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This study focuses on the ability of the global Land Data Assimilation System, LDAS-Monde, to improve the representation of land surface variables (LSVs) over Burkina-Faso through the joint assimilation of satellite derived surface soil moisture (SSM) and leaf area index (LAI) from January 2001 to June 2018. The LDAS-Monde offline system is forced by the latest European Centre for Medium-Range Weather Forecasts (ECMWF) atmospheric reanalysis ERA5 as well as ERA-Interim former reanalysis, leading to reanalyses of LSVs at 0.25° × 0.25° and 0.50° × 0.50° spatial resolution, respectively. Within LDAS-Monde, SSM and LAI observations from the Copernicus Global Land Service (CGLS) are assimilated with a simplified extended Kalman filter (SEKF) using the CO2-responsive version of the ISBA (Interactions between Soil, Biosphere, and Atmosphere) land surface model (LSM). First, it is shown that ERA5 better represents precipitation and incoming solar radiation than ERA-Interim former reanalysis from ECMWF based on in situ data. Results of four experiments are then compared: Open-loop simulation (i.e., no assimilation) and analysis (i.e., joint assimilation of SSM and LAI) forced by either ERA5 or ERA-Interim. After jointly assimilating SSM and LAI, it is noticed that the assimilation is able to impact soil moisture in the first top soil layers (the first 20 cm), and also in deeper soil layers (from 20 cm to 60 cm and below), as reflected by the structure of the SEKF Jacobians. The added value of using ERA5 reanalysis over ERA-Interim when used in LDAS-Monde is highlighted. The assimilation is able to improve the simulation of both SSM and LAI: The analyses add skill to both configurations, indicating the healthy behavior of LDAS-Monde. For LAI in particular, the southern region of the domain (dominated by a Sudan-Guinean climate) highlights a strong impact of the assimilation compared to the other two sub-regions of Burkina-Faso (dominated by Sahelian and Sudan-Sahelian climates). In the southern part of the domain, differences between the model and the observations are the largest, prior to any assimilation. These differences are linked to the model failing to represent the behavior of some specific vegetation species, which are known to put on leaves before the first rains of the season. The LDAS-Monde analysis is very efficient at compensating for this model weakness. Evapotranspiration estimates from the Global Land Evaporation Amsterdam Model (GLEAM) project as well as upscaled carbon uptake from the FLUXCOM project and sun-induced fluorescence from the Global Ozone Monitoring Experiment-2 (GOME-2) are used in the evaluation process, again demonstrating improvements in the representation of evapotranspiration and gross primary production after assimilation.

We analysed in the work how change in land use/land cover influences on flood characteristics (frequency and magnitude) using a model inter-comparison approach, statistical methods and two land use scenarios (land use scenario A and land use scenario B) for three time horizons. The derived land use maps from these scenarios were considered as forcing inputs to two physically based hydrological models (SWAT and WaSiM). The generalized Pareto distribution combined with the Poisson distribution was used to compute flood frequency and magnitude. Under land use scenario A, croplands increase at the annual rate of 0.7% while under land use scenario B, it increases by 1.13% between 2003 and 2029. The expansion of croplands indubitably enhances flood risks. Although there was a general agreement about the sense of the variation, the magnitude of change in flood characteristics was highly influenced by the model type. The rate of increase in flood quantiles simulated from SWAT (0.36-1.3% for 10-year flood) was smaller than the corresponding magnitude of changes simulated from WaSiM (2.6-7.0% for 10-year flood) whatever the scenarios. The expansion of agricultural and pasture lands at the yearly rate of 0.7% under land use scenario A (respectively, 1.13% under land use scenario B) leads to an increase of 3.6% (respectively, 5.4%) in 10-year flood by considering WaSiM. This study is among the first of its kind to establish a strong statistical relation between flood severity/frequency and agricultural land expansion and natural vegetation reduction. The results of this study are relevant and useful to the scientific research community as well as the decision makers for framing appropriate policy decisions towards the management of extreme events and the land use planning/management in future in the region.

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Adoption of good hygiene and sanitation practices is a major determining factor in household well-being and development of community members. In this study, the influence of socio-demographic factors on household hygiene and sanitation behaviour was assessed using interviews. The results revealed that 68% of households did not cover their containers during the transport of water; 58% of respondents defecated in water and 31% in the open air.Only 40% of households washed their hands with water and soap after defecation; 42% of respondents discharged their wastes into the water body while 34% discharged them into the natural environment; 49% of the respondents evacuated their wastewater into the water body while 51% preferred to pour them into the wild. Most of the practices of the populations were not conducive for ensuring healthy drinking water quality and preserving their