Economic and Land Use Impacts of Improving Water Use Efficiency in Irrigation in South Asia

This paper modifies and uses an advanced computable general equilibrium model coupled with biophysical data on land and water resources by Agro-Ecological Zone (AEZ) at the river basin level to examine the economy-wide consequences of im-provements in water use efficiency (WUE) in irrigation in South Asia. This is the first time the benefits of such improvements have been evaluated in an economy-wide context. It shows that such improvements increase production of food items, enhance food exports, and significantly improve food security in South Asia. Improvement in water use efficiency also leads to lower food prices, provides the opportunity to extend irrigated areas, decreases demand for cropland, and enhances reforestation. Im-provement in water use efficiency in irrigation also generates important net GDP gains across the South Asia region. Investments in improved WUE of up to 40% can be economically justified in Bangladesh, India, and Sri Lanka. However, in Nepal, for an improvement of more than 20% in WUE, the economic gains are smaller than costs from the associated investments. In Pakistan and rest of South Asia, an improvement in WUE of up to 30% appears to be economically profitable.

Machine learning enabled autonomous microstructural characterization in 3D samples

We introduce an unsupervised machine learning(ML)based technique for the identification and characterization of microstructures in three-dimensional(3D)samples obtained from molecular dynamics simulations,particle tracking data,or experiments.Our technique combines topology classification,image processing,and clustering algorithms,and can handle a wide range of microstructure types including grains in polycrystalline materials,voids in porous systems,and structures from self/directed assembly in soft-matter complex solutions.Our technique does not require a priori microstructure description of the target system and is insensitive to disorder such as extended defects in polycrystals arising from line and plane defects.We demonstrate quantitively that our technique provides unbiased microstructural information such as precise quantification of grains and their size distributions in 3D polycrystalline samples,characterizes features such as voids and porosity in 3D polymeric samples and micellar size distribution in 3D complex fluids.To demonstrate the efficacy of our ML approach,we benchmark it against a diverse set of synthetic data samples representing nanocrystalline metals,polymers and complex fluids as well as experimentally published characterization data.Our technique is computationally efficient and provides a way to quickly identify,track,and quantify complex microstructural features that impact the observed material behavior.