neural network (TNN) approach. The method is proposed for downscaling daily precipitation and temperature series for a region in northern Quebec, Canada. The downscaling models are developed and validated using large-scale predictor variables derived from the National Center for Environmental Prediction (NCEP) reanalysis data set. The performance of the temporal neural network downscaling model is also compared to a regression-based statistical downscaling model with emphasis on their ability in reproducing the observed climate variability and extremes. The downscaling results for the base period suggest that the TNN is an efficient method for downscaling both daily precipitation as well as daily maximum and minimum temperature series. Furthermore, the different model test results indicate that the TNN model mostly outperforms the statistical models for the downscaling of daily precipitation extremes and variability

Event-based model calibration approaches for selecting representative distributed parameters in semi-urban watersheds

The final publication is available at Elsevier via http://dx.doi.org/10.1016/j.advwatres.2018.05.013 © 2018. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/The objective of this study is to propose an event-based calibration approach for selecting representative semi-distributed hydrologic model parameters and to enhance peak flow prediction at multiple sites of a semi-urban catchment. The performance of three multi-site calibration approaches (multi-site simultaneous (MS-S), multi-site average objective function (MS-A) and multi-event multi-site (ME-MS)) and a benchmark at-catchment outlet (OU) calibration method, are compared in this study. Additional insightful contributions include assessing the nature of the spatio-temporal parameter variability among calibration events and developing an advanced event-based calibration approach to identify skillful model parameter-sets. This study used a SWMM5 hydrologic model in the Humber River Watershed located in Southern Ontario, Canada. For MS-S and OU calibration methods, the multi-objective calibration formulation is solved with the Pareto Archived Dynamically Dimensioned Search (PA-DDS) algorithm. For the MS-A and ME-MS methods, the single objective calibration formulation is solved with the Dynamically Dimensioned Search (DDS) algorithm. The results indicate that the MS-A calibration approach achieved better performance than other considered methods. Comparison between optimized model parameter sets showed that the DDS optimization in MS-A approach improved the model performance at multiple sites. The spatial and temporal variability analysis indicates a presence of uncertainty on sensitive parameters and most importantly on peak flow responses in an event-based calibration process. This finding implied the need to evaluate potential model parameters sets with a series of calibration steps as proposed herein. The proposed calibration and optimization formulation successfully identified representative model parameter set, which is more skillful than what is attainable when using simultaneous multi-site (MS-S), multi-event multi-site (MS-ME) or at basin outlet (OU) approach.Natural Sciences and Engineering Research Council of Canada [NETGP 451456

Influence of Reproductive Rhythm and Weaning Age on Fertility and Body Condition of Local Breed Does in the District of Abidjan

The objective of this study is to evaluate the reproductive performance of locally bred rabbits by comparing the production of females mated 11 days postpartum (semi-intensive R42) with those mated 25 days postpartum (extensive rhythm R56). Females are naturally protruding.120 rabbits selected from a private farm in Bingerville in the district of Abidjan were followed during the experiment. Receptivity and gestation rates were not significantly influenced (p>0.05) by the reproductive rhythm in the breeding females. Fertility in multiparous females showed a higher rate in the extensive rhythm (89-100%). The semi-intensive rhythm had the highest stillbirth rate (5.6%) and pre-weaning morbidity (14.03%). However, after weaning, morbidity was higher in bunnies in the extensive rhythm (13.6%). In the extensive rhythm, the highest values were observed for the number of weaned bunnies and the survival rate of breeding females. The extensive rhythm significantly increases the longevity of does with a high mortality rate of bunny rabbits. These results could be indicators for further investigation in the search for an optimum rate of rabbit reproduction

Régénération assistée du karité (Vitellaria paradoxa C. F. Gaertn.) dans les parcs agroforestiers au Burkina Faso

Le karité (Vitellaria paradoxa C. F. Gaertn.) présente une importance sociale, culturelle, économique et alimentaire pour le Burkina Faso où il occupe la quatrième place dans les produits d’exportation. La pérennité de cette espèce dont les populations rurales sont tributaires, est compromise par de multiples phénomènes dont le manque de régénération, les pratiques culturales et la coupe abusive du bois pour satisfaire les besoins énergétiques. L’objectif de cette étude est de proposer des méthodes adéquates pour rajeunir les parcs à karité au Burkina Faso. Cinq sites de recherche ont été choisis suivant un gradient phytogéographique : Sobaka, Noumoudara et Kakoumana (secteur sud soudanien), Gonsé (secteur nord soudanien) et Bouria (secteur sub sahélien). Les essais effectués dans chaque parcelle choisie, ont comporté trois répétitions et 7 traitements, représentés par les différentes techniques de régénération (plantation, transplantation, semis direct libre, semis dans les buissons, régénération naturelle assistéeRNA-, induction de drageon, induction de pousse adventive). Les résultats indiquent que le taux de survie de la régénération naturelle assistée est audessus de 70% après deux années de suivi. Les plantations et les semis directs dans les buissons sont aussi des techniques efficaces pour la régénération/restauration des parcs à karité, avec respectivement des taux de survie de 13,33% et 6,67% dans le secteur sub sahélien, 12,22% et 6% dans le nord soudanien, et 55,56% et 25,33% dans le sud soudanien après la troisième année de suivi. En conclusion, la régénération assistée est la technique la plus efficiente pour restaurer les parcs à karité. The Shea (Vitellaria paradoxa C. F. Gaertn.) is of enormous social, cultural, economic and nutritional importance for Burkina Faso where it ranks fourth in exports. The sustainability of this species on which rural populations depend is compromised by multiple phenomena, including the lack of regeneration of shea tree and the abusive cutting of wood to meet energy needs. This work aimed to propose adequate methods for rejuvenating shea parkland in Burkina Faso. Five sites distributed along a phytogeographic gradient were selected for tests : Sobaka, Noumoudara and Kakoumana (South-Sudanian phytogeographic zone), Gonsé (NorthSudanian phytogeographic zone) and Bouria (sub-Sahelian phytogeographic zone). The field trials included three repetitions by regeneration technique (planting, transplanting, direct sowing free, sowing in bushes and assisted natural regeneration -ANR-, sucker induction, induction of adventitious growth). The results indicate that the survival rate of assisted natural regeneration is above 70% after two years of follow-up. Planting and direct seeding in bushes are also effective techniques for the regeneration / restoration of shea tree parks with respectively survival rates of 13.33% and 6.67% in the sub sahelian sector, 12.22% and 6% in north sudanian, and 55.56% and 25.33% in south sudanian, after the third year of monitoring. In conclusion, in order to restore the shea parks, all seedlings regardless of their origins, will need to be assisted i

The evolving SARS-CoV-2 epidemic in Africa: Insights from rapidly expanding genomic surveillance

INTRODUCTION Investment in Africa over the past year with regard to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) sequencing has led to a massive increase in the number of sequences, which, to date, exceeds 100,000 sequences generated to track the pandemic on the continent. These sequences have profoundly affected how public health officials in Africa have navigated the COVID-19 pandemic. RATIONALE We demonstrate how the first 100,000 SARS-CoV-2 sequences from Africa have helped monitor the epidemic on the continent, how genomic surveillance expanded over the course of the pandemic, and how we adapted our sequencing methods to deal with an evolving virus. Finally, we also examine how viral lineages have spread across the continent in a phylogeographic framework to gain insights into the underlying temporal and spatial transmission dynamics for several variants of concern (VOCs). RESULTS Our results indicate that the number of countries in Africa that can sequence the virus within their own borders is growing and that this is coupled with a shorter turnaround time from the time of sampling to sequence submission. Ongoing evolution necessitated the continual updating of primer sets, and, as a result, eight primer sets were designed in tandem with viral evolution and used to ensure effective sequencing of the virus. The pandemic unfolded through multiple waves of infection that were each driven by distinct genetic lineages, with B.1-like ancestral strains associated with the first pandemic wave of infections in 2020. Successive waves on the continent were fueled by different VOCs, with Alpha and Beta cocirculating in distinct spatial patterns during the second wave and Delta and Omicron affecting the whole continent during the third and fourth waves, respectively. Phylogeographic reconstruction points toward distinct differences in viral importation and exportation patterns associated with the Alpha, Beta, Delta, and Omicron variants and subvariants, when considering both Africa versus the rest of the world and viral dissemination within the continent. Our epidemiological and phylogenetic inferences therefore underscore the heterogeneous nature of the pandemic on the continent and highlight key insights and challenges, for instance, recognizing the limitations of low testing proportions. We also highlight the early warning capacity that genomic surveillance in Africa has had for the rest of the world with the detection of new lineages and variants, the most recent being the characterization of various Omicron subvariants. CONCLUSION Sustained investment for diagnostics and genomic surveillance in Africa is needed as the virus continues to evolve. This is important not only to help combat SARS-CoV-2 on the continent but also because it can be used as a platform to help address the many emerging and reemerging infectious disease threats in Africa. In particular, capacity building for local sequencing within countries or within the continent should be prioritized because this is generally associated with shorter turnaround times, providing the most benefit to local public health authorities tasked with pandemic response and mitigation and allowing for the fastest reaction to localized outbreaks. These investments are crucial for pandemic preparedness and response and will serve the health of the continent well into the 21st century

Inter-Comparison of Different Bayesian Model Averaging Modifications in Streamflow Simulation

Bayesian model averaging (BMA) is a popular method using the advantages of forecast ensemble to enhance the reliability and accuracy of predictions. The inherent assumptions of the classical BMA has led to different variants. However, there is not a comprehensive examination of how these solutions improve the original BMA in the context of streamflow simulation. In this study, a scenario-based analysis was conducted for assessment of various modifications and how they affect BMA results. The evaluated modifications included using various streamflow ensembles, data transformation procedures, distribution types, standard deviation forms, and optimization methods. We applied the proposed analysis in two data-poor watersheds located in northern Ontario, Canada. The results indicate that using more representative distribution types do not significantly improve BMA-derived results, while the positive effect of implementing non-constant variance on BMA probabilistic performance cannot be ignored. Also, higher reliability was obtained by applying a data transformation procedure; however, it can reduce the results’ sharpness significantly. Moreover, although considering many streamflow simulations as ensemble members does not always enhance BMA results, using different forcing precipitation scenarios besides multi-models led to better BMA-based probabilistic simulations in data-poor watersheds. Also, the reliability of the expectation-maximization algorithm in estimating BMA parameters was confirmed