Motivation to Develop sDRIPS Package¶

Surface water irrigation remains vital for global food security, especially in agrarian economies reliant on canals, lakes, and reservoirs. However, climate variability—marked by rising temperatures, erratic precipitation, and altered seasonal water availability—has made effective water management increasingly complex. Farmers and irrigation managers often lack tools to incorporate short-term weather forecasts or estimate crop water demand at appropriate spatial scales, leading to inefficient practices such as overwatering or underwatering. These inefficiencies are compounded by the broader challenge of meeting agricultural water demand amid climate change, population growth, and inter-sectoral competition.

Existing advisory systems are either cost-prohibitive, region-specific, or technically limited due to coarse resolutions or lack of integration with local datasets. Additionally, there is limited research supporting water providers, such as canal operators, who face upstream allocation uncertainties and infrastructure constraints.

To bridge this gap, the sDRIPS (satellite Data Rendered Irrigation using Penman-Monteith and SEBAL) tool was developed—an open-source, Python-based, cloud-compatible package that generates irrigation advisories at both farm and command-area scales. Built on Earth observation data and evapotranspiration models, sDRIPS democratizes access to irrigation guidance.

Earlier versions of sDRIPS—tailored to projects in Bangladesh, the USA, and South Africa—highlighted the need for a unified, modular system. The new centralized sDRIPS package integrates these features, offering a flexible, configurable tool that supports both water users and providers. Hosted on GitHub and documented on ReadTheDocs, it includes tutorials and a command-line interface to support diverse users.