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Spatially explicit, near real time information on surface water dynamics is critical for understanding changes in water resources, and for long-term water security planning. The distribution of surface water across the African continent since 1984 and updated as every new Landsat scene becomes available is presented here, and validated for the continent for the first time. We applied the Water Observations from Space (WOfS) algorithm, developed and well-tested in Australia, to every Landsat scene acquired over Africa since the mid 1980s to provide spatial information on surface water dynamics over the past 30+ years. We assessed the accuracy of WOfS using aerial and satellite imagery. Four regional geospatial organisations, coordinated through the Digital Earth Africa Product Development Task Team, conducted the validation campaign and provided both the regional expertise and experience required for a continental-scale validation effort. We assessed whether the point was wet, dry, or cloud covered, for each of the 12 months in 2018, resulting in 34,800 labelled observations. As waterbodies larger than 100 km2 are easy to identify with Landsat resolution data and can thus boost accuracy, these were masked out. The resulting overall accuracy of the water classification was 82%. WOfS in Africa is expected to be used by ministries and departments of agriculture and water across the continent, by international organisations, academia, and the private sector. A large-scale collaborative effort, which included regional and technical skills spanning two continents was required to create a service that is regionally accurate and is both hosted on, and implemented operationally from, the African continent.

The availability of current land cover and land use (LCLU) information for monitoring the status of land resources has considerable value in ensuring sustainable land use planning and development. Similarly, the need to provide updated information on the extent of LCLU change in West Africa has become apparent, given the increasing demand for land resources driven by rapid population growth. Over the past decade, multiple projects have been undertaken to produce regional and national land cover maps. However, using different classification systems and legends has made updating and sharing land cover information challenging. This has resulted in the inefficient use of human and financial resources. The development of the Land Cover Meta Language (LCML) based on International Organization for Standardization (ISO) standards offers an opportunity to create a standardized classification system. This system would enable easier integration of regional and national data, efficient management of information, and better resource utilization in West Africa. This article emphasizes the process and the need for multistakeholder collaboration in developing a standardized land cover classification system for West Africa, which is currently nonexistent. It presents the survey data collected to evaluate historical, current, and future land cover mapping projects in the region and provides relevant use cases as examples for operationalizing a standardized land cover classification legend for West Africa.

Groundwater modeling is a useful tool for assessing sustainability in water resources planning. However, groundwater models are difficult to construct in regions with limited data availability, areas where planning is most crucial. We illustrate how remote sensing data can be used with limited in situ data to build and calibrate a regional groundwater model in the Goulbi Maradi alluvial aquifer in southern Niger in Western Africa. We used data from the NASA Gravity Recovery and Climate Experiment (GRACE) satellite mission to estimate recharge rates, the primary source of water to the aquifer. We used the groundwater storage changes obtained from GRACE data from 2009 to 2021 to establish an overall water budget. We used this water budget to back-calculate groundwater withdrawals from pumping in the region. There are only very limited historic data on withdrawals. This approach allowed us to calibrate the model and use it as a predictive tool to analyze the impact of various assumptions about future recharge and groundwater extraction patterns associated with the development of groundwater infrastructure in the region. The results indicate that water extraction from the Goulbi Maradi alluvial aquifer is sustainable, even if current groundwater extraction is increased by up to 28%.

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Recent studies have estimated annual flood loss at over two billion USD dollars in damage per year globally. This figure is likely to increase as populations grow, people move to flood prone areas, and flooding dynamics change. Digital Elevation Models are a primary input into many flood models; therefore, the accuracy and resolution of these data sets have implications for model accuracy that are not completely understood. An increasing number of DEMs are becoming open and freely available, which presents analysts with the challenge of selecting the most appropriate inputs for their specific use case, especially in countries such as Niger where these global DEMs have not been formally evaluated. This study compares the absolute vertical accuracy of four global and freely available DEMs: Shuttle Radar Topography Mission, ASTER, Advanced Land Observing Satellite, and MERIT, with reference to a high-resolution LiDAR DEM, in southern Niger. Specifically, the Height Above Nearest Drainage (HAND) model is used to investigate the impact of resolution and error on simulated flood extent. There are currently no studies that assess the vertical accuracy of global DEMs in Niger nor that investigate the impact of the DEM geometric variability on flood extents. The ALOS DEM showed the lowest Root Mean Square Error of 1.19 m when compared to the LiDAR DEM, while ASTER had the highest RMSE of 4.2 m. The results of the Height Above Nearest Drainage model showed that at floods under 3 m in depth, the higher resolution DEMs simulated less flood extent; however, once the flood depth exceeded 3 m, the higher the resolution the DEM, the larger the flood extent. These findings suggest that both absolute error and relative error, as well as resolution, should be addressed when applying a DEM to a flood model.

Accurate characterization of groundwater resources is required for sustainable management. Due to the cost of installing monitoring wells and challenges in collecting and managing in situ data, groundwater data are sparse—especially in developing countries. In this study, we demonstrate an analysis of long-term groundwater storage changes using temporally sparse but spatially dense well data, where each well had as few as one historical groundwater measurement. We developed methods to synthetically estimate groundwater table elevation (WTE) times series by clustering wells using two different methods; a uniform grid and k-means-constrained clustering to create pseudo-wells. These pseudo-wells had a more complete groundwater level time history, which we then temporally and spatially interpolated to analyze groundwater storage changes in an aquifer. We demonstrated these methods on the Beryl-Enterprise aquifer in Utah, USA, where other researchers quantified the groundwater storage depletion rate, and the wells had a large number of historical measurements. We randomly used one measurement per well and showed that our methods yielded storage depletion rates similar to published values. We applied the method to a region in southern Niger where wells had only one measurement per well, and showed that our estimated groundwater storage change trend reasonably matched that which was calculated using GRACE satellite data.

Accurately assessing groundwater storage changes in Niger is critical for long-term water resource management but is difficult due to sparse field data. We present a study of groundwater storage changes and recharge in Southern Niger, computed using data from NASA Gravity Recovery and Climate Experiment (GRACE) mission. We compute a groundwater storage anomaly estimate by subtracting the surface water anomaly provided by the Global Land Data Assimilation System (GLDAS) model from the GRACE total water storage anomaly. We use a statistical model to fill gaps in the GRACE data. We analyze the time period from 2002 to 2021, which corresponds to the life span of the GRACE mission, and show that there is little change in groundwater storage from 2002–2010, but a steep rise in storage from 2010–2021, which can partially be explained by a period of increased precipitation. We use the Water Table Fluctuation method to estimate recharge rates over this period and compare these values with previous estimates. We show that for the time range analyzed, groundwater resources in Niger are not being overutilized and could be further developed for beneficial use. Our estimated recharge rates compare favorably to previous estimates and provide managers with the data required to understand how much additional water could be extracted in a sustainable manner.