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This investigation attempts to understand the eco‐hydrology of, and accordingly suggest an option to manage floodwater for agriculture in, the understudied and data‐sparse ephemeral Baraka River Basin within the hyper‐arid region of Sudan. Reference is made to the major feature of the basin, that is, the Toker Delta spate irrigation scheme. A point‐to‐pixel comparison of gridded and ground‐based data sets is performed to enhance the estimates of rainfall. Analysis of remotely sensed land use/cover data is performed. The results show a significant reduction of the grassland and barren areas explained by a significant expansion of the cropland and open shrubland (invasive mesquite trees) areas in the delta. The cotton sown area is highly dependent on the flooded area and the discharge volume in the delta. However, the area of this major crop has declined since the early 1990s in favour of cultivation of more profitable food crops. Expansion of mesquite in the delta is problematic, taking hold under increased floodwater, and can only be manged by clearance to provide crop cultivation area. There is a great potential for floodwater harvesting during the rainfall season (June to September). A total seasonal runoff volume of around 4.6 and 10.8 billion cubic metres is estimated at 90 and 50% probabilities of exceedance (reliabilities), respectively. Rather than leaving the runoff generated from rainfall events to pass to the Red Sea or be consumed by mesquite trees, a location for runoff harvesting structure in a highly suitable area is proposed. Such a structure will support any policy shifts towards planning and managing the basin water resources for use in irrigating the agricultural scheme.
Proper satellite-based crop monitoring applications at the farm-level often require near-daily imagery at medium to high spatial resolution. The combination of data from different ongoing satellite missions Sentinel 2 (ESA) and Landsat 7/8 (NASA) provides this unprecedented opportunity at a global scale; however, this is rarely implemented because these procedures are data demanding and computationally intensive. This study developed a robust stream processing for the harmonization of Landsat 7, Landsat 8 and Sentinel 2 in the Google Earth Engine cloud platform, connecting the benefit of coherent data structure, built-in functions and computational power in the Google Cloud. The harmonized surface reflectance images were generated for two agricultural schemes in Bekaa (Lebanon) and Ninh Thuan (Vietnam) during 2018–2019. We evaluated the performance of several pre-processing steps needed for the harmonization including the image co-registration,
Bidirectional Reflectance Distribution Functions correction, topographic correction, and band adjustment. We found that the misregistration between Landsat 8 and Sentinel 2 images varied from 10 m in Ninh Thuan (Vietnam) to 32 m in Bekaa (Lebanon), and posed a great impact on the quality of the final harmonized data set if not treated. Analysis of a pair of overlapped L8-S2 images over the Bekaa region showed that, after the harmonization, all band-to-band spatial correlations were greatly improved. Finally, we demonstrated an application of the dense harmonized data set for crop mapping and monitoring. An harmonic (Fourier) analysis was applied to fit the detected unimodal, bimodal and trimodal shapes in the temporal NDVI patterns during one crop year in Ninh Thuan province. The derived phase and amplitude values of the crop cycles were combined with max-NDVI as an R-G-B false composite image. The final image was able to highlight croplands in bright colors (high phase and amplitude), while the non-crop areas were shown with grey/dark (low phase and amplitude). The harmonized data sets (with 30 m spatial resolution) along with the Google Earth Engine scripts used are provided for public use.
Water security is a major concern for water-scarce cities that face dynamic water challenges due to limited water supply, climate change and increasing water demand. Framing urban water security is challenging due to the complexity and uncertainties of the definitions and assessment frameworks concerning urban water security. Several studies have assessed water security by granting priority indicators equal weight without considering or adapting to the local conditions. This study develops a new urban water security assessment framework with application to the water-scarce city
of Madaba, Jordan. The study applies the new assessment framework on the study area and measures urban water security using the integrated urban water security index (IUWSI) and the analytic hierarchy process (AHP) as a decision management tool to prioritise and distinguish indicators that affect the four dimensions of urban water security: drinking water, ecosystems, climate change and water-related hazards, and socioeconomic aspects (DECS). The integrated urban water security index (IUWSI) highlights the state of water security and intervention strategies in Madaba. The study reveals that urban water security in Madaba is satisfactory to meet basic needs, with shortcomings in some aspects of the DECS. However, Madaba faces poor security in terms of managing climate- and water-related risks. The IUWSI framework assists with a rational and evidence-based decision-making process, which is important for enhancing water resources management in water-scarce cities.
Against the background of a worldwide decrease in the number of gauging stations,the estimation of river discharge using spaceborne data is crucial for hydrological research, rivermonitoring, and water resource management. Based on the at-many-stations hydraulic geometry(AMHG) concept, a novel approach is introduced for estimating river discharge using Sentinel-1time series within an automated workflow. By using a novel decile thresholding method, no a prioriknowledge of the AMHG function or proxy is used, as proposed in previous literature. With arelative root mean square error (RRMSE) of 19.5% for the whole period and a RRMSE of 15.8%considering only dry seasons, our method is a significant improvement relative to the optimizedAMHG method, achieving 38.5% and 34.5%, respectively. As the novel approach is embedded intoan automated workflow, it enables a global application for river discharge estimation using solelyremote sensing data. Starting with the mapping of river reaches, which have large differences inriver width overthe year, continuous river width time series are created using high-resolution andweather-independent SAR imaging. It is applied on a 28 km long section of the Mekong River nearVientiane, Laos, for the period from 2015 to 2018.