Assessment of vulnerability to water shortage in semi-arid river basins: The value of demand reduction and storage capacity

Highly Efficient Arginine Intercalated Graphene Oxide Composite Membranes for Water Desalination

Graphene oxide-based composite membranes have received enormous attention for highly efficient water desalination. Herein, we prepare arginine/graphene oxide (Arg/GO) composite membranes by surface functionalizing GO nanosheets with arginine amino acid. Arginine has a unique combination of hydroxyl and amino functional groups that cross-link GO nanosheets through hydrogen bonding and electrostatic interactions. The as-prepared Arg@GO composite membranes with different thicknesses are used to separate the salt and dye molecules. The 900-nm-thick Arg@GO composite membrane shows high rejection of 98% for NaCl and 99.8% for MgCl2, Ni(NO3)2, and Pb(NO3)2 with good water permeance. Such a membrane also shows a high separation efficiency (100%) for methylene blue, rhodamine B, and Evans blue dyes. At the same time, the ultrathin Arg@GO composite membrane (220 ± 10 nm) exhibits high water permeance of up to 2100 ± 10 L m-2 h-1 bar-1. Furthermore, the 900-nm-thick Arg@GO composite membrane is stable in an aqueous environment for 40 days with significantly less swelling. Therefore, these membranes can be utilized in future desalination and separation applications.

Real-time reservoir operation using inflow and irrigation demand forecasts in a reservoir-regulated river basin

Real-time reservoir operation using inflow and irrigation demand forecasts can help reservoir system managers make effective water management decisions. Forecasting of inflow and irrigation demands is challenging, owing to the variability of the weather variables that affect inflows and irrigation demands. In this context, bias-corrected Global Forecasting System (GFS) forecasts are used here in a hybrid approach (reservoir module with Long Short Term Memory (LSTM)) to forecast the reservoir inflows. Concurrently, the bias-corrected GFS forecasts are used in irrigation demand module to forecast the irrigation demands. The 'Scaled Distribution Mapping' method is used to bias-correct the GFS data of 1-5 days lead. The study area is the Damodar river basin, India, consisting of five major reservoirs: Tenughat and Konar located upstream of Panchet, and Tilaya situated upstream of Maithon. With the upstream reservoir outflow forecasts, the inflows are forecasted in Panchet and Maithon reservoirs with NSE values of 0.88-0.96 and 0.78-0.88, respectively, up to a 5-day lead. The irrigation demand module with bias-corrected GFS forecasts forecasted the irrigation demands close to the irrigation demands with the observed weather data. The percentage errors in irrigation demand forecasts of the Kharif (June-October) season at 1-5 days lead are 9.45 %, -15.45 %, -20.52 %, -26.36 %, -27.31 %, respectively. On the contrary, percentage errors in irrigation demand forecasts of Rabi (November-February) and Boro (January-May) are in the range of 8.17-8.79 % and 3.48-8.06 %, respectively. With the inflows and irrigation demand forecasts, the Panchet and Maithon reservoirs satisfied the downstream demands and reduced the floods. The inflow and irrigation demand forecasts, based on the GFS forecasts, have substantial potential for real-time reservoir operation, leading to efficient water management downstream.