Given the heightened competition for water in energy,food,and the environment in Africa,it is essential to implement sound integrated plans for basin or regional sustainable development.Zambezi River Basin(ZRB),one of...Given the heightened competition for water in energy,food,and the environment in Africa,it is essential to implement sound integrated plans for basin or regional sustainable development.Zambezi River Basin(ZRB),one of the least developed basins in the world,is under development with great ambition for hydropower and irrigation infrastructure.Here,we proposed a framework to assess different water usage trajectories for agricultural and hydropower development scenarios with data derived from big earth data method.Three future scenarios were set for irrigaiton expansion and development hydropower construction according to current plan,global average and high level,respectively.Using spatial analysis methods,average evapotranspiration(ET)difference before and after irrigation expansion and reservoir construction was used to estimate water usage trajectories.Results show that the total available water resource for ZRB is estimated as 111.8 km3.Due to irrigation and reservoirs construction,additional annual water consumption is estimated to be 0.9 and 14.2 km3 for 2017,respectively.By analyzing the water-energy-food-environment(WEFE)nexus given water availability constraints,we found that the water development boundary in the ZRB could support increases in both irrigation proportion and dam density to global average levels of 20%and 0.56/104 km2,respectively,without degrading the environment.The proposed paradigm for assessing water resources has the potential to endow the ZRB with significant capacity to support the achievement of relevant Sustainable Development Goals(SDGs).展开更多
This study designed an approach to derive land-cover in the South Africa with insufficient ground samples, and made a case demonstration in Nzhelele and Levhuvu catchments, South Africa. The method was developed based...This study designed an approach to derive land-cover in the South Africa with insufficient ground samples, and made a case demonstration in Nzhelele and Levhuvu catchments, South Africa. The method was developed based on an integration of Landsat 8, Sentinel-1, and Shuttle Radar Topography Mission(SRTM) Digital Elevation Model(DEM), and the Google Earth Engine(GEE) platform. Random forest classifier with 300 trees is employed as land-cover classification model. In order to overcome the defect of insufficient ground data, the stratified sampling method was used to generate the training and validation samples from the existing land-cover product. Likewise, in order to recognize different land-cover categories, the percentile and monthly median composites were employed to expand input metrics of random forest classifier. Results showed that the overall accuracy of the land-cover of Nzhelele and Levhuvu catchments, South Africa in 2017–2018 reached to 76.43%. Three important results can be drawn from our research. 1) The participation of Sentinel-1 data can slightly improve overall accuracy of land-cover while its contribution on land-cover classification varied with land types. 2) Under-fitting problem was observed in the training of non-dominant land-cover categories using the random sampling, the stratified sampling method is recommended to make sure the classification accuracy of non-dominant classes. 3) When related reflectance bands participated in the training process, individual Normalized Difference Vegetation index(NDVI), Enhanced Vegetation Index(EVI), Soil Adjusted Vegetation Index(SAVI), Normalized Difference Built-up Index(NDBI) have little effect on final land-cover classification result.展开更多
基金Under the auspices of National Natural Science Foundation of China(No.41861144019,W2412015,42301409)。
文摘Given the heightened competition for water in energy,food,and the environment in Africa,it is essential to implement sound integrated plans for basin or regional sustainable development.Zambezi River Basin(ZRB),one of the least developed basins in the world,is under development with great ambition for hydropower and irrigation infrastructure.Here,we proposed a framework to assess different water usage trajectories for agricultural and hydropower development scenarios with data derived from big earth data method.Three future scenarios were set for irrigaiton expansion and development hydropower construction according to current plan,global average and high level,respectively.Using spatial analysis methods,average evapotranspiration(ET)difference before and after irrigation expansion and reservoir construction was used to estimate water usage trajectories.Results show that the total available water resource for ZRB is estimated as 111.8 km3.Due to irrigation and reservoirs construction,additional annual water consumption is estimated to be 0.9 and 14.2 km3 for 2017,respectively.By analyzing the water-energy-food-environment(WEFE)nexus given water availability constraints,we found that the water development boundary in the ZRB could support increases in both irrigation proportion and dam density to global average levels of 20%and 0.56/104 km2,respectively,without degrading the environment.The proposed paradigm for assessing water resources has the potential to endow the ZRB with significant capacity to support the achievement of relevant Sustainable Development Goals(SDGs).
基金Under the auspices of National Natural Science Foundation of China(No.4171101213,41561144013,41991232)National Key R&D Program of China(No.2016YFC0503401,2016YFA0600304)International Partnership Program of Chinese Academy of Sciences(No.121311KYSB20170004)。
文摘This study designed an approach to derive land-cover in the South Africa with insufficient ground samples, and made a case demonstration in Nzhelele and Levhuvu catchments, South Africa. The method was developed based on an integration of Landsat 8, Sentinel-1, and Shuttle Radar Topography Mission(SRTM) Digital Elevation Model(DEM), and the Google Earth Engine(GEE) platform. Random forest classifier with 300 trees is employed as land-cover classification model. In order to overcome the defect of insufficient ground data, the stratified sampling method was used to generate the training and validation samples from the existing land-cover product. Likewise, in order to recognize different land-cover categories, the percentile and monthly median composites were employed to expand input metrics of random forest classifier. Results showed that the overall accuracy of the land-cover of Nzhelele and Levhuvu catchments, South Africa in 2017–2018 reached to 76.43%. Three important results can be drawn from our research. 1) The participation of Sentinel-1 data can slightly improve overall accuracy of land-cover while its contribution on land-cover classification varied with land types. 2) Under-fitting problem was observed in the training of non-dominant land-cover categories using the random sampling, the stratified sampling method is recommended to make sure the classification accuracy of non-dominant classes. 3) When related reflectance bands participated in the training process, individual Normalized Difference Vegetation index(NDVI), Enhanced Vegetation Index(EVI), Soil Adjusted Vegetation Index(SAVI), Normalized Difference Built-up Index(NDBI) have little effect on final land-cover classification result.
