An Assessment of Surface Water Detection Methods for Water Resource Management in the Nigerien Sahel 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 costeffective 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 semiarid Sahel has not been welldocumented 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 semiarid 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.

An objectbased image analysis approach to assess irrigationwater consumption from MODIS products in Ethiopia Abstract   Efficient waterresource management is essential with regard to food security, growing populations and climate change. This is especially important for low and middleincome (LMC) countries where food is often locally produced by traditional smallholder farming. Detailed knowledge of the spatiotemporal distribution of irrigationwater consumption provides valuable information to anticipate local food shortages and water scarcity as a result of climate variability. Yet, adequate techniques to quantify irrigationwater consumption at field level over large areas are lacking. Irrigation estimates generally have a coarse resolution making them inadequate for fieldlevel assessments.  This study developed a remotesensingbased approach to quantify spatiotemporal patterns of irrigationwater consumption at field level using the MODIS evapotranspiration product (MOD16A2) and existing landuse maps on the spatiotemporal distribution of irrigated agriculture. Objectbased image analysis was used to establish local evapotranspiration differences between irrigated and rainfed fields on a monthly basis, which are the irrigationwater consumption rates of the irrigated fields. This novel method was applied to a study area in the Central Rift Valley in Ethiopia where smallholder farming is dominant and only a few largescale farms are present. Comparison with irrigationwaterconsumption values of a local irrigation scheme showed that the monthly temporal dynamics were captured quite well, but lower values were calculated compared to the schemeu0027s field data. Comparison with two validated remotesensing based studies in Africa showed good agreement as irrigationwaterconsumption estimates were in the same order of magnitude. Irrigationwater consumption follows the temporal rainfall pattern, i.e. irrigation practices intensify with increased water availability. Surface water is commonly used for irrigation in the study area.  Our study shows that smallholder practices have a lower irrigationwater consumption compared to modern largescale farms by approximately a factor 3. Irrigationwater consumption in the area is considerable, especially during the dry season. On average 32 % of excess water (precipitation xe2x80x93 evapotranspiration) is consumed for irrigation. For smallholder irrigation and largescale irrigation specifically this is 28 % and 63 % respectively.  The objectbased approach presented here is an operational mapping method for fieldlevel irrigationwaterconsumption over large areas. MOD16A2 is a global opensource readilyavailable evapotranspiration product used here although an evapotranspiration product with a higher spatial resolution might be preferred. Our approach can provide irrigationwaterconsumption estimates over large areas in datapoor regions, which will increase the understanding of spatiotemporal patterns of smallholder irrigation and provide information to optimize water use.