This course aims to equip students with a comprehensive understanding and practical skills in the intersection of hydrology and information technology. The course begins with fundamental concepts and principles and progresses through the fundamentals of hydroinformatics and hydrology, emphasizing the use of computational tools, data analytics, and evolving modeling techniques. Hydrology and land surface models evolve over time as a result of advances in complex physics and the availability of space-borne observations, and understanding open-source and cutting-edge models is required to operate such systems effectively and, in particular, to improve the accuracy of hydrological forecasts, which in turn improves the effectiveness of water management. Through a combination of theoretical knowledge, hands-on applications, and case studies, the course aims to produce skilled hydroinformatics professionals capable of addressing contemporary water resource challenges. 

Upon completion of this course, students will gain a comprehensive understanding of hydroinformatics and hydrology, practical skills in utilizing computational tools, an improved understanding of designing and applying land information systems, and the ability to effectively address real-world hydrology problems.

None

I  Concepts and principles of hydroinformatics and hydrology

1. Overview of hydroinformatics and hydrological modeling

2. Computational tools and programming for hydroinformatics

3. Data analytics in hydroinformatics

4. Web-based applications and Decision Support Systems (DSS)

5. The evolution of hydrological and land surface modeling

6. Water, energy, and carbon cycles

7. Climate change and human intervention impact on hydrological modeling

II Land information systems in hydroinformatics

1. Scientific data structure and conventions

2. Navigating the open data landscape

3. Multivariate-multisensor observations 

4. Multiscale land information

5. Working with Unix and High-Performance Computing (HPC) systems

6. Understanding of land information systems: framework, modeling, and application

7. Data assimilation: optimizing model estimates using satellite observations

III Modern hydrological models and forecasting systems

1. Overview of the modern modeling and forecasting system

2. Operational forecasting case studies

3. Running models in a Unix environment

4. Modern global hydrological models

5. Fusing data streams: data assimilation practices

6. Weather Research and Forecasting Model (WRF) and WRF-Hydro

7. Regional perspectives: Thailand’s hydroinformatics system

No designated textbook, but class notes and handouts will be provided.

1. Fisher, R.A., Koven, C.D., 2020. Perspectives on the Future of Land Surface Models and the Challenges of Representing Complex Terrestrial Systems. Journal of Advances in Modeling Earth Systems 12, e2018MS001453. https://doi.org/10.1029/2018MS001453

2. Reichle, R.H., 2008. Data assimilation methods in the Earth sciences. Advances in Water Resources, Hydrologic Remote Sensing 31, 1411–1418. https://doi.org/10.1016/j.advwatres.2008.01.001

3. Tangdamrongsub, N., 2023. Comparative Analysis of Global Terrestrial Water Storage Simulations: Assessing CABLE, Noah-MP, PCR-GLOBWB, and GLDAS Performances during the GRACE and GRACE-FO Era. Water 15, 2456. https://doi.org/10.3390/w15132456

4. Chen, Y., Han, D., 2016. Big data and hydroinformatics. Journal of Hydroinformatics 18, 599–614. https://doi.org/10.2166/hydro.2016.180

1. Hydrology and Earth System Sciences (EGU)

2. Journal of Hydrology (Elsevier)

3. Journal of Hydrometeorology (AMS)

4. Journal of Advances in Modeling Earth Systems (AGU)

5. Geoscientific Model Development (EGU)

Lectures 30h

Hand-on exercises 15h

Group project presentations 03h

Self-study 90h

Teaching and learning methods include classroom lectures, exercises, homework, case studies, group project presentations and exams.

The final grade is calculated based on the following weight distribution: Exam (30%); Assignments (40%); Report and Presentations (30%). 

An "A" grade will be awarded to students who demonstrate exceptional understanding of AI and big data concepts and tools, effectively applying them to real-world data pertaining to water resource problems and exhibiting a proficient interpretation of the obtained results. A "B" grade will be given to students who exhibit solid performance in the subject matter, showcasing a comprehensive understanding of all the topics covered. Students who demonstrate satisfactory command over the subject matter will receive a "C" grade. A "D" grade will be assigned to students who possess limited knowledge of the subject matter.