EGU General Assembly 2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.

Localised Drought Early Warning using In-situ Groundwater Sensors

Will Veness, Wouter Buytaert, and Adrian Butler
Will Veness et al.
  • Grantham Institute, Imperial College London, London, United Kingdom

Drought Early Warning Systems (DEWSs) require data on spatial drought intensity and exposure to highlight the most-affected areas for early interventions. This data also provides evidence of drought severity to trigger early financing mechanisms. However, existing DEWSs are dependent on satellite-based parameters, which have a course spatial resolution and high measurement uncertainty. As a result, these indicators do not provide a reliable proxy for local groundwater availability during hydrological drought. This research explores groundwater monitoring for providing an alternative, direct index of groundwater availability for DEWSs, considering the increasing affordability and feasibility of monitoring due to advancements in modern sensors. Using in-situ observations collected from abstraction wells in Maroodi Jeex, Somaliland, a lumped parameter groundwater model has been calibrated that can forecast local groundwater levels during drought, by inputting seasonal and mid-range weather forecasts. The model can also simulate well water levels if the sensor is removed after 1 year, enabling an ongoing, locally calibrated groundwater index without the need for sensor maintenance. This suggests that national-scale groundwater monitoring in Somaliland is technically feasible, and it raises further research questions regarding how such a system can be funded, governed and maintained, as well as how this groundwater information would be practically used in the drought early warning early action process to inform management and financing decisions.

How to cite: Veness, W., Buytaert, W., and Butler, A.: Localised Drought Early Warning using In-situ Groundwater Sensors, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-4480,, 2022.