Metal-free functionalized carbonized cotton for efficient solar steam generation and wastewater treatment

Water desalination via solar steam generation is one of the most important technologies to address the increasingly pressing global water scarcity.

Application of Spectral Index-Based Logistic Regression to Detect Inland Water in the South Caucasus

The Caucasus is a diverse region with many climate zones that range from subtropical lowlands to mountainous alpine areas. The region is marked by irrigated croplands fed by irrigation canals, heavily vegetated wetlands, lakes, and reservoirs. In this study, we demonstrate the development of an improved surface water map based on a global water dataset to get a better understanding of the spatial distribution of small water bodies. First, we used the global water product from the European Commission Joint Research Center (JRC) to generate training data points by stratified random sampling. Next, we applied the optimal probability cut-off logistic regression model to develop surface water datasets for the entire Caucasus region, covering 19 Landsat tiles from May to October 2019. Finally, we used 6745 manually classified points (3261 non-water, 3484 water) to validate both the newly developed water dataset and the JRC global surface water dataset using an estimated proportion of area error matrix to evaluate accuracy. Our approach produced surface water extent maps with higher accuracy (89.2%) and detected 392 km2 more water than the global product (86.7% accuracy). We demonstrate that the newly developed method enables surface water detection of small ponds and lakes, flooded agricultural fields, and narrow irrigation channels, which are particularly important for mosquito-borne diseases.

The Global Water Body Layer from TanDEM-X Interferometric SAR Data

The interferometric synthetic aperture radar (InSAR) data set, acquired by the TanDEM-X (TerraSAR-X add-on for Digital Elevation Measurement) mission (TDM), represents a unique data source to derive geo-information products at a global scale. The complete Earth’s landmasses have been surveyed at least twice during the mission bistatic operation, which started at the end of 2010. Examples of the delivered global products are the TanDEM-X digital elevation model (DEM) (at a final independent posting of 12 m × 12 m) or the TanDEM-X global Forest/Non-Forest (FNF) map. The need for a reliable water product from TanDEM-X data was dictated by the limited accuracy and difficulty of use of the TDX Water Indication Mask (WAM), delivered as by-product of the global DEM, which jeopardizes its use for scientific applications, as well. Similarly as it has been done for the generation of the FNF map; in this work, we utilize the global data set of TanDEM-X quicklook images at 50 m × 50 m resolution, acquired between 2011 and 2016, to derive a new global water body layer (WBL), covering a range from −60∘ to +90∘ latitudes. The bistatic interferometric coherence is used as the primary input feature for performing water detection. We classify water surfaces in single TanDEM-X images, by considering the system’s geometric configuration and exploiting a watershed-based segmentation algorithm. Subsequently, single overlapping acquisitions are mosaicked together in a two-step logically weighting process to derive the global TDM WBL product, which comprises a binary averaged water/non-water layer as well as a permanent/temporary water indication layer. The accuracy of the new TDM WBL has been assessed over Europe, through a comparison with the Copernicus water and wetness layer, provided by the European Space Agency (ESA), at a 20 m × 20 m resolution. The F-score ranges from 83%, when considering all geocells (of 1∘ latitudes × 1∘ longitudes) over Europe, up to 93%, when considering only the geocells with a water content higher than 1%. At global scale, the quality of the product has been evaluated, by intercomparison, with other existing global water maps, resulting in an overall agreement that often exceeds 85% (F-score) when the content in the geocell is higher than 1%. The global TDM WBL presented in this study will be made available to the scientific community for free download and usage.

Distribution Matters: Sensitivity of Gridded Data of Population and Economic Conditions to Global Water Scarcity Assessment

Availability of water per capita is among the most fundamental water-scarcity indicators and has been used extensively in global grid-based water resources assessments. Recently, it has been extended to include the economic aspect, a proxy of the capability for water management. We applied the extended index globally under SSP–RCP scenarios using gridded population and economic conditions from two independent sources and unexpectedly found that the gridded data were significantly sensitive to global water-scarcity assessment. One projection assumed urban concentration of population and assets, whereas the other assumed dispersion. In analyses using multiple SSP–RCP scenarios representing a world of sustainability (SSP1–RCP2.6), regional rivalry (SSP3–RCP7.0), and fossil fuel development (SSP5–RCP8.5) in the future, multiple GCMs, and two gridded datasets showed that the water-scarce population ranges from 0.32–665 million. Uncertainties in the SSP–RCP and GCM scenarios were 6.58–489 million and 0.68–315 million, respectively. The population distribution assumption had a similar impact, with an uncertainty of 169–338 million. These results highlight the importance of the subregional distribution of socioeconomic factors for predicting the future global environment.