The International Water Management Institute (IWMI) is a leading global research organization dedicated to sustainable water and land resources management. Our mission is to improve the productivity, profitability, and resilience of water and land resources in support of food security, livelihoods, and environmental sustainability. Our teams are lead by researchers and scientists, and our projects are often materialized through prototypes that are engineered with our in-house expertise, upon which production-ready platforms must be designed so that they can be implemented for real world application in the field, through the engagement of technological partners for the implementation of such projects.

The Limpopo River Basin (LRB) is a region of immense environmental, socio-economic significance, extending across Botswana, South Africa, Zimbabwe, and Mozambique. It encompasses a diverse range of ecosystems, including forests, wetlands, and savannahs, supporting a rich variety of flora and fauna. The river and its tributaries provide essential water resources for agriculture, livestock, and fisheries, supporting millions of livelihoods. Despite these invaluable resources, the LRB faces challenges such as hydro-climatic extremes, land degradation, poor water quality, urbanization, and population growth, which threaten biodiversity and weaken local communities’ resilience to climate change. The basin also faces increasing competition for access to limited water resources and arable land, with women and youth disproportionately affected.

To address these pressing challenges, IWMI’s new project aims to create a comprehensive Digital Twin of the Limpopo River Basin. The LRB Digital Twin will serve as a virtual representation of the basin’s socio-ecological system, bridging the physical and digital worlds. This advanced approach combines environmental data, modelling, and analysis tools with digital technology to enable sustainable and inclusive water management.

This is an internationally/globally hired consultancy; therefore, individuals with relevant abilities from diverse locations around the world are encouraged to apply. The work arrangement for this consultancy is remote.

OBJECTIVE OF THE ASSIGNMENT:

The primary objective of this role is to contribute to the creation of a comprehensive Digital Twin of the Limpopo River Basin (LRB) by utilizing the Soil and Water Assessment Tool (SWAT) model to forecast water availability and support sustainable water management practices.

SCOPE OF WORK:

Responsibilities,

• Data Preprocessing: Gather, preprocess, and integrate input data from remote sensing and suitable stations for utilization in the SWAT model.
• Model Calibration: Calibrate the SWAT model using local weather station and stream flow data to ensure accuracy and reliability.
• Operationalization: Operationalize the SWAT model to generate consistent and reliable forecasts.
• Cloud Deployment: Deploy the SWAT model in an AWS environment for enhanced accessibility and scalability.
• Post-Processing and Integration: Incorporate post-processing of SWAT output for potential integration into the User Interface (UI) model.

EXPECTED DELIVERABLES:

• Preprocessing Scripts for input & output data for the SWAT Model.
• Automation Script for the Seasonal Forecast.
• Best Calibrated SWAT Model with new observed data.
• Provide 2024 water availability forecast.
• Deploy the Operational SWAT Model on AWS Cloud.
• Improvement & Validation.
• Documentation.
• Final Report / Peer review paper.

Requirements

QUALIFICATIONS & EXPERIENCE:

• Masters or PhD in hydrological modeling, environmental modeling. computational modeling or in a related discipline.
• At least 05 years of hands on experience in hydrological modeling, computational modeling.
• Proven experience in calibrating hydrological models (especially SWAT model) using local weather and stream flow data to ensure accuracy and reliability.

KNOWLEDGE, SKILLS & ABILITIES:

• Expertise in Hydrology: Profound understanding and practical experience in hydrological modeling and analysis, specifically with the SWAT model.
• Remote Sensing and Data Integration: Capability to preprocess and integrate diverse datasets, particularly from remote sensing sources, for input into hydrological models.
• Cloud Computing: Proficiency in deploying models in cloud environments, specifically AWS, for enhanced accessibility and scalability.
• Problem-Solving Skills: Ability to troubleshoot model-related issues and propose innovative solutions.
• Team Collaboration: Strong communication skills to collaborate effectively with multidisciplinary teams of researchers, scientists, and technological partners.
• Adaptability: Capacity to work in diverse and dynamic environments, especially in interdisciplinary projects addressing water management challenges.

Benefits

This is an internationally/globally hired consultancy; therefore, individuals with relevant abilities from diverse locations around the world are encouraged to apply. IWMI offers a competitive daily rate for this assignment. The duration of the contract will be for a period of six (06) months.

HOW TO APPLY: Apply for the position by following the application instructions at www.iwmi.org/jobs. We will be accepting applications through 24:00 (IST) on January 20, 2024 (applications will be reviewed on a rolling basis). Your application must include a CV, cover letter, and three (3) references, which may be contacted if you are shortlisted. Receipt of all applications will be acknowledged, but only short-listed candidates will be contacted.

IWMI believes that diversity powers our innovation, contributes to our excellence, and is critical for our mission. We offer a multi-cultural, multi-color, multi-generational and multi-disciplinary working environment. We are consciously creating an inclusive organization that reflects our global character and our commitment to gender equity. We, therefore, encourage applicants from all cultures, races, ethnicities, religions, sexes, national or regional origins, ages, disability status, sexual orientations, and gender identities.