Abstract
•A groundwater (GW) model is developed using MODFLOW-NWT in the Gilgel-Abay Catchment.•Data scarcity is dealt with citizen science initiative, and one-way CREST coupling.•Model simulates 39 years of hydraulic head at a monthly scale and 500 m resolution.•Calibration, validation and global model comparisons produce acceptable results.•The model findings will be helpful for future local-scale model developments in Ethiopia.
Groundwater (GW) is the main source of domestic water supply in Ethiopia (85%), however, despite widespread acknowledgement of its potential for resource-based development and climate change adaptation, the sector is still quite under-investigated. This is mainly due to the scarcity of in situ data, which are essential to building robust impact models. To address this, we developed a fine-resolution (500 m) GW model using MODFLOW-NWT, focusing on the Gilgel-Abay Catchment located in the Upper Blue Nile basin, fed with daily distributed input forcings of recharge and streamflow simulated by the Coupled Routing and Excess Storage (CREST) hydrological model. The model was calibrated against instantaneous observation records of GW table for 38 historical wells, and validated at selected sites using time series data collected from the Citizen Science Initiative (PIRE CSI), and the Innovation Lab for Small Scale Irrigation (ILSSI) project. An RMSE of 14.4 m (1.8% of range) was achieved for calibration and same for validation was 18.21 m and 15.76 m at the PIRE CSI and ILSSI sites, respectively. The findings of this research indicate substantial physical GW resource availability in the Gilgel-Abay region. Moreover, we expect the model to have multiscale future applications. These include obtaining dynamically downscaled boundary conditions for a local-scale GW model, to be developed in the next phase of our research. Further, an upscaled version of this model to encompass the entire Tana Basin would be developed to simulate lake-aquifer interactions. Finally, the approach of this research combining different types of datasets (e.g., reanalysis products, satellite data, citizen science data, etc.) is adaptable to other global data-scarce regions. Moreover, the method overcomes specific challenges associated to in situ data scarcity, limited knowledge on GW resources availability in the area, interaction with complex boundary conditions, and sensitivity under meteorological boundary forcings.