Surface observations are usually too few and far between to properly assess multidecadal variations at the local scale and characterize historical local extreme events at the same time. A data assimilation scheme has been recently presented to assimilate daily observations of temperature and precipitation into downscaled reconstructions from a global extended reanalysis through an Ensemble Kalman fitting approach and to derive high-resolution fields. Recent studies also showed that assimilating observations at high temporal resolution does not guarantee correct multidecadal variations. The current paper thus proposes (1) to apply the data assimilation scheme over France and over the 1871–2012 period based on the SCOPE Climate reconstructions background dataset and all available daily historical surface observations of temperature and precipitation, (2) to develop an assimilation scheme at the yearly timescale and to apply it over the same period and lastly, (3) to derive the FYRE Climate reanalysis, a 25-member ensemble hybrid dataset resulting from the daily and yearly assimilation schemes, spanning the whole 1871–2012 period at a daily and 8 km resolution over France. Assimilating daily observations only allows reconstructing accurately daily characteristics, but fails in reproducing robust multidecadal variations when compared to independent datasets. Combining the daily and yearly assimilation schemes, FYRE Climate clearly performs better than the SCOPE Climate background in terms of bias, error, and correlation, but also better than the Safran reference surface reanalysis over France available from 1958 onward only. FYRE Climate also succeeds in reconstructing both local extreme events and multidecadal variability. It is freely available at https://doi.org/10.5281/zenodo.4005573 (precipitation, Devers et al., 2020b) and https://doi.org/10.5281/zenodo.4006472 (temperature, Devers et al., 2020c).

The knowledge of historical French weather has recently been improved through the development of the Spatially COherent Probabilistic Extended (SCOPE) climate reconstructions. This high‐resolution ensemble daily reconstruction dataset of precipitation and temperature covers the period 1871–2012 and is derived through a statistical downscaling of the Twentieth Century Reanalysis. Historical surface observations – even though rather scarce and sparse – do exist from at least the beginning of the period considered, and this information does not currently feed SCOPE Climate. We propose to construct a new high‐resolution surface reanalysis over France – called FYRE Climate (French hYdrometeorological REanalysis Climate) – by assimilating daily temperature and precipitation observations into SCOPE Climate through an offline Ensemble Kalman Filter. The goal of the study is to test methodological choices for developing the future FYRE Climate surface reanalysis. The data assimilation scheme is evaluated over the 2009–2012 period with an increasing assimilated observation density reproducing historical densities. A consistent set of independent stations is retained for validation in terms of continuous ranked probability score, daily bias and daily correlation. Results highlight the importance of the localization as well as the Gaussian anamorphosis for precipitation. They show that: (a) the reanalysis has a lower uncertainty than the initial SCOPE Climate reconstructions, (b) its reliability is similar, and (c) the data assimilation allows reaching the performance of the reference Safran surface reanalysis over France, even for a density of observations as low as the one of 1950. These results thus pave the way for a full 140‐year high‐resolution precipitation and temperature surface reanalysis over France.

Vegetative filter strips are often used for protecting surface waters from pollution transferred by surface runoff in agricultural watersheds. In Europe, they are often prescribed along the stream banks, where a seasonal shallow water table (WT) could decrease the buffer zone efficiency. In spite of this potentially important effect, there are no systematic experimental or theoretical studies on the effect of this soil boundary condition on the VFS efficiency. In the companion paper (Muñoz-Carpena et al., 2018), we developed a physically based numerical algorithm (SWINGO) that allows the representation of soil infiltration with a shallow water table. Here we present the dynamic coupling of SWINGO with VFSMOD, an overland flow and transport mathematical model to study the WT influence on VFS efficiency in terms of reductions of overland flow, sediment, and pesticide transport. This new version of VFSMOD was applied to two contrasted benchmark field studies in France (sandy-loam soil in a Mediterranean semicontinental climate, and silty clay in a temperate oceanic climate), where limited testing of the model with field data on one of the sites showed promising results. The application showed that for the conditions of the studies, VFS efficiency decreases markedly when the water table is 0 to 1.5 m from the surface. In order to evaluate the relative importance of WT among other input factors controlling VFS efficiency, global sensitivity and uncertainty analysis (GSA) was applied on the benchmark studies. The most important factors found for VFS overland flow reduction were saturated hydraulic conductivity and WT depth, added to sediment characteristics and VFS dimensions for sediment and pesticide reductions. The relative importance of WT varied as a function of soil type (most important at the silty-clay soil) and hydraulic loading (rainfall + incoming runoff) at each site. The presence of WT introduced more complex responses dominated by strong interactions in the modeled system response, reducing the typical predominance of saturated hydraulic conductivity on infiltration under deep water table conditions. This study demonstrates that when present, the WT should be considered as a key hydrologic factor in buffer design and evaluation as a water quality mitigation practice.