Project description
The replenishment of groundwater resources, groundwater recharge, is affected by climate change. We recently published an analysis of global recharge data that showed that recharge rates are very sensitive to climate aridity. The current generation of global hydrological models cannot reproduce the strong response of groundwater recharge to climate. The central aim of this thesis is to assess how well observed recharge rates can be reproduced using a new physically based model that represent recharge processes better than existing global model codes. The study will simulate recharge rates at a small number of locations along a gradient from humid to semi-arid climates and will compare the results with published long-term recharge estimates (see Figure 1).
This work will use publicly available global datasets on climate, landforms and soils as inputs. The modelling will be conducted using the soil infiltration model code OpenRE that is written in the programming language Python, and a new extension that enables the simulation of preferential flow paths in large pores or cracks in the soil. The thesis will investigate how these processes affect recharge simulations under different climatic conditions. The work will contribute to a better understanding of groundwater recharge mechanisms and the effects of climate change on groundwater resources.
Proposed course plan during the master's degree (60 ECTS)
Fall:
GEO217 (external link) Hydrology, Ground Water and Geohazards (10 ECTS)
GEOV205 (external link) / Geographical Information Systems: Theory and Practice (10 ECTS)
GEOV300 (external link) / Scientific writing and communication in Earth Science (5 ECTS)
SDG213 (external link): Causes and consequences of Climate Change (10 ECTS)
Spring:
GEOV212 (external link) Hydrogeology (10 ECTS, course is still to be formally registered)
GEOV316 (external link) Practical Skills in Remote Sensing and Spatial analysis (10 ECTS)
GEO4360 – Field Methods in Hydrogeology (Uni Oslo, 5 ECTS)
Prerequisites
Experience with and willingness to learn Python is helpful for this project
External data
Publicly available data only
Field-, lab- and analysis work
none