Research

Ongoing projects

GROW: groundwater Sustainability and Crop production

Funder: European Research Council Starting Grant, granted to Inge de Graaf.

Run time: sept 2022- sept 2027

Groundwater is the largest accessible source of freshwater and is of critical importance for humans and the environment. Irrigation is by far the largest user of groundwater worldwide and accounts for 17% of the global cropland area and 40% of the global food production. Currently groundwater is overused in various regions around the world and negative impacts are experienced. To reduce and prevent negative impacts of groundwater overuse, groundwater must be used more sustainably. At the same time, global food demands will rise and crop production has to increase to secure adequate food supply worldwide. A daunting global challenge lies ahead in how to use groundwater sustainable while at the same time sufficient crop production, to support regional and global food security, should be maintained. In GROW we aim to assess how much groundwater is sustainably available worldwide and to what extent this available groundwater supports sufficient crop production to meet current and future food demands.

people involved: Bryan Marinelli, Sida Liu, Tijmen Willard, Fatemeh Karandish, Inge de Graaf.

Impacts of groundwater use on groundwater quality worldwide

Funder: WUR

Run time: 2023-2027

What is the impact of agricultural practices on groundwater quality? What is the contribution of groundwater to water quality in rivers, lakes and wetlands? How will groundwater quality change on future under climate change, increases in population and shifts in land use changes?

This are the type of questions we try to answer in this project. In this project we will integrate estimates of groundwater quality to groundwater quantity at the global scale to asses current and future groundwater quality and related impacts.

people involved: Floris Teuling, Nynke Hofstra, Inge de Graaf.

VIC-WUR development

Development of new version of the model VIC-WUR. Aiming for efficient coding and coupling of model components and modules, efficient writing of data, running in parallel etc.

people involved: Lisanne Nauta, Karun Datadien, Sida Liu, Inge de Graaf.