Reservoir construction and land use change are altering sediment transport within river systems at a global scale. Changes in sediment transport can impact river morphology, aquatic ecosystems, and ultimately the growth and retreat of delta environments. The Lower Mekong Basin is crucial to five neighboring countries for transportation, energy production, sustainable water supply, and food production. In response, countries have coordinated to develop programs for regional scale water quality monitoring that including surface sediment concentrations (SSSC); however, these programs are based on a limited number of point measurements and due to resource limitations, cannot provide comprehensive insights into sediment transport across all strategic locations within the Lower Mekong Basin. To augment in situ SSSC data from the current monitoring program, we developed an empirical model to estimate SSSC across the Lower Mekong Basin from Landsat observations. Model validation revealed that remotely sensed SSSC estimates captured the spatial and temporal dynamics in a range of aquatic environments (main stem of Mekong river, tributary systems, Mekong Floodplain, and reservoirs) while, on average, slightly underestimating SSSC by about 2 mg·L${}^{-1}$ across all settings. The operational SSSC model was developed and implemented using Google Earth Engine and Google App Engine was used to host an online application that allows users, without any knowledge of remote sensing, to access SSSC data across the region. Expanded access to SSSC data should be particularly helpful for resource managers and other stakeholders seeking to understand the dynamics between surface sediment concentrations and land use conversions, water policy, and energy production in a globally strategic region.

Water is a scarce, but essential resource in the Sahel. Rainfed ephemeral ponds and lakes that dot the landscape are necessary to the livelihoods of smallholder farmers and pastoralists who rely on these resources to irrigate crops and hydrate cattle. The remote location and dispersed nature of these water bodies limits typical methods of monitoring, such as with gauges; fortunately, remote sensing offers a quick and cost-effective means of regularly measuring surface water extent in these isolated regions. Dozens of operational methods exist to use remote sensing to identify waterbodies, however, their performance when identifying surface water in the semi-arid Sahel has not been well-documented and the limitations of these methods for the region are not well understood. Here, we evaluate two global dynamic surface water datasets, fifteen spectral indices developed to classify surface water extent, and three simple decision tree methods created specifically to identify surface water in semi-arid environments. We find that the existing global surface water datasets effectively minimize false positives, but greatly underestimate the presence and extent of smaller, more turbid water bodies that are essential to local livelihoods, an important limitation in their use for monitoring water availability. Three of fifteen spectral indices exhibited both high accuracy and threshold stability when evaluated over different areas and seasons. The three simple decision tree methods had mixed performance, with only one having an overall accuracy that compared to the best performing spectral indices. We find that while global surface water datasets may be appropriate for analysis at the global scale, other methods calibrated to the local environment may provide improved performance for more localized water monitoring needs.

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.

West Africa (WA) is prone to food insecurity due to climate-, economic-, conflict-related shocks, as well as high population growth and lack of proper adaptation strategies. As per the USAID’s Famine Early Warning Systems Network, which uses Integrated Phase Classification to classify acute food insecurity (AFI), between 2011 and early 2020, several parts of WA reported the “Stressed” phase of AFI >30% of the time. Food security and livelihood in the region relies substantially on rainfed farming and small-scale water holes. Droughts lead to water deficits resulting in adverse impacts on food production, human and livestock health and agricultural labor opportunities, leading to or worsening of food insecurity. Thus far, the focus of climate, drought outlooks and their impacts, to support food insecurity early warning in this region has mainly been on the seasonal scale (i.e., 3-6 months in future) forecasts whereas use of subseasonal scale (2-4 weeks in future) forecasts has been negligible. Recent advances in routine production (i.e. weekly) and open access to subseasonal forecasts provide an unprecedented opportunity to improve the existing climate services in the region by focusing on the impacts of subseasonal climate characteristics on food insecurity in the region. Here we report on an ongoing project with the AGRiculture HYdrology and METeorology Regional Centre (SERVIR’s WA Hub) that aims to develop a subseasonal water deficit forecasting system to support food insecurity early warning in the region. The presentation will describe  (i) the results of an ongoing analysis examining the influence of subseasonal climate characteristics (e.g. monthly climate variability, length of dry or wet spell) on food insecurity, as measures by different food insecurity indicators (such as vegetation index, food insecurity reports and household level health and malnutrition reports) and (ii) the major accomplishments towards implementation of the water deficit forecasting system, including development and evaluation of prototype products, (iii) capacity building and stakeholder engagement activities with National Meteorological and Hydrological Services across the region.