The PhD position on “Geospatial big data and machine learning based framework for hydrological decision support” is funded for four years in the framework of the MorSnow project “Monitoring and Quantifying Snowmelt contribution for Moroccan Water Management strategies in the context of Climate Change” at the CRSA center, Mohammed VI Polytechnic University, Morocco.

Water resources in Morocco are largely supplied, either directly or from reservoirs, by river basins originated from Mountainous regions. During the last few decades, numerous studies highlighted a significant change in both surface and groundwater due conjointly to climate change consequences and human activities where agriculture is considered as a main consumer of water. Additionally, future projections indicate a considerable change in direction and magnitude of both precipitation amount and temperature that will affect river’s discharges and snow water equivalent.

In this context, water balance components estimating represents a crucial information for water resources managers to plan and establish their strategies. To this end, Big Data and artificial intelligence technologies is showing great promise in many hydrological related applications such as planning optimum water systems, monitoring hydrological cycle parameters through big remote sensing and geographical information system, forecasting snowmelt runoff, and studying climate change impacts.

The general objective of this PhD proposal is to develop a geospatial platform gathering multi-source data, artificial intelligence techniques and hydrological models to simulate the main water balance components at various spatial and temporal scales in the Oum Er Rbia and Tensift River basins. The specific objectives are:

• Develop a strategy to acquire, processes and integrate a variety of data formats, varying from simple binary or CSV format to advanced self-describing Network Common Data Form (netCDF), Hierarchical Data Format (HDF), etc., within a geospatial and big data platform.

• Analyze the ability of reanalysis data to estimate streamflow in the ungauged basins through artificial intelligence algorithms and assess the influence of climatic variability on water availability using log-term datasets.

• Implement and develop automatic protocol to calibrate and validate, using both historical and experimental records, the appropriate sub-models which simulate the main processes governing hydrological cycle such as precipitation’s phases, interception, evapotranspiration, snowmelt, sublimation, runoff and surface-ground exchange. Sub-Models calibration and validation will be implemented at the local scale firstly and then within the basin scale considering the spatial variability.

• Combine remote sensing-based data (Snow cover products, Global Precipitation observation and algorithms “TRMM, GPM, CMORPH, PERSIAN”, and Soil Moisture Active Passive “SMAP”) and climate data from atmospheric reanalysis outputs (MERRA/ ERA5 reanalysis products) and General circulation models (GCMs) to compensate the lack of ground measurements when changing scales or basin’s level.

• Develop an optimizing technique for streamflow forecasting based on all previous data and technics to quantify water balance components and their response regarding the potential climate impact.

• Design and development of a Web-GIS decision support platform based on the previous developed models.

1. Experimental field work, including site instrumentation to measure meteo-hydrological and snow parameters in the study sites (Atlas mountains);
2. Pre-processing and analysis of big data sets;
3. Literature survey of the previous research works;
4. Development of new approaches based on machine learning and deep learning algorithms;
5. Design and implementation of hydrological sub-models for operational use;
6. Test and validation of the developed models;
7. Preparing peer-reviewed scientific publications and presentation for scientific conferences.

The ideal candidate should have completed a master's degree or equivalent either in Computer Science/Information Technologies/Computer Engineering, Mathematics, Data Analytics/Data Science or related disciplines.

The following skills are required :

• Good skills in computer programming (Python, Matlab, R);
• Knowledge in statistics, data science libraries, machine learning and deep learning;
• Good knowledge in Big Data Manipulation and Analysis;
• Background in hydrological and climatic measurements is desirable;
• Background in remote sensing techniques is desirable;
• Affinity with multidisciplinary research and (possibly) with the hydrology domain;
• Organizational skills to establish and maintain collaborations;
• Communicational skills and team spirit;
• Excellent written and oral English language skills including academic writing skills;
• Ability to work in collaboration with a multidisciplinary team both in the field and in the laboratory;
• Ability to carry out field experiments in mountainous environment.

The ideal candidate should have completed a master's degree or equivalent either in Computer Science/Information Technologies/Computer Engineering, Mathematics, Data Analytics/Data Science or related disciplines.

The following skills are required :

• Good skills in computer programming (Python, Matlab, R);
• Knowledge in statistics, data science libraries, machine learning and deep learning;
• Good knowledge in Big Data Manipulation and Analysis;
• Background in hydrological and climatic measurements is desirable;
• Background in remote sensing techniques is desirable;
• Affinity with multidisciplinary research and (possibly) with the hydrology domain;
• Organizational skills to establish and maintain collaborations;
• Communicational skills and team spirit;
• Excellent written and oral English language skills including academic writing skills;
• Ability to work in collaboration with a multidisciplinary team both in the field and in the laboratory;
• Ability to carry out field experiments in mountainous environment.