Earth system models are a central tool for advancing our understanding of the planet. Models allow us to combine process knowledge with numerous observations and serve as a digital laboratory, enabling us to conduct experiments that are not possible in the real world, such as simulating the effects of anthropogenic or climatic changes. Models are powerful tools that directly support policy and decision-making.
In our group, we develop (1) methods to precisely understand increasingly complex models,
(2) to make these complex instruments FAIR (Findability, Accessibility, Interoperability, and Reuse), and (3) to expand our knowledge of the global water cycle, especially in the context of global change. Our vision is to transform the way we create, use, share, and analyze complex Earth system models to 1) achieve a better understanding of large-scale processes and 2) conduct more robust risk assessments and identify pathways to sustainability.
You can find a current list of publications on ResearchGate and GoogleScholar:
Life on Earth depends on groundwater as a freshwater supplier and an essential component of the Earth system. Nevertheless, our knowledge of the causes of groundwater level changes is characterized by significant uncertainties. Therefore, according to the 2021 IPCC report, we currently cannot predict how climate change will affect the Earth’s groundwater resources.
In an effort to improve our process understanding of groundwater dynamics, we developed GROW (global GROundWater package). This user-friendly, quality-checked dataset combines groundwater level time series from around the world with Earth system variables that we assume have an influence on groundwater. GROW was developed to enable large-scale groundwater analyses without time-intensive data processing.
GROW contains:
- 187.317 Zeitreihen in täglicher, monatlicher oder jährlicher Auflösung
- from 41 countries – over 90% originate from either North America, Australia, or Europe
- most of them are 10 to 20 years long
- Time series with predominantly shallow groundwater levels (Median groundwater depth: 8 meters)
- 36 time series or attributes of meteorological, hydrological, geophysical, botanical, and anthropogenic variables (e.g., precipitation, distance to rivers, aquifer type, NDVI, proportion of irrigated cropland)
- 32 data characteristics (e.g., time series length, trend direction [increasing, decreasing, no trend], value jumps >= 50 m [present/not present], autocorrelation [present/not present], proportion of gaps)
The “State of Global Water Resources” Report by the World Meteorological Organization (WMO) is a key annual publication that provides a comprehensive assessment of the state of global water resources. It analyzes the impacts of climatic, environmental, and societal changes on the Earth’s water cycle, including precipitation, river flows, groundwater levels, and extreme events such as droughts and
floods. The report compares current data with long-term averages, highlights the impacts of climate change on the increasing irregularity of the hydrological cycle, and aims to inform decision-makers, support early warning systems, and contribute to achieving the UN Sustainable Development Goals.
The Earth System Modeling work group of Prof. Dr. Robert Reinecke at the Geographical Institute of Johannes Gutenberg University Mainz (JGU) plays a significant role in this important report. With its expertise in global hydrological modeling, particularly in the area of groundwater and its interactions, the work group provides crucial data, analyses, and model results. It assists in interpreting global data, evaluates deviations from normal hydrological conditions due to climate change, and helps to clearly communicate scientific findings, especially regarding the impacts of human activities and climate change on water resources.
The ReWaterGAP project focuses on modernizing and improving the global hydrological model (GHM) WaterGAP to enhance its sustainability and applicability in research. The project is carried out in collaboration with Goethe University Frankfurt and is funded by the DFG. Global hydrological models are crucial for understanding water flows and storage on continents and have made significant progress in recent decades. However, many existing model codes, including the original version of WaterGAP, are difficult to maintain and efficiently develop further due to increasing complexity. Issues such as non-modular design, inconsistent variable naming, insufficient documentation, and a lack of automated testing procedures hinder both the sustainability of research software and the reproducibility of study results.
To address these challenges, WaterGAP was completely reprogrammed in Python as part of the ReWaterGAP project. An agile project management approach was followed, and a modular Model-View-Controller architecture was implemented. Important development practices such as open-source licensing, version control, unit tests, linting, containerization, consistent and meaningful variable naming, and comprehensive internal and external documentation were integrated. Although the switch from C/C++ to Python doubled the execution time, the reprogramming led to significantly improved usability, maintainability, and extensibility of the software. The new WaterGAP is thus significantly more sustainable, easier to understand, and better suited for collaborative code development in diverse teams, which promotes the establishment of a community around the GHM and supports the FAIR4RS principles (Findable, Accessible, Interoperable, Reusable for Research Software).
The ISIMIP Groundwater Sector is a new, specialized area within the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP) https://www.isimip.org/, dedicated to modeling the impacts of global change on groundwater. As groundwater is an essential source of freshwater and plays a central role in climate change adaptation, this sector aims to create a more comprehensive understanding by combining and comparing multiple global, continental, and regional groundwater models. This is intended to reduce uncertainties in forecasts
reduced, enable more robust predictions, and identify the most effective methods for large-scale groundwater modeling. The Groundwater Sector complements existing ISIMIP sectors, thus contributing to a holistic analysis of climate impacts.
The Earth System Modeling work group actively contributes to the new ISIMIP Sector with its global groundwater model G³M, and Prof. Dr. Reinecke leads the sector together with Prof. Inge deGraaf from Wageningen University in the Netherlands.
The TRAILS project (Towards a multi-scale understanding of gRoundwater quAlity InterLinkageS), funded by the German Research Foundation (DFG), is an international network project that brings together 18 scientists from Germany, the Netherlands, Great Britain, and Australia. The focus is on investigating groundwater quality and developing and applying methods that enable a comprehensive understanding. The goal is to identify the spatial and temporal scales that are relevant for sustainable water governance and effective water management. In addition to the critical discussion and evaluation of existing approaches, the project also develops its own methodological concepts.
As part of the three-year project period, on-site workshops will be held at the Johannes Gutenberg-Universität Mainz, to which additional international experts will be invited to deepen the scientific debate through specialist lectures and discussions.
While a large number of studies already exist for surface waters such as rivers and lakes – from micro-scale to global analyses – knowledge of groundwater quality on a large-scale or even global level is still very limited. Groundwater is a central component of the world’s freshwater supply: in Germany alone, around 70% of drinking water comes from groundwater resources (UBA, 2024). It is also closely linked to surface waters.
The urgency is also underlined by current findings from the German Environment Agency (UBA, 2022): around a third of German groundwater bodies are currently in a ‘chemically poor condition’. Since direct measurement programs on supra-regional or even large-scale scales would involve considerable costs, high effort and long periods of time, TRAILS focuses on modeling approaches that should enable an integrative, cross-scale understanding.
To promote the understanding and application of the global hydrological model WaterGAP worldwide, we plan to establish an international WaterGAP Summer School. This initiative is aimed at young researchers who wish to use and further develop WaterGAP for their research in global water resources. WaterGAP is a powerful tool for simulating global water flows and storage, considering human influences, and provides crucial data for analyzing water scarcity, the impacts of climate change, and developing sustainable water management strategies.
The planned Summer School aims to provide an intensive, practice-oriented learning environment where participants can learn the intricacies of the model under the direct instruction of experienced developers and users. Given the complexity of global hydrological modeling, our goal is to lower the entry barriers and provide participants with the necessary knowledge for the correct application of the model to their specific geographical and thematic research questions, as well as for the well-founded interpretation of model results.
Another core objective of the initiative is to build a vibrant, international community of WaterGAP users and developers. As an open-source model, WaterGAP thrives on the contributions of its community. The Summer School is therefore intended to provide a platform to deepen knowledge of model architecture, programming standards, and collaborative development practices. We want to enable participants to actively contribute to identifying errors, validating model components, and developing new functionalities. In this way, the Summer School aims to accelerate innovation and ensure that WaterGAP remains a cutting-edge tool for addressing global water security challenges.
Please note: The WaterGAP Summer School is a planned initiative and is not currently taking place. We are working on the concept and will provide updates on future developments here.
For billions of people worldwide, especially those in coastal areas, groundwater is the primary source of drinking water. Globally, available groundwater resources are threatened by increasing water abstraction, especially for coastal aquifers, as they are additionally threatened by saltwater intrusion. At the same time, groundwater discharge into the oceans is an important process for aquatic ecosystems. The changing climate and rising sea levels will further alter coastal groundwater dynamics. Recently developed global groundwater models offer the opportunity to make these global challenges visible.
COASTGUARD aims to investigate the parameterization of these novel models at the ocean boundary condition more precisely and to quantify uncertainties. The project results will help the global research community to better understand large-scale coastal groundwater processes and relate them to local findings. COASTGUARD will not only contribute to a better understanding of the dynamics of coastal groundwater processes but also allow for implications regarding future freshwater availability. Furthermore, COASTGUARD will identify regions worldwide that are particularly affected by a changing climate. COASTGUARD thus offers a unique opportunity:
- to investigate uncertainties in global groundwater modeling and improve their parameterization at the crucial ocean interface,
- to provide new insights into which processes are dominant regarding the dynamics between groundwater and the sea on a global scale, as well as
- to present the global quantification of saltwater intrusion and groundwater discharge in the context of climate change and rising sea levels.
Software development has become an integral part of geosciences, as models and data processing become increasingly sophisticated. Paradoxically, however, it poses a threat to scientific progress, as reproducibility, a cornerstone of science, is rarely achieved. Software code is often either poorly written and documented or not shared at all; appropriate software licenses are rarely granted. This is particularly concerning because scientific results can have potentially controversial implications for stakeholders and policymakers and can influence public opinion in the long term.
In recent years, the progress towards open science has led to more and more publishers requiring access to data and source code alongside peer-reviewed manuscripts. Nevertheless, current studies show that results can rarely be reproduced.
In this project, we are conducting a survey within the geoscience community, which is being promoted via scientific blogs (AGU, EGU), research networks (researchgate.net and mailing lists), and social media. In doing so, we aim to investigate the causes of the lack of reproducibility. We take a look behind the scenes and show how the community develops and maintains complex code and what this means for reproducibility. Our survey covers background knowledge, community opinions, and behaviors related to reproducible software development.
We postulate that this lack of reproducibility could be due to insufficient reward within the scientific community, uncertainties regarding the correct licensing of software and other parts of the research compendium, and scientists’ lack of awareness of how to make software available in a way that allows for proper recognition of their work. We question presumed causes such as unclear guidelines from research institutions or the fact that software has been developed over decades by cohorts of researchers without a proper software development process and transparent licensing.
To this end, we also summarize solutions, such as adapting modern project management methods from computer engineering, which could ultimately reduce costs and increase the reproducibility of scientific research.
The OUTLAST project (September 2022 – August 2025), funded by the German Federal Ministry of Education and Research (BMBF), aims to develop the first global, multi-sectoral, and operational drought forecasting system. This system will quantify drought risks for sectors such as water supply, river ecosystems, and agriculture, and is intended to be implemented as a central component of the Global Hydrological Status and Outlook System (HydroSOS) of the World Meteorological Organization (WMO). In a co-design approach, the project works closely with pilot users, particularly in the Lake Victoria and West and Central Asia regions, to test the utility of drought forecasts and develop practical web portals and applications for drought management. Through this, OUTLAST aims to improve resilience to dry periods and support decision-makers in adapting to climate change. The project, which builds on the findings of the earlier GRoW project “GlobeDrought”, is supported by a consortium of research institutions, including Goethe University Frankfurt am Main, Karlsruhe Institute of Technology (KIT), Georg-August-Universität Göttingen, and the International Centre for Water Resources and Global Change (ICWRGC). Prof. Reinecke advises the consortium as a consultant on the implementation of the drought forecasting system.
AG Leadership
Doctoral Candidates
Student Assistants
Former Staff Members
Currently, there are no open positions. Unsolicited applications are always welcome.
If you are looking for a final thesis (master’s degree or Bachelor of Science) in our work group, you will find a list of possible topics below. Would you like to propose your own topic? Feel free to contact us.
Topics of past final theses from our work group:
- 2025
- BA: Groundwater Flood Risk Analysis: Influence of the Rhine on Groundwater Levels and Damage Potential in Mainz-Bingen, Rhineland-Palatinate
- BA: Drinking Water Scarcity in Small Island Developing States (SIDS): An Analysis of Regional Differences, Challenges, and Environmental and Anthropogenic Causes
- MA: An Analysis of the Influence of Statistical Indicators on the Functionality of the SONAR Algorithm for Automated Detection of Functional Relationships in Big Earth Data
- MA: Groundwater Dynamics and Wildfire Activity in Mediterranean Landscapes: A Case Study from the Iberian Peninsula
- MA: Analysis of Redispatch Events: An Integrative Study of Regional Weather and Consumption Data in Renewable Energy Production
- 2024
- BA: Algorithm-Based Classification of Groundwater Level Time Series in Germany
- MA: Concentration-Discharge Analysis for Baseflow Quantification: A New Approach
The lecture “Introduction to Cartography” provides a fundamental understanding of the concepts and technologies used in map creation. Participants learn how to collect, process, and present geographical information to convey geographical knowledge. A special focus is on the aspects of critical cartography, where the power dynamics and interests behind the representation of information on maps are examined.
The aim of the lecture is to provide participants with a solid foundation for working with maps. These include:
1. Understanding of the concepts and technologies used in map creation, including the use of projections and coordinate systems.
2. Ability to collect, process, and present geographical information to convey geographical knowledge.
3. Proficiency in the use of cartography software and tools, including map design and data visualization.
4. Ability to critically examine and understand the power dynamics and interests behind the representation of information on maps.
5. Understanding the importance of critical cartography for geographical research and society.
The lecture includes a combination of lectures and exercises, where participants can practically apply and discuss what they have learned. A final project offers participants the opportunity to deepen their understanding of cartography and improve their skills in applying concepts and technologies.
General Learning Objectives
· Understanding the history and development of cartography: Participants should gain a deeper understanding of the history of cartography and recognize how cartography has evolved over time.
· Understanding critical cartography: Participants should learn how maps can influence political, social, and cultural agendas and how to develop a critical stance towards maps to recognize the power dynamics behind the representation of information on maps.
· Mastery of cartographic representation: Participants should learn how to interpret and read maps, as well as understand the concepts and methods of cartographic representation.
· Mastery of projection types and coordinate systems: Participants should develop a deeper understanding of various projection types, such as Mercator, Transverse, and Conic projections, and understand the advantages and disadvantages of each projection. Furthermore, they should learn how different coordinate systems, such as the geographic coordinate system and the UTM coordinate system, are used in cartography and how to switch between them.
· Knowledge of modern technologies in cartography: Participants should be informed about modern technologies such as GIS and GPS and learn how these are used in cartography.
· Overview of current trends and developments in cartography: Participants should be informed about current trends and developments in cartography, e.g., open-source maps or big data maps.
The use of models in earth, environmental, and hydro sciences is ubiquitous. They are used for both scientific analyses and operational predictions. Each model is embedded in a modeling process that begins with the abstraction of the real system, the determination of parameters, the quantification of uncertainties, the formulation of objective functions, etc. In this course, the main elements of such a modeling process are discussed, and their adaptation to various models of earth, environmental, and water systems is explained and carried out. The lectures go hand in hand with practical exercises using software for implementing the modeling process (e.g., www.safetoolbox.info).
Basic knowledge of Python is an advantage but can also be acquired during the course. Students can work on specific elements of interest both in groups (in a “flipped classroom”) and individually (through a term paper).
Learning Outcomes:
1. Subject-specific Competencies:
Students are familiar with the most important elements of the modeling process.
2. Methodological Competencies:
Students
… are able to select these main elements and adapt them to their specific models and their complexity,
… know the theoretical foundations of these elements and are able to develop a modeling process for their model,
… are able to use selected software for implementing the modeling process, including global sensitivity analysis for understanding and quantifying uncertainties.
This course offers a comprehensive introduction to the analysis of Big Data in the context of the Earth system using the Python programming language. Participants learn how to effectively process and analyze large datasets, such as satellite data and time series from measuring stations. The course places a special emphasis on the practical application of Python and the use of data analysis tools and libraries.
Learning Objectives:
1. Python Programming: Learning the basics of Python programming and understanding how to apply this knowledge to large datasets.
2. Data Analysis: Understanding how to use Python to analyze data, recognize patterns, and make predictions.
3. Working with Large Datasets: Learning techniques for efficiently loading, processing, and analyzing large datasets.
4. Satellite Data: Understanding how to read, interpret, and analyze satellite data.
5. Time Series Analysis: Learning methods for analyzing time series data from measuring stations.
6. Data Visualization: Learning how to use Python libraries to create meaningful data visualizations.
7. Problem Solving: Developing the ability to identify and solve emerging problems in data analysis.
By the end of the course, participants will be able to effectively use Python to perform complex analyses of Big Data in the Earth system. They will also have a deeper understanding of the challenges and opportunities of working with large datasets.
