This page provides an overview of the AralDIF project as implemented on the Microsoft Azure public cloud platform. ‘Aral’ refers to the Aral Sea in central Asia, and DIF abbreviates ‘Dynamic Information System’. This is one of several related, regionally focused projects that implement hydrological models in concert with data from local stakeholders. The ultimate objective is to provide actionable public data concerning water resources in regions that are subject to water stress.
The NSF-sponsored work under the CC*IIE initiative is focused here on cloud adoption in research. Our perspective is researcher-centric and cloud-agnostic. It is therefore worth noting that this cloud implementation success story is based on the Microsoft Azure public cloud and built in part from the Microsoft Visual Studio integrated development environment.
Objective and Approach
The AralDIF program is an extension of the DIF (ccs/ccs_dif.html) framework for Central Asia. The primary focus domain is the Aral basin. The core project is a collaboration between the World Bank and stakeholders from seven different countries with drainage basins flowing into the Aral sea from the east.
Our task has been to implement a hydrologic model to understand changes in the regional water balance, quantify the effects of climate change on water resources and agricultural production, and translate these linkages into actionable information through a decision support system. In the process, we are also looking to build a system to encourage data sharing and collaboration in a geo-politically sensitive region.
In order to facilitate the data sharing process, we have developed APIs that allow users to subset data from cloud-based storage. We have also developed a website to help stakeholders visualize model input and outputs as well as perform minor analysis. Further, all data layers, model setup and parameters are stored on a virtual machine on a public cloud platform (MS Azure), thus negating the ability of a single country/stakeholder to dominate or manipulate the data. We have used publicly-available datasets for our analysis and data layers and used open-source tools for our cyberinfrastructure.