Hydrodynamic relationships between gravel pit lakes and aquifers: brief review and insights from numerical investigations

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Whole mitochondrial genomes unveil the impact of domestication on goat matrilineal variability

Background: The current extensive use of the domestic goat (Capra hircus) is the result of its medium size and high adaptability as multiple breeds. The extent to which its genetic variability was influenced by early domestication practices is largely unknown. A common standard by which to analyze maternally-inherited variability of livestock species is through complete sequencing of the entire mitogenome (mitochondrial DNA, mtDNA). Results: We present the first extensive survey of goat mitogenomic variability based on 84 complete sequences selected from an initial collection of 758 samples that represent 60 different breeds of C. hircus, as well as its wild sister species, bezoar (Capra aegagrus) from Iran. Our phylogenetic analyses dated the most recent common ancestor of C. hircus to ~460,000 years (ka) ago and identified five distinctive domestic haplogroups (A, B1, C1a, D1 and G). More than 90 % of goats examined were in haplogroup A. These domestic lineages are predominantly nested within C. aegagrus branches, diverged concomitantly at the interface between the Epipaleolithic and early Neolithic periods, and underwent a dramatic expansion starting from ~12–10 ka ago. Conclusions: Domestic goat mitogenomes descended from a small number of founding haplotypes that underwent domestication after surviving the last glacial maximum in the Near Eastern refuges. All modern haplotypes A probably descended from a single (or at most a few closely related) female C. aegagrus. Zooarchaelogical data indicate that domestication first occurred in Southeastern Anatolia. Goats accompanying the first Neolithic migration waves into the Mediterranean were already characterized by two ancestral A and C variants. The ancient separation of the C branch (~130 ka ago) suggests a genetically distinct population that could have been involved in a second event of domestication. The novel diagnostic mutational motifs defined here, which distinguish wild and domestic haplogroups, could be used to understand phylogenetic relationships among modern breeds and ancient remains and to evaluate whether selection differentially affected mitochondrial genome variants during the development of economically important breeds

Signatures-and-sensitivity-based multi-criteria variational calibration for distributed hydrological modeling applied to Mediterranean floods

Classical calibration methods in hydrology are commonly performed with a single cost function computed on long time series. Even though the hydrological model has acceptable scores in NSE and KGE, unbalancing problems can still arise between overall score and the model performance for flood events, and particularly flash floods. Enhancing multi-criteria calibration methods with multi-scale signatures to improve distributed flood modeling remains a challenge. In this study, the potential of hydrological signatures computed continuously and at the scale of flood events on long time series, is employed within various multi-criteria calibration approaches to attain a more efficient hydrological model. This work presents an improved and original signature-based calibration approach, implemented in the variational data assimilation algorithm of SMASH (Spatially distributed Modelling and ASsimilation for Hydrology) platform, applied over 141 catchments mostly located in the French Mediterranean region. Several signatures, especially flood event signatures are firstly computed, relying on a proposed automatic hydrograph segmentation algorithm. Suitable signatures for constraining the model are selected based on their global sensitivity analysis to model parameters. Several multi-criteria calibration strategies with the selected signatures are eventually performed, including a multi-objective optimization approach, and a single-objective optimization approach, that transforms the multi-criteria problem into a single-objective function. Note that in the first approach, the proposed technique based on a simple additive weighting method is used to select an optimal solution obtained from a set of non-inferior solutions. The suggested methods show that, for a global calibration, the average relative error in simulating the peak flow has been dropped from about 0.27 to 0.01-0.08 and from about 0.30 to 0.18-0.21 with various multi-criteria optimization strategies, respectively in calibration and temporal validation. For a distributed calibration, while the average NSE (resp. KGE) still slightly decreases from 0.78 (resp. 0.86) to 0.75 (resp. 0.81) in calibration, the quality of simulated peak flow has been enhanced about 1.5 times in average. In particular, the NSE (resp. KGE) calculated solely on 111 flood events which are picked from 23 downstream gauges has been improved from 0.80 (resp. 0.71) up to 0.83 (resp. 0.78) in median. These results have demonstrated the robustness and delicacy of the model constrained by the signatures for enhancing flash flood forecasting systems

Signatures-and-sensitivity-based multi-criteria variational calibration for distributed hydrological modeling applied to Mediterranean floods

Classical calibration methods in hydrology are commonly performed with a single cost function computed on long time series. Even though the hydrological model has acceptable scores in NSE and KGE, unbalancing problems can still arise between overall score and the model performance for flood events, and particularly flash floods. Enhancing multi-criteria calibration methods with multi-scale signatures to improve distributed flood modeling remains a challenge. In this study, the potential of hydrological signatures computed continuously and at the scale of flood events on long time series, is employed within various multi-criteria calibration approaches to attain a more efficient hydrological model. This work presents an improved and original signature-based calibration approach, implemented in the variational data assimilation algorithm of SMASH (Spatially distributed Modelling and ASsimilation for Hydrology) platform, applied over 141 catchments mostly located in the French Mediterranean region. Several signatures, especially flood event signatures are firstly computed, relying on a proposed automatic hydrograph segmentation algorithm. Suitable signatures for constraining the model are selected based on their global sensitivity analysis to model parameters. Several multi-criteria calibration strategies with the selected signatures are eventually performed, including a multi-objective optimization approach, and a single-objective optimization approach, that transforms the multi-criteria problem into a single-objective function. Note that in the first approach, the proposed technique based on a simple additive weighting method is used to select an optimal solution obtained from a set of non-inferior solutions. The suggested methods show that, for a global calibration, the average relative error in simulating the peak flow has been dropped from about 0.27 to 0.01-0.08 and from about 0.30 to 0.18-0.21 with various multi-criteria optimization strategies, respectively in calibration and temporal validation. For a distributed calibration, while the average NSE (resp. KGE) still slightly decreases from 0.78 (resp. 0.86) to 0.75 (resp. 0.81) in calibration, the quality of simulated peak flow has been enhanced about 1.5 times in average. In particular, the NSE (resp. KGE) calculated solely on 111 flood events which are picked from 23 downstream gauges has been improved from 0.80 (resp. 0.71) up to 0.83 (resp. 0.78) in median. These results have demonstrated the robustness and delicacy of the model constrained by the signatures for enhancing flash flood forecasting systems

SMASH -SPATIALLY DISTRIBUTED MODELLING AND ASSIMILATION FOR HYDROLOGY: PYTHON WRAPPING TOWARDS ENHANCED RESEARCH-TO-OPERATIONS TRANSFER

International audienceThe distributed SMASH platform is based on a gridded mesh and on a modular design. On each cell, the model features different hydrological components. Each component offers different modeling options such as snow modules, surface interception, production, transfer and percolation functions. At the grid scale, different routing models are implemented via a cell-to-cell numerical routing scheme. S MASH comes with its numerical adjoint model which is obtained by automatic differentiation with Tapenade. A variational data assimilation algorithm is implemented and helps to calibrate the distributed parameters or evaluate the model states. This algorithm uses the quasi-Newton lbfgs-b descent algorithm and the gradient of the cost function relative to the model parameters and states. This gradient is computed by a run of the adjoint model. T he numerical SMASH platform is a Fortran code. To gain in modularity and facilitate the use of SMASH in the research and engineering communities, a Python interface has been created with the new F90Wrap software. The original Fortran code has been revamped. The new structure enables to 1) control any inputs and outputs with Python, 2) keep an automatically differentiable and computationally efficient numerical Fortran model, 3) call a binary from the shell to preserve a backward compatibility with old practices. T he key to achieve this Python interface is to use Fortran modules and derived types to store all inputs and outputs variables. These Fortran structures are stored in different modules. F 90Wrap automatically generates the fortran functions and wrappers to give access to every component of each derived type. A Python class is generated to facilitate the use of these wrappers inside a Python code. T he Python object "model" aggregates all inputs/outputs variables required by SMASH. The "model" object comes with built-in methods to allow end users to perform simulations, calibrations, plotting and hdf5 export. The Python binding facilitates all post-processing since it does not requires I/O into text files anymore

Advanced Hybrid Data Assimilation for Parameter Regionalization within a Differentiable Spatially Distributed Hydrological Model and Uncertainty Correction with Bidirectional LSTM

Parameter regionalization for ungauged basins is an important and difficult topic in hydrology. The challenge becomes even more pronounced when seeking for flexible transfer operators that link physical descriptors to spatially distributed parameters of a conceptual model and impose spatial constraints needed, given sparse observation data. This work presents the Hybrid Data Assimilation Parameter Regionalization (HDA-PR) approach employing accurate adjoint-based cost gradients to learn complex transfer operators designed for high-resolution hydrological models. The core idea of HDA-PR lies in incorporating inferable regionalization mappings, such as multivariate regressions or neural networks, into a differentiable hydrological model and its variational data assimilation process. This integration enables the exploitation of the valuable information contained in heterogeneous datasets across extensive spatio-temporal computational domains, especially when dealing with high-dimensional regionalization problems. HDA-PR is thoroughly tested at multiple resolutions over several basin sets with contrasted sizes and characteristics, using worldwide and regional databases. The results showcase high discharge modeling performances in calibration and in spatio-temporal validation at pseudo-ungauged sites. The parametric stability and information extraction from databases are analyzed. Finally, a bidirectional LSTM network is trained on top of the regionalized hydrological model to predict modeling errors at multiple temporal scales. All methods are implemented in the open source SMASH package, and the regionalization method can be adapted for state-parameter correction from multi-source data, at multiple time scales such as for operational data assimilation. Furthermore, it can be applied to other differentiable geophysical models

Multi-gauge Hydrological Variational Data Assimilation: Regionalization Learning with Spatial Gradients using Multilayer Perceptron and Bayesian-Guided Multivariate Regression

International audienceTackling the difficult problem of estimating spatially distributed hydrological parameters, especially for floods on ungauged watercourses, this contribution presents a novel seamless regionalization technique for learning complex regional transfer functions designed for high-resolution hydrological models. The transfer functions rely on: (i) a multilayer perceptron enabling a seamless flow of gradient computation to employ machine learning optimization algorithms, or (ii) a multivariate regression mapping optimized by variational data assimilation algorithms and guided by Bayesian estimation, addressing the equifinality issue of feasible solutions. The approach involves incorporating the inferable regionalization mappings into a differentiable hydrological model and optimizing a cost function computed on multi-gauge data with accurate adjoint-based spatially distributed gradients

Multiscale Learnable Physical Modeling and Data Assimilation Framework: Application to High-Resolution Regionalized Hydrological Simulation of Flash Floods

To advance the discovery of scale-relevant hydrological laws while better exploiting massive multi-source data, merging machine learning into process-based modeling is compelling, as recently demonstrated in lumped hydrological modeling. This article introduces MLPM-PR, a new and powerful framework standing for Multiscale spatially distributed Learnable Physical Modeling and learnable Parameter Regionalization with data assimilation. MLPM-PR crucially builds on a differentiable model that couples (i) two neural networks for processes learning and parameters regionalization, (ii) grid-based conceptual hydrological operators, and (iii) a simple kinematic wave routing. The approach is tested on a challenging flash flood-prone multi-catchment modeling setup at high spatio-temporal resolution (1km, 1h). Discharge prediction performances highlight the accuracy and robustness of MLPM-PR compared to classical approaches in both spatial and temporal validation. The physical interpretability of spatially distributed parameters and internal states shows the nuanced behavior of the hybrid model and its adaptability to diverse hydrological responses

Le second âge du Fer en Aquitaine orientale. Apport des recherches réalisées récemment dans le Gers et ses marges

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