Data assimilation is widely used to improve flood forecasting capability, especially through parameter inference requiring statistical information on the uncertain input parameters (upstream discharge, friction coefficient) as well as on the variability of the water level and its sensitivity with respect to the inputs. For particle filter or ensemble Kalman filter, stochastically estimating probability density function and covariance matrices from a Monte Carlo random sampling requires a large ensemble of model evaluations, limiting their use in real-time application. To tackle this issue, fast surrogate models based on Polynomial Chaos and Gaussian Process can be used to represent the spatially distributed water level in place of solving the shallow water equations. This study investigates the use of these surrogates to estimate probability density functions and covariance matrices at a reduced computational cost and without the loss of accuracy, in the perspective of ensemble-based data assimilation. This study focuses on 1-D steady state flow simulated with MASCARET over the Garonne River (SouthWest France). Results show that both surrogates feature similar performance to the Monte-Carlo random sampling, but for a much smaller computational budget; a few MASCARET simulations (on the order of 10-100) are sufficient to accurately retrieve covariance matrices and probability density functions all along the river, even where the flow dynamic is more complex due to heterogeneous bathymetry.

Assessing epistemic uncertainties is considered as a milestone for improving numerical predictions of a dynamical system. In hydrodynamics, uncertainties in input parameters translate into uncertainties in simulated water levels through the shallow water equations. We investigate the ability of generalized polynomial chaos (gPC) surrogate to evaluate the probabilistic features of water level simulated by a 1-D hydraulic model (MASCARET) with the same accuracy as a classical Monte Carlo method but at a reduced computational cost. This study highlights that the water level probability density function and covariance matrix are better estimated with the polynomial surrogate model than with a Monte Carlo approach on the forward model given a limited budget of MASCARET evaluations. The gPC-surrogate performance is first assessed on an idealized channel with uniform geometry and then applied on

Abstract. The hydrology of Morocco is characterized by significant spatial variability. Precipitation follows a sharp gradient, decreasing from the north to the south. In order to redistribute the available water, a project has been proposed to transfer 860×106 m3 yr−1 from the wet north to the arid southern regions, namely the “Water Highway” project. The present study aims to address the viability of the project after accounting for the impacts of climate change in the watersheds located in the north. We perform regional climate model (RCM) simulations over the study region using boundary conditions from five different global circulation models (GCMs) and assuming two different emissions scenarios – RCP4.5 (with mitigation) and RCP8.5 (business as usual). The impact on precipitation and temperature are assessed, and the decrease in the available water quantity is estimated. Under RCP8.5, the project is likely not feasible. However, under the RCP4.5, a rescaled version of this project may be feasible depending on how much water is allocated to satisfy the local water demand in the north.

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(2020) 'Stochastic model reduction for polynomial chaos expansion of acoustic waves using proper orthogonal decomposition.', Reliability engineering system safety., 195. p. 106733.

Abstract. The High Atlas, culminating at more than 4000 m, is the water tower of Morocco. While plains receive less than 400 mm of precipitation in an average year, the mountains can get twice as much, often in the form of snow between November and March. Snowmelt thus accounts for a large fraction of the river discharge in the region, particularly during spring. In parallel, future climate change projections point towards a significant decline in precipitation and enhanced warming of temperature for the area. Here, we build on previous research results on snow and climate modelling in the High Atlas to make detailed projections of snowpack and river flow response to climate change in this region. We develop end-of-century snowpack projections using a distributed energy balance snow model based on SNOW-17 and high-resolution climate simulations over Morocco with the MIT Regional Climate Model (MRCM) under a mitigation (RCP4.5) and a business-as-usual (RCP8.5) scenario. Snowpack water content is projected to decline by up to 60 % under RCP4.5 and 80 % under RCP8.5 as a consequence of strong warming and drying in the region. We also implement a panel regression framework to relate runoff ratios to regional meteorological conditions in seven small sub-catchments in the High Atlas. Relative humidity and the fraction of solid-to-total precipitation are found to explain about 30 % of the inter-annual variability in runoff ratios. Due to projected future atmospheric drying and the associated decline in snow-to-precipitation ratio, a 5 %–30 % decrease in runoff ratios and 10 %–60 % decrease in precipitation are expected to lead to severe (20 %–70 %) declines in river discharge. Our results have important implications for water resources planning and sustainability of agriculture in this already water-stressed region.

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