Overland flow on agricultural fields may have some undesirable effects such as soil erosion, flood and pollutant transport. To better understand this phenomenon and limit its consequences, we developed a code using state-of-the-art numerical methods: FullSWOF (Full Shallow Water equations for Overland Flow), an object oriented code written in C++. It has been made open-source and can be downloaded from this http URL. The model is based on the classical system of Shallow Water (SW) (or Saint-Venant system). Numerical difficulties come from the numerous dry/wet transitions and the highly-variable topography encountered inside a field. It includes runon and rainfall inputs, infiltration (modified Green-Ampt equation), friction (Darcy-Weisbach and Manning formulas). First we present the numerical method for the resolution of the Shallow Water equations integrated in FullSWOF_2D (the two-dimension version). This method is based on hydrostatic reconstruction scheme, coupled with a semi-implicit friction term treatment. FullSWOF_2D has been previously validated using analytical solutions from the SWASHES library (Shallow Water Analytic Solutions for Hydraulic and Environmental Studies). Finally, FullSWOF_2D is run on a real topography measured on a runoff plot located in Thies (Senegal). Simulation results are compared with measured data. This experimental benchmark demonstrate the capabilities of FullSWOF to simulate adequately overland flow. FullSWOF could also be used for other environmental issues, such as river floods and dam-breaks.

This article expands the tau-omega model to properly simulate L-band microwave emission of the soil-snow-vegetation continuum through a closed-form solution of Maxwell's equations, considering the intervening dry snow layer as a loss-less medium. The feasibility and uncertainty of retrieving vegetation optical depth (VOD) and ground permittivity, given the noisy L-band brightness temperatures with 1 K (1-sigma), are demonstrated through controlled numerical experiments. For moderately dense vegetation canopy and a range of 100--400 $kg.m^{-3}$ snow density, the standard deviation of the retrieval errors is 0.1 and 3.5 for VOD and ground permittivity respectively. Using L-band observations from the Soil Moisture Active Passive (SMAP) satellite, a new data set of global estimates of VOD and ground permittivity are presented over the Arctic boreal forests and permafrost areas during winter months. In the absence of dense ground-based observations of ground permittivity and VOD, the retrievals are causally validated using dependent variables including above-ground biomass, tree height, and net ecosystem exchange. Time-series analyses promise that the new data set can expand our understanding of the land-atmosphere interactions and exchange of carbon fluxes over the Arctic landscape.